def _get_classifier_model():
classifier_model, core_model = (
bert_models.classifier_model(bert_config, tf.float32, num_classes,
max_seq_length))
classifier_model.optimizer = optimization.create_optimizer(
initial_lr, steps_per_epoch * epochs, warmup_steps)
return classifier_model, core_model
def _get_classifier_model():
"""Gets a classifier model."""
classifier_model, core_model = (
bert_models.classifier_model(bert_config, tf.float32, num_classes,
max_seq_length))
classifier_model.optimizer = optimization.create_optimizer(
initial_lr, steps_per_epoch * epochs, warmup_steps)
if FLAGS.fp16_implementation == 'graph_rewrite':
# Note: when flags_obj.fp16_implementation == "graph_rewrite", dtype as
# determined by flags_core.get_tf_dtype(flags_obj) would be 'float32'
# which will ensure tf.compat.v2.keras.mixed_precision and
# tf.train.experimental.enable_mixed_precision_graph_rewrite do not double
# up.
classifier_model.optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
classifier_model.optimizer)
return classifier_model, core_model
def export_classifier(model_export_path, input_meta_data):
"""Exports a trained model as a `SavedModel` for inference.
Args:
model_export_path: a string specifying the path to the SavedModel directory.
input_meta_data: dictionary containing meta data about input and model.
Raises:
Export path is not specified, got an empty string or None.
"""
if not model_export_path:
raise ValueError('Export path is not specified: %s' % model_export_path)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
classifier_model = bert_models.classifier_model(
bert_config, tf.float32, input_meta_data['num_labels'],
input_meta_data['max_seq_length'])[0]
model_saving_utils.export_bert_model(
model_export_path, model=classifier_model, checkpoint_dir=FLAGS.model_dir)
def _model_fn():
return bert_models.classifier_model(
bert_config, tf.float32, input_meta_data['num_labels'],
input_meta_data['max_seq_length'])[0]
