The GoogLeNet model is constructed from five sequential modules (labeled $b_1$ through $b_5$) followed by a fully connected output layer. The overall architecture diagram is shown in Fig. 8.4.2.

• Module $b_1$ (Stem): A $7 imes 7$ convolutional layer with $64$ output channels, stride $2$, and padding $3$, followed by ReLU activation and a $3 imes 3$ max-pooling layer (stride $2$, padding $1$). This module resembles the stems of AlexNet and LeNet.
• Module $b_2$: A $1 imes 1$ convolution with $64$ channels, then a $3 imes 3$ convolution that triples the channels to $192$, each followed by ReLU, concluding with $3 imes 3$ max-pooling (stride $2$, padding $1$).
• Module $b_3$: Two Inception blocks producing $64+128+32+32=256$ and $128+192+96+64=480$ output channels respectively, followed by $3 imes 3$ max-pooling.
• Module $b_4$: Five Inception blocks producing $512$, $512$, $512$, $528$, and $832$ output channels respectively, followed by $3 imes 3$ max-pooling.
• Module $b_5$: Two Inception blocks producing $832$ and $1024$ output channels respectively, followed by global average pooling (reducing each channel to $1 imes 1$) and a flatten operation.

Finally, a fully connected layer maps the $1024$-dimensional representation to the number of output classes.