Francois Chollet
444 linhas
21 KiB
Python
444 linhas
21 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import absolute_import
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from .. import backend as K
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from .. import activations
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from .. import initializers
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from .. import regularizers
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from .. import constraints
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from ..engine import Layer
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from ..engine import InputSpec
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from ..utils import conv_utils
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from ..legacy import interfaces
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class LocallyConnected1D(Layer):
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"""Locally-connected layer for 1D inputs.
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The `LocallyConnected1D` layer works similarly to
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the `Conv1D` layer, except that weights are unshared,
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that is, a different set of filters is applied at each different patch
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of the input.
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# Example
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```python
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# apply a unshared weight convolution 1d of length 3 to a sequence with
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# 10 timesteps, with 64 output filters
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model = Sequential()
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model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
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# now model.output_shape == (None, 8, 64)
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# add a new conv1d on top
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model.add(LocallyConnected1D(32, 3))
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# now model.output_shape == (None, 6, 32)
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```
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# Arguments
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filters: Integer, the dimensionality of the output space
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(i.e. the number output of filters in the convolution).
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kernel_size: An integer or tuple/list of a single integer,
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specifying the length of the 1D convolution window.
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strides: An integer or tuple/list of a single integer,
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specifying the stride length of the convolution.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: One of `"valid"` or `"same"` (case-insensitive).
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activation: Activation function to use
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(see [activations](../activations.md)).
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If you don't specify anything, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix
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(see [initializers](../initializers.md)).
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bias_initializer: Initializer for the bias vector
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(see [initializers](../initializers.md)).
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix
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(see [regularizer](../regularizers.md)).
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bias_regularizer: Regularizer function applied to the bias vector
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(see [regularizer](../regularizers.md)).
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation").
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(see [regularizer](../regularizers.md)).
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kernel_constraint: Constraint function applied to the kernel matrix
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(see [constraints](../constraints.md)).
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bias_constraint: Constraint function applied to the bias vector
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(see [constraints](../constraints.md)).
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# Input shape
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3D tensor with shape: `(batch_size, steps, input_dim)`
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# Output shape
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3D tensor with shape: `(batch_size, new_steps, filters)`
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`steps` value might have changed due to padding or strides.
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"""
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@interfaces.legacy_conv1d_support
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def __init__(self, filters,
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kernel_size,
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strides=1,
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padding='valid',
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data_format=None,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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bias_initializer='zeros',
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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bias_constraint=None,
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**kwargs):
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super(LocallyConnected1D, self).__init__(**kwargs)
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
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self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
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self.padding = conv_utils.normalize_padding(padding)
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if self.padding != 'valid':
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raise ValueError('Invalid border mode for LocallyConnected1D '
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'(only "valid" is supported): ' + padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.input_spec = InputSpec(ndim=3)
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def build(self, input_shape):
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input_dim = input_shape[2]
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if input_dim is None:
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raise ValueError('Axis 2 of input should be fully-defined. '
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'Found shape:', input_shape)
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output_length = conv_utils.conv_output_length(input_shape[1],
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self.kernel_size[0],
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self.padding,
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self.strides[0])
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self.kernel_shape = (output_length,
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self.kernel_size[0] * input_dim,
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self.filters)
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self.kernel = self.add_weight(
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self.kernel_shape,
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initializer=self.kernel_initializer,
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name='kernel',
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint)
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if self.use_bias:
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self.bias = self.add_weight(
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(output_length, self.filters),
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initializer=self.bias_initializer,
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name='bias',
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint)
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else:
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self.bias = None
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self.input_spec = InputSpec(ndim=3, axes={2: input_dim})
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self.built = True
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def compute_output_shape(self, input_shape):
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length = conv_utils.conv_output_length(input_shape[1],
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self.kernel_size[0],
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self.padding,
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self.strides[0])
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return (input_shape[0], length, self.filters)
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def call(self, inputs):
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stride = self.strides[0]
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output_length, feature_dim, filters = self.kernel_shape
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xs = []
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for i in range(output_length):
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slice_length = slice(i * stride,
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i * stride + self.kernel_size[0])
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xs.append(K.reshape(inputs[:, slice_length, :],
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(1, -1, feature_dim)))
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x_aggregate = K.concatenate(xs, axis=0)
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# Shape: `(output_length, batch_size, filters)`.
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output = K.batch_dot(x_aggregate, self.kernel)
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output = K.permute_dimensions(output, (1, 0, 2))
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if self.use_bias:
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output += K.reshape(self.bias, (1, output_length, filters))
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if self.activation is not None:
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output = self.activation(output)
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return output
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def get_config(self):
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config = {
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'filters': self.filters,
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'kernel_size': self.kernel_size,
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'strides': self.strides,
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'padding': self.padding,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'kernel_initializer': initializers.serialize(self.kernel_initializer),
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'bias_initializer': initializers.serialize(self.kernel_initializer),
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'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer': regularizers.serialize(self.bias_regularizer),
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'activity_regularizer': regularizers.serialize(self.activity_regularizer),
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'kernel_constraint': constraints.serialize(self.kernel_constraint),
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'bias_constraint': constraints.serialize(self.bias_constraint)
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}
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base_config = super(LocallyConnected1D, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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class LocallyConnected2D(Layer):
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"""Locally-connected layer for 2D inputs.
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The `LocallyConnected2D` layer works similarly
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to the `Conv2D` layer, except that weights are unshared,
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that is, a different set of filters is applied at each
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different patch of the input.
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# Examples
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```python
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# apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image
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# with `data_format="channels_last"`:
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model = Sequential()
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model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
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# now model.output_shape == (None, 30, 30, 64)
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# notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters
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# add a 3x3 unshared weights convolution on top, with 32 output filters:
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model.add(LocallyConnected2D(32, (3, 3)))
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# now model.output_shape == (None, 28, 28, 32)
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```
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# Arguments
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filters: Integer, the dimensionality of the output space
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(i.e. the number output of filters in the convolution).
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kernel_size: An integer or tuple/list of 2 integers, specifying the
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width and height of the 2D convolution window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the convolution along the width and height.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: one of `"valid"` or `"same"` (case-insensitive).
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch, width, height, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, width, height)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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activation: Activation function to use
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(see [activations](../activations.md)).
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If you don't specify anything, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix
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(see [initializers](../initializers.md)).
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bias_initializer: Initializer for the bias vector
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(see [initializers](../initializers.md)).
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix
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(see [regularizer](../regularizers.md)).
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bias_regularizer: Regularizer function applied to the bias vector
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(see [regularizer](../regularizers.md)).
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation").
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(see [regularizer](../regularizers.md)).
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kernel_constraint: Constraint function applied to the kernel matrix
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(see [constraints](../constraints.md)).
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bias_constraint: Constraint function applied to the bias vector
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(see [constraints](../constraints.md)).
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# Input shape
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4D tensor with shape:
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`(samples, channels, rows, cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(samples, rows, cols, channels)` if data_format='channels_last'.
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# Output shape
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4D tensor with shape:
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`(samples, filters, new_rows, new_cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
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`rows` and `cols` values might have changed due to padding.
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"""
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@interfaces.legacy_conv2d_support
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def __init__(self, filters,
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kernel_size,
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strides=(1, 1),
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padding='valid',
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data_format=None,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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bias_initializer='zeros',
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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bias_constraint=None,
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**kwargs):
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super(LocallyConnected2D, self).__init__(**kwargs)
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
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self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
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self.padding = conv_utils.normalize_padding(padding)
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if self.padding != 'valid':
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raise ValueError('Invalid border mode for LocallyConnected2D '
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'(only "valid" is supported): ' + padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.input_spec = InputSpec(ndim=4)
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def build(self, input_shape):
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if self.data_format == 'channels_last':
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input_row, input_col = input_shape[1:-1]
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input_filter = input_shape[3]
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else:
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input_row, input_col = input_shape[2:]
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input_filter = input_shape[1]
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if input_row is None or input_col is None:
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raise ValueError('The spatial dimensions of the inputs to '
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' a LocallyConnected2D layer '
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'should be fully-defined, but layer received '
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'the inputs shape ' + str(input_shape))
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output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0],
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self.padding, self.strides[0])
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output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1],
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self.padding, self.strides[1])
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self.output_row = output_row
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self.output_col = output_col
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self.kernel_shape = (output_row * output_col,
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self.kernel_size[0] * self.kernel_size[1] * input_filter,
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self.filters)
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self.kernel = self.add_weight(self.kernel_shape,
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initializer=self.kernel_initializer,
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name='kernel',
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint)
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if self.use_bias:
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self.bias = self.add_weight((output_row, output_col, self.filters),
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initializer=self.bias_initializer,
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name='bias',
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint)
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else:
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self.bias = None
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if self.data_format == 'channels_first':
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self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
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else:
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self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
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self.built = True
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def compute_output_shape(self, input_shape):
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if self.data_format == 'channels_first':
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rows = input_shape[2]
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cols = input_shape[3]
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elif self.data_format == 'channels_last':
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rows = input_shape[1]
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cols = input_shape[2]
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rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
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self.padding, self.strides[0])
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cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
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self.padding, self.strides[1])
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if self.data_format == 'channels_first':
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return (input_shape[0], self.filters, rows, cols)
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elif self.data_format == 'channels_last':
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return (input_shape[0], rows, cols, self.filters)
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def call(self, inputs):
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stride_row, stride_col = self.strides
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_, feature_dim, filters = self.kernel_shape
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if self.data_format == 'channels_first':
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if K.backend() == 'theano':
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output = []
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for i in range(self.output_row):
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for j in range(self.output_col):
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slice_row = slice(i * stride_row,
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i * stride_row + self.kernel_size[0])
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slice_col = slice(j * stride_col,
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j * stride_col + self.kernel_size[1])
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x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
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(1, -1, feature_dim))
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output.append(K.dot(x_flatten,
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self.kernel[i * self.output_col + j, :, :]))
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output = K.concatenate(output, axis=0)
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else:
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xs = []
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for i in range(self.output_row):
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for j in range(self.output_col):
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slice_row = slice(i * stride_row,
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i * stride_row + self.kernel_size[0])
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slice_col = slice(j * stride_col,
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j * stride_col + self.kernel_size[1])
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xs.append(K.reshape(inputs[:, :, slice_row, slice_col],
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(1, -1, feature_dim)))
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x_aggregate = K.concatenate(xs, axis=0)
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output = K.batch_dot(x_aggregate, self.kernel)
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output = K.reshape(output,
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(self.output_row, self.output_col, -1, filters))
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output = K.permute_dimensions(output, (2, 3, 0, 1))
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elif self.data_format == 'channels_last':
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xs = []
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for i in range(self.output_row):
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for j in range(self.output_col):
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slice_row = slice(i * stride_row,
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i * stride_row + self.kernel_size[0])
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slice_col = slice(j * stride_col,
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j * stride_col + self.kernel_size[1])
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xs.append(K.reshape(inputs[:, slice_row, slice_col, :],
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(1, -1, feature_dim)))
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x_aggregate = K.concatenate(xs, axis=0)
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output = K.batch_dot(x_aggregate, self.kernel)
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output = K.reshape(output,
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(self.output_row, self.output_col, -1, filters))
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output = K.permute_dimensions(output, (2, 0, 1, 3))
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if self.use_bias:
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if self.data_format == 'channels_first':
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output += K.reshape(self.bias,
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(1, filters, self.output_row, self.output_col))
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elif self.data_format == 'channels_last':
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output += K.reshape(self.bias,
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(1, self.output_row, self.output_col, filters))
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output = self.activation(output)
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return output
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def get_config(self):
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config = {
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'filters': self.filters,
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'kernel_size': self.kernel_size,
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'strides': self.strides,
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'padding': self.padding,
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'data_format': self.data_format,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'kernel_initializer': initializers.serialize(self.kernel_initializer),
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'bias_initializer': initializers.serialize(self.kernel_initializer),
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'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer': regularizers.serialize(self.bias_regularizer),
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'activity_regularizer': regularizers.serialize(self.activity_regularizer),
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'kernel_constraint': constraints.serialize(self.kernel_constraint),
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'bias_constraint': constraints.serialize(self.bias_constraint)
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}
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base_config = super(LocallyConnected2D, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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