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torstenholvatorstenholva
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Converted Python 2 to Python 3.
(Python35/Tools/scripts/2to3.py)
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‎input_data.py

Lines changed: 143 additions & 143 deletions
Original file line numberDiff line numberDiff line change
@@ -1,144 +1,144 @@
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"""Functions for downloading and reading MNIST data."""
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from __future__ import print_function
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import gzip
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import os
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import urllib
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import numpy
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SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
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def maybe_download(filename, work_directory):
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"""Download the data from Yann's website, unless it's already here."""
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if not os.path.exists(work_directory):
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os.mkdir(work_directory)
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filepath = os.path.join(work_directory, filename)
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if not os.path.exists(filepath):
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filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath)
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statinfo = os.stat(filepath)
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print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
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return filepath
18-
def _read32(bytestream):
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dt = numpy.dtype(numpy.uint32).newbyteorder('>')
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return numpy.frombuffer(bytestream.read(4), dtype=dt)
21-
def extract_images(filename):
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"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
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print('Extracting', filename)
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with gzip.open(filename) as bytestream:
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magic = _read32(bytestream)
26-
if magic != 2051:
27-
raise ValueError(
28-
'Invalid magic number %d in MNIST image file: %s' %
29-
(magic, filename))
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num_images = _read32(bytestream)
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rows = _read32(bytestream)
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cols = _read32(bytestream)
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buf = bytestream.read(rows * cols * num_images)
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data = numpy.frombuffer(buf, dtype=numpy.uint8)
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data = data.reshape(num_images, rows, cols, 1)
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return data
37-
def dense_to_one_hot(labels_dense, num_classes=10):
38-
"""Convert class labels from scalars to one-hot vectors."""
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num_labels = labels_dense.shape[0]
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index_offset = numpy.arange(num_labels) * num_classes
41-
labels_one_hot = numpy.zeros((num_labels, num_classes))
42-
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
43-
return labels_one_hot
44-
def extract_labels(filename, one_hot=False):
45-
"""Extract the labels into a 1D uint8 numpy array [index]."""
46-
print('Extracting', filename)
47-
with gzip.open(filename) as bytestream:
48-
magic = _read32(bytestream)
49-
if magic != 2049:
50-
raise ValueError(
51-
'Invalid magic number %d in MNIST label file: %s' %
52-
(magic, filename))
53-
num_items = _read32(bytestream)
54-
buf = bytestream.read(num_items)
55-
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
56-
if one_hot:
57-
return dense_to_one_hot(labels)
58-
return labels
59-
class DataSet(object):
60-
def __init__(self, images, labels, fake_data=False):
61-
if fake_data:
62-
self._num_examples = 10000
63-
else:
64-
assert images.shape[0] == labels.shape[0], (
65-
"images.shape: %s labels.shape: %s" % (images.shape,
66-
labels.shape))
67-
self._num_examples = images.shape[0]
68-
# Convert shape from [num examples, rows, columns, depth]
69-
# to [num examples, rows*columns] (assuming depth == 1)
70-
assert images.shape[3] == 1
71-
images = images.reshape(images.shape[0],
72-
images.shape[1] * images.shape[2])
73-
# Convert from [0, 255] -> [0.0, 1.0].
74-
images = images.astype(numpy.float32)
75-
images = numpy.multiply(images, 1.0 / 255.0)
76-
self._images = images
77-
self._labels = labels
78-
self._epochs_completed = 0
79-
self._index_in_epoch = 0
80-
@property
81-
def images(self):
82-
return self._images
83-
@property
84-
def labels(self):
85-
return self._labels
86-
@property
87-
def num_examples(self):
88-
return self._num_examples
89-
@property
90-
def epochs_completed(self):
91-
return self._epochs_completed
92-
def next_batch(self, batch_size, fake_data=False):
93-
"""Return the next `batch_size` examples from this data set."""
94-
if fake_data:
95-
fake_image = [1.0 for _ in xrange(784)]
96-
fake_label = 0
97-
return [fake_image for _ in xrange(batch_size)], [
98-
fake_label for _ in xrange(batch_size)]
99-
start = self._index_in_epoch
100-
self._index_in_epoch += batch_size
101-
if self._index_in_epoch > self._num_examples:
102-
# Finished epoch
103-
self._epochs_completed += 1
104-
# Shuffle the data
105-
perm = numpy.arange(self._num_examples)
106-
numpy.random.shuffle(perm)
107-
self._images = self._images[perm]
108-
self._labels = self._labels[perm]
109-
# Start next epoch
110-
start = 0
111-
self._index_in_epoch = batch_size
112-
assert batch_size <= self._num_examples
113-
end = self._index_in_epoch
114-
return self._images[start:end], self._labels[start:end]
115-
def read_data_sets(train_dir, fake_data=False, one_hot=False):
116-
class DataSets(object):
117-
pass
118-
data_sets = DataSets()
119-
if fake_data:
120-
data_sets.train = DataSet([], [], fake_data=True)
121-
data_sets.validation = DataSet([], [], fake_data=True)
122-
data_sets.test = DataSet([], [], fake_data=True)
123-
return data_sets
124-
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
125-
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
126-
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
127-
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
128-
VALIDATION_SIZE = 5000
129-
local_file = maybe_download(TRAIN_IMAGES, train_dir)
130-
train_images = extract_images(local_file)
131-
local_file = maybe_download(TRAIN_LABELS, train_dir)
132-
train_labels = extract_labels(local_file, one_hot=one_hot)
133-
local_file = maybe_download(TEST_IMAGES, train_dir)
134-
test_images = extract_images(local_file)
135-
local_file = maybe_download(TEST_LABELS, train_dir)
136-
test_labels = extract_labels(local_file, one_hot=one_hot)
137-
validation_images = train_images[:VALIDATION_SIZE]
138-
validation_labels = train_labels[:VALIDATION_SIZE]
139-
train_images = train_images[VALIDATION_SIZE:]
140-
train_labels = train_labels[VALIDATION_SIZE:]
141-
data_sets.train = DataSet(train_images, train_labels)
142-
data_sets.validation = DataSet(validation_images, validation_labels)
143-
data_sets.test = DataSet(test_images, test_labels)
1+
"""Functions for downloading and reading MNIST data."""
2+
3+
import gzip
4+
import os
5+
import urllib.request, urllib.parse, urllib.error
6+
import numpy
7+
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
8+
def maybe_download(filename, work_directory):
9+
"""Download the data from Yann's website, unless it's already here."""
10+
if not os.path.exists(work_directory):
11+
os.mkdir(work_directory)
12+
filepath = os.path.join(work_directory, filename)
13+
if not os.path.exists(filepath):
14+
filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
15+
statinfo = os.stat(filepath)
16+
print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
17+
return filepath
18+
def _read32(bytestream):
19+
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
20+
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
21+
def extract_images(filename):
22+
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
23+
print('Extracting', filename)
24+
with gzip.open(filename) as bytestream:
25+
magic = _read32(bytestream)
26+
if magic != 2051:
27+
raise ValueError(
28+
'Invalid magic number %d in MNIST image file: %s' %
29+
(magic, filename))
30+
num_images = _read32(bytestream)
31+
rows = _read32(bytestream)
32+
cols = _read32(bytestream)
33+
buf = bytestream.read(rows * cols * num_images)
34+
data = numpy.frombuffer(buf, dtype=numpy.uint8)
35+
data = data.reshape(num_images, rows, cols, 1)
36+
return data
37+
def dense_to_one_hot(labels_dense, num_classes=10):
38+
"""Convert class labels from scalars to one-hot vectors."""
39+
num_labels = labels_dense.shape[0]
40+
index_offset = numpy.arange(num_labels) * num_classes
41+
labels_one_hot = numpy.zeros((num_labels, num_classes))
42+
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
43+
return labels_one_hot
44+
def extract_labels(filename, one_hot=False):
45+
"""Extract the labels into a 1D uint8 numpy array [index]."""
46+
print('Extracting', filename)
47+
with gzip.open(filename) as bytestream:
48+
magic = _read32(bytestream)
49+
if magic != 2049:
50+
raise ValueError(
51+
'Invalid magic number %d in MNIST label file: %s' %
52+
(magic, filename))
53+
num_items = _read32(bytestream)
54+
buf = bytestream.read(num_items)
55+
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
56+
if one_hot:
57+
return dense_to_one_hot(labels)
58+
return labels
59+
class DataSet(object):
60+
def __init__(self, images, labels, fake_data=False):
61+
if fake_data:
62+
self._num_examples = 10000
63+
else:
64+
assert images.shape[0] == labels.shape[0], (
65+
"images.shape: %s labels.shape: %s" % (images.shape,
66+
labels.shape))
67+
self._num_examples = images.shape[0]
68+
# Convert shape from [num examples, rows, columns, depth]
69+
# to [num examples, rows*columns] (assuming depth == 1)
70+
assert images.shape[3] == 1
71+
images = images.reshape(images.shape[0],
72+
images.shape[1] * images.shape[2])
73+
# Convert from [0, 255] -> [0.0, 1.0].
74+
images = images.astype(numpy.float32)
75+
images = numpy.multiply(images, 1.0 / 255.0)
76+
self._images = images
77+
self._labels = labels
78+
self._epochs_completed = 0
79+
self._index_in_epoch = 0
80+
@property
81+
def images(self):
82+
return self._images
83+
@property
84+
def labels(self):
85+
return self._labels
86+
@property
87+
def num_examples(self):
88+
return self._num_examples
89+
@property
90+
def epochs_completed(self):
91+
return self._epochs_completed
92+
def next_batch(self, batch_size, fake_data=False):
93+
"""Return the next `batch_size` examples from this data set."""
94+
if fake_data:
95+
fake_image = [1.0 for _ in range(784)]
96+
fake_label = 0
97+
return [fake_image for _ in range(batch_size)], [
98+
fake_label for _ in range(batch_size)]
99+
start = self._index_in_epoch
100+
self._index_in_epoch += batch_size
101+
if self._index_in_epoch > self._num_examples:
102+
# Finished epoch
103+
self._epochs_completed += 1
104+
# Shuffle the data
105+
perm = numpy.arange(self._num_examples)
106+
numpy.random.shuffle(perm)
107+
self._images = self._images[perm]
108+
self._labels = self._labels[perm]
109+
# Start next epoch
110+
start = 0
111+
self._index_in_epoch = batch_size
112+
assert batch_size <= self._num_examples
113+
end = self._index_in_epoch
114+
return self._images[start:end], self._labels[start:end]
115+
def read_data_sets(train_dir, fake_data=False, one_hot=False):
116+
class DataSets(object):
117+
pass
118+
data_sets = DataSets()
119+
if fake_data:
120+
data_sets.train = DataSet([], [], fake_data=True)
121+
data_sets.validation = DataSet([], [], fake_data=True)
122+
data_sets.test = DataSet([], [], fake_data=True)
123+
return data_sets
124+
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
125+
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
126+
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
127+
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
128+
VALIDATION_SIZE = 5000
129+
local_file = maybe_download(TRAIN_IMAGES, train_dir)
130+
train_images = extract_images(local_file)
131+
local_file = maybe_download(TRAIN_LABELS, train_dir)
132+
train_labels = extract_labels(local_file, one_hot=one_hot)
133+
local_file = maybe_download(TEST_IMAGES, train_dir)
134+
test_images = extract_images(local_file)
135+
local_file = maybe_download(TEST_LABELS, train_dir)
136+
test_labels = extract_labels(local_file, one_hot=one_hot)
137+
validation_images = train_images[:VALIDATION_SIZE]
138+
validation_labels = train_labels[:VALIDATION_SIZE]
139+
train_images = train_images[VALIDATION_SIZE:]
140+
train_labels = train_labels[VALIDATION_SIZE:]
141+
data_sets.train = DataSet(train_images, train_labels)
142+
data_sets.validation = DataSet(validation_images, validation_labels)
143+
data_sets.test = DataSet(test_images, test_labels)
144144
return data_sets

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