def normalize_padding(value):
padding = value.lower()
allowed = {'valid', 'same', 'causal'}
if N.backend() == 'theano':
allowed.add('full')
if padding not in allowed:
raise ValueError(
'The `padding` argument must be one of "valid", "same" (or "causal" for Conv1D). '
'Received: ' + str(padding))
return padding
def time_dist_dense(x, w, b, dropout=None,
input_dim=None, output_dim=None,
timesteps=None, training=False):
"""
Apply 'W.y + b' for every temporal slice y of x
Inputs
------
@param x : tensor holding time series data
@param w : weight matrix
@param b : bias vector
@param is_training: is the caller in training phase or not
@param dropout : applies dropout to the operation
@param input_dim: (optional) dimensionality of the input
@param output_dim: (optional) dimensionality of the output
@param timesteps: (optional) number of timesteps
Returns
-------
Output tensor
"""
if(not input_dim):
input_dim = N.shape(x)[2]
if(not timesteps):
timesteps = N.shape(x)[1]
if(not output_dim):
output_dim = N.shape(w)[1]
if(dropout is not None and 0. < dropout < 1.):
ones = N.ones_like(N.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = N.dropout(ones, dropout)
expanded_dropout_matrix = N.repeat(dropout_matrix, timesteps)
if(training):
x = x * expanded_dropout_matrix
#collpse time dimension and batch dimension together
x = N.reshape(x, (-1, input_dim))
x = N.dot(x, w)
x += b
#reshape to 3D
if N.backend() == 'tensorflow':
x = N.reshape(x, N.stack([-1, timesteps, output_dim]))
x.set_shape([None, None, output_dim])
else:
x = N.reshape(x, (-1, timesteps, output_dim))
return x