text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Locally-connected layers.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.keras.python.keras import activations
  from tensorflow.contrib.keras.python.keras import backend as K
  from tensorflow.contrib.keras.python.keras import constraints
  from tensorflow.contrib.keras.python.keras import initializers
  from tensorflow.contrib.keras.python.keras import regularizers
  from tensorflow.contrib.keras.python.keras.engine import InputSpec
  from tensorflow.contrib.keras.python.keras.engine import Layer
  from tensorflow.contrib.keras.python.keras.utils import conv_utils
  from tensorflow.python.framework import tensor_shape
  
  
  class LocallyConnected1D(Layer):
    """Locally-connected layer for 1D inputs.
  
    The `LocallyConnected1D` layer works similarly to
    the `Conv1D` layer, except that weights are unshared,
    that is, a different set of filters is applied at each different patch
    of the input.
  
    Example:
    ```python
        # apply a unshared weight convolution 1d of length 3 to a sequence with
        # 10 timesteps, with 64 output filters
        model = Sequential()
        model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
        # now model.output_shape == (None, 8, 64)
        # add a new conv1d on top
        model.add(LocallyConnected1D(32, 3))
        # now model.output_shape == (None, 6, 32)
    ```
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of a single integer,
            specifying the length of the 1D convolution window.
        strides: An integer or tuple/list of a single integer,
            specifying the stride length of the convolution.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: One of `"valid"` or `"same"` (case-insensitive).
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        3D tensor with shape: `(batch_size, steps, input_dim)`
  
    Output shape:
        3D tensor with shape: `(batch_size, new_steps, filters)`
        `steps` value might have changed due to padding or strides.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(LocallyConnected1D, self).__init__(**kwargs)
      self.filters = filters
      self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
      self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
      self.padding = conv_utils.normalize_padding(padding)
      if self.padding != 'valid':
        raise ValueError('Invalid border mode for LocallyConnected1D '
                         '(only "valid" is supported): ' + padding)
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.activation = activations.get(activation)
      self.use_bias = use_bias
      self.kernel_initializer = initializers.get(kernel_initializer)
      self.bias_initializer = initializers.get(bias_initializer)
      self.kernel_regularizer = regularizers.get(kernel_regularizer)
      self.bias_regularizer = regularizers.get(bias_regularizer)
      self.activity_regularizer = regularizers.get(activity_regularizer)
      self.kernel_constraint = constraints.get(kernel_constraint)
      self.bias_constraint = constraints.get(bias_constraint)
      self.input_spec = InputSpec(ndim=3)
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      input_dim = input_shape[2]
      if input_dim is None:
        raise ValueError('Axis 2 of input should be fully-defined. '
                         'Found shape:', input_shape)
      output_length = conv_utils.conv_output_length(
          input_shape[1], self.kernel_size[0], self.padding, self.strides[0])
      self.kernel_shape = (output_length, self.kernel_size[0] * input_dim,
                           self.filters)
      self.kernel = self.add_weight(
          self.kernel_shape,
          initializer=self.kernel_initializer,
          name='kernel',
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint)
      if self.use_bias:
        self.bias = self.add_weight(
            (output_length, self.filters),
            initializer=self.bias_initializer,
            name='bias',
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint)
      else:
        self.bias = None
      self.input_spec = InputSpec(ndim=3, axes={2: input_dim})
      self.built = True
  
    def _compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      length = conv_utils.conv_output_length(input_shape[1], self.kernel_size[0],
                                             self.padding, self.strides[0])
      return tensor_shape.TensorShape([input_shape[0], length, self.filters])
  
    def call(self, inputs):
      stride = self.strides[0]
      output_length, feature_dim, filters = self.kernel_shape
  
      xs = []
      for i in range(output_length):
        slice_length = slice(i * stride, i * stride + self.kernel_size[0])
        xs.append(K.reshape(inputs[:, slice_length, :], (1, -1, feature_dim)))
      x_aggregate = K.concatenate(xs, axis=0)
      # Shape: `(output_length, batch_size, filters)`.
      output = K.batch_dot(x_aggregate, self.kernel)
      output = K.permute_dimensions(output, (1, 0, 2))
  
      if self.use_bias:
        output += K.reshape(self.bias, (1, output_length, filters))
      if self.activation is not None:
        output = self.activation(output)
      return output
  
    def get_config(self):
      config = {
          'filters':
              self.filters,
          'kernel_size':
              self.kernel_size,
          'strides':
              self.strides,
          'padding':
              self.padding,
          'activation':
              activations.serialize(self.activation),
          'use_bias':
              self.use_bias,
          'kernel_initializer':
              initializers.serialize(self.kernel_initializer),
          'bias_initializer':
              initializers.serialize(self.kernel_initializer),
          'kernel_regularizer':
              regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint':
              constraints.serialize(self.kernel_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(LocallyConnected1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class LocallyConnected2D(Layer):
    """Locally-connected layer for 2D inputs.
  
    The `LocallyConnected2D` layer works similarly
    to the `Conv2D` layer, except that weights are unshared,
    that is, a different set of filters is applied at each
    different patch of the input.
  
    Examples:
    ```python
        # apply a 3x3 unshared weights convolution with 64 output filters on a
        32x32 image
        # with `data_format="channels_last"`:
        model = Sequential()
        model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
        # now model.output_shape == (None, 30, 30, 64)
        # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64
        parameters
  
        # add a 3x3 unshared weights convolution on top, with 32 output filters:
        model.add(LocallyConnected2D(32, (3, 3)))
        # now model.output_shape == (None, 28, 28, 32)
    ```
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            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 "channels_last".
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        4D tensor with shape:
        `(samples, channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
        4D tensor with shape:
        `(samples, filters, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(LocallyConnected2D, self).__init__(**kwargs)
      self.filters = filters
      self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
      self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
      self.padding = conv_utils.normalize_padding(padding)
      if self.padding != 'valid':
        raise ValueError('Invalid border mode for LocallyConnected2D '
                         '(only "valid" is supported): ' + padding)
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.activation = activations.get(activation)
      self.use_bias = use_bias
      self.kernel_initializer = initializers.get(kernel_initializer)
      self.bias_initializer = initializers.get(bias_initializer)
      self.kernel_regularizer = regularizers.get(kernel_regularizer)
      self.bias_regularizer = regularizers.get(bias_regularizer)
      self.activity_regularizer = regularizers.get(activity_regularizer)
      self.kernel_constraint = constraints.get(kernel_constraint)
      self.bias_constraint = constraints.get(bias_constraint)
      self.input_spec = InputSpec(ndim=4)
  
    def build(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_last':
        input_row, input_col = input_shape[1:-1]
        input_filter = input_shape[3]
      else:
        input_row, input_col = input_shape[2:]
        input_filter = input_shape[1]
      if input_row is None or input_col is None:
        raise ValueError('The spatial dimensions of the inputs to '
                         ' a LocallyConnected2D layer '
                         'should be fully-defined, but layer received '
                         'the inputs shape ' + str(input_shape))
  
      output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0],
                                                 self.padding, self.strides[0])
      output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1],
                                                 self.padding, self.strides[1])
      self.output_row = output_row
      self.output_col = output_col
      self.kernel_shape = (output_row * output_col, self.kernel_size[0] *
                           self.kernel_size[1] * input_filter, self.filters)
      self.kernel = self.add_weight(
          self.kernel_shape,
          initializer=self.kernel_initializer,
          name='kernel',
          regularizer=self.kernel_regularizer,
          constraint=self.kernel_constraint)
      if self.use_bias:
        self.bias = self.add_weight(
            (output_row, output_col, self.filters),
            initializer=self.bias_initializer,
            name='bias',
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint)
      else:
        self.bias = None
      if self.data_format == 'channels_first':
        self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
      else:
        self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
      self.built = True
  
    def _compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        rows = input_shape[2]
        cols = input_shape[3]
      elif self.data_format == 'channels_last':
        rows = input_shape[1]
        cols = input_shape[2]
      rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
                                           self.padding, self.strides[0])
      cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
                                           self.padding, self.strides[1])
  
      if self.data_format == 'channels_first':
        return tensor_shape.TensorShape(
            [input_shape[0], self.filters, rows, cols])
      elif self.data_format == 'channels_last':
        return tensor_shape.TensorShape(
            [input_shape[0], rows, cols, self.filters])
  
    def call(self, inputs):
      stride_row, stride_col = self.strides
      _, feature_dim, filters = self.kernel_shape
  
      if self.data_format == 'channels_first':
        if K.backend() == 'theano':
          output = []
          for i in range(self.output_row):
            for j in range(self.output_col):
              slice_row = slice(i * stride_row,
                                i * stride_row + self.kernel_size[0])
              slice_col = slice(j * stride_col,
                                j * stride_col + self.kernel_size[1])
              x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
                                    (1, -1, feature_dim))
              output.append(
                  K.dot(x_flatten, self.kernel[i * self.output_col + j, :, :]))
          output = K.concatenate(output, axis=0)
        else:
          xs = []
          for i in range(self.output_row):
            for j in range(self.output_col):
              slice_row = slice(i * stride_row,
                                i * stride_row + self.kernel_size[0])
              slice_col = slice(j * stride_col,
                                j * stride_col + self.kernel_size[1])
              xs.append(
                  K.reshape(inputs[:, :, slice_row, slice_col], (1, -1,
                                                                 feature_dim)))
          x_aggregate = K.concatenate(xs, axis=0)
          output = K.batch_dot(x_aggregate, self.kernel)
        output = K.reshape(output, (self.output_row, self.output_col, -1,
                                    filters))
        output = K.permute_dimensions(output, (2, 3, 0, 1))
  
      elif self.data_format == 'channels_last':
        xs = []
        for i in range(self.output_row):
          for j in range(self.output_col):
            slice_row = slice(i * stride_row,
                              i * stride_row + self.kernel_size[0])
            slice_col = slice(j * stride_col,
                              j * stride_col + self.kernel_size[1])
            xs.append(
                K.reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim
                                                              )))
        x_aggregate = K.concatenate(xs, axis=0)
        output = K.batch_dot(x_aggregate, self.kernel)
        output = K.reshape(output, (self.output_row, self.output_col, -1,
                                    filters))
        output = K.permute_dimensions(output, (2, 0, 1, 3))
  
      if self.use_bias:
        if self.data_format == 'channels_first':
          output += K.reshape(self.bias, (1, filters, self.output_row,
                                          self.output_col))
        elif self.data_format == 'channels_last':
          output += K.reshape(self.bias, (1, self.output_row, self.output_col,
                                          filters))
      output = self.activation(output)
      return output
  
    def get_config(self):
      config = {
          'filters':
              self.filters,
          'kernel_size':
              self.kernel_size,
          'strides':
              self.strides,
          'padding':
              self.padding,
          'data_format':
              self.data_format,
          'activation':
              activations.serialize(self.activation),
          'use_bias':
              self.use_bias,
          'kernel_initializer':
              initializers.serialize(self.kernel_initializer),
          'bias_initializer':
              initializers.serialize(self.kernel_initializer),
          'kernel_regularizer':
              regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint':
              constraints.serialize(self.kernel_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(LocallyConnected2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  