Deep Learning with PyTorch

Reducing the size of the network

The size of the network in general refers to the number of layers or the number of weight parameters used in a network. In the example of image classification that we saw in the last chapter, we used a ResNet model that has 18 blocks consisting of different layers inside it. The torchvision library in PyTorch comes with ResNet models of different sizes starting from 18 blocks and going up to 152 blocks. Say, for example, if we are using a ResNet block with 152 blocks and the model is overfitting, then we can try using a ResNet with 101 blocks or 50 blocks. In the custom architectures we build, we can simply remove some intermediate linear layers, thus preventing our PyTorch models from memorizing the training ...