def __init__(self, graph, batch_size=32, en_maxlen=None, maxlen=None):
"""Creates a RNN sequence model for a given Graph instance."""
self.graph = graph
self.session = graph.session
self.saver = graph.saver
self.batch_size = batch_size
self._outputs = graph.outputs
self._final_state = graph.final_state
self._n_examples = graph.n_examples
self._predictions = graph.predictions
self._probs = graph.prediction_probs
self._samples = graph.samples
self.en_maxlen = en_maxlen
self.maxlen = maxlen
self.meta_learn = graph.meta_learn
self._loss = 'loss'
self._train = 'train'
self._meta_train = 'meta_train'
self._count = 'n'
self._policy_ent = 'ent_reg'
self._step_bc = data_utils.BatchConverter(
tuple_keys=['initial_state'], seq_keys=['inputs', 'encoded_context'])
self._step_ba = data_utils.BatchAggregator(
tuple_keys=[self._final_state], seq_keys=[self._outputs])
self._train_bc = data_utils.BatchConverter(['initial_state'],
'inputs targets context'.split())
self._train_ba = data_utils.BatchAggregator(
num_keys=[self._loss, self._policy_ent, self._count])
def __init__(self, graph, batch_size=32, en_maxlen=None, maxlen=None):
"""Creates a RNN seq2seq model for a given Graph object."""
super(RNNSeq2seqModel, self).__init__(
graph, batch_size=batch_size, en_maxlen=en_maxlen, maxlen=maxlen)
self._en_outputs = graph.en_outputs
self._initial_state = graph.initial_state
self._en_initial_state = graph.en_initial_state
self._encode_bc = data_utils.BatchConverter(
tuple_keys=[self._en_initial_state], seq_keys=['context'])
self._encode_ba = data_utils.BatchAggregator(
tuple_keys=[self._initial_state], seq_keys=[self._en_outputs])
def _predict(self, cell_outputs, predictions_node, temperature=1.0):
fetch_list = [predictions_node]
feed_dict = {self._outputs: cell_outputs}
bc = data_utils.BatchConverter(seq_keys=[self._outputs], maxlen=self.maxlen)
ba = data_utils.BatchAggregator(seq_keys=[predictions_node])
result_dict = self.run_epoch(
fetch_list, feed_dict, bc, ba, parameters=dict(temperature=temperature))
outputs = result_dict[predictions_node]
return outputs
def compute_step_logprobs(self,
inputs,
targets,
context=None,
initial_state=None,
parameters=None):
feed_dict = dict(
initial_state=initial_state,
inputs=inputs,
targets=targets,
context=context)
ba = data_utils.BatchAggregator(seq_keys=['step_logprobs'])
fetch_list = ['step_logprobs']
result_dict = self.run_epoch(
fetch_list, feed_dict, self._train_bc, ba, parameters=parameters)
logprobs = result_dict.get('step_logprobs', [])
return logprobs
def compute_probs(self,
inputs,
targets,
context=None,
initial_state=None,
parameters=None):
feed_dict = dict(
initial_state=initial_state,
inputs=inputs,
targets=targets,
context=context)
ba = data_utils.BatchAggregator(tuple_keys=['sequence_probs'])
fetch_list = ['sequence_probs']
result_dict = self.run_epoch(
fetch_list, feed_dict, self._train_bc, ba, parameters=parameters)
probs = [l[0] for l in result_dict.get('sequence_probs', [])]
return probs
def compute_scores(self,
inputs,
targets,
context=None,
initial_state=None,
parameters=None):
"""Computes the scores for the attn based score function."""
feed_dict = dict(
initial_state=initial_state,
inputs=inputs,
targets=targets,
context=context)
ba = data_utils.BatchAggregator(keep_keys=['scores'])
fetch_list = ['scores']
result_dict = self.run_epoch(
fetch_list, feed_dict, self._train_bc, ba, parameters=parameters)
scores = result_dict.get('scores', [])
return scores
