DeepLearning/LoadAndParseMNISTData.py at master · timestocome/DeepLearning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
#!/python

import pickle, gzip
import numpy
import theano
import theano.tensor as T


dataset = 'mnist.pkl.gz'

# Load the dataset
with gzip.open(dataset, 'rb') as f:
    try:
        train_set, valid_set, test_set = pickle.load(f, encoding='latin1')
    except:
        train_set, valid_set, test_set = pickle.load(f)


# shared variables can be quickly loaded onto the gpu
def shared_dataset(data_xy, borrow=True):

    data_x, data_y = data_xy
    shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX), borrow=borrow)
    shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX), borrow=borrow)

    # When storing data on the GPU it has to be stored as floats
    # therefore we will store the labels as ``floatX`` as well
    return shared_x, T.cast(shared_y, 'int32')


test_set_x, test_set_y = shared_dataset(test_set)
valid_set_x, valid_set_y = shared_dataset(valid_set)
train_set_x, train_set_y = shared_dataset(train_set)


datasets = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]

 # set up datasets
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]


 # set up constants
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]