If you were starting to wonder whether there was going to be anything different in this implementation, here it is. I'm going to use two convolutional layers, with batch normalization, and max pooling. This is going to require us to make quite a few choices, which of course we could choose to search as hyperparameters later. It's always better to get something working first though. As Donald Knuth would say, premature optimization is the root of all evil. We will use the following code snippet to define the two convolutional blocks:

# convolutional block 1
conv1 = Conv2D(64, kernel_size=(3,3), activation="relu", name="conv_1")(inputs)
batch1 = BatchNormalization(name="batch_norm_1")(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2), name="pool_1")(batch1)

# convolutional block 2
conv2 = Conv2D(32, kernel_size=(3,3), activation="relu", name="conv_2")(pool1)
batch2 = BatchNormalization(name="batch_norm_2")(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2), name="pool_2")(batch2)

So, clearly, we have two convolutional blocks here, that consist of a convolutional layer, a batch normalization layer, and a pooling layer.

In the first block, I'm using 64 3 x 3 filters with relu activations. I'm using valid (no) padding and a stride of 1. Batch normalization doesn't require any parameters and it isn't really trainable. The pooling layer is using 2 x 2 pooling windows, valid padding, and a stride of 2 (the dimension of the window).

The second block is very much the same; however, I'm halving the number of filters to 32.

While there are many knobs we could turn in this architecture, the one I would tune first is the kernel size of the convolutions. Kernel size tends to be an important choice. In fact, some modern neural network architectures such as Google's inception, allow us to use multiple filter sizes in the same convolutional layer.