To define LeNet code, we use a convolutional 2D module, which is:
keras.layers.convolutional.Conv2D(filters, kernel_size, padding='valid')
Here, filters is the number of convolution kernels to use (for example, the dimensionality of the output), kernel_size is an integer or tuple/list of two integers, specifying the width and height of the 2D convolution window (can be a single integer to specify the same value for all spatial dimensions), and padding='same' means that padding is used. There are two options: padding='valid' means that the convolution is only computed where the input and the filter fully overlap, and therefore the output is smaller than the input, while padding='same' means that we have an output that is ...
