Convolutional Layer of CNN in one Picture

A complete course at Stanford has devoted to Convolutional Neural Network.
The Course Notes (by Andrej Karpathy) are well written and they worth a look.

That course notes have inspired me to create a picture for summarising some concepts.

An interesting summary (adapted from here ) is the following:

Input Layer

• Size: $W_1 \times H_1 \times D_1$
• Hyperparameters:
• Number of filters $K$
• Dimension of the filter $F \times F \times D_1$
• Stride: $S$
• Amount of Zero Padding: $P$

Output Layer

• Size: $W_2 \times H_2 \times D_2$
• $W_2 = \frac{W_1 - F + 2P}{S} + 1$
• $H_2 = \frac{H_1 - F + 2P}{S} + 1$
• $D_2 = K$

The parameter sharing introduces $F \times F \times D_1$ per filter, for a total of $(F \times F \times D_1) \times K$ weights and $K$ biases

In the output volume, the d-th depth slice (of size $W_2 \times H2$) is the result of performing a valid convolution of the d-th filter over the input volume with a stride of $S$, and then offset by d-th bias.

Another interesting post on the Convolutional Neural Network is here