Stock_Prediction_Model_Stateless_LSTM.py 文件源码

def lstm_model(self):
        model = Sequential()
        first = True
        for idx in range(len(self.paras.model['hidden_layers'])):
            if idx == (len(self.paras.model['hidden_layers']) - 1):
                model.add(LSTM(int(self.paras.model['hidden_layers'][idx]), return_sequences=False))
                model.add(Activation(self.paras.model['activation']))
                model.add(Dropout(self.paras.model['dropout']))
            elif first == True:
                model.add(LSTM(input_shape=(None, int(self.paras.n_features)),
                               units=int(self.paras.model['hidden_layers'][idx]),
                               return_sequences=True))
                model.add(Activation(self.paras.model['activation']))
                model.add(Dropout(self.paras.model['dropout']))
                first = False
            else:
                model.add(LSTM(int(self.paras.model['hidden_layers'][idx]), return_sequences=True))
                model.add(Activation(self.paras.model['activation']))
                model.add(Dropout(self.paras.model['dropout']))

        if self.paras.model['optimizer'] == 'sgd':
            #optimizer = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
            optimizer = optimizers.SGD(lr=self.paras.model['learning_rate'], decay=1e-6, momentum=0.9, nesterov=True)
        elif self.paras.model['optimizer'] == 'rmsprop':
            #optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
            optimizer = optimizers.RMSprop(lr=self.paras.model['learning_rate']/10, rho=0.9, epsilon=1e-08, decay=0.0)
        elif self.paras.model['optimizer'] == 'adagrad':
            #optimizer = optimizers.Adagrad(lr=0.01, epsilon=1e-08, decay=0.0)
            optimizer = optimizers.Adagrad(lr=self.paras.model['learning_rate'], epsilon=1e-08, decay=0.0)
        elif self.paras.model['optimizer'] == 'adam':
            #optimizer = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
            optimizer = optimizers.Adam(lr=self.paras.model['learning_rate']/10, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
        elif self.paras.model['optimizer'] == 'adadelta':
            optimizer = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0)
        elif self.paras.model['optimizer'] == 'adamax':
            optimizer = optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
        elif self.paras.model['optimizer'] == 'nadam':
            optimizer = optimizers.Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004)
        else:
            optimizer = optimizers.Adam(lr=self.paras.model['learning_rate']/10, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

        # output layer
        model.add(Dense(units=self.paras.model['out_layer']))
        model.add(Activation(self.paras.model['out_activation']))
        model.compile(loss=self.paras.model['loss'], optimizer=optimizer, metrics=['accuracy'])

        return model