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Project: deep-learning-models (GitHub Link)

• deep-learning-models-master
  • run.py
  • Makefile
  • set_env.sh
  • LICENSE
  • models
    • layers
      • sampling.py
      • extras.py
      • normalization.py
      • shape.py
      • __init__.py
    • adgm.py
    • convadgm.py
    • dcgan.py
    • distributions
      • distributions.py
      • __init__.py
      • operations.py
    • softmax.py
    • vae.py
    • ssadgm.py
    • convvae.py
    • semisup_model.py
    • resnet.py
    • cnn.py
    • dadgm.py
    • model.py
    • sbn.py
    • __init__.py
    • mlp.py
    • hdgm.py
    • helpers.py
  • util
    • data.py
    • launch.py
    • fig.py
    • __init__.py
  • README.md
  • requirements.txt
  • optimizers
    • __init__.py
    • README.md
  • .gitignore

Deep Learning Model Zoo

This repository contains implementations of various deep learning algorithms in Theano/Lasagne.

Running a model

To run a model, you may use the run.py launch script.

python run.py train \
  --dataset <dataset> \
  --model <epochs> \
  --alg <opt_alg> \
  --n_batch <batch_size> \
  --lr <learning_rate> \
  -e <num_epochs> \
  -l <log_name>

Alternatively, you may use the Makefile included in the root dir; typing make train will start training. There are also several additional parameters that can be configured inside the Makefile.

The model will periodically save its weights and report training/validation losses in the logfile.

Algorithms

The following algorithms are available.

Supervised learning models

• softmax: simple softmax classifier
• mlp: multilayer perceptron
• cnn: convolutional neural network; solves mnist and achieves reasonably good accuracy on cifar10
• resnet: small residual network; achieves an accuracy in the 80's on cifar10

Semi-supervised models

• ssdadgm: semi-supervised deep generative models (in progress)

Unsupervised models

• vae: variational autoencoder
• convvae: convolutional variational autoencoder
• sbn: vae with discrete latent variables, trained with neural variational inference (reduces to sigmoid belief network)
• adgm: auxiliary deep generative model (unsupervised version)
• convadgm: convolutional auxiliary deep generative model (unsupervised version)
• dadgm: discrete-variable auxiliary deep generative model (unsupervised version, also trained with NVIL)
• dcgan: small deep convolutional generative adversarial network (tested on mnist)

Datasets

The following datasets are currently available:

• cifar10: color images divided into 10 classes (32x32x3)
• mnist: standard handwritten digits dataset (28x28)
• digits: sklearn digits dataset (8x8); can be used for quick debugging on a CPU

Optimization methods

Currently, we may train the models using:

• sgd: standard stochastic gradient descent
• adam: the Adam optimizer

Feedback

Send feedback to Volodymyr Kuleshov. Some models contain snippets from other users' repositories; let me know if I forgot to cite anyone.