def standard_nextstep_sample(cell, inputs, output_size, embedding, scope=None,
argmax=False, softmax_temperature=1):
"""Generate samples from the standard next step prediction model.
Assumes that we are modelling sequence of discrete symbols.
Args:
cell (tf.nn.rnn_cell.RNNCell): a cell to reproduce the model.
inputs: input variable, all but the first is ignored.
output_size (int): the size of the vocabulary.
embedding: the embedding matrix used.
scope (Optional): variable scope, needs to line up with what was used
to make the model for inference the first time around.
argmax (Optional[bool]): if True, then instead of sampling we simply
take the argmax of the logits, if False we put a softmax on
first. Defaults to False.
softmax_temperature (Optional[bool]): the temparature for softmax.
The logits are divided by this before feeding into the softmax:
a high value means higher entropy. Default 1.
Returns:
tuple: (initial_state, sequence, final_state) where
- *initial_state* is a variable for the starting state of the net.
- *sequence* is a list of len(inputs) length containing the sampled
symbols.
- *final_state* the finishing state of the network, to pass along.
"""
# have to be quite careful to ensure the scopes line up
with tf.variable_scope(scope or 'rnn') as scope:
inputs = tf.unpack(inputs)
batch_size = inputs[0].get_shape()[0].value
initial_state = cell.zero_state(batch_size, tf.float32)
# get the output weights
with tf.variable_scope('output_layer'):
weights = tf.get_variable('weights',
[cell.output_size, output_size])
biases = tf.get_variable('bias',
[output_size])
# choose an appropriate loop function
sequence = []
if argmax:
loop_fn = argmax_and_embed(embedding, output_list=sequence,
output_projection=(weights, biases))
else:
loop_fn = sample_and_embed(embedding, softmax_temperature,
output_list=sequence,
output_projection=(weights, biases))
all_states, fstate = tf.nn.seq2seq.rnn_decoder(
inputs, initial_state, cell, loop_function=loop_fn, scope=scope)
return [initial_state], sequence, [fstate]
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