def default_params(): params = AttentionSeq2Seq.default_params() params["discriminator_units"] = 256 params["discriminator_loss_multiplier"] = 10.0 params["discriminator_reverse_grad"] = False params["discriminator_mix_context"] = False return params
def create_model(self, params=None):
params_ = AttentionSeq2Seq.default_params().copy()
params_.update({"source.reverse": True})
params_.update(params or {})
return AttentionSeq2Seq(source_vocab_info=self.vocab_info,
target_vocab_info=self.vocab_info,
params=params_)
def create_model(self, mode, params=None):
params_ = AttentionSeq2Seq.default_params().copy()
params_.update(TEST_PARAMS)
params_.update({
"source.reverse": True,
"vocab_source": self.vocab_file.name,
"vocab_target": self.vocab_file.name,
})
params_.update(params or {})
return AttentionSeq2Seq(params=params_, mode=mode)
def create_model(self, mode, params=None):
params_ = AttentionSeq2Seq.default_params().copy()
params_.update(TEST_PARAMS)
params_.update({
"source.reverse": True,
"vocab_source": self.vocab_file.name,
"vocab_target": self.vocab_file.name,
})
params_.update(params or {})
return AttentionSeq2Seq(params=params_, mode=mode)
def default_params():
"""call in configurable class, return default params
"""
params = AttentionSeq2Seq.default_params().copy()
params.update({
"pointer_gen": True,
"coverage": True,
"embedding.share": True,
"attention.class": "AttentionLayerBahdanau",
"attention.params": {}, # Arbitrary attention layer parameters
"bridge.class": "seq2seq.models.bridges.ZeroBridge",
"encoder.class": "seq2seq.encoders.BidirectionalRNNEncoder",
"encoder.params": {}, # Arbitrary parameters for the encoder
"decoder.class": "seq2seq.decoders.CopyGenDecoder",
"decoder.params": {} # Arbitrary parameters for the decoder
})
return params
def test_model(source_path, target_path, vocab_path):
tf.logging.set_verbosity(tf.logging.INFO)
batch_size = 2
# Build model graph
mode = tf.contrib.learn.ModeKeys.TRAIN
params_ = AttentionSeq2Seq.default_params().copy()
params_.update({
"vocab_source": vocab_path,
"vocab_target": vocab_path,
})
model = AttentionSeq2Seq(params=params_, mode=mode)
tf.logging.info(vocab_path)
input_pipeline_ = input_pipeline.ParallelTextInputPipeline(params={
"source_files": [source_path],
"target_files": [target_path]
},
mode=mode)
input_fn = training_utils.create_input_fn(pipeline=input_pipeline_,
batch_size=batch_size)
features, labels = input_fn()
fetches = model(features, labels, None)
fetches = [_ for _ in fetches if _ is not None]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(tf.tables_initializer())
with tf.contrib.slim.queues.QueueRunners(sess):
fetches_ = sess.run(fetches)
return model, fetches_
def create_model(self):
return AttentionSeq2Seq(source_vocab_info=self.vocab_info,
target_vocab_info=self.vocab_info,
params=AttentionSeq2Seq.default_params())