steps_per_epoch gradient descent steps--the fitting process will go to the next epoch in this casebatches are samplesso it will take batches until you see your target of , samples when using fit_generatoryou can pass validation_data argumentmuch as with the fit method it' important to note that this argument is allowed to be data generatorbut it could also be tuple of numpy arrays if you pass generator as validation_datathen this generator is expected to yield batches of validation data endlesslythus you should also specify the validation_steps argumentwhich tells the process how many batches to draw from the validation generator for evaluation listing fit ing he model using bat ch generat or history model fit_generatortrain_generatorsteps_per_epoch= epochs= validation_data=validation_generatorvalidation_steps= it' good practice to always save your models after training listing saving he model model save('cats_and_dogs_small_ 'let' plot the loss and accuracy of the model over the training and validation data during training (see figures and listing displaying curves of loss and accuracy during raining import matplotlib pyplot as plt acc history history['acc'val_acc history history['val_acc'loss history history['loss'val_loss history history['val_loss'epochs range( len(acc plt plot(epochsacc'bo'label='training acc'plt plot(epochsval_acc' 'label='validation acc'plt title('training and validation accuracy'plt legend(plt figure(plt plot(epochsloss'bo'label='training loss'plt plot(epochsval_loss' 'label='validation loss'plt title('training and validation loss'plt legend(plt show(licensed to