def load_model(model_path):
model = build_model(training=False)
model.load_weights(model_path)
return model
def run():
batch_size = 63
epochs = 5000
data_process = DataProcess(use_word2cut=False)
model = build_model()
documents_length = data_process.get_documents_size(data_process.enc_ids_file, data_process.dec_ids_file)
if batch_size > documents_length:
print("ERROR--->" + u"语料数据量过少，请再添加一些")
return None
#自适应学习率
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=20, min_lr=1e-6, mode='min')
'''monitor: 需要监视的量，val_loss，val_acc
patience: 当early stop被激活(如发现loss相比上一个epoch训练没有下降)，则经过patience个epoch后停止训练
verbose: 信息展示模式
mode: 'auto','min','max'之一，在min模式训练，如果检测值停止下降则终止训练。在max模式下，当检测值不再上升的时候则停止训练。'''
early_stopping = EarlyStopping(monitor='val_loss', patience=50, verbose=2)
model.fit_generator(generator=generate_batch(batch_size=batch_size),
steps_per_epoch=int(documents_length / batch_size)+5, \
validation_data=generate_batch(batch_size=batch_size), \
validation_steps=int(documents_length / batch_size)+5,\
epochs=epochs, verbose=1, workers=2, use_multiprocessing=True,
callbacks=[reduce_lr,early_stopping])
model.save_weights("model/seq2seq_model_weights.h5", overwrite=True)
def run():
batch_size = 63
epochs = 5000
data_process = DataProcess(use_word2cut=False)
model = build_model()
documents_length = data_process.get_documents_size(
data_process.enc_ids_file, data_process.dec_ids_file)
if batch_size > documents_length:
print("ERROR--->" + u"语料数据量过少，请再添加一些")
return None
model.fit_generator(generator=generate_batch(batch_size=batch_size),
steps_per_epoch=int(documents_length / batch_size), \
epochs=epochs, verbose=1, workers=1)
model.save_weights("model/seq2seq_model_weights.h5", overwrite=True)