An example of fine-tuning an auto-encoder for classification. The example demonstrates how arbitrary modules can easily be extended to fix the weights and/or biases after loading a model. Additionally it shows how weights and biases can manually be copied between models with a different structure.
Minimal example of defining a custom Torch module on a custom data structure. This example defines a simple data structure wrapping two Torch tensors and defines a linear
nn.Module to operate on this data structure. While the backward pass is not implemented, the example illustrates how Torch can be extended for deep learning on custom data structures.
Simple LUA package to manually initialize the weights and biases of a network in Torch according to different strategies — these include uniform and normal initialization as well as heuristic and Xavier initialization. The package is easily extended to include additional initialization schemes and allows to initialize weights and biases using different strategies.
A simple convolutional auto-encoder implemented in Torch and trained using Torch’s
Auto-encoder in Torch using Torch’s
optim package and GPU acceleration.