def reconstruction(self) -> tf.Tensor:
"""
Computes the reconstruction of the input sequence.
Although an output projection is implicitly added by the decoder RNN, another linear transformation of the
decoder output is performed. This is done to preserve consistency of models between the unidirectional and
bidirectional cases, since in the latter case the decoder output sequence has twice as many features as the
input sequence. Thus, another linear projection is required in the bidirectional case.
Returns
-------
tf.Tensor
The reconstruction of the input sequence, of shape [max_time, batch_size, num_features]
"""
# shape: [num_windows, max_time * batch_size, decoder_frequency.output_size]
decoder_output = self.decoder.output
num_windows = decoder_output.shape.as_list()[
0] # must be known at graph-construction time
# shape: [max_time * batch_size, num_windows, decoder_frequency.output_size]
decoder_output = tf.transpose(decoder_output, [1, 0, 2])
# shape: [max_time * batch_size, num_windows * decoder_frequency.output_size]
decoder_output = tf.reshape(decoder_output, [
self.max_time * self.batch_size,
num_windows * self.decoder.output_size
])
# shape: [max_time * batch_size, num_features]
reconstruction = tf.tanh(
linear(decoder_output, output_size=self.num_features))
reconstruction = restore_time(inputs=reconstruction,
max_time=self.max_time,
batch_size=self.batch_size,
num_features=self.num_features)
tf.add_to_collection("reconstruction", reconstruction)
summaries.reconstruction_summaries(reconstruction, self.targets)
return reconstruction
def reconstruction(self) -> tf.Tensor:
"""
Computes the reconstruction of the input sequence.
Although an output projection is implicitly added by the decoder RNN, another linear transformation of the
decoder output is performed. This is done to preserve consistency of models between the unidirectional and
bidirectional cases, since in the latter case the decoder output sequence has twice as many features as the
input sequence. Thus, another linear projection is required in the bidirectional case.
Returns
-------
tf.Tensor
The reconstruction of the input sequence, of shape [max_time, batch_size, num_features]
"""
output = tf.tanh(
time_distributed_linear(inputs=self.decoder.output,
output_size=self.num_features))
tf.add_to_collection("reconstruction", output)
summaries.reconstruction_summaries(output, self.targets)
return output