Source code for datasets.binarized_mnist

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"""
Module ``datasets.binarized_mnist`` gives access to the binarized version of the MNIST dataset.

| **References:**
| On the Quantitative Analysis of Deep Belief Networks
| Salakhutdinov and Murray
| http://www.mit.edu/~rsalakhu/papers/dbn_ais.pdf
| The MNIST database of handwritten digits
| LeCun and Cortes
| http://yann.lecun.com/exdb/mnist/

"""

import mlpython.misc.io as mlio
import numpy as np
import os

[docs]def load(dir_path,load_to_memory=False): """ Loads a binarized version of MNIST. The data is given by a dictionary mapping from strings ``'train'``, ``'valid'`` and ``'test'`` to the associated pair of data and metadata. **Defined metadata:** * ``'input_size'`` * ``'length'`` """ input_size=784 dir_path = os.path.expanduser(dir_path) def load_line(line): tokens = line.split() return np.array([int(i) for i in tokens]) train_file,valid_file,test_file = [os.path.join(dir_path, 'binarized_mnist_' + ds + '.amat') for ds in ['train','valid','test']] # Get data train,valid,test = [mlio.load_from_file(f,load_line) for f in [train_file,valid_file,test_file]] lengths = [50000,10000,10000] if load_to_memory: train,valid,test = [mlio.MemoryDataset(d,[(input_size,)],[np.float64],l) for d,l in zip([train,valid,test],lengths)] # Get metadata train_meta,valid_meta,test_meta = [{'input_size':input_size, 'length':l} for l in lengths] return {'train':(train,train_meta),'valid':(valid,valid_meta),'test':(test,test_meta)}
[docs]def obtain(dir_path): """ Downloads the dataset to ``dir_path``. """ dir_path = os.path.expanduser(dir_path) print 'Downloading the dataset' import urllib urllib.urlretrieve('http://www.cs.toronto.edu/~larocheh/public/datasets/binarized_mnist/binarized_mnist_train.amat',os.path.join(dir_path,'binarized_mnist_train.amat')) urllib.urlretrieve('http://www.cs.toronto.edu/~larocheh/public/datasets/binarized_mnist/binarized_mnist_valid.amat',os.path.join(dir_path,'binarized_mnist_valid.amat')) urllib.urlretrieve('http://www.cs.toronto.edu/~larocheh/public/datasets/binarized_mnist/binarized_mnist_test.amat',os.path.join(dir_path,'binarized_mnist_test.amat')) print 'Done '