b68db1aaf6abe4d2cea8321cc6f1564228dd60f5,deepchem/models/tensorgraph/models/seqtoseq.py,SeqToSeq,_create_encoder,#SeqToSeq#Any#Any#,145

Before Change

      prev_layer = layers.GRU(
          self._embedding_dimension, self.batch_size, in_layers=prev_layer)
    prev_layer = layers.Gather(in_layers=[prev_layer, self._gather_indices])
    if self._variational:
      self._embedding_mean = layers.Dense(
          self._embedding_dimension, in_layers=prev_layer)
      self._embedding_stddev = layers.Dense(
          self._embedding_dimension, in_layers=prev_layer)
      prev_layer = layers.CombineMeanStd(
          [self._embedding_mean, self._embedding_stddev], training_only=True)
    return prev_layer

  def _create_decoder(self, n_layers, dropout):
    Create the decoder layers.

After Change

  def _create_encoder(self, n_layers, dropout):
    Create the encoder as a tf.keras.Model.
    input = self._create_features()
    gather_indices = Input(shape=(2,), dtype=tf.int32)
    prev_layer = input
    for i in range(n_layers):
      if dropout > 0.0:
        prev_layer = Dropout(rate=dropout)(prev_layer)
      prev_layer = GRU(
          self._embedding_dimension, return_sequences=True)(prev_layer)
    prev_layer = Lambda(lambda x: tf.gather_nd(x[0], x[1]))(
        [prev_layer, gather_indices])
    return tf.keras.Model(inputs=[input, gather_indices], outputs=prev_layer)

  def _create_decoder(self, n_layers, dropout):
    Create the decoder as a tf.keras.Model.

Instances