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- # -*- coding: UTF-8 -*-
- from keras.layers import Input, Dense, LSTM
- from keras.models import Model
- from keras.callbacks import ModelCheckpoint, EarlyStopping
- def get_keras_model(opt):
- input1 = Input(shape=(opt.Model['time_step'], opt.input_size))
- lstm = input1
- for i in range(opt.Model['lstm_layers']):
- lstm = LSTM(units=opt.Model['hidden_size'],dropout=opt.Model['dropout_rate'],return_sequences=True)(lstm)
- output = Dense(opt.output_size)(lstm)
- model = Model(input1, output)
- model.compile(loss='mse', optimizer='adam') # metrics=["mae"]
- return model
- def gpu_train_init():
- import tensorflow as tf
- from keras.backend.tensorflow_backend import set_session
- sess_config = tf.ConfigProto(log_device_placement=True, allow_soft_placement=True)
- sess_config.gpu_options.per_process_gpu_memory_fraction = 0.7 # 最多使用70%GPU内存
- sess_config.gpu_options.allow_growth=True # 初始化时不全部占满GPU显存, 按需分配
- sess = tf.Session(config=sess_config)
- set_session(sess)
- def train(opt, train_and_valid_data):
- if opt.use_cuda: gpu_train_init()
- train_X, train_Y, valid_X, valid_Y = train_and_valid_data
- model = get_keras_model(opt)
- model.summary()
- if opt.add_train:
- model.load_weights(opt.model_save_path + opt.model_name)
- check_point = ModelCheckpoint(filepath=opt.model_save_path + opt.model_name, monitor='val_loss',
- save_best_only=True, mode='auto')
- early_stop = EarlyStopping(monitor='val_loss', patience=opt.Model['patience'], mode='auto')
- model.fit(train_X, train_Y, batch_size=opt.Model['batch_size'], epochs=opt.Model['epoch'], verbose=2,
- validation_data=(valid_X, valid_Y), callbacks=[check_point, early_stop])
- def predict(config, test_X):
- model = get_keras_model(config)
- model.load_weights(config.model_save_path + config.model_name)
- result = model.predict(test_X, batch_size=1)
- result = result.reshape((-1, config.output_size))
- return result
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