def _create_model_from_scratch(albert_config, is_training, input_ids,
input_mask, segment_ids, use_one_hot_embeddings):
"""Creates an ALBERT model from scratch (as opposed to hub)."""
model = modeling.AlbertModel(
config=albert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
output_layer = model.get_pooled_output()
return output_layer
def __init__(self, is_training):
# Training or not
self.is_training = is_training
# Placeholder
self.input_ids = tf.placeholder(tf.int32,
shape=[None, hp.sequence_length],
name='input_ids')
self.input_masks = tf.placeholder(tf.int32,
shape=[None, hp.sequence_length],
name='input_masks')
self.segment_ids = tf.placeholder(tf.int32,
shape=[None, hp.sequence_length],
name='segment_ids')
self.label_ids = tf.placeholder(tf.float32,
shape=[None, hp.num_labels],
name='label_ids')
# Load BERT model
self.model = modeling.AlbertModel(config=bert_config,
is_training=self.is_training,
input_ids=self.input_ids,
input_mask=self.input_masks,
token_type_ids=self.segment_ids,
use_one_hot_embeddings=False)
# Get the feature vector by BERT
output_layer_init = self.model.get_sequence_output()
# Cell TextCNN
output_layer = cell_textcnn(output_layer_init, self.is_training)
# Hidden size
hidden_size = output_layer.shape[-1].value
# Full-connection
with tf.name_scope("Full-connection"):
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
logits = tf.nn.bias_add(
tf.matmul(output_layer, output_weights, transpose_b=True),
output_bias)
# Prediction sigmoid(Multi-label)
self.probabilities = tf.nn.sigmoid(logits)
with tf.variable_scope("Prediction"):
# Prediction
zero = tf.zeros_like(logits)
one = tf.ones_like(logits)
self.predictions = tf.where(logits < 0.5, x=zero, y=one)
with tf.variable_scope("loss"):
# Summary for tensorboard
if self.is_training:
self.accuracy = tf.reduce_mean(
tf.to_float(tf.equal(self.predictions, self.label_ids)))
tf.summary.scalar('accuracy', self.accuracy)
# Initial embedding by BERT
ckpt = tf.train.get_checkpoint_state(hp.saved_model_path)
checkpoint_suffix = ".index"
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path +
checkpoint_suffix):
print('=' * 10, 'Restoring model from checkpoint!', '=' * 10)
print("%s - Restoring model from checkpoint ~%s" %
(time_now_string(), ckpt.model_checkpoint_path))
else:
print('=' * 10, 'First time load BERT model!', '=' * 10)
tvars = tf.trainable_variables()
if hp.init_checkpoint:
(assignment_map, initialized_variable_names) = \
modeling.get_assignment_map_from_checkpoint(tvars,
hp.init_checkpoint)
tf.train.init_from_checkpoint(hp.init_checkpoint,
assignment_map)
# Loss and Optimizer
if self.is_training:
# Global_step
self.global_step = tf.Variable(0,
name='global_step',
trainable=False)
per_example_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.label_ids, logits=logits)
self.loss = tf.reduce_mean(per_example_loss)
# Optimizer BERT
train_examples = processor.get_train_examples(hp.data_dir)
num_train_steps = int(
len(train_examples) / hp.batch_size * hp.num_train_epochs)
num_warmup_steps = int(num_train_steps * hp.warmup_proportion)
print('num_train_steps', num_train_steps)
self.optimizer = optimization.create_optimizer(
self.loss,
hp.learning_rate,
num_train_steps,
num_warmup_steps,
hp.use_tpu,
Global_step=self.global_step)
# Summary for tensorboard
tf.summary.scalar('loss', self.loss)
self.merged = tf.summary.merge_all()