figure deep learning for computer vision smoot hed curves for raining and validation loss for fine-tuning the validation accuracy curve look much cleaner you're seeing nice absolute improvement in accuracyfrom about to above note that the loss curve doesn' show any real improvement (in factit' deterioratingyou may wonderhow could accuracy stay stable or improve if the loss isn' decreasingthe answer is simplewhat you display is an average of pointwise loss valuesbut what matters for accuracy is the distribution of the loss valuesnot their averagebecause accuracy is the result of binary thresholding of the class probability predicted by the model the model may still be improving even if this isn' reflected in the average loss you can now finally evaluate this model on the test datatest_generator test_datagen flow_from_directorytest_dirtarget_size=( )batch_size= class_mode='binary'test_losstest_acc model evaluate_generator(test_generatorsteps= print('test acc:'test_acchere you get test accuracy of in the original kaggle competition around this datasetthis would have been one of the top results but using modern deep-learning techniquesyou managed to reach this result using only small fraction of the training data available (about %there is huge difference between being able to train on , samples compared to , sampleslicensed to