I’m basically following the fine tuning instructions for efficientnet here. I use the model without top weights, freezing the rest, and train the model some, training and validation loss both ended around 1.7, and both accuracies were around 0.34. Then I saved the model, and loaded it again, and unfreezed the top 20 layers except for batch normalization ones. When I start training again, training loss is going down, between 1 and 2, and training accuracy is going up. But validation loss is in the hundreds, going up and down, and validation accuracy is oscillating between values of 0.01-0.3. Sometimes the validation accuracy goes down but the loss goes down also. Any ideas why the validation loss would be so high? I’m using ImageDataGenerator with a validation split of 0.2, and train_datagen.flow_from_dataframe with subsets for training and validation for each, for each training run. I saved the model with h5 format.