def predict(self):
"""Predicts result from the model."""
params, flags_obj, is_train = self.params, self.flags_obj, False
with tf.name_scope("model"):
model = transformer.create_model(params, is_train)
self._load_weights_if_possible(model, flags_obj.init_weight_path)
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
def train(self):
"""Trains the model."""
params, flags_obj, is_train = self.params, self.flags_obj, True
model = transformer.create_model(params, is_train)
opt = self._create_optimizer()
model.compile(opt, target_tensors=[])
model.summary()
self._load_weights_if_possible(model, flags_obj.init_weight_path)
cur_log_dir = _get_log_dir_or_default(flags_obj)
_ensure_dir(cur_log_dir)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = data_pipeline.train_input_fn(params)
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=params["num_parallel_calls"])
valid_ds = data_pipeline.eval_input_fn(params)
valid_ds = valid_ds.map(
map_data_fn, num_parallel_calls=params["num_parallel_calls"])
init_epoch = flags_obj.init_epoch or 0
init_steps = init_epoch * flags_obj.steps_per_epoch
callbacks = self._create_callbacks(cur_log_dir, init_steps, params)
history = model.fit(train_ds,
initial_epoch=init_epoch,
epochs=flags_obj.train_epochs,
steps_per_epoch=flags_obj.steps_per_epoch,
validation_data=valid_ds,
validation_steps=flags_obj.validation_steps,
callbacks=callbacks)
tf.compat.v1.logging.info("\nTrain history: {}".format(
history.history))
save_weight_path = os.path.join(cur_log_dir,
"saves-model-weights.hdf5")
save_model_path = os.path.join(cur_log_dir, "saves-model.hdf5")
model.save_weights(save_weight_path)
model.save(save_model_path)
def predict(self):
"""Predicts result from the model."""
self.params['train'] = False
params = self.params
flags_obj = self.flags_obj
with tf.name_scope("model"):
model = transformer.create_model(params, is_train=False)
self._load_weights_if_possible(
model, tf.train.latest_checkpoint(self.flags_obj.model_dir))
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
print(params)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
import time
start = time.time()
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
print('\n\n\n', time.time() - start)
