Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
Convolution
If in the MLP model the number of units characterizes the Dense layers, the kernel
characterizes the CNN operations. As shown in Figure 1.4.2, the kernel can be
visualized as a rectangular patch or window that slides through the whole image
from left to right, and top to bottom. This operation is called convolution. It
transforms the input image into a feature maps, which is a representation of what
the kernel has learned from the input image. The feature maps are then transformed
into another feature maps in the succeeding layer and so on. The number of feature
maps generated per Conv2D is controlled by the filters argument.
Figure 1.4.2: A 3 × 3 kernel is convolved with an MNIST digit image.
The convolution is shown in steps t n
and t n+1
where the kernel moved by a stride of 1 pixel to the right.
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