python - Cross-Validation in LSTM - ValueError: Input 0 of layer sequential_3 is incompatible with the layer

Question

I am trying to perform a 10-fold cross-validation on a LSTM, the code is the following:

                    # 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

The idea is to perform a 10-fold cross-validation with an EarlyStopping based on the lack of improvements on the validation accuracy. The first fold runs perfectly, but everytime the second fold is supposed to begin, I receive the error:

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

A note about my input:

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

So for some reasons, when the second fold begins X_train1.shape[1] appears to be equal to None. Has this ever happened to you? Thanks!

Answer 1

I can see straight away some strange things in the cycle you aim to implement. i think you can safely get rid of the

kfold.split(X_train1, y_train1)

before the for loop.

Then, you are not selecting the split istances but are just feeding the whole dataset X_train1. This looks better:

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]