def universal_transformer_gpt_model(
max_seq_length: int, vocabulary_size: int,
word_embedding_size: int, transformer_depth: int,
num_heads: int, transformer_dropout: float = 0.1,
embedding_dropout: float = 0.6,
l2_reg_penalty: float = 1e-6,
confidence_penalty_weight: float = 0.1):
"""
A model which is similar to the one described by OpenAI in paper
"Improving Language Understanding by Generative Pre-Training", except
that it relies L2 regularization of the word embedding matrix
(instead of the dropout), and uses Universal Transformer architecture.
"""
word_ids = Input(shape=(max_seq_length,), dtype='int32', name='word_ids')
l2_regularizer = (regularizers.l2(l2_reg_penalty) if l2_reg_penalty
else None)
embedding_layer = ReusableEmbedding(
vocabulary_size, word_embedding_size,
input_length=max_seq_length,
name='bpe_embeddings',
# Regularization is based on paper "A Comparative Study on
# Regularization Strategies for Embedding-based Neural Networks"
# https://arxiv.org/pdf/1508.03721.pdf
embeddings_regularizer=l2_regularizer)
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name='word_prediction_logits')
coordinate_embedding_layer = TransformerCoordinateEmbedding(
transformer_depth,
name='coordinate_embedding')
transformer_act_layer = TransformerACT(name='adaptive_computation_time')
transformer_block = TransformerBlock(
name='transformer', num_heads=num_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True, vanilla_wiring=False)
output_softmax_layer = Softmax(name='word_predictions')
next_step_input, embedding_matrix = embedding_layer(word_ids)
act_output = next_step_input
for i in range(transformer_depth):
next_step_input = coordinate_embedding_layer(next_step_input, step=i)
next_step_input = transformer_block(next_step_input)
next_step_input, act_output = transformer_act_layer(next_step_input)
transformer_act_layer.finalize()
next_step_input = act_output
word_predictions = output_softmax_layer(
output_layer([next_step_input, embedding_matrix]))
model = Model(inputs=[word_ids], outputs=[word_predictions])
# Penalty for confidence of the output distribution, as described in
# "Regularizing Neural Networks by Penalizing Confident
# Output Distributions" (https://arxiv.org/abs/1701.06548)
confidence_penalty = K.mean(
confidence_penalty_weight *
K.sum(word_predictions * K.log(word_predictions), axis=-1))
model.add_loss(confidence_penalty)
return model
def make_transformer_model(max_seq_length: int, vocabulary_size: int,
word_embedding_size: int, transformer_depth: int,
num_heads: int, transformer_dropout: float = 0.1,
embedding_dropout: float = 0.6,
l2_reg_penalty: float = 1e-6,
confidence_penalty_weight: float = 0.05):
word_ids = Input(shape=(max_seq_length,), dtype='int32', name='word_ids')
l2_regularizer = (regularizers.l2(l2_reg_penalty) if l2_reg_penalty
else None)
embedding_layer = ReusableEmbedding(
vocabulary_size, word_embedding_size,
input_length=max_seq_length,
name='bpe_embeddings',
# Regularization is based on paper "A Comparative Study on
# Regularization Strategies for Embedding-based Neural Networks"
# https://arxiv.org/pdf/1508.03721.pdf
embeddings_regularizer=l2_regularizer)
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name='word_prediction_logits')
coordinate_embedding_layer = TransformerCoordinateEmbedding(
transformer_depth,
name='coordinate_embedding')
transformer_act_layer = TransformerACT(name='adaptive_computation_time')
transformer_block = TransformerBlock(
name='transformer', num_heads=num_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True)
output_softmax_layer = Softmax(name='word_predictions')
next_step_input, embedding_matrix = embedding_layer(word_ids)
act_output = next_step_input
dropout_layer = Dropout(embedding_dropout, name='input_dropout')
next_step_input = dropout_layer(next_step_input)
for i in range(transformer_depth):
next_step_input = coordinate_embedding_layer(next_step_input, step=i)
next_step_input = transformer_block(next_step_input)
next_step_input, act_output = transformer_act_layer(next_step_input)
transformer_act_layer.finalize()
next_step_input = act_output
# depth_of_trainable_params(act_output)
word_predictions = output_softmax_layer(
output_layer([next_step_input, embedding_matrix]))
model = Model(inputs=[word_ids], outputs=[word_predictions])
confidence_penalty = K.mean(
confidence_penalty_weight *
K.sum(word_predictions * K.log(word_predictions), axis=-1))
model.add_loss(confidence_penalty)
return model
def transformer_bert_model(max_seq_length: int,
vocabulary_size: int,
word_embedding_size: int,
use_universal_transformer: bool,
transformer_depth: int,
num_heads: int,
transformer_dropout: float = 0.1,
embedding_dropout: float = 0.6,
l2_reg_penalty: float = 1e-4):
"""
Builds a BERT-based model (Bidirectional Encoder Representations
from Transformers) following paper "BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding"
(https://arxiv.org/abs/1810.04805)
Depending on the value passed with `use_universal_transformer` argument,
this function applies either an Adaptive Universal Transformer (2018)
or a vanilla Transformer (2017) to do the job (the original paper uses
vanilla Transformer).
"""
word_ids = Input(shape=(max_seq_length, ), dtype='int32', name='word_ids')
segment_ids = Input(shape=(max_seq_length, ),
dtype='int32',
name='segment_ids')
l2_regularizer = (regularizers.l2(l2_reg_penalty)
if l2_reg_penalty else None)
embedding_layer = ReusableEmbedding(
vocabulary_size,
word_embedding_size,
input_length=max_seq_length,
name='bpe_embeddings',
# Regularization is based on paper "A Comparative Study on
# Regularization Strategies for Embedding-based Neural Networks"
# https://arxiv.org/pdf/1508.03721.pdf
embeddings_regularizer=l2_regularizer)
segment_embedding_layer = Embedding(
2, # "Segment A" and "Segment B" embeddings
word_embedding_size,
name='segment_embeddings')
add_segment_layer = Add(name='add_segment')
output_layer = TiedOutputEmbedding(projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name='word_prediction_logits')
output_softmax_layer = Softmax(name='word_predictions')
coordinate_embedding_layer = TransformerCoordinateEmbedding(
transformer_depth if use_universal_transformer else 1,
name='coordinate_embedding')
next_step_input, embedding_matrix = embedding_layer(word_ids)
segment_embeddings = segment_embedding_layer(segment_ids)
if use_universal_transformer:
# Building a Universal Transformer (2018)
act_layer = TransformerACT(name='adaptive_computation_time')
transformer_block = TransformerBlock(
name='transformer',
num_heads=num_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
# Allow bi-directional attention
use_masking=False)
act_output = next_step_input
for i in range(transformer_depth):
next_step_input = coordinate_embedding_layer(next_step_input,
step=i)
next_step_input = add_segment_layer(
[next_step_input, segment_embeddings])
next_step_input = transformer_block(next_step_input)
next_step_input, act_output = act_layer(next_step_input)
act_layer.finalize()
next_step_input = act_output
else:
# Building a Vanilla Transformer (described in
# "Attention is all you need", 2017)
next_step_input = coordinate_embedding_layer(next_step_input, step=0)
next_step_input = add_segment_layer(
[next_step_input, segment_embeddings])
for i in range(transformer_depth):
next_step_input = (
TransformerBlock(
name='transformer' + str(i),
num_heads=num_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=False, # Allow bi-directional attention
vanilla_wiring=True)(next_step_input))
word_predictions = output_softmax_layer(
output_layer([next_step_input, embedding_matrix]))
cls_node_slice = (
# selecting the first output position in each sequence
# (responsible for classification)
Lambda(lambda x: x[:, 0], name='cls_node_slicer')(next_step_input))
class_prediction = (Dense(1, name='class_prediction',
activation='sigmoid')(cls_node_slice))
model = Model(inputs=[word_ids, segment_ids],
outputs=[word_predictions, class_prediction])
return model
def build_model(max_length: int,
embedding_matrix: Union[np.ndarray, Tuple[int]],
transformer_depth: int,
transformer_heads: int,
filters: List[int],
kernel_size: List[int],
pool_size: List[int],
conv_padding: str,
pool_padding: str,
dense_size: List[int],
loaded_model: Optional[str] = None,
fine_tune_model: bool = False,
l2_penalty: Optional[float] = None,
embedding_dropout: float = 0.6,
transformer_dropout: float = 0.1,
conv_dropout: float = 0.1,
dense_dropout: Union[float, List[float]] = 0.3,
classifier_dropout: float = 0.1,
train_lm=True) -> Model:
if not (len(filters) > 0 and len(kernel_size) > 0 and len(pool_size) > 0):
logger.error(
"There are no filters, kernel sizes or pool sizes specified for the CNN."
)
raise ValueError(
"There are no filters, kernel sizes or pool sizes specified for the CNN."
)
if type(dense_dropout) != list:
dense_dropout = [dense_dropout]
if len(dense_size) > 0 and len(dense_size) != len(dense_dropout):
max_list_length = max([len(dense_size), len(dense_dropout)])
new_dense_size = []
new_dense_dropout = []
for i in range(max_list_length):
new_dense_size.append(
dense_size[i] if i < len(dense_size) else dense_size[-1])
new_dense_dropout.append(dense_dropout[i] if i < len(dense_dropout)
else dense_dropout[-1])
dense_size = new_dense_size
dense_dropout = new_dense_dropout
logger.warning(
"Lists given for dense layer sizes and dense layer dropout rates are not the same length. "
"The shorter lists are padded using the last value to match the length of the longest."
)
if len(filters) != len(kernel_size) or len(filters) != len(
pool_size) or len(kernel_size) != len(pool_size):
max_list_length = max([len(filters), len(kernel_size), len(pool_size)])
new_filters = []
new_kernel_size = []
new_pool_size = []
for i in range(max_list_length):
new_filters.append(filters[i] if i < len(filters) else filters[-1])
new_kernel_size.append(
kernel_size[i] if i < len(kernel_size) else kernel_size[-1])
new_pool_size.append(
pool_size[i] if i < len(pool_size) else pool_size[-1])
filters = new_filters
kernel_size = new_kernel_size
pool_size = new_pool_size
logger.warning(
"Lists given for convolutional filters, kernel sizes and pooling sizes had different lengths. "
"The shorter lists are padded using the last value to match the length of the longest."
)
original_model = None
if loaded_model:
# load the specified model
original_model = load_model(loaded_model,
custom_objects={
"perplexity": perplexity,
"lm_accuracy": lm_accuracy
})
# regularizer for embedding layer
l2_regularizer = l2(l2_penalty) if l2_penalty else None
# input encoded as integers
raw_input = Input(shape=(max_length, ), name="input")
# embedding layer, initialised with embedding matrix weights for now
embedding_weights = [
original_model.get_layer(name="word_embedding").get_weights()[0]
if loaded_model else embedding_matrix
]
embedding_layer = ReusableEmbedding(
input_dim=(embedding_matrix[0] if type(embedding_matrix) == tuple else
embedding_matrix.shape[0]),
output_dim=(embedding_matrix[1] if type(embedding_matrix) == tuple else
embedding_matrix.shape[1]),
input_length=max_length,
name="word_embedding",
weights=(None if type(embedding_matrix) == tuple and not loaded_model
else embedding_weights),
embeddings_regularizer=l2_regularizer)
# "transpose" of embedding matrix to map back to vocabulary
if loaded_model:
output_weights = original_model.get_layer(
name="word_prediction_logits").get_weights()
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name="word_prediction_logits",
weights=output_weights)
else:
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name="word_prediction_logits")
# transformer as taken from here: https://github.com/kpot/keras-transformer/blob/master/example/models.py
if loaded_model:
position_weights = original_model.get_layer(
name="position_embedding").get_weights()
position_embedding = TransformerCoordinateEmbedding(
max_transformer_depth=1,
name="position_embedding",
weights=position_weights)
else:
position_embedding = TransformerCoordinateEmbedding(
max_transformer_depth=1, name="position_embedding")
transformer_input, embedding_matrix = embedding_layer(raw_input)
transformer_output = position_embedding(transformer_input, step=0)
for i in range(transformer_depth):
block_name = "transformer" + str(i)
# define transformer block
transformer_block = TransformerBlock(
name=block_name,
num_heads=transformer_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True,
vanilla_wiring=True)
# build the layers in the block because apparently you have to do that
if loaded_model:
if i == 0:
transformer_block.attention_layer.build(
original_model.get_layer(
"position_embedding").output_shape)
else:
transformer_block.attention_layer.build(
original_model.get_layer(
"transformer{}_normalization2".format(i -
1)).output_shape)
transformer_block.norm1_layer.build(
original_model.get_layer(block_name +
"_self_attention").output_shape)
transformer_block.norm2_layer.build(
original_model.get_layer(block_name +
"_normalization1").output_shape)
transformer_block.transition_layer.build(
original_model.get_layer(block_name +
"_normalization1").output_shape)
# set weights for all the contained layers manually
transformer_block.attention_layer.set_weights(
original_model.get_layer(
name=(block_name + "_self_attention")).get_weights())
transformer_block.norm1_layer.set_weights(
original_model.get_layer(
name=(block_name + "_normalization1")).get_weights())
transformer_block.norm2_layer.set_weights(
original_model.get_layer(
name=(block_name + "_normalization2")).get_weights())
transformer_block.transition_layer.set_weights(
original_model.get_layer(name=(block_name +
"_transition")).get_weights())
# pass output of last layer through transformer
transformer_output = transformer_block(transformer_output)
# nothing special to load for softmax
softmax_layer = Softmax(name="word_predictions")
lm_output_logits = output_layer([transformer_output, embedding_matrix])
lm_output = softmax_layer(lm_output_logits)
if not fine_tune_model:
m = Model(inputs=raw_input, outputs=lm_output)
return m
loaded_layer_names = []
if loaded_model:
loaded_layer_names = [layer.name for layer in original_model.layers]
# convolution layer(s)
conv_dropout = Dropout(conv_dropout, name="conv_dropout")
conv_output = transformer_output
for i in range(len(filters)):
# construct and possibly load convolutional layer
conv_layer_name = "conv_{}".format(i)
convolution = Conv1D(filters[i],
kernel_size[i],
padding=conv_padding,
activation="relu",
name=conv_layer_name)
if loaded_model and conv_layer_name in loaded_layer_names:
layer = original_model.get_layer(name=conv_layer_name)
convolution.build(layer.input_shape)
convolution.set_weights(layer.get_weights())
# construct max pooling, no weights to load
pooling = MaxPooling1D(pool_size[i],
padding=pool_padding,
name="max_pool_{}".format(i))
# get output/input of next layer
conv_output = pooling(convolution(conv_dropout(conv_output)))
# dense layer(s)
flatten = Flatten(name="flatten")
dense_output = flatten(conv_output)
for i in range(len(dense_size)):
# construct and possibly load dense layer
dense_layer_name = "dense_{}".format(i)
dense = Dense(dense_size[i], name=dense_layer_name)
if loaded_model and dense_layer_name in loaded_layer_names:
layer = original_model.get_layer(name=dense_layer_name)
dense.build(layer.input_shape)
dense.set_weights(layer.get_weights())
# nothing to load for dropout
dropout = Dropout(rate=dense_dropout[i],
name="dense_dropout_{}".format(i))
# get output
dense_output = dense(dropout(dense_output))
# classification layer
classifier_dropout = Dropout(classifier_dropout, name="classifier_dropout")
classifier = Dense(1, name="classifier")
classifier_prediction = Activation("sigmoid", name="classifier_prediction")
classifier_output = classifier_prediction(
classifier(classifier_dropout(dense_output)))
if train_lm:
m = Model(inputs=raw_input, outputs=[lm_output, classifier_output])
else:
m = Model(inputs=raw_input, outputs=classifier_output)
return m
def transformer_bert_model(max_seq_length: int,
time_window_size: int,
vocabulary_size: int,
concept_embedding_size: int,
depth: int,
num_heads: int,
transformer_dropout: float = 0.1,
embedding_dropout: float = 0.6,
l2_reg_penalty: float = 1e-4):
"""
Builds a BERT-based model (Bidirectional Encoder Representations
from Transformers) following paper "BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding"
(https://arxiv.org/abs/1810.04805)
Depending on the value passed with `use_universal_transformer` argument,
this function applies either an Adaptive Universal Transformer (2018)
or a vanilla Transformer (2017) to do the job (the original paper uses
vanilla Transformer).
"""
masked_concept_ids = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype='int32',
name='masked_concept_ids')
concept_ids = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype='int32',
name='concept_ids')
time_stamps = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype='int32',
name='time_stamps')
mask = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype='int32',
name='mask')
concept_mask = tf.expand_dims(tf.expand_dims(mask, axis=1), axis=1)
l2_regularizer = (tf.keras.regularizers.l2(l2_reg_penalty)
if l2_reg_penalty else None)
embedding_layer = ReusableEmbedding(
vocabulary_size,
concept_embedding_size,
input_length=max_seq_length,
name='bpe_embeddings',
# Regularization is based on paper "A Comparative Study on
# Regularization Strategies for Embedding-based Neural Networks"
# https://arxiv.org/pdf/1508.03721.pdf
embeddings_regularizer=l2_regularizer)
time_embedding_layer = TimeSelfAttention(vocab_size=vocabulary_size,
target_seq_len=max_seq_length,
context_seq_len=max_seq_length,
time_window_size=time_window_size,
return_logits=True)
encoder_layer = Encoder(name='encoder',
num_layers=depth,
d_model=concept_embedding_size,
num_heads=num_heads,
dropout_rate=transformer_dropout)
output_layer = TiedOutputEmbedding(projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name='concept_prediction_logits')
softmax_layer = tf.keras.layers.Softmax(name='concept_predictions')
coordinate_embedding_layer = TransformerCoordinateEmbedding(
1, name='coordinate_embedding')
next_step_input, embedding_matrix = embedding_layer(masked_concept_ids)
# Building a Vanilla Transformer (described in
# "Attention is all you need", 2017)
next_step_input = coordinate_embedding_layer(next_step_input, step=0)
# shape = (batch_size, seq_len, seq_len)
time_attention = time_embedding_layer([concept_ids, time_stamps, mask])
# pad a dimension to accommodate the head split
time_attention = tf.expand_dims(time_attention, axis=1)
next_step_input, _ = encoder_layer(next_step_input, concept_mask,
time_attention)
concept_predictions = softmax_layer(
output_layer([next_step_input, embedding_matrix]))
model = tf.keras.Model(
inputs=[masked_concept_ids, concept_ids, time_stamps, mask],
outputs=[concept_predictions])
return model
def vanilla_transformer_gpt_model(
max_seq_length: int, vocabulary_size: int,
word_embedding_size: int, transformer_depth: int,
num_heads: int, transformer_dropout: float = 0.1,
embedding_dropout: float = 0.6,
l2_reg_penalty: float = 1e-6,
confidence_penalty_weight: float = 0.1,
agglomerative_attention: bool = False,
use_convolutions: bool = False,
use_coordinate_embeddings: bool = True,
convolution_width: int = 0,
dropout_cls: Type[Layer] = Dropout
):
"""
A model which is almost identical to the one described by OpenAI in paper
"Improving Language Understanding by Generative Pre-Training", except
that it uses L2 regularization of the word embedding matrix,
instead of the dropout.
"""
word_ids = Input(shape=(max_seq_length,), dtype='int32', name='word_ids')
l2_regularizer = (keras.regularizers.l2(l2_reg_penalty) if l2_reg_penalty
else None)
embedding_layer = ReusableEmbedding(
vocabulary_size, word_embedding_size,
input_length=max_seq_length,
name='bpe_embeddings',
# Regularization is based on paper "A Comparative Study on
# Regularization Strategies for Embedding-based Neural Networks"
# https://arxiv.org/pdf/1508.03721.pdf
embeddings_regularizer=l2_regularizer)
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name='word_prediction_logits')
conv_layer = keras.layers.Conv1D(
word_embedding_size, convolution_width, padding='causal',
activation='relu', kernel_initializer='he_uniform', name='convolution')
coordinate_embedding_layer = TransformerCoordinateEmbedding(
1,
name='coordinate_embedding')
output_softmax_layer = Softmax(name='word_predictions')
next_step_input, embedding_matrix = embedding_layer(word_ids)
if use_convolutions:
next_step_input = conv_layer(next_step_input)
if use_coordinate_embeddings:
next_step_input = coordinate_embedding_layer(next_step_input, step=0)
for i in range(transformer_depth):
next_step_input = (
TransformerBlock(
name='transformer' + str(i), num_heads=num_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True,
vanilla_wiring=True,
agglomerative_attention=agglomerative_attention,
dropout_cls=dropout_cls,
)
(next_step_input))
word_predictions = output_softmax_layer(
output_layer([next_step_input, embedding_matrix]))
model = keras.models.Model(inputs=[word_ids], outputs=[word_predictions])
# Penalty for confidence of the output distribution, as described in
# "Regularizing Neural Networks by Penalizing Confident
# Output Distributions" (https://arxiv.org/abs/1701.06548)
confidence_penalty = K.mean(
confidence_penalty_weight *
K.sum(word_predictions * K.log(word_predictions), axis=-1))
model.add_loss(confidence_penalty)
return model
def build_model(max_length: int,
embedding_matrix: Union[np.ndarray, Tuple[int]],
transformer_depth: int,
transformer_heads: int,
cell_type: str,
cell_size: int,
cell_stack_size: int,
filters: List[int],
kernel_size: List[int],
pool_size: List[int],
conv_padding: str,
pool_padding: str,
dense_size: List[int],
loaded_model: Optional[str] = None,
l2_penalty: Optional[float] = None,
embedding_dropout: float = 0.6,
transformer_dropout: float = 0.1,
conv_dropout: float = 0.1,
dense_dropout: Union[float, List[float]] = 0.3,
classifier_dropout: float = 0.1,
no_cnn: bool = False,
train_lm: bool = True) -> Model:
if type(dense_dropout) != list:
dense_dropout = [dense_dropout]
if len(dense_size) > 0 and len(dense_size) != len(dense_dropout):
max_list_length = max([len(dense_size), len(dense_dropout)])
new_dense_size = []
new_dense_dropout = []
for i in range(max_list_length):
new_dense_size.append(
dense_size[i] if i < len(dense_size) else dense_size[-1])
new_dense_dropout.append(dense_dropout[i] if i < len(dense_dropout)
else dense_dropout[-1])
dense_size = new_dense_size
dense_dropout = new_dense_dropout
logger.warning(
"Lists given for dense layer sizes and dense layer dropout rates are not the same length. "
"The shorter lists are padded using the last value to match the length of the longest."
)
if not no_cnn and len(filters) != len(kernel_size) or len(filters) != len(
pool_size) or len(kernel_size) != len(pool_size):
max_list_length = max([len(filters), len(kernel_size), len(pool_size)])
new_filters = []
new_kernel_size = []
new_pool_size = []
for i in range(max_list_length):
new_filters.append(filters[i] if i < len(filters) else filters[-1])
new_kernel_size.append(
kernel_size[i] if i < len(kernel_size) else kernel_size[-1])
new_pool_size.append(
pool_size[i] if i < len(pool_size) else pool_size[-1])
filters = new_filters
kernel_size = new_kernel_size
pool_size = new_pool_size
logger.warning(
"Lists given for convolutional filters, kernel sizes and pooling sizes had different lengths. "
"The shorter lists are padded using the last value to match the length of the longest."
)
cell_type_name = cell_type.lower()
if cell_type_name == "lstm":
cell_type = LSTM
elif cell_type_name == "gru":
cell_type = GRU
# regularizer for embedding layer
l2_regularizer = l2(l2_penalty) if l2_penalty else None
# input encoded as integers
raw_input = Input(shape=(max_length, ), name="input")
# embedding layer, initialised with embedding matrix weights for now
embedding_layer = ReusableEmbedding(
input_dim=(embedding_matrix[0] if type(embedding_matrix) == tuple else
embedding_matrix.shape[0]),
output_dim=(embedding_matrix[1] if type(embedding_matrix) == tuple else
embedding_matrix.shape[1]),
input_length=max_length,
name="word_embedding",
weights=(None
if type(embedding_matrix) == tuple else [embedding_matrix]),
embeddings_regularizer=l2_regularizer)
# "transpose" of embedding matrix to map back to vocabulary
lm_output = TiedOutputEmbedding(projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name="word_prediction_logits")
# transformer as taken from here: https://github.com/kpot/keras-transformer/blob/master/example/models.py
position_embedding = TransformerCoordinateEmbedding(
max_transformer_depth=1, name="position_embedding")
transformer_blocks = []
for i in range(transformer_depth):
block_name = "transformer" + str(i)
# define transformer block
transformer_block = TransformerBlock(
name=block_name,
num_heads=transformer_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True,
vanilla_wiring=True)
transformer_blocks.append(transformer_block)
# nothing special to load for softmax
lm_prediction = Softmax(name="word_predictions")
# RNN
rnn_cells = []
for i in range(cell_stack_size - 1):
cell_name = "{}_{}".format(cell_type_name, i)
cell = Bidirectional(cell_type(cell_size,
return_sequences=True,
name=cell_name),
name="bidirectional_{}".format(cell_name))
rnn_cells.append(cell)
cell_name = "{}_{}".format(cell_type_name, cell_stack_size - 1)
cell = Bidirectional(cell_type(cell_size,
return_sequences=(not no_cnn),
name=cell_name),
name="bidirectional_{}".format(cell_name))
rnn_cells.append(cell)
# flattening for RNN/transformer outputs and CNN outputs
flatten = Flatten(name="flatten")
# convolution layer(s)
conv_dropout = Dropout(conv_dropout, name="conv_dropout")
conv_layers = []
for i in range(len(filters)):
# construct and possibly load convolutional layer
conv_layer_name = "conv_{}".format(i)
convolution = Conv1D(filters[i],
kernel_size[i],
padding=conv_padding,
activation="relu",
name=conv_layer_name)
# construct max pooling, no weights to load
pooling = MaxPooling1D(pool_size[i],
padding=pool_padding,
name="max_pool_{}".format(i))
conv_layers.append((convolution, pooling))
# dense layer(s)
dense_layers = []
for i in range(len(dense_size)):
# nothing to load for dropout
dropout = Dropout(rate=dense_dropout[i],
name="dense_dropout_{}".format(i))
# construct and possibly load dense layer
dense_layer_name = "dense_{}".format(i)
dense = Dense(dense_size[i], name=dense_layer_name)
# get output
dense_layers.append((dropout, dense))
# classification layer
classifier_dropout = Dropout(classifier_dropout, name="classifier_dropout")
classifier = Dense(1, name="classifier")
classifier_prediction = Activation("sigmoid", name="classifier_prediction")
# embedding used for both parts of the model
embedded, embedding_matrix = embedding_layer(raw_input)
# BUILD THE ACTUAL MODEL
# transformer
transformer_output = position_embedding(embedded, step=0)
for tb in transformer_blocks:
transformer_output = tb(transformer_output)
# language model loss if it is used
lm_output = lm_output([transformer_output, embedding_matrix])
lm_output = lm_prediction(lm_output)
# RNN
rnn_output = embedded
for rc in rnn_cells:
rnn_output = rc(rnn_output)
# handle concatenation of both outputs
if no_cnn:
# flatten both and concatenate them
concat = Concatenate(name="concat_flattened")
classifier_output = concat(
[flatten(transformer_output),
flatten(rnn_output)])
else:
# concatenate the extracted features
concat = Concatenate(name="concat_features")
classifier_output = concat([transformer_output, rnn_output])
# pass through CNN
for cl in conv_layers:
classifier_output = cl[1](cl[0](conv_dropout(classifier_output)))
# pass through dense layer(s)
classifier_output = flatten(classifier_output)
for dl in dense_layers:
classifier_output = dl[1](dl[0](classifier_output))
# do classification
classifier_output = classifier_prediction(
classifier(classifier_dropout(classifier_output)))
if train_lm:
m = Model(inputs=raw_input, outputs=[lm_output, classifier_output])
else:
m = Model(inputs=raw_input, outputs=classifier_output)
return m
def build_model(max_length,
loaded_model=None,
fine_tune_model=False,
embedding_matrix=None,
transformer_depth=8,
transformer_heads=8,
l2_penalty=None,
embedding_dropout=0.6,
transformer_dropout=0.1,
classifier_dropout=0.1,
transformer_output_handling="flatten",
print_info=False,
train_lm=True):
original_model = None
if loaded_model:
# load the specified model
original_model = load_model(loaded_model,
custom_objects={
"perplexity":
perplexity,
"lm_accuracy":
lm_accuracy,
"SeqSelfAttention":
SeqSelfAttention,
"ScaledDotProductAttention":
ScaledDotProductAttention
})
# regularizer for embedding layer
l2_regularizer = l2(l2_penalty) if l2_penalty else None
# input encoded as integers
raw_input = Input(shape=(max_length, ), name="input")
# embedding layer, initialised with embedding matrix weights for now
embedding_weights = [
original_model.get_layer(name="word_embedding").get_weights()[0]
if loaded_model else embedding_matrix
]
embedding_layer = ReusableEmbedding(
input_dim=(embedding_matrix[0] if type(embedding_matrix) == tuple else
embedding_matrix.shape[0]),
output_dim=(embedding_matrix[1] if type(embedding_matrix) == tuple else
embedding_matrix.shape[1]),
input_length=max_length,
name="word_embedding",
weights=(None if type(embedding_matrix) == tuple and not loaded_model
else embedding_weights),
embeddings_regularizer=l2_regularizer)
# "transpose" of embedding matrix to map back to vocabulary
if loaded_model:
output_weights = original_model.get_layer(
name="word_prediction_logits").get_weights()
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name="word_prediction_logits",
weights=output_weights)
else:
output_layer = TiedOutputEmbedding(
projection_regularizer=l2_regularizer,
projection_dropout=embedding_dropout,
name="word_prediction_logits")
# transformer as taken from here: https://github.com/kpot/keras-transformer/blob/master/example/models.py
if loaded_model:
position_weights = original_model.get_layer(
name="position_embedding").get_weights()
position_embedding = TransformerCoordinateEmbedding(
max_transformer_depth=1,
name="position_embedding",
weights=position_weights)
else:
position_embedding = TransformerCoordinateEmbedding(
max_transformer_depth=1, name="position_embedding")
transformer_input, embedding_matrix = embedding_layer(raw_input)
transformer_output = position_embedding(transformer_input, step=0)
for i in range(transformer_depth):
block_name = "transformer" + str(i)
# define transformer block
transformer_block = TransformerBlock(
name=block_name,
num_heads=transformer_heads,
residual_dropout=transformer_dropout,
attention_dropout=transformer_dropout,
use_masking=True,
vanilla_wiring=True)
# build the layers in the block because apparently you have to do that
if loaded_model:
if i == 0:
transformer_block.attention_layer.build(
original_model.get_layer(
"position_embedding").output_shape)
else:
transformer_block.attention_layer.build(
original_model.get_layer(
"transformer{}_normalization2".format(i -
1)).output_shape)
transformer_block.norm1_layer.build(
original_model.get_layer(block_name +
"_self_attention").output_shape)
transformer_block.norm2_layer.build(
original_model.get_layer(block_name +
"_normalization1").output_shape)
transformer_block.transition_layer.build(
original_model.get_layer(block_name +
"_normalization1").output_shape)
# set weights for all the contained layers manually
transformer_block.attention_layer.set_weights(
original_model.get_layer(
name=(block_name + "_self_attention")).get_weights())
transformer_block.norm1_layer.set_weights(
original_model.get_layer(
name=(block_name + "_normalization1")).get_weights())
transformer_block.norm2_layer.set_weights(
original_model.get_layer(
name=(block_name + "_normalization2")).get_weights())
transformer_block.transition_layer.set_weights(
original_model.get_layer(name=(block_name +
"_transition")).get_weights())
# pass output of last layer through transformer
transformer_output = transformer_block(transformer_output)
if print_info:
logger.debug("transformer_output shape: {}".format(
K.int_shape(transformer_output[0]
if fine_tune_model else transformer_output)))
# nothing special to load for softmax
softmax_layer = Softmax(name="word_predictions")
lm_output_logits = output_layer([transformer_output, embedding_matrix])
lm_output = softmax_layer(lm_output_logits)
if print_info:
logger.debug("lm_output_logits shape: {}".format(
K.int_shape(lm_output_logits)))
logger.debug("output shape: {}".format(K.int_shape(lm_output)))
if not fine_tune_model:
m = Model(inputs=raw_input, outputs=lm_output)
return m
loaded_layer_names = []
if loaded_model:
loaded_layer_names = [layer.name for layer in original_model.layers]
# for concatenation transformer outputs early
flatten = Flatten(name="flatten_transformer_output")
max_pooling = Lambda(lambda x: K.max(x, axis=1), name="max_pooling")
mean_pooling = Lambda(lambda x: K.mean(x, axis=1), name="mean_pooling")
self_attention = SeqSelfAttention(name="self_attention")
scaled_dot_attention = ScaledDotProductAttention(
name="scaled_dot_attention")
dropout = Dropout(rate=classifier_dropout, name="classifier_dropout")
options = {
"flatten": flatten,
"max_pooling": max_pooling,
"mean_pooling": mean_pooling,
"self_attention": self_attention,
"scaled_dot_attention": scaled_dot_attention
}
dense = Dense(2, activation=None, name="dense")
if loaded_model and "dense" in loaded_layer_names:
layer = original_model.get_layer(name="dense")
dense.build(layer.input_shape)
dense.set_weights(layer.get_weights())
pooling_layer = options[transformer_output_handling]
if loaded_model and transformer_output_handling in loaded_layer_names:
layer = original_model.get_layer(name=transformer_output_handling)
pooling_layer.build(layer.input_shape)
pooling_layer.set_weights(layer.get_weights())
if "attention" in transformer_output_handling:
handled_output = flatten(pooling_layer(transformer_output))
else:
handled_output = pooling_layer(transformer_output)
classifier_logits = dense(dropout(handled_output))
classifier_output = Softmax(
name="classifier_prediction")(classifier_logits)
if train_lm:
m = Model(inputs=raw_input, outputs=[lm_output, classifier_output])
else:
m = Model(inputs=raw_input, outputs=classifier_output)
# m = Model(inputs=raw_input, outputs=lm_output)
return m
