def train(self, train_data, valid_data):
train_D = DataGenerator(train_data, self.tokenizer,
CONFIG['batch_size'], CONFIG['max_len'])
valid_D = DataGenerator(valid_data, self.tokenizer,
CONFIG['batch_size'], CONFIG['max_len'])
output = Lambda(lambda x: x[:, 0])(self.albert_model.output)
output = Dense(1, activation='sigmoid')(output)
self.model = Model(self.albert_model.input, output)
save = ModelCheckpoint(os.path.join(self.model_path),
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='auto')
early_stopping = EarlyStopping(monitor='val_acc',
min_delta=0,
patience=3,
verbose=1,
mode='auto')
callbacks = [save, early_stopping]
if self.initial_model:
x1_in = Input(shape=(None, ))
x2_in = Input(shape=(None, ))
x_in = self.albert_model([x1_in, x2_in])
x_in = Lambda(lambda x: x[:, 0])(x_in)
p = Dense(1, activation='sigmoid')(x_in)
self.model = Model([x1_in, x2_in], p)
else:
self.model = self.albert_model
self.model.compile(
loss='binary_crossentropy',
# optimizer=RAdam(1e-5), # 用足够小的学习率
optimizer=PiecewiseLinearLearningRate(Adam(1e-5), {
1000: 1e-5,
2000: 6e-5
}),
metrics=[
'accuracy', process.get_precision, process.get_recall,
process.get_f1
])
self.model.summary()
self.model.fit_generator(
train_D.__iter__(),
steps_per_epoch=len(train_D),
epochs=CONFIG['epochs'],
validation_data=valid_D.__iter__(),
validation_steps=len(valid_D),
callbacks=callbacks,
use_multiprocessing=CONFIG['use_multiprocessing'],
)
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='auto')
early_stopping = EarlyStopping(monitor='val_acc',
min_delta=0,
patience=8,
verbose=1,
mode='auto')
callbacks = [save, early_stopping]
model.compile(
loss='sparse_categorical_crossentropy',
# optimizer=Adam(1e-5), # 用足够小的学习率
optimizer=PiecewiseLinearLearningRate(Adam(1e-5), {
1000: 1e-5,
2000: 6e-5
}),
metrics=['accuracy'])
model.summary()
train_D = data_generator(train_data)
valid_D = data_generator(valid_data)
model.fit_generator(train_D.__iter__(),
steps_per_epoch=len(train_D),
epochs=100000,
validation_data=valid_D.__iter__(),
validation_steps=len(valid_D),
callbacks=callbacks)
from keras.optimizers import Adam
model = load_pretrained_model(
config_path,
checkpoint_path,
keep_words=keep_words, # 只保留keep_words中的字，精简原字表
albert=True)
output = Lambda(lambda x: x[:, 0])(model.output)
output = Dense(1, activation='sigmoid')(output)
model = Model(model.input, output)
model.compile(
loss='binary_crossentropy',
# optimizer=Adam(1e-5), # 用足够小的学习率
optimizer=PiecewiseLinearLearningRate(Adam(1e-4), {
1000: 1,
2000: 0.1
}),
metrics=['accuracy'])
model.summary()
train_D = data_generator(train_data)
valid_D = data_generator(valid_data)
model.fit_generator(train_D.__iter__(),
steps_per_epoch=len(train_D),
epochs=10,
validation_data=valid_D.__iter__(),
validation_steps=len(valid_D))