Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

Last update: Oct 06, 2022

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Deep Learning forward-thinking-pytorch

Overview

forward-thinking-pytorch

Pytorch implementation of Forward Thinking: Building and Training Neural Networks One Layer at a Time

Requirements

Python 2.7
numpy
pytorch
Matplotlib

Usage

  $ ./run.sh

to run forward-thinking & forward-thinking (deep) & backpropagation & backpropagation (deep).

For forward-thinking experiment (5 layers),

$ python forward_thinking.py

If you want to do this with very deep model (20 layers),

$ python forward_thinking.py --epoch 200 --deep

As a comparison, learn by backpropagation:

$ python backpropagate.py

Backpropagate with very deep model (20 layers),

$ python backpropagate.py --epoch 200 --deep

Result

For 5 layers, forward-thinking learns slightly faster than backpropagation. Dips are observed when new layers are added.

When the model becomes very deep (20 layers), backpropagation cannot train the model. On the other hand, forward-thinking can train the model as usual.

Owner

Kim Heecheol

University of Tokyo, Intelligent systems & Informatics Lab.

GitHub Repository https://arxiv.org/pdf/1706.02480.pdf

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Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

Related tags

Overview

forward-thinking-pytorch

Requirements

Usage

Result

Owner

Kim Heecheol

"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

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