add files

model/model_keras.py

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+from keras.layers import Input, Dense, LSTM
+from keras.models import Model
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+
+
+def get_keras_model(config):
+	input1 = Input(shape=(config.time_step, config.input_size))
+	lstm = input1
+	for i in range(config.lstm_layers):
+		lstm = LSTM(units=config.hidden_size, dropout=config.dropout_rate, return_sequences=True)(lstm)
+	output = Dense(config.output_size)(lstm)
+	model = Model(input1, output)
+	model.compile(loss='mse', optimizer='adam')
+	return model
+
+
+def train(config, train_X, train_Y, valid_X, valid_Y):
+	model = get_keras_model(config)
+	model.summary()
+	if config.add_train:
+		model.load_weights(config.model_save_path + config.model_name)
+
+	check_point = ModelCheckpoint(filepath=config.model_save_path + config.model_name, monitor='val_loss',
+	                              save_best_only=True, mode='auto')
+	early_stop = EarlyStopping(monitor='val_loss', patience=config.patience, mode='auto')
+	model.fit(train_X, train_Y, batch_size=config.batch_size, epochs=config.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