def build(config, is_training):
if not isinstance(config, bidirectional_rnn_pb2.BidirectionalRnn):
raise ValueError(
'config not of type bidirectional_rnn_pb2.BidirectionalRnn')
if config.static:
brnn_class = bidirectional_rnn.StaticBidirectionalRnn
else:
brnn_class = bidirectional_rnn.DynamicBidirectionalRnn
fw_cell_object = rnn_cell_builder.build(config.fw_bw_rnn_cell)
bw_cell_object = rnn_cell_builder.build(config.fw_bw_rnn_cell)
rnn_regularizer_object = hyperparams_builder._build_regularizer(
config.rnn_regularizer)
fc_hyperparams_object = None
if config.num_output_units > 0:
if config.fc_hyperparams.op != hyperparams_pb2.Hyperparams.FC:
raise ValueError('op type must be FC')
fc_hyperparams_object = hyperparams_builder.build(
config.fc_hyperparams, is_training)
return brnn_class(fw_cell_object,
bw_cell_object,
rnn_regularizer=rnn_regularizer_object,
num_output_units=config.num_output_units,
fc_hyperparams=fc_hyperparams_object,
summarize_activations=config.summarize_activations)
def build(config, is_training):
if not isinstance(config, predictor_pb2.Predictor):
raise ValueError('config not of type predictor_pb2.AttentionPredictor')
predictor_oneof = config.WhichOneof('predictor_oneof')
if predictor_oneof == 'attention_predictor':
predictor_config = config.attention_predictor
rnn_cell_object = rnn_cell_builder.build(predictor_config.rnn_cell)
rnn_regularizer_object = hyperparams_builder._build_regularizer(predictor_config.rnn_regularizer)
label_map_object = label_map_builder.build(predictor_config.label_map)
loss_object = loss_builder.build(predictor_config.loss)
if not predictor_config.HasField('lm_rnn_cell'):
lm_rnn_cell_object = None
else:
lm_rnn_cell_object = _build_language_model_rnn_cell(predictor_config.lm_rnn_cell)
attention_predictor_object = attention_predictor.AttentionPredictor(
rnn_cell=rnn_cell_object,
rnn_regularizer=rnn_regularizer_object,
num_attention_units=predictor_config.num_attention_units,
max_num_steps=predictor_config.max_num_steps,
multi_attention=predictor_config.multi_attention,
beam_width=predictor_config.beam_width,
reverse=predictor_config.reverse,
label_map=label_map_object,
loss=loss_object,
sync=predictor_config.sync,
lm_rnn_cell=lm_rnn_cell_object,
is_training=is_training
)
return attention_predictor_object
else:
raise ValueError('Unknown predictor_oneof: {}'.format(predictor_oneof))
def _build_language_model_rnn_cell(config):
if not isinstance(config, predictor_pb2.LanguageModelRnnCell):
raise ValueError('config not of type predictor_pb2.LanguageModelRnnCell')
rnn_cell_list = [
rnn_cell_builder.build(rnn_cell_config) for rnn_cell_config in config.rnn_cell
]
lm_rnn_cell = rnn.MultiRNNCell(rnn_cell_list)
return lm_rnn_cell
def test_build_gru_cell(self):
rnn_cell_text_proto = """
gru_cell {
num_units: 1024
initializer { orthogonal_initializer { seed: 1 } }
}
"""
rnn_cell_proto = rnn_cell_pb2.RnnCell()
text_format.Merge(rnn_cell_text_proto, rnn_cell_proto)
rnn_cell_object = rnn_cell_builder.build(rnn_cell_proto)
self.assertEqual(rnn_cell_object.state_size, 1024)
def test_build_lstm_cell(self):
rnn_cell_text_proto = """
lstm_cell {
num_units: 1024
use_peepholes: true
forget_bias: 1.5
initializer { orthogonal_initializer { seed: 1 } }
}
"""
rnn_cell_proto = rnn_cell_pb2.RnnCell()
text_format.Merge(rnn_cell_text_proto, rnn_cell_proto)
rnn_cell_object = rnn_cell_builder.build(rnn_cell_proto)
lstm_state_tuple = rnn_cell_object.state_size
self.assertEqual(lstm_state_tuple[0], 1024)
self.assertEqual(lstm_state_tuple[1], 1024)