Convolutional layer

Two main architectural considerations are associated with the convolutional layer in Keras. The first is to do with the number of filters to employ in the given layer, whereas the second denotes the size of the filters themselves. So, let's see how this is implemented by initializing a blank sequential model and adding our first convolutional layer to it:

model=sequential()
#First Convolutional layer 
model.add(Conv2D(16,(5,5), padding = 'same', activation = 'relu', input_shape = (64,64,3)))
model.add(BatchNormalization())

Defining the number and size of the filters

As we saw previously, we define the layer by embodying it with 16 filters, each with a height and width of 5 x 5. In...