Style fixes (#7073)

examples/image_ocr.py

@@ -136,8 +136,8 @@ def shuffle_mats_or_lists(matrix_list, stop_ind=None):
         elif isinstance(mat, list):
             ret.append([mat[i] for i in a])
         else:
-            raise TypeError('shuffle_mats_or_lists only supports '
-                            'numpy.array and list objects')
+            raise TypeError('`shuffle_mats_or_lists` only supports '
+                            'numpy.array and list objects.')
     return ret
 
 

keras/applications/resnet50.py

@@ -77,7 +77,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
 
 
 def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
-    """conv_block is the block that has a conv layer at shortcut
+    """A block that has a conv layer at shortcut.
 
     # Arguments
         input_tensor: input tensor

keras/backend/cntk_backend.py

@@ -652,16 +652,16 @@ def _normalize_axis(axis, x):
 
     nones = _get_dynamic_axis_num(x)
 
-    if type(axis) is tuple:
+    if isinstance(axis, tuple):
         _axis = list(axis)
-    elif type(axis) is int:
+    elif isinstance(axis, int):
         _axis = [axis]
-    elif type(axis) is list:
+    elif isinstance(axis, list):
         _axis = list(axis)
     else:
         _axis = axis
 
-    if type(_axis) is list:
+    if isinstance(_axis, list):
         for i, a in enumerate(_axis):
             if a is not None and a < 0:
                 _axis[i] = (a % ndim)

keras/backend/theano_backend.py

@@ -446,10 +446,14 @@ def transpose(x):
 
 
 def gather(reference, indices):
-    """reference: a tensor.
-    indices: an int tensor of indices.
+    """Retrieves the elements of indices `indices` in the tensor `reference`.
 
-    Return: a tensor of same type as reference.
+    # Arguments
+        reference: A tensor.
+        indices: An integer tensor of indices.
+
+    # Returns
+        A tensor of same type as `reference`.
     """
     y = reference[indices]
     if hasattr(reference, '_keras_shape') and hasattr(indices, '_keras_shape'):
@@ -930,7 +934,7 @@ def tile(x, n):
                     output_shape += (None,)
                 else:
                     output_shape += (i * j,)
-        elif type(n) is int:
+        elif isinstance(n, int):
             output_shape = x._keras_shape[:-1]
             if x._keras_shape[-1] is None:
                 output_shape += (None,)
@@ -1138,8 +1142,8 @@ def pattern_broadcast(x, broatcastable):
 
 def get_value(x):
     if not hasattr(x, 'get_value'):
-        raise TypeError('get_value() can only be called on a variable. '
-                        'If you have an expression instead, use eval().')
+        raise TypeError('`get_value` can only be called on a variable. '
+                        'If you have an expression instead, use `eval()`.')
     return x.get_value()
 
 
@@ -1386,7 +1390,18 @@ def rnn(step_function, inputs, initial_states,
 
 
 def switch(condition, then_expression, else_expression):
-    """condition: scalar tensor.
+    """Switches between two operations depending on a scalar value.
+
+    Note that both `then_expression` and `else_expression`
+    should be symbolic tensors of the *same shape*.
+
+    # Arguments
+        condition: scalar tensor (`int` or `bool`).
+        then_expression: either a tensor, or a callable that returns a tensor.
+        else_expression: either a tensor, or a callable that returns a tensor.
+
+    # Returns
+        The selected tensor.
     """
     if callable(then_expression):
         then_expression = then_expression()

keras/datasets/cifar100.py

@@ -19,7 +19,7 @@ def load_data(label_mode='fine'):
         ValueError: in case of invalid `label_mode`.
     """
     if label_mode not in ['fine', 'coarse']:
-        raise ValueError('label_mode must be one of "fine" "coarse".')
+        raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.')
 
     dirname = 'cifar-100-python'
     origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'

keras/engine/training.py

@@ -563,7 +563,7 @@ def _standardize_weights(y, sample_weight=None, class_weight=None,
         return sample_weight
     elif isinstance(class_weight, dict):
         if len(y.shape) > 2:
-            raise ValueError('class_weight not supported for '
+            raise ValueError('`class_weight` not supported for '
                              '3+ dimensional targets.')
         if y.shape[1] > 1:
             y_classes = y.argmax(axis=1)
@@ -1756,7 +1756,7 @@ class Model(Container):
             elif len(validation_data) == 3:
                 val_x, val_y, val_sample_weight = validation_data
             else:
-                raise ValueError('validation_data should be a tuple '
+                raise ValueError('`validation_data` should be a tuple '
                                  '`(val_x, val_y, val_sample_weight)` '
                                  'or `(val_x, val_y)`. Found: ' +
                                  str(validation_data))
@@ -1795,7 +1795,7 @@ class Model(Container):
                     generator_output = next(output_generator)
 
                     if not hasattr(generator_output, '__len__'):
-                        raise ValueError('output of generator should be '
+                        raise ValueError('Output of generator should be '
                                          'a tuple `(x, y, sample_weight)` '
                                          'or `(x, y)`. Found: ' +
                                          str(generator_output))
@@ -1805,7 +1805,7 @@ class Model(Container):
                     elif len(generator_output) == 3:
                         x, y, sample_weight = generator_output
                     else:
-                        raise ValueError('output of generator should be '
+                        raise ValueError('Output of generator should be '
                                          'a tuple `(x, y, sample_weight)` '
                                          'or `(x, y)`. Found: ' +
                                          str(generator_output))
@@ -1939,7 +1939,7 @@ class Model(Container):
             while steps_done < steps:
                 generator_output = next(output_generator)
                 if not hasattr(generator_output, '__len__'):
-                    raise ValueError('output of generator should be a tuple '
+                    raise ValueError('Output of generator should be a tuple '
                                      '(x, y, sample_weight) '
                                      'or (x, y). Found: ' +
                                      str(generator_output))
@@ -1949,7 +1949,7 @@ class Model(Container):
                 elif len(generator_output) == 3:
                     x, y, sample_weight = generator_output
                 else:
-                    raise ValueError('output of generator should be a tuple '
+                    raise ValueError('Output of generator should be a tuple '
                                      '(x, y, sample_weight) '
                                      'or (x, y). Found: ' +
                                      str(generator_output))

keras/layers/core.py

@@ -193,8 +193,8 @@ class SpatialDropout2D(Dropout):
         if data_format is None:
             data_format = K.image_data_format()
         if data_format not in {'channels_last', 'channels_first'}:
-            raise ValueError('data_format must be in '
-                             '{"channels_last", "channels_first"}')
+            raise ValueError('`data_format` must be in '
+                             '{`"channels_last"`, `"channels_first"`}')
         self.data_format = data_format
         self.input_spec = InputSpec(ndim=4)
 
@@ -246,8 +246,8 @@ class SpatialDropout3D(Dropout):
         if data_format is None:
             data_format = K.image_data_format()
         if data_format not in {'channels_last', 'channels_first'}:
-            raise ValueError('data_format must be in '
-                             '{"channels_last", "channels_first"}')
+            raise ValueError('`data_format` must be in '
+                             '{`"channels_last"`, `"channels_first"`}')
         self.data_format = data_format
         self.input_spec = InputSpec(ndim=5)
 
@@ -637,7 +637,7 @@ class Lambda(Layer):
         else:
             shape = self._output_shape(input_shape)
             if not isinstance(shape, (list, tuple)):
-                raise ValueError('output_shape function must return a tuple')
+                raise ValueError('`output_shape` function must return a tuple.')
             return tuple(shape)
 
     def call(self, inputs, mask=None):

keras/preprocessing/image.py

@@ -139,7 +139,7 @@ def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,
         ValueError: if `zoom_range` isn't a tuple.
     """
     if len(zoom_range) != 2:
-        raise ValueError('zoom_range should be a tuple or list of two floats. '
+        raise ValueError('`zoom_range` should be a tuple or list of two floats. '
                          'Received arg: ', zoom_range)
 
     if zoom_range[0] == 1 and zoom_range[1] == 1:
@@ -421,8 +421,8 @@ class ImageDataGenerator(object):
         self.preprocessing_function = preprocessing_function
 
         if data_format not in {'channels_last', 'channels_first'}:
-            raise ValueError('data_format should be "channels_last" (channel after row and '
-                             'column) or "channels_first" (channel before row and column). '
+            raise ValueError('`data_format` should be `"channels_last"` (channel after row and '
+                             'column) or `"channels_first"` (channel before row and column). '
                              'Received arg: ', data_format)
         self.data_format = data_format
         if data_format == 'channels_first':
@@ -443,7 +443,7 @@ class ImageDataGenerator(object):
         elif len(zoom_range) == 2:
             self.zoom_range = [zoom_range[0], zoom_range[1]]
         else:
-            raise ValueError('zoom_range should be a float or '
+            raise ValueError('`zoom_range` should be a float or '
                              'a tuple or list of two floats. '
                              'Received arg: ', zoom_range)