예제 #1
0
def _time_distributed_dense(x,
                            w,
                            b=None,
                            dropout=None,
                            input_dim=None,
                            output_dim=None,
                            timesteps=None,
                            training=None):
  """Apply `y . w + b` for every temporal slice y of x.

  Arguments:
      x: input tensor.
      w: weight matrix.
      b: optional bias vector.
      dropout: whether to apply dropout (same dropout mask
          for every temporal slice of the input).
      input_dim: integer; optional dimensionality of the input.
      output_dim: integer; optional dimensionality of the output.
      timesteps: integer; optional number of timesteps.
      training: training phase tensor or boolean.

  Returns:
      Output tensor.
  """
  if not input_dim:
    input_dim = K.shape(x)[2]
  if not timesteps:
    timesteps = K.shape(x)[1]
  if not output_dim:
    output_dim = K.shape(w)[1]

  if dropout is not None and 0. < dropout < 1.:
    # apply the same dropout pattern at every timestep
    ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
    dropout_matrix = K.dropout(ones, dropout)
    expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
    x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)

  # collapse time dimension and batch dimension together
  x = K.reshape(x, (-1, input_dim))
  x = K.dot(x, w)
  if b is not None:
    x = K.bias_add(x, b)
  # reshape to 3D tensor
  if K.backend() == 'tensorflow':
    x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
    x.set_shape([None, None, output_dim])
  else:
    x = K.reshape(x, (-1, timesteps, output_dim))
  return x
 def dropped_inputs():  # pylint: disable=function-redefined
     return K.dropout(ones, self.recurrent_dropout)
 def dropped_inputs():
     return K.dropout(ones, self.dropout)
예제 #4
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 def dropped_inputs():  # pylint: disable=function-redefined
   return K.dropout(ones, self.recurrent_dropout)
예제 #5
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 def dropped_inputs():
   return K.dropout(ones, self.dropout)