Pytorch implementation of the Equilibrium Propagation algorithm as introduced in Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation. This project is licensed under the terms of the MIT license.
You can run the models using the run_energy_model_mnist.py script which provides the following options:
python run_energy_model_mnist.py -h
usage: run_energy_model_mnist.py [-h] [--batch_size BATCH_SIZE]
[--c_energy {cross_entropy,squared_error}]
[--dimensions DIMENSIONS [DIMENSIONS ...]]
[--energy {cond_gaussian,restr_hopfield}]
[--epochs EPOCHS] [--fast_ff_init]
[--learning_rate LEARNING_RATE]
[--log_dir LOG_DIR]
[--nonlinearity {leaky_relu,relu,sigmoid,tanh}]
[--optimizer {adam,adagrad,sgd}]
[--seed SEED]
Train an energy-based model on MNIST using Equilibrium Propagation.
optional arguments:
-h, --help show this help message and exit
--batch_size BATCH_SIZE
Size of mini batches during training.
--c_energy {cross_entropy,squared_error}
Supervised learning cost function.
--dimensions DIMENSIONS [DIMENSIONS ...]
Dimensions of the neural network.
--energy {cond_gaussian,restr_hopfield}
Type of energy-based model.
--epochs EPOCHS Number of epochs to train.
--fast_ff_init Flag to enable fast feedforward initialization.
--learning_rate LEARNING_RATE
Learning rate of the optimizer.
--log_dir LOG_DIR Subdirectory within ./log/ to store logs.
--nonlinearity {leaky_relu,relu,sigmoid,tanh}
Nonlinearity between network layers.
--optimizer {adam,adagrad,sgd}
Optimizer used to train the model.
--seed SEED Random seed for pytorch
The default configurations for unspecified parameters are stored in /etc/.
Documentation is auto-generated from docstrings using pdoc3 . --html --force --output-dir docs.
Two demo runs for the conditional Gaussian and the restricted Hopfield model using the default configuration can be found in the /log/ directory. They can be reproduced with:
#!/bin/bash
python run_energy_model_mnist.py --energy cond_gaussian --c_energy cross_entropy --seed 2019
python run_energy_model_mnist.py --energy restr_hopfield --c_energy squared_error --seed 2019python 3.6
pytorch 1.1.0
torchvision 0.3.0