This repository contains source code for the experiments in a paper titled Semi-Supervised Learning with Ladder Networks by A Rasmus, H Valpola, M Honkala, M Berglund, and T Raiko.
Required libraries
Install Theano, Blocks Stable 0.2, Fuel Stable 0.2
Refer to the Blocks installation instructions for details but use tag v0.2 instead. Something along:
pip install git+git://github.com/mila-udem/[email protected]
pip install git+git://github.com/mila-udem/[email protected]
Fuel comes with Blocks, but you need to download and convert the datasets. Refer to the Fuel documentation. One might need to rename the converted files.
fuel-download mnist
fuel-convert mnist --dtype float32
fuel-download cifar10
fuel-convert cifar10
Alternatively, one can use the environment.yml file that is provided in this repo to create an conda environment.
- First install anaconda from https://www.continuum.io/downloads. Then,
conda env create -f environment.ymlsource activate ladder- The environment should be good to go!
Models in the paper
The following commands train the models with seed 1. The reported numbers in the paper are averages over several random seeds. These commands use all the training samples for training (--unlabeled-samples 60000) and none are used for validation. This results in a lot of NaNs being printed during the trainining, since the validation statistics are not available. If you want to observe the validation error and costs during the training, use --unlabeled-samples 50000.
MNIST all labels
# Full
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 1000,1,0.01,0.01,0.01,0.01,0.01 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_full
# Bottom
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,0,0,0,0,0,0 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_bottom
# Gamma model
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,2 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_gamma
# Supervised baseline
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 60000 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_all_baseline
MNIST 100 labels
# Full
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 1000,10,0.1,0.1,0.1,0.1,0.1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_full
# Bottom-only
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 5000,0,0,0,0,0,0 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_bottom
# Gamma
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,0.5 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_gamma
# Supervised baseline
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 100 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_100_baseline
MNIST 1000 labels
# Full
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,20,0.1,0.1,0.1,0.1,0.1 --f-local-noise-std 0.2 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_full
# Bottom-only
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,0,0,0,0,0,0 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_bottom
# Gamma model
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,10 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_gamma
# Supervised baseline
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 1000 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_1000_baseline
MNIST 50 labels
# Full model
run.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,20,0.1,0.1,0.1,0.1,0.1 --labeled-samples 50 --unlabeled-samples 60000 --seed 1 -- mnist_50_full
MNIST convolutional models
# Conv-FC
run.py train --encoder-layers convv:1000:26:1:1-convv:500:1:1:1-convv:250:1:1:1-convv:250:1:1:1-convv:250:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec gauss --denoising-cost-x 1000,10,0.1,0.1,0.1,0.1,0.1,0.1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_conv_fc
# Conv-Small, Gamma
run.py train --encoder-layers convf:32:5:1:1-maxpool:2:2-convv:64:3:1:1-convf:64:3:1:1-maxpool:2:2-convv:128:3:1:1-convv:10:1:1:1-globalmeanpool:6:6-fc:10 --decoder-spec 0-0-0-0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,0,0,0,1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_conv_gamma
# Conv-Small, supervised baseline. Overfits easily, so keep training short.
run.py train --encoder-layers convf:32:5:1:1-maxpool:2:2-convv:64:3:1:1-convf:64:3:1:1-maxpool:2:2-convv:128:3:1:1-convv:10:1:1:1-globalmeanpool:6:6-fc:10 --decoder-spec 0-0-0-0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0,0,0,0 --num-epochs 20 --lrate-decay 0.5 --f-local-noise-std 0.45 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_conv_baseline
CIFAR models
# Conv-Large, Gamma
./run.py train --encoder-layers convv:96:3:1:1-convf:96:3:1:1-convf:96:3:1:1-maxpool:2:2-convv:192:3:1:1-convf:192:3:1:1-convv:192:3:1:1-maxpool:2:2-convv:192:3:1:1-convv:192:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec 0-0-0-0-0-0-0-0-0-0-0-0-gauss --dataset cifar10 --act leakyrelu --denoising-cost-x 0,0,0,0,0,0,0,0,0,0,0,0,4.0 --num-epochs 70 --lrate-decay 0.86 --seed 1 --whiten-zca 3072 --contrast-norm 55 --top-c False --labeled-samples 4000 --unlabeled-samples 50000 -- cifar_4k_gamma
# Conv-Large, supervised baseline. Overfits easily, so keep training short.
./run.py train --encoder-layers convv:96:3:1:1-convf:96:3:1:1-convf:96:3:1:1-maxpool:2:2-convv:192:3:1:1-convf:192:3:1:1-convv:192:3:1:1-maxpool:2:2-convv:192:3:1:1-convv:192:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec 0-0-0-0-0-0-0-0-0-0-0-0-0 --dataset cifar10 --act leakyrelu --denoising-cost-x 0,0,0,0,0,0,0,0,0,0,0,0,0 --num-epochs 20 --lrate-decay 0.5 --seed 1 --whiten-zca 3072 --contrast-norm 55 --top-c False --labeled-samples 4000 --unlabeled-samples 50000 -- cifar_4k_baseline
Evaluating models with testset
After training a model, you can infer the results on a test set by performing the evaluate command. An example use after training a model:
./run.py evaluate results/mnist_all_bottom0
18 Sep 21, 2022
1 Feb 14, 2022
4.1k Jan 09, 2023
5 Jan 10, 2022
4 Oct 28, 2022
7 Nov 02, 2022
11.2k Jan 09, 2023
159 Dec 19, 2022
402 Jan 02, 2023
2 Jun 07, 2022
242 Jan 02, 2023
3 Sep 17, 2022
0 Aug 28, 2021
17 Dec 05, 2022
17 Dec 09, 2022
1 Jun 19, 2022
2 Jan 02, 2022
1 Nov 12, 2021
1 May 21, 2022
57 Dec 11, 2022