abhiskk / Ladder
Licence: gpl-3.0
Implementation of Ladder Network in PyTorch.
Stars: ✭ 37
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ladder
Implementation of Ladder Network and Stacked Denoising Autoencoder in PyTorch.
Requirements
Training ladder
-
Run
python utils/mnist_data.pyto create the MNIST dataset. -
Run the following command to train the ladder network:
python ladder/ladder.py --batch 100 --epochs 20 --noise_std 0.2 --data_dir data
Status: The unsupervised loss starts at a high value because of which the network overfits the unsupervised loss and the supervised performance is bad. Current best accuracy on MNIST validation set using 3000 labelled and 47000 unlabelled examples: 98.33%.
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