Python LSTM中的交叉验证-ValueError:层顺序_3的输入0与层不兼容

我试图在LSTM上执行10倍交叉验证，代码如下：

                    # Initialising the RNN
                    regressor = Sequential()
                    
                    # Adding the first LSTM layer and some Dropout regularisation
                    regressor.add(LSTM(units = 350, return_sequences = True, input_shape = (X_train1.shape[1], len(columns1))))
                    regressor.add(Dropout(0.5))
                    
                    # Adding a second LSTM layer and some Dropout regularisation
                    regressor.add(LSTM(units = 350, return_sequences = True))
                    regressor.add(Dropout(0.5))
                    
                    # Adding a third LSTM layer and some Dropout regularisation
                    regressor.add(LSTM(units = 350, return_sequences = True))
                    regressor.add(Dropout(0.5))
                    
                    # Adding a fourth LSTM layer and some Dropout regularisation
                    regressor.add(LSTM(units = 350))
                    regressor.add(Dropout(0.5))
                    
                    # Adding the output layer
                    regressor.add(Dense(units = 1)) 
                    
                    # Compiling the RNN
                    regressor.compile(optimizer = 'rmsprop', loss = 'mean_squared_error',metrics=['accuracy']) 
                        
                    # RNN TRAINING
                    
                    kfold = KFold(n_splits=10, shuffle=True, random_state=0) 
                    val_accuracies = []
                    test_accuracies = []
                    
                
                    i = 1
                    df_metrics = pd.DataFrame()
                    
                    
                    kfold.split(X_train1, y_train1)
                    
                    #for train_index, test_index in kfold.split(disease_df):
                    for train_index, test_index in kfold.split(X_train1, y_train1):
                        
                    
                        #callback = EarlyStopping(monitor='val_accuracy', patience=10,restore_best_weights=True)
                        # Fitting the RNN to the Training set (RUN/TRAIN the model)
                        history = regressor.fit(X_train1, y_train1, epochs = 100, batch_size = 25, validation_split = 0.1, callbacks=[EarlyStopping('val_accuracy', mode='max',patience=5)])
                        
                        i+=1

我们的想法是，在验证准确性缺乏改进的基础上，进行10倍交叉验证，并提前终止。第一次折叠运行良好，但每次第二次折叠开始时，我都会收到错误：

    ValueError: Input 0 of layer sequential_3 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 68)  

关于我的输入的说明：

 X_train1.shape[1] = 1
 len(columns1) = 68

因此，由于某些原因，当第二次折叠开始时，X_train1.shape[1]似乎等于零。你有过这样的经历吗？

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谢谢

我可以马上看到您打算实施的周期中的一些奇怪的事情。 我想你可以安全地摆脱这个问题

kfold.split(X_train1, y_train1)

在for循环之前

然后，您不选择分割距离，而只是输入整个数据集X_train1。这看起来更好：

from sklearn.model_selection import KFold
kf = KFold(n_splits=2)


for train_index, test_index in kf.split(X_train1):
 print("TRAIN:", train_index, "TEST:", test_index)
 X_train, X_test = X_train1[train_index], X_train1[test_index]
 y_train, y_test = y_train1[train_index], y_train1[test_index]

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谢谢，所以基本上第二个折叠没有什么需要处理的，因为它不是一个折叠，但是我在第一个折叠中传递了整个数据集。我明白了，现在，谢谢你的支持，这件事让我感到困惑是的，事实上你没有从sklearn应用k折叠。希望有帮助！