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  3. Image Processing with Keras in Python

Exercise

Visualizing kernel responses

One of the ways to interpret the weights of a neural network is to see how the kernels stored in these weights "see" the world. That is, what properties of an image are emphasized by this kernel. In this exercise, we will do that by convolving an image with the kernel and visualizing the result. Given images in the test_data variable, a function called extract_kernel() that extracts a kernel from the provided network, and the function called convolution() that we defined in the first chapter, extract the kernel, load the data from a file and visualize it with matplotlib.

A deep CNN model, a function convolution(), along with the kernel you extracted in an earlier exercise is available in your workspace.

Ready to take your deep learning to the next level? Check out Advanced Deep Learning with Keras to see how the Keras functional API lets you build domain knowledge to solve new types of problems.

Instructions

100 XP
  • Use the convolution() function to convolve the extracted kernel with the first channel of the fourth item in the image array.
  • Visualize the resulting convolution with imshow().