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 1conv1 = Conv2D(64, kernel_size=(3,3), activation="relu", name="conv_1")(inputs)batch1 = BatchNormalization(name="batch_norm_1")(conv1)pool1 = MaxPooling2D(pool_size ...
