train_model.fit(
pretrain_generator.generator(),
steps_per_epoch=len(pretrain_generator),
epochs=pretrain_epochs,
callbacks=[checkpoint, csv_logger],
)
# build task fine-tune model
# reload weights without mlm
# bert_without_mlm = build_transformer_model(checkpoint_path=model_saved_path,
# config_path=config_path, with_mlm=False)
idx = 11
feed_forward_name = 'Transformer-%d-FeedForward' % idx
bert_without_mlm = bert.layers[feed_forward_name]
output = Lambda(lambda x: x[:, 0])(bert_without_mlm.output)
output = Dense(num_classes, activation='softmax')(output)
model = Model(bert.inputs, output)
model.summary()
model.compile(loss='sparse_categorical_crossentropy',
optimizer=Adam(fine_tune_lr),
metrics=['acc'])
evaluator = Evaluator()
model.fit_generator(train_generator.generator(),
steps_per_epoch=len(train_generator),
epochs=fine_tune_epochs,
callbacks=[evaluator])
return_keras_model=False,
prefix='Predecessor-')
# 判别模型
x_in = Input(shape=K.int_shape(predecessor.output)[1:])
x = Lambda(lambda x: x[:, 0])(x_in)
x = Dense(units=num_classes, activation='softmax')(x)
classifier = Model(x_in, x)
predecessor_model = Model(predecessor.inputs, classifier(predecessor.output))
predecessor_model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(1e-5), # 用足够小的学习率
metrics=['sparse_categorical_accuracy'],
)
predecessor_model.summary()
# predecessor_model_3
output = predecessor_model.layers[31].output # 第3层transform
output = Lambda(lambda x: x[:, 0])(output)
dense = ScaleDense(lr_multiplier=5,
units=num_classes,
activation='softmax',
weights=predecessor_model.layers[-1].get_weights())
output = dense(output)
predecessor_3_model = Model(predecessor_model.inputs, output)
predecessor_3_model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(1e-5), # 用足够小的学习率
metrics=['sparse_categorical_accuracy'],
# 判别模型
x_in = Input(shape=K.int_shape(predecessor.output)[1:])
x = Dense(num_labels)(x_in)
CRF = ConditionalRandomField(lr_multiplier=2)
x = CRF(x)
classifier = Model(x_in, x)
opt = Adam(learning_rate=lr)
predecessor_model = Model(predecessor.inputs, classifier(predecessor.outputs))
predecessor_model.compile(
loss=predecessor_model.layers[-1].layers[-1].sparse_loss,
optimizer=opt,
metrics=[CRF.sparse_accuracy])
predecessor_model.summary()
successor_model = Model(successor.inputs, classifier(successor.outputs))
successor_model.compile(loss=successor_model.layers[-1].layers[-1].sparse_loss,
optimizer=opt,
metrics=[CRF.sparse_accuracy])
successor_model.summary()
theseus_model = bert_of_theseus(predecessor, successor, classifier)
theseus_model.compile(loss=theseus_model.layers[-1].layers[-1].sparse_loss,
optimizer=opt,
metrics=[CRF.sparse_accuracy])
theseus_model.summary()
class NamedEntityRecognizer(ViterbiDecoder):
pooler = bert.model.outputs[0]
classification_output = Dense(units=num_classes,
activation='softmax',
name='classifier')(pooler)
classifier = Model(bert.model.inputs, classification_output)
seq2seq = Model(bert.model.inputs, bert.model.outputs[1])
outputs = TotalLoss([2])(bert.model.inputs + bert.model.outputs)
# outputs = Dense(num_classes, activation='softmax')(outputs)
train_model = Model(bert.model.inputs, [classification_output, outputs])
train_model.compile(loss=['sparse_categorical_crossentropy', None],
optimizer=Adam(1e-5),
metrics=['acc'])
train_model.summary()
def evaluate(val_data=valid_generator):
total = 0.
right = 0.
for x, y_true in tqdm(val_data):
y_pred = classifier.predict(x).argmax(axis=-1)
y_true = y_true[:, 0]
total += len(y_true)
right += (y_true == y_pred).sum()
print(total, right)
return right / total
class Evaluator(keras.callbacks.Callback):
