Datasets input at model.fit produce unexpected results of training loss vs validation loss

Im trying to train a neural network (VAE) using tensorflow and Im getting different results based on the type of input in the model.fit. When I input arrays I get normal difference between the validation loss and the total loss. When I input a dataset based on the same input I get a normal total loss and a really small validation loss. I havent changed the model. The only things that changes is the input format. The code for when I input an array. train slices is (2627,138,138,1) and define the batch size in the model.fit train_slices = preprocess_data(CropTumor, file_array[train_dataset]) val_slices = preprocess_data(CropTumor, file_array[val_dataset]) # reset model weights before training VAE.set_weights(initial_weights) # fit model fit_results = VAE.fit(train_slices,train_slices, epochs=1000, validation_data=(val_slices,val_slices), callbacks=[early_stopping_kfold, tensorboard_callback], batch_size=batch_sz, verbose=2 ) The output Epoch 1/1000 2022-08-01 11:56:35.683852: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8401 2022-08-01 11:56:36.371780: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-08-01 11:56:36.461054: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 672/672 - 7s - loss: 537.2896 - val_loss: 213.7070 - 7s/epoch - 11ms/step Epoch 2/1000 672/672 - 5s - loss: 248.5211 - v…