Hands-On Image Processing with Python

Training phase

The following code block shows how to fit the model on the training dataset. Let's use 20k images from the training dataset to train the VGG-16 model and 5k images as validation dataset to evaluate the model while training. The weights=None parameter value must be passed to the VGG16() function to ensure that the network is trained from scratch. Note, this will take a long time if not run on a GPU, so a GPU is recommended. 

For installing TensorFlow with a GPU, refer to this article: https://medium.com/@raza.shahzad/setting-up-tensorflow-gpu-keras-in-conda-on-windows-10-75d4fd498198.

With 20 epochs, the accuracy achieved on the validation dataset is 78.38%. We can tune the hyperparameters to increase the accuracy of the model ...