Model Layers
Explore the structure of SqueezeNet by learning to build its initial convolution and max pooling layers. Understand how modifications adapt the model for smaller images in CIFAR-10, and gain practical experience coding these layers to enhance image recognition performance.
We'll cover the following...
We'll cover the following...
Chapter Goals:
- Learn the high-level architecture of SqueezeNet
- Understand the use of non-fire module convolution layers
A. Overview
As mentioned in the previous chapter, we'll be building a condensed version of the SqueezeNet model. The differences between our model and the original are:
- Our model uses only the first 4 fire modules from the original SqueezeNet (rather than all 8)
- The initial convolution layer for our model uses fewer