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TAG»LUA«

18thMARCH2017

SNIPPET

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.

Interested?

14thMARCH2017

SNIPPET

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.

Interested?

11thMARCH2017

SNIPPET

A simple custom abs-criterion in Torch extending nn.Criterion.

Interested?

09thMARCH2017

SNIPPET

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.

Interested?

09thMARCH2017

SNIPPET

A simple convolutional auto-encoder implemented in Torch and trained using Torch’s optim package.

Interested?

21thFEBRUARY2017

SNIPPET

Auto-encoder in Torch using Torch’s optim package and GPU acceleration.

Interested?

21thFEBRUARY2017

SNIPPET

Auto-encoder in Torch using Torch’s optim package.

Interested?

20thFEBRUARY2017

SNIPPET

Simple auto-encoder example implemented in Torch (following the documentation)

Interested?

20thFEBRUARY2017

SNIPPET

Auto-encoder implemented in Torch using Torch’s cunn for GPU-acceleration.

Interested?

20thFEBRUARY2017

SNIPPET

Following the documentation, this snippet illustrates the implementation of a simple auto-encoder using Torch’s nn.StochasticGradient trainer.

Interested?