def build_logits(self, features, mode=None):
""" Building graph of KD Student
Args:
features (`OrderedDict`): A dict mapping raw input to tensors
mode (`bool): tell the model whether it is under training
Returns:
logits (`list`): logits for all the layers, list of shape of [None, num_labels]
label_ids (`Tensor`): label_ids, shape of [None]
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
preprocessor = preprocessors.get_preprocessor(
self.config.pretrain_model_name_or_path,
user_defined_config=self.config)
bert_backbone = model_zoo.get_pretrained_model(
self.config.pretrain_model_name_or_path)
if mode != tf.estimator.ModeKeys.PREDICT:
teacher_logits, input_ids, input_mask, segment_ids, label_ids = preprocessor(
features)
else:
teacher_logits, input_ids, input_mask, segment_ids = preprocessor(
features)
label_ids = None
teacher_n_layers = int(
teacher_logits.shape[1]) / self.config.num_labels - 1
self.teacher_logits = [
teacher_logits[:, i * self.config.num_labels:(i + 1) *
self.config.num_labels]
for i in range(teacher_n_layers + 1)
]
if self.config.train_probes:
bert_model = bert_backbone.bert
embedding_output = bert_model.embeddings([input_ids, segment_ids],
training=is_training)
attention_mask = layers.get_attn_mask_bert(input_ids, input_mask)
all_hidden_outputs, all_att_outputs = bert_model.encoder(
[embedding_output, attention_mask], training=is_training)
# Get teacher Probes
logits = layers.HiddenLayerProbes(
self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name="probes")([embedding_output, all_hidden_outputs])
else:
_, pooled_output = bert_backbone(
[input_ids, input_mask, segment_ids], mode=mode)
pooled_output = tf.layers.dropout(pooled_output,
rate=self.config.dropout_rate,
training=is_training)
logits = layers.Dense(
self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='app/ez_dense')(pooled_output)
logits = [logits]
return logits, label_ids
def build_logits(self, features, mode=None):
""" Building graph of KD Teacher
Args:
features (`OrderedDict`): A dict mapping raw input to tensors
mode (`bool): tell the model whether it is under training
Returns:
logits (`list`): logits for all the layers, list of shape of [None, num_labels]
label_ids (`Tensor`): label_ids, shape of [None]
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
preprocessor = preprocessors.get_preprocessor(
self.config.pretrain_model_name_or_path,
user_defined_config=self.config)
bert_backbone = model_zoo.get_pretrained_model(
self.config.pretrain_model_name_or_path)
# Serialize raw text to get input tensors
input_ids, input_mask, segment_ids, label_id = preprocessor(features)
if self.config.train_probes:
# Get BERT all hidden states
bert_model = bert_backbone.bert
embedding_output = bert_model.embeddings([input_ids, segment_ids],
training=is_training)
attention_mask = layers.get_attn_mask_bert(input_ids, input_mask)
all_hidden_outputs, all_att_outputs = bert_model.encoder(
[embedding_output, attention_mask], training=is_training)
# Get teacher Probes
logits = layers.HiddenLayerProbes(
self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name="probes")([embedding_output, all_hidden_outputs])
self.tvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
"probes/")
else:
_, pooled_output = bert_backbone(
[input_ids, input_mask, segment_ids], mode=mode)
pooled_output = tf.layers.dropout(pooled_output,
rate=self.config.dropout_rate,
training=is_training)
logits = layers.Dense(
self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='app/ez_dense')(pooled_output)
logits = [logits]
if mode == tf.estimator.ModeKeys.PREDICT:
return {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
"label_id": label_id,
"logits": tf.concat(logits, axis=-1)
}
else:
return logits, label_id
def build_logits(self, features, mode=None):
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path)
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path)
global_step = tf.train.get_or_create_global_step()
tnews_dense = layers.Dense(
15,
kernel_initializer=layers.get_initializer(0.02),
name='tnews_dense')
ocemotion_dense = layers.Dense(
7,
kernel_initializer=layers.get_initializer(0.02),
name='ocemotion_dense')
ocnli_dense = layers.Dense(
3,
kernel_initializer=layers.get_initializer(0.02),
name='ocnli_dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
outputs = model([input_ids, input_mask, segment_ids], mode=mode)
pooled_output = outputs[1]
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output = tf.nn.dropout(pooled_output, keep_prob=0.9)
logits = tf.case([
(tf.equal(tf.mod(global_step, 3),
0), lambda: tnews_dense(pooled_output)),
(tf.equal(tf.mod(global_step, 3),
1), lambda: ocemotion_dense(pooled_output)),
(tf.equal(tf.mod(global_step, 3),
2), lambda: ocnli_dense(pooled_output)),
],
exclusive=True)
if mode == tf.estimator.ModeKeys.PREDICT:
ret = {
"tnews_logits": tnews_dense(pooled_output),
"ocemotion_logits": ocemotion_dense(pooled_output),
"ocnli_logits": ocnli_dense(pooled_output),
"label_ids": label_ids
}
return ret
return logits, label_ids
def build_logits(self, features, mode=None):
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path)
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path)
global_step = tf.train.get_or_create_global_step()
tnews_dense = layers.Dense(
15,
kernel_initializer=layers.get_initializer(0.02),
name='tnews_dense')
ocemotion_dense = layers.Dense(
7,
kernel_initializer=layers.get_initializer(0.02),
name='ocemotion_dense')
ocnli_dense = layers.Dense(
3,
kernel_initializer=layers.get_initializer(0.02),
name='ocnli_dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
outputs_tnews = model([input_ids[0], input_mask[0], segment_ids[0]],
mode=mode)
pooled_output_tnews = outputs_tnews[1]
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output_tnews = tf.nn.dropout(pooled_output_tnews,
keep_prob=0.2)
logits_tnews = tnews_dense(pooled_output_tnews)
outputs_ocemotion = model(
[input_ids[1], input_mask[1], segment_ids[1]], mode=mode)
pooled_output_ocemotion = outputs_ocemotion[1]
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output_ocemotion = tf.nn.dropout(pooled_output_ocemotion,
keep_prob=0.2)
logits_ocemotion = ocemotion_dense(pooled_output_ocemotion)
outputs_ocnli = model([input_ids[2], input_mask[2], segment_ids[2]],
mode=mode)
pooled_output_ocnli = outputs_ocnli[1]
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output_ocnli = tf.nn.dropout(pooled_output_ocnli,
keep_prob=0.5)
logits_ocnli = ocnli_dense(pooled_output_ocnli)
return [logits_tnews, logits_ocemotion,
logits_ocnli], [label_ids[0], label_ids[1], label_ids[2]]
def build_logits(self, features, mode=None):
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path,
user_defined_config=self.user_defined_config)
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path)
dense = layers.Dense(self.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
outputs = model([input_ids, input_mask, segment_ids], mode=mode)
pooled_output = outputs[1]
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output = tf.nn.dropout(pooled_output, keep_prob=0.9)
logits = dense(pooled_output)
if mode == tf.estimator.ModeKeys.PREDICT:
return logits
return logits, label_ids
def __init__(self, config, **kwargs):
super(AlbertBackbone, self).__init__(**kwargs)
self.embeddings = layers.AlbertEmbeddings(config, name="embeddings")
self.embedding_hidden_mapping_in = layers.Dense(
config.hidden_size,
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="encoder/embedding_hidden_mapping_in",
)
self.encoder = AlbertEncoder(config, name="encoder/transformer")
self.pooler = layers.Dense(units=config.hidden_size,
activation='tanh',
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="pooler/dense")
def __init__(self, config, **kwargs):
super(AlbertSelfOutput, self).__init__(**kwargs)
self.dense = layers.Dense(config.hidden_size,
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="dense")
self.dropout = layers.Dropout(config.hidden_dropout_prob)
def build_logits(self, features, mode=None):
""" Building BERT text match graph
Args:
features (`OrderedDict`): A dict mapping raw input to tensors
mode (`bool`): tell the model whether it is under training
Returns:
logits (`Tensor`): The output after the last dense layer. Shape of [None, num_labels]
label_ids (`Tensor`): label_ids, shape of [None]
"""
bert_preprocessor = preprocessors.get_preprocessor(
self.config.pretrain_model_name_or_path,
user_defined_config=self.config)
input_ids, input_mask, segment_ids, label_ids = bert_preprocessor(
features)
bert_backbone = model_zoo.get_pretrained_model(
self.config.pretrain_model_name_or_path)
_, pool_output = bert_backbone([input_ids, input_mask, segment_ids],
mode=mode)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
pool_output = tf.layers.dropout(pool_output,
rate=self.config.dropout_rate,
training=is_training)
logits = layers.Dense(self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='app/ez_dense')(pool_output)
self.check_and_init_from_checkpoint(mode)
return logits, label_ids
def build(self, input_shape):
self.output_weights = self.add_weight(
shape=[self.hidden_size, self.patch_feature_size],
initializer=layers.get_initializer(self.initializer_range),
trainable=True,
name="output_weights")
super(ImageBertMPMHead, self).build(input_shape)
def build_logits(self, features, mode=None):
""" Building DAM text match graph
Args:
features (`OrderedDict`): A dict mapping raw input to tensors
mode (`bool`): tell the model whether it is under training
Returns:
logits (`Tensor`): The output after the last dense layer. Shape of [None, num_labels]
label_ids (`Tensor`): label_ids, shape of [None]
"""
text_preprocessor = DeepTextPreprocessor(self.config, mode=mode)
text_a_indices, text_a_masks, text_b_indices, text_b_masks, label_ids = text_preprocessor(
features)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
word_embeddings = self._add_word_embeddings(
vocab_size=text_preprocessor.vocab.size,
embed_size=self.config.embedding_size,
pretrained_word_embeddings=text_preprocessor.
pretrained_word_embeddings,
trainable=not self.config.fix_embedding)
a_embeds = tf.nn.embedding_lookup(word_embeddings, text_a_indices)
b_embeds = tf.nn.embedding_lookup(word_embeddings, text_b_indices)
dam_output_features = layers.DAMEncoder(self.config.hidden_size)(
[a_embeds, b_embeds, text_a_masks, text_b_masks],
training=is_training)
dam_output_features = tf.layers.dropout(
dam_output_features,
rate=0.2,
training=is_training,
name='dam_out_features_dropout')
dam_output_features = layers.Dense(
self.config.hidden_size,
activation=tf.nn.relu,
kernel_initializer=layers.get_initializer(0.02),
name='dam_out_features_projection')(dam_output_features)
logits = layers.Dense(self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='output_layer')(dam_output_features)
self.check_and_init_from_checkpoint(mode)
return logits, label_ids
def __init__(self, config, **kwargs):
super(BertHAEBackbone, self).__init__(config, **kwargs)
self.config = config
self.encoder = layers.Encoder(config, name="encoder")
self.pooler = layers.Dense(
units=config.hidden_size,
activation='tanh',
kernel_initializer=layers.get_initializer(config.initializer_range),
name="pooler/dense")
def __init__(self, config, **kwargs):
super(AlbertIntermediate, self).__init__(**kwargs)
self.dense = layers.Dense(config.intermediate_size,
activation=layers.gelu_new,
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="dense")
self.dense_output = AlbertOutput(config, name="output")
self.dropout = layers.Dropout(config.hidden_dropout_prob)
def _add_word_embeddings(self, vocab_size, embed_size, pretrained_word_embeddings=None, trainable=False):
with tf.name_scope("input_representations"):
if pretrained_word_embeddings is not None:
tf.logging.info("Initialize word embedding from pretrained")
word_embedding_initializer = tf.constant_initializer(pretrained_word_embeddings)
else:
word_embedding_initializer = layers.get_initializer(0.02)
word_embeddings = tf.get_variable("word_embeddings",
[vocab_size, embed_size],
dtype=tf.float32, initializer=word_embedding_initializer,
trainable=trainable)
return word_embeddings
def __init__(self, config, **kwargs):
super(VideoBertEmbeddings, self).__init__(**kwargs)
self.clip_feature_size = config.clip_feature_size
self.clip_size = config.clip_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.max_clip_position_embeddings = config.max_clip_position_embeddings
self.LayerNorm = layers.LayerNormalization
self.dropout = layers.Dropout(config.hidden_dropout_prob)
self.initializer = layers.get_initializer(self.initializer_range)
def __init__(self, config, **kwargs):
super(ImageBertBackbone, self).__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = layers.BertEmbeddings(config, name="embeddings")
self.image_embeddings = ImageEmbeddings(config,
name="image_embeddings")
self.encoder = layers.Encoder(config, name="encoder")
self.pooler = layers.Dense(units=config.hidden_size,
activation='tanh',
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="pooler/dense")
def build_logits(self, features, mode=None):
bert_preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path,
app_model_name="pretrain_language_model",
user_defined_config=self.user_defined_config)
if _APP_FLAGS.distribution_strategy == "WhaleStrategy" or \
self.config.distribution_strategy == "WhaleStrategy":
tf.logging.info("*********Calling Whale Encoder***********")
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path,
enable_whale=True,
input_sequence_length=_APP_FLAGS.input_sequence_length)
else:
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path,
input_sequence_length=_APP_FLAGS.input_sequence_length)
if _APP_FLAGS.loss == "mlm+nsp" or _APP_FLAGS.loss == "mlm+sop":
input_ids, input_mask, segment_ids, masked_lm_positions, \
masked_lm_ids, masked_lm_weights, next_sentence_labels = bert_preprocessor(features)
lm_logits, nsp_logits, _ = model(
[input_ids, input_mask, segment_ids],
masked_lm_positions=masked_lm_positions,
output_features=False,
mode=mode)
return (lm_logits, nsp_logits), (masked_lm_ids, masked_lm_weights,
next_sentence_labels)
elif _APP_FLAGS.loss == "mlm":
task_1_dense = layers.Dense(
2,
kernel_initializer=layers.get_initializer(0.02),
name='task_1_dense')
input_ids, input_mask, segment_ids, masked_lm_positions, \
masked_lm_ids, masked_lm_weights, task_1_label = bert_preprocessor(features)
lm_logits, _, pooled_output = model(
[input_ids, input_mask, segment_ids],
masked_lm_positions=masked_lm_positions,
output_features=False,
mode=mode)
task_1_logits = task_1_dense(pooled_output)
return (lm_logits,
task_1_logits), (masked_lm_ids, masked_lm_weights,
task_1_label)
def __init__(self, config, **kwargs):
super(ImageEmbeddings, self).__init__(**kwargs)
self.patch_feature_size = config.patch_feature_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.patch_type_vocab_size = config.patch_type_vocab_size
self.max_patch_position_embeddings = config.max_patch_position_embeddings
self.LayerNorm = layers.LayerNormalization
self.dropout_input = layers.Dropout(config.hidden_dropout_prob)
self.dropout_output = layers.Dropout(config.hidden_dropout_prob)
self.initializer = layers.get_initializer(self.initializer_range)
def build_logits(self, features, mode=None):
text_preprocessor = DeepTextPreprocessor(self.config, mode=mode)
text_a_indices, text_a_masks, text_b_indices, text_b_masks, label_ids = text_preprocessor(
features)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
word_embeddings = self._add_word_embeddings(
vocab_size=text_preprocessor.vocab.size,
embed_size=self.config.embedding_size,
pretrained_word_embeddings=text_preprocessor.
pretrained_word_embeddings,
trainable=not self.config.fix_embedding)
a_embeds = tf.nn.embedding_lookup(word_embeddings, text_a_indices)
b_embeds = tf.nn.embedding_lookup(word_embeddings, text_b_indices)
hcnn_output_features = layers.HybridCNNEncoder(
num_filters=self.config.hidden_size,
l2_reg=self.config.l2_reg,
filter_size=self.config.filter_size)(
[a_embeds, b_embeds, text_a_masks, text_b_masks])
hcnn_output_features = tf.layers.dropout(
hcnn_output_features,
rate=0.2,
training=is_training,
name='dam_out_features_dropout')
hcnn_output_features = layers.Dense(
self.config.hidden_size,
activation=tf.nn.relu,
kernel_initializer=layers.get_initializer(0.02),
name='dam_out_features_projection')(hcnn_output_features)
logits = layers.Dense(self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='output_layer')(hcnn_output_features)
self.check_and_init_from_checkpoint(mode)
return logits, label_ids
def build_logits(self, features, mode=None):
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path, user_defined_config=self.config)
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path)
dense = layers.Dense(self.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
outputs = model([input_ids, input_mask, segment_ids], mode=mode)
pooled_output = outputs[1]
logits = dense(pooled_output)
return logits, label_ids
def __init__(self, config, **kwargs):
self.embeddings = layers.BertEmbeddings(config, name="embeddings")
if not kwargs.pop('enable_whale', False):
self.encoder = layers.Encoder(config, name="encoder")
else:
self.encoder = layers.Encoder_whale(config, name="encoder")
self.pooler = layers.Dense(units=config.hidden_size,
activation='tanh',
kernel_initializer=layers.get_initializer(
config.initializer_range),
name="pooler/dense")
super(BertBackbone, self).__init__(config, **kwargs)
def build_logits(self, features, mode=None):
# 负责对原始数据进行预处理，生成模型需要的特征，比如：input_ids, input_mask, segment_ids等
preprocessor = preprocessors.get_preprocessor(self.pretrain_model_name_or_path,
user_defined_config=self.user_defined_config)
# 负责构建网络的backbone
model = model_zoo.get_pretrained_model(self.pretrain_model_name_or_path)
dense = layers.Dense(self.num_labels, kernel_initializer=layers.get_initializer(0.02), name='dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
_, pooled_output = model([input_ids, input_mask, segment_ids], mode=mode)
logits = dense(pooled_output)
return logits, label_ids
def build_logits(self, features, mode=None):
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path)
model = model_zoo.get_pretrained_model(
self.pretrain_model_name_or_path)
dense = layers.Dense(self.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='dense')
input_ids, input_mask, segment_ids, label_ids = preprocessor(features)
outputs = model([input_ids, input_mask, segment_ids], mode=mode)
pooled_output = outputs[1]
logits = dense(pooled_output)
if mode == tf.estimator.ModeKeys.PREDICT:
ret = {"logits": logits}
return ret
return logits, label_ids
def build_logits(self, features, mode=None):
""" Building DAM text match graph
Args:
features (`OrderedDict`): A dict mapping raw input to tensors
mode (`bool`): tell the model whether it is under training
Returns:
logits (`Tensor`): The output after the last dense layer. Shape of [None, num_labels]
label_ids (`Tensor`): label_ids, shape of [None]
"""
text_preprocessor = DeepTextPreprocessor(self.config, mode=mode)
text_indices, text_masks, _, _, label_ids = text_preprocessor(features)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
word_embeddings = self._add_word_embeddings(
vocab_size=text_preprocessor.vocab.size,
embed_size=self.config.embedding_size,
pretrained_word_embeddings=text_preprocessor.
pretrained_word_embeddings,
trainable=not self.config.fix_embedding)
text_embeds = tf.nn.embedding_lookup(word_embeddings, text_indices)
output_features = layers.TextCNNEncoder(
num_filters=self.config.num_filters,
filter_sizes=self.config.filter_sizes,
embed_size=self.config.embedding_size,
max_seq_len=self.config.sequence_length,
)([text_embeds, text_masks], training=is_training)
output_features = tf.layers.dropout(output_features,
rate=self.config.dropout_rate,
training=is_training,
name='output_features')
logits = layers.Dense(self.config.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='output_layer')(output_features)
self.check_and_init_from_checkpoint(mode)
return logits, label_ids
def build_logits(self, features, mode=None):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
preprocessor = preprocessors.get_preprocessor(
self.pretrain_model_name_or_path, user_defined_config=self.config)
bert_backbone = model_zoo.get_pretrained_model(
self.config.pretrain_model_name_or_path)
dense = layers.Dense(self.num_labels,
kernel_initializer=layers.get_initializer(0.02),
name='dense')
input_ids, input_mask, segment_ids, label_ids, domains, weights = preprocessor(
features)
self.domains = domains
self.weights = weights
hidden_size = bert_backbone.config.hidden_size
self.domain_logits = dict()
bert_model = bert_backbone.bert
embedding_output = bert_model.embeddings([input_ids, segment_ids],
training=is_training)
attention_mask = layers.get_attn_mask_bert(input_ids, input_mask)
encoder_outputs = bert_model.encoder(
[embedding_output, attention_mask], training=is_training)
encoder_outputs = encoder_outputs[0]
pooled_output = bert_model.pooler(encoder_outputs[-1][:, 0])
if mode == tf.estimator.ModeKeys.TRAIN:
pooled_output = tf.nn.dropout(pooled_output, keep_prob=0.9)
with tf.variable_scope("mft", reuse=tf.AUTO_REUSE):
# add domain network
logits = dense(pooled_output)
domains = tf.squeeze(domains)
domain_embedded_matrix = tf.get_variable(
"domain_projection", [num_domains, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
domain_embedded = tf.nn.embedding_lookup(domain_embedded_matrix,
domains)
for layer_index in layer_indexes:
content_tensor = tf.reduce_mean(encoder_outputs[layer_index],
axis=1)
content_tensor_with_domains = domain_embedded + content_tensor
domain_weights = tf.get_variable(
"domain_weights", [num_domains, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
domain_bias = tf.get_variable(
"domain_bias", [num_domains],
initializer=tf.zeros_initializer())
current_domain_logits = tf.matmul(content_tensor_with_domains,
domain_weights,
transpose_b=True)
current_domain_logits = tf.nn.bias_add(current_domain_logits,
domain_bias)
self.domain_logits["domain_logits_" +
str(layer_index)] = current_domain_logits
return logits, label_ids