def model_fn(features, labels, mode, params):
code_id = features[str_code_id]
token_ids = features[str_desc_tokens]
#mode = tf.estimator.ModeKeys.TRAIN
loss = build_model(code_id, token_ids, dim, max_seq, n_input_voca,
n_output_voca)
#logging_hook = tf.train.LoggingTensorHook({"loss": loss}, every_n_iter=1)
logging_hook = _LoggerHook(loss, 1)
num_train_step = 1000
train_op = create_optimizer(loss, lr, num_train_step, 0, False)
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
voca_mask = features["voca_mask"]
segment_ids = features["segment_ids"]
masked_lm_positions = features["masked_lm_positions"]
masked_lm_ids = features["masked_lm_ids"]
masked_lm_weights = features["masked_lm_weights"]
next_sentence_labels = features["next_sentence_labels"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = modeling.IBertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
voca_mask=voca_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss,
masked_lm_log_probs) = get_masked_lm_output(
bert_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions, masked_lm_ids,
masked_lm_weights)
(next_sentence_loss, next_sentence_example_loss,
next_sentence_log_probs) = get_next_sentence_output(
bert_config, model.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss + next_sentence_loss
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint,
assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(total_loss, learning_rate,
num_train_steps,
num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(masked_lm_example_loss, masked_lm_log_probs,
masked_lm_ids, masked_lm_weights,
next_sentence_example_loss, next_sentence_log_probs,
next_sentence_labels):
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(
masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = tf.argmax(masked_lm_log_probs,
axis=-1,
output_type=tf.int32)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss,
[-1])
masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = tf.reshape(
next_sentence_log_probs,
[-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = tf.argmax(next_sentence_log_probs,
axis=-1,
output_type=tf.int32)
next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
next_sentence_accuracy = tf.metrics.accuracy(
labels=next_sentence_labels,
predictions=next_sentence_predictions)
next_sentence_mean_loss = tf.metrics.mean(
values=next_sentence_example_loss)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
eval_metrics = (metric_fn, [
masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, next_sentence_example_loss,
next_sentence_log_probs, next_sentence_labels
])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
raise ValueError("Only TRAIN and EVAL modes are supported: %s" %
(mode))
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_real_example = None
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, logits,
probabilities) = create_model(bert_config, is_training, input_ids,
input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint,
assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(total_loss, learning_rate,
num_train_steps,
num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits,
is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
accuracy = tf.metrics.accuracy(labels=label_ids,
predictions=predictions,
weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss,
weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn, [
per_example_loss, label_ids, logits, is_real_example
])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"probabilities": probabilities},
scaffold_fn=scaffold_fn)
return output_spec