text: # Copyright 2018 MLBenchmark Group. 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.
  # ==============================================================================
  """Implementation of fully connected network."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  class FeedFowardNetwork(tf.layers.Layer):
    """Fully connected feedforward network."""
  
    def __init__(self, hidden_size, filter_size, relu_dropout, train, regularizer=None):
      super(FeedFowardNetwork, self).__init__()
      self.hidden_size = hidden_size
      self.filter_size = filter_size
      self.relu_dropout = relu_dropout
      self.train = train
  
      # regularizer = tf.contrib.layers.l2_regularizer(0.0005)
  
      self.filter_dense_layer = tf.layers.Dense(
          filter_size,
          use_bias=True,
          activation=tf.nn.relu,
          name="filter_layer",
          kernel_regularizer=regularizer,
          bias_regularizer=regularizer
      )
      self.output_dense_layer = tf.layers.Dense(
          hidden_size,
          use_bias=True,
          name="output_layer",
          kernel_regularizer=regularizer,
          bias_regularizer=regularizer )
  
    def call(self, x, padding=None):
      # Retrieve dynamically known shapes
      batch_size = tf.shape(x)[0]
      length = tf.shape(x)[1]
  
      if padding is not None:
        with tf.name_scope("remove_padding"):
          # Flatten padding to [batch_size*length]
          pad_mask = tf.reshape(padding, [-1])
  
          nonpad_ids = tf.cast(tf.where(pad_mask < 1e-9), dtype=tf.int32)
  
          # Reshape x to [batch_size*length, hidden_size] to remove padding
          x = tf.reshape(x, [-1, self.hidden_size])
          x = tf.gather_nd(x, indices=nonpad_ids)
  
          # Reshape x from 2 dimensions to 3 dimensions.
          x.set_shape([None, self.hidden_size])
          x = tf.expand_dims(x, axis=0)
  
      output = self.filter_dense_layer(x)
      if self.train:
        output = tf.nn.dropout(output, keep_prob = 1 - self.relu_dropout)
      output = self.output_dense_layer(output)
  
      if padding is not None:
        with tf.name_scope("re_add_padding"):
          output = tf.squeeze(output, axis=0)
          output = tf.scatter_nd(
              indices=nonpad_ids,
              updates=output,
              shape=[batch_size * length, self.hidden_size]
          )
          output = tf.reshape(output, [batch_size, length, self.hidden_size])
      return output
  
  