In this section, we are going to define the content cost function and then formalize the function a bit more by calculating the derivative, which we will use for further applications as well. We will use transfer learning or a pre-trained convolution architecture, such as VGG-16, but this time in a different way. Instead of using the model for this prediction for the softmax layer, we will use the layer's knowledge or their ability to capture the features of the images as depicted in the following diagram:
As we saw in the first section, What are convolution network layers learning, the first layers of the neural network capture rather low-level features, such as shapes or maybe colors, and as we move deeper, the layers are detecting more high-level features, and at the same time also capture a bigger part of the image, as shown in the preceding...
