model.add(LSTM(units=number_units)) model.add(Dropout(dropout_fraction)) # Output layer model.add(Dense(1)) Python 代码片段概述了 LSTM 长短期记忆 RNN 循环神经网络模型的结构。LSTM 模型是一种特殊的 RNN,非常适合基于时间序列数据进行预测,这使得它们对于算法交易非常有用,在算法交易中可以使用过去的股...
# 模型评估train_predict=model.predict(X_train)test_predict=model.predict(X_test)# 可视化结果importmatplotlib.pyplotasplt# 反归一化train_predict=scaler.inverse_transform(train_predict)test_predict=scaler.inverse_transform(test_predict)actual_data=scaler.inverse_transform(scaled_data)# 绘制图形plt.figure...
return_sequences=True,input_shape=(30,5))) grid_model.add(LSTM(50)) grid_model.add(Dropout(0.2)) grid_model.add(Dense(1))grid_model.compile(loss = 'mse',optimizer = optimizer) return grid_modelgrid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY))p...
prediction_test=model.predict(testX) 如果直接model.predict(testX),testX的形状是(1029,10,5),是一个批量预测,输出prediction_test是一个(1029,3,5)的三维数组,prediction_test[0]就是第一个样本未来3天5个标签的预测结果,prediction_test[1]就是第二个样本未来3天5个标签的预测结果... 看一下第一个测...
# 进行预测 test_data = data[-time_step:] test_data = test_data.reshape(1, time_step, 1) predicted_value = model.predict(test_data) print("Predicted value:", predicted_value[0][0]) 从预测结果中获取概率值: 由于LSTM模型本身不直接输出概率值,但我们可以将模型的输出通过sigmoid函数(或其他...
LSTM生成和预测 模型训练超过100期,并生成预测。 #生成LSTM网络 model = Sequential() model.add(LSTM(4,input_shape =(1,previous))) model.fit(X\_train,Y\_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将标准化后的数据转换为原始数据 trainpr...
model.add(LSTM(4,input_shape =(1,previous))) model.fit(X_train,Y_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将标准化后的数据转换为原始数据 trainpred = scaler.inverse_transform(trainpred) ...
model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2]))) model.add(Dense(y.shape[1], activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X, y, nb_epoch=500, batch_size=1, verbose=2)# summarize performance of...
model.add(LSTM(4,input_shape =(1,previous))) model.fit(X_train,Y_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将预测转换回正常值 trainpred = scaler.inverse_transform(trainpred) ...
model.add(LSTM(4,input_shape =(1,previous))) model.fit(X_train,Y_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将标准化后的数据转换为原始数据 trainpred = scaler.inverse_transform(trainpred) ...