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Since we have fine-tuned the model by training our custom dense layers on our data, let us check the performance of the model.
evaluate method on the
vgg_model to get the accuracy of its performance on the test data.
vgg_model_loss, vgg_model_acc = vgg_model.<< your code comes here >>(test_set_x_orig,test_set_y_orig)
Print the accuracy
print('Test accuracy using VGG16 model as the base:', vgg_model_acc)
You could play around with different value of hyper-parameters, like the learning rate, number of epochs, number of dropout neurons, dropout value, etc, and get a better model which yields better performance.
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