Prepare new PyPI release.

* make clipnorm work with embeddings layer

* test for embedding + clipnorm

* update embedding_with_clipnorm test nb_epoch to epochs

.github/stale.yml

@@ -0,0 +1,19 @@
+# Configuration for probot-stale - https://github.com/probot/stale
+
+# Number of days of inactivity before an Issue or Pull Request becomes stale
+daysUntilStale: 90
+# Number of days of inactivity before a stale Issue or Pull Request is closed
+daysUntilClose: 30
+# Issues or Pull Requests with these labels will never be considered stale
+exemptLabels:
+  - bug
+  - Announcement
+  - help wanted
+  - To investigate
+# Label to use when marking as stale
+staleLabel: stale
+# Comment to post when marking as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed after 30 days if no further activity 
+  occurs, but feel free to re-open a closed issue if needed.

.gitignore

@@ -8,8 +8,10 @@ keras/datasets/data/*
 keras/datasets/temp/*
 docs/site/*
 docs/theme/*
+docs/sources/*
 tags
 Keras.egg-info
+examples/img/*
 
 # test-related
 .coverage

.travis.yml

@@ -4,9 +4,11 @@ language: python
 matrix:
     include:
         - python: 2.7
-          env: KERAS_BACKEND=theano TEST_MODE=PEP8
+          env: KERAS_BACKEND=tensorflow TEST_MODE=PEP8
         - python: 2.7
-          env: KERAS_BACKEND=theano TEST_MODE=INTEGRATION_TESTS
+          env: KERAS_BACKEND=tensorflow TEST_MODE=INTEGRATION_TESTS
+        - python: 3.5
+          env: KERAS_BACKEND=tensorflow TEST_MODE=DOC
         - python: 2.7
           env: KERAS_BACKEND=tensorflow
         - python: 3.5
@@ -15,6 +17,10 @@ matrix:
           env: KERAS_BACKEND=theano
         - python: 3.5
           env: KERAS_BACKEND=theano
+        - python: 2.7
+          env: KERAS_BACKEND=cntk
+        - python: 3.5
+          env: KERAS_BACKEND=cntk
 install:
   # code below is taken from http://conda.pydata.org/docs/travis.html
   # We do this conditionally because it saves us some downloading if the
@@ -34,7 +40,7 @@ install:
 
   - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib pandas pytest h5py
   - source activate test-environment
-  - pip install git+git://github.com/Theano/Theano.git
+  - pip install theano
 
   # install PIL for preprocessing tests
   - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
@@ -45,12 +51,25 @@ install:
 
   - pip install -e .[tests]
 
-  # install TensorFlow
+  # install TensorFlow (CPU version).
+  - pip install tensorflow
+  
+  # install cntk
   - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
-      pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp27-none-linux_x86_64.whl;
+      pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.0-cp27-cp27mu-linux_x86_64.whl;
     elif [[ "$TRAVIS_PYTHON_VERSION" == "3.5" ]]; then
-      pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl;
+      pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.0-cp35-cp35m-linux_x86_64.whl;
     fi
+
+  #install open mpi
+  - rm -rf ~/mpi
+  - mkdir ~/mpi
+  - pushd ~/mpi
+  - wget http://cntk.ai/PythonWheel/ForKeras/depends/openmpi_1.10-3.zip
+  - unzip ./openmpi_1.10-3.zip
+  - sudo dpkg -i openmpi_1.10-3.deb
+  - popd
+
 # command to run tests
 script:
   # run keras backend init to initialize backend config
@@ -64,8 +83,8 @@ script:
        PYTHONPATH=$PWD:$PYTHONPATH py.test tests/integration_tests;
     elif [[ "$TEST_MODE" == "PEP8" ]]; then
        PYTHONPATH=$PWD:$PYTHONPATH py.test --pep8 -m pep8 -n0;
+    elif [[ "$TEST_MODE" == "DOC" ]]; then
+        PYTHONPATH=$PWD:$PYTHONPATH py.test tests/test_documentation.py;
     else
-       PYTHONPATH=$PWD:$PYTHONPATH py.test tests/ --ignore=tests/integration_tests;
+       PYTHONPATH=$PWD:$PYTHONPATH py.test tests/ --ignore=tests/integration_tests --ignore=tests/test_documentation.py --cov=keras tests/ --cov-fail-under 78 --cov-report term-missing;
     fi
-after_success:
-  - coveralls

CONTRIBUTING.md

@@ -19,6 +19,7 @@ To easily update Theano: `pip install git+git://github.com/Theano/Theano.git --u
 
 The more information you provide, the easier it is for us to validate that there is a bug and the faster we'll be able to take action. If you want your issue to be resolved quickly, following the steps above is crucial.
 
+---
 
 ## Requesting a Feature
 
@@ -30,35 +31,52 @@ You can also use Github issues to request features you would like to see in Kera
 
 3. After discussing the feature you may choose to attempt a Pull Request. If you're at all able, start writing some code. We always have more work to do than time to do it. If you can write some code then that will speed the process along.
 
+
+---
+
+## Requests for Contributions
+
+[This is the board](https://github.com/fchollet/keras/projects/1) where we list current outstanding issues and features to be added. If you want to start contributing to Keras, this is the place to start.
+
+
+---
+
 ## Pull Requests
 
-We love pull requests. Here's a quick guide:
+**Where should I submit my pull request?**
+
+1. **Keras improvements and bugfixes** go to the [Keras `master` branch](https://github.com/fchollet/keras/tree/master).
+2. **New features** such as layers and datasets go to [keras-contrib](https://github.com/farizrahman4u/keras-contrib). Unless it is a new feature listed in [Requests for Contributions](https://github.com/fchollet/keras/projects/1), in which case it belongs in core Keras.
+
+Here's a quick guide to submitting your improvements:
 
 1. If your PR introduces a change in functionality, make sure you start by opening an issue to discuss whether the change should be made, and how to handle it. This will save you from having your PR closed down the road! Of course, if your PR is a simple bug fix, you don't need to do that.
 
 2. Write the code. This is the hard part!
 
-3. Make sure any new function or class you introduce has proper docstrings. Make sure any code you touch still has up-to-date docstrings and documentation.
+3. Make sure any new function or class you introduce has proper docstrings. Make sure any code you touch still has up-to-date docstrings and documentation. **Docstring style should be respected.** In particular, they should be formatted in MarkDown, and there should be sections for `Arguments`, `Returns`, `Raises` (if applicable). Look at other docstrings in the codebase for examples.
 
 4. Write tests. Your code should have full unit test coverage. If you want to see your PR merged promptly, this is crucial.
 
 5. Run our test suite locally. It's easy: from the Keras folder, simply run: `py.test tests/`.
-  - You will need to install the test requirements as well: `pip install -e .[tests]`.
+    - You will need to install the test requirements as well: `pip install -e .[tests]`.
 
 6. Make sure all tests are passing:
-  - with the Theano backend, on Python 2.7 and Python 3.5
-  - with the TensorFlow backend, on Python 2.7
+    - with the Theano backend, on Python 2.7 and Python 3.5. Make sure you have the development version of Theano.
+    - with the TensorFlow backend, on Python 2.7 and Python 3.5. Make sure you have the development version of TensorFlow.
 
 7. We use PEP8 syntax conventions, but we aren't dogmatic when it comes to line length. Make sure your lines stay reasonably sized, though. To make your life easier, we recommend running a PEP8 linter:
-  - Install PEP8 packages: `pip install pep8 pytest-pep8 autopep8`
-  - Run a standalone PEP8 check: `py.test --pep8 -m pep8`
-  - You can automatically fix some PEP8 error by running: `autopep8 -i --select <errors> <FILENAME>` for example: `autopep8 -i --select E128 tests/keras/backend/test_backends.py`
+    - Install PEP8 packages: `pip install pep8 pytest-pep8 autopep8`
+    - Run a standalone PEP8 check: `py.test --pep8 -m pep8`
+    - You can automatically fix some PEP8 error by running: `autopep8 -i --select <errors> <FILENAME>` for example: `autopep8 -i --select E128 tests/keras/backend/test_backends.py`
 
-8. When committing, use appropriate, descriptive commit messages. Make sure that your branch history is not a string of "bug fix", "fix", "oops", etc. When submitting your PR, squash your commits into a single commit with an appropriate commit message, to make sure the project history stays clean and readable. See ['rebase and squash'](http://rebaseandsqua.sh/) for technical help on how to squash your commits.
+8. When committing, use appropriate, descriptive commit messages.
 
 9. Update the documentation. If introducing new functionality, make sure you include code snippets demonstrating the usage of your new feature.
 
-10. Submit your PR. If your changes have been approved in a previous discussion, and if you have complete (and passing) unit tests, your PR is likely to be merged promptly. Otherwise, well...
+10. Submit your PR. If your changes have been approved in a previous discussion, and if you have complete (and passing) unit tests as well as proper docstrings/documentation, your PR is likely to be merged promptly. Otherwise, well...
+
+---
 
 ## Adding new examples
 

LICENSE

@@ -8,8 +8,12 @@ All contributions by Google:
 Copyright (c) 2015, Google, Inc.
 All rights reserved.
 
+All contributions by Microsoft:
+Copyright (c) 2017, Microsoft, Inc.
+All rights reserved.
+
 All other contributions:
-Copyright (c) 2015, the respective contributors.
+Copyright (c) 2015 - 2017, the respective contributors.
 All rights reserved.
 
 Each contributor holds copyright over their respective contributions.

README.md

@@ -1,18 +1,16 @@
-# Keras: Deep Learning library for TensorFlow and Theano
+# Keras: Deep Learning for Python
 
 [![Build Status](https://travis-ci.org/fchollet/keras.svg?branch=master)](https://travis-ci.org/fchollet/keras)
 [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/fchollet/keras/blob/master/LICENSE)
 
-
 ## You have just found Keras.
 
-Keras is a high-level neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*
+Keras is a high-level neural networks API, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow), [CNTK](https://github.com/Microsoft/cntk) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*
 
 Use Keras if you need a deep learning library that:
 
-- Allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
+- Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
 - Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-- Supports arbitrary connectivity schemes (including multi-input and multi-output training).
 - Runs seamlessly on CPU and GPU.
 
 Read the documentation at [Keras.io](http://keras.io).
@@ -25,11 +23,11 @@ Keras is compatible with: __Python 2.7-3.5__.
 
 ## Guiding principles
 
+- __User friendliness.__ Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
+
 - __Modularity.__ A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
 
-- __Minimalism.__ Each module should be kept short and simple. Every piece of code should be transparent upon first reading. No black magic: it hurts iteration speed and ability to innovate.
-
-- __Easy extensibility.__ New modules are dead simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
+- __Easy extensibility.__ New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
 
 - __Work with Python__. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
 
@@ -39,9 +37,9 @@ Keras is compatible with: __Python 2.7-3.5__.
 
 ## Getting started: 30 seconds to Keras
 
-The core data structure of Keras is a __model__, a way to organize layers. The main type of model is the [`Sequential`](http://keras.io/getting-started/sequential-model-guide) model, a linear stack of layers. For more complex architectures, you should use the [Keras functional API](http://keras.io/getting-started/functional-api-guide).
+The core data structure of Keras is a __model__, a way to organize layers. The simplest type of model is the [`Sequential`](http://keras.io/getting-started/sequential-model-guide) model, a linear stack of layers. For more complex architectures, you should use the [Keras functional API](http://keras.io/getting-started/functional-api-guide), which allows to build arbitrary graphs of layers.
 
-Here's the `Sequential` model:
+Here is the `Sequential` model:
 
 ```python
 from keras.models import Sequential
@@ -54,45 +52,52 @@ Stacking layers is as easy as `.add()`:
 ```python
 from keras.layers import Dense, Activation
 
-model.add(Dense(output_dim=64, input_dim=100))
+model.add(Dense(units=64, input_dim=100))
 model.add(Activation('relu'))
-model.add(Dense(output_dim=10))
+model.add(Dense(units=10))
 model.add(Activation('softmax'))
 ```
 
 Once your model looks good, configure its learning process with `.compile()`:
+
 ```python
-model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model.compile(loss='categorical_crossentropy',
+              optimizer='sgd',
+              metrics=['accuracy'])
 ```
 
 If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
 ```python
-from keras.optimizers import SGD
-model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True))
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True))
 ```
 
 You can now iterate on your training data in batches:
+
 ```python
-model.fit(X_train, Y_train, nb_epoch=5, batch_size=32)
+# x_train and y_train are Numpy arrays --just like in the Scikit-Learn API.
+model.fit(x_train, y_train, epochs=5, batch_size=32)
 ```
 
 Alternatively, you can feed batches to your model manually:
+
 ```python
-model.train_on_batch(X_batch, Y_batch)
+model.train_on_batch(x_batch, y_batch)
 ```
 
 Evaluate your performance in one line:
+
 ```python
-loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
+loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
 ```
 
 Or generate predictions on new data:
+
 ```python
-classes = model.predict_classes(X_test, batch_size=32)
-proba = model.predict_proba(X_test, batch_size=32)
+classes = model.predict(x_test, batch_size=128)
 ```
 
-Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
+Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
 
 For a more in-depth tutorial about Keras, you can check out:
 
@@ -110,7 +115,7 @@ In the [examples folder](https://github.com/fchollet/keras/tree/master/examples)
 Keras uses the following dependencies:
 
 - numpy, scipy
-- pyyaml
+- yaml
 - HDF5 and h5py (optional, required if you use model saving/loading functions)
 - Optional but recommended if you use CNNs: cuDNN.
 
@@ -118,7 +123,12 @@ Keras uses the following dependencies:
 *When using the TensorFlow backend:*
 
 - TensorFlow
-    - [See installation instructions](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)
+    - [See installation instructions](https://www.tensorflow.org/install/).
+
+*When using the CNTK backend:*
+
+- CNTK
+    - [See installation instructions](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine).
 
 *When using the Theano backend:*
 
@@ -138,7 +148,7 @@ sudo pip install keras
 ------------------
 
 
-## Switching from TensorFlow to Theano
+## Switching from TensorFlow to CNTK or Theano
 
 By default, Keras will use TensorFlow as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
 

docker/Dockerfile

@@ -7,10 +7,10 @@ RUN mkdir -p $CONDA_DIR && \
     echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh && \
     apt-get update && \
     apt-get install -y wget git libhdf5-dev g++ graphviz && \
-    wget --quiet https://repo.continuum.io/miniconda/Miniconda3-3.9.1-Linux-x86_64.sh && \
-    echo "6c6b44acdd0bc4229377ee10d52c8ac6160c336d9cdd669db7371aa9344e1ac3 *Miniconda3-3.9.1-Linux-x86_64.sh" | sha256sum -c - && \
-    /bin/bash /Miniconda3-3.9.1-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
-    rm Miniconda3-3.9.1-Linux-x86_64.sh
+    wget --quiet https://repo.continuum.io/miniconda/Miniconda3-4.2.12-Linux-x86_64.sh && \
+    echo "c59b3dd3cad550ac7596e0d599b91e75d88826db132e4146030ef471bb434e9a *Miniconda3-4.2.12-Linux-x86_64.sh" | sha256sum -c - && \
+    /bin/bash /Miniconda3-4.2.12-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
+    rm Miniconda3-4.2.12-Linux-x86_64.sh
 
 ENV NB_USER keras
 ENV NB_UID 1000
@@ -24,13 +24,14 @@ RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
 USER keras
 
 # Python
-ARG python_version=3.5.2
-ARG tensorflow_version=0.12.0rc0-cp35-cp35m
+ARG python_version=3.5
+
 RUN conda install -y python=${python_version} && \
-    pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-${tensorflow_version}-linux_x86_64.whl && \
-    pip install git+git://github.com/Theano/Theano.git && \
-    pip install ipdb pytest pytest-cov python-coveralls coverage==3.7.1 pytest-xdist pep8 pytest-pep8 pydot_ng && \
-    conda install Pillow scikit-learn notebook pandas matplotlib nose pyyaml six h5py && \
+    pip install --upgrade pip && \
+    pip install tensorflow-gpu && \
+    conda install Pillow scikit-learn notebook pandas matplotlib mkl nose pyyaml six h5py && \
+    conda install theano pygpu && \
+    git clone git://github.com/fchollet/keras.git /src && pip install -e /src[tests] && \
     pip install git+git://github.com/fchollet/keras.git && \
     conda clean -yt
 

docker/theanorc

@@ -1,5 +1,5 @@
 [global]
 floatX = float32
 optimizer=None
-device = gpu
+device = cuda
 

docs/autogen.py

@@ -8,9 +8,7 @@ Index
 - Getting started
     Getting started with the sequential model
     Getting started with the functional api
-    Examples
     FAQ
-    Installation guide
 
 - Models
     About Keras models
@@ -26,31 +24,39 @@ Index
         explain common layer functions: get_weights, set_weights, get_config
         explain input_shape
         explain usage on non-Keras tensors
-    Core layers
-    Convolutional
-    Recurrent
-    Embeddings
-    Normalization
-    Advanced activations
-    Noise
+    Core Layers
+    Convolutional Layers
+    Pooling Layers
+    Locally-connected Layers
+    Recurrent Layers
+    Embedding Layers
+    Merge Layers
+    Advanced Activations Layers
+    Normalization Layers
+    Noise Layers
+    Layer Wrappers
+    Writing your own Keras layers
 
 - Preprocessing
-    Image preprocessing
-    Text preprocessing
-    Sequence preprocessing
+    Sequence Preprocessing
+    Text Preprocessing
+    Image Preprocessing
 
-Objectives
+Losses
 Metrics
 Optimizers
 Activations
 Callbacks
 Datasets
+Applications
 Backend
-Initializations
+Initializers
 Regularizers
 Constraints
 Visualization
 Scikit-learn API
+Utils
+Contributing
 
 '''
 from __future__ import print_function
@@ -65,7 +71,10 @@ if sys.version[0] == '2':
     reload(sys)
     sys.setdefaultencoding('utf8')
 
-from keras.layers import convolutional
+import keras
+from keras import utils
+from keras import layers
+from keras import initializers
 from keras.layers import pooling
 from keras.layers import local
 from keras.layers import recurrent
@@ -79,7 +88,7 @@ from keras import optimizers
 from keras import callbacks
 from keras import models
 from keras.engine import topology
-from keras import objectives
+from keras import losses
 from keras import metrics
 from keras import backend
 from keras import constraints
@@ -98,6 +107,9 @@ EXCLUDE = {
     'get_session',
     'set_session',
     'CallbackList',
+    'serialize',
+    'deserialize',
+    'get',
 }
 
 PAGES = [
@@ -108,14 +120,13 @@ PAGES = [
             models.Sequential.fit,
             models.Sequential.evaluate,
             models.Sequential.predict,
-            models.Sequential.predict_classes,
-            models.Sequential.predict_proba,
             models.Sequential.train_on_batch,
             models.Sequential.test_on_batch,
             models.Sequential.predict_on_batch,
             models.Sequential.fit_generator,
             models.Sequential.evaluate_generator,
             models.Sequential.predict_generator,
+            models.Sequential.get_layer,
         ],
     },
     {
@@ -137,40 +148,35 @@ PAGES = [
     {
         'page': 'layers/core.md',
         'classes': [
-            core.Dense,
-            core.Activation,
-            core.Dropout,
-            core.Flatten,
-            core.Reshape,
-            core.Permute,
-            core.RepeatVector,
-            topology.Merge,
-            core.Lambda,
-            core.ActivityRegularization,
-            core.Masking,
-            core.Highway,
-            core.MaxoutDense,
+            layers.Dense,
+            layers.Activation,
+            layers.Dropout,
+            layers.Flatten,
+            layers.Reshape,
+            layers.Permute,
+            layers.RepeatVector,
+            layers.Lambda,
+            layers.ActivityRegularization,
+            layers.Masking,
         ],
     },
     {
         'page': 'layers/convolutional.md',
         'classes': [
-            convolutional.Convolution1D,
-            convolutional.AtrousConvolution1D,
-            convolutional.Convolution2D,
-            convolutional.AtrousConvolution2D,
-            convolutional.SeparableConvolution2D,
-            convolutional.Deconvolution2D,
-            convolutional.Convolution3D,
-            convolutional.Cropping1D,
-            convolutional.Cropping2D,
-            convolutional.Cropping3D,
-            convolutional.UpSampling1D,
-            convolutional.UpSampling2D,
-            convolutional.UpSampling3D,
-            convolutional.ZeroPadding1D,
-            convolutional.ZeroPadding2D,
-            convolutional.ZeroPadding3D,
+            layers.Conv1D,
+            layers.Conv2D,
+            layers.SeparableConv2D,
+            layers.Conv2DTranspose,
+            layers.Conv3D,
+            layers.Cropping1D,
+            layers.Cropping2D,
+            layers.Cropping3D,
+            layers.UpSampling1D,
+            layers.UpSampling2D,
+            layers.UpSampling3D,
+            layers.ZeroPadding1D,
+            layers.ZeroPadding2D,
+            layers.ZeroPadding3D,
         ],
     },
     {
@@ -224,6 +230,25 @@ PAGES = [
         'page': 'layers/noise.md',
         'all_module_classes': [noise],
     },
+    {
+        'page': 'layers/merge.md',
+        'classes': [
+            layers.Add,
+            layers.Multiply,
+            layers.Average,
+            layers.Maximum,
+            layers.Concatenate,
+            layers.Dot,
+        ],
+        'functions': [
+            layers.add,
+            layers.multiply,
+            layers.average,
+            layers.maximum,
+            layers.concatenate,
+            layers.dot,
+        ]
+    },
     {
         'page': 'layers/wrappers.md',
         'all_module_classes': [wrappers],
@@ -232,6 +257,15 @@ PAGES = [
         'page': 'metrics.md',
         'all_module_functions': [metrics],
     },
+    {
+        'page': 'losses.md',
+        'all_module_functions': [losses],
+    },
+    {
+        'page': 'initializers.md',
+        'all_module_functions': [initializers],
+        'all_module_classes': [initializers],
+    },
     {
         'page': 'optimizers.md',
         'all_module_classes': [optimizers],
@@ -240,36 +274,19 @@ PAGES = [
         'page': 'callbacks.md',
         'all_module_classes': [callbacks],
     },
+    {
+        'page': 'activations.md',
+        'all_module_functions': [activations],
+    },
     {
         'page': 'backend.md',
         'all_module_functions': [backend],
     },
     {
-        'page': 'utils/data_utils.md',
-        'functions': [
-            data_utils.get_file,
-        ]
-    },
-    {
-        'page': 'utils/io_utils.md',
-        'classes': [
-            io_utils.HDF5Matrix
-        ],
-    },
-    {
-        'page': 'utils/layer_utils.md',
-        'functions': [
-            layer_utils.layer_from_config,
-        ]
-    },
-    {
-        'page': 'utils/np_utils.md',
-        'all_module_functions': [np_utils]
-    },
-    {
-        'page': 'utils/generic_utils.md',
-        'all_module_functions': [generic_utils],
-        'classes': [generic_utils.CustomObjectScope]
+        'page': 'utils.md',
+        'all_module_functions': [utils],
+        'classes': [utils.CustomObjectScope,
+                    utils.HDF5Matrix]
     },
 ]
 
@@ -303,7 +320,9 @@ def get_classes_ancestors(classes):
 
 
 def get_function_signature(function, method=True):
-    signature = inspect.getargspec(function)
+    signature = getattr(function, '_legacy_support_signature', None)
+    if signature is None:
+        signature = inspect.getargspec(function)
     defaults = signature.defaults
     if method:
         args = signature.args[1:]
@@ -368,7 +387,7 @@ def process_class_docstring(docstring):
                        r'\n    __\1__\n\n',
                        docstring)
 
-    docstring = re.sub(r'    ([^\s\\]+):(.*)\n',
+    docstring = re.sub(r'    ([^\s\\\(]+):(.*)\n',
                        r'    - __\1__:\2\n',
                        docstring)
 
@@ -386,7 +405,7 @@ def process_function_docstring(docstring):
                        r'\n        __\1__\n\n',
                        docstring)
 
-    docstring = re.sub(r'    ([^\s\\]+):(.*)\n',
+    docstring = re.sub(r'    ([^\s\\\(]+):(.*)\n',
                        r'    - __\1__:\2\n',
                        docstring)
 
@@ -410,6 +429,14 @@ for subdir, dirs, fnames in os.walk('templates'):
             new_fpath = fpath.replace('templates', 'sources')
             shutil.copy(fpath, new_fpath)
 
+# Take care of index page.
+readme = open('../README.md').read()
+index = open('templates/index.md').read()
+index = index.replace('{{autogenerated}}', readme[readme.find('##'):])
+f = open('sources/index.md', 'w')
+f.write(index)
+f.close()
+
 print('Starting autogeneration.')
 for page_data in PAGES:
     blocks = []
@@ -463,7 +490,11 @@ for page_data in PAGES:
         docstring = function.__doc__
         if docstring:
             subblocks.append(process_function_docstring(docstring))
-            blocks.append('\n\n'.join(subblocks))
+        blocks.append('\n\n'.join(subblocks))
+
+    if not blocks:
+        raise RuntimeError('Found no content for page ' +
+                           page_data['page'])
 
     mkdown = '\n----\n\n'.join(blocks)
     # save module page.
@@ -483,3 +514,5 @@ for page_data in PAGES:
     if not os.path.exists(subdir):
         os.makedirs(subdir)
     open(path, 'w').write(mkdown)
+
+shutil.copyfile('../CONTRIBUTING.md', 'sources/contributing.md')

docs/mkdocs.yml

@@ -1,15 +1,14 @@
 site_name: Keras Documentation
 theme: readthedocs
+#theme_dir: theme
 docs_dir: sources
 repo_url: http://github.com/fchollet/keras
 site_url: http://keras.io/
-# theme_dir: theme
 site_description: 'Documentation for Keras, the Python Deep Learning library.'
 
 dev_addr: '0.0.0.0:8000'
 google_analytics: ['UA-61785484-1', 'keras.io']
 
-
 pages:
 - Home: index.md
 - Getting started:
@@ -28,6 +27,7 @@ pages:
   - Locally-connected Layers: layers/local.md
   - Recurrent Layers: layers/recurrent.md
   - Embedding Layers: layers/embeddings.md
+  - Merge Layers: layers/merge.md
   - Advanced Activations Layers: layers/advanced-activations.md
   - Normalization Layers: layers/normalization.md
   - Noise layers: layers/noise.md
@@ -37,7 +37,7 @@ pages:
   - Sequence Preprocessing: preprocessing/sequence.md
   - Text Preprocessing: preprocessing/text.md
   - Image Preprocessing: preprocessing/image.md
-- Objectives: objectives.md
+- Losses: losses.md
 - Metrics: metrics.md
 - Optimizers: optimizers.md
 - Activations: activations.md
@@ -45,17 +45,10 @@ pages:
 - Datasets: datasets.md
 - Applications: applications.md
 - Backend: backend.md
-- Initializations: initializations.md
+- Initializers: initializers.md
 - Regularizers: regularizers.md
 - Constraints: constraints.md
 - Visualization: visualization.md
 - Scikit-learn API: scikit-learn-api.md
-- Utils:
-  - Data Utils: utils/data_utils.md
-  - I/O Utils: utils/io_utils.md
-  - Layer Utils: utils/layer_utils.md
-  - Numpy Utils: utils/np_utils.md
-  - Generic Utils: utils/generic_utils.md
-
-
-
+- Utils: utils.md
+- Contributing: contributing.md

docs/templates/activations.md

@@ -4,39 +4,31 @@
 Activations can either be used through an `Activation` layer, or through the `activation` argument supported by all forward layers:
 
 ```python
-from keras.layers.core import Activation, Dense
+from keras.layers import Activation, Dense
 
 model.add(Dense(64))
 model.add(Activation('tanh'))
 ```
-is equivalent to:
+
+This is equivalent to:
+
 ```python
 model.add(Dense(64, activation='tanh'))
 ```
 
-You can also pass an element-wise Theano/TensorFlow function as an activation:
+You can also pass an element-wise Tensorflow/Theano function as an activation:
 
 ```python
 from keras import backend as K
 
-def tanh(x):
-    return K.tanh(x)
-
-model.add(Dense(64, activation=tanh))
-model.add(Activation(tanh))
+model.add(Dense(64, activation=K.tanh))
+model.add(Activation(K.tanh))
 ```
 
 ## Available activations
 
-- __softmax__: Softmax applied across inputs last dimension. Expects shape either `(nb_samples, nb_timesteps, nb_dims)` or `(nb_samples, nb_dims)`.
-- __softplus__
-- __softsign__
-- __relu__
-- __tanh__
-- __sigmoid__
-- __hard_sigmoid__
-- __linear__
+{{autogenerated}}
 
-## On Advanced Activations
+## On "Advanced Activations"
 
-Activations that are more complex than a simple Theano/TensorFlow function (eg. learnable activations, configurable activations, etc.) are available as [Advanced Activation layers](layers/advanced-activations.md), and can be found in the module `keras.layers.advanced_activations`. These include PReLU and LeakyReLU.
+Activations that are more complex than a simple Tensorflow/Theano function (eg. learnable activations, which maintain a state) are available as [Advanced Activation layers](layers/advanced-activations.md), and can be found in the module `keras.layers.advanced_activations`. These include `PReLU` and `LeakyReLU`.

docs/templates/applications.md

@@ -15,14 +15,10 @@ Weights are downloaded automatically when instantiating a model. They are stored
 - [ResNet50](#resnet50)
 - [InceptionV3](#inceptionv3)
 
-All of these architectures (except Xception) are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_dim_ordering=tf`, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth".
+All of these architectures (except Xception) are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image data format set in your Keras configuration file at `~/.keras/keras.json`. For instance, if you have set `image_data_format=channels_last`, then any model loaded from this repository will get built according to the TensorFlow data format convention, "Width-Height-Depth".
 
 The Xception model is only available for TensorFlow, due to its reliance on `SeparableConvolution` layers.
 
-### Model for music audio file auto-tagging (taking as input Mel-spectrograms):
-
-- [MusicTaggerCRNN](#musictaggercrnn)
-
 -----
 
 ## Usage examples for image classification models
@@ -79,7 +75,7 @@ from keras.models import Model
 import numpy as np
 
 base_model = VGG19(weights='imagenet')
-model = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output)
+model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)
 
 img_path = 'elephant.jpg'
 img = image.load_img(img_path, target_size=(224, 224))
@@ -111,7 +107,7 @@ x = Dense(1024, activation='relu')(x)
 predictions = Dense(200, activation='softmax')(x)
 
 # this is the model we will train
-model = Model(input=base_model.input, output=predictions)
+model = Model(inputs=base_model.input, outputs=predictions)
 
 # first: train only the top layers (which were randomly initialized)
 # i.e. freeze all convolutional InceptionV3 layers
@@ -134,10 +130,10 @@ for i, layer in enumerate(base_model.layers):
    print(i, layer.name)
 
 # we chose to train the top 2 inception blocks, i.e. we will freeze
-# the first 172 layers and unfreeze the rest:
-for layer in model.layers[:172]:
+# the first 249 layers and unfreeze the rest:
+for layer in model.layers[:249]:
    layer.trainable = False
-for layer in model.layers[172:]:
+for layer in model.layers[249:]:
    layer.trainable = True
 
 # we need to recompile the model for these modifications to take effect
@@ -158,7 +154,7 @@ from keras.applications.inception_v3 import InceptionV3
 from keras.layers import Input
 
 # this could also be the output a different Keras model or layer
-input_tensor = Input(shape=(224, 224, 3))  # this assumes K.image_dim_ordering() == 'tf'
+input_tensor = Input(shape=(224, 224, 3))  # this assumes K.image_data_format() == 'channels_last'
 
 model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True)
 ```
@@ -172,7 +168,6 @@ model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=T
 - [VGG19](#vgg19)
 - [ResNet50](#resnet50)
 - [InceptionV3](#inceptionv3)
-- [MusicTaggerCRNN](#musictaggercrnn)
 
 -----
 
@@ -191,7 +186,7 @@ and a top-5 validation accuracy of 0.945.
 
 Note that this model is only available for the TensorFlow backend,
 due to its reliance on `SeparableConvolution` layers. Additionally it only supports
-the dimension ordering "tf" (width, height, channels).
+the data format "channels_last" (height, width, channels).
 
 The default input size for this model is 299x299.
 
@@ -206,6 +201,17 @@ The default input size for this model is 299x299.
     It should have exactly 3 inputs channels,
     and width and height should be no smaller than 71.
     E.g. `(150, 150, 3)` would be one valid value.
+- pooling: Optional pooling mode for feature extraction
+    when `include_top` is `False`.
+    - `None` means that the output of the model will be
+        the 4D tensor output of the
+        last convolutional layer.
+    - `avg` means that global average pooling
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a 2D tensor.
+    - `max` means that global max pooling will
+        be applied.
 - classes: optional number of classes to classify images 
     into, only to be specified if `include_top` is True, and 
     if no `weights` argument is specified.
@@ -235,7 +241,7 @@ keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tenso
 VGG16 model, with weights pre-trained on ImageNet.
 
 This model is available for both the Theano and TensorFlow backend, and can be built both
-with "th" dim ordering (channels, width, height) or "tf" dim ordering (width, height, channels).
+with "channels_first" data format (channels, height, width) or "channels_last" data format (height, width, channels).
 
 The default input size for this model is 224x224.
 
@@ -246,11 +252,22 @@ The default input size for this model is 224x224.
 - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
 - input_shape: optional shape tuple, only to be specified
     if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `tf` dim ordering)
-    or `(3, 224, 244)` (with `th` dim ordering).
+    has to be `(224, 224, 3)` (with `channels_last` data format)
+    or `(3, 224, 224)` (with `channels_first` data format).
     It should have exactly 3 inputs channels,
     and width and height should be no smaller than 48.
     E.g. `(200, 200, 3)` would be one valid value.
+- pooling: Optional pooling mode for feature extraction
+    when `include_top` is `False`.
+    - `None` means that the output of the model will be
+        the 4D tensor output of the
+        last convolutional layer.
+    - `avg` means that global average pooling
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a 2D tensor.
+    - `max` means that global max pooling will
+        be applied.
 - classes: optional number of classes to classify images 
     into, only to be specified if `include_top` is True, and 
     if no `weights` argument is specified.
@@ -280,7 +297,7 @@ keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tenso
 VGG19 model, with weights pre-trained on ImageNet.
 
 This model is available for both the Theano and TensorFlow backend, and can be built both
-with "th" dim ordering (channels, width, height) or "tf" dim ordering (width, height, channels).
+with "channels_first" data format (channels, height, width) or "channels_last" data format (height, width, channels).
 
 The default input size for this model is 224x224.
 
@@ -291,11 +308,22 @@ The default input size for this model is 224x224.
 - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
 - input_shape: optional shape tuple, only to be specified
     if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `tf` dim ordering)
-    or `(3, 224, 244)` (with `th` dim ordering).
+    has to be `(224, 224, 3)` (with `channels_last` data format)
+    or `(3, 224, 224)` (with `channels_first` data format).
     It should have exactly 3 inputs channels,
     and width and height should be no smaller than 48.
     E.g. `(200, 200, 3)` would be one valid value.
+- pooling: Optional pooling mode for feature extraction
+    when `include_top` is `False`.
+    - `None` means that the output of the model will be
+        the 4D tensor output of the
+        last convolutional layer.
+    - `avg` means that global average pooling
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a 2D tensor.
+    - `max` means that global max pooling will
+        be applied.
 - classes: optional number of classes to classify images 
     into, only to be specified if `include_top` is True, and 
     if no `weights` argument is specified.
@@ -326,7 +354,7 @@ keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet', input
 ResNet50 model, with weights pre-trained on ImageNet.
 
 This model is available for both the Theano and TensorFlow backend, and can be built both
-with "th" dim ordering (channels, width, height) or "tf" dim ordering (width, height, channels).
+with "channels_first" data format (channels, height, width) or "channels_last" data format (height, width, channels).
 
 The default input size for this model is 224x224.
 
@@ -338,11 +366,22 @@ The default input size for this model is 224x224.
 - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
 - input_shape: optional shape tuple, only to be specified
     if `include_top` is False (otherwise the input shape
-    has to be `(224, 224, 3)` (with `tf` dim ordering)
-    or `(3, 224, 244)` (with `th` dim ordering).
+    has to be `(224, 224, 3)` (with `channels_last` data format)
+    or `(3, 224, 224)` (with `channels_first` data format).
     It should have exactly 3 inputs channels,
     and width and height should be no smaller than 197.
     E.g. `(200, 200, 3)` would be one valid value.
+- pooling: Optional pooling mode for feature extraction
+    when `include_top` is `False`.
+    - `None` means that the output of the model will be
+        the 4D tensor output of the
+        last convolutional layer.
+    - `avg` means that global average pooling
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a 2D tensor.
+    - `max` means that global max pooling will
+        be applied.
 - classes: optional number of classes to classify images 
     into, only to be specified if `include_top` is True, and 
     if no `weights` argument is specified.
@@ -371,7 +410,7 @@ keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet'
 Inception V3 model, with weights pre-trained on ImageNet.
 
 This model is available for both the Theano and TensorFlow backend, and can be built both
-with "th" dim ordering (channels, width, height) or "tf" dim ordering (width, height, channels).
+with "channels_first" data format (channels, height, width) or "channels_last" data format (height, width, channels).
 
 The default input size for this model is 299x299.
 
@@ -383,11 +422,22 @@ The default input size for this model is 299x299.
 - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
 - input_shape: optional shape tuple, only to be specified
     if `include_top` is False (otherwise the input shape
-    has to be `(299, 299, 3)` (with `tf` dim ordering)
-    or `(3, 299, 299)` (with `th` dim ordering).
+    has to be `(299, 299, 3)` (with `channels_last` data format)
+    or `(3, 299, 299)` (with `channels_first` data format).
     It should have exactly 3 inputs channels,
     and width and height should be no smaller than 139.
     E.g. `(150, 150, 3)` would be one valid value.
+- pooling: Optional pooling mode for feature extraction
+    when `include_top` is `False`.
+    - `None` means that the output of the model will be
+        the 4D tensor output of the
+        last convolutional layer.
+    - `avg` means that global average pooling
+        will be applied to the output of the
+        last convolutional layer, and thus
+        the output of the model will be a 2D tensor.
+    - `max` means that global max pooling will
+        be applied.
 - classes: optional number of classes to classify images 
     into, only to be specified if `include_top` is True, and 
     if no `weights` argument is specified.
@@ -402,68 +452,4 @@ A Keras model instance.
 
 ### License
 
-These weights are trained by ourselves and are released under the MIT license.
-
------
-
-## MusicTaggerCRNN
-
-
-```python
-keras.applications.music_tagger_crnn.MusicTaggerCRNN(weights='msd', input_tensor=None, include_top=True)
-```
-
-A convolutional-recurrent model taking as input a vectorized representation of the MelSpectrogram of a music track and capable of outputting the musical genre of the track. You can use `keras.applications.music_tagger_crnn.preprocess_input` to convert a sound file to a vectorized spectrogram. This requires to have installed the [Librosa](http://librosa.github.io/librosa/) library. See [the usage example](#music-tagging-and-feature-extraction-with-musictaggercrnn).
-
-### Arguments
-
-- weights: one of `None` (random initialization) or "msd" (pre-training on [Million Song Dataset](http://labrosa.ee.columbia.edu/millionsong/)).
-- input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
-- include_top: whether to include the 1 fully-connected layer (output layer) at the top of the network. If False, the network outputs 32-dim features.
-- classes: optional number of classes to classify images 
-    into, only to be specified if `include_top` is True, and 
-    if no `weights` argument is specified.
-    
-### Returns
-
-A Keras model instance.
-
-### References
-
-- [Convolutional Recurrent Neural Networks for Music Classification](https://arxiv.org/abs/1609.04243)
-
-### License
-
-These weights are ported from the ones [released by Keunwoo Choi](https://github.com/keunwoochoi/music-auto_tagging-keras) under the [MIT license](https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/LICENSE.md).
-
-### Examples: music tagging and audio feature extraction
-
-```python
-from keras.applications.music_tagger_crnn import MusicTaggerCRNN
-from keras.applications.music_tagger_crnn import preprocess_input, decode_predictions
-import numpy as np
-
-# 1. Tagging
-model = MusicTaggerCRNN(weights='msd')
-
-audio_path = 'audio_file.mp3'
-melgram = preprocess_input(audio_path)
-melgrams = np.expand_dims(melgram, axis=0)
-
-preds = model.predict(melgrams)
-print('Predicted:')
-print(decode_predictions(preds))
-# print: ('Predicted:', [[('rock', 0.097071797), ('pop', 0.042456303), ('alternative', 0.032439161), ('indie', 0.024491295), ('female vocalists', 0.016455274)]])
-
-#. 2. Feature extraction
-model = MusicTaggerCRNN(weights='msd', include_top=False)
-
-audio_path = 'audio_file.mp3'
-melgram = preprocess_input(audio_path)
-melgrams = np.expand_dims(melgram, axis=0)
-
-feats = model.predict(melgrams)
-print('Features:')
-print(feats[0, :10])
-# print: ('Features:', [-0.19160545 0.94259131 -0.9991011 0.47644514 -0.19089699 0.99033844 0.1103896 -0.00340496 0.14823607 0.59856361])
-```
+These weights are released under [the Apache License](https://github.com/tensorflow/models/blob/master/LICENSE).

docs/templates/backend.md

@@ -4,12 +4,13 @@
 
 Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.
 
-At this time, Keras has two backend implementations available: the **TensorFlow** backend and the **Theano** backend.
+At this time, Keras has three backend implementations available: the **TensorFlow** backend, the **Theano** backend, and the **CNTK** backend.
 
 - [TensorFlow](http://www.tensorflow.org/) is an open-source symbolic tensor manipulation framework developed by Google, Inc.
 - [Theano](http://deeplearning.net/software/theano/) is an open-source symbolic tensor manipulation framework developed by LISA/MILA Lab at Université de Montréal.
+- [CNTK](https://www.microsoft.com/en-us/cognitive-toolkit/) is an open-source, commercial-grade toolkit for deep learning developed by Microsoft.
 
-In the future, we are likely to add more backend options. If you are interested in developing a new backend, get in touch!
+In the future, we are likely to add more backend options.
 
 ----
 
@@ -17,22 +18,24 @@ In the future, we are likely to add more backend options. If you are interested
 
 If you have run Keras at least once, you will find the Keras configuration file at:
 
-`~/.keras/keras.json`
+`$HOME/.keras/keras.json`
 
 If it isn't there, you can create it.
 
+**NOTE for Windows Users:** Please change `$HOME` with `%USERPROFILE%`.
+
 The default configuration file looks like this:
 
 ```
 {
-    "image_dim_ordering": "tf",
+    "image_data_format": "channels_last",
     "epsilon": 1e-07,
     "floatx": "float32",
     "backend": "tensorflow"
 }
 ```
 
-Simply change the field `backend` to either `"theano"` or `"tensorflow"`, and Keras will use the new configuration next time you run any Keras code.
+Simply change the field `backend` to `"theano"`, `"tensorflow"`, or `"cntk"`, and Keras will use the new configuration next time you run any Keras code.
 
 You can also define the environment variable ``KERAS_BACKEND`` and this will
 override what is defined in your config file :
@@ -49,34 +52,34 @@ Using TensorFlow backend.
 
 ```
 {
-    "image_dim_ordering": "tf",
+    "image_data_format": "channels_last",
     "epsilon": 1e-07,
     "floatx": "float32",
     "backend": "tensorflow"
 }
 ```
 
-You can change these settings by editing `~/.keras/keras.json`. 
+You can change these settings by editing `$HOME/.keras/keras.json`. 
 
-* `image_dim_ordering`: string, either `"tf"` or `"th"`. It specifies which dimension ordering convention Keras will follow. (`keras.backend.image_dim_ordering()` returns it.)
-  - For 2D data (e.g. image), `"tf"` assumes `(rows, cols, channels)` while `"th"` assumes `(channels, rows, cols)`. 
-  - For 3D data, `"tf"` assumes `(conv_dim1, conv_dim2, conv_dim3, channels)` while `"th"` assumes `(channels, conv_dim1, conv_dim2, conv_dim3)`.
+* `image_data_format`: string, either `"channels_last"` or `"channels_first"`. It specifies which data format convention Keras will follow. (`keras.backend.image_data_format()` returns it.)
+  - For 2D data (e.g. image), `"channels_last"` assumes `(rows, cols, channels)` while `"channels_first"` assumes `(channels, rows, cols)`. 
+  - For 3D data, `"channels_last"` assumes `(conv_dim1, conv_dim2, conv_dim3, channels)` while `"channels_first"` assumes `(channels, conv_dim1, conv_dim2, conv_dim3)`.
 * `epsilon`: float, a numeric fuzzing constant used to avoid dividing by zero in some operations.
 * `floatx`: string, `"float16"`, `"float32"`, or `"float64"`. Default float precision.
-* `backend`: string, `"tensorflow"` or `"theano"`.
+* `backend`: string, `"tensorflow"`, `"theano"`, or `"cntk"`.
 
 ----
 
 ## Using the abstract Keras backend to write new code
 
-If you want the Keras modules you write to be compatible with both Theano and TensorFlow, you have to write them via the abstract Keras backend API. Here's an intro.
+If you want the Keras modules you write to be compatible with both Theano (`th`) and TensorFlow (`tf`), you have to write them via the abstract Keras backend API. Here's an intro.
 
 You can import the backend module via:
 ```python
 from keras import backend as K
 ```
 
-The code below instantiates an input placeholder. It's equivalent to `tf.placeholder()` or `T.matrix()`, `T.tensor3()`, etc.
+The code below instantiates an input placeholder. It's equivalent to `tf.placeholder()` or `th.tensor.matrix()`, `th.tensor.tensor3()`, etc.
 
 ```python
 input = K.placeholder(shape=(2, 4, 5))
@@ -86,9 +89,10 @@ input = K.placeholder(shape=(None, 4, 5))
 input = K.placeholder(ndim=3)
 ```
 
-The code below instantiates a shared variable. It's equivalent to `tf.variable()` or `theano.shared()`.
+The code below instantiates a shared variable. It's equivalent to `tf.Variable()` or `th.shared()`.
 
 ```python
+import numpy as np
 val = np.random.random((3, 4, 5))
 var = K.variable(value=val)
 
@@ -101,11 +105,16 @@ var = K.ones(shape=(3, 4, 5))
 Most tensor operations you will need can be done as you would in TensorFlow or Theano:
 
 ```python
+# Initializing Tensors with Random Numbers
+b = K.random_uniform_variable(shape=(3, 4)). # Uniform distribution
+c = K.random_normal_variable(shape=(3, 4)). # Gaussian distribution
+d = K.random_normal_variable(shape=(3, 4)).
+# Tensor Arithmetics
 a = b + c * K.abs(d)
 c = K.dot(a, K.transpose(b))
-a = K.sum(b, axis=2)
+a = K.sum(b, axis=1)
 a = K.softmax(b)
-a = concatenate([b, c], axis=-1)
+a = K.concatenate([b, c], axis=-1)
 # etc...
 ```
 

docs/templates/callbacks.md

@@ -1,6 +1,6 @@
 ## Usage of callbacks
 
-A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks (as the keyword argument `callbacks`) to the `.fit()` method of the `Sequential` model. The relevant methods of the callbacks will then be called at each stage of the training. 
+A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks (as the keyword argument `callbacks`) to the `.fit()` method of the `Sequential` or `Model` classes. The relevant methods of the callbacks will then be called at each stage of the training. 
 
 ---
 
@@ -36,14 +36,14 @@ class LossHistory(keras.callbacks.Callback):
         self.losses.append(logs.get('loss'))
 
 model = Sequential()
-model.add(Dense(10, input_dim=784, init='uniform'))
+model.add(Dense(10, input_dim=784, kernel_initializer='uniform'))
 model.add(Activation('softmax'))
 model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
 
 history = LossHistory()
-model.fit(X_train, Y_train, batch_size=128, nb_epoch=20, verbose=0, callbacks=[history])
+model.fit(x_train, y_train, batch_size=128, epochs=20, verbose=0, callbacks=[history])
 
-print history.losses
+print(history.losses)
 # outputs
 '''
 [0.66047596406559383, 0.3547245744908703, ..., 0.25953155204159617, 0.25901699725311789]
@@ -58,15 +58,13 @@ print history.losses
 from keras.callbacks import ModelCheckpoint
 
 model = Sequential()
-model.add(Dense(10, input_dim=784, init='uniform'))
+model.add(Dense(10, input_dim=784, kernel_initializer='uniform'))
 model.add(Activation('softmax'))
 model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
 
 '''
 saves the model weights after each epoch if the validation loss decreased
 '''
-checkpointer = ModelCheckpoint(filepath="/tmp/weights.hdf5", verbose=1, save_best_only=True)
-model.fit(X_train, Y_train, batch_size=128, nb_epoch=20, verbose=0, validation_data=(X_test, Y_test), callbacks=[checkpointer])
-
+checkpointer = ModelCheckpoint(filepath='/tmp/weights.hdf5', verbose=1, save_best_only=True)
+model.fit(x_train, y_train, batch_size=128, epochs=20, verbose=0, validation_data=(X_test, Y_test), callbacks=[checkpointer])
 ```
-

docs/templates/constraints.md

@@ -2,21 +2,21 @@
 
 Functions from the `constraints` module allow setting constraints (eg. non-negativity) on network parameters during optimization.
 
-The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`, `TimeDistributedDense`, `MaxoutDense`, `Convolution1D`, `Convolution2D` and `Convolution3D` have a unified API.
+The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`, `Conv1D`, `Conv2D` and `Conv3D` have a unified API.
 
 These layers expose 2 keyword arguments:
 
-- `W_constraint` for the main weights matrix
-- `b_constraint` for the bias.
+- `kernel_constraint` for the main weights matrix
+- `bias_constraint` for the bias.
 
 
 ```python
 from keras.constraints import maxnorm
-model.add(Dense(64, W_constraint = maxnorm(2)))
+model.add(Dense(64, kernel_constraint=max_norm(2.)))
 ```
 
 ## Available constraints
 
-- __maxnorm__(m=2): maximum-norm constraint
-- __nonneg__(): non-negativity constraint
-- __unitnorm__(): unit-norm constraint, enforces the matrix to have unit norm along the last axis
+- __max_norm__(max_value=2, axis=0): maximum-norm constraint
+- __non_neg__(): non-negativity constraint
+- __unit_norm__(): unit-norm constraint, enforces the matrix to have unit norm along the last axis

docs/templates/datasets.md

@@ -9,13 +9,14 @@ Dataset of 50,000 32x32 color training images, labeled over 10 categories, and 1
 ```python
 from keras.datasets import cifar10
 
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
 ```
 
-- __Return:__
+- __Returns:__
     - 2 tuples:
-        - __X_train, X_test__: uint8 array of RGB image data with shape (nb_samples, 3, 32, 32).
-        - __y_train, y_test__: uint8 array of category labels (integers in range 0-9) with shape (nb_samples,).
+        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32).
+        - __y_train, y_test__: uint8 array of category labels (integers in range 0-9) with shape (num_samples,).
+
 
 ---
 
@@ -28,18 +29,19 @@ Dataset of 50,000 32x32 color training images, labeled over 100 categories, and
 ```python
 from keras.datasets import cifar100
 
-(X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode='fine')
+(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')
 ```
 
-- __Return:__
+- __Returns:__
     - 2 tuples:
-        - __X_train, X_test__: uint8 array of RGB image data with shape (nb_samples, 3, 32, 32).
-        - __y_train, y_test__: uint8 array of category labels with shape (nb_samples,).
+        - __x_train, x_test__: uint8 array of RGB image data with shape (num_samples, 3, 32, 32).
+        - __y_train, y_test__: uint8 array of category labels with shape (num_samples,).
 
 - __Arguments:__
 
     - __label_mode__: "fine" or "coarse".
 
+
 ---
 
 ## IMDB Movie reviews sentiment classification
@@ -53,8 +55,8 @@ As a convention, "0" does not stand for a specific word, but instead is used to
 ```python
 from keras.datasets import imdb
 
-(X_train, y_train), (X_test, y_test) = imdb.load_data(path="imdb_full.pkl",
-                                                      nb_words=None,
+(x_train, y_train), (x_test, y_test) = imdb.load_data(path="imdb.npz",
+                                                      num_words=None,
                                                       skip_top=0,
                                                       maxlen=None,
                                                       seed=113,
@@ -62,24 +64,25 @@ from keras.datasets import imdb
                                                       oov_char=2,
                                                       index_from=3)
 ```
-- __Return:__
+- __Returns:__
     - 2 tuples:
-        - __X_train, X_test__: list of sequences, which are lists of indexes (integers). If the nb_words argument was specific, the maximum possible index value is nb_words-1. If the maxlen argument was specified, the largest possible sequence length is maxlen.
+        - __x_train, x_test__: list of sequences, which are lists of indexes (integers). If the num_words argument was specific, the maximum possible index value is num_words-1. If the maxlen argument was specified, the largest possible sequence length is maxlen.
         - __y_train, y_test__: list of integer labels (1 or 0). 
 
 - __Arguments:__
 
-    - __path__: if you do have the data locally (at `'~/.keras/datasets/' + path`), if will be downloaded to this location (in cPickle format).
-    - __nb_words__: integer or None. Top most frequent words to consider. Any less frequent word will appear as 0 in the sequence data.
-    - __skip_top__: integer. Top most frequent words to ignore (they will appear as 0s in the sequence data).
+    - __path__: if you do not have the data locally (at `'~/.keras/datasets/' + path`), it will be downloaded to this location.
+    - __num_words__: integer or None. Top most frequent words to consider. Any less frequent word will appear as `oov_char` value in the sequence data.
+    - __skip_top__: integer. Top most frequent words to ignore (they will appear as `oov_char` value in the sequence data).
     - __maxlen__: int. Maximum sequence length. Any longer sequence will be truncated.
     - __seed__: int. Seed for reproducible data shuffling.
-    - __start_char__: char. The start of a sequence will be marked with this character.
+    - __start_char__: int. The start of a sequence will be marked with this character.
         Set to 1 because 0 is usually the padding character.
-    - __oov_char__: char. words that were cut out because of the `nb_words`
+    - __oov_char__: int. words that were cut out because of the `num_words`
         or `skip_top` limit will be replaced with this character.
     - __index_from__: int. Index actual words with this index and higher.
 
+
 ---
 
 ## Reuters newswire topics classification
@@ -91,8 +94,8 @@ Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with the IM
 ```python
 from keras.datasets import reuters
 
-(X_train, y_train), (X_test, y_test) = reuters.load_data(path="reuters.pkl",
-                                                         nb_words=None,
+(x_train, y_train), (x_test, y_test) = reuters.load_data(path="reuters.npz",
+                                                         num_words=None,
                                                          skip_top=0,
                                                          maxlen=None,
                                                          test_split=0.2,
@@ -104,20 +107,23 @@ from keras.datasets import reuters
 
 The specifications are the same as that of the IMDB dataset, with the addition of:
 
-    - __test_split__: float. Fraction of the dataset to be used as test data.
+- __test_split__: float. Fraction of the dataset to be used as test data.
 
 This dataset also makes available the word index used for encoding the sequences:
 
 ```python
-word_index = reuters.get_word_index(path="reuters_word_index.pkl")
+word_index = reuters.get_word_index(path="reuters_word_index.json")
 ```
 
-- __Return:__ A dictionary where key are words (str) and values are indexes (integer). eg. `word_index["giraffe"]` might return `1234`. 
+- __Returns:__ A dictionary where key are words (str) and values are indexes (integer). eg. `word_index["giraffe"]` might return `1234`. 
 
 - __Arguments:__
 
-    - __path__: if you do have the index file locally (at `'~/.keras/datasets/' + path`), if will be downloaded to this location (in cPickle format).
+    - __path__: if you do not have the index file locally (at `'~/.keras/datasets/' + path`), it will be downloaded to this location.
     
+
+---
+
 ## MNIST database of handwritten digits
 
 Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
@@ -127,14 +133,44 @@ Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set
 ```python
 from keras.datasets import mnist
 
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 ```
 
-- __Return:__
+- __Returns:__
     - 2 tuples:
-        - __X_train, X_test__: uint8 array of grayscale image data with shape (nb_samples, 28, 28).
-        - __y_train, y_test__: uint8 array of digit labels (integers in range 0-9) with shape (nb_samples,).
+        - __x_train, x_test__: uint8 array of grayscale image data with shape (num_samples, 28, 28).
+        - __y_train, y_test__: uint8 array of digit labels (integers in range 0-9) with shape (num_samples,).
 
 - __Arguments:__
 
-    - __path__: if you do have the index file locally (at `'~/.keras/datasets/' + path`), if will be downloaded to this location (in cPickle format).
+    - __path__: if you do not have the index file locally (at `'~/.keras/datasets/' + path`), it will be downloaded to this location.
+
+
+---
+
+## Boston housing price regression dataset
+
+
+Dataset taken from the StatLib library which is maintained at Carnegie Mellon University. 
+
+Samples contain 13 attributes of houses at different locations around the Boston suburbs in the late 1970s.
+Targets are the median values of the houses at a location (in k$).
+
+
+### Usage:
+
+```python
+from keras.datasets import boston_housing
+
+(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
+```
+
+- __Arguments:__
+    - __path__: path where to cache the dataset locally
+        (relative to ~/.keras/datasets).
+    - __seed__: Random seed for shuffling the data
+        before computing the test split.
+    - __test_split__: fraction of the data to reserve as test set.
+
+- __Returns:__
+    Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

docs/templates/getting-started/faq.md

@@ -2,7 +2,7 @@
 
 - [How should I cite Keras?](#how-should-i-cite-keras)
 - [How can I run Keras on GPU?](#how-can-i-run-keras-on-gpu)
-- [What does \["sample", "batch", "epoch"\] mean?](#what-does-sample-batch-epoch-mean)
+- [What does "sample", "batch", "epoch" mean?](#what-does-sample-batch-epoch-mean)
 - [How can I save a Keras model?](#how-can-i-save-a-keras-model)
 - [Why is the training loss much higher than the testing loss?](#why-is-the-training-loss-much-higher-than-the-testing-loss)
 - [How can I obtain the output of an intermediate layer?](#how-can-i-obtain-the-output-of-an-intermediate-layer)
@@ -15,6 +15,8 @@
 - [How can I use stateful RNNs?](#how-can-i-use-stateful-rnns)
 - [How can I remove a layer from a Sequential model?](#how-can-i-remove-a-layer-from-a-sequential-model)
 - [How can I use pre-trained models in Keras?](#how-can-i-use-pre-trained-models-in-keras)
+- [How can I use HDF5 inputs with Keras?](#how-can-i-use-hdf5-inputs-with-keras)
+- [Where is the Keras configuration filed stored?](#where-is-the-keras-configuration-filed-stored)
 
 ---
 
@@ -25,7 +27,7 @@ Please cite Keras in your publications if it helps your research. Here is an exa
 ```
 @misc{chollet2015keras,
   title={Keras},
-  author={Chollet, Fran\c{c}ois},
+  author={Chollet, Fran\c{c}ois and others},
   year={2015},
   publisher={GitHub},
   howpublished={\url{https://github.com/fchollet/keras}},
@@ -36,7 +38,8 @@ Please cite Keras in your publications if it helps your research. Here is an exa
 
 ### How can I run Keras on GPU?
 
-If you are running on the TensorFlow backend, your code will automatically run on GPU if any available GPU is detected.
+If you are running on the TensorFlow or CNTK backends, your code will automatically run on GPU if any available GPU is detected.
+
 If you are running on the Theano backend, you can use one of the following methods:
 
 Method 1: use Theano flags.
@@ -57,7 +60,7 @@ theano.config.floatX = 'float32'
 
 ---
 
-### What does \["sample", "batch", "epoch"\] mean?
+### What does "sample", "batch", "epoch" mean?
 
 Below are some common definitions that are necessary to know and understand to correctly utilize Keras:
 
@@ -120,6 +123,7 @@ from keras.models import model_from_json
 model = model_from_json(json_string)
 
 # model reconstruction from YAML
+from keras.models import model_from_yaml
 model = model_from_yaml(yaml_string)
 ```
 
@@ -149,16 +153,16 @@ For example:
 """
 Assume original model looks like this:
     model = Sequential()
-    model.add(Dense(2, input_dim=3, name="dense_1"))
-    model.add(Dense(3, name="dense_2"))
+    model.add(Dense(2, input_dim=3, name='dense_1'))
+    model.add(Dense(3, name='dense_2'))
     ...
     model.save_weights(fname)
 """
 
 # new model
 model = Sequential()
-model.add(Dense(2, input_dim=3, name="dense_1"))  # will be loaded
-model.add(Dense(10, name="new_dense"))  # will not be loaded
+model.add(Dense(2, input_dim=3, name='dense_1'))  # will be loaded
+model.add(Dense(10, name='new_dense'))  # will not be loaded
 
 # load weights from first model; will only affect the first layer, dense_1.
 model.load_weights(fname, by_name=True)
@@ -184,8 +188,8 @@ from keras.models import Model
 model = ...  # create the original model
 
 layer_name = 'my_layer'
-intermediate_layer_model = Model(input=model.input,
-                                 output=model.get_layer(layer_name).output)
+intermediate_layer_model = Model(inputs=model.input,
+                                 outputs=model.get_layer(layer_name).output)
 intermediate_output = intermediate_layer_model.predict(data)
 ```
 
@@ -197,7 +201,7 @@ from keras import backend as K
 # with a Sequential model
 get_3rd_layer_output = K.function([model.layers[0].input],
                                   [model.layers[3].output])
-layer_output = get_3rd_layer_output([X])[0]
+layer_output = get_3rd_layer_output([x])[0]
 ```
 
 Similarly, you could build a Theano and TensorFlow function directly.
@@ -210,19 +214,19 @@ get_3rd_layer_output = K.function([model.layers[0].input, K.learning_phase()],
                                   [model.layers[3].output])
 
 # output in test mode = 0
-layer_output = get_3rd_layer_output([X, 0])[0]
+layer_output = get_3rd_layer_output([x, 0])[0]
 
 # output in train mode = 1
-layer_output = get_3rd_layer_output([X, 1])[0]
+layer_output = get_3rd_layer_output([x, 1])[0]
 ```
 
 ---
 
 ### How can I use Keras with datasets that don't fit in memory?
 
-You can do batch training using `model.train_on_batch(X, y)` and `model.test_on_batch(X, y)`. See the [models documentation](/models/sequential).
+You can do batch training using `model.train_on_batch(x, y)` and `model.test_on_batch(x, y)`. See the [models documentation](/models/sequential).
 
-Alternatively, you can write a generator that yields batches of training data and use the method `model.fit_generator(data_generator, samples_per_epoch, nb_epoch)`.
+Alternatively, you can write a generator that yields batches of training data and use the method `model.fit_generator(data_generator, steps_per_epoch, epochs)`.
 
 You can see batch training in action in our [CIFAR10 example](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py).
 
@@ -235,7 +239,7 @@ You can use an `EarlyStopping` callback:
 ```python
 from keras.callbacks import EarlyStopping
 early_stopping = EarlyStopping(monitor='val_loss', patience=2)
-model.fit(X, y, validation_split=0.2, callbacks=[early_stopping])
+model.fit(x, y, validation_split=0.2, callbacks=[early_stopping])
 ```
 
 Find out more in the [callbacks documentation](/callbacks).
@@ -264,7 +268,7 @@ Validation data is never shuffled.
 The `model.fit` method returns an `History` callback, which has a `history` attribute containing the lists of successive losses and other metrics.
 
 ```python
-hist = model.fit(X, y, validation_split=0.2)
+hist = model.fit(x, y, validation_split=0.2)
 print(hist.history)
 ```
 
@@ -311,12 +315,13 @@ Making a RNN stateful means that the states for the samples of each batch will b
 When using stateful RNNs, it is therefore assumed that:
 
 - all batches have the same number of samples
-- If `X1` and `X2` are successive batches of samples, then `X2[i]` is the follow-up sequence to `X1[i]`, for every `i`.
+- If `x1` and `x2` are successive batches of samples, then `x2[i]` is the follow-up sequence to `x1[i]`, for every `i`.
 
 To use statefulness in RNNs, you need to:
 
-- explicitly specify the batch size you are using, by passing a `batch_input_shape` argument to the first layer in your model. It should be a tuple of integers, e.g. `(32, 10, 16)` for a 32-samples batch of sequences of 10 timesteps with 16 features per timestep.
+- explicitly specify the batch size you are using, by passing a `batch_size` argument to the first layer in your model. E.g. `batch_size=32` for a 32-samples batch of sequences of 10 timesteps with 16 features per timestep.
 - set `stateful=True` in your RNN layer(s).
+- specify `shuffle=False` when calling fit().
 
 To reset the states accumulated:
 
@@ -327,20 +332,20 @@ Example:
 
 ```python
 
-X  # this is our input data, of shape (32, 21, 16)
+x  # this is our input data, of shape (32, 21, 16)
 # we will feed it to our model in sequences of length 10
 
 model = Sequential()
-model.add(LSTM(32, batch_input_shape=(32, 10, 16), stateful=True))
+model.add(LSTM(32, input_shape=(10, 16), batch_size=32, stateful=True))
 model.add(Dense(16, activation='softmax'))
 
 model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
 
 # we train the network to predict the 11th timestep given the first 10:
-model.train_on_batch(X[:, :10, :], np.reshape(X[:, 10, :], (32, 16)))
+model.train_on_batch(x[:, :10, :], np.reshape(x[:, 10, :], (32, 16)))
 
 # the state of the network has changed. We can feed the follow-up sequences:
-model.train_on_batch(X[:, 10:20, :], np.reshape(X[:, 20, :], (32, 16)))
+model.train_on_batch(x[:, 10:20, :], np.reshape(x[:, 20, :], (32, 16)))
 
 # let's reset the states of the LSTM layer:
 model.reset_states()
@@ -401,3 +406,51 @@ The VGG16 model is also the basis for several Keras example scripts:
 - [Style transfer](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py)
 - [Feature visualization](https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py)
 - [Deep dream](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py)
+
+---
+
+### How can I use HDF5 inputs with Keras?
+
+You can use the `HDF5Matrix` class from `keras.utils.io_utils`. See [the HDF5Matrix documentation](/utils/#hdf5matrix) for details.
+
+You can also directly use a HDF5 dataset:
+
+```python
+import h5py
+with h5py.File('input/file.hdf5', 'r') as f:
+    x_data = f['x_data']
+    model.predict(x_data)
+```
+
+---
+
+### Where is the Keras configuration filed stored?
+
+The default directory where all Keras data is stored is:
+
+```bash
+$HOME/.keras/
+```
+
+Note that Windows users should replace `$HOME` with `%USERPROFILE%`.
+In case Keras cannot create the above directory (e.g. due to permission issues), `/tmp/.keras/` is used as a backup.
+
+The Keras configuration file is a JSON file stored at `$HOME/.keras/keras.json`. The default configuration file looks like this:
+
+```
+{
+    "image_data_format": "channels_last",
+    "epsilon": 1e-07,
+    "floatx": "float32",
+    "backend": "tensorflow"
+}
+```
+
+It contains the following fields:
+
+- The image data format to be used as default by image processing layers and utilities (either `channels_last` or `channels_first`).
+- The `epsilon` numerical fuzz factor to be used to prevent division by zero in some operations.
+- The default float data type.
+- The default backend. See the [backend documentation](/backend).
+
+Likewise, cached dataset files, such as those downloaded with [`get_file()`](/utils/#get_file), are stored by default in `$HOME/.keras/datasets/`.

docs/templates/getting-started/functional-api-guide.md

@@ -8,7 +8,7 @@ Let's start with something simple.
 
 -----
 
-## First example: fully connected network
+## First example: a densely-connected network
 
 The `Sequential` model is probably a better choice to implement such a network, but it helps to start with something really simple.
 
@@ -20,7 +20,7 @@ The `Sequential` model is probably a better choice to implement such a network,
 from keras.layers import Input, Dense
 from keras.models import Model
 
-# this returns a tensor
+# This returns a tensor
 inputs = Input(shape=(784,))
 
 # a layer instance is callable on a tensor, and returns a tensor
@@ -28,9 +28,9 @@ x = Dense(64, activation='relu')(inputs)
 x = Dense(64, activation='relu')(x)
 predictions = Dense(10, activation='softmax')(x)
 
-# this creates a model that includes
+# This creates a model that includes
 # the Input layer and three Dense layers
-model = Model(input=inputs, output=predictions)
+model = Model(inputs=inputs, outputs=predictions)
 model.compile(optimizer='rmsprop',
               loss='categorical_crossentropy',
               metrics=['accuracy'])
@@ -45,7 +45,7 @@ With the functional API, it is easy to re-use trained models: you can treat any
 
 ```python
 x = Input(shape=(784,))
-# this works, and returns the 10-way softmax we defined above.
+# This works, and returns the 10-way softmax we defined above.
 y = model(x)
 ```
 
@@ -54,11 +54,11 @@ This can allow, for instance, to quickly create models that can process *sequenc
 ```python
 from keras.layers import TimeDistributed
 
-# input tensor for sequences of 20 timesteps,
+# Input tensor for sequences of 20 timesteps,
 # each containing a 784-dimensional vector
 input_sequences = Input(shape=(20, 784))
 
-# this applies our previous model to every timestep in the input sequences.
+# This applies our previous model to every timestep in the input sequences.
 # the output of the previous model was a 10-way softmax,
 # so the output of the layer below will be a sequence of 20 vectors of size 10.
 processed_sequences = TimeDistributed(model)(input_sequences)
@@ -83,18 +83,18 @@ The main input will receive the headline, as a sequence of integers (each intege
 The integers will be between 1 and 10,000 (a vocabulary of 10,000 words) and the sequences will be 100 words long.
 
 ```python
-from keras.layers import Input, Embedding, LSTM, Dense, merge
+from keras.layers import Input, Embedding, LSTM, Dense
 from keras.models import Model
 
-# headline input: meant to receive sequences of 100 integers, between 1 and 10000.
-# note that we can name any layer by passing it a "name" argument.
+# Headline input: meant to receive sequences of 100 integers, between 1 and 10000.
+# Note that we can name any layer by passing it a "name" argument.
 main_input = Input(shape=(100,), dtype='int32', name='main_input')
 
-# this embedding layer will encode the input sequence
+# This embedding layer will encode the input sequence
 # into a sequence of dense 512-dimensional vectors.
 x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)
 
-# a LSTM will transform the vector sequence into a single vector,
+# A LSTM will transform the vector sequence into a single vector,
 # containing information about the entire sequence
 lstm_out = LSTM(32)(x)
 ```
@@ -109,21 +109,21 @@ At this point, we feed into the model our auxiliary input data by concatenating
 
 ```python
 auxiliary_input = Input(shape=(5,), name='aux_input')
-x = merge([lstm_out, auxiliary_input], mode='concat')
+x = keras.layers.concatenate([lstm_out, auxiliary_input])
 
-# we stack a deep fully-connected network on top
+# We stack a deep densely-connected network on top
 x = Dense(64, activation='relu')(x)
 x = Dense(64, activation='relu')(x)
 x = Dense(64, activation='relu')(x)
 
-# and finally we add the main logistic regression layer
+# And finally we add the main logistic regression layer
 main_output = Dense(1, activation='sigmoid', name='main_output')(x)
 ```
 
 This defines a model with two inputs and two outputs:
 
 ```python
-model = Model(input=[main_input, auxiliary_input], output=[main_output, auxiliary_output])
+model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
 ```
 
 We compile the model and assign a weight of 0.2 to the auxiliary loss.
@@ -139,7 +139,7 @@ We can train the model by passing it lists of input arrays and target arrays:
 
 ```python
 model.fit([headline_data, additional_data], [labels, labels],
-          nb_epoch=50, batch_size=32)
+          epochs=50, batch_size=32)
 ```
 
 Since our inputs and outputs are named (we passed them a "name" argument),
@@ -150,10 +150,10 @@ model.compile(optimizer='rmsprop',
               loss={'main_output': 'binary_crossentropy', 'aux_output': 'binary_crossentropy'},
               loss_weights={'main_output': 1., 'aux_output': 0.2})
 
-# and trained it via:
+# And trained it via:
 model.fit({'main_input': headline_data, 'aux_input': additional_data},
           {'main_output': labels, 'aux_output': labels},
-          nb_epoch=50, batch_size=32)
+          epochs=50, batch_size=32)
 ```
 
 -----
@@ -171,7 +171,8 @@ Because the problem is symmetric, the mechanism that encodes the first tweet sho
 Let's build this with the functional API. We will take as input for a tweet a binary matrix of shape `(140, 256)`, i.e. a sequence of 140 vectors of size 256, where each dimension in the 256-dimensional vector encodes the presence/absence of a character (out of an alphabet of 256 frequent characters).
 
 ```python
-from keras.layers import Input, LSTM, Dense, merge
+import keras
+from keras.layers import Input, LSTM, Dense
 from keras.models import Model
 
 tweet_a = Input(shape=(140, 256))
@@ -181,31 +182,31 @@ tweet_b = Input(shape=(140, 256))
 To share a layer across different inputs, simply instantiate the layer once, then call it on as many inputs as you want:
 
 ```python
-# this layer can take as input a matrix
+# This layer can take as input a matrix
 # and will return a vector of size 64
 shared_lstm = LSTM(64)
 
-# when we reuse the same layer instance
+# When we reuse the same layer instance
 # multiple times, the weights of the layer
 # are also being reused
 # (it is effectively *the same* layer)
 encoded_a = shared_lstm(tweet_a)
 encoded_b = shared_lstm(tweet_b)
 
-# we can then concatenate the two vectors:
-merged_vector = merge([encoded_a, encoded_b], mode='concat', concat_axis=-1)
+# We can then concatenate the two vectors:
+merged_vector = keras.layers.concatenate([encoded_a, encoded_b], axis=-1)
 
-# and add a logistic regression on top
+# And add a logistic regression on top
 predictions = Dense(1, activation='sigmoid')(merged_vector)
 
-# we define a trainable model linking the
+# We define a trainable model linking the
 # tweet inputs to the predictions
-model = Model(input=[tweet_a, tweet_b], output=predictions)
+model = Model(inputs=[tweet_a, tweet_b], outputs=predictions)
 
 model.compile(optimizer='rmsprop',
               loss='binary_crossentropy',
               metrics=['accuracy'])
-model.fit([data_a, data_b], labels, nb_epoch=10)
+model.fit([data_a, data_b], labels, epochs=10)
 ```
 
 Let's pause to take a look at how to read the shared layer's output or output shape.
@@ -255,16 +256,16 @@ assert lstm.get_output_at(1) == encoded_b
 
 Simple enough, right?
 
-The same is true for the properties `input_shape` and `output_shape`: as long as the layer has only one node, or as long as all nodes have the same input/output shape, then the notion of "layer output/input shape" is well defined, and that one shape will be returned by `layer.output_shape`/`layer.input_shape`. But if, for instance, you apply a same `Convolution2D` layer to an input of shape `(3, 32, 32)`, and then to an input of shape `(3, 64, 64)`, the layer will have multiple input/output shapes, and you will have to fetch them by specifying the index of the node they belong to:
+The same is true for the properties `input_shape` and `output_shape`: as long as the layer has only one node, or as long as all nodes have the same input/output shape, then the notion of "layer output/input shape" is well defined, and that one shape will be returned by `layer.output_shape`/`layer.input_shape`. But if, for instance, you apply a same `Conv2D` layer to an input of shape `(3, 32, 32)`, and then to an input of shape `(3, 64, 64)`, the layer will have multiple input/output shapes, and you will have to fetch them by specifying the index of the node they belong to:
 
 ```python
 a = Input(shape=(3, 32, 32))
 b = Input(shape=(3, 64, 64))
 
-conv = Convolution2D(16, 3, 3, border_mode='same')
+conv = Conv2D(16, (3, 3), padding='same')
 conved_a = conv(a)
 
-# only one input so far, the following will work:
+# Only one input so far, the following will work:
 assert conv.input_shape == (None, 3, 32, 32)
 
 conved_b = conv(b)
@@ -284,20 +285,20 @@ Code examples are still the best way to get started, so here are a few more.
 For more information about the Inception architecture, see [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842).
 
 ```python
-from keras.layers import merge, Convolution2D, MaxPooling2D, Input
+from keras.layers import Conv2D, MaxPooling2D, Input
 
 input_img = Input(shape=(3, 256, 256))
 
-tower_1 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(input_img)
-tower_1 = Convolution2D(64, 3, 3, border_mode='same', activation='relu')(tower_1)
+tower_1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
+tower_1 = Conv2D(64, (3, 3), padding='same', activation='relu')(tower_1)
 
-tower_2 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(input_img)
-tower_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(tower_2)
+tower_2 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
+tower_2 = Conv2D(64, (5, 5), padding='same', activation='relu')(tower_2)
 
-tower_3 = MaxPooling2D((3, 3), strides=(1, 1), border_mode='same')(input_img)
-tower_3 = Convolution2D(64, 1, 1, border_mode='same', activation='relu')(tower_3)
+tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img)
+tower_3 = Conv2D(64, (1, 1), padding='same', activation='relu')(tower_3)
 
-output = merge([tower_1, tower_2, tower_3], mode='concat', concat_axis=1)
+output = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=1)
 ```
 
 ### Residual connection on a convolution layer
@@ -305,14 +306,14 @@ output = merge([tower_1, tower_2, tower_3], mode='concat', concat_axis=1)
 For more information about residual networks, see [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385).
 
 ```python
-from keras.layers import merge, Convolution2D, Input
+from keras.layers import Conv2D, Input
 
 # input tensor for a 3-channel 256x256 image
 x = Input(shape=(3, 256, 256))
 # 3x3 conv with 3 output channels (same as input channels)
-y = Convolution2D(3, 3, 3, border_mode='same')(x)
+y = Conv2D(3, (3, 3), padding='same')(x)
 # this returns x + y.
-z = merge([x, y], mode='sum')
+z = keras.layers.add([x, y])
 ```
 
 ### Shared vision model
@@ -320,27 +321,27 @@ z = merge([x, y], mode='sum')
 This model re-uses the same image-processing module on two inputs, to classify whether two MNIST digits are the same digit or different digits.
 
 ```python
-from keras.layers import merge, Convolution2D, MaxPooling2D, Input, Dense, Flatten
+from keras.layers import Conv2D, MaxPooling2D, Input, Dense, Flatten
 from keras.models import Model
 
-# first, define the vision modules
+# First, define the vision modules
 digit_input = Input(shape=(1, 27, 27))
-x = Convolution2D(64, 3, 3)(digit_input)
-x = Convolution2D(64, 3, 3)(x)
+x = Conv2D(64, (3, 3))(digit_input)
+x = Conv2D(64, (3, 3))(x)
 x = MaxPooling2D((2, 2))(x)
 out = Flatten()(x)
 
 vision_model = Model(digit_input, out)
 
-# then define the tell-digits-apart model
+# Then define the tell-digits-apart model
 digit_a = Input(shape=(1, 27, 27))
 digit_b = Input(shape=(1, 27, 27))
 
-# the vision model will be shared, weights and all
+# The vision model will be shared, weights and all
 out_a = vision_model(digit_a)
 out_b = vision_model(digit_b)
 
-concatenated = merge([out_a, out_b], mode='concat')
+concatenated = keras.layers.concatenate([out_a, out_b])
 out = Dense(1, activation='sigmoid')(concatenated)
 
 classification_model = Model([digit_a, digit_b], out)
@@ -353,46 +354,46 @@ This model can select the correct one-word answer when asked a natural-language
 It works by encoding the question into a vector, encoding the image into a vector, concatenating the two, and training on top a logistic regression over some vocabulary of potential answers.
 
 ```python
-from keras.layers import Convolution2D, MaxPooling2D, Flatten
-from keras.layers import Input, LSTM, Embedding, Dense, merge
+from keras.layers import Conv2D, MaxPooling2D, Flatten
+from keras.layers import Input, LSTM, Embedding, Dense
 from keras.models import Model, Sequential
 
-# first, let's define a vision model using a Sequential model.
-# this model will encode an image into a vector.
+# First, let's define a vision model using a Sequential model.
+# This model will encode an image into a vector.
 vision_model = Sequential()
-vision_model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same', input_shape=(3, 224, 224)))
-vision_model.add(Convolution2D(64, 3, 3, activation='relu'))
+vision_model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(3, 224, 224)))
+vision_model.add(Conv2D(64, (3, 3), activation='relu'))
 vision_model.add(MaxPooling2D((2, 2)))
-vision_model.add(Convolution2D(128, 3, 3, activation='relu', border_mode='same'))
-vision_model.add(Convolution2D(128, 3, 3, activation='relu'))
+vision_model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
+vision_model.add(Conv2D(128, (3, 3), activation='relu'))
 vision_model.add(MaxPooling2D((2, 2)))
-vision_model.add(Convolution2D(256, 3, 3, activation='relu', border_mode='same'))
-vision_model.add(Convolution2D(256, 3, 3, activation='relu'))
-vision_model.add(Convolution2D(256, 3, 3, activation='relu'))
+vision_model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
+vision_model.add(Conv2D(256, (3, 3), activation='relu'))
+vision_model.add(Conv2D(256, (3, 3), activation='relu'))
 vision_model.add(MaxPooling2D((2, 2)))
 vision_model.add(Flatten())
 
-# now let's get a tensor with the output of our vision model:
+# Now let's get a tensor with the output of our vision model:
 image_input = Input(shape=(3, 224, 224))
 encoded_image = vision_model(image_input)
 
-# next, let's define a language model to encode the question into a vector.
-# each question will be at most 100 word long,
+# Next, let's define a language model to encode the question into a vector.
+# Each question will be at most 100 word long,
 # and we will index words as integers from 1 to 9999.
 question_input = Input(shape=(100,), dtype='int32')
 embedded_question = Embedding(input_dim=10000, output_dim=256, input_length=100)(question_input)
 encoded_question = LSTM(256)(embedded_question)
 
-# let's concatenate the question vector and the image vector:
-merged = merge([encoded_question, encoded_image], mode='concat')
+# Let's concatenate the question vector and the image vector:
+merged = keras.layers.concatenate([encoded_question, encoded_image])
 
-# and let's train a logistic regression over 1000 words on top:
+# And let's train a logistic regression over 1000 words on top:
 output = Dense(1000, activation='softmax')(merged)
 
-# this is our final model:
-vqa_model = Model(input=[image_input, question_input], output=output)
+# This is our final model:
+vqa_model = Model(inputs=[image_input, question_input], outputs=output)
 
-# the next stage would be training this model on actual data.
+# The next stage would be training this model on actual data.
 ```
 
 ### Video question answering model
@@ -403,19 +404,19 @@ Now that we have trained our image QA model, we can quickly turn it into a video
 from keras.layers import TimeDistributed
 
 video_input = Input(shape=(100, 3, 224, 224))
-# this is our video encoded via the previously trained vision_model (weights are reused)
+# This is our video encoded via the previously trained vision_model (weights are reused)
 encoded_frame_sequence = TimeDistributed(vision_model)(video_input)  # the output will be a sequence of vectors
 encoded_video = LSTM(256)(encoded_frame_sequence)  # the output will be a vector
 
-# this is a model-level representation of the question encoder, reusing the same weights as before:
-question_encoder = Model(input=question_input, output=encoded_question)
+# This is a model-level representation of the question encoder, reusing the same weights as before:
+question_encoder = Model(inputs=question_input, outputs=encoded_question)
 
-# let's use it to encode the question:
+# Let's use it to encode the question:
 video_question_input = Input(shape=(100,), dtype='int32')
 encoded_video_question = question_encoder(video_question_input)
 
-# and this is our video question answering model:
-merged = merge([encoded_video, encoded_video_question], mode='concat')
+# And this is our video question answering model:
+merged = keras.layers.concatenate([encoded_video, encoded_video_question])
 output = Dense(1000, activation='softmax')(merged)
-video_qa_model = Model(input=[video_input, video_question_input], output=output)
+video_qa_model = Model(inputs=[video_input, video_question_input], outputs=output)
 ```

docs/templates/getting-started/sequential-model-guide.md

@@ -9,7 +9,7 @@ from keras.models import Sequential
 from keras.layers import Dense, Activation
 
 model = Sequential([
-    Dense(32, input_dim=784),
+    Dense(32, input_shape=(784,)),
     Activation('relu'),
     Dense(10),
     Activation('softmax'),
@@ -30,192 +30,101 @@ model.add(Activation('relu'))
 
 The model needs to know what input shape it should expect. For this reason, the first layer in a `Sequential` model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape. There are several possible ways to do this:
 
-- pass an `input_shape` argument to the first layer. This is a shape tuple (a tuple of integers or `None` entries, where `None` indicates that any positive integer may be expected). In `input_shape`, the batch dimension is not included.
-- pass instead a `batch_input_shape` argument, where the batch dimension is included. This is useful for specifying a fixed batch size (e.g. with stateful RNNs).
-- some 2D layers, such as `Dense`, support the specification of their input shape via the argument `input_dim`, and some 3D temporal layers support the arguments `input_dim` and `input_length`.
+- Pass an `input_shape` argument to the first layer. This is a shape tuple (a tuple of integers or `None` entries, where `None` indicates that any positive integer may be expected). In `input_shape`, the batch dimension is not included.
+- Some 2D layers, such as `Dense`, support the specification of their input shape via the argument `input_dim`, and some 3D temporal layers support the arguments `input_dim` and `input_length`.
+- If you ever need to specify a fixed batch size for your inputs (this is useful for stateful recurrent networks), you can pass a `batch_size` argument to a layer. If you pass both `batch_size=32` and `input_shape=(6, 8)` to a layer, it will then expect every batch of inputs to have the batch shape `(32, 6, 8)`.
 
-As such, the following three snippets are strictly equivalent:
+As such, the following snippets are strictly equivalent:
 ```python
 model = Sequential()
 model.add(Dense(32, input_shape=(784,)))
 ```
 ```python
 model = Sequential()
-model.add(Dense(32, batch_input_shape=(None, 784)))
-# note that batch dimension is "None" here,
-# so the model will be able to process batches of any size.
-```
-```python
-model = Sequential()
 model.add(Dense(32, input_dim=784))
 ```
 
-And so are the following three snippets:
-```python
-model = Sequential()
-model.add(LSTM(32, input_shape=(10, 64)))
-```
-```python
-model = Sequential()
-model.add(LSTM(32, batch_input_shape=(None, 10, 64)))
-```
-```python
-model = Sequential()
-model.add(LSTM(32, input_length=10, input_dim=64))
-```
-
-----
-
-## The Merge layer
-
-Multiple `Sequential` instances can be merged into a single output via a `Merge` layer. The output is a layer that can be added as first layer in a new `Sequential` model. For instance, here's a model with two separate input branches getting merged:
-
-```python
-from keras.layers import Merge
-
-left_branch = Sequential()
-left_branch.add(Dense(32, input_dim=784))
-
-right_branch = Sequential()
-right_branch.add(Dense(32, input_dim=784))
-
-merged = Merge([left_branch, right_branch], mode='concat')
-
-final_model = Sequential()
-final_model.add(merged)
-final_model.add(Dense(10, activation='softmax'))
-```
-
-<img src="https://s3.amazonaws.com/keras.io/img/two_branches_sequential_model.png" alt="two branch Sequential" style="width: 400px;"/>
-
-Such a two-branch model can then be trained via e.g.:
-
-```python
-final_model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
-final_model.fit([input_data_1, input_data_2], targets)  # we pass one data array per model input
-```
-
-The `Merge` layer supports a number of pre-defined modes:
-
-- `sum` (default): element-wise sum
-- `concat`: tensor concatenation. You can specify the concatenation axis via the argument `concat_axis`.
-- `mul`: element-wise multiplication
-- `ave`: tensor average
-- `dot`: dot product. You can specify which axes to reduce along via the argument `dot_axes`.
-- `cos`: cosine proximity between vectors in 2D tensors.
-
-You can also pass a function as the `mode` argument, allowing for arbitrary transformations:
-
-```python
-merged = Merge([left_branch, right_branch], mode=lambda x: x[0] - x[1])
-```
-
-Now you know enough to be able to define *almost* any model with Keras. For complex models that cannot be expressed via `Sequential` and `Merge`, you can use [the functional API](/getting-started/functional-api-guide).
-
-
 ----
 
 ## Compilation
 
 Before training a model, you need to configure the learning process, which is done via the `compile` method. It receives three arguments:
 
-- an optimizer. This could be the string identifier of an existing optimizer (such as `rmsprop` or `adagrad`), or an instance of the `Optimizer` class. See: [optimizers](/optimizers).
-- a loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (such as `categorical_crossentropy` or `mse`), or it can be an objective function. See: [objectives](/objectives).
-- a list of metrics. For any classification problem you will want to set this to `metrics=['accuracy']`. A metric could be the string identifier of an existing metric or a custom metric function.  Custom metric function should return either a single tensor value or a dict `metric_name -> metric_value`. See: [metrics](/metrics).
+- An optimizer. This could be the string identifier of an existing optimizer (such as `rmsprop` or `adagrad`), or an instance of the `Optimizer` class. See: [optimizers](/optimizers).
+- A loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (such as `categorical_crossentropy` or `mse`), or it can be an objective function. See: [losses](/losses).
+- A list of metrics. For any classification problem you will want to set this to `metrics=['accuracy']`. A metric could be the string identifier of an existing metric or a custom metric function.
 
 ```python
-# for a multi-class classification problem
+# For a multi-class classification problem
 model.compile(optimizer='rmsprop',
               loss='categorical_crossentropy',
               metrics=['accuracy'])
 
-# for a binary classification problem
+# For a binary classification problem
 model.compile(optimizer='rmsprop',
               loss='binary_crossentropy',
               metrics=['accuracy'])
 
-# for a mean squared error regression problem
+# For a mean squared error regression problem
 model.compile(optimizer='rmsprop',
               loss='mse')
 
-# for custom metrics
+# For custom metrics
 import keras.backend as K
 
 def mean_pred(y_true, y_pred):
     return K.mean(y_pred)
 
-def false_rates(y_true, y_pred):
-    false_neg = ...
-    false_pos = ...
-    return {
-        'false_neg': false_neg,
-        'false_pos': false_pos,
-    }
-
 model.compile(optimizer='rmsprop',
               loss='binary_crossentropy',
-              metrics=['accuracy', mean_pred, false_rates])
+              metrics=['accuracy', mean_pred])
 ```
 
 ----
 
 ## Training
 
-Keras models are trained on Numpy arrays of input data and labels. For training a model, you will typically use the `fit` function. [Read its documentation here](/models/sequential). 
+Keras models are trained on Numpy arrays of input data and labels. For training a model, you will typically use the `fit` function. [Read its documentation here](/models/sequential).
 
 ```python
-# for a single-input model with 2 classes (binary):
+# For a single-input model with 2 classes (binary classification):
 
 model = Sequential()
-model.add(Dense(1, input_dim=784, activation='sigmoid'))
+model.add(Dense(32, activation='relu', input_dim=100))
+model.add(Dense(1, activation='sigmoid'))
 model.compile(optimizer='rmsprop',
               loss='binary_crossentropy',
               metrics=['accuracy'])
 
-# generate dummy data
+# Generate dummy data
 import numpy as np
-data = np.random.random((1000, 784))
+data = np.random.random((1000, 100))
 labels = np.random.randint(2, size=(1000, 1))
 
-# train the model, iterating on the data in batches
-# of 32 samples
-model.fit(data, labels, nb_epoch=10, batch_size=32)
+# Train the model, iterating on the data in batches of 32 samples
+model.fit(data, labels, epochs=10, batch_size=32)
 ```
+
 ```python
-# for a multi-input model with 10 classes:
-
-left_branch = Sequential()
-left_branch.add(Dense(32, input_dim=784))
-
-right_branch = Sequential()
-right_branch.add(Dense(32, input_dim=784))
-
-merged = Merge([left_branch, right_branch], mode='concat')
+# For a single-input model with 10 classes (categorical classification):
 
 model = Sequential()
-model.add(merged)
+model.add(Dense(32, activation='relu', input_dim=100))
 model.add(Dense(10, activation='softmax'))
-
 model.compile(optimizer='rmsprop',
               loss='categorical_crossentropy',
               metrics=['accuracy'])
 
-# generate dummy data
+# Generate dummy data
 import numpy as np
-from keras.utils.np_utils import to_categorical
-data_1 = np.random.random((1000, 784))
-data_2 = np.random.random((1000, 784))
-
-# these are integers between 0 and 9
+data = np.random.random((1000, 100))
 labels = np.random.randint(10, size=(1000, 1))
-# we convert the labels to a binary matrix of size (1000, 10)
-# for use with categorical_crossentropy
-labels = to_categorical(labels, 10)
 
-# train the model
-# note that we are passing a list of Numpy arrays as training data
-# since the model has 2 inputs
-model.fit([data_1, data_2], labels, nb_epoch=10, batch_size=32)
+# Convert labels to categorical one-hot encoding
+one_hot_labels = keras.utils.to_categorical(labels, num_classes=10)
+
+# Train the model, iterating on the data in batches of 32 samples
+model.fit(data, one_hot_labels, epochs=10, batch_size=32)
 ```
 
 ----
@@ -225,7 +134,7 @@ model.fit([data_1, data_2], labels, nb_epoch=10, batch_size=32)
 
 Here are a few examples to get you started!
 
-In the examples folder, you will also find example models for real datasets:
+In the [examples folder](https://github.com/fchollet/keras/tree/master/examples), you will also find example models for real datasets:
 
 - CIFAR10 small images classification: Convolutional Neural Network (CNN) with realtime data augmentation
 - IMDB movie review sentiment classification: LSTM over sequences of words
@@ -243,51 +152,50 @@ from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation
 from keras.optimizers import SGD
 
+# Generate dummy data
+import numpy as np
+x_train = np.random.random((1000, 20))
+y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
+x_test = np.random.random((100, 20))
+y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
+
 model = Sequential()
 # Dense(64) is a fully-connected layer with 64 hidden units.
 # in the first layer, you must specify the expected input data shape:
 # here, 20-dimensional vectors.
-model.add(Dense(64, input_dim=20, init='uniform'))
-model.add(Activation('tanh'))
-model.add(Dropout(0.5))
-model.add(Dense(64, init='uniform'))
-model.add(Activation('tanh'))
-model.add(Dropout(0.5))
-model.add(Dense(10, init='uniform'))
-model.add(Activation('softmax'))
-
-sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
-model.compile(loss='categorical_crossentropy',
-              optimizer=sgd,
-              metrics=['accuracy'])
-
-model.fit(X_train, y_train,
-          nb_epoch=20,
-          batch_size=16)
-score = model.evaluate(X_test, y_test, batch_size=16)
-```
-
-
-### Alternative implementation of a similar MLP:
-
-```python
-model = Sequential()
-model.add(Dense(64, input_dim=20, activation='relu'))
+model.add(Dense(64, activation='relu', input_dim=20))
 model.add(Dropout(0.5))
 model.add(Dense(64, activation='relu'))
 model.add(Dropout(0.5))
 model.add(Dense(10, activation='softmax'))
 
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss='categorical_crossentropy',
-              optimizer='adadelta',
+              optimizer=sgd,
               metrics=['accuracy'])
+
+model.fit(x_train, y_train,
+          epochs=20,
+          batch_size=128)
+score = model.evaluate(x_test, y_test, batch_size=128)
 ```
 
 
 ### MLP for binary classification:
+
 ```python
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+
+# Generate dummy data
+x_train = np.random.random((1000, 20))
+y_train = np.random.randint(2, size=(1000, 1))
+x_test = np.random.random((100, 20))
+y_test = np.random.randint(2, size=(100, 1))
+
 model = Sequential()
-model.add(Dense(64, input_dim=20, init='uniform', activation='relu'))
+model.add(Dense(64, input_dim=20, activation='relu'))
 model.add(Dropout(0.5))
 model.add(Dense(64, activation='relu'))
 model.add(Dropout(0.5))
@@ -296,47 +204,53 @@ model.add(Dense(1, activation='sigmoid'))
 model.compile(loss='binary_crossentropy',
               optimizer='rmsprop',
               metrics=['accuracy'])
+
+model.fit(x_train, y_train,
+          epochs=20,
+          batch_size=128)
+score = model.evaluate(x_test, y_test, batch_size=128)
 ```
 
 
 ### VGG-like convnet:
 
 ```python
+import numpy as np
+import keras
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
 from keras.optimizers import SGD
 
+# Generate dummy data
+x_train = np.random.random((100, 100, 100, 3))
+y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
+x_test = np.random.random((20, 100, 100, 3))
+y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)
+
 model = Sequential()
-# input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
+# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
 # this applies 32 convolution filters of size 3x3 each.
-model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(3, 100, 100)))
-model.add(Activation('relu'))
-model.add(Convolution2D(32, 3, 3))
-model.add(Activation('relu'))
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
+model.add(Conv2D(32, (3, 3), activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 
-model.add(Convolution2D(64, 3, 3, border_mode='valid'))
-model.add(Activation('relu'))
-model.add(Convolution2D(64, 3, 3))
-model.add(Activation('relu'))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(Conv2D(64, (3, 3), activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 
 model.add(Flatten())
-# Note: Keras does automatic shape inference.
-model.add(Dense(256))
-model.add(Activation('relu'))
+model.add(Dense(256, activation='relu'))
 model.add(Dropout(0.5))
+model.add(Dense(10, activation='softmax'))
 
-model.add(Dense(10))
-model.add(Activation('softmax'))
-
-sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss='categorical_crossentropy', optimizer=sgd)
 
-model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)
+model.fit(x_train, y_train, batch_size=32, epochs=10)
+score = model.evaluate(x_test, y_test, batch_size=32)
 ```
 
 
@@ -344,89 +258,50 @@ model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)
 
 ```python
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation
+from keras.layers import Dense, Dropout
 from keras.layers import Embedding
 from keras.layers import LSTM
 
 model = Sequential()
-model.add(Embedding(max_features, 256, input_length=maxlen))
-model.add(LSTM(output_dim=128, activation='sigmoid', inner_activation='hard_sigmoid'))
+model.add(Embedding(max_features, output_dim=256))
+model.add(LSTM(128))
 model.add(Dropout(0.5))
-model.add(Dense(1))
-model.add(Activation('sigmoid'))
+model.add(Dense(1, activation='sigmoid'))
 
 model.compile(loss='binary_crossentropy',
               optimizer='rmsprop',
               metrics=['accuracy'])
 
-model.fit(X_train, Y_train, batch_size=16, nb_epoch=10)
-score = model.evaluate(X_test, Y_test, batch_size=16)
+model.fit(x_train, y_train, batch_size=16, epochs=10)
+score = model.evaluate(x_test, y_test, batch_size=16)
 ```
 
-### Architecture for learning image captions with a convnet and a Gated Recurrent Unit:
-(word-level embedding, caption of maximum length 16 words).
-
-Note that getting this to work well will require using a bigger convnet, initialized with pre-trained weights.
+### Sequence classification with 1D convolutions:
 
 ```python
-max_caption_len = 16
-vocab_size = 10000
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+from keras.layers import Embedding
+from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
 
-# first, let's define an image model that
-# will encode pictures into 128-dimensional vectors.
-# it should be initialized with pre-trained weights.
-image_model = Sequential()
-image_model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(3, 100, 100)))
-image_model.add(Activation('relu'))
-image_model.add(Convolution2D(32, 3, 3))
-image_model.add(Activation('relu'))
-image_model.add(MaxPooling2D(pool_size=(2, 2)))
-
-image_model.add(Convolution2D(64, 3, 3, border_mode='valid'))
-image_model.add(Activation('relu'))
-image_model.add(Convolution2D(64, 3, 3))
-image_model.add(Activation('relu'))
-image_model.add(MaxPooling2D(pool_size=(2, 2)))
-
-image_model.add(Flatten())
-image_model.add(Dense(128))
-
-# let's load the weights from a save file.
-image_model.load_weights('weight_file.h5')
-
-# next, let's define a RNN model that encodes sequences of words
-# into sequences of 128-dimensional word vectors.
-language_model = Sequential()
-language_model.add(Embedding(vocab_size, 256, input_length=max_caption_len))
-language_model.add(GRU(output_dim=128, return_sequences=True))
-language_model.add(TimeDistributed(Dense(128)))
-
-# let's repeat the image vector to turn it into a sequence.
-image_model.add(RepeatVector(max_caption_len))
-
-# the output of both models will be tensors of shape (samples, max_caption_len, 128).
-# let's concatenate these 2 vector sequences.
 model = Sequential()
-model.add(Merge([image_model, language_model], mode='concat', concat_axis=-1))
-# let's encode this vector sequence into a single vector
-model.add(GRU(256, return_sequences=False))
-# which will be used to compute a probability
-# distribution over what the next word in the caption should be!
-model.add(Dense(vocab_size))
-model.add(Activation('softmax'))
+model.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 100)))
+model.add(Conv1D(64, 3, activation='relu'))
+model.add(MaxPooling1D(3))
+model.add(Conv1D(128, 3, activation='relu'))
+model.add(Conv1D(128, 3, activation='relu'))
+model.add(GlobalAveragePooling1D())
+model.add(Dropout(0.5))
+model.add(Dense(1, activation='sigmoid'))
 
-model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
+model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
 
-# "images" is a numpy float array of shape (nb_samples, nb_channels=3, width, height).
-# "captions" is a numpy integer array of shape (nb_samples, max_caption_len)
-# containing word index sequences representing partial captions.
-# "next_words" is a numpy float array of shape (nb_samples, vocab_size)
-# containing a categorical encoding (0s and 1s) of the next word in the corresponding
-# partial caption.
-model.fit([images, partial_captions], next_words, batch_size=16, nb_epoch=100)
+model.fit(x_train, y_train, batch_size=16, epochs=10)
+score = model.evaluate(x_test, y_test, batch_size=16)
 ```
 
-
 ### Stacked LSTM for sequence classification
 
 In this model, we stack 3 LSTM layers on top of each other,
@@ -445,7 +320,7 @@ import numpy as np
 
 data_dim = 16
 timesteps = 8
-nb_classes = 10
+num_classes = 10
 
 # expected input data shape: (batch_size, timesteps, data_dim)
 model = Sequential()
@@ -459,16 +334,16 @@ model.compile(loss='categorical_crossentropy',
               optimizer='rmsprop',
               metrics=['accuracy'])
 
-# generate dummy training data
+# Generate dummy training data
 x_train = np.random.random((1000, timesteps, data_dim))
-y_train = np.random.random((1000, nb_classes))
+y_train = np.random.random((1000, num_classes))
 
-# generate dummy validation data
+# Generate dummy validation data
 x_val = np.random.random((100, timesteps, data_dim))
-y_val = np.random.random((100, nb_classes))
+y_val = np.random.random((100, num_classes))
 
 model.fit(x_train, y_train,
-          batch_size=64, nb_epoch=5,
+          batch_size=64, epochs=5,
           validation_data=(x_val, y_val))
 ```
 
@@ -479,7 +354,7 @@ A stateful recurrent model is one for which the internal states (memories) obtai
 of samples are reused as initial states for the samples of the next batch. This allows to process longer sequences
 while keeping computational complexity manageable.
 
-[You can read more about stateful RNNs in the FAQ.](/faq/#how-can-i-use-stateful-rnns)
+[You can read more about stateful RNNs in the FAQ.](/getting-started/faq/#how-can-i-use-stateful-rnns)
 
 ```python
 from keras.models import Sequential
@@ -488,11 +363,11 @@ import numpy as np
 
 data_dim = 16
 timesteps = 8
-nb_classes = 10
+num_classes = 10
 batch_size = 32
 
-# expected input batch shape: (batch_size, timesteps, data_dim)
-# note that we have to provide the full batch_input_shape since the network is stateful.
+# Expected input batch shape: (batch_size, timesteps, data_dim)
+# Note that we have to provide the full batch_input_shape since the network is stateful.
 # the sample of index i in batch k is the follow-up for the sample i in batch k-1.
 model = Sequential()
 model.add(LSTM(32, return_sequences=True, stateful=True,
@@ -505,63 +380,15 @@ model.compile(loss='categorical_crossentropy',
               optimizer='rmsprop',
               metrics=['accuracy'])
 
-# generate dummy training data
+# Generate dummy training data
 x_train = np.random.random((batch_size * 10, timesteps, data_dim))
-y_train = np.random.random((batch_size * 10, nb_classes))
+y_train = np.random.random((batch_size * 10, num_classes))
 
-# generate dummy validation data
+# Generate dummy validation data
 x_val = np.random.random((batch_size * 3, timesteps, data_dim))
-y_val = np.random.random((batch_size * 3, nb_classes))
+y_val = np.random.random((batch_size * 3, num_classes))
 
 model.fit(x_train, y_train,
-          batch_size=batch_size, nb_epoch=5,
+          batch_size=batch_size, epochs=5, shuffle=False,
           validation_data=(x_val, y_val))
 ```
-
-
-### Two merged LSTM encoders for classification over two parallel sequences
-
-In this model, two input sequences are encoded into vectors by two separate LSTM modules.
-
-These two vectors are then concatenated, and a fully connected network is trained on top of the concatenated representations.
-
-<img src="https://keras.io/img/dual_lstm.png" alt="Dual LSTM" style="width: 600px;"/>
-
-```python
-from keras.models import Sequential
-from keras.layers import Merge, LSTM, Dense
-import numpy as np
-
-data_dim = 16
-timesteps = 8
-nb_classes = 10
-
-encoder_a = Sequential()
-encoder_a.add(LSTM(32, input_shape=(timesteps, data_dim)))
-
-encoder_b = Sequential()
-encoder_b.add(LSTM(32, input_shape=(timesteps, data_dim)))
-
-decoder = Sequential()
-decoder.add(Merge([encoder_a, encoder_b], mode='concat'))
-decoder.add(Dense(32, activation='relu'))
-decoder.add(Dense(nb_classes, activation='softmax'))
-
-decoder.compile(loss='categorical_crossentropy',
-                optimizer='rmsprop',
-                metrics=['accuracy'])
-
-# generate dummy training data
-x_train_a = np.random.random((1000, timesteps, data_dim))
-x_train_b = np.random.random((1000, timesteps, data_dim))
-y_train = np.random.random((1000, nb_classes))
-
-# generate dummy validation data
-x_val_a = np.random.random((100, timesteps, data_dim))
-x_val_b = np.random.random((100, timesteps, data_dim))
-y_val = np.random.random((100, nb_classes))
-
-decoder.fit([x_train_a, x_train_b], y_train,
-            batch_size=64, nb_epoch=5,
-            validation_data=([x_val_a, x_val_b], y_val))
-```

docs/templates/index.md

@@ -1,165 +1,3 @@
-# Keras: Deep Learning library for Theano and TensorFlow
+# Keras: The Python Deep Learning library
 
-## You have just found Keras.
-
-Keras is a high-level neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*
-
-Use Keras if you need a deep learning library that:
-
-- Allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
-- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-- Supports arbitrary connectivity schemes (including multi-input and multi-output training).
-- Runs seamlessly on CPU and GPU.
-
-Read the documentation at [Keras.io](http://keras.io).
-
-Keras is compatible with: __Python 2.7-3.5__.
-
-
-------------------
-
-
-## Guiding principles
-
-- __Modularity.__ A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
-
-- __Minimalism.__ Each module should be kept short and simple. Every piece of code should be transparent upon first reading. No black magic: it hurts iteration speed and ability to innovate.
-
-- __Easy extensibility.__ New modules are dead simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
-
-- __Work with Python__. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
-
-
-------------------
-
-
-## Getting started: 30 seconds to Keras
-
-The core data structure of Keras is a __model__, a way to organize layers. The main type of model is the [`Sequential`](http://keras.io/getting-started/sequential-model-guide) model, a linear stack of layers. For more complex architectures, you should use the [Keras functional API](http://keras.io/getting-started/functional-api-guide).
-
-Here's the `Sequential` model:
-
-```python
-from keras.models import Sequential
-
-model = Sequential()
-```
-
-Stacking layers is as easy as `.add()`:
-
-```python
-from keras.layers import Dense, Activation
-
-model.add(Dense(output_dim=64, input_dim=100))
-model.add(Activation('relu'))
-model.add(Dense(output_dim=10))
-model.add(Activation('softmax'))
-```
-
-Once your model looks good, configure its learning process with `.compile()`:
-```python
-model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
-```
-
-If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
-```python
-from keras.optimizers import SGD
-model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True))
-```
-
-You can now iterate on your training data in batches:
-```python
-model.fit(X_train, Y_train, nb_epoch=5, batch_size=32)
-```
-
-Alternatively, you can feed batches to your model manually:
-```python
-model.train_on_batch(X_batch, Y_batch)
-```
-
-Evaluate your performance in one line:
-```python
-loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
-```
-
-Or generate predictions on new data:
-```python
-classes = model.predict_classes(X_test, batch_size=32)
-proba = model.predict_proba(X_test, batch_size=32)
-```
-
-Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
-
-For a more in-depth tutorial about Keras, you can check out:
-
-- [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide)
-- [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide)
-
-In the [examples folder](https://github.com/fchollet/keras/tree/master/examples) of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
-
-
-------------------
-
-
-## Installation
-
-Keras uses the following dependencies:
-
-- numpy, scipy
-- pyyaml
-- HDF5 and h5py (optional, required if you use model saving/loading functions)
-- Optional but recommended if you use CNNs: cuDNN.
-
-
-*When using the TensorFlow backend:*
-
-- TensorFlow
-    - [See installation instructions](https://github.com/tensorflow/tensorflow#download-and-setup).
-
-*When using the Theano backend:*
-
-- Theano
-    - [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
-
-To install Keras, `cd` to the Keras folder and run the install command:
-```sh
-sudo python setup.py install
-```
-
-You can also install Keras from PyPI:
-```sh
-sudo pip install keras
-```
-
-------------------
-
-
-## Switching from TensorFlow to Theano
-
-By default, Keras will use TensorFlow as its tensor manipulation library. [Follow these instructions](http://keras.io/backend/) to configure the Keras backend.
-
-------------------
-
-
-## Support
-
-You can ask questions and join the development discussion:
-
-- On the [Keras Google group](https://groups.google.com/forum/#!forum/keras-users).
-- On the [Keras Slack channel](https://kerasteam.slack.com). Use [this link](https://keras-slack-autojoin.herokuapp.com/) to request an invitation to the channel.
-
-You can also post **bug reports and feature requests** (only) in [Github issues](https://github.com/fchollet/keras/issues). Make sure to read [our guidelines](https://github.com/fchollet/keras/blob/master/CONTRIBUTING.md) first.
-
-
-------------------
-
-
-## Why this name, Keras?
-
-Keras (κέρας) means _horn_ in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the _Odyssey_, where dream spirits (_Oneiroi_, singular _Oneiros_) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).
-
-Keras was initially developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
-
->_"Oneiroi are beyond our unravelling --who can be sure what tale they tell? Not all that men look for comes to pass. Two gates there are that give passage to fleeting Oneiroi; one is made of horn, one of ivory. The Oneiroi that pass through sawn ivory are deceitful, bearing a message that will not be fulfilled; those that come out through polished horn have truth behind them, to be accomplished for men who see them."_ Homer, Odyssey 19. 562 ff (Shewring translation).
-
-------------------
+{{autogenerated}}

docs/templates/initializations.md

@@ -1,51 +0,0 @@
-
-## Usage of initializations
-
-Initializations define the way to set the initial random weights of Keras layers.
-
-The keyword arguments used for passing initializations to layers will depend on the layer. Usually it is simply `init`:
-
-```python
-model.add(Dense(64, init='uniform'))
-```
-
-## Available initializations
-
-- __uniform__
-- __lecun_uniform__: Uniform initialization scaled by the square root of the number of inputs (LeCun 98).
-- __normal__
-- __identity__: Use with square 2D layers (`shape[0] == shape[1]`).
-- __orthogonal__: Use with square 2D layers (`shape[0] == shape[1]`).
-- __zero__
-- __one__
-- __glorot_normal__: Gaussian initialization scaled by fan_in + fan_out (Glorot 2010)
-- __glorot_uniform__
-- __he_normal__: Gaussian initialization scaled by fan_in (He et al., 2014)
-- __he_uniform__
-
-
-An initialization may be passed as a string (must match one of the available initializations above), or as a callable.
-If a callable, then it must take two arguments: `shape` (shape of the variable to initialize) and `name` (name of the variable),
-and it must return a variable (e.g. output of `K.variable()`):
-
-```python
-from keras import backend as K
-import numpy as np
-
-def my_init(shape, name=None):
-    value = np.random.random(shape)
-    return K.variable(value, name=name)
-
-model.add(Dense(64, init=my_init))
-```
-
-You could also use functions from `keras.initializations` in this way:
-
-```python
-from keras import initializations
-
-def my_init(shape, name=None):
-    return initializations.normal(shape, scale=0.01, name=name)
-
-model.add(Dense(64, init=my_init))
-```

docs/templates/initializers.md

@@ -0,0 +1,43 @@
+## Usage of initializers
+
+Initializations define the way to set the initial random weights of Keras layers.
+
+The keyword arguments used for passing initializers to layers will depend on the layer. Usually it is simply `kernel_initializer` and `bias_initializer`:
+
+```python
+model.add(Dense(64,
+                kernel_initializer='random_uniform',
+                bias_initializer='zeros'))
+```
+
+## Available initializers
+
+The following built-in initializers are available as part of the `keras.initializers` module:
+
+{{autogenerated}}
+
+
+An initializer may be passed as a string (must match one of the available initializers above), or as a callable:
+
+```python
+from keras import initializers
+
+model.add(Dense(64, kernel_initializer=initializers.random_normal(stddev=0.01)))
+
+# also works; will use the default parameters.
+model.add(Dense(64, kernel_initializer='random_normal'))
+```
+
+
+## Using custom initializers
+
+If passing a custom callable, then it must take the argument `shape` (shape of the variable to initialize) and `dtype` (dtype of generated values):
+
+```python
+from keras import backend as K
+
+def my_init(shape, dtype=None):
+    return K.random_normal(shape, dtype=dtype)
+
+model.add(Dense(64, kernel_initializer=my_init))
+```

docs/templates/layers/about-keras-layers.md

@@ -15,11 +15,11 @@ reconstructed_layer = Dense.from_config(config)
 Or:
 
 ```python
-from keras.utils.layer_utils import layer_from_config
+from keras import layers
 
 config = layer.get_config()
-layer = layer_from_config({'class_name': layer.__class__.__name__,
-                           'config': config})
+layer = layers.deserialize({'class_name': layer.__class__.__name__,
+                            'config': config})
 ```
 
 If a layer has a single node (i.e. if it isn't a shared layer), you can get its input tensor, output tensor, input shape and output shape via:

docs/templates/layers/writing-your-own-keras-layers.md

@@ -2,11 +2,11 @@
 
 For simple, stateless custom operations, you are probably better off using `layers.core.Lambda` layers. But for any custom operation that has trainable weights, you should implement your own layer.
 
-Here is the skeleton of a Keras layer, **as of Keras 1.2.0** (if you have an older version, please upgrade). There are only three methods you need to implement:
+Here is the skeleton of a Keras layer, **as of Keras 2.0** (if you have an older version, please upgrade). There are only three methods you need to implement:
 
 - `build(input_shape)`: this is where you will define your weights. This method must set `self.built = True`, which can be done by calling `super([Layer], self).build()`.
 - `call(x)`: this is where the layer's logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to `call`: the input tensor.
-- `get_output_shape_for(input_shape)`: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.
+- `compute_output_shape(input_shape)`: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.
 
 ```python
 from keras import backend as K
@@ -14,21 +14,23 @@ from keras.engine.topology import Layer
 import numpy as np
 
 class MyLayer(Layer):
+
     def __init__(self, output_dim, **kwargs):
         self.output_dim = output_dim
         super(MyLayer, self).__init__(**kwargs)
 
     def build(self, input_shape):
         # Create a trainable weight variable for this layer.
-        self.W = self.add_weight(shape=(input_shape[1], self.output_dim),
-                                 initializer='uniform',
-                                 trainable=True)
+        self.kernel = self.add_weight(name='kernel', 
+                                      shape=(input_shape[1], self.output_dim),
+                                      initializer='uniform',
+                                      trainable=True)
         super(MyLayer, self).build(input_shape)  # Be sure to call this somewhere!
 
-    def call(self, x, mask=None):
-        return K.dot(x, self.W)
+    def call(self, x):
+        return K.dot(x, self.kernel)
 
-    def get_output_shape_for(self, input_shape):
+    def compute_output_shape(self, input_shape):
         return (input_shape[0], self.output_dim)
 ```
 

docs/templates/losses.md

@@ -0,0 +1,37 @@
+
+## Usage of loss functions
+
+A loss function (or objective function, or optimization score function) is one of the two parameters required to compile a model:
+
+```python
+model.compile(loss='mean_squared_error', optimizer='sgd')
+```
+
+```python
+from keras import losses
+
+model.compile(loss=losses.mean_squared_error, optimizer='sgd')
+```
+
+You can either pass the name of an existing loss function, or pass a TensorFlow/Theano symbolic function that returns a scalar for each data-point and takes the following two arguments:
+
+- __y_true__: True labels. TensorFlow/Theano tensor.
+- __y_pred__: Predictions. TensorFlow/Theano tensor of the same shape as y_true.
+
+The actual optimized objective is the mean of the output array across all datapoints.
+
+For a few examples of such functions, check out the [losses source](https://github.com/fchollet/keras/blob/master/keras/losses.py).
+
+## Available loss functions
+
+{{autogenerated}}
+
+----
+
+**Note**: when using the `categorical_crossentropy` loss, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros expect for a 1 at the index corresponding to the class of the sample). In order to convert *integer targets* into *categorical targets*, you can use the Keras utility `to_categorical`:
+
+```python
+from keras.utils.np_utils import to_categorical
+
+categorical_labels = to_categorical(int_labels, num_classes=None)
+```

docs/templates/metrics.md

@@ -3,7 +3,21 @@
 
 A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the `metrics` parameter when a model is compiled.
 
-A metric function is similar to an [objective function](/objectives), except that the results from evaluating a metric are not used when training the model.
+```python
+model.compile(loss='mean_squared_error',
+              optimizer='sgd',
+              metrics=['mae', 'acc'])
+```
+
+```python
+from keras import metrics
+
+model.compile(loss='mean_squared_error',
+              optimizer='sgd',
+              metrics=[metrics.mae, metrics.categorical_accuracy])
+```
+
+A metric function is similar to an [loss function](/losses), except that the results from evaluating a metric are not used when training the model.
 
 You can either pass the name of an existing metric, or pass a Theano/TensorFlow symbolic function (see [Custom metrics](#custom-metrics)).
 
@@ -26,26 +40,17 @@ You can either pass the name of an existing metric, or pass a Theano/TensorFlow
 
 ## Custom metrics
 
-Custom metrics can be defined and passed via the compilation step. The
+Custom metrics can be passed at the compilation step. The
 function would need to take `(y_true, y_pred)` as arguments and return
-either a single tensor value or a dict `metric_name -> metric_value`.
+a single tensor value.
 
 ```python
-# for custom metrics
 import keras.backend as K
 
 def mean_pred(y_true, y_pred):
     return K.mean(y_pred)
 
-def false_rates(y_true, y_pred):
-    false_neg = ...
-    false_pos = ...
-    return {
-        'false_neg': false_neg,
-        'false_pos': false_pos,
-    }
-
 model.compile(optimizer='rmsprop',
               loss='binary_crossentropy',
-              metrics=['accuracy', mean_pred, false_rates])
-```
+              metrics=['accuracy', mean_pred])
+```

docs/templates/models/model.md

@@ -1,6 +1,6 @@
 # Model class API
 
-In the functional API, given an input tensor and output tensor, you can instantiate a `Model` via:
+In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a `Model` via:
 
 ```python
 from keras.models import Model
@@ -8,7 +8,7 @@ from keras.layers import Input, Dense
 
 a = Input(shape=(32,))
 b = Dense(32)(a)
-model = Model(input=a, output=b)
+model = Model(inputs=a, outputs=b)
 ```
 
 This model will include all layers required in the computation of `b` given `a`.
@@ -16,7 +16,7 @@ This model will include all layers required in the computation of `b` given `a`.
 In the case of multi-input or multi-output models, you can use lists as well:
 
 ```python
-model = Model(input=[a1, a2], output=[b1, b3, b3])
+model = Model(inputs=[a1, a2], outputs=[b1, b3, b3])
 ```
 
 For a detailed introduction of what `Model` can do, read [this guide to the Keras functional API](/getting-started/functional-api-guide).

docs/templates/objectives.md

@@ -1,40 +0,0 @@
-
-## Usage of objectives
-
-An objective function (or loss function, or optimization score function) is one of the two parameters required to compile a model:
-
-```python
-model.compile(loss='mean_squared_error', optimizer='sgd')
-```
-
-You can either pass the name of an existing objective, or pass a Theano/TensorFlow symbolic function that returns a scalar for each data-point and takes the following two arguments:
-
-- __y_true__: True labels. Theano/TensorFlow tensor.
-- __y_pred__: Predictions. Theano/TensorFlow tensor of the same shape as y_true.
-
-The actual optimized objective is the mean of the output array across all datapoints.
-
-For a few examples of such functions, check out the [objectives source](https://github.com/fchollet/keras/blob/master/keras/objectives.py).
-
-## Available objectives
-
-- __mean_squared_error__ / __mse__
-- __mean_absolute_error__ / __mae__
-- __mean_absolute_percentage_error__ / __mape__
-- __mean_squared_logarithmic_error__ / __msle__
-- __squared_hinge__
-- __hinge__
-- __binary_crossentropy__: Also known as logloss. 
-- __categorical_crossentropy__: Also known as multiclass logloss. __Note__: using this objective requires that your labels are binary arrays of shape `(nb_samples, nb_classes)`.
-- __sparse_categorical_crossentropy__: As above but accepts sparse labels. __Note__: this objective still requires that your labels have the same number of dimensions as your outputs; you may need to add a length-1 dimension to the shape of your labels, e.g with `np.expand_dims(y, -1)`.
-- __kullback_leibler_divergence__ / __kld__: Information gain from a predicted probability distribution Q to a true probability distribution P. Gives a measure of difference between both distributions.
-- __poisson__: Mean of `(predictions - targets * log(predictions))`
-- __cosine_proximity__: The opposite (negative) of the mean cosine proximity between predictions and targets.
-
-**Note**: when using the `categorical_crossentropy` objective, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros expect for a 1 at the index corresponding to the class of the sample). In order to convert *integer targets* into *categorical targets*, you can use the Keras utility `to_categorical`:
-
-```python
-from keras.utils.np_utils import to_categorical
-
-categorical_labels = to_categorical(int_labels, nb_classes=None)
-```

docs/templates/optimizers.md

@@ -4,12 +4,14 @@
 An optimizer is one of the two arguments required for compiling a Keras model:
 
 ```python
+from keras import optimizers
+
 model = Sequential()
-model.add(Dense(64, init='uniform', input_dim=10))
+model.add(Dense(64, init='uniform', input_shape=(10,)))
 model.add(Activation('tanh'))
 model.add(Activation('softmax'))
 
-sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss='mean_squared_error', optimizer=sgd)
 ```
 
@@ -27,16 +29,20 @@ model.compile(loss='mean_squared_error', optimizer='sgd')
 The parameters `clipnorm` and `clipvalue` can be used with all optimizers to control gradient clipping:
 
 ```python
-# all parameter gradients will be clipped to
+from keras import optimizers
+
+# All parameter gradients will be clipped to
 # a maximum norm of 1.
-sgd = SGD(lr=0.01, clipnorm=1.)
+sgd = optimizers.SGD(lr=0.01, clipnorm=1.)
 ```
 
 ```python
-# all parameter gradients will be clipped to
+from keras import optimizers
+
+# All parameter gradients will be clipped to
 # a maximum value of 0.5 and
 # a minimum value of -0.5.
-sgd = SGD(lr=0.01, clipvalue=0.5)
+sgd = optimizers.SGD(lr=0.01, clipvalue=0.5)
 ```
 
 ---

docs/templates/preprocessing/image.md

@@ -7,6 +7,7 @@ keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,
     featurewise_std_normalization=False,
     samplewise_std_normalization=False,
     zca_whitening=False,
+    zca_epsilon=1e-6,
     rotation_range=0.,
     width_shift_range=0.,
     height_shift_range=0.,
@@ -18,7 +19,8 @@ keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,
     horizontal_flip=False,
     vertical_flip=False,
     rescale=None,
-    dim_ordering=K.image_dim_ordering())
+    preprocessing_function=None,
+    data_format=K.image_data_format())
 ```
 
 Generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches) indefinitely.
@@ -28,6 +30,7 @@ Generate batches of tensor image data with real-time data augmentation. The data
     - __samplewise_center__: Boolean. Set each sample mean to 0.
     - __featurewise_std_normalization__: Boolean. Divide inputs by std of the dataset, feature-wise.
     - __samplewise_std_normalization__: Boolean. Divide each input by its std.
+    - __zca_epsilon__: epsilon for ZCA whitening. Default is 1e-6.
     - __zca_whitening__: Boolean. Apply ZCA whitening.
     - __rotation_range__: Int. Degree range for random rotations.
     - __width_shift_range__: Float (fraction of total width). Range for random horizontal shifts.
@@ -42,27 +45,32 @@ Generate batches of tensor image data with real-time data augmentation. The data
     - __rescale__: rescaling factor. Defaults to None. If None or 0, no rescaling is applied,
             otherwise we multiply the data by the value provided (before applying
             any other transformation).
-    - __dim_ordering__: One of {"th", "tf"}.
-        "tf" mode means that the images should have shape `(samples, height, width, channels)`,
-        "th" mode means that the images should have shape `(samples, channels, height, width)`.
-        It defaults to the `image_dim_ordering` value found in your
+    - __preprocessing_function__: function that will be implied on each input.
+            The function will run before any other modification on it.
+            The function should take one argument:
+            one image (Numpy tensor with rank 3),
+            and should output a Numpy tensor with the same shape.
+    - _data_format_: One of {"channels_first", "channels_last"}.
+        "channels_last" mode means that the images should have shape `(samples, height, width, channels)`,
+        "channels_first" mode means that the images should have shape `(samples, channels, height, width)`.
+        It defaults to the `image_data_format` value found in your
         Keras config file at `~/.keras/keras.json`.
-        If you never set it, then it will be "tf".
+        If you never set it, then it will be "channels_last".
 
 - __Methods__:
-    - __fit(X)__: Compute the internal data stats related to the data-dependent transformations, based on an array of sample data.
+    - __fit(x)__: Compute the internal data stats related to the data-dependent transformations, based on an array of sample data.
         Only required if featurewise_center or featurewise_std_normalization or zca_whitening.
         - __Arguments__:
-            - __X__: sample data. Should have rank 4.
+            - __x__: sample data. Should have rank 4.
                 In case of grayscale data,
                 the channels axis should have value 1, and in case
                 of RGB data, it should have value 3.
             - __augment__: Boolean (default: False). Whether to fit on randomly augmented samples.
             - __rounds__: int (default: 1). If augment, how many augmentation passes over the data to use.
             - __seed__: int (default: None). Random seed.
-    - __flow(X, y)__: Takes numpy data & label arrays, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop.
+    - __flow(x, y)__: Takes numpy data & label arrays, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop.
         - __Arguments__:
-            - __X__: data. Should have rank 4.
+            - __x__: data. Should have rank 4.
                 In case of grayscale data,
                 the channels axis should have value 1, and in case
                 of RGB data, it should have value 3.
@@ -72,7 +80,7 @@ Generate batches of tensor image data with real-time data augmentation. The data
             - __seed__: int (default: None).
             - __save_to_dir__: None or str (default: None). This allows you to optimally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
             - __save_prefix__: str (default: `''`). Prefix to use for filenames of saved pictures (only relevant if `save_to_dir` is set).
-            - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "jpeg".
+            - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "png".
         - __yields__: Tuples of `(x, y)` where `x` is a numpy array of image data and `y` is a numpy array of corresponding labels.
             The generator loops indefinitely.
     - __flow_from_directory(directory)__: Takes the path to a directory, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop.
@@ -82,25 +90,25 @@ Generate batches of tensor image data with real-time data augmentation. The data
                 See [this script](https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d) for more details.
             - __target_size__: tuple of integers, default: `(256, 256)`. The dimensions to which all images found will be resized.
             - __color_mode__: one of "grayscale", "rbg". Default: "rgb". Whether the images will be converted to have 1 or 3 color channels.
-            - __classes__: optional list of class subdirectories (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list of classes will be automatically inferred (and the order of the classes, which will map to the label indices, will be alphanumeric).
-            - __class_mode__: one of "categorical", "binary", "sparse" or None. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels. If None, no labels are returned (the generator will only yield batches of image data, which is useful to use `model.predict_generator()`, `model.evaluate_generator()`, etc.).
+            - __classes__: optional list of class subdirectories (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list of classes will be automatically inferred from the subdirectory names/structure under `directory`, where each subdirectory will be treated as a different class (and the order of the classes, which will map to the label indices, will be alphanumeric). The dictionary containing the mapping from class names to class indices can be obtained via the attribute `class_indices`.
+            - __class_mode__: one of "categorical", "binary", "sparse" or None. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels. If None, no labels are returned (the generator will only yield batches of image data, which is useful to use `model.predict_generator()`, `model.evaluate_generator()`, etc.). Please note that in case of class_mode None, the data still needs to reside in a subdirectory of `directory` for it to work correctly.
             - __batch_size__: size of the batches of data (default: 32).
             - __shuffle__: whether to shuffle the data (default: True)
             - __seed__: optional random seed for shuffling and transformations.
             - __save_to_dir__: None or str (default: None). This allows you to optimally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
             - __save_prefix__: str. Prefix to use for filenames of saved pictures (only relevant if `save_to_dir` is set).
-            - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "jpeg".
+            - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "png".
             - __follow_links__: whether to follow symlinks inside class subdirectories (default: False).
 
 
 - __Examples__:
 
-Example of using `.flow(X, y)`:
+Example of using `.flow(x, y)`:
 
 ```python
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+y_train = np_utils.to_categorical(y_train, num_classes)
+y_test = np_utils.to_categorical(y_test, num_classes)
 
 datagen = ImageDataGenerator(
     featurewise_center=True,
@@ -112,20 +120,20 @@ datagen = ImageDataGenerator(
 
 # compute quantities required for featurewise normalization
 # (std, mean, and principal components if ZCA whitening is applied)
-datagen.fit(X_train)
+datagen.fit(x_train)
 
 # fits the model on batches with real-time data augmentation:
-model.fit_generator(datagen.flow(X_train, Y_train, batch_size=32),
-                    samples_per_epoch=len(X_train), nb_epoch=nb_epoch)
+model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
+                    steps_per_epoch=len(x_train) / 32, epochs=epochs)
 
 # here's a more "manual" example
-for e in range(nb_epoch):
-    print 'Epoch', e
+for e in range(epochs):
+    print('Epoch', e)
     batches = 0
-    for X_batch, Y_batch in datagen.flow(X_train, Y_train, batch_size=32):
-        loss = model.train(X_batch, Y_batch)
+    for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
+        model.fit(x_batch, y_batch)
         batches += 1
-        if batches >= len(X_train) / 32:
+        if batches >= len(x_train) / 32:
             # we need to break the loop by hand because
             # the generator loops indefinitely
             break
@@ -156,10 +164,10 @@ validation_generator = test_datagen.flow_from_directory(
 
 model.fit_generator(
         train_generator,
-        samples_per_epoch=2000,
-        nb_epoch=50,
+        steps_per_epoch=2000,
+        epochs=50,
         validation_data=validation_generator,
-        nb_val_samples=800)
+        validation_steps=800)
 ```
 
 Example of transforming images and masks together.
@@ -195,6 +203,6 @@ train_generator = zip(image_generator, mask_generator)
 
 model.fit_generator(
     train_generator,
-    samples_per_epoch=2000,
-    nb_epoch=50)
+    steps_per_epoch=2000,
+    epochs=50)
 ```

docs/templates/preprocessing/sequence.md

@@ -5,9 +5,9 @@ keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype='int32'
     padding='pre', truncating='pre', value=0.)
 ```
 
-Transform a list of `nb_samples` sequences (lists of scalars) into a 2D Numpy array of shape `(nb_samples, nb_timesteps)`. `nb_timesteps` is either the `maxlen` argument if provided, or the length of the longest sequence otherwise. Sequences that are shorter than `nb_timesteps` are padded with `value` at the end. Sequences longer than `nb_timesteps` are truncated so that it fits the desired length. Position where padding or truncation happens is determined by `padding` or `truncating`, respectively.
+Transform a list of `num_samples` sequences (lists of scalars) into a 2D Numpy array of shape `(num_samples, num_timesteps)`. `num_timesteps` is either the `maxlen` argument if provided, or the length of the longest sequence otherwise. Sequences that are shorter than `num_timesteps` are padded with `value` at the end. Sequences longer than `num_timesteps` are truncated so that it fits the desired length. Position where padding or truncation happens is determined by `padding` or `truncating`, respectively.
 
-- __Return__: 2D Numpy array of shape `(nb_samples, nb_timesteps)`.
+- __Return__: 2D Numpy array of shape `(num_samples, num_timesteps)`.
 
 - __Arguments__:
     - __sequences__: List of lists of int or float.

docs/templates/preprocessing/text.md

@@ -3,7 +3,7 @@
 
 ```python
 keras.preprocessing.text.text_to_word_sequence(text, 
-    filters=base_filter(), lower=True, split=" ")
+    filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=" ")
 ```
 
 Split a sentence into a list of words.
@@ -12,7 +12,7 @@ Split a sentence into a list of words.
 
 - __Arguments__:
     - __text__: str.
-    - __filters__: list (or concatenation) of characters to filter out, such as punctuation. Default: base_filter(), includes basic punctuation, tabs, and newlines.
+    - __filters__: list (or concatenation) of characters to filter out, such as punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n' , includes basic punctuation, tabs, and newlines.
     - __lower__: boolean. Whether to set the text to lowercase.
     - __split__: str. Separator for word splitting.
 
@@ -20,7 +20,7 @@ Split a sentence into a list of words.
 
 ```python
 keras.preprocessing.text.one_hot(text, n,
-    filters=base_filter(), lower=True, split=" ")
+    filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=" ")
 ```
 
 One-hot encode a text into a list of word indexes in a vocabulary of size n.
@@ -33,14 +33,15 @@ One-hot encode a text into a list of word indexes in a vocabulary of size n.
 ## Tokenizer
 
 ```python
-keras.preprocessing.text.Tokenizer(nb_words=None, filters=base_filter(), 
-    lower=True, split=" ")
+keras.preprocessing.text.Tokenizer(num_words=None, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 
+    lower=True, split=" ", char_level=False)
 ```
 
 Class for vectorizing texts, or/and turning texts into sequences (=list of word indexes, where the word of rank i in the dataset (starting at 1) has index i).
 
 - __Arguments__: Same as `text_to_word_sequence` above.
-    - __nb_words__: None or int. Maximum number of words to work with (if set, tokenization will be restricted to the top nb_words most common words in the dataset).
+    - __num_words__: None or int. Maximum number of words to work with (if set, tokenization will be restricted to the top num_words most common words in the dataset).
+    - __char_level__: if True, every character will be treated as a token.
 
 - __Methods__:
 
@@ -57,7 +58,7 @@ Class for vectorizing texts, or/and turning texts into sequences (=list of word
         - __Return__: yield one sequence per input text.
 
     - __texts_to_matrix(texts)__:
-        - __Return__: numpy array of shape `(len(texts), nb_words)`.
+        - __Return__: numpy array of shape `(len(texts), num_words)`.
         - __Arguments__:
             - __texts__: list of texts to vectorize.
             - __mode__: one of "binary", "count", "tfidf", "freq" (default: "binary").
@@ -67,7 +68,7 @@ Class for vectorizing texts, or/and turning texts into sequences (=list of word
             - __sequences__: list of sequences to train on. 
 
     - __sequences_to_matrix(sequences)__:
-        - __Return__: numpy array of shape `(len(sequences), nb_words)`.
+        - __Return__: numpy array of shape `(len(sequences), num_words)`.
         - __Arguments__:
             - __sequences__: list of sequences to vectorize.
             - __mode__: one of "binary", "count", "tfidf", "freq" (default: "binary").

docs/templates/regularizers.md

@@ -2,39 +2,45 @@
 
 Regularizers allow to apply penalties on layer parameters or layer activity during optimization. These penalties are incorporated in the loss function that the network optimizes.
 
-The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`, `TimeDistributedDense`, `MaxoutDense`, `Convolution1D`, `Convolution2D` and `Convolution3D` have a unified API.
+The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`, `Conv1D`, `Conv2D` and `Conv3D` have a unified API.
 
 These layers expose 3 keyword arguments:
 
-- `W_regularizer`: instance of `keras.regularizers.WeightRegularizer`
-- `b_regularizer`: instance of `keras.regularizers.WeightRegularizer`
-- `activity_regularizer`: instance of `keras.regularizers.ActivityRegularizer`
+- `kernel_regularizer`: instance of `keras.regularizers.Regularizer`
+- `bias_regularizer`: instance of `keras.regularizers.Regularizer`
+- `activity_regularizer`: instance of `keras.regularizers.Regularizer`
 
 
 ## Example
 
 ```python
-from keras.regularizers import l2, activity_l2
-model.add(Dense(64, input_dim=64, W_regularizer=l2(0.01), activity_regularizer=activity_l2(0.01)))
+from keras import regularizers
+model.add(Dense(64, input_dim=64,
+                kernel_regularizer=regularizers.l2(0.01),
+                activity_regularizer=regularizers.l1(0.01)))
 ```
 
 ## Available penalties
 
 ```python
-keras.regularizers.WeightRegularizer(l1=0., l2=0.)
+keras.regularizers.l1(0.)
+keras.regularizers.l2(0.)
+keras.regularizers.l1_l2(0.)
 ```
 
+## Developing new regularizers
+
+Any function that takes in a weight matrix and returns a loss contribution tensor can be used as a regularizer, e.g.:
+
 ```python
-keras.regularizers.ActivityRegularizer(l1=0., l2=0.)
+from keras import backend as K
+
+def l1_reg(weight_matrix):
+    return 0.01 * K.sum(K.abs(weight_matrix))
+
+model.add(Dense(64, input_dim=64,
+                kernel_regularizer=l1_reg)
 ```
 
-## Shortcuts
-
-These are shortcut functions available in `keras.regularizers`.
-
-- __l1__(l=0.01): L1 weight regularization penalty, also known as LASSO
-- __l2__(l=0.01): L2 weight regularization penalty, also known as weight decay, or Ridge
-- __l1l2__(l1=0.01, l2=0.01): L1-L2 weight regularization penalty, also known as ElasticNet
-- __activity_l1__(l=0.01): L1 activity regularization
-- __activity_l2__(l=0.01): L2 activity regularization
-- __activity_l1l2__(l1=0.01, l2=0.01): L1+L2 activity regularization
+Alternatively, you can write your regularizers in an object-oriented way;
+see the [keras/regularizers.py](https://github.com/fchollet/keras/blob/master/keras/regularizers.py) module for examples.

docs/templates/scikit-learn-api.md

@@ -30,7 +30,7 @@ its arguments, so that you could create the estimator without passing any
 values to `sk_params`.
 
 `sk_params` could also accept parameters for calling `fit`, `predict`,
-`predict_proba`, and `score` methods (e.g., `nb_epoch`, `batch_size`).
+`predict_proba`, and `score` methods (e.g., `epochs`, `batch_size`).
 fitting (predicting) parameters are selected in the following order:
 
 1. Values passed to the dictionary arguments of
@@ -42,4 +42,4 @@ fitting (predicting) parameters are selected in the following order:
 When using scikit-learn's `grid_search` API, legal tunable parameters are
 those you could pass to `sk_params`, including fitting parameters.
 In other words, you could use `grid_search` to search for the best
-`batch_size` or `nb_epoch` as well as the model parameters.
+`batch_size` or `epochs` as well as the model parameters.

docs/templates/visualization.md

@@ -1,16 +1,16 @@
 
 ## Model visualization
 
-The `keras.utils.visualize_util` module provides utility functions to plot
-a Keras model (using graphviz).
+The `keras.utils.vis_utils` module provides utility functions to plot
+a Keras model (using `graphviz`).
 
 This will plot a graph of the model and save it to a file:
 ```python
-from keras.utils.visualize_util import plot
-plot(model, to_file='model.png')
+from keras.utils import plot_model
+plot_model(model, to_file='model.png')
 ```
 
-`plot` takes two optional arguments:
+`plot_model` takes two optional arguments:
 
 - `show_shapes` (defaults to False) controls whether output shapes are shown in the graph.
 - `show_layer_names` (defaults to True) controls whether layer names are shown in the graph.
@@ -19,7 +19,7 @@ You can also directly obtain the `pydot.Graph` object and render it yourself,
 for example to show it in an ipython notebook :
 ```python
 from IPython.display import SVG
-from keras.utils.visualize_util import model_to_dot
+from keras.utils.vis_utils import model_to_dot
 
 SVG(model_to_dot(model).create(prog='dot', format='svg'))
 ```

examples/addition_rnn.py

@@ -23,13 +23,11 @@ Four digits inverted:
 
 Five digits inverted:
 + One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
-
 '''
 
 from __future__ import print_function
 from keras.models import Sequential
-from keras.engine.training import slice_X
-from keras.layers import Activation, TimeDistributed, Dense, RepeatVector, recurrent
+from keras import layers
 import numpy as np
 from six.moves import range
 
@@ -57,15 +55,15 @@ class CharacterTable(object):
             num_rows: Number of rows in the returned one hot encoding. This is
                 used to keep the # of rows for each data the same.
         """
-        X = np.zeros((num_rows, len(self.chars)))
+        x = np.zeros((num_rows, len(self.chars)))
         for i, c in enumerate(C):
-            X[i, self.char_indices[c]] = 1
-        return X
+            x[i, self.char_indices[c]] = 1
+        return x
 
-    def decode(self, X, calc_argmax=True):
+    def decode(self, x, calc_argmax=True):
         if calc_argmax:
-            X = X.argmax(axis=-1)
-        return ''.join(self.indices_char[x] for x in X)
+            x = x.argmax(axis=-1)
+        return ''.join(self.indices_char[x] for x in x)
 
 
 class colors:
@@ -95,7 +93,7 @@ while len(questions) < TRAINING_SIZE:
                     for i in range(np.random.randint(1, DIGITS + 1))))
     a, b = f(), f()
     # Skip any addition questions we've already seen
-    # Also skip any such that X+Y == Y+X (hence the sorting).
+    # Also skip any such that x+Y == Y+x (hence the sorting).
     key = tuple(sorted((a, b)))
     if key in seen:
         continue
@@ -115,35 +113,35 @@ while len(questions) < TRAINING_SIZE:
 print('Total addition questions:', len(questions))
 
 print('Vectorization...')
-X = np.zeros((len(questions), MAXLEN, len(chars)), dtype=np.bool)
+x = np.zeros((len(questions), MAXLEN, len(chars)), dtype=np.bool)
 y = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype=np.bool)
 for i, sentence in enumerate(questions):
-    X[i] = ctable.encode(sentence, MAXLEN)
+    x[i] = ctable.encode(sentence, MAXLEN)
 for i, sentence in enumerate(expected):
     y[i] = ctable.encode(sentence, DIGITS + 1)
 
-# Shuffle (X, y) in unison as the later parts of X will almost all be larger
+# Shuffle (x, y) in unison as the later parts of x will almost all be larger
 # digits.
 indices = np.arange(len(y))
 np.random.shuffle(indices)
-X = X[indices]
+x = x[indices]
 y = y[indices]
 
 # Explicitly set apart 10% for validation data that we never train over.
-split_at = len(X) - len(X) // 10
-(X_train, X_val) = (slice_X(X, 0, split_at), slice_X(X, split_at))
-(y_train, y_val) = (y[:split_at], y[split_at:])
+split_at = len(x) - len(x) // 10
+(x_train, x_val) = x[:split_at], x[split_at:]
+(y_train, y_val) = y[:split_at], y[split_at:]
 
 print('Training Data:')
-print(X_train.shape)
+print(x_train.shape)
 print(y_train.shape)
 
 print('Validation Data:')
-print(X_val.shape)
+print(x_val.shape)
 print(y_val.shape)
 
 # Try replacing GRU, or SimpleRNN.
-RNN = recurrent.LSTM
+RNN = layers.LSTM
 HIDDEN_SIZE = 128
 BATCH_SIZE = 128
 LAYERS = 1
@@ -152,24 +150,24 @@ print('Build model...')
 model = Sequential()
 # "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE.
 # Note: In a situation where your input sequences have a variable length,
-# use input_shape=(None, nb_feature).
+# use input_shape=(None, num_feature).
 model.add(RNN(HIDDEN_SIZE, input_shape=(MAXLEN, len(chars))))
 # As the decoder RNN's input, repeatedly provide with the last hidden state of
 # RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum
 # length of output, e.g., when DIGITS=3, max output is 999+999=1998.
-model.add(RepeatVector(DIGITS + 1))
+model.add(layers.RepeatVector(DIGITS + 1))
 # The decoder RNN could be multiple layers stacked or a single layer.
 for _ in range(LAYERS):
     # By setting return_sequences to True, return not only the last output but
-    # all the outputs so far in the form of (nb_samples, timesteps,
+    # all the outputs so far in the form of (num_samples, timesteps,
     # output_dim). This is necessary as TimeDistributed in the below expects
     # the first dimension to be the timesteps.
     model.add(RNN(HIDDEN_SIZE, return_sequences=True))
 
 # Apply a dense layer to the every temporal slice of an input. For each of step
 # of the output sequence, decide which character should be chosen.
-model.add(TimeDistributed(Dense(len(chars))))
-model.add(Activation('softmax'))
+model.add(layers.TimeDistributed(layers.Dense(len(chars))))
+model.add(layers.Activation('softmax'))
 model.compile(loss='categorical_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])
@@ -181,15 +179,17 @@ for iteration in range(1, 200):
     print()
     print('-' * 50)
     print('Iteration', iteration)
-    model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=1,
-              validation_data=(X_val, y_val))
+    model.fit(x_train, y_train,
+              batch_size=BATCH_SIZE,
+              epochs=1,
+              validation_data=(x_val, y_val))
     # Select 10 samples from the validation set at random so we can visualize
     # errors.
     for i in range(10):
-        ind = np.random.randint(0, len(X_val))
-        rowX, rowy = X_val[np.array([ind])], y_val[np.array([ind])]
-        preds = model.predict_classes(rowX, verbose=0)
-        q = ctable.decode(rowX[0])
+        ind = np.random.randint(0, len(x_val))
+        rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]
+        preds = model.predict_classes(rowx, verbose=0)
+        q = ctable.decode(rowx[0])
         correct = ctable.decode(rowy[0])
         guess = ctable.decode(preds[0], calc_argmax=False)
         print('Q', q[::-1] if INVERT else q)

examples/antirectifier.py

@@ -2,7 +2,7 @@
 
 We build a custom activation layer called 'Antirectifier',
 which modifies the shape of the tensor that passes through it.
-We need to specify two methods: `get_output_shape_for` and `call`.
+We need to specify two methods: `compute_output_shape` and `call`.
 
 Note that the same result can also be achieved via a Lambda layer.
 
@@ -11,14 +11,14 @@ backend (`K`), our code can run both on TensorFlow and Theano.
 '''
 
 from __future__ import print_function
+import keras
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Layer, Activation
+from keras import layers
 from keras.datasets import mnist
 from keras import backend as K
-from keras.utils import np_utils
 
 
-class Antirectifier(Layer):
+class Antirectifier(layers.Layer):
     '''This is the combination of a sample-wise
     L2 normalization with the concatenation of the
     positive part of the input with the negative part
@@ -46,50 +46,50 @@ class Antirectifier(Layer):
         classification accuracy as an equivalent ReLU-based network.
     '''
 
-    def get_output_shape_for(self, input_shape):
+    def compute_output_shape(self, input_shape):
         shape = list(input_shape)
         assert len(shape) == 2  # only valid for 2D tensors
         shape[-1] *= 2
         return tuple(shape)
 
-    def call(self, x, mask=None):
-        x -= K.mean(x, axis=1, keepdims=True)
-        x = K.l2_normalize(x, axis=1)
-        pos = K.relu(x)
-        neg = K.relu(-x)
+    def call(self, inputs):
+        inputs -= K.mean(inputs, axis=1, keepdims=True)
+        inputs = K.l2_normalize(inputs, axis=1)
+        pos = K.relu(inputs)
+        neg = K.relu(-inputs)
         return K.concatenate([pos, neg], axis=1)
 
 # global parameters
 batch_size = 128
-nb_classes = 10
-nb_epoch = 40
+num_classes = 10
+epochs = 40
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 # build the model
 model = Sequential()
-model.add(Dense(256, input_shape=(784,)))
+model.add(layers.Dense(256, input_shape=(784,)))
 model.add(Antirectifier())
-model.add(Dropout(0.1))
-model.add(Dense(256))
+model.add(layers.Dropout(0.1))
+model.add(layers.Dense(256))
 model.add(Antirectifier())
-model.add(Dropout(0.1))
-model.add(Dense(10))
-model.add(Activation('softmax'))
+model.add(layers.Dropout(0.1))
+model.add(layers.Dense(10))
+model.add(layers.Activation('softmax'))
 
 # compile the model
 model.compile(loss='categorical_crossentropy',
@@ -97,9 +97,11 @@ model.compile(loss='categorical_crossentropy',
               metrics=['accuracy'])
 
 # train the model
-model.fit(X_train, Y_train,
-          batch_size=batch_size, nb_epoch=nb_epoch,
-          verbose=1, validation_data=(X_test, Y_test))
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
 
 # next, compare with an equivalent network
 # with2x bigger Dense layers and ReLU

examples/babi_memnn.py

@@ -12,11 +12,11 @@ References:
 Reaches 98.6% accuracy on task 'single_supporting_fact_10k' after 120 epochs.
 Time per epoch: 3s on CPU (core i7).
 '''
-
 from __future__ import print_function
-from keras.models import Sequential
+
+from keras.models import Sequential, Model
 from keras.layers.embeddings import Embedding
-from keras.layers import Activation, Dense, Merge, Permute, Dropout
+from keras.layers import Input, Activation, Dense, Permute, Dropout, add, dot, concatenate
 from keras.layers import LSTM
 from keras.utils.data_utils import get_file
 from keras.preprocessing.sequence import pad_sequences
@@ -38,7 +38,8 @@ def tokenize(sent):
 def parse_stories(lines, only_supporting=False):
     '''Parse stories provided in the bAbi tasks format
 
-    If only_supporting is true, only the sentences that support the answer are kept.
+    If only_supporting is true, only the sentences
+    that support the answer are kept.
     '''
     data = []
     story = []
@@ -68,9 +69,12 @@ def parse_stories(lines, only_supporting=False):
 
 
 def get_stories(f, only_supporting=False, max_length=None):
-    '''Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story.
+    '''Given a file name, read the file,
+    retrieve the stories,
+    and then convert the sentences into a single story.
 
-    If max_length is supplied, any stories longer than max_length tokens will be discarded.
+    If max_length is supplied,
+    any stories longer than max_length tokens will be discarded.
     '''
     data = parse_stories(f.readlines(), only_supporting=only_supporting)
     flatten = lambda data: reduce(lambda x, y: x + y, data)
@@ -85,7 +89,8 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
     for story, query, answer in data:
         x = [word_idx[w] for w in story]
         xq = [word_idx[w] for w in query]
-        y = np.zeros(len(word_idx) + 1)  # let's not forget that index 0 is reserved
+        # let's not forget that index 0 is reserved
+        y = np.zeros(len(word_idx) + 1)
         y[word_idx[answer]] = 1
         X.append(x)
         Xq.append(xq)
@@ -93,7 +98,6 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
     return (pad_sequences(X, maxlen=story_maxlen),
             pad_sequences(Xq, maxlen=query_maxlen), np.array(Y))
 
-
 try:
     path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')
 except:
@@ -116,7 +120,11 @@ print('Extracting stories for the challenge:', challenge_type)
 train_stories = get_stories(tar.extractfile(challenge.format('train')))
 test_stories = get_stories(tar.extractfile(challenge.format('test')))
 
-vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train_stories + test_stories)))
+vocab = set()
+for story, q, answer in train_stories + test_stories:
+    vocab |= set(story + q + [answer])
+vocab = sorted(vocab)
+
 # Reserve 0 for masking via pad_sequences
 vocab_size = len(vocab) + 1
 story_maxlen = max(map(len, (x for x, _, _ in train_stories + test_stories)))
@@ -135,8 +143,14 @@ print('-')
 print('Vectorizing the word sequences...')
 
 word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
-inputs_train, queries_train, answers_train = vectorize_stories(train_stories, word_idx, story_maxlen, query_maxlen)
-inputs_test, queries_test, answers_test = vectorize_stories(test_stories, word_idx, story_maxlen, query_maxlen)
+inputs_train, queries_train, answers_train = vectorize_stories(train_stories,
+                                                               word_idx,
+                                                               story_maxlen,
+                                                               query_maxlen)
+inputs_test, queries_test, answers_test = vectorize_stories(test_stories,
+                                                            word_idx,
+                                                            story_maxlen,
+                                                            query_maxlen)
 
 print('-')
 print('inputs: integer tensor of shape (samples, max_length)')
@@ -153,13 +167,25 @@ print('answers_test shape:', answers_test.shape)
 print('-')
 print('Compiling...')
 
+# placeholders
+input_sequence = Input((story_maxlen,))
+question = Input((query_maxlen,))
+
+# encoders
 # embed the input sequence into a sequence of vectors
 input_encoder_m = Sequential()
 input_encoder_m.add(Embedding(input_dim=vocab_size,
-                              output_dim=64,
-                              input_length=story_maxlen))
+                              output_dim=64))
 input_encoder_m.add(Dropout(0.3))
 # output: (samples, story_maxlen, embedding_dim)
+
+# embed the input into a sequence of vectors of size query_maxlen
+input_encoder_c = Sequential()
+input_encoder_c.add(Embedding(input_dim=vocab_size,
+                              output_dim=query_maxlen))
+input_encoder_c.add(Dropout(0.3))
+# output: (samples, story_maxlen, query_maxlen)
+
 # embed the question into a sequence of vectors
 question_encoder = Sequential()
 question_encoder.add(Embedding(input_dim=vocab_size,
@@ -167,44 +193,43 @@ question_encoder.add(Embedding(input_dim=vocab_size,
                                input_length=query_maxlen))
 question_encoder.add(Dropout(0.3))
 # output: (samples, query_maxlen, embedding_dim)
-# compute a 'match' between input sequence elements (which are vectors)
-# and the question vector sequence
-match = Sequential()
-match.add(Merge([input_encoder_m, question_encoder],
-                mode='dot',
-                dot_axes=[2, 2]))
-match.add(Activation('softmax'))
-# output: (samples, story_maxlen, query_maxlen)
-# embed the input into a single vector with size = story_maxlen:
-input_encoder_c = Sequential()
-input_encoder_c.add(Embedding(input_dim=vocab_size,
-                              output_dim=query_maxlen,
-                              input_length=story_maxlen))
-input_encoder_c.add(Dropout(0.3))
-# output: (samples, story_maxlen, query_maxlen)
-# sum the match vector with the input vector:
-response = Sequential()
-response.add(Merge([match, input_encoder_c], mode='sum'))
-# output: (samples, story_maxlen, query_maxlen)
-response.add(Permute((2, 1)))  # output: (samples, query_maxlen, story_maxlen)
 
-# concatenate the match vector with the question vector,
-# and do logistic regression on top
-answer = Sequential()
-answer.add(Merge([response, question_encoder], mode='concat', concat_axis=-1))
+# encode input sequence and questions (which are indices)
+# to sequences of dense vectors
+input_encoded_m = input_encoder_m(input_sequence)
+input_encoded_c = input_encoder_c(input_sequence)
+question_encoded = question_encoder(question)
+
+# compute a 'match' between the first input vector sequence
+# and the question vector sequence
+# shape: `(samples, story_maxlen, query_maxlen)`
+match = dot([input_encoded_m, question_encoded], axes=(2, 2))
+match = Activation('softmax')(match)
+
+# add the match matrix with the second input vector sequence
+response = add([match, input_encoded_c])  # (samples, story_maxlen, query_maxlen)
+response = Permute((2, 1))(response)  # (samples, query_maxlen, story_maxlen)
+
+# concatenate the match matrix with the question vector sequence
+answer = concatenate([response, question_encoded])
+
 # the original paper uses a matrix multiplication for this reduction step.
 # we choose to use a RNN instead.
-answer.add(LSTM(32))
-# one regularization layer -- more would probably be needed.
-answer.add(Dropout(0.3))
-answer.add(Dense(vocab_size))
-# we output a probability distribution over the vocabulary
-answer.add(Activation('softmax'))
+answer = LSTM(32)(answer)  # (samples, 32)
 
-answer.compile(optimizer='rmsprop', loss='categorical_crossentropy',
-               metrics=['accuracy'])
-# Note: you could use a Graph model to avoid repeat the input twice
-answer.fit([inputs_train, queries_train, inputs_train], answers_train,
-           batch_size=32,
-           nb_epoch=120,
-           validation_data=([inputs_test, queries_test, inputs_test], answers_test))
+# one regularization layer -- more would probably be needed.
+answer = Dropout(0.3)(answer)
+answer = Dense(vocab_size)(answer)  # (samples, vocab_size)
+# we output a probability distribution over the vocabulary
+answer = Activation('softmax')(answer)
+
+# build the final model
+model = Model([input_sequence, question], answer)
+model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# train
+model.fit([inputs_train, queries_train], answers_train,
+          batch_size=32,
+          epochs=120,
+          validation_data=([inputs_test, queries_test], answers_test))

examples/babi_rnn.py

@@ -12,7 +12,7 @@ QA2 - Two Supporting Facts   | 20               | 50.0
 QA3 - Three Supporting Facts | 20               | 20.5
 QA4 - Two Arg. Relations     | 61               | 62.9
 QA5 - Three Arg. Relations   | 70               | 61.9
-QA6 - Yes/No Questions       | 48               | 50.7
+QA6 - yes/No Questions       | 48               | 50.7
 QA7 - Counting               | 49               | 78.9
 QA8 - Lists/Sets             | 45               | 77.2
 QA9 - Simple Negation        | 64               | 64.0
@@ -62,13 +62,12 @@ import re
 import tarfile
 
 import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.utils.data_utils import get_file
 from keras.layers.embeddings import Embedding
-from keras.layers import Dense, Merge, Dropout, RepeatVector
+from keras import layers
 from keras.layers import recurrent
-from keras.models import Sequential
+from keras.models import Model
 from keras.preprocessing.sequence import pad_sequences
 
 
@@ -84,7 +83,8 @@ def tokenize(sent):
 def parse_stories(lines, only_supporting=False):
     '''Parse stories provided in the bAbi tasks format
 
-    If only_supporting is true, only the sentences that support the answer are kept.
+    If only_supporting is true,
+    only the sentences that support the answer are kept.
     '''
     data = []
     story = []
@@ -114,9 +114,11 @@ def parse_stories(lines, only_supporting=False):
 
 
 def get_stories(f, only_supporting=False, max_length=None):
-    '''Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story.
+    '''Given a file name, read the file, retrieve the stories,
+    and then convert the sentences into a single story.
 
-    If max_length is supplied, any stories longer than max_length tokens will be discarded.
+    If max_length is supplied,
+    any stories longer than max_length tokens will be discarded.
     '''
     data = parse_stories(f.readlines(), only_supporting=only_supporting)
     flatten = lambda data: reduce(lambda x, y: x + y, data)
@@ -125,18 +127,19 @@ def get_stories(f, only_supporting=False, max_length=None):
 
 
 def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
-    X = []
-    Xq = []
-    Y = []
+    xs = []
+    xqs = []
+    ys = []
     for story, query, answer in data:
         x = [word_idx[w] for w in story]
         xq = [word_idx[w] for w in query]
-        y = np.zeros(len(word_idx) + 1)  # let's not forget that index 0 is reserved
+        # let's not forget that index 0 is reserved
+        y = np.zeros(len(word_idx) + 1)
         y[word_idx[answer]] = 1
-        X.append(x)
-        Xq.append(xq)
-        Y.append(y)
-    return pad_sequences(X, maxlen=story_maxlen), pad_sequences(Xq, maxlen=query_maxlen), np.array(Y)
+        xs.append(x)
+        xqs.append(xq)
+        ys.append(y)
+    return pad_sequences(xs, maxlen=story_maxlen), pad_sequences(xqs, maxlen=query_maxlen), np.array(ys)
 
 RNN = recurrent.LSTM
 EMBED_HIDDEN_SIZE = 50
@@ -144,7 +147,10 @@ SENT_HIDDEN_SIZE = 100
 QUERY_HIDDEN_SIZE = 100
 BATCH_SIZE = 32
 EPOCHS = 40
-print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN, EMBED_HIDDEN_SIZE, SENT_HIDDEN_SIZE, QUERY_HIDDEN_SIZE))
+print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN,
+                                                           EMBED_HIDDEN_SIZE,
+                                                           SENT_HIDDEN_SIZE,
+                                                           QUERY_HIDDEN_SIZE))
 
 try:
     path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')
@@ -165,47 +171,53 @@ challenge = 'tasks_1-20_v1-2/en/qa2_two-supporting-facts_{}.txt'
 train = get_stories(tar.extractfile(challenge.format('train')))
 test = get_stories(tar.extractfile(challenge.format('test')))
 
-vocab = sorted(reduce(lambda x, y: x | y, (set(story + q + [answer]) for story, q, answer in train + test)))
+vocab = set()
+for story, q, answer in train + test:
+    vocab |= set(story + q + [answer])
+vocab = sorted(vocab)
+
 # Reserve 0 for masking via pad_sequences
 vocab_size = len(vocab) + 1
 word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
 story_maxlen = max(map(len, (x for x, _, _ in train + test)))
 query_maxlen = max(map(len, (x for _, x, _ in train + test)))
 
-X, Xq, Y = vectorize_stories(train, word_idx, story_maxlen, query_maxlen)
-tX, tXq, tY = vectorize_stories(test, word_idx, story_maxlen, query_maxlen)
+x, xq, y = vectorize_stories(train, word_idx, story_maxlen, query_maxlen)
+tx, txq, ty = vectorize_stories(test, word_idx, story_maxlen, query_maxlen)
 
 print('vocab = {}'.format(vocab))
-print('X.shape = {}'.format(X.shape))
-print('Xq.shape = {}'.format(Xq.shape))
-print('Y.shape = {}'.format(Y.shape))
+print('x.shape = {}'.format(x.shape))
+print('xq.shape = {}'.format(xq.shape))
+print('y.shape = {}'.format(y.shape))
 print('story_maxlen, query_maxlen = {}, {}'.format(story_maxlen, query_maxlen))
 
 print('Build model...')
 
-sentrnn = Sequential()
-sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE,
-                      input_length=story_maxlen))
-sentrnn.add(Dropout(0.3))
+sentence = layers.Input(shape=(story_maxlen,), dtype='int32')
+encoded_sentence = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(sentence)
+encoded_sentence = layers.Dropout(0.3)(encoded_sentence)
 
-qrnn = Sequential()
-qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE,
-                   input_length=query_maxlen))
-qrnn.add(Dropout(0.3))
-qrnn.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
-qrnn.add(RepeatVector(story_maxlen))
+question = layers.Input(shape=(query_maxlen,), dtype='int32')
+encoded_question = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(question)
+encoded_question = layers.Dropout(0.3)(encoded_question)
+encoded_question = RNN(EMBED_HIDDEN_SIZE)(encoded_question)
+encoded_question = layers.RepeatVector(story_maxlen)(encoded_question)
 
-model = Sequential()
-model.add(Merge([sentrnn, qrnn], mode='sum'))
-model.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
-model.add(Dropout(0.3))
-model.add(Dense(vocab_size, activation='softmax'))
+merged = layers.add([encoded_sentence, encoded_question])
+merged = RNN(EMBED_HIDDEN_SIZE)(merged)
+merged = layers.Dropout(0.3)(merged)
+preds = layers.Dense(vocab_size, activation='softmax')(merged)
 
+model = Model([sentence, question], preds)
 model.compile(optimizer='adam',
               loss='categorical_crossentropy',
               metrics=['accuracy'])
 
 print('Training')
-model.fit([X, Xq], Y, batch_size=BATCH_SIZE, nb_epoch=EPOCHS, validation_split=0.05)
-loss, acc = model.evaluate([tX, tXq], tY, batch_size=BATCH_SIZE)
+model.fit([x, xq], y,
+          batch_size=BATCH_SIZE,
+          epochs=EPOCHS,
+          validation_split=0.05)
+loss, acc = model.evaluate([tx, txq], ty,
+                           batch_size=BATCH_SIZE)
 print('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))

examples/cifar10_cnn.py

@@ -1,53 +1,48 @@
 '''Train a simple deep CNN on the CIFAR10 small images dataset.
 
 GPU run command with Theano backend (with TensorFlow, the GPU is automatically used):
-    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
+    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatx=float32 python cifar10_cnn.py
 
 It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.
 (it's still underfitting at that point, though).
 '''
 
 from __future__ import print_function
+import keras
 from keras.datasets import cifar10
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
+from keras.layers import Conv2D, MaxPooling2D
 
 batch_size = 32
-nb_classes = 10
-nb_epoch = 200
+num_classes = 10
+epochs = 200
 data_augmentation = True
 
-# input image dimensions
-img_rows, img_cols = 32, 32
-# The CIFAR10 images are RGB.
-img_channels = 3
-
 # The data, shuffled and split between train and test sets:
-(X_train, y_train), (X_test, y_test) = cifar10.load_data()
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # Convert class vectors to binary class matrices.
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
 
-model.add(Convolution2D(32, 3, 3, border_mode='same',
-                        input_shape=X_train.shape[1:]))
+model.add(Conv2D(32, (3, 3), padding='same',
+                 input_shape=x_train.shape[1:]))
 model.add(Activation('relu'))
-model.add(Convolution2D(32, 3, 3))
+model.add(Conv2D(32, (3, 3)))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 
-model.add(Convolution2D(64, 3, 3, border_mode='same'))
+model.add(Conv2D(64, (3, 3), padding='same'))
 model.add(Activation('relu'))
-model.add(Convolution2D(64, 3, 3))
+model.add(Conv2D(64, (3, 3)))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
@@ -56,25 +51,28 @@ model.add(Flatten())
 model.add(Dense(512))
 model.add(Activation('relu'))
 model.add(Dropout(0.5))
-model.add(Dense(nb_classes))
+model.add(Dense(num_classes))
 model.add(Activation('softmax'))
 
+# initiate RMSprop optimizer
+opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
+
 # Let's train the model using RMSprop
 model.compile(loss='categorical_crossentropy',
-              optimizer='rmsprop',
+              optimizer=opt,
               metrics=['accuracy'])
 
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
 
 if not data_augmentation:
     print('Not using data augmentation.')
-    model.fit(X_train, Y_train,
+    model.fit(x_train, y_train,
               batch_size=batch_size,
-              nb_epoch=nb_epoch,
-              validation_data=(X_test, Y_test),
+              epochs=epochs,
+              validation_data=(x_test, y_test),
               shuffle=True)
 else:
     print('Using real-time data augmentation.')
@@ -91,13 +89,13 @@ else:
         horizontal_flip=True,  # randomly flip images
         vertical_flip=False)  # randomly flip images
 
-    # Compute quantities required for featurewise normalization
+    # Compute quantities required for feature-wise normalization
     # (std, mean, and principal components if ZCA whitening is applied).
-    datagen.fit(X_train)
+    datagen.fit(x_train)
 
     # Fit the model on the batches generated by datagen.flow().
-    model.fit_generator(datagen.flow(X_train, Y_train,
+    model.fit_generator(datagen.flow(x_train, y_train,
                                      batch_size=batch_size),
-                        samples_per_epoch=X_train.shape[0],
-                        nb_epoch=nb_epoch,
-                        validation_data=(X_test, Y_test))
+                        steps_per_epoch=x_train.shape[0] // batch_size,
+                        epochs=epochs,
+                        validation_data=(x_test, y_test))

examples/conv_filter_visualization.py

@@ -5,6 +5,7 @@ This script can run on CPU in a few minutes (with the TensorFlow backend).
 Results example: http://i.imgur.com/4nj4KjN.jpg
 '''
 from __future__ import print_function
+
 from scipy.misc import imsave
 import numpy as np
 import time
@@ -34,7 +35,7 @@ def deprocess_image(x):
 
     # convert to RGB array
     x *= 255
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         x = x.transpose((1, 2, 0))
     x = np.clip(x, 0, 255).astype('uint8')
     return x
@@ -67,7 +68,7 @@ for filter_index in range(0, 200):
     # we build a loss function that maximizes the activation
     # of the nth filter of the layer considered
     layer_output = layer_dict[layer_name].output
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         loss = K.mean(layer_output[:, filter_index, :, :])
     else:
         loss = K.mean(layer_output[:, :, :, filter_index])
@@ -85,7 +86,7 @@ for filter_index in range(0, 200):
     step = 1.
 
     # we start from a gray image with some random noise
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         input_img_data = np.random.random((1, 3, img_width, img_height))
     else:
         input_img_data = np.random.random((1, img_width, img_height, 3))

examples/conv_lstm.py

@@ -3,7 +3,7 @@ This network is used to predict the next frame of an artificially
 generated movie which contains moving squares.
 """
 from keras.models import Sequential
-from keras.layers.convolutional import Convolution3D
+from keras.layers.convolutional import Conv3D
 from keras.layers.convolutional_recurrent import ConvLSTM2D
 from keras.layers.normalization import BatchNormalization
 import numpy as np
@@ -14,27 +14,26 @@ import pylab as plt
 # of identical shape.
 
 seq = Sequential()
-seq.add(ConvLSTM2D(nb_filter=40, nb_row=3, nb_col=3,
+seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                    input_shape=(None, 40, 40, 1),
-                   border_mode='same', return_sequences=True))
+                   padding='same', return_sequences=True))
 seq.add(BatchNormalization())
 
-seq.add(ConvLSTM2D(nb_filter=40, nb_row=3, nb_col=3,
-                   border_mode='same', return_sequences=True))
+seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
+                   padding='same', return_sequences=True))
 seq.add(BatchNormalization())
 
-seq.add(ConvLSTM2D(nb_filter=40, nb_row=3, nb_col=3,
-                   border_mode='same', return_sequences=True))
+seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
+                   padding='same', return_sequences=True))
 seq.add(BatchNormalization())
 
-seq.add(ConvLSTM2D(nb_filter=40, nb_row=3, nb_col=3,
-                   border_mode='same', return_sequences=True))
+seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
+                   padding='same', return_sequences=True))
 seq.add(BatchNormalization())
 
-seq.add(Convolution3D(nb_filter=1, kernel_dim1=1, kernel_dim2=3,
-                      kernel_dim3=3, activation='sigmoid',
-                      border_mode='same', dim_ordering='tf'))
-
+seq.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
+               activation='sigmoid',
+               padding='same', data_format='channels_last'))
 seq.compile(loss='binary_crossentropy', optimizer='adadelta')
 
 
@@ -101,7 +100,7 @@ def generate_movies(n_samples=1200, n_frames=15):
 # Train the network
 noisy_movies, shifted_movies = generate_movies(n_samples=1200)
 seq.fit(noisy_movies[:1000], shifted_movies[:1000], batch_size=10,
-        nb_epoch=300, validation_split=0.05)
+        epochs=300, validation_split=0.05)
 
 # Testing the network on one movie
 # feed it with the first 7 positions and then

examples/deep_dream.py

@@ -8,23 +8,16 @@ e.g.:
 ```
 python deep_dream.py img/mypic.jpg results/dream
 ```
-
-It is preferable to run this script on GPU, for speed.
-If running on CPU, prefer the TensorFlow backend (much faster).
-
-Example results: http://i.imgur.com/FX6ROg9.jpg
 '''
 from __future__ import print_function
+
 from keras.preprocessing.image import load_img, img_to_array
 import numpy as np
-from scipy.misc import imsave
-from scipy.optimize import fmin_l_bfgs_b
-import time
+import scipy
 import argparse
 
-from keras.applications import vgg16
+from keras.applications import inception_v3
 from keras import backend as K
-from keras.layers import Input
 
 parser = argparse.ArgumentParser(description='Deep Dreams with Keras.')
 parser.add_argument('base_image_path', metavar='base', type=str,
@@ -36,185 +29,167 @@ args = parser.parse_args()
 base_image_path = args.base_image_path
 result_prefix = args.result_prefix
 
-# dimensions of the generated picture.
-img_height = 600
-img_width = 600
-
-# path to the model weights file.
-weights_path = 'vgg16_weights.h5'
-
-# some settings we found interesting
-saved_settings = {
-    'bad_trip': {'features': {'block4_conv1': 0.05,
-                              'block4_conv2': 0.01,
-                              'block4_conv3': 0.01},
-                 'continuity': 0.1,
-                 'dream_l2': 0.8,
-                 'jitter': 5},
-    'dreamy': {'features': {'block5_conv1': 0.05,
-                            'block5_conv2': 0.02},
-               'continuity': 0.1,
-               'dream_l2': 0.02,
-               'jitter': 0},
+# These are the names of the layers
+# for which we try to maximize activation,
+# as well as their weight in the final loss
+# we try to maximize.
+# You can tweak these setting to obtain new visual effects.
+settings = {
+    'features': {
+        'mixed2': 0.2,
+        'mixed3': 0.5,
+        'mixed4': 2.,
+        'mixed5': 1.5,
+    },
 }
-# the settings we will use in this experiment
-settings = saved_settings['dreamy']
-
-# util function to open, resize and format pictures into appropriate tensors
 
 
 def preprocess_image(image_path):
-    img = load_img(image_path, target_size=(img_height, img_width))
+    # Util function to open, resize and format pictures
+    # into appropriate tensors.
+    img = load_img(image_path)
     img = img_to_array(img)
     img = np.expand_dims(img, axis=0)
-    img = vgg16.preprocess_input(img)
+    img = inception_v3.preprocess_input(img)
     return img
 
-# util function to convert a tensor into a valid image
-
 
 def deprocess_image(x):
-    if K.image_dim_ordering() == 'th':
-        x = x.reshape((3, img_height, img_width))
+    # Util function to convert a tensor into a valid image.
+    if K.image_data_format() == 'channels_first':
+        x = x.reshape((3, x.shape[2], x.shape[3]))
         x = x.transpose((1, 2, 0))
     else:
-        x = x.reshape((img_height, img_width, 3))
-    # Remove zero-center by mean pixel
-    x[:, :, 0] += 103.939
-    x[:, :, 1] += 116.779
-    x[:, :, 2] += 123.68
-    # 'BGR'->'RGB'
-    x = x[:, :, ::-1]
+        x = x.reshape((x.shape[1], x.shape[2], 3))
+    x /= 2.
+    x += 0.5
+    x *= 255.
     x = np.clip(x, 0, 255).astype('uint8')
     return x
 
-if K.image_dim_ordering() == 'th':
-    img_size = (3, img_height, img_width)
-else:
-    img_size = (img_height, img_width, 3)
-# this will contain our generated image
-dream = Input(batch_shape=(1,) + img_size)
+K.set_learning_phase(0)
 
-# build the VGG16 network with our placeholder
-# the model will be loaded with pre-trained ImageNet weights
-model = vgg16.VGG16(input_tensor=dream,
-                    weights='imagenet', include_top=False)
+# Build the InceptionV3 network with our placeholder.
+# The model will be loaded with pre-trained ImageNet weights.
+model = inception_v3.InceptionV3(weights='imagenet',
+                                 include_top=False)
+dream = model.input
 print('Model loaded.')
 
-# get the symbolic outputs of each "key" layer (we gave them unique names).
+# Get the symbolic outputs of each "key" layer (we gave them unique names).
 layer_dict = dict([(layer.name, layer) for layer in model.layers])
 
-# continuity loss util function
-
-
-def continuity_loss(x):
-    assert K.ndim(x) == 4
-    if K.image_dim_ordering() == 'th':
-        a = K.square(x[:, :, :img_height - 1, :img_width - 1] -
-                     x[:, :, 1:, :img_width - 1])
-        b = K.square(x[:, :, :img_height - 1, :img_width - 1] -
-                     x[:, :, :img_height - 1, 1:])
-    else:
-        a = K.square(x[:, :img_height - 1, :img_width - 1, :] -
-                     x[:, 1:, :img_width - 1, :])
-        b = K.square(x[:, :img_height - 1, :img_width - 1, :] -
-                     x[:, :img_height - 1, 1:, :])
-    return K.sum(K.pow(a + b, 1.25))
-
-# define the loss
+# Define the loss.
 loss = K.variable(0.)
 for layer_name in settings['features']:
-    # add the L2 norm of the features of a layer to the loss
+    # Add the L2 norm of the features of a layer to the loss.
     assert layer_name in layer_dict.keys(), 'Layer ' + layer_name + ' not found in model.'
     coeff = settings['features'][layer_name]
     x = layer_dict[layer_name].output
-    shape = layer_dict[layer_name].output_shape
-    # we avoid border artifacts by only involving non-border pixels in the loss
-    if K.image_dim_ordering() == 'th':
-        loss -= coeff * K.sum(K.square(x[:, :, 2: shape[2] - 2, 2: shape[3] - 2])) / np.prod(shape[1:])
+    # We avoid border artifacts by only involving non-border pixels in the loss.
+    scaling = K.prod(K.cast(K.shape(x), 'float32'))
+    if K.image_data_format() == 'channels_first':
+        loss += coeff * K.sum(K.square(x[:, :, 2: -2, 2: -2])) / scaling
     else:
-        loss -= coeff * K.sum(K.square(x[:, 2: shape[1] - 2, 2: shape[2] - 2, :])) / np.prod(shape[1:])
+        loss += coeff * K.sum(K.square(x[:, 2: -2, 2: -2, :])) / scaling
 
-# add continuity loss (gives image local coherence, can result in an artful blur)
-loss += settings['continuity'] * continuity_loss(dream) / np.prod(img_size)
-# add image L2 norm to loss (prevents pixels from taking very high values, makes image darker)
-loss += settings['dream_l2'] * K.sum(K.square(dream)) / np.prod(img_size)
+# Compute the gradients of the dream wrt the loss.
+grads = K.gradients(loss, dream)[0]
+# Normalize gradients.
+grads /= K.maximum(K.mean(K.abs(grads)), 1e-7)
 
-# feel free to further modify the loss as you see fit, to achieve new effects...
-
-# compute the gradients of the dream wrt the loss
-grads = K.gradients(loss, dream)
-
-outputs = [loss]
-if isinstance(grads, (list, tuple)):
-    outputs += grads
-else:
-    outputs.append(grads)
-
-f_outputs = K.function([dream], outputs)
+# Set up function to retrieve the value
+# of the loss and gradients given an input image.
+outputs = [loss, grads]
+fetch_loss_and_grads = K.function([dream], outputs)
 
 
 def eval_loss_and_grads(x):
-    x = x.reshape((1,) + img_size)
-    outs = f_outputs([x])
+    outs = fetch_loss_and_grads([x])
     loss_value = outs[0]
-    if len(outs[1:]) == 1:
-        grad_values = outs[1].flatten().astype('float64')
-    else:
-        grad_values = np.array(outs[1:]).flatten().astype('float64')
+    grad_values = outs[1]
     return loss_value, grad_values
 
-# this Evaluator class makes it possible
-# to compute loss and gradients in one pass
-# while retrieving them via two separate functions,
-# "loss" and "grads". This is done because scipy.optimize
-# requires separate functions for loss and gradients,
-# but computing them separately would be inefficient.
+
+def resize_img(img, size):
+    img = np.copy(img)
+    if K.image_data_format() == 'channels_first':
+        factors = (1, 1,
+                   float(size[0]) / img.shape[2],
+                   float(size[1]) / img.shape[3])
+    else:
+        factors = (1,
+                   float(size[0]) / img.shape[1],
+                   float(size[1]) / img.shape[2],
+                   1)
+    return scipy.ndimage.zoom(img, factors, order=1)
 
 
-class Evaluator(object):
-
-    def __init__(self):
-        self.loss_value = None
-        self.grad_values = None
-
-    def loss(self, x):
-        assert self.loss_value is None
+def gradient_ascent(x, iterations, step, max_loss=None):
+    for i in range(iterations):
         loss_value, grad_values = eval_loss_and_grads(x)
-        self.loss_value = loss_value
-        self.grad_values = grad_values
-        return self.loss_value
+        if max_loss is not None and loss_value > max_loss:
+            break
+        print('..Loss value at', i, ':', loss_value)
+        x += step * grad_values
+    return x
 
-    def grads(self, x):
-        assert self.loss_value is not None
-        grad_values = np.copy(self.grad_values)
-        self.loss_value = None
-        self.grad_values = None
-        return grad_values
 
-evaluator = Evaluator()
+def save_img(img, fname):
+    pil_img = deprocess_image(np.copy(img))
+    scipy.misc.imsave(fname, pil_img)
 
-# run scipy-based optimization (L-BFGS) over the pixels of the generated image
-# so as to minimize the loss
-x = preprocess_image(base_image_path)
-for i in range(5):
-    print('Start of iteration', i)
-    start_time = time.time()
 
-    # add a random jitter to the initial image. This will be reverted at decoding time
-    random_jitter = (settings['jitter'] * 2) * (np.random.random(img_size) - 0.5)
-    x += random_jitter
+"""Process:
 
-    # run L-BFGS for 7 steps
-    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
-                                     fprime=evaluator.grads, maxfun=7)
-    print('Current loss value:', min_val)
-    # decode the dream and save it
-    x = x.reshape(img_size)
-    x -= random_jitter
-    img = deprocess_image(np.copy(x))
-    fname = result_prefix + '_at_iteration_%d.png' % i
-    imsave(fname, img)
-    end_time = time.time()
-    print('Image saved as', fname)
-    print('Iteration %d completed in %ds' % (i, end_time - start_time))
+- Load the original image.
+- Define a number of processing scales (i.e. image shapes),
+    from smallest to largest.
+- Resize the original image to the smallest scale.
+- For every scale, starting with the smallest (i.e. current one):
+    - Run gradient ascent
+    - Upscale image to the next scale
+    - Reinject the detail that was lost at upscaling time
+- Stop when we are back to the original size.
+
+To obtain the detail lost during upscaling, we simply
+take the original image, shrink it down, upscale it,
+and compare the result to the (resized) original image.
+"""
+
+
+# Playing with these hyperparameters will also allow you to achieve new effects
+step = 0.01  # Gradient ascent step size
+num_octave = 3  # Number of scales at which to run gradient ascent
+octave_scale = 1.4  # Size ratio between scales
+iterations = 20  # Number of ascent steps per scale
+max_loss = 10.
+
+img = preprocess_image(base_image_path)
+if K.image_data_format() == 'channels_first':
+    original_shape = img.shape[2:]
+else:
+    original_shape = img.shape[1:3]
+successive_shapes = [original_shape]
+for i in range(1, num_octave):
+    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
+    successive_shapes.append(shape)
+successive_shapes = successive_shapes[::-1]
+original_img = np.copy(img)
+shrunk_original_img = resize_img(img, successive_shapes[0])
+
+for shape in successive_shapes:
+    print('Processing image shape', shape)
+    img = resize_img(img, shape)
+    img = gradient_ascent(img,
+                          iterations=iterations,
+                          step=step,
+                          max_loss=max_loss)
+    upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)
+    same_size_original = resize_img(original_img, shape)
+    lost_detail = same_size_original - upscaled_shrunk_original_img
+
+    img += lost_detail
+    shrunk_original_img = resize_img(original_img, shape)
+
+save_img(img, fname=result_prefix + '.png')

examples/image_ocr.py

@@ -41,9 +41,10 @@ import numpy as np
 from scipy import ndimage
 import pylab
 from keras import backend as K
-from keras.layers.convolutional import Convolution2D, MaxPooling2D
+from keras.layers.convolutional import Conv2D, MaxPooling2D
 from keras.layers import Input, Dense, Activation
-from keras.layers import Reshape, Lambda, merge
+from keras.layers import Reshape, Lambda
+from keras.layers.merge import add, concatenate
 from keras.models import Model
 from keras.layers.recurrent import GRU
 from keras.optimizers import SGD
@@ -126,9 +127,9 @@ def shuffle_mats_or_lists(matrix_list, stop_ind=None):
         stop_ind = len_val
     assert stop_ind <= len_val
 
-    a = range(stop_ind)
+    a = list(range(stop_ind))
     np.random.shuffle(a)
-    a += range(stop_ind, len_val)
+    a += list(range(stop_ind, len_val))
     for mat in matrix_list:
         if isinstance(mat, np.ndarray):
             ret.append(mat[a])
@@ -235,7 +236,7 @@ class TextImageGenerator(keras.callbacks.Callback):
     def get_batch(self, index, size, train):
         # width and height are backwards from typical Keras convention
         # because width is the time dimension when it gets fed into the RNN
-        if K.image_dim_ordering() == 'th':
+        if K.image_data_format() == 'channels_first':
             X_data = np.ones([size, 1, self.img_w, self.img_h])
         else:
             X_data = np.ones([size, self.img_w, self.img_h, 1])
@@ -248,7 +249,7 @@ class TextImageGenerator(keras.callbacks.Callback):
             # Mix in some blank inputs.  This seems to be important for
             # achieving translational invariance
             if train and i > size - 4:
-                if K.image_dim_ordering() == 'th':
+                if K.image_data_format() == 'channels_first':
                     X_data[i, 0, 0:self.img_w, :] = self.paint_func('')[0, :, :].T
                 else:
                     X_data[i, 0:self.img_w, :, 0] = self.paint_func('',)[0, :, :].T
@@ -257,7 +258,7 @@ class TextImageGenerator(keras.callbacks.Callback):
                 label_length[i] = 1
                 source_str.append('')
             else:
-                if K.image_dim_ordering() == 'th':
+                if K.image_data_format() == 'channels_first':
                     X_data[i, 0, 0:self.img_w, :] = self.paint_func(self.X_text[index + i])[0, :, :].T
                 else:
                     X_data[i, 0:self.img_w, :, 0] = self.paint_func(self.X_text[index + i])[0, :, :].T
@@ -383,7 +384,7 @@ class VizCallback(keras.callbacks.Callback):
             cols = 1
         for i in range(self.num_display_words):
             pylab.subplot(self.num_display_words // cols, cols, i + 1)
-            if K.image_dim_ordering() == 'th':
+            if K.image_data_format() == 'channels_first':
                 the_input = word_batch['the_input'][i, 0, :, :]
             else:
                 the_input = word_batch['the_input'][i, :, :, 0]
@@ -403,19 +404,19 @@ def train(run_name, start_epoch, stop_epoch, img_w):
     val_words = int(words_per_epoch * (val_split))
 
     # Network parameters
-    conv_num_filters = 16
-    filter_size = 3
+    conv_filters = 16
+    kernel_size = (3, 3)
     pool_size = 2
     time_dense_size = 32
     rnn_size = 512
 
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         input_shape = (1, img_w, img_h)
     else:
         input_shape = (img_w, img_h, 1)
 
     fdir = os.path.dirname(get_file('wordlists.tgz',
-                                    origin='http://www.isosemi.com/datasets/wordlists.tgz', untar=True))
+                                    origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))
 
     img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),
                                  bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),
@@ -427,14 +428,16 @@ def train(run_name, start_epoch, stop_epoch, img_w):
                                  )
     act = 'relu'
     input_data = Input(name='the_input', shape=input_shape, dtype='float32')
-    inner = Convolution2D(conv_num_filters, filter_size, filter_size, border_mode='same',
-                          activation=act, init='he_normal', name='conv1')(input_data)
+    inner = Conv2D(conv_filters, kernel_size, padding='same',
+                   activation=act, kernel_initializer='he_normal',
+                   name='conv1')(input_data)
     inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)
-    inner = Convolution2D(conv_num_filters, filter_size, filter_size, border_mode='same',
-                          activation=act, init='he_normal', name='conv2')(inner)
+    inner = Conv2D(conv_filters, kernel_size, padding='same',
+                   activation=act, kernel_initializer='he_normal',
+                   name='conv2')(inner)
     inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)
 
-    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_num_filters)
+    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)
     inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)
 
     # cuts down input size going into RNN:
@@ -442,17 +445,17 @@ def train(run_name, start_epoch, stop_epoch, img_w):
 
     # Two layers of bidirecitonal GRUs
     # GRU seems to work as well, if not better than LSTM:
-    gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(inner)
-    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru1_b')(inner)
-    gru1_merged = merge([gru_1, gru_1b], mode='sum')
-    gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged)
-    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru2_b')(gru1_merged)
+    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)
+    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
+    gru1_merged = add([gru_1, gru_1b])
+    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
+    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
 
     # transforms RNN output to character activations:
-    inner = Dense(img_gen.get_output_size(), init='he_normal',
-                  name='dense2')(merge([gru_2, gru_2b], mode='concat'))
+    inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',
+                  name='dense2')(concatenate([gru_2, gru_2b]))
     y_pred = Activation('softmax', name='softmax')(inner)
-    Model(input=[input_data], output=y_pred).summary()
+    Model(inputs=input_data, outputs=y_pred).summary()
 
     labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')
     input_length = Input(name='input_length', shape=[1], dtype='int64')
@@ -464,7 +467,7 @@ def train(run_name, start_epoch, stop_epoch, img_w):
     # clipnorm seems to speeds up convergence
     sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
 
-    model = Model(input=[input_data, labels, input_length, label_length], output=[loss_out])
+    model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
 
     # the loss calc occurs elsewhere, so use a dummy lambda func for the loss
     model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
@@ -476,8 +479,8 @@ def train(run_name, start_epoch, stop_epoch, img_w):
 
     viz_cb = VizCallback(run_name, test_func, img_gen.next_val())
 
-    model.fit_generator(generator=img_gen.next_train(), samples_per_epoch=(words_per_epoch - val_words),
-                        nb_epoch=stop_epoch, validation_data=img_gen.next_val(), nb_val_samples=val_words,
+    model.fit_generator(generator=img_gen.next_train(), steps_per_epoch=(words_per_epoch - val_words),
+                        epochs=stop_epoch, validation_data=img_gen.next_val(), validation_steps=val_words,
                         callbacks=[viz_cb, img_gen], initial_epoch=start_epoch)
 
 

examples/imdb_bidirectional_lstm.py

@@ -6,7 +6,6 @@ Time per epoch on CPU (Core i7): ~150s.
 
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
@@ -15,19 +14,21 @@ from keras.datasets import imdb
 
 
 max_features = 20000
-maxlen = 100  # cut texts after this number of words (among top max_features most common words)
+# cut texts after this number of words
+# (among top max_features most common words)
+maxlen = 100
 batch_size = 32
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
 
-print("Pad sequences (samples x time)")
-X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
-X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+print('Pad sequences (samples x time)')
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 y_train = np.array(y_train)
 y_test = np.array(y_test)
 
@@ -41,7 +42,7 @@ model.add(Dense(1, activation='sigmoid'))
 model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
 
 print('Train...')
-model.fit(X_train, y_train,
+model.fit(x_train, y_train,
           batch_size=batch_size,
-          nb_epoch=4,
-          validation_data=[X_test, y_test])
+          epochs=4,
+          validation_data=[x_test, y_test])

examples/imdb_cnn.py

@@ -7,37 +7,34 @@ Gets to 0.89 test accuracy after 2 epochs.
 '''
 
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation
 from keras.layers import Embedding
-from keras.layers import Convolution1D, GlobalMaxPooling1D
+from keras.layers import Conv1D, GlobalMaxPooling1D
 from keras.datasets import imdb
 
-
 # set parameters:
 max_features = 5000
 maxlen = 400
 batch_size = 32
 embedding_dims = 50
-nb_filter = 250
-filter_length = 3
+filters = 250
+kernel_size = 3
 hidden_dims = 250
-nb_epoch = 2
+epochs = 2
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
 
 print('Pad sequences (samples x time)')
-X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
-X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 
 print('Build model...')
 model = Sequential()
@@ -46,16 +43,16 @@ model = Sequential()
 # our vocab indices into embedding_dims dimensions
 model.add(Embedding(max_features,
                     embedding_dims,
-                    input_length=maxlen,
-                    dropout=0.2))
+                    input_length=maxlen))
+model.add(Dropout(0.2))
 
-# we add a Convolution1D, which will learn nb_filter
+# we add a Convolution1D, which will learn filters
 # word group filters of size filter_length:
-model.add(Convolution1D(nb_filter=nb_filter,
-                        filter_length=filter_length,
-                        border_mode='valid',
-                        activation='relu',
-                        subsample_length=1))
+model.add(Conv1D(filters,
+                 kernel_size,
+                 padding='valid',
+                 activation='relu',
+                 strides=1))
 # we use max pooling:
 model.add(GlobalMaxPooling1D())
 
@@ -71,7 +68,7 @@ model.add(Activation('sigmoid'))
 model.compile(loss='binary_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])
-model.fit(X_train, y_train,
+model.fit(x_train, y_train,
           batch_size=batch_size,
-          nb_epoch=nb_epoch,
-          validation_data=(X_test, y_test))
+          epochs=epochs,
+          validation_data=(x_test, y_test))

examples/imdb_cnn_lstm.py

@@ -4,34 +4,31 @@ classification task.
 Gets to 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.
 '''
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation
 from keras.layers import Embedding
 from keras.layers import LSTM
-from keras.layers import Convolution1D, MaxPooling1D
+from keras.layers import Conv1D, MaxPooling1D
 from keras.datasets import imdb
 
-
 # Embedding
 max_features = 20000
 maxlen = 100
 embedding_size = 128
 
 # Convolution
-filter_length = 5
-nb_filter = 64
-pool_length = 4
+kernel_size = 5
+filters = 64
+pool_size = 4
 
 # LSTM
 lstm_output_size = 70
 
 # Training
 batch_size = 30
-nb_epoch = 2
+epochs = 2
 
 '''
 Note:
@@ -40,27 +37,27 @@ Only 2 epochs are needed as the dataset is very small.
 '''
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
 
 print('Pad sequences (samples x time)')
-X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
-X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 
 print('Build model...')
 
 model = Sequential()
 model.add(Embedding(max_features, embedding_size, input_length=maxlen))
 model.add(Dropout(0.25))
-model.add(Convolution1D(nb_filter=nb_filter,
-                        filter_length=filter_length,
-                        border_mode='valid',
-                        activation='relu',
-                        subsample_length=1))
-model.add(MaxPooling1D(pool_length=pool_length))
+model.add(Conv1D(filters,
+                 kernel_size,
+                 padding='valid',
+                 activation='relu',
+                 strides=1))
+model.add(MaxPooling1D(pool_size=pool_size))
 model.add(LSTM(lstm_output_size))
 model.add(Dense(1))
 model.add(Activation('sigmoid'))
@@ -70,8 +67,10 @@ model.compile(loss='binary_crossentropy',
               metrics=['accuracy'])
 
 print('Train...')
-model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
-          validation_data=(X_test, y_test))
-score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          validation_data=(x_test, y_test))
+score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
 print('Test score:', score)
 print('Test accuracy:', acc)

examples/imdb_fasttext.py

@@ -7,12 +7,11 @@ https://arxiv.org/abs/1607.01759
 
 Results on IMDB datasets with uni and bi-gram embeddings:
     Uni-gram: 0.8813 test accuracy after 5 epochs. 8s/epoch on i7 cpu.
-    Bi-gram : 0.9056 test accuracy after 5 epochs. 2s/epoch on GTX 980M gpu.
+    Bi-gram : 0.9056 test accuracy after 5 epochs. 2s/epoch on GTx 980M gpu.
 '''
 
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
@@ -70,20 +69,20 @@ max_features = 20000
 maxlen = 400
 batch_size = 32
 embedding_dims = 50
-nb_epoch = 5
+epochs = 5
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
-print('Average train sequence length: {}'.format(np.mean(list(map(len, X_train)), dtype=int)))
-print('Average test sequence length: {}'.format(np.mean(list(map(len, X_test)), dtype=int)))
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
+print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
+print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
 
 if ngram_range > 1:
     print('Adding {}-gram features'.format(ngram_range))
     # Create set of unique n-gram from the training set.
     ngram_set = set()
-    for input_list in X_train:
+    for input_list in x_train:
         for i in range(2, ngram_range + 1):
             set_of_ngram = create_ngram_set(input_list, ngram_value=i)
             ngram_set.update(set_of_ngram)
@@ -98,17 +97,17 @@ if ngram_range > 1:
     # max_features is the highest integer that could be found in the dataset.
     max_features = np.max(list(indice_token.keys())) + 1
 
-    # Augmenting X_train and X_test with n-grams features
-    X_train = add_ngram(X_train, token_indice, ngram_range)
-    X_test = add_ngram(X_test, token_indice, ngram_range)
-    print('Average train sequence length: {}'.format(np.mean(list(map(len, X_train)), dtype=int)))
-    print('Average test sequence length: {}'.format(np.mean(list(map(len, X_test)), dtype=int)))
+    # Augmenting x_train and x_test with n-grams features
+    x_train = add_ngram(x_train, token_indice, ngram_range)
+    x_test = add_ngram(x_test, token_indice, ngram_range)
+    print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
+    print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
 
 print('Pad sequences (samples x time)')
-X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
-X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 
 print('Build model...')
 model = Sequential()
@@ -130,7 +129,7 @@ model.compile(loss='binary_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])
 
-model.fit(X_train, y_train,
+model.fit(x_train, y_train,
           batch_size=batch_size,
-          nb_epoch=nb_epoch,
-          validation_data=(X_test, y_test))
+          epochs=epochs,
+          validation_data=(x_test, y_test))

examples/imdb_lstm.py

@@ -11,12 +11,10 @@ Some configurations won't converge.
 from what you see with CNNs/MLPs/etc.
 '''
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
-from keras.layers import Dense, Activation, Embedding
+from keras.layers import Dense, Embedding
 from keras.layers import LSTM
 from keras.datasets import imdb
 
@@ -25,22 +23,21 @@ maxlen = 80  # cut texts after this number of words (among top max_features most
 batch_size = 32
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
 
 print('Pad sequences (samples x time)')
-X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
-X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 
 print('Build model...')
 model = Sequential()
-model.add(Embedding(max_features, 128, dropout=0.2))
-model.add(LSTM(128, dropout_W=0.2, dropout_U=0.2))  # try using a GRU instead, for fun
-model.add(Dense(1))
-model.add(Activation('sigmoid'))
+model.add(Embedding(max_features, 128))
+model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
+model.add(Dense(1, activation='sigmoid'))
 
 # try using different optimizers and different optimizer configs
 model.compile(loss='binary_crossentropy',
@@ -48,9 +45,11 @@ model.compile(loss='binary_crossentropy',
               metrics=['accuracy'])
 
 print('Train...')
-model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=15,
-          validation_data=(X_test, y_test))
-score, acc = model.evaluate(X_test, y_test,
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=15,
+          validation_data=(x_test, y_test))
+score, acc = model.evaluate(x_test, y_test,
                             batch_size=batch_size)
 print('Test score:', score)
 print('Test accuracy:', acc)

examples/lstm_benchmark.py

@@ -1,19 +1,19 @@
 '''Compare LSTM implementations on the IMDB sentiment classification task.
 
-consume_less='cpu' preprocesses input to the LSTM which typically results in
+implementation=0 preprocesses input to the LSTM which typically results in
 faster computations at the expense of increased peak memory usage as the
 preprocessed input must be kept in memory.
 
-consume_less='mem' does away with the preprocessing, meaning that it might take
+implementation=1 does away with the preprocessing, meaning that it might take
 a little longer, but should require less peak memory.
 
-consume_less='gpu' concatenates the input, output and forget gate's weights
+implementation=2 concatenates the input, output and forget gate's weights
 into one, large matrix, resulting in faster computation time as the GPU can
 utilize more cores, at the expense of reduced regularization because the same
 dropout is shared across the gates.
 
-Note that the relative performance of the different `consume_less` modes
-can vary depending on your device, your model and the size of your data.
+Note that the relative performance of the different implementations can
+vary depending on your device, your model and the size of your data.
 '''
 
 import time
@@ -22,7 +22,7 @@ import matplotlib.pyplot as plt
 
 from keras.preprocessing import sequence
 from keras.models import Sequential
-from keras.layers import Embedding, Dense, LSTM
+from keras.layers import Embedding, Dense, LSTM, Dropout
 from keras.datasets import imdb
 
 max_features = 20000
@@ -30,21 +30,26 @@ max_length = 80
 embedding_dim = 256
 batch_size = 128
 epochs = 10
-modes = ['cpu', 'mem', 'gpu']
+modes = [0, 1, 2]
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)
+(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)
 X_train = sequence.pad_sequences(X_train, max_length)
 X_test = sequence.pad_sequences(X_test, max_length)
 
 # Compile and train different models while meauring performance.
 results = []
 for mode in modes:
-    print('Testing mode: consume_less="{}"'.format(mode))
+    print('Testing mode: implementation={}'.format(mode))
 
     model = Sequential()
-    model.add(Embedding(max_features, embedding_dim, input_length=max_length, dropout=0.2))
-    model.add(LSTM(embedding_dim, dropout_W=0.2, dropout_U=0.2, consume_less=mode))
+    model.add(Embedding(max_features, embedding_dim,
+                        input_length=max_length))
+    model.add(Dropout(0.2))
+    model.add(LSTM(embedding_dim,
+                   dropout=0.2,
+                   recurrent_dropout=0.2,
+                   implementation=mode))
     model.add(Dense(1, activation='sigmoid'))
     model.compile(loss='binary_crossentropy',
                   optimizer='adam',
@@ -53,7 +58,7 @@ for mode in modes:
     start_time = time.time()
     history = model.fit(X_train, y_train,
                         batch_size=batch_size,
-                        nb_epoch=epochs,
+                        epochs=epochs,
                         validation_data=(X_test, y_test))
     average_time_per_epoch = (time.time() - start_time) / epochs
 

examples/lstm_text_generation.py

@@ -20,7 +20,7 @@ import numpy as np
 import random
 import sys
 
-path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
+path = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
 text = open(path).read().lower()
 print('corpus length:', len(text))
 
@@ -73,7 +73,9 @@ for iteration in range(1, 60):
     print()
     print('-' * 50)
     print('Iteration', iteration)
-    model.fit(X, y, batch_size=128, nb_epoch=1)
+    model.fit(X, y,
+              batch_size=128,
+              epochs=1)
 
     start_index = random.randint(0, len(text) - maxlen - 1)
 

examples/mnist_acgan.py

@@ -6,8 +6,8 @@ MNIST dataset. See https://arxiv.org/abs/1610.09585 for more details.
 
 You should start to see reasonable images after ~5 epochs, and good images
 by ~15 epochs. You should use a GPU, as the convolution-heavy operations are
-very slow on the CPU. Prefer the TensorFlow backend if you plan on iterating, as
-the compilation time can be a blocker using Theano.
+very slow on the CPU. Prefer the TensorFlow backend if you plan on iterating,
+as the compilation time can be a blocker using Theano.
 
 Timings:
 
@@ -33,9 +33,10 @@ from six.moves import range
 
 import keras.backend as K
 from keras.datasets import mnist
-from keras.layers import Input, Dense, Reshape, Flatten, Embedding, merge, Dropout
+from keras import layers
+from keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropout
 from keras.layers.advanced_activations import LeakyReLU
-from keras.layers.convolutional import UpSampling2D, Convolution2D
+from keras.layers.convolutional import UpSampling2D, Conv2D
 from keras.models import Sequential, Model
 from keras.optimizers import Adam
 from keras.utils.generic_utils import Progbar
@@ -43,7 +44,7 @@ import numpy as np
 
 np.random.seed(1337)
 
-K.set_image_dim_ordering('th')
+K.set_image_data_format('channels_first')
 
 
 def build_generator(latent_size):
@@ -57,17 +58,20 @@ def build_generator(latent_size):
 
     # upsample to (..., 14, 14)
     cnn.add(UpSampling2D(size=(2, 2)))
-    cnn.add(Convolution2D(256, 5, 5, border_mode='same',
-                          activation='relu', init='glorot_normal'))
+    cnn.add(Conv2D(256, 5, padding='same',
+                   activation='relu',
+                   kernel_initializer='glorot_normal'))
 
     # upsample to (..., 28, 28)
     cnn.add(UpSampling2D(size=(2, 2)))
-    cnn.add(Convolution2D(128, 5, 5, border_mode='same',
-                          activation='relu', init='glorot_normal'))
+    cnn.add(Conv2D(128, 5, padding='same',
+                   activation='relu',
+                   kernel_initializer='glorot_normal'))
 
     # take a channel axis reduction
-    cnn.add(Convolution2D(1, 2, 2, border_mode='same',
-                          activation='tanh', init='glorot_normal'))
+    cnn.add(Conv2D(1, 2, padding='same',
+                   activation='tanh',
+                   kernel_initializer='glorot_normal'))
 
     # this is the z space commonly refered to in GAN papers
     latent = Input(shape=(latent_size, ))
@@ -77,14 +81,14 @@ def build_generator(latent_size):
 
     # 10 classes in MNIST
     cls = Flatten()(Embedding(10, latent_size,
-                              init='glorot_normal')(image_class))
+                              embeddings_initializer='glorot_normal')(image_class))
 
     # hadamard product between z-space and a class conditional embedding
-    h = merge([latent, cls], mode='mul')
+    h = layers.multiply([latent, cls])
 
     fake_image = cnn(h)
 
-    return Model(input=[latent, image_class], output=fake_image)
+    return Model([latent, image_class], fake_image)
 
 
 def build_discriminator():
@@ -92,20 +96,20 @@ def build_discriminator():
     # the reference paper
     cnn = Sequential()
 
-    cnn.add(Convolution2D(32, 3, 3, border_mode='same', subsample=(2, 2),
-                          input_shape=(1, 28, 28)))
+    cnn.add(Conv2D(32, 3, padding='same', strides=2,
+                   input_shape=(1, 28, 28)))
     cnn.add(LeakyReLU())
     cnn.add(Dropout(0.3))
 
-    cnn.add(Convolution2D(64, 3, 3, border_mode='same', subsample=(1, 1)))
+    cnn.add(Conv2D(64, 3, padding='same', strides=1))
     cnn.add(LeakyReLU())
     cnn.add(Dropout(0.3))
 
-    cnn.add(Convolution2D(128, 3, 3, border_mode='same', subsample=(2, 2)))
+    cnn.add(Conv2D(128, 3, padding='same', strides=2))
     cnn.add(LeakyReLU())
     cnn.add(Dropout(0.3))
 
-    cnn.add(Convolution2D(256, 3, 3, border_mode='same', subsample=(1, 1)))
+    cnn.add(Conv2D(256, 3, padding='same', strides=1))
     cnn.add(LeakyReLU())
     cnn.add(Dropout(0.3))
 
@@ -122,12 +126,12 @@ def build_discriminator():
     fake = Dense(1, activation='sigmoid', name='generation')(features)
     aux = Dense(10, activation='softmax', name='auxiliary')(features)
 
-    return Model(input=image, output=[fake, aux])
+    return Model(image, [fake, aux])
 
 if __name__ == '__main__':
 
     # batch and latent size taken from the paper
-    nb_epochs = 50
+    epochs = 50
     batch_size = 100
     latent_size = 100
 
@@ -156,7 +160,7 @@ if __name__ == '__main__':
     # we only want to be able to train generation for the combined model
     discriminator.trainable = False
     fake, aux = discriminator(fake)
-    combined = Model(input=[latent, image_class], output=[fake, aux])
+    combined = Model([latent, image_class], [fake, aux])
 
     combined.compile(
         optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
@@ -172,21 +176,21 @@ if __name__ == '__main__':
     X_test = (X_test.astype(np.float32) - 127.5) / 127.5
     X_test = np.expand_dims(X_test, axis=1)
 
-    nb_train, nb_test = X_train.shape[0], X_test.shape[0]
+    num_train, num_test = X_train.shape[0], X_test.shape[0]
 
     train_history = defaultdict(list)
     test_history = defaultdict(list)
 
-    for epoch in range(nb_epochs):
-        print('Epoch {} of {}'.format(epoch + 1, nb_epochs))
+    for epoch in range(epochs):
+        print('Epoch {} of {}'.format(epoch + 1, epochs))
 
-        nb_batches = int(X_train.shape[0] / batch_size)
-        progress_bar = Progbar(target=nb_batches)
+        num_batches = int(X_train.shape[0] / batch_size)
+        progress_bar = Progbar(target=num_batches)
 
         epoch_gen_loss = []
         epoch_disc_loss = []
 
-        for index in range(nb_batches):
+        for index in range(num_batches):
             progress_bar.update(index)
             # generate a new batch of noise
             noise = np.random.uniform(-1, 1, (batch_size, latent_size))
@@ -218,28 +222,29 @@ if __name__ == '__main__':
             noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))
             sampled_labels = np.random.randint(0, 10, 2 * batch_size)
 
-            # we want to train the genrator to trick the discriminator
+            # we want to train the generator to trick the discriminator
             # For the generator, we want all the {fake, not-fake} labels to say
             # not-fake
             trick = np.ones(2 * batch_size)
 
             epoch_gen_loss.append(combined.train_on_batch(
-                [noise, sampled_labels.reshape((-1, 1))], [trick, sampled_labels]))
+                [noise, sampled_labels.reshape((-1, 1))],
+                [trick, sampled_labels]))
 
         print('\nTesting for epoch {}:'.format(epoch + 1))
 
         # evaluate the testing loss here
 
         # generate a new batch of noise
-        noise = np.random.uniform(-1, 1, (nb_test, latent_size))
+        noise = np.random.uniform(-1, 1, (num_test, latent_size))
 
         # sample some labels from p_c and generate images from them
-        sampled_labels = np.random.randint(0, 10, nb_test)
+        sampled_labels = np.random.randint(0, 10, num_test)
         generated_images = generator.predict(
             [noise, sampled_labels.reshape((-1, 1))], verbose=False)
 
         X = np.concatenate((X_test, generated_images))
-        y = np.array([1] * nb_test + [0] * nb_test)
+        y = np.array([1] * num_test + [0] * num_test)
         aux_y = np.concatenate((y_test, sampled_labels), axis=0)
 
         # see if the discriminator can figure itself out...
@@ -249,10 +254,10 @@ if __name__ == '__main__':
         discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)
 
         # make new noise
-        noise = np.random.uniform(-1, 1, (2 * nb_test, latent_size))
-        sampled_labels = np.random.randint(0, 10, 2 * nb_test)
+        noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))
+        sampled_labels = np.random.randint(0, 10, 2 * num_test)
 
-        trick = np.ones(2 * nb_test)
+        trick = np.ones(2 * num_test)
 
         generator_test_loss = combined.evaluate(
             [noise, sampled_labels.reshape((-1, 1))],

examples/mnist_cnn.py

@@ -6,77 +6,65 @@ Gets to 99.25% test accuracy after 12 epochs
 '''
 
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
-
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
 from keras import backend as K
 
 batch_size = 128
-nb_classes = 10
-nb_epoch = 12
+num_classes = 10
+epochs = 12
 
 # input image dimensions
 img_rows, img_cols = 28, 28
-# number of convolutional filters to use
-nb_filters = 32
-# size of pooling area for max pooling
-pool_size = (2, 2)
-# convolution kernel size
-kernel_size = (3, 3)
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-if K.image_dim_ordering() == 'th':
-    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
-    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
+if K.image_data_format() == 'channels_first':
+    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
     input_shape = (1, img_rows, img_cols)
 else:
-    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
-    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
     input_shape = (img_rows, img_cols, 1)
 
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
-
-model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
-                        border_mode='valid',
-                        input_shape=input_shape))
-model.add(Activation('relu'))
-model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=pool_size))
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
-
 model.add(Flatten())
-model.add(Dense(128))
-model.add(Activation('relu'))
+model.add(Dense(128, activation='relu'))
 model.add(Dropout(0.5))
-model.add(Dense(nb_classes))
-model.add(Activation('softmax'))
+model.add(Dense(num_classes, activation='softmax'))
 
-model.compile(loss='categorical_crossentropy',
-              optimizer='adadelta',
+model.compile(loss=keras.losses.categorical_crossentropy,
+              optimizer=keras.optimizers.Adadelta(),
               metrics=['accuracy'])
 
-model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
-          verbose=1, validation_data=(X_test, Y_test))
-score = model.evaluate(X_test, Y_test, verbose=0)
-print('Test score:', score[0])
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
 print('Test accuracy:', score[1])

examples/mnist_hierarchical_rnn.py

@@ -26,62 +26,65 @@ After 5 epochs: train acc: 0.9858, val acc: 0.9864
 """
 from __future__ import print_function
 
+import keras
 from keras.datasets import mnist
 from keras.models import Model
 from keras.layers import Input, Dense, TimeDistributed
 from keras.layers import LSTM
-from keras.utils import np_utils
 
 # Training parameters.
 batch_size = 32
-nb_classes = 10
-nb_epochs = 5
+num_classes = 10
+epochs = 5
 
 # Embedding dimensions.
 row_hidden = 128
 col_hidden = 128
 
 # The data, shuffled and split between train and test sets.
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
 # Reshapes data to 4D for Hierarchical RNN.
-X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
-X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
+x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # Converts class vectors to binary class matrices.
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
-row, col, pixel = X_train.shape[1:]
+row, col, pixel = x_train.shape[1:]
 
 # 4D input.
 x = Input(shape=(row, col, pixel))
 
 # Encodes a row of pixels using TimeDistributed Wrapper.
-encoded_rows = TimeDistributed(LSTM(output_dim=row_hidden))(x)
+encoded_rows = TimeDistributed(LSTM(row_hidden))(x)
 
 # Encodes columns of encoded rows.
 encoded_columns = LSTM(col_hidden)(encoded_rows)
 
 # Final predictions and model.
-prediction = Dense(nb_classes, activation='softmax')(encoded_columns)
-model = Model(input=x, output=prediction)
+prediction = Dense(num_classes, activation='softmax')(encoded_columns)
+model = Model(x, prediction)
 model.compile(loss='categorical_crossentropy',
               optimizer='rmsprop',
               metrics=['accuracy'])
 
 # Training.
-model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
-          verbose=1, validation_data=(X_test, Y_test))
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
 
 # Evaluation.
-scores = model.evaluate(X_test, Y_test, verbose=0)
+scores = model.evaluate(x_test, y_test, verbose=0)
 print('Test loss:', scores[0])
 print('Test accuracy:', scores[1])

examples/mnist_irnn.py

@@ -3,7 +3,7 @@ with pixel-by-pixel sequential MNIST in
 "A Simple Way to Initialize Recurrent Networks of Rectified Linear Units"
 by Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
 
-arXiv:1504.00941v2 [cs.NE] 7 Apr 2015
+arxiv:1504.00941v2 [cs.NE] 7 Apr 2015
 http://arxiv.org/pdf/1504.00941v2.pdf
 
 Optimizer is replaced with RMSprop which yields more stable and steady
@@ -15,56 +15,59 @@ Reaches 0.93 train/test accuracy after 900 epochs
 
 from __future__ import print_function
 
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers import Dense, Activation
 from keras.layers import SimpleRNN
-from keras.initializations import normal, identity
+from keras import initializers
 from keras.optimizers import RMSprop
-from keras.utils import np_utils
 
 batch_size = 32
-nb_classes = 10
-nb_epochs = 200
+num_classes = 10
+epochs = 200
 hidden_units = 100
 
 learning_rate = 1e-6
 clip_norm = 1.0
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(X_train.shape[0], -1, 1)
-X_test = X_test.reshape(X_test.shape[0], -1, 1)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(x_train.shape[0], -1, 1)
+x_test = x_test.reshape(x_test.shape[0], -1, 1)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 print('Evaluate IRNN...')
 model = Sequential()
-model.add(SimpleRNN(output_dim=hidden_units,
-                    init=lambda shape, name: normal(shape, scale=0.001, name=name),
-                    inner_init=lambda shape, name: identity(shape, scale=1.0, name=name),
+model.add(SimpleRNN(hidden_units,
+                    kernel_initializer=initializers.RandomNormal(stddev=0.001),
+                    recurrent_initializer=initializers.Identity(gain=1.0),
                     activation='relu',
-                    input_shape=X_train.shape[1:]))
-model.add(Dense(nb_classes))
+                    input_shape=x_train.shape[1:]))
+model.add(Dense(num_classes))
 model.add(Activation('softmax'))
 rmsprop = RMSprop(lr=learning_rate)
 model.compile(loss='categorical_crossentropy',
               optimizer=rmsprop,
               metrics=['accuracy'])
 
-model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
-          verbose=1, validation_data=(X_test, Y_test))
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
 
-scores = model.evaluate(X_test, Y_test, verbose=0)
+scores = model.evaluate(x_test, y_test, verbose=0)
 print('IRNN test score:', scores[0])
 print('IRNN test accuracy:', scores[1])

examples/mnist_mlp.py

@@ -6,45 +6,40 @@ Gets to 98.40% test accuracy after 20 epochs
 '''
 
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
-from keras.layers.core import Dense, Dropout, Activation
+from keras.layers import Dense, Dropout
 from keras.optimizers import RMSprop
-from keras.utils import np_utils
 
 
 batch_size = 128
-nb_classes = 10
-nb_epoch = 20
+num_classes = 10
+epochs = 20
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
-model.add(Dense(512, input_shape=(784,)))
-model.add(Activation('relu'))
+model.add(Dense(512, activation='relu', input_shape=(784,)))
 model.add(Dropout(0.2))
-model.add(Dense(512))
-model.add(Activation('relu'))
+model.add(Dense(512, activation='relu'))
 model.add(Dropout(0.2))
-model.add(Dense(10))
-model.add(Activation('softmax'))
+model.add(Dense(10, activation='softmax'))
 
 model.summary()
 
@@ -52,9 +47,11 @@ model.compile(loss='categorical_crossentropy',
               optimizer=RMSprop(),
               metrics=['accuracy'])
 
-history = model.fit(X_train, Y_train,
-                    batch_size=batch_size, nb_epoch=nb_epoch,
-                    verbose=1, validation_data=(X_test, Y_test))
-score = model.evaluate(X_test, Y_test, verbose=0)
-print('Test score:', score[0])
+history = model.fit(x_train, y_train,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
 print('Test accuracy:', score[1])

examples/mnist_net2net.py

@@ -26,7 +26,7 @@ Notes
 
 Experiments
 - Teacher model: a basic CNN model trained on MNIST for 3 epochs.
-- Net2WiderNet exepriment:
+- Net2WiderNet experiment:
   + Student model has a wider Conv2D layer and a wider FC layer.
   + Comparison of 'random-padding' vs 'net2wider' weight initialization.
   + With both methods, student model should immediately perform as well as
@@ -44,7 +44,7 @@ Experiments
     when a Dropout layer is used.
 
 Results
-- Tested with 'Theano' backend and 'th' image_dim_ordering.
+- Tested with 'Theano' backend and 'channels_first' image_data_format.
 - Running on GPU GeForce GTX 980M
 - Performance Comparisons - validation loss values during first 3 epochs:
 (1) teacher_model:             0.075    0.041    0.041
@@ -57,16 +57,17 @@ Results
 from __future__ import print_function
 from six.moves import xrange
 import numpy as np
-np.random.seed(1337)
-
+import keras
 from keras.models import Sequential
 from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten
 from keras.optimizers import SGD
-from keras.utils import np_utils
 from keras.datasets import mnist
 
-input_shape = (1, 28, 28)  # image shape
-nb_class = 10  # number of class
+if keras.backend.image_data_format() == 'channels_first':
+    input_shape = (1, 28, 28)  # image shape
+else:
+    input_shape = (28, 28, 1)  # image shape
+num_class = 10  # number of class
 
 
 # load and pre-process data
@@ -75,7 +76,7 @@ def preprocess_input(x):
 
 
 def preprocess_output(y):
-    return np_utils.to_categorical(y)
+    return keras.utils.to_categorical(y)
 
 (train_x, train_y), (validation_x, validation_y) = mnist.load_data()
 train_x, validation_x = map(preprocess_input, [train_x, validation_x])
@@ -88,17 +89,17 @@ print('validation_x shape:', validation_x.shape,
 
 # knowledge transfer algorithms
 def wider2net_conv2d(teacher_w1, teacher_b1, teacher_w2, new_width, init):
-    '''Get initial weights for a wider conv2d layer with a bigger nb_filter,
+    '''Get initial weights for a wider conv2d layer with a bigger filters,
     by 'random-padding' or 'net2wider'.
 
     # Arguments
         teacher_w1: `weight` of conv2d layer to become wider,
-          of shape (nb_filter1, nb_channel1, kh1, kw1)
+          of shape (filters1, num_channel1, kh1, kw1)
         teacher_b1: `bias` of conv2d layer to become wider,
-          of shape (nb_filter1, )
+          of shape (filters1, )
         teacher_w2: `weight` of next connected conv2d layer,
-          of shape (nb_filter2, nb_channel2, kh2, kw2)
-        new_width: new `nb_filter` for the wider conv2d layer
+          of shape (filters2, num_channel2, kh2, kw2)
+        new_width: new `filters` for the wider conv2d layer
         init: initialization algorithm for new weights,
           either 'random-pad' or 'net2wider'
     '''
@@ -107,7 +108,7 @@ def wider2net_conv2d(teacher_w1, teacher_b1, teacher_w2, new_width, init):
     assert teacher_w1.shape[0] == teacher_b1.shape[0], (
         'weight and bias from same layer should have compatible shapes')
     assert new_width > teacher_w1.shape[0], (
-        'new width (nb_filter) should be bigger than the existing one')
+        'new width (filters) should be bigger than the existing one')
 
     n = new_width - teacher_w1.shape[0]
     if init == 'random-pad':
@@ -193,13 +194,13 @@ def deeper2net_conv2d(teacher_w):
 
     # Arguments
         teacher_w: `weight` of previous conv2d layer,
-          of shape (nb_filter, nb_channel, kh, kw)
+          of shape (filters, num_channel, kh, kw)
     '''
-    nb_filter, nb_channel, kh, kw = teacher_w.shape
-    student_w = np.zeros((nb_filter, nb_filter, kh, kw))
-    for i in xrange(nb_filter):
+    filters, num_channel, kh, kw = teacher_w.shape
+    student_w = np.zeros((filters, filters, kh, kw))
+    for i in xrange(filters):
         student_w[i, i, (kh - 1) / 2, (kw - 1) / 2] = 1.
-    student_b = np.zeros(nb_filter)
+    student_b = np.zeros(filters)
     return student_w, student_b
 
 
@@ -213,30 +214,31 @@ def copy_weights(teacher_model, student_model, layer_names):
 
 
 # methods to construct teacher_model and student_models
-def make_teacher_model(train_data, validation_data, nb_epoch=3):
+def make_teacher_model(train_data, validation_data, epochs=3):
     '''Train a simple CNN as teacher model.
     '''
     model = Sequential()
-    model.add(Conv2D(64, 3, 3, input_shape=input_shape,
-                     border_mode='same', name='conv1'))
-    model.add(MaxPooling2D(name='pool1'))
-    model.add(Conv2D(64, 3, 3, border_mode='same', name='conv2'))
-    model.add(MaxPooling2D(name='pool2'))
+    model.add(Conv2D(64, 3, input_shape=input_shape,
+                     padding='same', name='conv1'))
+    model.add(MaxPooling2D(2, name='pool1'))
+    model.add(Conv2D(64, 3, padding='same', name='conv2'))
+    model.add(MaxPooling2D(2, name='pool2'))
     model.add(Flatten(name='flatten'))
     model.add(Dense(64, activation='relu', name='fc1'))
-    model.add(Dense(nb_class, activation='softmax', name='fc2'))
+    model.add(Dense(num_class, activation='softmax', name='fc2'))
     model.compile(loss='categorical_crossentropy',
                   optimizer=SGD(lr=0.01, momentum=0.9),
                   metrics=['accuracy'])
 
     train_x, train_y = train_data
-    history = model.fit(train_x, train_y, nb_epoch=nb_epoch,
+    history = model.fit(train_x, train_y,
+                        epochs=epochs,
                         validation_data=validation_data)
     return model, history
 
 
 def make_wider_student_model(teacher_model, train_data,
-                             validation_data, init, nb_epoch=3):
+                             validation_data, init, epochs=3):
     '''Train a wider student model based on teacher_model,
        with either 'random-pad' (baseline) or 'net2wider'
     '''
@@ -245,15 +247,15 @@ def make_wider_student_model(teacher_model, train_data,
 
     model = Sequential()
     # a wider conv1 compared to teacher_model
-    model.add(Conv2D(new_conv1_width, 3, 3, input_shape=input_shape,
-                     border_mode='same', name='conv1'))
-    model.add(MaxPooling2D(name='pool1'))
-    model.add(Conv2D(64, 3, 3, border_mode='same', name='conv2'))
-    model.add(MaxPooling2D(name='pool2'))
+    model.add(Conv2D(new_conv1_width, 3, input_shape=input_shape,
+                     padding='same', name='conv1'))
+    model.add(MaxPooling2D(2, name='pool1'))
+    model.add(Conv2D(64, 3, padding='same', name='conv2'))
+    model.add(MaxPooling2D(2, name='pool2'))
     model.add(Flatten(name='flatten'))
     # a wider fc1 compared to teacher model
     model.add(Dense(new_fc1_width, activation='relu', name='fc1'))
-    model.add(Dense(nb_class, activation='softmax', name='fc2'))
+    model.add(Dense(num_class, activation='softmax', name='fc2'))
 
     # The weights for other layers need to be copied from teacher_model
     # to student_model, except for widened layers
@@ -279,32 +281,33 @@ def make_wider_student_model(teacher_model, train_data,
                   metrics=['accuracy'])
 
     train_x, train_y = train_data
-    history = model.fit(train_x, train_y, nb_epoch=nb_epoch,
+    history = model.fit(train_x, train_y,
+                        epochs=epochs,
                         validation_data=validation_data)
     return model, history
 
 
 def make_deeper_student_model(teacher_model, train_data,
-                              validation_data, init, nb_epoch=3):
+                              validation_data, init, epochs=3):
     '''Train a deeper student model based on teacher_model,
        with either 'random-init' (baseline) or 'net2deeper'
     '''
     model = Sequential()
-    model.add(Conv2D(64, 3, 3, input_shape=input_shape,
-                     border_mode='same', name='conv1'))
-    model.add(MaxPooling2D(name='pool1'))
-    model.add(Conv2D(64, 3, 3, border_mode='same', name='conv2'))
+    model.add(Conv2D(64, 3, input_shape=input_shape,
+                     padding='same', name='conv1'))
+    model.add(MaxPooling2D(2, name='pool1'))
+    model.add(Conv2D(64, 3, padding='same', name='conv2'))
     # add another conv2d layer to make original conv2 deeper
     if init == 'net2deeper':
         prev_w, _ = model.get_layer('conv2').get_weights()
         new_weights = deeper2net_conv2d(prev_w)
-        model.add(Conv2D(64, 3, 3, border_mode='same',
+        model.add(Conv2D(64, 3, padding='same',
                          name='conv2-deeper', weights=new_weights))
     elif init == 'random-init':
-        model.add(Conv2D(64, 3, 3, border_mode='same', name='conv2-deeper'))
+        model.add(Conv2D(64, 3, padding='same', name='conv2-deeper'))
     else:
         raise ValueError('Unsupported weight initializer: %s' % init)
-    model.add(MaxPooling2D(name='pool2'))
+    model.add(MaxPooling2D(2, name='pool2'))
     model.add(Flatten(name='flatten'))
     model.add(Dense(64, activation='relu', name='fc1'))
     # add another fc layer to make original fc1 deeper
@@ -316,7 +319,7 @@ def make_deeper_student_model(teacher_model, train_data,
         model.add(Dense(64, activation='relu', name='fc1-deeper'))
     else:
         raise ValueError('Unsupported weight initializer: %s' % init)
-    model.add(Dense(nb_class, activation='softmax', name='fc2'))
+    model.add(Dense(num_class, activation='softmax', name='fc2'))
 
     # copy weights for other layers
     copy_weights(teacher_model, model, layer_names=[
@@ -327,7 +330,8 @@ def make_deeper_student_model(teacher_model, train_data,
                   metrics=['accuracy'])
 
     train_x, train_y = train_data
-    history = model.fit(train_x, train_y, nb_epoch=nb_epoch,
+    history = model.fit(train_x, train_y,
+                        epochs=epochs,
                         validation_data=validation_data)
     return model, history
 
@@ -345,16 +349,16 @@ def net2wider_experiment():
     print('\nbuilding teacher model ...')
     teacher_model, _ = make_teacher_model(train_data,
                                           validation_data,
-                                          nb_epoch=3)
+                                          epochs=3)
 
     print('\nbuilding wider student model by random padding ...')
     make_wider_student_model(teacher_model, train_data,
                              validation_data, 'random-pad',
-                             nb_epoch=3)
+                             epochs=3)
     print('\nbuilding wider student model by net2wider ...')
     make_wider_student_model(teacher_model, train_data,
                              validation_data, 'net2wider',
-                             nb_epoch=3)
+                             epochs=3)
 
 
 def net2deeper_experiment():
@@ -369,16 +373,16 @@ def net2deeper_experiment():
     print('\nbuilding teacher model ...')
     teacher_model, _ = make_teacher_model(train_data,
                                           validation_data,
-                                          nb_epoch=3)
+                                          epochs=3)
 
     print('\nbuilding deeper student model by random init ...')
     make_deeper_student_model(teacher_model, train_data,
                               validation_data, 'random-init',
-                              nb_epoch=3)
+                              epochs=3)
     print('\nbuilding deeper student model by net2deeper ...')
     make_deeper_student_model(teacher_model, train_data,
                               validation_data, 'net2deeper',
-                              nb_epoch=3)
+                              epochs=3)
 
 # run the experiments
 net2wider_experiment()

examples/mnist_siamese_graph.py

@@ -13,7 +13,6 @@ Gets to 99.5% test accuracy after 20 epochs.
 from __future__ import absolute_import
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 import random
 from keras.datasets import mnist
@@ -25,7 +24,7 @@ from keras import backend as K
 
 def euclidean_distance(vects):
     x, y = vects
-    return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))
+    return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
 
 
 def eucl_dist_output_shape(shapes):
@@ -38,7 +37,8 @@ def contrastive_loss(y_true, y_pred):
     http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
     '''
     margin = 1
-    return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
+    return K.mean(y_true * K.square(y_pred) +
+                  (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
 
 
 def create_pairs(x, digit_indices):
@@ -79,22 +79,22 @@ def compute_accuracy(predictions, labels):
 
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
 input_dim = 784
-nb_epoch = 20
+epochs = 20
 
 # create training+test positive and negative pairs
 digit_indices = [np.where(y_train == i)[0] for i in range(10)]
-tr_pairs, tr_y = create_pairs(X_train, digit_indices)
+tr_pairs, tr_y = create_pairs(x_train, digit_indices)
 
 digit_indices = [np.where(y_test == i)[0] for i in range(10)]
-te_pairs, te_y = create_pairs(X_test, digit_indices)
+te_pairs, te_y = create_pairs(x_test, digit_indices)
 
 # network definition
 base_network = create_base_network(input_dim)
@@ -108,17 +108,18 @@ input_b = Input(shape=(input_dim,))
 processed_a = base_network(input_a)
 processed_b = base_network(input_b)
 
-distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])
+distance = Lambda(euclidean_distance,
+                  output_shape=eucl_dist_output_shape)([processed_a, processed_b])
 
-model = Model(input=[input_a, input_b], output=distance)
+model = Model([input_a, input_b], distance)
 
 # train
 rms = RMSprop()
 model.compile(loss=contrastive_loss, optimizer=rms)
 model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
-          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
           batch_size=128,
-          nb_epoch=nb_epoch)
+          epochs=epochs,
+          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))
 
 # compute final accuracy on training and test sets
 pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])

examples/mnist_sklearn_wrapper.py

@@ -4,64 +4,62 @@ Builds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best m
 '''
 
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
+from keras.layers import Conv2D, MaxPooling2D
 from keras.wrappers.scikit_learn import KerasClassifier
 from keras import backend as K
 from sklearn.grid_search import GridSearchCV
 
 
-nb_classes = 10
+num_classes = 10
 
 # input image dimensions
 img_rows, img_cols = 28, 28
 
 # load training data and do basic data normalization
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
-if K.image_dim_ordering() == 'th':
-    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
-    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
+if K.image_data_format() == 'channels_first':
+    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
     input_shape = (1, img_rows, img_cols)
 else:
-    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
-    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
     input_shape = (img_rows, img_cols, 1)
 
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
 
 # convert class vectors to binary class matrices
-y_train = np_utils.to_categorical(y_train, nb_classes)
-y_test = np_utils.to_categorical(y_test, nb_classes)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
 
 
-def make_model(dense_layer_sizes, nb_filters, nb_conv, nb_pool):
+def make_model(dense_layer_sizes, filters, kernel_size, pool_size):
     '''Creates model comprised of 2 convolutional layers followed by dense layers
 
-    dense_layer_sizes: List of layer sizes. This list has one number for each layer
-    nb_filters: Number of convolutional filters in each convolutional layer
-    nb_conv: Convolutional kernel size
-    nb_pool: Size of pooling area for max pooling
+    dense_layer_sizes: List of layer sizes.
+        This list has one number for each layer
+    filters: Number of convolutional filters in each convolutional layer
+    kernel_size: Convolutional kernel size
+    pool_size: Size of pooling area for max pooling
     '''
 
     model = Sequential()
-
-    model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
-                            border_mode='valid',
-                            input_shape=input_shape))
+    model.add(Conv2D(filters, kernel_size,
+                     padding='valid',
+                     input_shape=input_shape))
     model.add(Activation('relu'))
-    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
+    model.add(Conv2D(filters, kernel_size))
     model.add(Activation('relu'))
-    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(MaxPooling2D(pool_size=pool_size))
     model.add(Dropout(0.25))
 
     model.add(Flatten())
@@ -69,7 +67,7 @@ def make_model(dense_layer_sizes, nb_filters, nb_conv, nb_pool):
         model.add(Dense(layer_size))
     model.add(Activation('relu'))
     model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
+    model.add(Dense(num_classes))
     model.add(Activation('softmax'))
 
     model.compile(loss='categorical_crossentropy',
@@ -82,15 +80,15 @@ dense_size_candidates = [[32], [64], [32, 32], [64, 64]]
 my_classifier = KerasClassifier(make_model, batch_size=32)
 validator = GridSearchCV(my_classifier,
                          param_grid={'dense_layer_sizes': dense_size_candidates,
-                                     # nb_epoch is avail for tuning even when not
+                                     # epochs is avail for tuning even when not
                                      # an argument to model building function
-                                     'nb_epoch': [3, 6],
-                                     'nb_filters': [8],
-                                     'nb_conv': [3],
-                                     'nb_pool': [2]},
-                         scoring='log_loss',
+                                     'epochs': [3, 6],
+                                     'filters': [8],
+                                     'kernel_size': [3],
+                                     'pool_size': [2]},
+                         scoring='neg_log_loss',
                          n_jobs=1)
-validator.fit(X_train, y_train)
+validator.fit(x_train, y_train)
 
 print('The parameters of the best model are: ')
 print(validator.best_params_)
@@ -99,6 +97,6 @@ print(validator.best_params_)
 # validator.best_estimator_.model returns the (unwrapped) keras model
 best_model = validator.best_estimator_.model
 metric_names = best_model.metrics_names
-metric_values = best_model.evaluate(X_test, y_test)
+metric_values = best_model.evaluate(x_test, y_test)
 for metric, value in zip(metric_names, metric_values):
     print(metric, ': ', value)

examples/mnist_swwae.py

@@ -2,21 +2,21 @@
 MNIST dataset.  It exemplifies two influential methods that have been developed
 in the past few years.
 
-The first is the idea of properly "unpooling." During any max pool, the
-exact location (the "where") of the maximal value in a pooled receptive field
+The first is the idea of properly 'unpooling.' During any max pool, the
+exact location (the 'where') of the maximal value in a pooled receptive field
 is lost, however it can be very useful in the overall reconstruction of an
-input image.  Therefore, if the "where" is handed from the encoder
-to the corresponding decoder layer, features being decoded can be "placed" in
+input image.  Therefore, if the 'where' is handed from the encoder
+to the corresponding decoder layer, features being decoded can be 'placed' in
 the right location, allowing for reconstructions of much higher fidelity.
 
 References:
 [1]
-"Visualizing and Understanding Convolutional Networks"
+'Visualizing and Understanding Convolutional Networks'
 Matthew D Zeiler, Rob Fergus
 https://arxiv.org/abs/1311.2901v3
 
 [2]
-"Stacked What-Where Auto-encoders"
+'Stacked What-Where Auto-encoders'
 Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann LeCun
 https://arxiv.org/abs/1506.02351v8
 
@@ -34,42 +34,67 @@ applied as a bias because we know the MNIST digits are mapped to [0,1].
 
 References:
 [3]
-"Deep Residual Learning for Image Recognition"
+'Deep Residual Learning for Image Recognition'
 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
 https://arxiv.org/abs/1512.03385v1
 
 [4]
-"Identity Mappings in Deep Residual Networks"
+'Identity Mappings in Deep Residual Networks'
 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
 https://arxiv.org/abs/1603.05027v3
 
 '''
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337)  # for reproducibility
 
 from keras.datasets import mnist
 from keras.models import Model
-from keras.layers import Activation, merge
-from keras.layers import UpSampling2D, Convolution2D, MaxPooling2D
-from keras.layers import Input, BatchNormalization
+from keras.layers import Activation
+from keras.layers import UpSampling2D, Conv2D, MaxPooling2D
+from keras.layers import Input, BatchNormalization, ELU
 import matplotlib.pyplot as plt
 import keras.backend as K
+from keras import layers
 
 
-def convresblock(x, nfeats=8, ksize=3, nskipped=2):
-    ''' The proposed residual block from [4]'''
-    y0 = Convolution2D(nfeats, ksize, ksize, border_mode='same')(x)
+def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):
+    """The proposed residual block from [4].
+
+    Running with elu=True will use ELU nonlinearity and running with
+    elu=False will use BatchNorm + RELU nonlinearity.  While ELU's are fast
+    due to the fact they do not suffer from BatchNorm overhead, they may
+    overfit because they do not offer the stochastic element of the batch
+    formation process of BatchNorm, which acts as a good regularizer.
+
+    # Arguments
+        x: 4D tensor, the tensor to feed through the block
+        nfeats: Integer, number of feature maps for conv layers.
+        ksize: Integer, width and height of conv kernels in first convolution.
+        nskipped: Integer, number of conv layers for the residual function.
+        elu: Boolean, whether to use ELU or BN+RELU.
+
+    # Input shape
+        4D tensor with shape:
+        `(batch, channels, rows, cols)`
+
+    # Output shape
+        4D tensor with shape:
+        `(batch, filters, rows, cols)`
+    """
+    y0 = Conv2D(nfeats, ksize, padding='same')(x)
     y = y0
     for i in range(nskipped):
-        y = BatchNormalization(mode=0, axis=1)(y)
-        y = Activation('relu')(y)
-        y = Convolution2D(nfeats, ksize, ksize, border_mode='same')(y)
-    return merge([y0, y], mode='sum')
+        if elu:
+            y = ELU()(y)
+        else:
+            y = BatchNormalization(axis=1)(y)
+            y = Activation('relu')(y)
+        y = Conv2D(nfeats, 1, padding='same')(y)
+    return layers.add([y0, y])
 
 
 def getwhere(x):
-    ''' Calculate the "where" mask that contains switches indicating which
+    ''' Calculate the 'where' mask that contains switches indicating which
     index contained the max value when MaxPool2D was applied.  Using the
     gradient of the sum is a nice trick to keep everything high level.'''
     y_prepool, y_postpool = x
@@ -82,24 +107,24 @@ if K.backend() == 'tensorflow':
                        'of a gradient, which isn\'t '
                        'supported for all TF ops.')
 
-# This example assume 'th' dim ordering.
-K.set_image_dim_ordering('th')
+# This example assume 'channels_first' data format.
+K.set_image_data_format('channels_first')
 
 # input image dimensions
 img_rows, img_cols = 28, 28
 
 # the data, shuffled and split between train and test sets
-(X_train, _), (X_test, _) = mnist.load_data()
+(x_train, _), (x_test, _) = mnist.load_data()
 
-X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
-X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-print('X_train shape:', X_train.shape)
-print(X_train.shape[0], 'train samples')
-print(X_test.shape[0], 'test samples')
+x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
 
 # The size of the kernel used for the MaxPooling2D
 pool_size = 2
@@ -110,47 +135,47 @@ pool_sizes = np.array([1, 1, 1, 1, 1]) * pool_size
 # The convolution kernel size
 ksize = 3
 # Number of epochs to train for
-nb_epoch = 5
+epochs = 5
 # Batch size during training
 batch_size = 128
 
 if pool_size == 2:
     # if using a 5 layer net of pool_size = 2
-    X_train = np.pad(X_train, [[0, 0], [0, 0], [2, 2], [2, 2]],
+    x_train = np.pad(x_train, [[0, 0], [0, 0], [2, 2], [2, 2]],
                      mode='constant')
-    X_test = np.pad(X_test, [[0, 0], [0, 0], [2, 2], [2, 2]], mode='constant')
+    x_test = np.pad(x_test, [[0, 0], [0, 0], [2, 2], [2, 2]], mode='constant')
     nlayers = 5
 elif pool_size == 3:
     # if using a 3 layer net of pool_size = 3
-    X_train = X_train[:, :, :-1, :-1]
-    X_test = X_test[:, :, :-1, :-1]
+    x_train = x_train[:, :, :-1, :-1]
+    x_test = x_test[:, :, :-1, :-1]
     nlayers = 3
 else:
     import sys
-    sys.exit("Script supports pool_size of 2 and 3.")
+    sys.exit('Script supports pool_size of 2 and 3.')
 
 # Shape of input to train on (note that model is fully convolutional however)
-input_shape = X_train.shape[1:]
+input_shape = x_train.shape[1:]
 # The final list of the size of axis=1 for all layers, including input
 nfeats_all = [input_shape[0]] + nfeats
 
-# First build the encoder, all the while keeping track of the "where" masks
+# First build the encoder, all the while keeping track of the 'where' masks
 img_input = Input(shape=input_shape)
 
-# We push the "where" masks to the following list
+# We push the 'where' masks to the following list
 wheres = [None] * nlayers
 y = img_input
 for i in range(nlayers):
     y_prepool = convresblock(y, nfeats=nfeats_all[i + 1], ksize=ksize)
     y = MaxPooling2D(pool_size=(pool_sizes[i], pool_sizes[i]))(y_prepool)
-    wheres[i] = merge([y_prepool, y], mode=getwhere,
-                      output_shape=lambda x: x[0])
+    wheres[i] = layers.Lambda(
+        getwhere, output_shape=lambda x: x[0])([y_prepool, y])
 
-# Now build the decoder, and use the stored "where" masks to place the features
+# Now build the decoder, and use the stored 'where' masks to place the features
 for i in range(nlayers):
     ind = nlayers - 1 - i
     y = UpSampling2D(size=(pool_sizes[ind], pool_sizes[ind]))(y)
-    y = merge([y, wheres[ind]], mode='mul')
+    y = layers.multiply([y, wheres[ind]])
     y = convresblock(y, nfeats=nfeats_all[ind], ksize=ksize)
 
 # Use hard_simgoid to clip range of reconstruction
@@ -161,16 +186,18 @@ model = Model(img_input, y)
 model.compile('adam', 'mse')
 
 # Fit the model
-model.fit(X_train, X_train, validation_data=(X_test, X_test),
-          batch_size=batch_size, nb_epoch=nb_epoch)
+model.fit(x_train, x_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          validation_data=(x_test, x_test))
 
 # Plot
-X_recon = model.predict(X_test[:25])
-X_plot = np.concatenate((X_test[:25], X_recon), axis=1)
-X_plot = X_plot.reshape((5, 10, input_shape[-2], input_shape[-1]))
-X_plot = np.vstack([np.hstack(x) for x in X_plot])
+x_recon = model.predict(x_test[:25])
+x_plot = np.concatenate((x_test[:25], x_recon), axis=1)
+x_plot = x_plot.reshape((5, 10, input_shape[-2], input_shape[-1]))
+x_plot = np.vstack([np.hstack(x) for x in x_plot])
 plt.figure()
 plt.axis('off')
 plt.title('Test Samples: Originals/Reconstructions')
-plt.imshow(X_plot, interpolation='none', cmap='gray')
+plt.imshow(x_plot, interpolation='none', cmap='gray')
 plt.savefig('reconstructions.png')

examples/mnist_transfer_cnn.py

@@ -12,100 +12,99 @@ and 99.2% for the last five digits after transfer + fine-tuning.
 '''
 
 from __future__ import print_function
-import numpy as np
+
 import datetime
-
-np.random.seed(1337)  # for reproducibility
-
+import keras
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Convolution2D, MaxPooling2D
-from keras.utils import np_utils
+from keras.layers import Conv2D, MaxPooling2D
 from keras import backend as K
 
 now = datetime.datetime.now
 
 batch_size = 128
-nb_classes = 5
-nb_epoch = 5
+num_classes = 5
+epochs = 5
 
 # input image dimensions
 img_rows, img_cols = 28, 28
 # number of convolutional filters to use
-nb_filters = 32
+filters = 32
 # size of pooling area for max pooling
 pool_size = 2
 # convolution kernel size
 kernel_size = 3
 
-if K.image_dim_ordering() == 'th':
+if K.image_data_format() == 'channels_first':
     input_shape = (1, img_rows, img_cols)
 else:
     input_shape = (img_rows, img_cols, 1)
 
 
-def train_model(model, train, test, nb_classes):
-    X_train = train[0].reshape((train[0].shape[0],) + input_shape)
-    X_test = test[0].reshape((test[0].shape[0],) + input_shape)
-    X_train = X_train.astype('float32')
-    X_test = X_test.astype('float32')
-    X_train /= 255
-    X_test /= 255
-    print('X_train shape:', X_train.shape)
-    print(X_train.shape[0], 'train samples')
-    print(X_test.shape[0], 'test samples')
+def train_model(model, train, test, num_classes):
+    x_train = train[0].reshape((train[0].shape[0],) + input_shape)
+    x_test = test[0].reshape((test[0].shape[0],) + input_shape)
+    x_train = x_train.astype('float32')
+    x_test = x_test.astype('float32')
+    x_train /= 255
+    x_test /= 255
+    print('x_train shape:', x_train.shape)
+    print(x_train.shape[0], 'train samples')
+    print(x_test.shape[0], 'test samples')
 
     # convert class vectors to binary class matrices
-    Y_train = np_utils.to_categorical(train[1], nb_classes)
-    Y_test = np_utils.to_categorical(test[1], nb_classes)
+    y_train = keras.utils.to_categorical(train[1], num_classes)
+    y_test = keras.utils.to_categorical(test[1], num_classes)
 
     model.compile(loss='categorical_crossentropy',
                   optimizer='adadelta',
                   metrics=['accuracy'])
 
     t = now()
-    model.fit(X_train, Y_train,
-              batch_size=batch_size, nb_epoch=nb_epoch,
+    model.fit(x_train, y_train,
+              batch_size=batch_size,
+              epochs=epochs,
               verbose=1,
-              validation_data=(X_test, Y_test))
+              validation_data=(x_test, y_test))
     print('Training time: %s' % (now() - t))
-    score = model.evaluate(X_test, Y_test, verbose=0)
+    score = model.evaluate(x_test, y_test, verbose=0)
     print('Test score:', score[0])
     print('Test accuracy:', score[1])
 
 
 # the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
 # create two datasets one with digits below 5 and one with 5 and above
-X_train_lt5 = X_train[y_train < 5]
+x_train_lt5 = x_train[y_train < 5]
 y_train_lt5 = y_train[y_train < 5]
-X_test_lt5 = X_test[y_test < 5]
+x_test_lt5 = x_test[y_test < 5]
 y_test_lt5 = y_test[y_test < 5]
 
-X_train_gte5 = X_train[y_train >= 5]
-y_train_gte5 = y_train[y_train >= 5] - 5  # make classes start at 0 for
-X_test_gte5 = X_test[y_test >= 5]         # np_utils.to_categorical
+x_train_gte5 = x_train[y_train >= 5]
+y_train_gte5 = y_train[y_train >= 5] - 5
+x_test_gte5 = x_test[y_test >= 5]
 y_test_gte5 = y_test[y_test >= 5] - 5
 
 # define two groups of layers: feature (convolutions) and classification (dense)
 feature_layers = [
-    Convolution2D(nb_filters, kernel_size, kernel_size,
-                  border_mode='valid',
-                  input_shape=input_shape),
+    Conv2D(filters, kernel_size,
+           padding='valid',
+           input_shape=input_shape),
     Activation('relu'),
-    Convolution2D(nb_filters, kernel_size, kernel_size),
+    Conv2D(filters, kernel_size),
     Activation('relu'),
-    MaxPooling2D(pool_size=(pool_size, pool_size)),
+    MaxPooling2D(pool_size=pool_size),
     Dropout(0.25),
     Flatten(),
 ]
+
 classification_layers = [
     Dense(128),
     Activation('relu'),
     Dropout(0.5),
-    Dense(nb_classes),
+    Dense(num_classes),
     Activation('softmax')
 ]
 
@@ -114,8 +113,8 @@ model = Sequential(feature_layers + classification_layers)
 
 # train model for 5-digit classification [0..4]
 train_model(model,
-            (X_train_lt5, y_train_lt5),
-            (X_test_lt5, y_test_lt5), nb_classes)
+            (x_train_lt5, y_train_lt5),
+            (x_test_lt5, y_test_lt5), num_classes)
 
 # freeze feature layers and rebuild model
 for l in feature_layers:
@@ -123,5 +122,5 @@ for l in feature_layers:
 
 # transfer: train dense layers for new classification task [5..9]
 train_model(model,
-            (X_train_gte5, y_train_gte5),
-            (X_test_gte5, y_test_gte5), nb_classes)
+            (x_train_gte5, y_train_gte5),
+            (x_test_gte5, y_test_gte5), num_classes)

examples/neural_doodle.py

@@ -77,8 +77,8 @@ content_img_path = args.content_image
 target_img_prefix = args.target_image_prefix
 use_content_img = content_img_path is not None
 
-nb_labels = args.nlabels
-nb_colors = 3  # RGB
+num_labels = args.nlabels
+num_colors = 3  # RGB
 # determine image sizes based on target_mask
 ref_img = imread(target_mask_path)
 img_nrows, img_ncols = ref_img.shape[:2]
@@ -103,7 +103,7 @@ def preprocess_image(image_path):
 
 
 def deprocess_image(x):
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         x = x.reshape((3, img_nrows, img_ncols))
         x = x.transpose((1, 2, 0))
     else:
@@ -122,7 +122,7 @@ def kmeans(xs, k):
     assert xs.ndim == 2
     try:
         from sklearn.cluster import k_means
-        _, labels, _ = k_means(xs.astype("float64"), k)
+        _, labels, _ = k_means(xs.astype('float64'), k)
     except ImportError:
         from scipy.cluster.vq import kmeans2
         _, labels = kmeans2(xs, k, missing='raise')
@@ -132,7 +132,7 @@ def kmeans(xs, k):
 def load_mask_labels():
     '''Load both target and style masks.
     A mask image (nr x nc) with m labels/colors will be loaded
-    as a 4D boolean tensor: (1, m, nr, nc) for 'th' or (1, nr, nc, m) for 'tf'
+    as a 4D boolean tensor: (1, m, nr, nc) for 'channels_first' or (1, nr, nc, m) for 'channels_last'
     '''
     target_mask_img = load_img(target_mask_path,
                                target_size=(img_nrows, img_ncols))
@@ -140,33 +140,33 @@ def load_mask_labels():
     style_mask_img = load_img(style_mask_path,
                               target_size=(img_nrows, img_ncols))
     style_mask_img = img_to_array(style_mask_img)
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         mask_vecs = np.vstack([style_mask_img.reshape((3, -1)).T,
                                target_mask_img.reshape((3, -1)).T])
     else:
         mask_vecs = np.vstack([style_mask_img.reshape((-1, 3)),
                                target_mask_img.reshape((-1, 3))])
 
-    labels = kmeans(mask_vecs, nb_labels)
+    labels = kmeans(mask_vecs, num_labels)
     style_mask_label = labels[:img_nrows *
                               img_ncols].reshape((img_nrows, img_ncols))
     target_mask_label = labels[img_nrows *
                                img_ncols:].reshape((img_nrows, img_ncols))
 
-    stack_axis = 0 if K.image_dim_ordering() == 'th' else -1
-    style_mask = np.stack([style_mask_label == r for r in xrange(nb_labels)],
+    stack_axis = 0 if K.image_data_format() == 'channels_first' else -1
+    style_mask = np.stack([style_mask_label == r for r in xrange(num_labels)],
                           axis=stack_axis)
-    target_mask = np.stack([target_mask_label == r for r in xrange(nb_labels)],
+    target_mask = np.stack([target_mask_label == r for r in xrange(num_labels)],
                            axis=stack_axis)
 
     return (np.expand_dims(style_mask, axis=0),
             np.expand_dims(target_mask, axis=0))
 
 # Create tensor variables for images
-if K.image_dim_ordering() == 'th':
-    shape = (1, nb_colors, img_nrows, img_ncols)
+if K.image_data_format() == 'channels_first':
+    shape = (1, num_colors, img_nrows, img_ncols)
 else:
-    shape = (1, img_nrows, img_ncols, nb_colors)
+    shape = (1, img_nrows, img_ncols, num_colors)
 
 style_image = K.variable(preprocess_image(style_img_path))
 target_image = K.placeholder(shape=shape)
@@ -179,8 +179,8 @@ images = K.concatenate([style_image, target_image, content_image], axis=0)
 
 # Create tensor variables for masks
 raw_style_mask, raw_target_mask = load_mask_labels()
-style_mask = K.variable(raw_style_mask.astype("float32"))
-target_mask = K.variable(raw_target_mask.astype("float32"))
+style_mask = K.variable(raw_style_mask.astype('float32'))
+target_mask = K.variable(raw_target_mask.astype('float32'))
 masks = K.concatenate([style_mask, target_mask], axis=0)
 
 # index constants for images and tasks variables
@@ -191,13 +191,13 @@ STYLE, TARGET, CONTENT = 0, 1, 2
 image_model = vgg19.VGG19(include_top=False, input_tensor=images)
 
 # mask model as a series of pooling
-mask_input = Input(tensor=masks, shape=(None, None, None), name="mask_input")
+mask_input = Input(tensor=masks, shape=(None, None, None), name='mask_input')
 x = mask_input
 for layer in image_model.layers[1:]:
     name = 'mask_%s' % layer.name
     if 'conv' in layer.name:
-        x = AveragePooling2D((3, 3), strides=(
-            1, 1), name=name, border_mode="same")(x)
+        x = AveragePooling2D((3, 3), padding='same', strides=(
+            1, 1), name=name)(x)
     elif 'pool' in layer.name:
         x = AveragePooling2D((2, 2), name=name)(x)
 mask_model = Model(mask_input, x)
@@ -228,18 +228,19 @@ def region_style_loss(style_image, target_image, style_mask, target_mask):
     '''
     assert 3 == K.ndim(style_image) == K.ndim(target_image)
     assert 2 == K.ndim(style_mask) == K.ndim(target_mask)
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         masked_style = style_image * style_mask
         masked_target = target_image * target_mask
-        nb_channels = K.shape(style_image)[0]
+        num_channels = K.shape(style_image)[0]
     else:
         masked_style = K.permute_dimensions(
             style_image, (2, 0, 1)) * style_mask
         masked_target = K.permute_dimensions(
             target_image, (2, 0, 1)) * target_mask
-        nb_channels = K.shape(style_image)[-1]
-    s = gram_matrix(masked_style) / K.mean(style_mask) / nb_channels
-    c = gram_matrix(masked_target) / K.mean(target_mask) / nb_channels
+        num_channels = K.shape(style_image)[-1]
+    num_channels = K.cast(num_channels, dtype='float32')
+    s = gram_matrix(masked_style) / K.mean(style_mask) / num_channels
+    c = gram_matrix(masked_target) / K.mean(target_mask) / num_channels
     return K.mean(K.square(s - c))
 
 
@@ -250,8 +251,8 @@ def style_loss(style_image, target_image, style_masks, target_masks):
     assert 3 == K.ndim(style_image) == K.ndim(target_image)
     assert 3 == K.ndim(style_masks) == K.ndim(target_masks)
     loss = K.variable(0)
-    for i in xrange(nb_labels):
-        if K.image_dim_ordering() == 'th':
+    for i in xrange(num_labels):
+        if K.image_data_format() == 'channels_first':
             style_mask = style_masks[i, :, :]
             target_mask = target_masks[i, :, :]
         else:
@@ -268,7 +269,7 @@ def content_loss(content_image, target_image):
 
 def total_variation_loss(x):
     assert 4 == K.ndim(x)
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -
                      x[:, :, 1:, :img_ncols - 1])
         b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -
@@ -310,7 +311,7 @@ f_outputs = K.function([target_image], outputs)
 
 
 def eval_loss_and_grads(x):
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         x = x.reshape((1, 3, img_nrows, img_ncols))
     else:
         x = x.reshape((1, img_nrows, img_ncols, 3))
@@ -346,7 +347,7 @@ class Evaluator(object):
 evaluator = Evaluator()
 
 # Generate images by iterative optimization
-if K.image_dim_ordering() == 'th':
+if K.image_data_format() == 'channels_first':
     x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.
 else:
     x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.

examples/neural_style_transfer.py

@@ -57,7 +57,7 @@ from scipy.optimize import fmin_l_bfgs_b
 import time
 import argparse
 
-from keras.applications import vgg16
+from keras.applications import vgg19
 from keras import backend as K
 
 parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
@@ -99,14 +99,14 @@ def preprocess_image(image_path):
     img = load_img(image_path, target_size=(img_nrows, img_ncols))
     img = img_to_array(img)
     img = np.expand_dims(img, axis=0)
-    img = vgg16.preprocess_input(img)
+    img = vgg19.preprocess_input(img)
     return img
 
 # util function to convert a tensor into a valid image
 
 
 def deprocess_image(x):
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         x = x.reshape((3, img_nrows, img_ncols))
         x = x.transpose((1, 2, 0))
     else:
@@ -125,7 +125,7 @@ base_image = K.variable(preprocess_image(base_image_path))
 style_reference_image = K.variable(preprocess_image(style_reference_image_path))
 
 # this will contain our generated image
-if K.image_dim_ordering() == 'th':
+if K.image_data_format() == 'channels_first':
     combination_image = K.placeholder((1, 3, img_nrows, img_ncols))
 else:
     combination_image = K.placeholder((1, img_nrows, img_ncols, 3))
@@ -137,7 +137,7 @@ input_tensor = K.concatenate([base_image,
 
 # build the VGG16 network with our 3 images as input
 # the model will be loaded with pre-trained ImageNet weights
-model = vgg16.VGG16(input_tensor=input_tensor,
+model = vgg19.VGG19(input_tensor=input_tensor,
                     weights='imagenet', include_top=False)
 print('Model loaded.')
 
@@ -152,7 +152,7 @@ outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
 
 def gram_matrix(x):
     assert K.ndim(x) == 3
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         features = K.batch_flatten(x)
     else:
         features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
@@ -189,7 +189,7 @@ def content_loss(base, combination):
 
 def total_variation_loss(x):
     assert K.ndim(x) == 4
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])
         b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])
     else:
@@ -199,7 +199,7 @@ def total_variation_loss(x):
 
 # combine these loss functions into a single scalar
 loss = K.variable(0.)
-layer_features = outputs_dict['block4_conv2']
+layer_features = outputs_dict['block5_conv2']
 base_image_features = layer_features[0, :, :, :]
 combination_features = layer_features[2, :, :, :]
 loss += content_weight * content_loss(base_image_features,
@@ -229,7 +229,7 @@ f_outputs = K.function([combination_image], outputs)
 
 
 def eval_loss_and_grads(x):
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         x = x.reshape((1, 3, img_nrows, img_ncols))
     else:
         x = x.reshape((1, img_nrows, img_ncols, 3))
@@ -273,10 +273,7 @@ evaluator = Evaluator()
 
 # run scipy-based optimization (L-BFGS) over the pixels of the generated image
 # so as to minimize the neural style loss
-if K.image_dim_ordering() == 'th':
-    x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.
-else:
-    x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.
+x = preprocess_image(base_image_path)
 
 for i in range(iterations):
     print('Start of iteration', i)

examples/pretrained_word_embeddings.py

@@ -12,17 +12,17 @@ http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html
 '''
 
 from __future__ import print_function
-import os
-import numpy as np
-np.random.seed(1337)
 
+import os
+import sys
+import numpy as np
 from keras.preprocessing.text import Tokenizer
 from keras.preprocessing.sequence import pad_sequences
-from keras.utils.np_utils import to_categorical
+from keras.utils import to_categorical
 from keras.layers import Dense, Input, Flatten
 from keras.layers import Conv1D, MaxPooling1D, Embedding
 from keras.models import Model
-import sys
+
 
 BASE_DIR = ''
 GLOVE_DIR = BASE_DIR + '/glove.6B/'
@@ -66,14 +66,18 @@ for name in sorted(os.listdir(TEXT_DATA_DIR)):
                     f = open(fpath)
                 else:
                     f = open(fpath, encoding='latin-1')
-                texts.append(f.read())
+                t = f.read()
+                i = t.find('\n\n')  # skip header
+                if 0 < i:
+                    t = t[i:]
+                texts.append(t)
                 f.close()
                 labels.append(label_id)
 
 print('Found %s texts.' % len(texts))
 
 # finally, vectorize the text samples into a 2D integer tensor
-tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)
+tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
 tokenizer.fit_on_texts(texts)
 sequences = tokenizer.texts_to_sequences(texts)
 
@@ -91,18 +95,18 @@ indices = np.arange(data.shape[0])
 np.random.shuffle(indices)
 data = data[indices]
 labels = labels[indices]
-nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
+num_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
 
-x_train = data[:-nb_validation_samples]
-y_train = labels[:-nb_validation_samples]
-x_val = data[-nb_validation_samples:]
-y_val = labels[-nb_validation_samples:]
+x_train = data[:-num_validation_samples]
+y_train = labels[:-num_validation_samples]
+x_val = data[-num_validation_samples:]
+y_val = labels[-num_validation_samples:]
 
 print('Preparing embedding matrix.')
 
 # prepare embedding matrix
-nb_words = min(MAX_NB_WORDS, len(word_index))
-embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))
+num_words = min(MAX_NB_WORDS, len(word_index))
+embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))
 for word, i in word_index.items():
     if i >= MAX_NB_WORDS:
         continue
@@ -113,7 +117,7 @@ for word, i in word_index.items():
 
 # load pre-trained word embeddings into an Embedding layer
 # note that we set trainable = False so as to keep the embeddings fixed
-embedding_layer = Embedding(nb_words,
+embedding_layer = Embedding(num_words,
                             EMBEDDING_DIM,
                             weights=[embedding_matrix],
                             input_length=MAX_SEQUENCE_LENGTH,
@@ -139,6 +143,7 @@ model.compile(loss='categorical_crossentropy',
               optimizer='rmsprop',
               metrics=['acc'])
 
-# happy learning!
-model.fit(x_train, y_train, validation_data=(x_val, y_val),
-          nb_epoch=2, batch_size=128)
+model.fit(x_train, y_train,
+          batch_size=128,
+          epochs=10,
+          validation_data=(x_val, y_val))

examples/reuters_mlp.py

@@ -1,58 +1,60 @@
 '''Trains and evaluate a simple MLP
 on the Reuters newswire topic classification task.
 '''
-
 from __future__ import print_function
-import numpy as np
-np.random.seed(1337)  # for reproducibility
 
+import numpy as np
+import keras
 from keras.datasets import reuters
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation
-from keras.utils import np_utils
 from keras.preprocessing.text import Tokenizer
 
 max_words = 1000
 batch_size = 32
-nb_epoch = 5
+epochs = 5
 
 print('Loading data...')
-(X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
-print(len(X_train), 'train sequences')
-print(len(X_test), 'test sequences')
+(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words,
+                                                         test_split=0.2)
+print(len(x_train), 'train sequences')
+print(len(x_test), 'test sequences')
 
-nb_classes = np.max(y_train) + 1
-print(nb_classes, 'classes')
+num_classes = np.max(y_train) + 1
+print(num_classes, 'classes')
 
 print('Vectorizing sequence data...')
-tokenizer = Tokenizer(nb_words=max_words)
-X_train = tokenizer.sequences_to_matrix(X_train, mode='binary')
-X_test = tokenizer.sequences_to_matrix(X_test, mode='binary')
-print('X_train shape:', X_train.shape)
-print('X_test shape:', X_test.shape)
+tokenizer = Tokenizer(num_words=max_words)
+x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')
+x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')
+print('x_train shape:', x_train.shape)
+print('x_test shape:', x_test.shape)
 
-print('Convert class vector to binary class matrix (for use with categorical_crossentropy)')
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
-print('Y_train shape:', Y_train.shape)
-print('Y_test shape:', Y_test.shape)
+print('Convert class vector to binary class matrix '
+      '(for use with categorical_crossentropy)')
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+print('y_train shape:', y_train.shape)
+print('y_test shape:', y_test.shape)
 
 print('Building model...')
 model = Sequential()
 model.add(Dense(512, input_shape=(max_words,)))
 model.add(Activation('relu'))
 model.add(Dropout(0.5))
-model.add(Dense(nb_classes))
+model.add(Dense(num_classes))
 model.add(Activation('softmax'))
 
 model.compile(loss='categorical_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])
 
-history = model.fit(X_train, Y_train,
-                    nb_epoch=nb_epoch, batch_size=batch_size,
-                    verbose=1, validation_split=0.1)
-score = model.evaluate(X_test, Y_test,
+history = model.fit(x_train, y_train,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_split=0.1)
+score = model.evaluate(x_test, y_test,
                        batch_size=batch_size, verbose=1)
 print('Test score:', score[0])
 print('Test accuracy:', score[1])

examples/stateful_lstm.py

@@ -36,7 +36,7 @@ def gen_cosine_amp(amp=100, period=1000, x0=0, xn=50000, step=1, k=0.0001):
     return cos
 
 
-print('Generating Data')
+print('Generating Data...')
 cos = gen_cosine_amp()
 print('Input shape:', cos.shape)
 
@@ -44,13 +44,13 @@ expected_output = np.zeros((len(cos), 1))
 for i in range(len(cos) - lahead):
     expected_output[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
 
-print('Output shape')
-print(expected_output.shape)
+print('Output shape:', expected_output.shape)
 
-print('Creating Model')
+print('Creating Model...')
 model = Sequential()
 model.add(LSTM(50,
-               batch_input_shape=(batch_size, tsteps, 1),
+               input_shape=(tsteps, 1),
+               batch_size=batch_size,
                return_sequences=True,
                stateful=True))
 model.add(LSTM(50,
@@ -62,11 +62,18 @@ model.compile(loss='mse', optimizer='rmsprop')
 print('Training')
 for i in range(epochs):
     print('Epoch', i, '/', epochs)
-    model.fit(cos,
-              expected_output,
+
+    # Note that the last state for sample i in a batch will
+    # be used as initial state for sample i in the next batch.
+    # Thus we are simultaneously training on batch_size series with
+    # lower resolution than the original series contained in cos.
+    # Each of these series are offset by one step and can be
+    # extracted with cos[i::batch_size].
+
+    model.fit(cos, expected_output,
               batch_size=batch_size,
+              epochs=1,
               verbose=1,
-              nb_epoch=1,
               shuffle=False)
     model.reset_states()
 

examples/variational_autoencoder.py

@@ -6,19 +6,20 @@ import numpy as np
 import matplotlib.pyplot as plt
 from scipy.stats import norm
 
-from keras.layers import Input, Dense, Lambda
+from keras.layers import Input, Dense, Lambda, Layer
 from keras.models import Model
 from keras import backend as K
-from keras import objectives
+from keras import metrics
 from keras.datasets import mnist
 
 batch_size = 100
 original_dim = 784
 latent_dim = 2
 intermediate_dim = 256
-nb_epoch = 50
+epochs = 50
 epsilon_std = 1.0
 
+
 x = Input(batch_shape=(batch_size, original_dim))
 h = Dense(intermediate_dim, activation='relu')(x)
 z_mean = Dense(latent_dim)(h)
@@ -28,7 +29,7 @@ z_log_var = Dense(latent_dim)(h)
 def sampling(args):
     z_mean, z_log_var = args
     epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.,
-                              std=epsilon_std)
+                              stddev=epsilon_std)
     return z_mean + K.exp(z_log_var / 2) * epsilon
 
 # note that "output_shape" isn't necessary with the TensorFlow backend
@@ -41,13 +42,29 @@ h_decoded = decoder_h(z)
 x_decoded_mean = decoder_mean(h_decoded)
 
 
-def vae_loss(x, x_decoded_mean):
-    xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
-    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
-    return xent_loss + kl_loss
+# Custom loss layer
+class CustomVariationalLayer(Layer):
+    def __init__(self, **kwargs):
+        self.is_placeholder = True
+        super(CustomVariationalLayer, self).__init__(**kwargs)
+
+    def vae_loss(self, x, x_decoded_mean):
+        xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
+        kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
+        return K.mean(xent_loss + kl_loss)
+
+    def call(self, inputs):
+        x = inputs[0]
+        x_decoded_mean = inputs[1]
+        loss = self.vae_loss(x, x_decoded_mean)
+        self.add_loss(loss, inputs=inputs)
+        # We won't actually use the output.
+        return x
+
+y = CustomVariationalLayer()([x, x_decoded_mean])
+vae = Model(x, y)
+vae.compile(optimizer='rmsprop', loss=None)
 
-vae = Model(x, x_decoded_mean)
-vae.compile(optimizer='rmsprop', loss=vae_loss)
 
 # train the VAE on MNIST digits
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -57,9 +74,9 @@ x_test = x_test.astype('float32') / 255.
 x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
 x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
 
-vae.fit(x_train, x_train,
+vae.fit(x_train,
         shuffle=True,
-        nb_epoch=nb_epoch,
+        epochs=epochs,
         batch_size=batch_size,
         validation_data=(x_test, x_test))
 

examples/variational_autoencoder_deconv.py

@@ -7,41 +7,46 @@ import numpy as np
 import matplotlib.pyplot as plt
 from scipy.stats import norm
 
-from keras.layers import Input, Dense, Lambda, Flatten, Reshape
-from keras.layers import Convolution2D, Deconvolution2D
+from keras.layers import Input, Dense, Lambda, Flatten, Reshape, Layer
+from keras.layers import Conv2D, Conv2DTranspose
 from keras.models import Model
 from keras import backend as K
-from keras import objectives
+from keras import metrics
 from keras.datasets import mnist
 
 # input image dimensions
 img_rows, img_cols, img_chns = 28, 28, 1
 # number of convolutional filters to use
-nb_filters = 64
+filters = 64
 # convolution kernel size
-nb_conv = 3
+num_conv = 3
 
 batch_size = 100
-if K.image_dim_ordering() == 'th':
+if K.image_data_format() == 'channels_first':
     original_img_size = (img_chns, img_rows, img_cols)
 else:
     original_img_size = (img_rows, img_cols, img_chns)
 latent_dim = 2
 intermediate_dim = 128
 epsilon_std = 1.0
-nb_epoch = 5
+epochs = 5
 
 x = Input(batch_shape=(batch_size,) + original_img_size)
-conv_1 = Convolution2D(img_chns, 2, 2, border_mode='same', activation='relu')(x)
-conv_2 = Convolution2D(nb_filters, 2, 2,
-                       border_mode='same', activation='relu',
-                       subsample=(2, 2))(conv_1)
-conv_3 = Convolution2D(nb_filters, nb_conv, nb_conv,
-                       border_mode='same', activation='relu',
-                       subsample=(1, 1))(conv_2)
-conv_4 = Convolution2D(nb_filters, nb_conv, nb_conv,
-                       border_mode='same', activation='relu',
-                       subsample=(1, 1))(conv_3)
+conv_1 = Conv2D(img_chns,
+                kernel_size=(2, 2),
+                padding='same', activation='relu')(x)
+conv_2 = Conv2D(filters,
+                kernel_size=(2, 2),
+                padding='same', activation='relu',
+                strides=(2, 2))(conv_1)
+conv_3 = Conv2D(filters,
+                kernel_size=num_conv,
+                padding='same', activation='relu',
+                strides=1)(conv_2)
+conv_4 = Conv2D(filters,
+                kernel_size=num_conv,
+                padding='same', activation='relu',
+                strides=1)(conv_3)
 flat = Flatten()(conv_4)
 hidden = Dense(intermediate_dim, activation='relu')(flat)
 
@@ -52,7 +57,7 @@ z_log_var = Dense(latent_dim)(hidden)
 def sampling(args):
     z_mean, z_log_var = args
     epsilon = K.random_normal(shape=(batch_size, latent_dim),
-                              mean=0., std=epsilon_std)
+                              mean=0., stddev=epsilon_std)
     return z_mean + K.exp(z_log_var) * epsilon
 
 # note that "output_shape" isn't necessary with the TensorFlow backend
@@ -61,36 +66,36 @@ z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
 
 # we instantiate these layers separately so as to reuse them later
 decoder_hid = Dense(intermediate_dim, activation='relu')
-decoder_upsample = Dense(nb_filters * 14 * 14, activation='relu')
+decoder_upsample = Dense(filters * 14 * 14, activation='relu')
 
-if K.image_dim_ordering() == 'th':
-    output_shape = (batch_size, nb_filters, 14, 14)
+if K.image_data_format() == 'channels_first':
+    output_shape = (batch_size, filters, 14, 14)
 else:
-    output_shape = (batch_size, 14, 14, nb_filters)
+    output_shape = (batch_size, 14, 14, filters)
 
 decoder_reshape = Reshape(output_shape[1:])
-decoder_deconv_1 = Deconvolution2D(nb_filters, nb_conv, nb_conv,
-                                   output_shape,
-                                   border_mode='same',
-                                   subsample=(1, 1),
+decoder_deconv_1 = Conv2DTranspose(filters,
+                                   kernel_size=num_conv,
+                                   padding='same',
+                                   strides=1,
                                    activation='relu')
-decoder_deconv_2 = Deconvolution2D(nb_filters, nb_conv, nb_conv,
-                                   output_shape,
-                                   border_mode='same',
-                                   subsample=(1, 1),
+decoder_deconv_2 = Conv2DTranspose(filters, num_conv,
+                                   padding='same',
+                                   strides=1,
                                    activation='relu')
-if K.image_dim_ordering() == 'th':
-    output_shape = (batch_size, nb_filters, 29, 29)
+if K.image_data_format() == 'channels_first':
+    output_shape = (batch_size, filters, 29, 29)
 else:
-    output_shape = (batch_size, 29, 29, nb_filters)
-decoder_deconv_3_upsamp = Deconvolution2D(nb_filters, 2, 2,
-                                          output_shape,
-                                          border_mode='valid',
-                                          subsample=(2, 2),
+    output_shape = (batch_size, 29, 29, filters)
+decoder_deconv_3_upsamp = Conv2DTranspose(filters,
+                                          kernel_size=(3, 3),
+                                          strides=(2, 2),
+                                          padding='valid',
                                           activation='relu')
-decoder_mean_squash = Convolution2D(img_chns, 2, 2,
-                                    border_mode='valid',
-                                    activation='sigmoid')
+decoder_mean_squash = Conv2D(img_chns,
+                             kernel_size=2,
+                             padding='valid',
+                             activation='sigmoid')
 
 hid_decoded = decoder_hid(z)
 up_decoded = decoder_upsample(hid_decoded)
@@ -101,17 +106,31 @@ x_decoded_relu = decoder_deconv_3_upsamp(deconv_2_decoded)
 x_decoded_mean_squash = decoder_mean_squash(x_decoded_relu)
 
 
-def vae_loss(x, x_decoded_mean):
-    # NOTE: binary_crossentropy expects a batch_size by dim
-    # for x and x_decoded_mean, so we MUST flatten these!
-    x = K.flatten(x)
-    x_decoded_mean = K.flatten(x_decoded_mean)
-    xent_loss = img_rows * img_cols * objectives.binary_crossentropy(x, x_decoded_mean)
-    kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
-    return xent_loss + kl_loss
+# Custom loss layer
+class CustomVariationalLayer(Layer):
+    def __init__(self, **kwargs):
+        self.is_placeholder = True
+        super(CustomVariationalLayer, self).__init__(**kwargs)
 
-vae = Model(x, x_decoded_mean_squash)
-vae.compile(optimizer='rmsprop', loss=vae_loss)
+    def vae_loss(self, x, x_decoded_mean_squash):
+        x = K.flatten(x)
+        x_decoded_mean_squash = K.flatten(x_decoded_mean_squash)
+        xent_loss = img_rows * img_cols * metrics.binary_crossentropy(x, x_decoded_mean_squash)
+        kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
+        return K.mean(xent_loss + kl_loss)
+
+    def call(self, inputs):
+        x = inputs[0]
+        x_decoded_mean_squash = inputs[1]
+        loss = self.vae_loss(x, x_decoded_mean_squash)
+        self.add_loss(loss, inputs=inputs)
+        # We don't use this output.
+        return x
+
+
+y = CustomVariationalLayer()([x, x_decoded_mean_squash])
+vae = Model(x, y)
+vae.compile(optimizer='rmsprop', loss=None)
 vae.summary()
 
 # train the VAE on MNIST digits
@@ -124,9 +143,9 @@ x_test = x_test.reshape((x_test.shape[0],) + original_img_size)
 
 print('x_train.shape:', x_train.shape)
 
-vae.fit(x_train, x_train,
+vae.fit(x_train,
         shuffle=True,
-        nb_epoch=nb_epoch,
+        epochs=epochs,
         batch_size=batch_size,
         validation_data=(x_test, x_test))
 

keras/__init__.py

@@ -1,4 +1,7 @@
 from __future__ import absolute_import
+
+from . import activations
+from . import applications
 from . import backend
 from . import datasets
 from . import engine
@@ -8,11 +11,13 @@ from . import utils
 from . import wrappers
 from . import callbacks
 from . import constraints
-from . import initializations
+from . import initializers
 from . import metrics
 from . import models
-from . import objectives
+from . import losses
 from . import optimizers
 from . import regularizers
+# Importable from root because it's technically not a layer
+from .layers import Input
 
-__version__ = '1.2.2'
+__version__ = '2.0.5'

keras/activations.py

@@ -1,20 +1,33 @@
 from __future__ import absolute_import
+import six
+import warnings
 from . import backend as K
-from .utils.generic_utils import get_from_module
+from .utils.generic_utils import deserialize_keras_object
+from .engine import Layer
 
 
-def softmax(x):
+def softmax(x, axis=-1):
+    """Softmax activation function.
+
+    # Arguments
+        x : Tensor.
+        axis: Integer, axis along which the softmax normalization is applied.
+
+    # Returns
+        Tensor, output of softmax transformation.
+
+    # Raises
+        ValueError: In case `dim(x) == 1`.
+    """
     ndim = K.ndim(x)
     if ndim == 2:
         return K.softmax(x)
-    elif ndim == 3:
-        e = K.exp(x - K.max(x, axis=-1, keepdims=True))
-        s = K.sum(e, axis=-1, keepdims=True)
+    elif ndim > 2:
+        e = K.exp(x - K.max(x, axis=axis, keepdims=True))
+        s = K.sum(e, axis=axis, keepdims=True)
         return e / s
     else:
-        raise ValueError('Cannot apply softmax to a tensor '
-                         'that is not 2D or 3D. '
-                         'Here, ndim=' + str(ndim))
+        raise ValueError('Cannot apply softmax to a tensor that is 1D')
 
 
 def elu(x, alpha=1.0):
@@ -49,7 +62,32 @@ def linear(x):
     return x
 
 
+def serialize(activation):
+    return activation.__name__
+
+
+def deserialize(name, custom_objects=None):
+    return deserialize_keras_object(name,
+                                    module_objects=globals(),
+                                    custom_objects=custom_objects,
+                                    printable_module_name='activation function')
+
+
 def get(identifier):
     if identifier is None:
         return linear
-    return get_from_module(identifier, globals(), 'activation function')
+    if isinstance(identifier, six.string_types):
+        identifier = str(identifier)
+        return deserialize(identifier)
+    elif callable(identifier):
+        if isinstance(identifier, Layer):
+            warnings.warn((
+                'Do not pass a layer instance (such as {identifier}) as the '
+                'activation argument of another layer. Instead, advanced '
+                'activation layers should be used just like any other '
+                'layer in a model.'
+            ).format(identifier=identifier.__class__.__name__))
+        return identifier
+    else:
+        raise ValueError('Could not interpret '
+                         'activation function identifier:', identifier)

keras/applications/audio_conv_utils.py

@@ -1,92 +0,0 @@
-import numpy as np
-from .. import backend as K
-
-try:
-    import librosa
-except ImportError:
-    librosa = None
-
-
-TAGS = ['rock', 'pop', 'alternative', 'indie', 'electronic',
-        'female vocalists', 'dance', '00s', 'alternative rock', 'jazz',
-        'beautiful', 'metal', 'chillout', 'male vocalists',
-        'classic rock', 'soul', 'indie rock', 'Mellow', 'electronica',
-        '80s', 'folk', '90s', 'chill', 'instrumental', 'punk',
-        'oldies', 'blues', 'hard rock', 'ambient', 'acoustic',
-        'experimental', 'female vocalist', 'guitar', 'Hip-Hop',
-        '70s', 'party', 'country', 'easy listening',
-        'sexy', 'catchy', 'funk', 'electro', 'heavy metal',
-        'Progressive rock', '60s', 'rnb', 'indie pop',
-        'sad', 'House', 'happy']
-
-
-def preprocess_input(audio_path, dim_ordering='default'):
-    """Reads an audio file and outputs a Mel-spectrogram.
-
-    # Arguments
-        audio_path: path to the target audio file.
-        dim_ordering: data format for the output spectrogram image.
-
-    # Returns
-        3D Numpy tensor encoding the Mel-spectrogram.
-
-    # Raises
-        ImportError: if librosa is not available.
-    """
-    if dim_ordering == 'default':
-        dim_ordering = K.image_dim_ordering()
-    assert dim_ordering in {'tf', 'th'}
-
-    if librosa is None:
-        raise ImportError('Librosa is required to process audio files. '
-                          'Install it via `pip install librosa` or visit '
-                          'http://librosa.github.io/librosa/ for details.')
-
-    # mel-spectrogram parameters
-    sr = 12000
-    n_fft = 512
-    n_mels = 96
-    hop_length = 256
-    duration = 29.12
-
-    src, sr = librosa.load(audio_path, sr=sr)
-    n_sample = src.shape[0]
-    n_sample_wanted = int(duration * sr)
-
-    # trim the signal at the center
-    if n_sample < n_sample_wanted:  # if too short
-        src = np.hstack((src, np.zeros((int(duration * sr) - n_sample,))))
-    elif n_sample > n_sample_wanted:  # if too long
-        src = src[(n_sample - n_sample_wanted) // 2:
-                  (n_sample + n_sample_wanted) // 2]
-
-    logam = librosa.logamplitude
-    melgram = librosa.feature.melspectrogram
-    x = logam(melgram(y=src, sr=sr, hop_length=hop_length,
-                      n_fft=n_fft, n_mels=n_mels) ** 2,
-              ref_power=1.0)
-
-    if dim_ordering == 'th':
-        x = np.expand_dims(x, axis=0)
-    elif dim_ordering == 'tf':
-        x = np.expand_dims(x, axis=3)
-    return x
-
-
-def decode_predictions(preds, top_n=5):
-    """Decode the output of a music tagger model.
-
-    # Arguments
-        preds: 2-dimensional numpy array
-        top_n: integer in [0, 50], number of items to show.
-
-    # Returns
-        Decoded output.
-    """
-    assert len(preds.shape) == 2 and preds.shape[1] == 50
-    results = []
-    for pred in preds:
-        result = zip(TAGS, pred)
-        result = sorted(result, key=lambda x: x[1], reverse=True)
-        results.append(result[:top_n])
-    return results

keras/applications/imagenet_utils.py

@@ -1,4 +1,3 @@
-import numpy as np
 import json
 
 from ..utils.data_utils import get_file
@@ -8,21 +7,21 @@ CLASS_INDEX = None
 CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'
 
 
-def preprocess_input(x, dim_ordering='default'):
+def preprocess_input(x, data_format=None):
     """Preprocesses a tensor encoding a batch of images.
 
     # Arguments
         x: input Numpy tensor, 4D.
-        dim_ordering: data format of the image tensor.
+        data_format: data format of the image tensor.
 
     # Returns
         Preprocessed tensor.
     """
-    if dim_ordering == 'default':
-        dim_ordering = K.image_dim_ordering()
-    assert dim_ordering in {'tf', 'th'}
+    if data_format is None:
+        data_format = K.image_data_format()
+    assert data_format in {'channels_last', 'channels_first'}
 
-    if dim_ordering == 'th':
+    if data_format == 'channels_first':
         # 'RGB'->'BGR'
         x = x[:, ::-1, :, :]
         # Zero-center by mean pixel
@@ -78,7 +77,7 @@ def decode_predictions(preds, top=5):
 def _obtain_input_shape(input_shape,
                         default_size,
                         min_size,
-                        dim_ordering,
+                        data_format,
                         include_top):
     """Internal utility to compute/validate an ImageNet model's input shape.
 
@@ -87,7 +86,7 @@ def _obtain_input_shape(input_shape,
             or a user-provided shape to be validated.
         default_size: default input width/height for the model.
         min_size: minimum input width/height accepted by the model.
-        dim_ordering: image data format to use.
+        data_format: image data format to use.
         include_top: whether the model is expected to
             be linked to a classifier via a Flatten layer.
 
@@ -97,7 +96,7 @@ def _obtain_input_shape(input_shape,
     # Raises
         ValueError: in case of invalid argument values.
     """
-    if dim_ordering == 'th':
+    if data_format == 'channels_first':
         default_shape = (3, default_size, default_size)
     else:
         default_shape = (default_size, default_size, 3)
@@ -108,7 +107,7 @@ def _obtain_input_shape(input_shape,
                                  '`input_shape` should be ' + str(default_shape) + '.')
         input_shape = default_shape
     else:
-        if dim_ordering == 'th':
+        if data_format == 'channels_first':
             if input_shape is not None:
                 if len(input_shape) != 3:
                     raise ValueError('`input_shape` must be a tuple of three integers.')

keras/applications/inception_v3.py

@@ -1,14 +1,9 @@
 # -*- coding: utf-8 -*-
 """Inception V3 model for Keras.
 
-Note that the ImageNet weights provided are from a model that had not fully converged.
-Inception v3 should be able to reach 6.9% top-5 error, but our model
-only gets to 7.8% (same as a fully-converged ResNet 50).
-For comparison, VGG16 only gets to 9.9%, quite a bit worse.
-
-Also, do note that the input image format for this model is different than for
-the VGG16 and ResNet models (299x299 instead of 224x224), and that the input preprocessing function
-is also different (same as Xception).
+Note that the input image format for this model is different than for
+the VGG16 and ResNet models (299x299 instead of 224x224),
+and that the input preprocessing function is also different (same as Xception).
 
 # Reference
 
@@ -21,25 +16,49 @@ from __future__ import absolute_import
 import warnings
 
 from ..models import Model
-from ..layers import Flatten, Dense, Input, BatchNormalization, merge
-from ..layers import Convolution2D, MaxPooling2D, AveragePooling2D
+from .. import layers
+from ..layers import Activation
+from ..layers import Dense
+from ..layers import Input
+from ..layers import BatchNormalization
+from ..layers import Conv2D
+from ..layers import MaxPooling2D
+from ..layers import AveragePooling2D
+from ..layers import GlobalAveragePooling2D
+from ..layers import GlobalMaxPooling2D
 from ..engine.topology import get_source_inputs
-from ..utils.layer_utils import convert_all_kernels_in_model
 from ..utils.data_utils import get_file
 from .. import backend as K
-from .imagenet_utils import decode_predictions, _obtain_input_shape
+from .imagenet_utils import decode_predictions
+from .imagenet_utils import _obtain_input_shape
 
 
-TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels.h5'
-TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels.h5'
-TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels_notop.h5'
-TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
+WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
 
 
-def conv2d_bn(x, nb_filter, nb_row, nb_col,
-              border_mode='same', subsample=(1, 1),
+def conv2d_bn(x,
+              filters,
+              num_row,
+              num_col,
+              padding='same',
+              strides=(1, 1),
               name=None):
     """Utility function to apply conv + BN.
+
+    # Arguments
+        x: input tensor.
+        filters: filters in `Conv2D`.
+        num_row: height of the convolution kernel.
+        num_col: width of the convolution kernel.
+        padding: padding mode in `Conv2D`.
+        strides: strides in `Conv2D`.
+        name: name of the ops; will become `name + '_conv'`
+            for the convolution and `name + '_bn'` for the
+            batch norm layer.
+
+    # Returns
+        Output tensor after applying `Conv2D` and `BatchNormalization`.
     """
     if name is not None:
         bn_name = name + '_bn'
@@ -47,34 +66,38 @@ def conv2d_bn(x, nb_filter, nb_row, nb_col,
     else:
         bn_name = None
         conv_name = None
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         bn_axis = 1
     else:
         bn_axis = 3
-    x = Convolution2D(nb_filter, nb_row, nb_col,
-                      subsample=subsample,
-                      activation='relu',
-                      border_mode=border_mode,
-                      name=conv_name)(x)
-    x = BatchNormalization(axis=bn_axis, name=bn_name)(x)
+    x = Conv2D(
+        filters, (num_row, num_col),
+        strides=strides,
+        padding=padding,
+        use_bias=False,
+        name=conv_name)(x)
+    x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
+    x = Activation('relu', name=name)(x)
     return x
 
 
-def InceptionV3(include_top=True, weights='imagenet',
-                input_tensor=None, input_shape=None,
+def InceptionV3(include_top=True,
+                weights='imagenet',
+                input_tensor=None,
+                input_shape=None,
+                pooling=None,
                 classes=1000):
-    """Instantiate the Inception v3 architecture,
-    optionally loading weights pre-trained
+    """Instantiates the Inception v3 architecture.
+
+    Optionally loads weights pre-trained
     on ImageNet. Note that when using TensorFlow,
     for best performance you should set
-    `image_dim_ordering="tf"` in your Keras config
+    `image_data_format="channels_last"` in your Keras config
     at ~/.keras/keras.json.
-
     The model and the weights are compatible with both
-    TensorFlow and Theano. The dimension ordering
+    TensorFlow and Theano. The data format
     convention used by the model is the one
     specified in your Keras config file.
-
     Note that the default input image size for this model is 299x299.
 
     # Arguments
@@ -86,17 +109,32 @@ def InceptionV3(include_top=True, weights='imagenet',
             to use as image input for the model.
         input_shape: optional shape tuple, only to be specified
             if `include_top` is False (otherwise the input shape
-            has to be `(299, 299, 3)` (with `tf` dim ordering)
-            or `(3, 299, 299)` (with `th` dim ordering).
+            has to be `(299, 299, 3)` (with `channels_last` data format)
+            or `(3, 299, 299)` (with `channels_first` data format).
             It should have exactly 3 inputs channels,
             and width and height should be no smaller than 139.
             E.g. `(150, 150, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional layer.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional layer, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
         classes: optional number of classes to classify images
             into, only to be specified if `include_top` is True, and
             if no `weights` argument is specified.
 
     # Returns
         A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
     """
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
@@ -108,11 +146,12 @@ def InceptionV3(include_top=True, weights='imagenet',
                          ' as true, `classes` should be 1000')
 
     # Determine proper input shape
-    input_shape = _obtain_input_shape(input_shape,
-                                      default_size=299,
-                                      min_size=139,
-                                      dim_ordering=K.image_dim_ordering(),
-                                      include_top=include_top)
+    input_shape = _obtain_input_shape(
+        input_shape,
+        default_size=299,
+        min_size=139,
+        data_format=K.image_data_format(),
+        include_top=include_top)
 
     if input_tensor is None:
         img_input = Input(shape=input_shape)
@@ -122,50 +161,82 @@ def InceptionV3(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
 
-    if K.image_dim_ordering() == 'th':
+    if K.image_data_format() == 'channels_first':
         channel_axis = 1
     else:
         channel_axis = 3
 
-    x = conv2d_bn(img_input, 32, 3, 3, subsample=(2, 2), border_mode='valid')
-    x = conv2d_bn(x, 32, 3, 3, border_mode='valid')
+    x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
+    x = conv2d_bn(x, 32, 3, 3, padding='valid')
     x = conv2d_bn(x, 64, 3, 3)
     x = MaxPooling2D((3, 3), strides=(2, 2))(x)
 
-    x = conv2d_bn(x, 80, 1, 1, border_mode='valid')
-    x = conv2d_bn(x, 192, 3, 3, border_mode='valid')
+    x = conv2d_bn(x, 80, 1, 1, padding='valid')
+    x = conv2d_bn(x, 192, 3, 3, padding='valid')
     x = MaxPooling2D((3, 3), strides=(2, 2))(x)
 
     # mixed 0, 1, 2: 35 x 35 x 256
-    for i in range(3):
-        branch1x1 = conv2d_bn(x, 64, 1, 1)
+    branch1x1 = conv2d_bn(x, 64, 1, 1)
 
-        branch5x5 = conv2d_bn(x, 48, 1, 1)
-        branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
-
-        branch3x3dbl = conv2d_bn(x, 64, 1, 1)
-        branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
-        branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
-
-        branch_pool = AveragePooling2D(
-            (3, 3), strides=(1, 1), border_mode='same')(x)
-        branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
-        x = merge([branch1x1, branch5x5, branch3x3dbl, branch_pool],
-                  mode='concat', concat_axis=channel_axis,
-                  name='mixed' + str(i))
-
-    # mixed 3: 17 x 17 x 768
-    branch3x3 = conv2d_bn(x, 384, 3, 3, subsample=(2, 2), border_mode='valid')
+    branch5x5 = conv2d_bn(x, 48, 1, 1)
+    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
 
     branch3x3dbl = conv2d_bn(x, 64, 1, 1)
     branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
-    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3,
-                             subsample=(2, 2), border_mode='valid')
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+
+    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
+    branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
+    x = layers.concatenate(
+        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
+        axis=channel_axis,
+        name='mixed0')
+
+    # mixed 1: 35 x 35 x 256
+    branch1x1 = conv2d_bn(x, 64, 1, 1)
+
+    branch5x5 = conv2d_bn(x, 48, 1, 1)
+    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
+
+    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+
+    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
+    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
+    x = layers.concatenate(
+        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
+        axis=channel_axis,
+        name='mixed1')
+
+    # mixed 2: 35 x 35 x 256
+    branch1x1 = conv2d_bn(x, 64, 1, 1)
+
+    branch5x5 = conv2d_bn(x, 48, 1, 1)
+    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
+
+    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+
+    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
+    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
+    x = layers.concatenate(
+        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
+        axis=channel_axis,
+        name='mixed2')
+
+    # mixed 3: 17 x 17 x 768
+    branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')
+
+    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
+    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
+    branch3x3dbl = conv2d_bn(
+        branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')
 
     branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
-    x = merge([branch3x3, branch3x3dbl, branch_pool],
-              mode='concat', concat_axis=channel_axis,
-              name='mixed3')
+    x = layers.concatenate(
+        [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3')
 
     # mixed 4: 17 x 17 x 768
     branch1x1 = conv2d_bn(x, 192, 1, 1)
@@ -180,11 +251,12 @@ def InceptionV3(include_top=True, weights='imagenet',
     branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
     branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
 
-    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x)
+    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
     branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
-    x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
-              mode='concat', concat_axis=channel_axis,
-              name='mixed4')
+    x = layers.concatenate(
+        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
+        axis=channel_axis,
+        name='mixed4')
 
     # mixed 5, 6: 17 x 17 x 768
     for i in range(2):
@@ -201,11 +273,12 @@ def InceptionV3(include_top=True, weights='imagenet',
         branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
 
         branch_pool = AveragePooling2D(
-            (3, 3), strides=(1, 1), border_mode='same')(x)
+            (3, 3), strides=(1, 1), padding='same')(x)
         branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
-        x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
-                  mode='concat', concat_axis=channel_axis,
-                  name='mixed' + str(5 + i))
+        x = layers.concatenate(
+            [branch1x1, branch7x7, branch7x7dbl, branch_pool],
+            axis=channel_axis,
+            name='mixed' + str(5 + i))
 
     # mixed 7: 17 x 17 x 768
     branch1x1 = conv2d_bn(x, 192, 1, 1)
@@ -214,33 +287,33 @@ def InceptionV3(include_top=True, weights='imagenet',
     branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
     branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
 
-    branch7x7dbl = conv2d_bn(x, 160, 1, 1)
+    branch7x7dbl = conv2d_bn(x, 192, 1, 1)
     branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
     branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
     branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
     branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
 
-    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x)
+    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
     branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
-    x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
-              mode='concat', concat_axis=channel_axis,
-              name='mixed7')
+    x = layers.concatenate(
+        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
+        axis=channel_axis,
+        name='mixed7')
 
     # mixed 8: 8 x 8 x 1280
     branch3x3 = conv2d_bn(x, 192, 1, 1)
     branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
-                          subsample=(2, 2), border_mode='valid')
+                          strides=(2, 2), padding='valid')
 
     branch7x7x3 = conv2d_bn(x, 192, 1, 1)
     branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
     branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
-    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3,
-                            subsample=(2, 2), border_mode='valid')
+    branch7x7x3 = conv2d_bn(
+        branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')
 
-    branch_pool = AveragePooling2D((3, 3), strides=(2, 2))(x)
-    x = merge([branch3x3, branch7x7x3, branch_pool],
-              mode='concat', concat_axis=channel_axis,
-              name='mixed8')
+    branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
+    x = layers.concatenate(
+        [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8')
 
     # mixed 9: 8 x 8 x 2048
     for i in range(2):
@@ -249,29 +322,32 @@ def InceptionV3(include_top=True, weights='imagenet',
         branch3x3 = conv2d_bn(x, 384, 1, 1)
         branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
         branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
-        branch3x3 = merge([branch3x3_1, branch3x3_2],
-                          mode='concat', concat_axis=channel_axis,
-                          name='mixed9_' + str(i))
+        branch3x3 = layers.concatenate(
+            [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i))
 
         branch3x3dbl = conv2d_bn(x, 448, 1, 1)
         branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
         branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
         branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
-        branch3x3dbl = merge([branch3x3dbl_1, branch3x3dbl_2],
-                             mode='concat', concat_axis=channel_axis)
+        branch3x3dbl = layers.concatenate(
+            [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis)
 
         branch_pool = AveragePooling2D(
-            (3, 3), strides=(1, 1), border_mode='same')(x)
+            (3, 3), strides=(1, 1), padding='same')(x)
         branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
-        x = merge([branch1x1, branch3x3, branch3x3dbl, branch_pool],
-                  mode='concat', concat_axis=channel_axis,
-                  name='mixed' + str(9 + i))
-
+        x = layers.concatenate(
+            [branch1x1, branch3x3, branch3x3dbl, branch_pool],
+            axis=channel_axis,
+            name='mixed' + str(9 + i))
     if include_top:
         # Classification block
-        x = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x)
-        x = Flatten(name='flatten')(x)
+        x = GlobalAveragePooling2D(name='avg_pool')(x)
         x = Dense(classes, activation='softmax', name='predictions')(x)
+    else:
+        if pooling == 'avg':
+            x = GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = GlobalMaxPooling2D()(x)
 
     # Ensure that the model takes into account
     # any potential predecessors of `input_tensor`.
@@ -284,42 +360,29 @@ def InceptionV3(include_top=True, weights='imagenet',
 
     # load weights
     if weights == 'imagenet':
-        if K.image_dim_ordering() == 'th':
-            if include_top:
-                weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels.h5',
-                                        TH_WEIGHTS_PATH,
-                                        cache_subdir='models',
-                                        md5_hash='b3baf3070cc4bf476d43a2ea61b0ca5f')
-            else:
-                weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels_notop.h5',
-                                        TH_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models',
-                                        md5_hash='79aaa90ab4372b4593ba3df64e142f05')
-            model.load_weights(weights_path)
+        if K.image_data_format() == 'channels_first':
             if K.backend() == 'tensorflow':
                 warnings.warn('You are using the TensorFlow backend, yet you '
                               'are using the Theano '
-                              'image dimension ordering convention '
-                              '(`image_dim_ordering="th"`). '
+                              'image data format convention '
+                              '(`image_data_format="channels_first"`). '
                               'For best performance, set '
-                              '`image_dim_ordering="tf"` in '
+                              '`image_data_format="channels_last"` in '
                               'your Keras config '
                               'at ~/.keras/keras.json.')
-                convert_all_kernels_in_model(model)
+        if include_top:
+            weights_path = get_file(
+                'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
+                WEIGHTS_PATH,
+                cache_subdir='models',
+                md5_hash='9a0d58056eeedaa3f26cb7ebd46da564')
         else:
-            if include_top:
-                weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
-                                        TF_WEIGHTS_PATH,
-                                        cache_subdir='models',
-                                        md5_hash='fe114b3ff2ea4bf891e9353d1bbfb32f')
-            else:
-                weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
-                                        TF_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models',
-                                        md5_hash='2f3609166de1d967d1a481094754f691')
-            model.load_weights(weights_path)
-            if K.backend() == 'theano':
-                convert_all_kernels_in_model(model)
+            weights_path = get_file(
+                'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                WEIGHTS_PATH_NO_TOP,
+                cache_subdir='models',
+                md5_hash='bcbd6486424b2319ff4ef7d526e38f63')
+        model.load_weights(weights_path)
     return model
 
 

keras/applications/music_tagger_crnn.py

@@ -1,160 +0,0 @@
-# -*- coding: utf-8 -*-
-"""MusicTaggerCRNN model for Keras.
-
-# Reference:
-
-- [Music-auto_tagging-keras](https://github.com/keunwoochoi/music-auto_tagging-keras)
-
-"""
-from __future__ import print_function
-from __future__ import absolute_import
-
-from .. import backend as K
-from ..layers import Input, Dense
-from ..models import Model
-from ..layers import Dense, Dropout, Reshape, Permute
-from ..layers.convolutional import Convolution2D
-from ..layers.convolutional import MaxPooling2D, ZeroPadding2D
-from ..layers.normalization import BatchNormalization
-from ..layers.advanced_activations import ELU
-from ..layers.recurrent import GRU
-from ..engine.topology import get_source_inputs
-from ..utils.data_utils import get_file
-from ..utils.layer_utils import convert_all_kernels_in_model
-from .audio_conv_utils import decode_predictions, preprocess_input
-
-TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.3/music_tagger_crnn_weights_tf_kernels_th_dim_ordering.h5'
-TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.3/music_tagger_crnn_weights_tf_kernels_tf_dim_ordering.h5'
-
-
-def MusicTaggerCRNN(weights='msd', input_tensor=None,
-                    include_top=True, classes=50):
-    """Instantiate the MusicTaggerCRNN architecture,
-    optionally loading weights pre-trained
-    on Million Song Dataset. Note that when using TensorFlow,
-    for best performance you should set
-    `image_dim_ordering="tf"` in your Keras config
-    at ~/.keras/keras.json.
-
-    The model and the weights are compatible with both
-    TensorFlow and Theano. The dimension ordering
-    convention used by the model is the one
-    specified in your Keras config file.
-
-    For preparing mel-spectrogram input, see
-    `audio_conv_utils.py` in [applications](https://github.com/fchollet/keras/tree/master/keras/applications).
-    You will need to install [Librosa](http://librosa.github.io/librosa/)
-    to use it.
-
-    # Arguments
-        weights: one of `None` (random initialization)
-            or "msd" (pre-training on ImageNet).
-        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
-            to use as image input for the model.
-        include_top: whether to include the 1 fully-connected
-            layer (output layer) at the top of the network.
-            If False, the network outputs 32-dim features.
-        classes: optional number of classes to classify images
-            into, only to be specified if `include_top` is True, and
-            if no `weights` argument is specified.
-
-    # Returns
-        A Keras model instance.
-    """
-    if weights not in {'msd', None}:
-        raise ValueError('The `weights` argument should be either '
-                         '`None` (random initialization) or `msd` '
-                         '(pre-training on Million Song Dataset).')
-
-    if weights == 'msd' and include_top and classes != 50:
-        raise ValueError('If using `weights` as msd with `include_top`'
-                         ' as true, `classes` should be 50')
-    # Determine proper input shape
-    if K.image_dim_ordering() == 'th':
-        input_shape = (1, 96, 1366)
-    else:
-        input_shape = (96, 1366, 1)
-
-    if input_tensor is None:
-        melgram_input = Input(shape=input_shape)
-    else:
-        if not K.is_keras_tensor(input_tensor):
-            melgram_input = Input(tensor=input_tensor, shape=input_shape)
-        else:
-            melgram_input = input_tensor
-
-    # Determine input axis
-    if K.image_dim_ordering() == 'th':
-        channel_axis = 1
-        freq_axis = 2
-        time_axis = 3
-    else:
-        channel_axis = 3
-        freq_axis = 1
-        time_axis = 2
-
-    # Input block
-    x = ZeroPadding2D(padding=(0, 37))(melgram_input)
-    x = BatchNormalization(axis=time_axis, name='bn_0_freq')(x)
-
-    # Conv block 1
-    x = Convolution2D(64, 3, 3, border_mode='same', name='conv1')(x)
-    x = BatchNormalization(axis=channel_axis, mode=0, name='bn1')(x)
-    x = ELU()(x)
-    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(x)
-
-    # Conv block 2
-    x = Convolution2D(128, 3, 3, border_mode='same', name='conv2')(x)
-    x = BatchNormalization(axis=channel_axis, mode=0, name='bn2')(x)
-    x = ELU()(x)
-    x = MaxPooling2D(pool_size=(3, 3), strides=(3, 3), name='pool2')(x)
-
-    # Conv block 3
-    x = Convolution2D(128, 3, 3, border_mode='same', name='conv3')(x)
-    x = BatchNormalization(axis=channel_axis, mode=0, name='bn3')(x)
-    x = ELU()(x)
-    x = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool3')(x)
-
-    # Conv block 4
-    x = Convolution2D(128, 3, 3, border_mode='same', name='conv4')(x)
-    x = BatchNormalization(axis=channel_axis, mode=0, name='bn4')(x)
-    x = ELU()(x)
-    x = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), name='pool4')(x)
-
-    # reshaping
-    if K.image_dim_ordering() == 'th':
-        x = Permute((3, 1, 2))(x)
-    x = Reshape((15, 128))(x)
-
-    # GRU block 1, 2, output
-    x = GRU(32, return_sequences=True, name='gru1')(x)
-    x = GRU(32, return_sequences=False, name='gru2')(x)
-
-    if include_top:
-        x = Dense(classes, activation='sigmoid', name='output')(x)
-
-    # Ensure that the model takes into account
-    # any potential predecessors of `input_tensor`.
-    if input_tensor is not None:
-        inputs = get_source_inputs(input_tensor)
-    else:
-        inputs = melgram_input
-    # Create model.
-    model = Model(inputs, x, name='music_tagger_crnn')
-
-    if weights is None:
-        return model
-    else:
-        # Load weights
-        if K.image_dim_ordering() == 'tf':
-            weights_path = get_file('music_tagger_crnn_weights_tf_kernels_tf_dim_ordering.h5',
-                                    TF_WEIGHTS_PATH,
-                                    cache_subdir='models')
-        else:
-            weights_path = get_file('music_tagger_crnn_weights_tf_kernels_th_dim_ordering.h5',
-                                    TH_WEIGHTS_PATH,
-                                    cache_subdir='models')
-        model.load_weights(weights_path, by_name=True)
-        if K.backend() == 'theano':
-            convert_all_kernels_in_model(model)
-        return model

keras/applications/resnet50.py

@@ -12,55 +12,66 @@ from __future__ import absolute_import
 
 import warnings
 
-from ..layers import merge, Input
-from ..layers import Dense, Activation, Flatten
-from ..layers import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D
+from ..layers import Input
+from .. import layers
+from ..layers import Dense
+from ..layers import Activation
+from ..layers import Flatten
+from ..layers import Conv2D
+from ..layers import MaxPooling2D
+from ..layers import ZeroPadding2D
+from ..layers import AveragePooling2D
+from ..layers import GlobalAveragePooling2D
+from ..layers import GlobalMaxPooling2D
 from ..layers import BatchNormalization
 from ..models import Model
 from .. import backend as K
 from ..engine.topology import get_source_inputs
-from ..utils.layer_utils import convert_all_kernels_in_model
+from ..utils import layer_utils
 from ..utils.data_utils import get_file
-from .imagenet_utils import decode_predictions, preprocess_input, _obtain_input_shape
+from .imagenet_utils import decode_predictions
+from .imagenet_utils import preprocess_input
+from .imagenet_utils import _obtain_input_shape
 
 
-TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels.h5'
-TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
-TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5'
-TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
+WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
 
 
 def identity_block(input_tensor, kernel_size, filters, stage, block):
-    """The identity_block is the block that has no conv layer at shortcut
+    """The identity block is the block that has no conv layer at shortcut.
 
     # Arguments
         input_tensor: input tensor
-        kernel_size: defualt 3, the kernel size of middle conv layer at main path
-        filters: list of integers, the nb_filters of 3 conv layer at main path
+        kernel_size: default 3, the kernel size of middle conv layer at main path
+        filters: list of integers, the filterss of 3 conv layer at main path
         stage: integer, current stage label, used for generating layer names
         block: 'a','b'..., current block label, used for generating layer names
+
+    # Returns
+        Output tensor for the block.
     """
-    nb_filter1, nb_filter2, nb_filter3 = filters
-    if K.image_dim_ordering() == 'tf':
+    filters1, filters2, filters3 = filters
+    if K.image_data_format() == 'channels_last':
         bn_axis = 3
     else:
         bn_axis = 1
     conv_name_base = 'res' + str(stage) + block + '_branch'
     bn_name_base = 'bn' + str(stage) + block + '_branch'
 
-    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
+    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
     x = Activation('relu')(x)
 
-    x = Convolution2D(nb_filter2, kernel_size, kernel_size,
-                      border_mode='same', name=conv_name_base + '2b')(x)
+    x = Conv2D(filters2, kernel_size,
+               padding='same', name=conv_name_base + '2b')(x)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
     x = Activation('relu')(x)
 
-    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
+    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
 
-    x = merge([x, input_tensor], mode='sum')
+    x = layers.add([x, input_tensor])
     x = Activation('relu')(x)
     return x
 
@@ -70,79 +81,99 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
 
     # Arguments
         input_tensor: input tensor
-        kernel_size: defualt 3, the kernel size of middle conv layer at main path
-        filters: list of integers, the nb_filters of 3 conv layer at main path
+        kernel_size: default 3, the kernel size of middle conv layer at main path
+        filters: list of integers, the filterss of 3 conv layer at main path
         stage: integer, current stage label, used for generating layer names
         block: 'a','b'..., current block label, used for generating layer names
 
-    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
-    And the shortcut should have subsample=(2,2) as well
+    # Returns
+        Output tensor for the block.
+
+    Note that from stage 3, the first conv layer at main path is with strides=(2,2)
+    And the shortcut should have strides=(2,2) as well
     """
-    nb_filter1, nb_filter2, nb_filter3 = filters
-    if K.image_dim_ordering() == 'tf':
+    filters1, filters2, filters3 = filters
+    if K.image_data_format() == 'channels_last':
         bn_axis = 3
     else:
         bn_axis = 1
     conv_name_base = 'res' + str(stage) + block + '_branch'
     bn_name_base = 'bn' + str(stage) + block + '_branch'
 
-    x = Convolution2D(nb_filter1, 1, 1, subsample=strides,
-                      name=conv_name_base + '2a')(input_tensor)
+    x = Conv2D(filters1, (1, 1), strides=strides,
+               name=conv_name_base + '2a')(input_tensor)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
     x = Activation('relu')(x)
 
-    x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',
-                      name=conv_name_base + '2b')(x)
+    x = Conv2D(filters2, kernel_size, padding='same',
+               name=conv_name_base + '2b')(x)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
     x = Activation('relu')(x)
 
-    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
+    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
 
-    shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides,
-                             name=conv_name_base + '1')(input_tensor)
+    shortcut = Conv2D(filters3, (1, 1), strides=strides,
+                      name=conv_name_base + '1')(input_tensor)
     shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
 
-    x = merge([x, shortcut], mode='sum')
+    x = layers.add([x, shortcut])
     x = Activation('relu')(x)
     return x
 
 
 def ResNet50(include_top=True, weights='imagenet',
              input_tensor=None, input_shape=None,
+             pooling=None,
              classes=1000):
-    """Instantiate the ResNet50 architecture,
-    optionally loading weights pre-trained
+    """Instantiates the ResNet50 architecture.
+
+    Optionally loads weights pre-trained
     on ImageNet. Note that when using TensorFlow,
     for best performance you should set
-    `image_dim_ordering="tf"` in your Keras config
+    `image_data_format="channels_last"` in your Keras config
     at ~/.keras/keras.json.
 
     The model and the weights are compatible with both
-    TensorFlow and Theano. The dimension ordering
+    TensorFlow and Theano. The data format
     convention used by the model is the one
     specified in your Keras config file.
 
     # Arguments
-        include_top: whether to include the 3 fully-connected
-            layers at the top of the network.
+        include_top: whether to include the fully-connected
+            layer at the top of the network.
         weights: one of `None` (random initialization)
             or "imagenet" (pre-training on ImageNet).
         input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
             to use as image input for the model.
         input_shape: optional shape tuple, only to be specified
             if `include_top` is False (otherwise the input shape
-            has to be `(224, 224, 3)` (with `tf` dim ordering)
-            or `(3, 224, 244)` (with `th` dim ordering).
+            has to be `(224, 224, 3)` (with `channels_last` data format)
+            or `(3, 224, 224)` (with `channels_first` data format).
             It should have exactly 3 inputs channels,
             and width and height should be no smaller than 197.
             E.g. `(200, 200, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional layer.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional layer, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
         classes: optional number of classes to classify images
             into, only to be specified if `include_top` is True, and
             if no `weights` argument is specified.
 
     # Returns
         A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
     """
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
@@ -157,7 +188,7 @@ def ResNet50(include_top=True, weights='imagenet',
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
                                       min_size=197,
-                                      dim_ordering=K.image_dim_ordering(),
+                                      data_format=K.image_data_format(),
                                       include_top=include_top)
 
     if input_tensor is None:
@@ -167,13 +198,13 @@ def ResNet50(include_top=True, weights='imagenet',
             img_input = Input(tensor=input_tensor, shape=input_shape)
         else:
             img_input = input_tensor
-    if K.image_dim_ordering() == 'tf':
+    if K.image_data_format() == 'channels_last':
         bn_axis = 3
     else:
         bn_axis = 1
 
     x = ZeroPadding2D((3, 3))(img_input)
-    x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)
+    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
     x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
     x = Activation('relu')(x)
     x = MaxPooling2D((3, 3), strides=(2, 2))(x)
@@ -203,6 +234,11 @@ def ResNet50(include_top=True, weights='imagenet',
     if include_top:
         x = Flatten()(x)
         x = Dense(classes, activation='softmax', name='fc1000')(x)
+    else:
+        if pooling == 'avg':
+            x = GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = GlobalMaxPooling2D()(x)
 
     # Ensure that the model takes into account
     # any potential predecessors of `input_tensor`.
@@ -215,40 +251,32 @@ def ResNet50(include_top=True, weights='imagenet',
 
     # load weights
     if weights == 'imagenet':
-        if K.image_dim_ordering() == 'th':
-            if include_top:
-                weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels.h5',
-                                        TH_WEIGHTS_PATH,
-                                        cache_subdir='models',
-                                        md5_hash='1c1f8f5b0c8ee28fe9d950625a230e1c')
-            else:
-                weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels_notop.h5',
-                                        TH_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models',
-                                        md5_hash='f64f049c92468c9affcd44b0976cdafe')
-            model.load_weights(weights_path)
-            if K.backend() == 'tensorflow':
-                warnings.warn('You are using the TensorFlow backend, yet you '
-                              'are using the Theano '
-                              'image dimension ordering convention '
-                              '(`image_dim_ordering="th"`). '
-                              'For best performance, set '
-                              '`image_dim_ordering="tf"` in '
-                              'your Keras config '
-                              'at ~/.keras/keras.json.')
-                convert_all_kernels_in_model(model)
+        if include_top:
+            weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH,
+                                    cache_subdir='models',
+                                    md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
         else:
+            weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                                    WEIGHTS_PATH_NO_TOP,
+                                    cache_subdir='models',
+                                    md5_hash='a268eb855778b3df3c7506639542a6af')
+        model.load_weights(weights_path)
+        if K.backend() == 'theano':
+            layer_utils.convert_all_kernels_in_model(model)
             if include_top:
-                weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
-                                        TF_WEIGHTS_PATH,
-                                        cache_subdir='models',
-                                        md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
-            else:
-                weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
-                                        TF_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models',
-                                        md5_hash='a268eb855778b3df3c7506639542a6af')
-            model.load_weights(weights_path)
-            if K.backend() == 'theano':
-                convert_all_kernels_in_model(model)
+                maxpool = model.get_layer(name='avg_pool')
+                shape = maxpool.output_shape[1:]
+                dense = model.get_layer(name='fc1000')
+                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
+
+        if K.image_data_format() == 'channels_first' and K.backend() == 'tensorflow':
+            warnings.warn('You are using the TensorFlow backend, yet you '
+                          'are using the Theano '
+                          'image data format convention '
+                          '(`image_data_format="channels_first"`). '
+                          'For best performance, set '
+                          '`image_data_format="channels_last"` in '
+                          'your Keras config '
+                          'at ~/.keras/keras.json.')
     return model

keras/applications/vgg16.py

@@ -12,33 +12,40 @@ from __future__ import absolute_import
 import warnings
 
 from ..models import Model
-from ..layers import Flatten, Dense, Input
-from ..layers import Convolution2D, MaxPooling2D
+from ..layers import Flatten
+from ..layers import Dense
+from ..layers import Input
+from ..layers import Conv2D
+from ..layers import MaxPooling2D
+from ..layers import GlobalAveragePooling2D
+from ..layers import GlobalMaxPooling2D
 from ..engine.topology import get_source_inputs
-from ..utils.layer_utils import convert_all_kernels_in_model
+from ..utils import layer_utils
 from ..utils.data_utils import get_file
 from .. import backend as K
-from .imagenet_utils import decode_predictions, preprocess_input, _obtain_input_shape
+from .imagenet_utils import decode_predictions
+from .imagenet_utils import preprocess_input
+from .imagenet_utils import _obtain_input_shape
 
 
-TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5'
-TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
-TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'
-TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
+WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
 
 
 def VGG16(include_top=True, weights='imagenet',
           input_tensor=None, input_shape=None,
+          pooling=None,
           classes=1000):
-    """Instantiate the VGG16 architecture,
-    optionally loading weights pre-trained
+    """Instantiates the VGG16 architecture.
+
+    Optionally loads weights pre-trained
     on ImageNet. Note that when using TensorFlow,
     for best performance you should set
-    `image_dim_ordering="tf"` in your Keras config
+    `image_data_format="channels_last"` in your Keras config
     at ~/.keras/keras.json.
 
     The model and the weights are compatible with both
-    TensorFlow and Theano. The dimension ordering
+    TensorFlow and Theano. The data format
     convention used by the model is the one
     specified in your Keras config file.
 
@@ -51,17 +58,32 @@ def VGG16(include_top=True, weights='imagenet',
             to use as image input for the model.
         input_shape: optional shape tuple, only to be specified
             if `include_top` is False (otherwise the input shape
-            has to be `(224, 224, 3)` (with `tf` dim ordering)
-            or `(3, 224, 244)` (with `th` dim ordering).
+            has to be `(224, 224, 3)` (with `channels_last` data format)
+            or `(3, 224, 224)` (with `channels_first` data format).
             It should have exactly 3 inputs channels,
             and width and height should be no smaller than 48.
             E.g. `(200, 200, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional layer.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional layer, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
         classes: optional number of classes to classify images
             into, only to be specified if `include_top` is True, and
             if no `weights` argument is specified.
 
     # Returns
         A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
     """
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
@@ -75,7 +97,7 @@ def VGG16(include_top=True, weights='imagenet',
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
                                       min_size=48,
-                                      dim_ordering=K.image_dim_ordering(),
+                                      data_format=K.image_data_format(),
                                       include_top=include_top)
 
     if input_tensor is None:
@@ -86,31 +108,31 @@ def VGG16(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
     # Block 1
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
 
     # Block 2
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
 
     # Block 3
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
 
     # Block 4
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
 
     # Block 5
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
 
     if include_top:
@@ -119,6 +141,11 @@ def VGG16(include_top=True, weights='imagenet',
         x = Dense(4096, activation='relu', name='fc1')(x)
         x = Dense(4096, activation='relu', name='fc2')(x)
         x = Dense(classes, activation='softmax', name='predictions')(x)
+    else:
+        if pooling == 'avg':
+            x = GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = GlobalMaxPooling2D()(x)
 
     # Ensure that the model takes into account
     # any potential predecessors of `input_tensor`.
@@ -131,36 +158,32 @@ def VGG16(include_top=True, weights='imagenet',
 
     # load weights
     if weights == 'imagenet':
-        if K.image_dim_ordering() == 'th':
+        if include_top:
+            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH,
+                                    cache_subdir='models')
+        else:
+            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                                    WEIGHTS_PATH_NO_TOP,
+                                    cache_subdir='models')
+        model.load_weights(weights_path)
+        if K.backend() == 'theano':
+            layer_utils.convert_all_kernels_in_model(model)
+
+        if K.image_data_format() == 'channels_first':
             if include_top:
-                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels.h5',
-                                        TH_WEIGHTS_PATH,
-                                        cache_subdir='models')
-            else:
-                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5',
-                                        TH_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models')
-            model.load_weights(weights_path)
+                maxpool = model.get_layer(name='block5_pool')
+                shape = maxpool.output_shape[1:]
+                dense = model.get_layer(name='fc1')
+                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
+
             if K.backend() == 'tensorflow':
                 warnings.warn('You are using the TensorFlow backend, yet you '
                               'are using the Theano '
-                              'image dimension ordering convention '
-                              '(`image_dim_ordering="th"`). '
+                              'image data format convention '
+                              '(`image_data_format="channels_first"`). '
                               'For best performance, set '
-                              '`image_dim_ordering="tf"` in '
+                              '`image_data_format="channels_last"` in '
                               'your Keras config '
                               'at ~/.keras/keras.json.')
-                convert_all_kernels_in_model(model)
-        else:
-            if include_top:
-                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
-                                        TF_WEIGHTS_PATH,
-                                        cache_subdir='models')
-            else:
-                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
-                                        TF_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models')
-            model.load_weights(weights_path)
-            if K.backend() == 'theano':
-                convert_all_kernels_in_model(model)
     return model

keras/applications/vgg19.py

@@ -12,33 +12,40 @@ from __future__ import absolute_import
 import warnings
 
 from ..models import Model
-from ..layers import Flatten, Dense, Input
-from ..layers import Convolution2D, MaxPooling2D
+from ..layers import Flatten
+from ..layers import Dense
+from ..layers import Input
+from ..layers import Conv2D
+from ..layers import MaxPooling2D
+from ..layers import GlobalAveragePooling2D
+from ..layers import GlobalMaxPooling2D
 from ..engine.topology import get_source_inputs
-from ..utils.layer_utils import convert_all_kernels_in_model
+from ..utils import layer_utils
 from ..utils.data_utils import get_file
 from .. import backend as K
-from .imagenet_utils import decode_predictions, preprocess_input, _obtain_input_shape
+from .imagenet_utils import decode_predictions
+from .imagenet_utils import preprocess_input
+from .imagenet_utils import _obtain_input_shape
 
 
-TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels.h5'
-TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
-TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels_notop.h5'
-TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'
+WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'
 
 
 def VGG19(include_top=True, weights='imagenet',
           input_tensor=None, input_shape=None,
+          pooling=None,
           classes=1000):
-    """Instantiate the VGG19 architecture,
-    optionally loading weights pre-trained
+    """Instantiates the VGG19 architecture.
+
+    Optionally loads weights pre-trained
     on ImageNet. Note that when using TensorFlow,
     for best performance you should set
-    `image_dim_ordering="tf"` in your Keras config
+    `image_data_format="channels_last"` in your Keras config
     at ~/.keras/keras.json.
 
     The model and the weights are compatible with both
-    TensorFlow and Theano. The dimension ordering
+    TensorFlow and Theano. The data format
     convention used by the model is the one
     specified in your Keras config file.
 
@@ -51,17 +58,32 @@ def VGG19(include_top=True, weights='imagenet',
             to use as image input for the model.
         input_shape: optional shape tuple, only to be specified
             if `include_top` is False (otherwise the input shape
-            has to be `(224, 224, 3)` (with `tf` dim ordering)
-            or `(3, 224, 244)` (with `th` dim ordering).
+            has to be `(224, 224, 3)` (with `channels_last` data format)
+            or `(3, 224, 224)` (with `channels_first` data format).
             It should have exactly 3 inputs channels,
             and width and height should be no smaller than 48.
             E.g. `(200, 200, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional layer.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional layer, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
         classes: optional number of classes to classify images
             into, only to be specified if `include_top` is True, and
             if no `weights` argument is specified.
 
     # Returns
         A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
     """
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
@@ -75,7 +97,7 @@ def VGG19(include_top=True, weights='imagenet',
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
                                       min_size=48,
-                                      dim_ordering=K.image_dim_ordering(),
+                                      data_format=K.image_data_format(),
                                       include_top=include_top)
 
     if input_tensor is None:
@@ -86,34 +108,34 @@ def VGG19(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
     # Block 1
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
 
     # Block 2
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
 
     # Block 3
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv4')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
 
     # Block 4
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv4')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
 
     # Block 5
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv4')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
 
     if include_top:
@@ -122,6 +144,11 @@ def VGG19(include_top=True, weights='imagenet',
         x = Dense(4096, activation='relu', name='fc1')(x)
         x = Dense(4096, activation='relu', name='fc2')(x)
         x = Dense(classes, activation='softmax', name='predictions')(x)
+    else:
+        if pooling == 'avg':
+            x = GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = GlobalMaxPooling2D()(x)
 
     # Ensure that the model takes into account
     # any potential predecessors of `input_tensor`.
@@ -134,36 +161,32 @@ def VGG19(include_top=True, weights='imagenet',
 
     # load weights
     if weights == 'imagenet':
-        if K.image_dim_ordering() == 'th':
+        if include_top:
+            weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH,
+                                    cache_subdir='models')
+        else:
+            weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                                    WEIGHTS_PATH_NO_TOP,
+                                    cache_subdir='models')
+        model.load_weights(weights_path)
+        if K.backend() == 'theano':
+            layer_utils.convert_all_kernels_in_model(model)
+
+        if K.image_data_format() == 'channels_first':
             if include_top:
-                weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels.h5',
-                                        TH_WEIGHTS_PATH,
-                                        cache_subdir='models')
-            else:
-                weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels_notop.h5',
-                                        TH_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models')
-            model.load_weights(weights_path)
+                maxpool = model.get_layer(name='block5_pool')
+                shape = maxpool.output_shape[1:]
+                dense = model.get_layer(name='fc1')
+                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
+
             if K.backend() == 'tensorflow':
                 warnings.warn('You are using the TensorFlow backend, yet you '
                               'are using the Theano '
-                              'image dimension ordering convention '
-                              '(`image_dim_ordering="th"`). '
+                              'image data format convention '
+                              '(`image_data_format="channels_first"`). '
                               'For best performance, set '
-                              '`image_dim_ordering="tf"` in '
+                              '`image_data_format="channels_last"` in '
                               'your Keras config '
                               'at ~/.keras/keras.json.')
-                convert_all_kernels_in_model(model)
-        else:
-            if include_top:
-                weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
-                                        TF_WEIGHTS_PATH,
-                                        cache_subdir='models')
-            else:
-                weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
-                                        TF_WEIGHTS_PATH_NO_TOP,
-                                        cache_subdir='models')
-            model.load_weights(weights_path)
-            if K.backend() == 'theano':
-                convert_all_kernels_in_model(model)
     return model

keras/applications/xception.py

@@ -23,12 +23,21 @@ from __future__ import absolute_import
 import warnings
 
 from ..models import Model
-from ..layers import Dense, Input, BatchNormalization, Activation, merge
-from ..layers import Conv2D, SeparableConv2D, MaxPooling2D, GlobalAveragePooling2D
+from .. import layers
+from ..layers import Dense
+from ..layers import Input
+from ..layers import BatchNormalization
+from ..layers import Activation
+from ..layers import Conv2D
+from ..layers import SeparableConv2D
+from ..layers import MaxPooling2D
+from ..layers import GlobalAveragePooling2D
+from ..layers import GlobalMaxPooling2D
 from ..engine.topology import get_source_inputs
 from ..utils.data_utils import get_file
 from .. import backend as K
-from .imagenet_utils import decode_predictions, _obtain_input_shape
+from .imagenet_utils import decode_predictions
+from .imagenet_utils import _obtain_input_shape
 
 
 TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5'
@@ -37,13 +46,15 @@ TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/relea
 
 def Xception(include_top=True, weights='imagenet',
              input_tensor=None, input_shape=None,
+             pooling=None,
              classes=1000):
-    """Instantiate the Xception architecture,
-    optionally loading weights pre-trained
+    """Instantiates the Xception architecture.
+
+    Optionally loads weights pre-trained
     on ImageNet. This model is available for TensorFlow only,
     and can only be used with inputs following the TensorFlow
-    dimension ordering `(width, height, channels)`.
-    You should set `image_dim_ordering="tf"` in your Keras config
+    data format `(width, height, channels)`.
+    You should set `image_data_format="channels_last"` in your Keras config
     located at ~/.keras/keras.json.
 
     Note that the default input image size for this model is 299x299.
@@ -61,12 +72,29 @@ def Xception(include_top=True, weights='imagenet',
             It should have exactly 3 inputs channels,
             and width and height should be no smaller than 71.
             E.g. `(150, 150, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional layer.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional layer, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
         classes: optional number of classes to classify images
             into, only to be specified if `include_top` is True, and
             if no `weights` argument is specified.
 
     # Returns
         A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
+        RuntimeError: If attempting to run this model with a
+            backend that does not support separable convolutions.
     """
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
@@ -80,26 +108,26 @@ def Xception(include_top=True, weights='imagenet',
     if K.backend() != 'tensorflow':
         raise RuntimeError('The Xception model is only available with '
                            'the TensorFlow backend.')
-    if K.image_dim_ordering() != 'tf':
+    if K.image_data_format() != 'channels_last':
         warnings.warn('The Xception model is only available for the '
-                      'input dimension ordering "tf" '
+                      'input data format "channels_last" '
                       '(width, height, channels). '
                       'However your settings specify the default '
-                      'dimension ordering "th" (channels, width, height). '
-                      'You should set `image_dim_ordering="tf"` in your Keras '
+                      'data format "channels_first" (channels, width, height). '
+                      'You should set `image_data_format="channels_last"` in your Keras '
                       'config located at ~/.keras/keras.json. '
                       'The model being returned right now will expect inputs '
-                      'to follow the "tf" dimension ordering.')
-        K.set_image_dim_ordering('tf')
-        old_dim_ordering = 'th'
+                      'to follow the "channels_last" data format.')
+        K.set_image_data_format('channels_last')
+        old_data_format = 'channels_first'
     else:
-        old_dim_ordering = None
+        old_data_format = None
 
     # Determine proper input shape
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=299,
                                       min_size=71,
-                                      dim_ordering=K.image_dim_ordering(),
+                                      data_format=K.image_data_format(),
                                       include_top=include_top)
 
     if input_tensor is None:
@@ -110,95 +138,100 @@ def Xception(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
 
-    x = Conv2D(32, 3, 3, subsample=(2, 2), bias=False, name='block1_conv1')(img_input)
+    x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
     x = BatchNormalization(name='block1_conv1_bn')(x)
     x = Activation('relu', name='block1_conv1_act')(x)
-    x = Conv2D(64, 3, 3, bias=False, name='block1_conv2')(x)
+    x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
     x = BatchNormalization(name='block1_conv2_bn')(x)
     x = Activation('relu', name='block1_conv2_act')(x)
 
-    residual = Conv2D(128, 1, 1, subsample=(2, 2),
-                      border_mode='same', bias=False)(x)
+    residual = Conv2D(128, (1, 1), strides=(2, 2),
+                      padding='same', use_bias=False)(x)
     residual = BatchNormalization()(residual)
 
-    x = SeparableConv2D(128, 3, 3, border_mode='same', bias=False, name='block2_sepconv1')(x)
+    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
     x = BatchNormalization(name='block2_sepconv1_bn')(x)
     x = Activation('relu', name='block2_sepconv2_act')(x)
-    x = SeparableConv2D(128, 3, 3, border_mode='same', bias=False, name='block2_sepconv2')(x)
+    x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
     x = BatchNormalization(name='block2_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), border_mode='same', name='block2_pool')(x)
-    x = merge([x, residual], mode='sum')
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
+    x = layers.add([x, residual])
 
-    residual = Conv2D(256, 1, 1, subsample=(2, 2),
-                      border_mode='same', bias=False)(x)
+    residual = Conv2D(256, (1, 1), strides=(2, 2),
+                      padding='same', use_bias=False)(x)
     residual = BatchNormalization()(residual)
 
     x = Activation('relu', name='block3_sepconv1_act')(x)
-    x = SeparableConv2D(256, 3, 3, border_mode='same', bias=False, name='block3_sepconv1')(x)
+    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
     x = BatchNormalization(name='block3_sepconv1_bn')(x)
     x = Activation('relu', name='block3_sepconv2_act')(x)
-    x = SeparableConv2D(256, 3, 3, border_mode='same', bias=False, name='block3_sepconv2')(x)
+    x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
     x = BatchNormalization(name='block3_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), border_mode='same', name='block3_pool')(x)
-    x = merge([x, residual], mode='sum')
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
+    x = layers.add([x, residual])
 
-    residual = Conv2D(728, 1, 1, subsample=(2, 2),
-                      border_mode='same', bias=False)(x)
+    residual = Conv2D(728, (1, 1), strides=(2, 2),
+                      padding='same', use_bias=False)(x)
     residual = BatchNormalization()(residual)
 
     x = Activation('relu', name='block4_sepconv1_act')(x)
-    x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name='block4_sepconv1')(x)
+    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
     x = BatchNormalization(name='block4_sepconv1_bn')(x)
     x = Activation('relu', name='block4_sepconv2_act')(x)
-    x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name='block4_sepconv2')(x)
+    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
     x = BatchNormalization(name='block4_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), border_mode='same', name='block4_pool')(x)
-    x = merge([x, residual], mode='sum')
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
+    x = layers.add([x, residual])
 
     for i in range(8):
         residual = x
         prefix = 'block' + str(i + 5)
 
         x = Activation('relu', name=prefix + '_sepconv1_act')(x)
-        x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name=prefix + '_sepconv1')(x)
+        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x)
         x = BatchNormalization(name=prefix + '_sepconv1_bn')(x)
         x = Activation('relu', name=prefix + '_sepconv2_act')(x)
-        x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name=prefix + '_sepconv2')(x)
+        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x)
         x = BatchNormalization(name=prefix + '_sepconv2_bn')(x)
         x = Activation('relu', name=prefix + '_sepconv3_act')(x)
-        x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name=prefix + '_sepconv3')(x)
+        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x)
         x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)
 
-        x = merge([x, residual], mode='sum')
+        x = layers.add([x, residual])
 
-    residual = Conv2D(1024, 1, 1, subsample=(2, 2),
-                      border_mode='same', bias=False)(x)
+    residual = Conv2D(1024, (1, 1), strides=(2, 2),
+                      padding='same', use_bias=False)(x)
     residual = BatchNormalization()(residual)
 
     x = Activation('relu', name='block13_sepconv1_act')(x)
-    x = SeparableConv2D(728, 3, 3, border_mode='same', bias=False, name='block13_sepconv1')(x)
+    x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
     x = BatchNormalization(name='block13_sepconv1_bn')(x)
     x = Activation('relu', name='block13_sepconv2_act')(x)
-    x = SeparableConv2D(1024, 3, 3, border_mode='same', bias=False, name='block13_sepconv2')(x)
+    x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
     x = BatchNormalization(name='block13_sepconv2_bn')(x)
 
-    x = MaxPooling2D((3, 3), strides=(2, 2), border_mode='same', name='block13_pool')(x)
-    x = merge([x, residual], mode='sum')
+    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
+    x = layers.add([x, residual])
 
-    x = SeparableConv2D(1536, 3, 3, border_mode='same', bias=False, name='block14_sepconv1')(x)
+    x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
     x = BatchNormalization(name='block14_sepconv1_bn')(x)
     x = Activation('relu', name='block14_sepconv1_act')(x)
 
-    x = SeparableConv2D(2048, 3, 3, border_mode='same', bias=False, name='block14_sepconv2')(x)
+    x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
     x = BatchNormalization(name='block14_sepconv2_bn')(x)
     x = Activation('relu', name='block14_sepconv2_act')(x)
 
     if include_top:
         x = GlobalAveragePooling2D(name='avg_pool')(x)
         x = Dense(classes, activation='softmax', name='predictions')(x)
+    else:
+        if pooling == 'avg':
+            x = GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = GlobalMaxPooling2D()(x)
 
     # Ensure that the model takes into account
     # any potential predecessors of `input_tensor`.
@@ -221,8 +254,8 @@ def Xception(include_top=True, weights='imagenet',
                                     cache_subdir='models')
         model.load_weights(weights_path)
 
-    if old_dim_ordering:
-        K.set_image_dim_ordering(old_dim_ordering)
+    if old_data_format:
+        K.set_image_data_format(old_data_format)
     return model
 
 

keras/backend/__init__.py

@@ -7,59 +7,75 @@ from .common import epsilon
 from .common import floatx
 from .common import set_epsilon
 from .common import set_floatx
-from .common import get_uid
 from .common import cast_to_floatx
-from .common import image_dim_ordering
-from .common import set_image_dim_ordering
-from .common import is_keras_tensor
-from .common import legacy_weight_ordering
-from .common import set_legacy_weight_ordering
+from .common import image_data_format
+from .common import set_image_data_format
 
+# Obtain Keras base dir path: either ~/.keras or /tmp.
 _keras_base_dir = os.path.expanduser('~')
 if not os.access(_keras_base_dir, os.W_OK):
     _keras_base_dir = '/tmp'
-
 _keras_dir = os.path.join(_keras_base_dir, '.keras')
-if not os.path.exists(_keras_dir):
-    os.makedirs(_keras_dir)
 
 # Default backend: TensorFlow.
 _BACKEND = 'tensorflow'
 
+# Attempt to read Keras config file.
 _config_path = os.path.expanduser(os.path.join(_keras_dir, 'keras.json'))
 if os.path.exists(_config_path):
-    _config = json.load(open(_config_path))
+    try:
+        _config = json.load(open(_config_path))
+    except ValueError:
+        _config = {}
     _floatx = _config.get('floatx', floatx())
     assert _floatx in {'float16', 'float32', 'float64'}
     _epsilon = _config.get('epsilon', epsilon())
     assert isinstance(_epsilon, float)
     _backend = _config.get('backend', _BACKEND)
-    assert _backend in {'theano', 'tensorflow'}
-    _image_dim_ordering = _config.get('image_dim_ordering',
-                                      image_dim_ordering())
-    assert _image_dim_ordering in {'tf', 'th'}
+    assert _backend in {'theano', 'tensorflow', 'cntk'}
+    _image_data_format = _config.get('image_data_format',
+                                     image_data_format())
+    assert _image_data_format in {'channels_last', 'channels_first'}
 
     set_floatx(_floatx)
     set_epsilon(_epsilon)
-    set_image_dim_ordering(_image_dim_ordering)
+    set_image_data_format(_image_data_format)
     _BACKEND = _backend
 
-# save config file
-if not os.path.exists(_config_path):
-    _config = {'floatx': floatx(),
-               'epsilon': epsilon(),
-               'backend': _BACKEND,
-               'image_dim_ordering': image_dim_ordering()}
-    with open(_config_path, 'w') as f:
-        f.write(json.dumps(_config, indent=4))
+# Save config file, if possible.
+if not os.path.exists(_keras_dir):
+    try:
+        os.makedirs(_keras_dir)
+    except OSError:
+        # Except permission denied and potential race conditions
+        # in multi-threaded environments.
+        pass
 
+if not os.path.exists(_config_path):
+    _config = {
+        'floatx': floatx(),
+        'epsilon': epsilon(),
+        'backend': _BACKEND,
+        'image_data_format': image_data_format()
+    }
+    try:
+        with open(_config_path, 'w') as f:
+            f.write(json.dumps(_config, indent=4))
+    except IOError:
+        # Except permission denied.
+        pass
+
+# Set backend based on KERAS_BACKEND flag, if applicable.
 if 'KERAS_BACKEND' in os.environ:
     _backend = os.environ['KERAS_BACKEND']
-    assert _backend in {'theano', 'tensorflow'}
+    assert _backend in {'theano', 'tensorflow', 'cntk'}
     _BACKEND = _backend
 
-# import backend
-if _BACKEND == 'theano':
+# Import backend functions.
+if _BACKEND == 'cntk':
+    sys.stderr.write('Using CNTK backend\n')
+    from .cntk_backend import *
+elif _BACKEND == 'theano':
     sys.stderr.write('Using Theano backend.\n')
     from .theano_backend import *
 elif _BACKEND == 'tensorflow':
@@ -72,5 +88,14 @@ else:
 def backend():
     """Publicly accessible method
     for determining the current backend.
+
+    # Returns
+        String, the name of the backend Keras is currently using.
+
+    # Example
+    ```python
+        >>> keras.backend.backend()
+        'tensorflow'
+    ```
     """
     return _BACKEND

keras/backend/common.py

@@ -1,13 +1,9 @@
 import numpy as np
 
-from collections import defaultdict
-
 # the type of float to use throughout the session.
 _FLOATX = 'float32'
 _EPSILON = 10e-8
-_UID_PREFIXES = defaultdict(int)
-_IMAGE_DIM_ORDERING = 'tf'
-_LEGACY_WEIGHT_ORDERING = False
+_IMAGE_DATA_FORMAT = 'channels_last'
 
 
 def epsilon():
@@ -48,7 +44,7 @@ def set_epsilon(e):
 
 
 def floatx():
-    """Returns the default float type, as a string
+    """Returns the default float type, as a string.
     (e.g. 'float16', 'float32', 'float64').
 
     # Returns
@@ -112,106 +108,81 @@ def cast_to_floatx(x):
     return np.asarray(x, dtype=_FLOATX)
 
 
-def image_dim_ordering():
-    """Returns the default image dimension ordering
-    convention ('th' or 'tf').
+def image_data_format():
+    """Returns the default image data format convention ('channels_first' or 'channels_last').
 
     # Returns
-        A string, either `'th'` or `'tf'`
+        A string, either `'channels_first'` or `'channels_last'`
 
     # Example
     ```python
-        >>> keras.backend.image_dim_ordering()
-        'th'
+        >>> keras.backend.image_data_format()
+        'channels_first'
     ```
     """
-    return _IMAGE_DIM_ORDERING
+    return _IMAGE_DATA_FORMAT
 
 
+def set_image_data_format(data_format):
+    """Sets the value of the data format convention.
+
+    # Arguments
+        data_format: string. `'channels_first'` or `'channels_last'`.
+
+    # Example
+    ```python
+        >>> from keras import backend as K
+        >>> K.image_data_format()
+        'channels_first'
+        >>> K.set_image_data_format('channels_last')
+        >>> K.image_data_format()
+        'channels_last'
+    ```
+    """
+    global _IMAGE_DATA_FORMAT
+    if data_format not in {'channels_last', 'channels_first'}:
+        raise ValueError('Unknown data_format:', data_format)
+    _IMAGE_DATA_FORMAT = str(data_format)
+
+
+# Legacy methods
+
 def set_image_dim_ordering(dim_ordering):
-    """Sets the value of the image dimension
-    ordering convention ('th' or 'tf').
+    """Legacy setter for `image_data_format`.
 
     # Arguments
-        dim_ordering: string. `'th'` or `'tf'`.
+        dim_ordering: string. `tf` or `th`.
 
     # Example
     ```python
         >>> from keras import backend as K
-        >>> K.image_dim_ordering()
-        'th'
-        >>> K.set_image_dim_ordering('tf')
-        >>> K.image_dim_ordering()
-        'tf'
+        >>> K.image_data_format()
+        'channels_first'
+        >>> K.set_image_data_format('channels_last')
+        >>> K.image_data_format()
+        'channels_last'
     ```
+
+    # Raises
+        ValueError: if `dim_ordering` is invalid.
     """
-    global _IMAGE_DIM_ORDERING
+    global _IMAGE_DATA_FORMAT
     if dim_ordering not in {'tf', 'th'}:
         raise ValueError('Unknown dim_ordering:', dim_ordering)
-    _IMAGE_DIM_ORDERING = str(dim_ordering)
-
-
-def get_uid(prefix=''):
-    """Provides a unique UID given a string prefix.
-
-    # Arguments
-        prefix: string.
-
-    # Returns
-        An integer.
-
-    # Example
-    ```
-        >>> keras.backend.get_uid('dense')
-        >>> 1
-        >>> keras.backend.get_uid('dense')
-        >>> 2
-    ```
-
-    """
-    _UID_PREFIXES[prefix] += 1
-    return _UID_PREFIXES[prefix]
-
-
-def reset_uids():
-    global _UID_PREFIXES
-    _UID_PREFIXES = defaultdict(int)
-
-
-def is_keras_tensor(x):
-    """Returns whether `x` is a Keras tensor.
-
-    # Arguments
-        x: a potential tensor.
-
-    # Returns
-        A boolean: whether the argument is a Keras tensor.
-
-    # Examples
-    ```python
-        >>> from keras import backend as K
-        >>> np_var = numpy.array([1, 2])
-        >>> K.is_keras_tensor(np_var)
-        False
-        >>> keras_var = K.variable(np_var)
-        >>> K.is_keras_tensor(keras_var)  # A variable is not a Tensor.
-        False
-        >>> keras_placeholder = K.placeholder(shape=(2, 4, 5))
-        >>> K.is_keras_tensor(keras_placeholder)  # A placeholder is a Tensor.
-        True
-    ```
-    """
-    if hasattr(x, '_keras_shape'):
-        return True
+    if dim_ordering == 'th':
+        data_format = 'channels_first'
     else:
-        return False
+        data_format = 'channels_last'
+    _IMAGE_DATA_FORMAT = data_format
 
 
-def set_legacy_weight_ordering(value):
-    global _LEGACY_WEIGHT_ORDERING
-    assert value in {True, False}
-    _LEGACY_WEIGHT_ORDERING = value
+def image_dim_ordering():
+    """Legacy getter for `image_data_format`.
 
-
-def legacy_weight_ordering():
-    return _LEGACY_WEIGHT_ORDERING
+    # Returns
+        string, one of `'th'`, `'tf'`
+    """
+    if _IMAGE_DATA_FORMAT == 'channels_first':
+        return 'th'
+    else:
+        return 'tf'

keras/callbacks.py

@@ -3,6 +3,7 @@ from __future__ import print_function
 
 import os
 import csv
+import six
 
 import numpy as np
 import time
@@ -13,8 +14,7 @@ from collections import deque
 from collections import OrderedDict
 from collections import Iterable
 from .utils.generic_utils import Progbar
-from keras import backend as K
-from pkg_resources import parse_version
+from . import backend as K
 
 try:
     import requests
@@ -23,6 +23,7 @@ except ImportError:
 
 if K.backend() == 'tensorflow':
     import tensorflow as tf
+    from tensorflow.contrib.tensorboard.plugins import projector
 
 
 class CallbackList(object):
@@ -138,6 +139,9 @@ class CallbackList(object):
         for callback in self.callbacks:
             callback.on_train_end(logs)
 
+    def __iter__(self):
+        return iter(self.callbacks)
+
 
 class Callback(object):
     """Abstract base class used to build new callbacks.
@@ -167,7 +171,7 @@ class Callback(object):
     """
 
     def __init__(self):
-        pass
+        self.validation_data = None
 
     def set_params(self, params):
         self.params = params
@@ -223,29 +227,69 @@ class BaseLogger(Callback):
                     logs[k] = self.totals[k] / self.seen
 
 
+class TerminateOnNaN(Callback):
+    """Callback that terminates training when a NaN loss is encountered."""
+
+    def __init__(self):
+        super(TerminateOnNaN, self).__init__()
+
+    def on_batch_end(self, batch, logs=None):
+        logs = logs or {}
+        loss = logs.get('loss')
+        if loss is not None:
+            if np.isnan(loss) or np.isinf(loss):
+                print('Batch %d: Invalid loss, terminating training' % (batch))
+                self.model.stop_training = True
+
+
 class ProgbarLogger(Callback):
     """Callback that prints metrics to stdout.
+
+    # Arguments
+        count_mode: One of "steps" or "samples".
+            Whether the progress bar should
+            count samples seens or steps (batches) seen.
+
+    # Raises
+        ValueError: In case of invalid `count_mode`.
     """
 
+    def __init__(self, count_mode='samples'):
+        super(ProgbarLogger, self).__init__()
+        if count_mode == 'samples':
+            self.use_steps = False
+        elif count_mode == 'steps':
+            self.use_steps = True
+        else:
+            raise ValueError('Unknown `count_mode`: ' + str(count_mode))
+
     def on_train_begin(self, logs=None):
         self.verbose = self.params['verbose']
-        self.nb_epoch = self.params['nb_epoch']
+        self.epochs = self.params['epochs']
 
     def on_epoch_begin(self, epoch, logs=None):
         if self.verbose:
-            print('Epoch %d/%d' % (epoch + 1, self.nb_epoch))
-            self.progbar = Progbar(target=self.params['nb_sample'],
+            print('Epoch %d/%d' % (epoch + 1, self.epochs))
+            if self.use_steps:
+                target = self.params['steps']
+            else:
+                target = self.params['samples']
+            self.target = target
+            self.progbar = Progbar(target=self.target,
                                    verbose=self.verbose)
         self.seen = 0
 
     def on_batch_begin(self, batch, logs=None):
-        if self.seen < self.params['nb_sample']:
+        if self.seen < self.target:
             self.log_values = []
 
     def on_batch_end(self, batch, logs=None):
         logs = logs or {}
         batch_size = logs.get('size', 0)
-        self.seen += batch_size
+        if self.use_steps:
+            self.seen += 1
+        else:
+            self.seen += batch_size
 
         for k in self.params['metrics']:
             if k in logs:
@@ -253,7 +297,7 @@ class ProgbarLogger(Callback):
 
         # Skip progbar update for the last batch;
         # will be handled by on_epoch_end.
-        if self.verbose and self.seen < self.params['nb_sample']:
+        if self.verbose and self.seen < self.target:
             self.progbar.update(self.seen, self.log_values)
 
     def on_epoch_end(self, epoch, logs=None):
@@ -438,7 +482,9 @@ class EarlyStopping(Callback):
             self.min_delta *= -1
 
     def on_train_begin(self, logs=None):
-        self.wait = 0  # Allow instances to be re-used
+        # Allow instances to be re-used
+        self.wait = 0
+        self.stopped_epoch = 0
         self.best = np.Inf if self.monitor_op == np.less else -np.Inf
 
     def on_epoch_end(self, epoch, logs=None):
@@ -475,8 +521,7 @@ class RemoteMonitor(Callback):
         field: String; JSON field under which the data will be stored.
         headers: Dictionary; optional custom HTTP headers.
             Defaults to:
-            `{'Accept': 'application/json',
-              'Content-Type': 'application/json'}`
+            `{'Accept': 'application/json', 'Content-Type': 'application/json'}`
     """
 
     def __init__(self,
@@ -550,23 +595,44 @@ class TensorBoard(Callback):
     tensorboard --logdir=/full_path_to_your_logs
     ```
     You can find more information about TensorBoard
-    [here](https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html).
+    [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
 
     # Arguments
         log_dir: the path of the directory where to save the log
-            files to be parsed by Tensorboard
+            files to be parsed by TensorBoard.
         histogram_freq: frequency (in epochs) at which to compute activation
-            histograms for the layers of the model. If set to 0,
-            histograms won't be computed.
-        write_graph: whether to visualize the graph in Tensorboard.
+            and weight histograms for the layers of the model. If set to 0,
+            histograms won't be computed. Validation data (or split) must be
+            specified for histogram visualizations.
+        write_graph: whether to visualize the graph in TensorBoard.
             The log file can become quite large when
             write_graph is set to True.
+        write_grads: whether to visualize gradient histograms in TensorBoard.
+            `histogram_freq` must be greater than 0.
+        batch_size: size of batch of inputs to feed to the network
+            for histograms computation.
+        write_images: whether to write model weights to visualize as
+            image in TensorBoard.
+        embeddings_freq: frequency (in epochs) at which selected embedding
+            layers will be saved.
+        embeddings_layer_names: a list of names of layers to keep eye on. If
+            None or empty list all the embedding layer will be watched.
+        embeddings_metadata: a dictionary which maps layer name to a file name
+            in which metadata for this embedding layer is saved. See the
+            [details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
+            about metadata files format. In case if the same metadata file is
+            used for all embedding layers, string can be passed.
     """
 
     def __init__(self, log_dir='./logs',
                  histogram_freq=0,
+                 batch_size=32,
                  write_graph=True,
-                 write_images=False):
+                 write_grads=False,
+                 write_images=False,
+                 embeddings_freq=0,
+                 embeddings_layer_names=None,
+                 embeddings_metadata=None):
         super(TensorBoard, self).__init__()
         if K.backend() != 'tensorflow':
             raise RuntimeError('TensorBoard callback only works '
@@ -575,7 +641,12 @@ class TensorBoard(Callback):
         self.histogram_freq = histogram_freq
         self.merged = None
         self.write_graph = write_graph
+        self.write_grads = write_grads
         self.write_images = write_images
+        self.embeddings_freq = embeddings_freq
+        self.embeddings_layer_names = embeddings_layer_names
+        self.embeddings_metadata = embeddings_metadata or {}
+        self.batch_size = batch_size
 
     def set_model(self, model):
         self.model = model
@@ -584,75 +655,126 @@ class TensorBoard(Callback):
             for layer in self.model.layers:
 
                 for weight in layer.weights:
-                    if hasattr(tf, 'histogram_summary'):
-                        tf.histogram_summary(weight.name, weight)
-                    else:
-                        tf.summary.histogram(weight.name, weight)
-
+                    tf.summary.histogram(weight.name, weight)
+                    if self.write_grads:
+                        grads = model.optimizer.get_gradients(model.total_loss,
+                                                              weight)
+                        tf.summary.histogram('{}_grad'.format(weight.name), grads)
                     if self.write_images:
                         w_img = tf.squeeze(weight)
-
-                        shape = w_img.get_shape()
-                        if len(shape) > 1 and shape[0] > shape[1]:
-                            w_img = tf.transpose(w_img)
-
-                        if len(shape) == 1:
-                            w_img = tf.expand_dims(w_img, 0)
-
-                        w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
-
-                        if hasattr(tf, 'image_summary'):
-                            tf.image_summary(weight.name, w_img)
+                        shape = K.int_shape(w_img)
+                        if len(shape) == 2:  # dense layer kernel case
+                            if shape[0] > shape[1]:
+                                w_img = tf.transpose(w_img)
+                                shape = K.int_shape(w_img)
+                            w_img = tf.reshape(w_img, [1,
+                                                       shape[0],
+                                                       shape[1],
+                                                       1])
+                        elif len(shape) == 3:  # convnet case
+                            if K.image_data_format() == 'channels_last':
+                                # switch to channels_first to display
+                                # every kernel as a separate image
+                                w_img = tf.transpose(w_img, perm=[2, 0, 1])
+                                shape = K.int_shape(w_img)
+                            w_img = tf.reshape(w_img, [shape[0],
+                                                       shape[1],
+                                                       shape[2],
+                                                       1])
+                        elif len(shape) == 1:  # bias case
+                            w_img = tf.reshape(w_img, [1,
+                                                       shape[0],
+                                                       1,
+                                                       1])
                         else:
-                            tf.summary.image(weight.name, w_img)
+                            # not possible to handle 3D convnets etc.
+                            continue
+
+                        shape = K.int_shape(w_img)
+                        assert len(shape) == 4 and shape[-1] in [1, 3, 4]
+                        tf.summary.image(weight.name, w_img)
 
                 if hasattr(layer, 'output'):
-                    if hasattr(tf, 'histogram_summary'):
-                        tf.histogram_summary('{}_out'.format(layer.name),
-                                             layer.output)
-                    else:
-                        tf.summary.histogram('{}_out'.format(layer.name),
-                                             layer.output)
-
-        if hasattr(tf, 'merge_all_summaries'):
-            self.merged = tf.merge_all_summaries()
-        else:
-            self.merged = tf.summary.merge_all()
+                    tf.summary.histogram('{}_out'.format(layer.name),
+                                         layer.output)
+        self.merged = tf.summary.merge_all()
 
         if self.write_graph:
-            if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
-                self.writer = tf.summary.FileWriter(self.log_dir,
-                                                    self.sess.graph)
-            elif parse_version(tf.__version__) >= parse_version('0.8.0'):
-                self.writer = tf.train.SummaryWriter(self.log_dir,
-                                                     self.sess.graph)
-            else:
-                self.writer = tf.train.SummaryWriter(self.log_dir,
-                                                     self.sess.graph_def)
+            self.writer = tf.summary.FileWriter(self.log_dir,
+                                                self.sess.graph)
         else:
-            if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
-                self.writer = tf.summary.FileWriter(self.log_dir)
+            self.writer = tf.summary.FileWriter(self.log_dir)
+
+        if self.embeddings_freq:
+            embeddings_layer_names = self.embeddings_layer_names
+
+            if not embeddings_layer_names:
+                embeddings_layer_names = [layer.name for layer in self.model.layers
+                                          if type(layer).__name__ == 'Embedding']
+
+            embeddings = {layer.name: layer.weights[0]
+                          for layer in self.model.layers
+                          if layer.name in embeddings_layer_names}
+
+            self.saver = tf.train.Saver(list(embeddings.values()))
+
+            embeddings_metadata = {}
+
+            if not isinstance(self.embeddings_metadata, str):
+                embeddings_metadata = self.embeddings_metadata
             else:
-                self.writer = tf.train.SummaryWriter(self.log_dir)
+                embeddings_metadata = {layer_name: self.embeddings_metadata
+                                       for layer_name in embeddings.keys()}
+
+            config = projector.ProjectorConfig()
+            self.embeddings_ckpt_path = os.path.join(self.log_dir,
+                                                     'keras_embedding.ckpt')
+
+            for layer_name, tensor in embeddings.items():
+                embedding = config.embeddings.add()
+                embedding.tensor_name = tensor.name
+
+                if layer_name in embeddings_metadata:
+                    embedding.metadata_path = embeddings_metadata[layer_name]
+
+            projector.visualize_embeddings(self.writer, config)
 
     def on_epoch_end(self, epoch, logs=None):
         logs = logs or {}
 
-        if self.model.validation_data and self.histogram_freq:
+        if self.validation_data and self.histogram_freq:
             if epoch % self.histogram_freq == 0:
-                # TODO: implement batched calls to sess.run
-                # (current call will likely go OOM on GPU)
+
+                val_data = self.validation_data
+                tensors = (self.model.inputs +
+                           self.model.targets +
+                           self.model.sample_weights)
+
                 if self.model.uses_learning_phase:
-                    cut_v_data = len(self.model.inputs)
-                    val_data = self.model.validation_data[:cut_v_data] + [0]
-                    tensors = self.model.inputs + [K.learning_phase()]
-                else:
-                    val_data = self.model.validation_data
-                    tensors = self.model.inputs
-                feed_dict = dict(zip(tensors, val_data))
-                result = self.sess.run([self.merged], feed_dict=feed_dict)
-                summary_str = result[0]
-                self.writer.add_summary(summary_str, epoch)
+                    tensors += [K.learning_phase()]
+
+                assert len(val_data) == len(tensors)
+                val_size = val_data[0].shape[0]
+                i = 0
+                while i < val_size:
+                    step = min(self.batch_size, val_size - i)
+                    batch_val = []
+                    batch_val.append(val_data[0][i:i + step])
+                    batch_val.append(val_data[1][i:i + step])
+                    batch_val.append(val_data[2][i:i + step])
+                    if self.model.uses_learning_phase:
+                        batch_val.append(val_data[3])
+                    feed_dict = dict(zip(tensors, batch_val))
+                    result = self.sess.run([self.merged], feed_dict=feed_dict)
+                    summary_str = result[0]
+                    self.writer.add_summary(summary_str, epoch)
+                    i += self.batch_size
+
+        if self.embeddings_freq and self.embeddings_ckpt_path:
+            if epoch % self.embeddings_freq == 0:
+                self.saver.save(self.sess,
+                                self.embeddings_ckpt_path,
+                                epoch)
 
         for name, value in logs.items():
             if name in ['batch', 'size']:
@@ -678,9 +800,9 @@ class ReduceLROnPlateau(Callback):
 
     # Example
         ```python
-            reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
-                                          patience=5, min_lr=0.001)
-            model.fit(X_train, Y_train, callbacks=[reduce_lr])
+        reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
+                                      patience=5, min_lr=0.001)
+        model.fit(X_train, Y_train, callbacks=[reduce_lr])
         ```
 
     # Arguments
@@ -787,8 +909,8 @@ class CSVLogger(Callback):
 
     # Example
         ```python
-            csv_logger = CSVLogger('training.log')
-            model.fit(X_train, Y_train, callbacks=[csv_logger])
+        csv_logger = CSVLogger('training.log')
+        model.fit(X_train, Y_train, callbacks=[csv_logger])
         ```
 
     # Arguments
@@ -805,23 +927,26 @@ class CSVLogger(Callback):
         self.writer = None
         self.keys = None
         self.append_header = True
+        self.file_flags = 'b' if six.PY2 and os.name == 'nt' else ''
         super(CSVLogger, self).__init__()
 
     def on_train_begin(self, logs=None):
         if self.append:
             if os.path.exists(self.filename):
-                with open(self.filename) as f:
+                with open(self.filename, 'r' + self.file_flags) as f:
                     self.append_header = not bool(len(f.readline()))
-            self.csv_file = open(self.filename, 'a')
+            self.csv_file = open(self.filename, 'a' + self.file_flags)
         else:
-            self.csv_file = open(self.filename, 'w')
+            self.csv_file = open(self.filename, 'w' + self.file_flags)
 
     def on_epoch_end(self, epoch, logs=None):
         logs = logs or {}
 
         def handle_value(k):
             is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
-            if isinstance(k, Iterable) and not is_zero_dim_ndarray:
+            if isinstance(k, six.string_types):
+                return k
+            elif isinstance(k, Iterable) and not is_zero_dim_ndarray:
                 return '"[%s]"' % (', '.join(map(str, k)))
             else:
                 return k
@@ -853,6 +978,7 @@ class LambdaCallback(Callback):
     This callback is constructed with anonymous functions that will be called
     at the appropriate time. Note that the callbacks expects positional
     arguments, as:
+
      - `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
         `epoch`, `logs`
      - `on_batch_begin` and `on_batch_end` expect two positional arguments:

keras/constraints.py

@@ -1,15 +1,17 @@
 from __future__ import absolute_import
+import six
 from . import backend as K
-from .utils.generic_utils import get_from_module
+from .utils.generic_utils import serialize_keras_object
+from .utils.generic_utils import deserialize_keras_object
 
 
 class Constraint(object):
 
-    def __call__(self, p):
-        return p
+    def __call__(self, w):
+        return w
 
     def get_config(self):
-        return {'name': self.__class__.__name__}
+        return {}
 
 
 class MaxNorm(Constraint):
@@ -25,7 +27,7 @@ class MaxNorm(Constraint):
             has shape `(input_dim, output_dim)`,
             set `axis` to `0` to constrain each weight vector
             of length `(input_dim,)`.
-            In a `Convolution2D` layer with `dim_ordering="tf"`,
+            In a `Convolution2D` layer with `data_format="channels_last"`,
             the weight tensor has shape
             `(rows, cols, input_depth, output_depth)`,
             set `axis` to `[0, 1, 2]`
@@ -36,19 +38,18 @@ class MaxNorm(Constraint):
         - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
     """
 
-    def __init__(self, m=2, axis=0):
-        self.m = m
+    def __init__(self, max_value=2, axis=0):
+        self.max_value = max_value
         self.axis = axis
 
-    def __call__(self, p):
-        norms = K.sqrt(K.sum(K.square(p), axis=self.axis, keepdims=True))
-        desired = K.clip(norms, 0, self.m)
-        p *= (desired / (K.epsilon() + norms))
-        return p
+    def __call__(self, w):
+        norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
+        desired = K.clip(norms, 0, self.max_value)
+        w *= (desired / (K.epsilon() + norms))
+        return w
 
     def get_config(self):
-        return {'name': self.__class__.__name__,
-                'm': self.m,
+        return {'max_value': self.max_value,
                 'axis': self.axis}
 
 
@@ -56,9 +57,9 @@ class NonNeg(Constraint):
     """Constrains the weights to be non-negative.
     """
 
-    def __call__(self, p):
-        p *= K.cast(p >= 0., K.floatx())
-        return p
+    def __call__(self, w):
+        w *= K.cast(K.greater_equal(w, 0.), K.floatx())
+        return w
 
 
 class UnitNorm(Constraint):
@@ -70,7 +71,7 @@ class UnitNorm(Constraint):
             has shape `(input_dim, output_dim)`,
             set `axis` to `0` to constrain each weight vector
             of length `(input_dim,)`.
-            In a `Convolution2D` layer with `dim_ordering="tf"`,
+            In a `Convolution2D` layer with `data_format="channels_last"`,
             the weight tensor has shape
             `(rows, cols, input_depth, output_depth)`,
             set `axis` to `[0, 1, 2]`
@@ -81,14 +82,13 @@ class UnitNorm(Constraint):
     def __init__(self, axis=0):
         self.axis = axis
 
-    def __call__(self, p):
-        return p / (K.epsilon() + K.sqrt(K.sum(K.square(p),
+    def __call__(self, w):
+        return w / (K.epsilon() + K.sqrt(K.sum(K.square(w),
                                                axis=self.axis,
                                                keepdims=True)))
 
     def get_config(self):
-        return {'name': self.__class__.__name__,
-                'axis': self.axis}
+        return {'axis': self.axis}
 
 
 class MinMaxNorm(Constraint):
@@ -98,10 +98,11 @@ class MinMaxNorm(Constraint):
     to have the norm between a lower bound and an upper bound.
 
     # Arguments
-        low: the minimum norm for the incoming weights.
-        high: the maximum norm for the incoming weights.
+        min_value: the minimum norm for the incoming weights.
+        max_value: the maximum norm for the incoming weights.
         rate: rate for enforcing the constraint: weights will be
-            rescaled to yield (1 - rate) * norm + rate * norm.clip(low, high).
+            rescaled to yield
+            `(1 - rate) * norm + rate * norm.clip(min_value, max_value)`.
             Effectively, this means that rate=1.0 stands for strict
             enforcement of the constraint, while rate<1.0 means that
             weights will be rescaled at each step to slowly move
@@ -118,33 +119,62 @@ class MinMaxNorm(Constraint):
             to constrain the weights of each filter tensor of size
             `(rows, cols, input_depth)`.
     """
-    def __init__(self, low=0.0, high=1.0, rate=1.0, axis=0):
-        self.low = low
-        self.high = high
+
+    def __init__(self, min_value=0.0, max_value=1.0, rate=1.0, axis=0):
+        self.min_value = min_value
+        self.max_value = max_value
         self.rate = rate
         self.axis = axis
 
-    def __call__(self, p):
-        norms = K.sqrt(K.sum(K.square(p), axis=self.axis, keepdims=True))
-        desired = self.rate * K.clip(norms, self.low, self.high) + (1 - self.rate) * norms
-        p *= (desired / (K.epsilon() + norms))
-        return p
+    def __call__(self, w):
+        norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
+        desired = (self.rate * K.clip(norms, self.min_value, self.max_value) +
+                   (1 - self.rate) * norms)
+        w *= (desired / (K.epsilon() + norms))
+        return w
 
     def get_config(self):
-        return {'name': self.__class__.__name__,
-                'low': self.low,
-                'high': self.high,
+        return {'min_value': self.min_value,
+                'max_value': self.max_value,
                 'rate': self.rate,
                 'axis': self.axis}
 
 
 # Aliases.
 
-maxnorm = MaxNorm
-nonneg = NonNeg
-unitnorm = UnitNorm
+max_norm = MaxNorm
+non_neg = NonNeg
+unit_norm = UnitNorm
+min_max_norm = MinMaxNorm
 
 
-def get(identifier, kwargs=None):
-    return get_from_module(identifier, globals(), 'constraint',
-                           instantiate=True, kwargs=kwargs)
+# Legacy aliases.
+maxnorm = max_norm
+nonneg = non_neg
+unitnorm = unit_norm
+
+
+def serialize(constraint):
+    return serialize_keras_object(constraint)
+
+
+def deserialize(config, custom_objects=None):
+    return deserialize_keras_object(config,
+                                    module_objects=globals(),
+                                    custom_objects=custom_objects,
+                                    printable_module_name='constraint')
+
+
+def get(identifier):
+    if identifier is None:
+        return None
+    if isinstance(identifier, dict):
+        return deserialize(identifier)
+    elif isinstance(identifier, six.string_types):
+        config = {'class_name': str(identifier), 'config': {}}
+        return deserialize(config)
+    elif callable(identifier):
+        return identifier
+    else:
+        raise ValueError('Could not interpret constraint identifier:',
+                         identifier)

keras/datasets/__init__.py

@@ -0,0 +1,8 @@
+from __future__ import absolute_import
+
+from . import mnist
+from . import imdb
+from . import reuters
+from . import cifar10
+from . import cifar100
+from . import boston_housing

keras/datasets/boston_housing.py

@@ -0,0 +1,36 @@
+from ..utils.data_utils import get_file
+import numpy as np
+
+
+def load_data(path='boston_housing.npz', seed=113, test_split=0.2):
+    """Loads the Boston Housing dataset.
+
+    # Arguments
+        path: path where to cache the dataset locally
+            (relative to ~/.keras/datasets).
+        seed: Random seed for shuffling the data
+            before computing the test split.
+        test_split: fraction of the data to reserve as test set.
+
+    # Returns
+        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
+    """
+    assert 0 <= test_split < 1
+    path = get_file(path,
+                    origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
+                    file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
+    f = np.load(path)
+    x = f['x']
+    y = f['y']
+    f.close()
+
+    np.random.seed(seed)
+    np.random.shuffle(x)
+    np.random.seed(seed)
+    np.random.shuffle(y)
+
+    x_train = np.array(x[:int(len(x) * (1 - test_split))])
+    y_train = np.array(y[:int(len(x) * (1 - test_split))])
+    x_test = np.array(x[int(len(x) * (1 - test_split)):])
+    y_test = np.array(y[int(len(x) * (1 - test_split)):])
+    return (x_train, y_train), (x_test, y_test)

keras/datasets/cifar10.py

@@ -16,10 +16,10 @@ def load_data():
     origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
     path = get_file(dirname, origin=origin, untar=True)
 
-    nb_train_samples = 50000
+    num_train_samples = 50000
 
-    x_train = np.zeros((nb_train_samples, 3, 32, 32), dtype='uint8')
-    y_train = np.zeros((nb_train_samples,), dtype='uint8')
+    x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
+    y_train = np.zeros((num_train_samples,), dtype='uint8')
 
     for i in range(1, 6):
         fpath = os.path.join(path, 'data_batch_' + str(i))
@@ -33,7 +33,7 @@ def load_data():
     y_train = np.reshape(y_train, (len(y_train), 1))
     y_test = np.reshape(y_test, (len(y_test), 1))
 
-    if K.image_dim_ordering() == 'tf':
+    if K.image_data_format() == 'channels_last':
         x_train = x_train.transpose(0, 2, 3, 1)
         x_test = x_test.transpose(0, 2, 3, 1)
 

keras/datasets/cifar100.py

@@ -34,7 +34,7 @@ def load_data(label_mode='fine'):
     y_train = np.reshape(y_train, (len(y_train), 1))
     y_test = np.reshape(y_test, (len(y_test), 1))
 
-    if K.image_dim_ordering() == 'tf':
+    if K.image_data_format() == 'channels_last':
         x_train = x_train.transpose(0, 2, 3, 1)
         x_test = x_test.transpose(0, 2, 3, 1)
 

keras/datasets/imdb.py

@@ -1,20 +1,19 @@
 from __future__ import absolute_import
-from six.moves import cPickle
-import gzip
 from ..utils.data_utils import get_file
 from six.moves import zip
 import numpy as np
-import sys
+import json
+import warnings
 
 
-def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
+def load_data(path='imdb.npz', num_words=None, skip_top=0,
               maxlen=None, seed=113,
-              start_char=1, oov_char=2, index_from=3):
+              start_char=1, oov_char=2, index_from=3, **kwargs):
     """Loads the IMDB dataset.
 
     # Arguments
         path: where to cache the data (relative to `~/.keras/dataset`).
-        nb_words: max number of words to include. Words are ranked
+        num_words: max number of words to include. Words are ranked
             by how often they occur (in the training set) and only
             the most frequent words are kept
         skip_top: skip the top N most frequently occuring words
@@ -23,7 +22,7 @@ def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
         seed: random seed for sample shuffling.
         start_char: The start of a sequence will be marked with this character.
             Set to 1 because 0 is usually the padding character.
-        oov_char: words that were cut out because of the `nb_words`
+        oov_char: words that were cut out because of the `num_words`
             or `skip_top` limit will be replaced with this character.
         index_from: index actual words with this index and higher.
 
@@ -36,20 +35,25 @@ def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
 
     Note that the 'out of vocabulary' character is only used for
     words that were present in the training set but are not included
-    because they're not making the `nb_words` cut here.
-    Words that were not seen in the trining set but are in the test set
+    because they're not making the `num_words` cut here.
+    Words that were not seen in the training set but are in the test set
     have simply been skipped.
     """
+    # Legacy support
+    if 'nb_words' in kwargs:
+        warnings.warn('The `nb_words` argument in `load_data` '
+                      'has been renamed `num_words`.')
+        num_words = kwargs.pop('nb_words')
+    if kwargs:
+        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
+
     path = get_file(path,
-                    origin='https://s3.amazonaws.com/text-datasets/imdb_full.pkl',
-                    md5_hash='d091312047c43cf9e4e38fef92437263')
-
-    if path.endswith('.gz'):
-        f = gzip.open(path, 'rb')
-    else:
-        f = open(path, 'rb')
-
-    (x_train, labels_train), (x_test, labels_test) = cPickle.load(f)
+                    origin='https://s3.amazonaws.com/text-datasets/imdb.npz')
+    f = np.load(path)
+    x_train = f['x_train']
+    labels_train = f['y_train']
+    x_test = f['x_test']
+    labels_test = f['y_test']
     f.close()
 
     np.random.seed(seed)
@@ -62,8 +66,8 @@ def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
     np.random.seed(seed * 2)
     np.random.shuffle(labels_test)
 
-    xs = x_train + x_test
-    labels = labels_train + labels_test
+    xs = np.concatenate([x_train, x_test])
+    labels = np.concatenate([labels_train, labels_test])
 
     if start_char is not None:
         xs = [[start_char] + [w + index_from for w in x] for x in xs]
@@ -79,24 +83,24 @@ def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
                 new_labels.append(y)
         xs = new_xs
         labels = new_labels
-    if not xs:
-        raise ValueError('After filtering for sequences shorter than maxlen=' +
-                         str(maxlen) + ', no sequence was kept. '
-                         'Increase maxlen.')
-    if not nb_words:
-        nb_words = max([max(x) for x in xs])
+        if not xs:
+            raise ValueError('After filtering for sequences shorter than maxlen=' +
+                             str(maxlen) + ', no sequence was kept. '
+                             'Increase maxlen.')
+    if not num_words:
+        num_words = max([max(x) for x in xs])
 
     # by convention, use 2 as OOV word
     # reserve 'index_from' (=3 by default) characters:
     # 0 (padding), 1 (start), 2 (OOV)
     if oov_char is not None:
-        xs = [[oov_char if (w >= nb_words or w < skip_top) else w for w in x] for x in xs]
+        xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x] for x in xs]
     else:
         new_xs = []
         for x in xs:
             nx = []
             for w in x:
-                if w >= nb_words or w < skip_top:
+                if skip_top <= w < num_words:
                     nx.append(w)
             new_xs.append(nx)
         xs = new_xs
@@ -110,7 +114,7 @@ def load_data(path='imdb_full.pkl', nb_words=None, skip_top=0,
     return (x_train, y_train), (x_test, y_test)
 
 
-def get_word_index(path='imdb_word_index.pkl'):
+def get_word_index(path='imdb_word_index.json'):
     """Retrieves the dictionary mapping word indices back to words.
 
     # Arguments
@@ -120,14 +124,8 @@ def get_word_index(path='imdb_word_index.pkl'):
         The word index dictionary.
     """
     path = get_file(path,
-                    origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.pkl',
-                    md5_hash='72d94b01291be4ff843198d3b0e1e4d7')
-    f = open(path, 'rb')
-
-    if sys.version_info < (3,):
-        data = cPickle.load(f)
-    else:
-        data = cPickle.load(f, encoding='latin1')
-
+                    origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json')
+    f = open(path)
+    data = json.load(f)
     f.close()
     return data

keras/datasets/mnist.py

@@ -1,10 +1,8 @@
-import gzip
 from ..utils.data_utils import get_file
-from six.moves import cPickle
-import sys
+import numpy as np
 
 
-def load_data(path='mnist.pkl.gz'):
+def load_data(path='mnist.npz'):
     """Loads the MNIST dataset.
 
     # Arguments
@@ -14,17 +12,11 @@ def load_data(path='mnist.pkl.gz'):
     # Returns
         Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
     """
-    path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.pkl.gz')
-
-    if path.endswith('.gz'):
-        f = gzip.open(path, 'rb')
-    else:
-        f = open(path, 'rb')
-
-    if sys.version_info < (3,):
-        data = cPickle.load(f)
-    else:
-        data = cPickle.load(f, encoding='bytes')
-
+    path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')
+    f = np.load(path)
+    x_train = f['x_train']
+    y_train = f['y_train']
+    x_test = f['x_test']
+    y_test = f['y_test']
     f.close()
-    return data  # (x_train, y_train), (x_test, y_test)
+    return (x_train, y_train), (x_test, y_test)

keras/datasets/reuters.py

@@ -1,20 +1,20 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import
 from ..utils.data_utils import get_file
-from six.moves import cPickle
 from six.moves import zip
 import numpy as np
-import sys
+import json
+import warnings
 
 
-def load_data(path='reuters.pkl', nb_words=None, skip_top=0,
+def load_data(path='reuters.npz', num_words=None, skip_top=0,
               maxlen=None, test_split=0.2, seed=113,
-              start_char=1, oov_char=2, index_from=3):
+              start_char=1, oov_char=2, index_from=3, **kwargs):
     """Loads the Reuters newswire classification dataset.
 
     # Arguments
         path: where to cache the data (relative to `~/.keras/dataset`).
-        nb_words: max number of words to include. Words are ranked
+        num_words: max number of words to include. Words are ranked
             by how often they occur (in the training set) and only
             the most frequent words are kept
         skip_top: skip the top N most frequently occuring words
@@ -24,7 +24,7 @@ def load_data(path='reuters.pkl', nb_words=None, skip_top=0,
         seed: random seed for sample shuffling.
         start_char: The start of a sequence will be marked with this character.
             Set to 1 because 0 is usually the padding character.
-        oov_char: words that were cut out because of the `nb_words`
+        oov_char: words that were cut out because of the `num_words`
             or `skip_top` limit will be replaced with this character.
         index_from: index actual words with this index and higher.
 
@@ -33,15 +33,23 @@ def load_data(path='reuters.pkl', nb_words=None, skip_top=0,
 
     Note that the 'out of vocabulary' character is only used for
     words that were present in the training set but are not included
-    because they're not making the `nb_words` cut here.
-    Words that were not seen in the trining set but are in the test set
+    because they're not making the `num_words` cut here.
+    Words that were not seen in the training set but are in the test set
     have simply been skipped.
     """
+    # Legacy support
+    if 'nb_words' in kwargs:
+        warnings.warn('The `nb_words` argument in `load_data` '
+                      'has been renamed `num_words`.')
+        num_words = kwargs.pop('nb_words')
+    if kwargs:
+        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
 
-    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters.pkl')
-    f = open(path, 'rb')
-    xs, labels = cPickle.load(f)
-    f.close()
+    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters.npz')
+    npzfile = np.load(path)
+    xs = npzfile['x']
+    labels = npzfile['y']
+    npzfile.close()
 
     np.random.seed(seed)
     np.random.shuffle(xs)
@@ -63,34 +71,34 @@ def load_data(path='reuters.pkl', nb_words=None, skip_top=0,
         xs = new_xs
         labels = new_labels
 
-    if not nb_words:
-        nb_words = max([max(x) for x in xs])
+    if not num_words:
+        num_words = max([max(x) for x in xs])
 
     # by convention, use 2 as OOV word
     # reserve 'index_from' (=3 by default) characters:
     # 0 (padding), 1 (start), 2 (OOV)
     if oov_char is not None:
-        xs = [[oov_char if (w >= nb_words or w < skip_top) else w for w in x] for x in xs]
+        xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x] for x in xs]
     else:
         new_xs = []
         for x in xs:
             nx = []
             for w in x:
-                if w >= nb_words or w < skip_top:
+                if skip_top <= w < num_words:
                     nx.append(w)
             new_xs.append(nx)
         xs = new_xs
 
-    x_train = xs[:int(len(xs) * (1 - test_split))]
-    y_train = labels[:int(len(xs) * (1 - test_split))]
+    x_train = np.array(xs[:int(len(xs) * (1 - test_split))])
+    y_train = np.array(labels[:int(len(xs) * (1 - test_split))])
 
-    x_test = xs[int(len(xs) * (1 - test_split)):]
-    y_test = labels[int(len(xs) * (1 - test_split)):]
+    x_test = np.array(xs[int(len(xs) * (1 - test_split)):])
+    y_test = np.array(labels[int(len(xs) * (1 - test_split)):])
 
     return (x_train, y_train), (x_test, y_test)
 
 
-def get_word_index(path='reuters_word_index.pkl'):
+def get_word_index(path='reuters_word_index.json'):
     """Retrieves the dictionary mapping word indices back to words.
 
     # Arguments
@@ -99,13 +107,8 @@ def get_word_index(path='reuters_word_index.pkl'):
     # Returns
         The word index dictionary.
     """
-    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.pkl')
-    f = open(path, 'rb')
-
-    if sys.version_info < (3,):
-        data = cPickle.load(f)
-    else:
-        data = cPickle.load(f, encoding='latin1')
-
+    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json')
+    f = open(path)
+    data = json.load(f)
     f.close()
     return data

keras/engine/__init__.py

@@ -4,7 +4,5 @@ from .topology import InputSpec
 from .topology import Input
 from .topology import InputLayer
 from .topology import Layer
-from .topology import Merge
-from .topology import merge
 from .topology import get_source_inputs
 from .training import Model

keras/initializations.py

@@ -1,121 +0,0 @@
-from __future__ import absolute_import
-import numpy as np
-from . import backend as K
-from .utils.generic_utils import get_from_module
-
-
-def get_fans(shape, dim_ordering='th'):
-    if len(shape) == 2:
-        fan_in = shape[0]
-        fan_out = shape[1]
-    elif len(shape) == 4 or len(shape) == 5:
-        # Assuming convolution kernels (2D or 3D).
-        # TH kernel shape: (depth, input_depth, ...)
-        # TF kernel shape: (..., input_depth, depth)
-        if dim_ordering == 'th':
-            receptive_field_size = np.prod(shape[2:])
-            fan_in = shape[1] * receptive_field_size
-            fan_out = shape[0] * receptive_field_size
-        elif dim_ordering == 'tf':
-            receptive_field_size = np.prod(shape[:2])
-            fan_in = shape[-2] * receptive_field_size
-            fan_out = shape[-1] * receptive_field_size
-        else:
-            raise ValueError('Invalid dim_ordering: ' + dim_ordering)
-    else:
-        # No specific assumptions.
-        fan_in = np.sqrt(np.prod(shape))
-        fan_out = np.sqrt(np.prod(shape))
-    return fan_in, fan_out
-
-
-def uniform(shape, scale=0.05, name=None, dim_ordering='th'):
-    return K.random_uniform_variable(shape, -scale, scale, name=name)
-
-
-def normal(shape, scale=0.05, name=None, dim_ordering='th'):
-    return K.random_normal_variable(shape, 0.0, scale, name=name)
-
-
-def lecun_uniform(shape, name=None, dim_ordering='th'):
-    """LeCun uniform variance scaling initializer.
-
-    # References
-        LeCun 98, Efficient Backprop,
-        http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
-    """
-    fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
-    scale = np.sqrt(3. / fan_in)
-    return uniform(shape, scale, name=name)
-
-
-def glorot_normal(shape, name=None, dim_ordering='th'):
-    """Glorot normal variance scaling initializer.
-
-    # References
-        Glorot & Bengio, AISTATS 2010
-    """
-    fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
-    s = np.sqrt(2. / (fan_in + fan_out))
-    return normal(shape, s, name=name)
-
-
-def glorot_uniform(shape, name=None, dim_ordering='th'):
-    fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
-    s = np.sqrt(6. / (fan_in + fan_out))
-    return uniform(shape, s, name=name)
-
-
-def he_normal(shape, name=None, dim_ordering='th'):
-    """He normal variance scaling initializer.
-
-    # References
-        He et al., http://arxiv.org/abs/1502.01852
-    """
-    fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
-    s = np.sqrt(2. / fan_in)
-    return normal(shape, s, name=name)
-
-
-def he_uniform(shape, name=None, dim_ordering='th'):
-    """He uniform variance scaling initializer.
-    """
-    fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
-    s = np.sqrt(6. / fan_in)
-    return uniform(shape, s, name=name)
-
-
-def orthogonal(shape, scale=1.1, name=None, dim_ordering='th'):
-    """Orthogonal initializer.
-
-    # References
-        Saxe et al., http://arxiv.org/abs/1312.6120
-    """
-    flat_shape = (shape[0], np.prod(shape[1:]))
-    a = np.random.normal(0.0, 1.0, flat_shape)
-    u, _, v = np.linalg.svd(a, full_matrices=False)
-    # Pick the one with the correct shape.
-    q = u if u.shape == flat_shape else v
-    q = q.reshape(shape)
-    return K.variable(scale * q[:shape[0], :shape[1]], name=name)
-
-
-def identity(shape, scale=1, name=None, dim_ordering='th'):
-    if len(shape) != 2 or shape[0] != shape[1]:
-        raise ValueError('Identity matrix initialization can only be used '
-                         'for 2D square matrices.')
-    else:
-        return K.variable(scale * np.identity(shape[0]), name=name)
-
-
-def zero(shape, name=None, dim_ordering='th'):
-    return K.zeros(shape, name=name)
-
-
-def one(shape, name=None, dim_ordering='th'):
-    return K.ones(shape, name=name)
-
-
-def get(identifier, **kwargs):
-    return get_from_module(identifier, globals(),
-                           'initialization', kwargs=kwargs)

keras/initializers.py

@@ -0,0 +1,471 @@
+from __future__ import absolute_import
+import numpy as np
+import six
+from . import backend as K
+from .utils.generic_utils import serialize_keras_object
+from .utils.generic_utils import deserialize_keras_object
+
+
+class Initializer(object):
+    """Initializer base class: all initializers inherit from this class.
+    """
+
+    def __call__(self, shape, dtype=None):
+        raise NotImplementedError
+
+    def get_config(self):
+        return {}
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(**config)
+
+
+class Zeros(Initializer):
+    """Initializer that generates tensors initialized to 0.
+    """
+
+    def __call__(self, shape, dtype=None):
+        return K.constant(0, shape=shape, dtype=dtype)
+
+
+class Ones(Initializer):
+    """Initializer that generates tensors initialized to 1.
+    """
+
+    def __call__(self, shape, dtype=None):
+        return K.constant(1, shape=shape, dtype=dtype)
+
+
+class Constant(Initializer):
+    """Initializer that generates tensors initialized to a constant value.
+
+    # Arguments
+        value: float; the value of the generator tensors.
+    """
+
+    def __init__(self, value=0):
+        self.value = value
+
+    def __call__(self, shape, dtype=None):
+        return K.constant(self.value, shape=shape, dtype=dtype)
+
+    def get_config(self):
+        return {'value': self.value}
+
+
+class RandomNormal(Initializer):
+    """Initializer that generates tensors with a normal distribution.
+
+    # Arguments
+        mean: a python scalar or a scalar tensor. Mean of the random values
+          to generate.
+        stddev: a python scalar or a scalar tensor. Standard deviation of the
+          random values to generate.
+        seed: A Python integer. Used to seed the random generator.
+    """
+
+    def __init__(self, mean=0., stddev=0.05, seed=None):
+        self.mean = mean
+        self.stddev = stddev
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        return K.random_normal(shape, self.mean, self.stddev,
+                               dtype=dtype, seed=self.seed)
+
+    def get_config(self):
+        return {
+            'mean': self.mean,
+            'stddev': self.stddev,
+            'seed': self.seed
+        }
+
+
+class RandomUniform(Initializer):
+    """Initializer that generates tensors with a uniform distribution.
+
+    # Arguments
+        minval: A python scalar or a scalar tensor. Lower bound of the range
+          of random values to generate.
+        maxval: A python scalar or a scalar tensor. Upper bound of the range
+          of random values to generate.  Defaults to 1 for float types.
+        seed: A Python integer. Used to seed the random generator.
+    """
+
+    def __init__(self, minval=-0.05, maxval=0.05, seed=None):
+        self.minval = minval
+        self.maxval = maxval
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        return K.random_uniform(shape, self.minval, self.maxval,
+                                dtype=dtype, seed=self.seed)
+
+    def get_config(self):
+        return {
+            'minval': self.minval,
+            'maxval': self.maxval,
+            'seed': self.seed,
+        }
+
+
+class TruncatedNormal(Initializer):
+    """Initializer that generates a truncated normal distribution.
+
+    These values are similar to values from a `RandomNormal`
+    except that values more than two standard deviations from the mean
+    are discarded and re-drawn. This is the recommended initializer for
+    neural network weights and filters.
+
+    # Arguments
+        mean: a python scalar or a scalar tensor. Mean of the random values
+          to generate.
+        stddev: a python scalar or a scalar tensor. Standard deviation of the
+          random values to generate.
+        seed: A Python integer. Used to seed the random generator.
+    """
+
+    def __init__(self, mean=0., stddev=0.05, seed=None):
+        self.mean = mean
+        self.stddev = stddev
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        return K.truncated_normal(shape, self.mean, self.stddev,
+                                  dtype=dtype, seed=self.seed)
+
+    def get_config(self):
+        return {
+            'mean': self.mean,
+            'stddev': self.stddev,
+            'seed': self.seed
+        }
+
+
+class VarianceScaling(Initializer):
+    """Initializer capable of adapting its scale to the shape of weights.
+
+    With `distribution="normal"`, samples are drawn from a truncated normal
+    distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
+
+        - number of input units in the weight tensor, if mode = "fan_in"
+        - number of output units, if mode = "fan_out"
+        - average of the numbers of input and output units, if mode = "fan_avg"
+
+    With `distribution="uniform"`,
+    samples are drawn from a uniform distribution
+    within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
+
+    # Arguments
+        scale: Scaling factor (positive float).
+        mode: One of "fan_in", "fan_out", "fan_avg".
+        distribution: Random distribution to use. One of "normal", "uniform".
+        seed: A Python integer. Used to seed the random generator.
+
+    # Raises
+        ValueError: In case of an invalid value for the "scale", mode" or
+          "distribution" arguments.
+    """
+
+    def __init__(self, scale=1.0,
+                 mode='fan_in',
+                 distribution='normal',
+                 seed=None):
+        if scale <= 0.:
+            raise ValueError('`scale` must be a positive float. Got:', scale)
+        mode = mode.lower()
+        if mode not in {'fan_in', 'fan_out', 'fan_avg'}:
+            raise ValueError('Invalid `mode` argument: '
+                             'expected on of {"fan_in", "fan_out", "fan_avg"} '
+                             'but got', mode)
+        distribution = distribution.lower()
+        if distribution not in {'normal', 'uniform'}:
+            raise ValueError('Invalid `distribution` argument: '
+                             'expected one of {"normal", "uniform"} '
+                             'but got', distribution)
+        self.scale = scale
+        self.mode = mode
+        self.distribution = distribution
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        fan_in, fan_out = _compute_fans(shape)
+        scale = self.scale
+        if self.mode == 'fan_in':
+            scale /= max(1., fan_in)
+        elif self.mode == 'fan_out':
+            scale /= max(1., fan_out)
+        else:
+            scale /= max(1., float(fan_in + fan_out) / 2)
+        if self.distribution == 'normal':
+            stddev = np.sqrt(scale)
+            return K.truncated_normal(shape, 0., stddev,
+                                      dtype=dtype, seed=self.seed)
+        else:
+            limit = np.sqrt(3. * scale)
+            return K.random_uniform(shape, -limit, limit,
+                                    dtype=dtype, seed=self.seed)
+
+    def get_config(self):
+        return {
+            'scale': self.scale,
+            'mode': self.mode,
+            'distribution': self.distribution,
+            'seed': self.seed
+        }
+
+
+class Orthogonal(Initializer):
+    """Initializer that generates a random orthogonal matrix.
+
+    # Arguments
+        gain: Multiplicative factor to apply to the orthogonal matrix.
+        seed: A Python integer. Used to seed the random generator.
+
+    # References
+        Saxe et al., http://arxiv.org/abs/1312.6120
+    """
+
+    def __init__(self, gain=1., seed=None):
+        self.gain = gain
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        num_rows = 1
+        for dim in shape[:-1]:
+            num_rows *= dim
+        num_cols = shape[-1]
+        flat_shape = (num_rows, num_cols)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+        a = np.random.normal(0.0, 1.0, flat_shape)
+        u, _, v = np.linalg.svd(a, full_matrices=False)
+        # Pick the one with the correct shape.
+        q = u if u.shape == flat_shape else v
+        q = q.reshape(shape)
+        return self.gain * q[:shape[0], :shape[1]]
+
+    def get_config(self):
+        return {
+            'gain': self.gain,
+            'seed': self.seed
+        }
+
+
+class Identity(Initializer):
+    """Initializer that generates the identity matrix.
+
+    Only use for square 2D matrices.
+
+    # Arguments
+        gain: Multiplicative factor to apply to the identity matrix.
+    """
+
+    def __init__(self, gain=1.):
+        self.gain = gain
+
+    def __call__(self, shape, dtype=None):
+        if len(shape) != 2 or shape[0] != shape[1]:
+            raise ValueError('Identity matrix initializer can only be used '
+                             'for 2D square matrices.')
+        else:
+            return self.gain * np.identity(shape[0])
+
+    def get_config(self):
+        return {
+            'gain': self.gain
+        }
+
+
+def lecun_uniform(seed=None):
+    """LeCun uniform initializer.
+
+    It draws samples from a uniform distribution within [-limit, limit]
+    where `limit` is `sqrt(3 / fan_in)`
+    where `fan_in` is the number of input units in the weight tensor.
+
+    # Arguments
+        seed: A Python integer. Used to seed the random generator.
+
+    # Returns
+        An initializer.
+
+    # References
+        LeCun 98, Efficient Backprop,
+        http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
+    """
+    return VarianceScaling(scale=1.,
+                           mode='fan_in',
+                           distribution='uniform',
+                           seed=seed)
+
+
+def glorot_normal(seed=None):
+    """Glorot normal initializer, also called Xavier normal initializer.
+
+    It draws samples from a truncated normal distribution centered on 0
+    with `stddev = sqrt(2 / (fan_in + fan_out))`
+    where `fan_in` is the number of input units in the weight tensor
+    and `fan_out` is the number of output units in the weight tensor.
+
+    # Arguments
+        seed: A Python integer. Used to seed the random generator.
+
+    # Returns
+        An initializer.
+
+    # References
+        Glorot & Bengio, AISTATS 2010
+        http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
+    """
+    return VarianceScaling(scale=1.,
+                           mode='fan_avg',
+                           distribution='normal',
+                           seed=seed)
+
+
+def glorot_uniform(seed=None):
+    """Glorot uniform initializer, also called Xavier uniform initializer.
+
+    It draws samples from a uniform distribution within [-limit, limit]
+    where `limit` is `sqrt(6 / (fan_in + fan_out))`
+    where `fan_in` is the number of input units in the weight tensor
+    and `fan_out` is the number of output units in the weight tensor.
+
+    # Arguments
+        seed: A Python integer. Used to seed the random generator.
+
+    # Returns
+        An initializer.
+
+    # References
+        Glorot & Bengio, AISTATS 2010
+        http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
+    """
+    return VarianceScaling(scale=1.,
+                           mode='fan_avg',
+                           distribution='uniform',
+                           seed=seed)
+
+
+def he_normal(seed=None):
+    """He normal initializer.
+
+    It draws samples from a truncated normal distribution centered on 0
+    with `stddev = sqrt(2 / fan_in)`
+    where `fan_in` is the number of input units in the weight tensor.
+
+    # Arguments
+        seed: A Python integer. Used to seed the random generator.
+
+    # Returns
+        An initializer.
+
+    # References
+        He et al., http://arxiv.org/abs/1502.01852
+    """
+    return VarianceScaling(scale=2.,
+                           mode='fan_in',
+                           distribution='normal',
+                           seed=seed)
+
+
+def he_uniform(seed=None):
+    """He uniform variance scaling initializer.
+
+    It draws samples from a uniform distribution within [-limit, limit]
+    where `limit` is `sqrt(6 / fan_in)`
+    where `fan_in` is the number of input units in the weight tensor.
+
+    # Arguments
+        seed: A Python integer. Used to seed the random generator.
+
+    # Returns
+        An initializer.
+
+    # References
+        He et al., http://arxiv.org/abs/1502.01852
+    """
+    return VarianceScaling(scale=2.,
+                           mode='fan_in',
+                           distribution='uniform',
+                           seed=seed)
+
+
+# Compatibility aliases
+
+zero = zeros = Zeros
+one = ones = Ones
+constant = Constant
+uniform = random_uniform = RandomUniform
+normal = random_normal = RandomNormal
+truncated_normal = TruncatedNormal
+identity = Identity
+orthogonal = Orthogonal
+
+# Utility functions
+
+
+def _compute_fans(shape, data_format='channels_last'):
+    """Computes the number of input and output units for a weight shape.
+
+    # Arguments
+        shape: Integer shape tuple.
+        data_format: Image data format to use for convolution kernels.
+            Note that all kernels in Keras are standardized on the
+            `channels_last` ordering (even when inputs are set
+            to `channels_first`).
+
+    # Returns
+        A tuple of scalars, `(fan_in, fan_out)`.
+
+    # Raises
+        ValueError: in case of invalid `data_format` argument.
+    """
+    if len(shape) == 2:
+        fan_in = shape[0]
+        fan_out = shape[1]
+    elif len(shape) in {3, 4, 5}:
+        # Assuming convolution kernels (1D, 2D or 3D).
+        # TH kernel shape: (depth, input_depth, ...)
+        # TF kernel shape: (..., input_depth, depth)
+        if data_format == 'channels_first':
+            receptive_field_size = np.prod(shape[2:])
+            fan_in = shape[1] * receptive_field_size
+            fan_out = shape[0] * receptive_field_size
+        elif data_format == 'channels_last':
+            receptive_field_size = np.prod(shape[:2])
+            fan_in = shape[-2] * receptive_field_size
+            fan_out = shape[-1] * receptive_field_size
+        else:
+            raise ValueError('Invalid data_format: ' + data_format)
+    else:
+        # No specific assumptions.
+        fan_in = np.sqrt(np.prod(shape))
+        fan_out = np.sqrt(np.prod(shape))
+    return fan_in, fan_out
+
+
+def serialize(initializer):
+    return serialize_keras_object(initializer)
+
+
+def deserialize(config, custom_objects=None):
+    return deserialize_keras_object(config,
+                                    module_objects=globals(),
+                                    custom_objects=custom_objects,
+                                    printable_module_name='initializer')
+
+
+def get(identifier):
+    if isinstance(identifier, dict):
+        return deserialize(identifier)
+    elif isinstance(identifier, six.string_types):
+        config = {'class_name': str(identifier), 'config': {}}
+        return deserialize(config)
+    elif callable(identifier):
+        return identifier
+    else:
+        raise ValueError('Could not interpret initializer identifier:',
+                         identifier)

keras/layers/__init__.py

@@ -1,5 +1,11 @@
 from __future__ import absolute_import
-from ..engine import Layer, Input, InputLayer, Merge, merge, InputSpec
+
+from ..utils.generic_utils import deserialize_keras_object
+from ..engine import Layer
+from ..engine import Input
+from ..engine import InputLayer
+from ..engine import InputSpec
+from .merge import *
 from .core import *
 from .convolutional import *
 from .pooling import *
@@ -11,3 +17,38 @@ from .noise import *
 from .advanced_activations import *
 from .wrappers import *
 from .convolutional_recurrent import *
+from ..legacy.layers import *
+
+
+def serialize(layer):
+    """Serialize a layer.
+
+    # Arguments
+        layer: a Layer object.
+
+    # Returns
+        dictionary with config.
+    """
+    return {'class_name': layer.__class__.__name__,
+            'config': layer.get_config()}
+
+
+def deserialize(config, custom_objects=None):
+    """Instantiate a layer from a config dictionary.
+
+    # Arguments
+        config: dict of the form {'class_name': str, 'config': dict}
+        custom_objects: dict mapping class names (or function names)
+            of custom (non-Keras) objects to class/functions
+
+    # Returns
+        Layer instance (may be Model, Sequential, Layer...)
+    """
+    from .. import models
+    globs = globals()  # All layers.
+    globs['Model'] = models.Model
+    globs['Sequential'] = models.Sequential
+    return deserialize_keras_object(config,
+                                    module_objects=globs,
+                                    custom_objects=custom_objects,
+                                    printable_module_name='layer')

keras/layers/advanced_activations.py

@@ -1,7 +1,13 @@
-from .. import initializations
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import
+
+from .. import initializers
+from .. import regularizers
+from .. import constraints
 from ..engine import Layer
+from ..engine import InputSpec
 from .. import backend as K
-import numpy as np
+from ..legacy import interfaces
 
 
 class LeakyReLU(Layer):
@@ -27,12 +33,12 @@ class LeakyReLU(Layer):
     """
 
     def __init__(self, alpha=0.3, **kwargs):
-        self.supports_masking = True
-        self.alpha = alpha
         super(LeakyReLU, self).__init__(**kwargs)
+        self.supports_masking = True
+        self.alpha = K.cast_to_floatx(alpha)
 
-    def call(self, x, mask=None):
-        return K.relu(x, alpha=self.alpha)
+    def call(self, inputs):
+        return K.relu(inputs, alpha=self.alpha)
 
     def get_config(self):
         config = {'alpha': self.alpha}
@@ -44,9 +50,9 @@ class PReLU(Layer):
     """Parametric Rectified Linear Unit.
 
     It follows:
-    `f(x) = alphas * x for x < 0`,
+    `f(x) = alpha * x for x < 0`,
     `f(x) = x for x >= 0`,
-    where `alphas` is a learned array with the same shape as x.
+    where `alpha` is a learned array with the same shape as x.
 
     # Input shape
         Arbitrary. Use the keyword argument `input_shape`
@@ -57,8 +63,9 @@ class PReLU(Layer):
         Same shape as the input.
 
     # Arguments
-        init: initialization function for the weights.
-        weights: initial weights, as a list of a single Numpy array.
+        alpha_initializer: initializer function for the weights.
+        alpha_regularizer: regularizer for the weights.
+        alpha_constraint: constraint for the weights.
         shared_axes: the axes along which to share learnable
             parameters for the activation function.
             For example, if the incoming feature maps
@@ -72,43 +79,61 @@ class PReLU(Layer):
         - [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852)
     """
 
-    def __init__(self, init='zero', weights=None, shared_axes=None, **kwargs):
+    @interfaces.legacy_prelu_support
+    def __init__(self, alpha_initializer='zeros',
+                 alpha_regularizer=None,
+                 alpha_constraint=None,
+                 shared_axes=None,
+                 **kwargs):
+        super(PReLU, self).__init__(**kwargs)
         self.supports_masking = True
-        self.init = initializations.get(init)
-        self.initial_weights = weights
-        if not isinstance(shared_axes, (list, tuple)):
+        self.alpha_initializer = initializers.get(alpha_initializer)
+        self.alpha_regularizer = regularizers.get(alpha_regularizer)
+        self.alpha_constraint = constraints.get(alpha_constraint)
+        if shared_axes is None:
+            self.shared_axes = None
+        elif not isinstance(shared_axes, (list, tuple)):
             self.shared_axes = [shared_axes]
         else:
             self.shared_axes = list(shared_axes)
-        super(PReLU, self).__init__(**kwargs)
 
     def build(self, input_shape):
         param_shape = list(input_shape[1:])
         self.param_broadcast = [False] * len(param_shape)
-        if self.shared_axes[0] is not None:
+        if self.shared_axes is not None:
             for i in self.shared_axes:
                 param_shape[i - 1] = 1
                 self.param_broadcast[i - 1] = True
+        self.alpha = self.add_weight(shape=param_shape,
+                                     name='alpha',
+                                     initializer=self.alpha_initializer,
+                                     regularizer=self.alpha_regularizer,
+                                     constraint=self.alpha_constraint)
+        # Set input spec
+        axes = {}
+        if self.shared_axes:
+            for i in range(1, len(input_shape)):
+                if i not in self.shared_axes:
+                    axes[i] = input_shape[i]
+        self.input_spec = InputSpec(ndim=len(input_shape), axes=axes)
+        self.built = True
 
-        self.alphas = self.init(param_shape,
-                                name='{}_alphas'.format(self.name))
-        self.trainable_weights = [self.alphas]
-
-        if self.initial_weights is not None:
-            self.set_weights(self.initial_weights)
-            del self.initial_weights
-
-    def call(self, x, mask=None):
-        pos = K.relu(x)
+    def call(self, inputs, mask=None):
+        pos = K.relu(inputs)
         if K.backend() == 'theano':
-            neg = (K.pattern_broadcast(self.alphas, self.param_broadcast) *
-                   (x - K.abs(x)) * 0.5)
+            neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) *
+                   (inputs - K.abs(inputs)) * 0.5)
         else:
-            neg = self.alphas * (x - K.abs(x)) * 0.5
+            neg = -self.alpha * K.relu(-inputs)
         return pos + neg
 
     def get_config(self):
-        config = {'init': self.init.__name__}
+        config = {
+            'alpha_initializer': initializers.serialize(self.alpha_initializer),
+            'alpha_regularizer': regularizers.serialize(self.alpha_regularizer),
+            'alpha_constraint': constraints.serialize(self.alpha_constraint),
+            'shared_axes': self.shared_axes
+        }
         base_config = super(PReLU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
@@ -136,12 +161,12 @@ class ELU(Layer):
     """
 
     def __init__(self, alpha=1.0, **kwargs):
+        super(ELU, self).__init__(**kwargs)
         self.supports_masking = True
         self.alpha = K.cast_to_floatx(alpha)
-        super(ELU, self).__init__(**kwargs)
 
-    def call(self, x, mask=None):
-        return K.elu(x, self.alpha)
+    def call(self, inputs):
+        return K.elu(inputs, self.alpha)
 
     def get_config(self):
         config = {'alpha': float(self.alpha)}
@@ -149,82 +174,6 @@ class ELU(Layer):
         return dict(list(base_config.items()) + list(config.items()))
 
 
-class ParametricSoftplus(Layer):
-    """Parametric Softplus.
-
-    It follows:
-    `f(x) = alpha * log(1 + exp(beta * x))`
-
-    # Input shape
-        Arbitrary. Use the keyword argument `input_shape`
-        (tuple of integers, does not include the samples axis)
-        when using this layer as the first layer in a model.
-
-    # Output shape
-        Same shape as the input.
-
-    # Arguments
-        alpha_init: float. Initial value of the alpha weights.
-        beta_init: float. Initial values of the beta weights.
-        weights: initial weights, as a list of 2 numpy arrays.
-        shared_axes: the axes along which to share learnable
-            parameters for the activation function.
-            For example, if the incoming feature maps
-            are from a 2D convolution
-            with output shape `(batch, height, width, channels)`,
-            and you wish to share parameters across space
-            so that each filter only has one set of parameters,
-            set `shared_axes=[1, 2]`.
-
-    # References
-        - [Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003143)
-    """
-
-    def __init__(self, alpha_init=0.2, beta_init=5.0,
-                 weights=None, shared_axes=None, **kwargs):
-        self.supports_masking = True
-        self.alpha_init = K.cast_to_floatx(alpha_init)
-        self.beta_init = K.cast_to_floatx(beta_init)
-        self.initial_weights = weights
-        if not isinstance(shared_axes, (list, tuple)):
-            self.shared_axes = [shared_axes]
-        else:
-            self.shared_axes = list(shared_axes)
-        super(ParametricSoftplus, self).__init__(**kwargs)
-
-    def build(self, input_shape):
-        param_shape = list(input_shape[1:])
-        self.param_broadcast = [False] * len(param_shape)
-        if self.shared_axes[0] is not None:
-            for i in self.shared_axes:
-                param_shape[i - 1] = 1
-                self.param_broadcast[i - 1] = True
-
-        self.alphas = K.variable(self.alpha_init * np.ones(param_shape),
-                                 name='{}_alphas'.format(self.name))
-        self.betas = K.variable(self.beta_init * np.ones(param_shape),
-                                name='{}_betas'.format(self.name))
-        self.trainable_weights = [self.alphas, self.betas]
-
-        if self.initial_weights is not None:
-            self.set_weights(self.initial_weights)
-            del self.initial_weights
-
-    def call(self, x, mask=None):
-        if K.backend() == 'theano':
-            return (K.softplus(K.pattern_broadcast(self.betas,
-                                                   self.param_broadcast) * x) *
-                    K.pattern_broadcast(self.alphas, self.param_broadcast))
-        else:
-            return K.softplus(self.betas * x) * self.alphas
-
-    def get_config(self):
-        config = {'alpha_init': float(self.alpha_init),
-                  'beta_init': float(self.beta_init)}
-        base_config = super(ParametricSoftplus, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))
-
-
 class ThresholdedReLU(Layer):
     """Thresholded Rectified Linear Unit.
 
@@ -248,116 +197,14 @@ class ThresholdedReLU(Layer):
     """
 
     def __init__(self, theta=1.0, **kwargs):
+        super(ThresholdedReLU, self).__init__(**kwargs)
         self.supports_masking = True
         self.theta = K.cast_to_floatx(theta)
-        super(ThresholdedReLU, self).__init__(**kwargs)
 
-    def call(self, x, mask=None):
-        return x * K.cast(x > self.theta, K.floatx())
+    def call(self, inputs, mask=None):
+        return inputs * K.cast(K.greater(inputs, self.theta), K.floatx())
 
     def get_config(self):
         config = {'theta': float(self.theta)}
         base_config = super(ThresholdedReLU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
-
-
-class SReLU(Layer):
-    """S-shaped Rectified Linear Unit.
-
-    It follows:
-    `f(x) = t^r + a^r(x - t^r) for x >= t^r`,
-    `f(x) = x for t^r > x > t^l`,
-    `f(x) = t^l + a^l(x - t^l) for x <= t^l`.
-
-    # Input shape
-        Arbitrary. Use the keyword argument `input_shape`
-        (tuple of integers, does not include the samples axis)
-        when using this layer as the first layer in a model.
-
-    # Output shape
-        Same shape as the input.
-
-    # Arguments
-        t_left_init: initialization function for the left part intercept
-        a_left_init: initialization function for the left part slope
-        t_right_init: initialization function for the right part intercept
-        a_right_init: initialization function for the right part slope
-        shared_axes: the axes along which to share learnable
-            parameters for the activation function.
-            For example, if the incoming feature maps
-            are from a 2D convolution
-            with output shape `(batch, height, width, channels)`,
-            and you wish to share parameters across space
-            so that each filter only has one set of parameters,
-            set `shared_axes=[1, 2]`.
-
-    # References
-        - [Deep Learning with S-shaped Rectified Linear Activation Units](http://arxiv.org/abs/1512.07030)
-    """
-
-    def __init__(self, t_left_init='zero', a_left_init='glorot_uniform',
-                 t_right_init='glorot_uniform', a_right_init='one',
-                 shared_axes=None, **kwargs):
-        self.supports_masking = True
-        self.t_left_init = t_left_init
-        self.a_left_init = a_left_init
-        self.t_right_init = t_right_init
-        self.a_right_init = a_right_init
-        if not isinstance(shared_axes, (list, tuple)):
-            self.shared_axes = [shared_axes]
-        else:
-            self.shared_axes = list(shared_axes)
-        super(SReLU, self).__init__(**kwargs)
-
-    def build(self, input_shape):
-        param_shape = list(input_shape[1:])
-        self.param_broadcast = [False] * len(param_shape)
-        if self.shared_axes[0] is not None:
-            for i in self.shared_axes:
-                param_shape[i - 1] = 1
-                self.param_broadcast[i - 1] = True
-
-        t_left_init = initializations.get(self.t_left_init)
-        a_left_init = initializations.get(self.a_left_init)
-        t_right_init = initializations.get(self.t_right_init)
-        a_right_init = initializations.get(self.a_right_init)
-
-        self.t_left = t_left_init(param_shape,
-                                  name='{}_t_left'.format(self.name))
-        self.a_left = a_left_init(param_shape,
-                                  name='{}_a_left'.format(self.name))
-        self.t_right = t_right_init(param_shape,
-                                    name='{}_t_right'.format(self.name))
-        self.a_right = a_right_init(param_shape,
-                                    name='{}_a_right'.format(self.name))
-        # ensure the the right part is always to the right of the left
-        self.t_right_actual = self.t_left + K.abs(self.t_right)
-        self.trainable_weights = [self.t_left, self.a_left,
-                                  self.t_right, self.a_right]
-
-    def call(self, x, mask=None):
-        if K.backend() == 'theano':
-            t_left = K.pattern_broadcast(self.t_left, self.param_broadcast)
-            a_left = K.pattern_broadcast(self.a_left, self.param_broadcast)
-            a_right = K.pattern_broadcast(self.a_right, self.param_broadcast)
-            t_right_actual = K.pattern_broadcast(self.t_right_actual,
-                                                 self.param_broadcast)
-        else:
-            t_left = self.t_left
-            a_left = self.a_left
-            a_right = self.a_right
-            t_right_actual = self.t_right_actual
-
-        y_left_and_center = t_left + K.relu(x - t_left,
-                                            a_left,
-                                            t_right_actual - t_left)
-        y_right = K.relu(x - t_right_actual) * a_right
-        return y_left_and_center + y_right
-
-    def get_config(self):
-        config = {'t_left_init': self.t_left_init,
-                  'a_left_init': self.a_left_init,
-                  't_right_init': self.t_right_init,
-                  'a_right_init': self.a_right_init}
-        base_config = super(SReLU, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))