Revisiting the bird species identifier to use images

In this section, we're going to revisit the bird species identifier from before. This time, we're going to update it to use neural networks and deep learning. Can you recall the birds dataset? It has 200 different species of birds across 12,000 images. Unlike last time, we won't be using the human-labeled attributes, and instead we'll use the actual images without any pre-processing. In our first attempt, we're going to build a custom convolutional neural network, just like we did for the mathematical symbols classifier.

Let's go to the code. We will start with the typical imports:

We'll make some convenience variables, the rows and columns of the image, the width and height, and the number of channels, RGB, though every bird image will be equal. Even though they're not all necessarily the same size, we're going to resize them to this size so that they're all consistent:

Now, this project introduces an interesting feature on Keras called...