Stock_Prediction_Model_Stateless_LSTM.py 文件源码

def LSTM(self, argsDict):
        self.paras.batch_size             = argsDict["batch_size"]
        self.paras.model['dropout']       = argsDict['dropout']
        self.paras.model['activation']    = argsDict["activation"]
        self.paras.model['optimizer']     = argsDict["optimizer"]
        self.paras.model['learning_rate'] = argsDict["learning_rate"]

        print(self.paras.batch_size, self.paras.model['dropout'], self.paras.model['activation'], self.paras.model['optimizer'], self.paras.model['learning_rate'])

        model = self.lstm_model()
        model.fit(self.train_x, self.train_y,
              batch_size=self.paras.batch_size,
              epochs=self.paras.epoch,
              verbose=0,
              callbacks=[EarlyStopping(monitor='loss', patience=5)]
              )

        score, mse = model.evaluate(self.test_x, self.test_y, verbose=0)
        y_pred=model.predict(self.test_x)
        reca=Recall_s(self.test_y,y_pred)
        return -reca