def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
unique_ids = features["unique_ids"]
input_ids = features["input_ids"]
input_mask = features["input_mask"]
input_type_ids = features["input_type_ids"]
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
with jit_scope():
model = modeling.BertModel(config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=input_type_ids)
if mode != tf.estimator.ModeKeys.PREDICT:
raise ValueError("Only PREDICT modes are supported: %s" %
(mode))
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
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)
all_layers = model.get_all_encoder_layers()
predictions = {
"unique_id": unique_ids,
}
for (i, layer_index) in enumerate(layer_indexes):
predictions["layer_output_%d" %
i] = all_layers[layer_index]
from tensorflow.python.estimator.model_fn import EstimatorSpec
output_spec = EstimatorSpec(mode=mode, predictions=predictions)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
from tensorflow.python.estimator.model_fn import EstimatorSpec
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_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, logits,
probabilities) = self.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 = {}
if init_checkpoint:
(assignment_map, initialized_variable_names) \
= modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
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)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps,
num_warmup_steps, False)
output_spec = EstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits):
predictions = tf.argmax(logits,
axis=-1,
output_type=tf.int32)
accuracy = tf.metrics.accuracy(label_ids, predictions)
auc = tf.metrics.auc(label_ids, predictions)
loss = tf.metrics.mean(per_example_loss)
return {
"eval_accuracy": accuracy,
"eval_auc": auc,
"eval_loss": loss,
}
eval_metrics = metric_fn(per_example_loss, label_ids, logits)
output_spec = EstimatorSpec(mode=mode,
loss=total_loss,
eval_metric_ops=eval_metrics)
else:
output_spec = EstimatorSpec(mode=mode,
predictions=probabilities)
return output_spec