https://github.com/divamgupta/ladder_network_keras

Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !
https://github.com/divamgupta/ladder_network_keras

Last synced: 3 months ago
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Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

• Host: GitHub
• URL: https://github.com/divamgupta/ladder_network_keras
• Owner: divamgupta
• Created: 2019-02-22T20:01:18.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-04-30T09:58:08.000Z (over 3 years ago)
• Last Synced: 2024-08-01T17:33:26.309Z (6 months ago)
• Language: Python
• Size: 7.81 KB
• Stars: 101
• Watchers: 6
• Forks: 33
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# Semi-Supervised Learning with Ladder Networks in Keras

This is an implementation of Ladder Network in Keras. Ladder network is a model for semi-supervised learning. Refer to the paper titled [_Semi-Supervised Learning with Ladder Networks_](http://arxiv.org/abs/1507.02672) by A Rasmus, H Valpola, M Honkala,M Berglund, and T Raiko

This implementation was used in the official code of our paper  [Unsupervised Clustering using Pseudo-semi-supervised Learning

](https://openreview.net/pdf?id=rJlnxkSYPS). The code can be found [here](https://github.com/divamgupta/deep_clustering_kingdra) and the blog post can be found [here](https://divamgupta.com/unsupervised-learning/2020/10/31/pseudo-semi-supervised-learning-for-unsupervised-clustering.html)

The model achives **98%** test accuracy on MNIST with just **100 labeled examples**. 

The code only works with Tensorflow backend.

## Requirements

- Python 2.7+/3.6+

- Tensorflow (1.4.0)

- numpy

- keras (2.1.4) 

Note that other versions of tensorflow/keras should also work.

## How to use

Load the dataset

```python

from keras.datasets import mnist

import keras

import random

# get the dataset

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 28*28).astype('float32')/255.0

x_test = x_test.reshape(10000, 28*28).astype('float32')/255.0

y_train = keras.utils.to_categorical( y_train )

y_test = keras.utils.to_categorical( y_test )

# only select 100 training samples 

idxs_annot = range( x_train.shape[0])

random.seed(0)

random.shuffle( idxs_annot )

idxs_annot = idxs_annot[ :100 ]

x_train_unlabeled = x_train

x_train_labeled = x_train[ idxs_annot ]

y_train_labeled = y_train[ idxs_annot  ]

```

Repeat the labeled dataset to match the shapes

```python

n_rep = x_train_unlabeled.shape[0] / x_train_labeled.shape[0]

x_train_labeled_rep = np.concatenate([x_train_labeled]*n_rep)

y_train_labeled_rep = np.concatenate([y_train_labeled]*n_rep)

```

Initialize the model

```python

from ladder_net import get_ladder_network_fc

inp_size = 28*28 # size of mnist dataset 

n_classes = 10

model = get_ladder_network_fc( layer_sizes = [ inp_size , 1000, 500, 250, 250, 250, n_classes ]  )

```

Train the model

```python

model.fit([ x_train_labeled_rep , x_train_unlabeled   ] , y_train_labeled_rep , epochs=100)

```

Get the test accuracy 

```python

from sklearn.metrics import accuracy_score

y_test_pr = model.test_model.predict(x_test , batch_size=100 )

print "test accuracy" , accuracy_score(y_test.argmax(-1) , y_test_pr.argmax(-1)  )

```