mirror of
https://github.com/TheAlgorithms/Python.git
synced 2025-07-05 17:34:49 +08:00
GitHub Action formats our code with psf/black (#1569)
* GitHub Action formats our code with psf/black @poyea Your review please. * fixup! Format Python code with psf/black push
This commit is contained in:
Christian Clauss
@ -34,20 +34,20 @@ from tensorflow.python.framework import random_seed
|
||||
from tensorflow.python.platform import gfile
|
||||
from tensorflow.python.util.deprecation import deprecated
|
||||
|
||||
_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
|
||||
_Datasets = collections.namedtuple("_Datasets", ["train", "validation", "test"])
|
||||
|
||||
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
|
||||
DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
|
||||
DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
|
||||
|
||||
|
||||
def _read32(bytestream):
|
||||
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
|
||||
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
|
||||
dt = numpy.dtype(numpy.uint32).newbyteorder(">")
|
||||
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.data to implement this functionality.')
|
||||
@deprecated(None, "Please use tf.data to implement this functionality.")
|
||||
def _extract_images(f):
|
||||
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
|
||||
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
|
||||
|
||||
Args:
|
||||
f: A file object that can be passed into a gzip reader.
|
||||
@ -59,34 +59,35 @@ def _extract_images(f):
|
||||
ValueError: If the bytestream does not start with 2051.
|
||||
|
||||
"""
|
||||
print('Extracting', f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2051:
|
||||
raise ValueError('Invalid magic number %d in MNIST image file: %s' %
|
||||
(magic, f.name))
|
||||
num_images = _read32(bytestream)
|
||||
rows = _read32(bytestream)
|
||||
cols = _read32(bytestream)
|
||||
buf = bytestream.read(rows * cols * num_images)
|
||||
data = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
data = data.reshape(num_images, rows, cols, 1)
|
||||
return data
|
||||
print("Extracting", f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2051:
|
||||
raise ValueError(
|
||||
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name)
|
||||
)
|
||||
num_images = _read32(bytestream)
|
||||
rows = _read32(bytestream)
|
||||
cols = _read32(bytestream)
|
||||
buf = bytestream.read(rows * cols * num_images)
|
||||
data = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
data = data.reshape(num_images, rows, cols, 1)
|
||||
return data
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.one_hot on tensors.')
|
||||
@deprecated(None, "Please use tf.one_hot on tensors.")
|
||||
def _dense_to_one_hot(labels_dense, num_classes):
|
||||
"""Convert class labels from scalars to one-hot vectors."""
|
||||
num_labels = labels_dense.shape[0]
|
||||
index_offset = numpy.arange(num_labels) * num_classes
|
||||
labels_one_hot = numpy.zeros((num_labels, num_classes))
|
||||
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
|
||||
return labels_one_hot
|
||||
"""Convert class labels from scalars to one-hot vectors."""
|
||||
num_labels = labels_dense.shape[0]
|
||||
index_offset = numpy.arange(num_labels) * num_classes
|
||||
labels_one_hot = numpy.zeros((num_labels, num_classes))
|
||||
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
|
||||
return labels_one_hot
|
||||
|
||||
|
||||
@deprecated(None, 'Please use tf.data to implement this functionality.')
|
||||
@deprecated(None, "Please use tf.data to implement this functionality.")
|
||||
def _extract_labels(f, one_hot=False, num_classes=10):
|
||||
"""Extract the labels into a 1D uint8 numpy array [index].
|
||||
"""Extract the labels into a 1D uint8 numpy array [index].
|
||||
|
||||
Args:
|
||||
f: A file object that can be passed into a gzip reader.
|
||||
@ -99,37 +100,43 @@ def _extract_labels(f, one_hot=False, num_classes=10):
|
||||
Raises:
|
||||
ValueError: If the bystream doesn't start with 2049.
|
||||
"""
|
||||
print('Extracting', f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2049:
|
||||
raise ValueError('Invalid magic number %d in MNIST label file: %s' %
|
||||
(magic, f.name))
|
||||
num_items = _read32(bytestream)
|
||||
buf = bytestream.read(num_items)
|
||||
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
if one_hot:
|
||||
return _dense_to_one_hot(labels, num_classes)
|
||||
return labels
|
||||
print("Extracting", f.name)
|
||||
with gzip.GzipFile(fileobj=f) as bytestream:
|
||||
magic = _read32(bytestream)
|
||||
if magic != 2049:
|
||||
raise ValueError(
|
||||
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name)
|
||||
)
|
||||
num_items = _read32(bytestream)
|
||||
buf = bytestream.read(num_items)
|
||||
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
|
||||
if one_hot:
|
||||
return _dense_to_one_hot(labels, num_classes)
|
||||
return labels
|
||||
|
||||
|
||||
class _DataSet(object):
|
||||
"""Container class for a _DataSet (deprecated).
|
||||
"""Container class for a _DataSet (deprecated).
|
||||
|
||||
THIS CLASS IS DEPRECATED.
|
||||
"""
|
||||
|
||||
@deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
|
||||
' from tensorflow/models.')
|
||||
def __init__(self,
|
||||
images,
|
||||
labels,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
seed=None):
|
||||
"""Construct a _DataSet.
|
||||
@deprecated(
|
||||
None,
|
||||
"Please use alternatives such as official/mnist/_DataSet.py"
|
||||
" from tensorflow/models.",
|
||||
)
|
||||
def __init__(
|
||||
self,
|
||||
images,
|
||||
labels,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
seed=None,
|
||||
):
|
||||
"""Construct a _DataSet.
|
||||
|
||||
one_hot arg is used only if fake_data is true. `dtype` can be either
|
||||
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
|
||||
@ -146,101 +153,105 @@ class _DataSet(object):
|
||||
reshape: Bool. If True returned images are returned flattened to vectors.
|
||||
seed: The random seed to use.
|
||||
"""
|
||||
seed1, seed2 = random_seed.get_seed(seed)
|
||||
# If op level seed is not set, use whatever graph level seed is returned
|
||||
numpy.random.seed(seed1 if seed is None else seed2)
|
||||
dtype = dtypes.as_dtype(dtype).base_dtype
|
||||
if dtype not in (dtypes.uint8, dtypes.float32):
|
||||
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
|
||||
dtype)
|
||||
if fake_data:
|
||||
self._num_examples = 10000
|
||||
self.one_hot = one_hot
|
||||
else:
|
||||
assert images.shape[0] == labels.shape[0], (
|
||||
'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
|
||||
self._num_examples = images.shape[0]
|
||||
seed1, seed2 = random_seed.get_seed(seed)
|
||||
# If op level seed is not set, use whatever graph level seed is returned
|
||||
numpy.random.seed(seed1 if seed is None else seed2)
|
||||
dtype = dtypes.as_dtype(dtype).base_dtype
|
||||
if dtype not in (dtypes.uint8, dtypes.float32):
|
||||
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
|
||||
if fake_data:
|
||||
self._num_examples = 10000
|
||||
self.one_hot = one_hot
|
||||
else:
|
||||
assert (
|
||||
images.shape[0] == labels.shape[0]
|
||||
), "images.shape: %s labels.shape: %s" % (images.shape, labels.shape)
|
||||
self._num_examples = images.shape[0]
|
||||
|
||||
# Convert shape from [num examples, rows, columns, depth]
|
||||
# to [num examples, rows*columns] (assuming depth == 1)
|
||||
if reshape:
|
||||
assert images.shape[3] == 1
|
||||
images = images.reshape(images.shape[0],
|
||||
images.shape[1] * images.shape[2])
|
||||
if dtype == dtypes.float32:
|
||||
# Convert from [0, 255] -> [0.0, 1.0].
|
||||
images = images.astype(numpy.float32)
|
||||
images = numpy.multiply(images, 1.0 / 255.0)
|
||||
self._images = images
|
||||
self._labels = labels
|
||||
self._epochs_completed = 0
|
||||
self._index_in_epoch = 0
|
||||
# Convert shape from [num examples, rows, columns, depth]
|
||||
# to [num examples, rows*columns] (assuming depth == 1)
|
||||
if reshape:
|
||||
assert images.shape[3] == 1
|
||||
images = images.reshape(
|
||||
images.shape[0], images.shape[1] * images.shape[2]
|
||||
)
|
||||
if dtype == dtypes.float32:
|
||||
# Convert from [0, 255] -> [0.0, 1.0].
|
||||
images = images.astype(numpy.float32)
|
||||
images = numpy.multiply(images, 1.0 / 255.0)
|
||||
self._images = images
|
||||
self._labels = labels
|
||||
self._epochs_completed = 0
|
||||
self._index_in_epoch = 0
|
||||
|
||||
@property
|
||||
def images(self):
|
||||
return self._images
|
||||
@property
|
||||
def images(self):
|
||||
return self._images
|
||||
|
||||
@property
|
||||
def labels(self):
|
||||
return self._labels
|
||||
@property
|
||||
def labels(self):
|
||||
return self._labels
|
||||
|
||||
@property
|
||||
def num_examples(self):
|
||||
return self._num_examples
|
||||
@property
|
||||
def num_examples(self):
|
||||
return self._num_examples
|
||||
|
||||
@property
|
||||
def epochs_completed(self):
|
||||
return self._epochs_completed
|
||||
@property
|
||||
def epochs_completed(self):
|
||||
return self._epochs_completed
|
||||
|
||||
def next_batch(self, batch_size, fake_data=False, shuffle=True):
|
||||
"""Return the next `batch_size` examples from this data set."""
|
||||
if fake_data:
|
||||
fake_image = [1] * 784
|
||||
if self.one_hot:
|
||||
fake_label = [1] + [0] * 9
|
||||
else:
|
||||
fake_label = 0
|
||||
return [fake_image for _ in xrange(batch_size)
|
||||
], [fake_label for _ in xrange(batch_size)]
|
||||
start = self._index_in_epoch
|
||||
# Shuffle for the first epoch
|
||||
if self._epochs_completed == 0 and start == 0 and shuffle:
|
||||
perm0 = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm0)
|
||||
self._images = self.images[perm0]
|
||||
self._labels = self.labels[perm0]
|
||||
# Go to the next epoch
|
||||
if start + batch_size > self._num_examples:
|
||||
# Finished epoch
|
||||
self._epochs_completed += 1
|
||||
# Get the rest examples in this epoch
|
||||
rest_num_examples = self._num_examples - start
|
||||
images_rest_part = self._images[start:self._num_examples]
|
||||
labels_rest_part = self._labels[start:self._num_examples]
|
||||
# Shuffle the data
|
||||
if shuffle:
|
||||
perm = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm)
|
||||
self._images = self.images[perm]
|
||||
self._labels = self.labels[perm]
|
||||
# Start next epoch
|
||||
start = 0
|
||||
self._index_in_epoch = batch_size - rest_num_examples
|
||||
end = self._index_in_epoch
|
||||
images_new_part = self._images[start:end]
|
||||
labels_new_part = self._labels[start:end]
|
||||
return numpy.concatenate((images_rest_part, images_new_part),
|
||||
axis=0), numpy.concatenate(
|
||||
(labels_rest_part, labels_new_part), axis=0)
|
||||
else:
|
||||
self._index_in_epoch += batch_size
|
||||
end = self._index_in_epoch
|
||||
return self._images[start:end], self._labels[start:end]
|
||||
def next_batch(self, batch_size, fake_data=False, shuffle=True):
|
||||
"""Return the next `batch_size` examples from this data set."""
|
||||
if fake_data:
|
||||
fake_image = [1] * 784
|
||||
if self.one_hot:
|
||||
fake_label = [1] + [0] * 9
|
||||
else:
|
||||
fake_label = 0
|
||||
return (
|
||||
[fake_image for _ in xrange(batch_size)],
|
||||
[fake_label for _ in xrange(batch_size)],
|
||||
)
|
||||
start = self._index_in_epoch
|
||||
# Shuffle for the first epoch
|
||||
if self._epochs_completed == 0 and start == 0 and shuffle:
|
||||
perm0 = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm0)
|
||||
self._images = self.images[perm0]
|
||||
self._labels = self.labels[perm0]
|
||||
# Go to the next epoch
|
||||
if start + batch_size > self._num_examples:
|
||||
# Finished epoch
|
||||
self._epochs_completed += 1
|
||||
# Get the rest examples in this epoch
|
||||
rest_num_examples = self._num_examples - start
|
||||
images_rest_part = self._images[start : self._num_examples]
|
||||
labels_rest_part = self._labels[start : self._num_examples]
|
||||
# Shuffle the data
|
||||
if shuffle:
|
||||
perm = numpy.arange(self._num_examples)
|
||||
numpy.random.shuffle(perm)
|
||||
self._images = self.images[perm]
|
||||
self._labels = self.labels[perm]
|
||||
# Start next epoch
|
||||
start = 0
|
||||
self._index_in_epoch = batch_size - rest_num_examples
|
||||
end = self._index_in_epoch
|
||||
images_new_part = self._images[start:end]
|
||||
labels_new_part = self._labels[start:end]
|
||||
return (
|
||||
numpy.concatenate((images_rest_part, images_new_part), axis=0),
|
||||
numpy.concatenate((labels_rest_part, labels_new_part), axis=0),
|
||||
)
|
||||
else:
|
||||
self._index_in_epoch += batch_size
|
||||
end = self._index_in_epoch
|
||||
return self._images[start:end], self._labels[start:end]
|
||||
|
||||
|
||||
@deprecated(None, 'Please write your own downloading logic.')
|
||||
@deprecated(None, "Please write your own downloading logic.")
|
||||
def _maybe_download(filename, work_directory, source_url):
|
||||
"""Download the data from source url, unless it's already here.
|
||||
"""Download the data from source url, unless it's already here.
|
||||
|
||||
Args:
|
||||
filename: string, name of the file in the directory.
|
||||
@ -250,83 +261,90 @@ def _maybe_download(filename, work_directory, source_url):
|
||||
Returns:
|
||||
Path to resulting file.
|
||||
"""
|
||||
if not gfile.Exists(work_directory):
|
||||
gfile.MakeDirs(work_directory)
|
||||
filepath = os.path.join(work_directory, filename)
|
||||
if not gfile.Exists(filepath):
|
||||
urllib.request.urlretrieve(source_url, filepath)
|
||||
with gfile.GFile(filepath) as f:
|
||||
size = f.size()
|
||||
print('Successfully downloaded', filename, size, 'bytes.')
|
||||
return filepath
|
||||
if not gfile.Exists(work_directory):
|
||||
gfile.MakeDirs(work_directory)
|
||||
filepath = os.path.join(work_directory, filename)
|
||||
if not gfile.Exists(filepath):
|
||||
urllib.request.urlretrieve(source_url, filepath)
|
||||
with gfile.GFile(filepath) as f:
|
||||
size = f.size()
|
||||
print("Successfully downloaded", filename, size, "bytes.")
|
||||
return filepath
|
||||
|
||||
|
||||
@deprecated(None, 'Please use alternatives such as:'
|
||||
' tensorflow_datasets.load(\'mnist\')')
|
||||
def read_data_sets(train_dir,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
validation_size=5000,
|
||||
seed=None,
|
||||
source_url=DEFAULT_SOURCE_URL):
|
||||
if fake_data:
|
||||
@deprecated(
|
||||
None, "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"
|
||||
)
|
||||
def read_data_sets(
|
||||
train_dir,
|
||||
fake_data=False,
|
||||
one_hot=False,
|
||||
dtype=dtypes.float32,
|
||||
reshape=True,
|
||||
validation_size=5000,
|
||||
seed=None,
|
||||
source_url=DEFAULT_SOURCE_URL,
|
||||
):
|
||||
if fake_data:
|
||||
|
||||
def fake():
|
||||
return _DataSet([], [],
|
||||
fake_data=True,
|
||||
one_hot=one_hot,
|
||||
dtype=dtype,
|
||||
seed=seed)
|
||||
def fake():
|
||||
return _DataSet(
|
||||
[], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed
|
||||
)
|
||||
|
||||
train = fake()
|
||||
validation = fake()
|
||||
test = fake()
|
||||
return _Datasets(train=train, validation=validation, test=test)
|
||||
|
||||
if not source_url: # empty string check
|
||||
source_url = DEFAULT_SOURCE_URL
|
||||
|
||||
train_images_file = "train-images-idx3-ubyte.gz"
|
||||
train_labels_file = "train-labels-idx1-ubyte.gz"
|
||||
test_images_file = "t10k-images-idx3-ubyte.gz"
|
||||
test_labels_file = "t10k-labels-idx1-ubyte.gz"
|
||||
|
||||
local_file = _maybe_download(
|
||||
train_images_file, train_dir, source_url + train_images_file
|
||||
)
|
||||
with gfile.Open(local_file, "rb") as f:
|
||||
train_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(
|
||||
train_labels_file, train_dir, source_url + train_labels_file
|
||||
)
|
||||
with gfile.Open(local_file, "rb") as f:
|
||||
train_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
local_file = _maybe_download(
|
||||
test_images_file, train_dir, source_url + test_images_file
|
||||
)
|
||||
with gfile.Open(local_file, "rb") as f:
|
||||
test_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(
|
||||
test_labels_file, train_dir, source_url + test_labels_file
|
||||
)
|
||||
with gfile.Open(local_file, "rb") as f:
|
||||
test_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
if not 0 <= validation_size <= len(train_images):
|
||||
raise ValueError(
|
||||
"Validation size should be between 0 and {}. Received: {}.".format(
|
||||
len(train_images), validation_size
|
||||
)
|
||||
)
|
||||
|
||||
validation_images = train_images[:validation_size]
|
||||
validation_labels = train_labels[:validation_size]
|
||||
train_images = train_images[validation_size:]
|
||||
train_labels = train_labels[validation_size:]
|
||||
|
||||
options = dict(dtype=dtype, reshape=reshape, seed=seed)
|
||||
|
||||
train = _DataSet(train_images, train_labels, **options)
|
||||
validation = _DataSet(validation_images, validation_labels, **options)
|
||||
test = _DataSet(test_images, test_labels, **options)
|
||||
|
||||
train = fake()
|
||||
validation = fake()
|
||||
test = fake()
|
||||
return _Datasets(train=train, validation=validation, test=test)
|
||||
|
||||
if not source_url: # empty string check
|
||||
source_url = DEFAULT_SOURCE_URL
|
||||
|
||||
train_images_file = 'train-images-idx3-ubyte.gz'
|
||||
train_labels_file = 'train-labels-idx1-ubyte.gz'
|
||||
test_images_file = 't10k-images-idx3-ubyte.gz'
|
||||
test_labels_file = 't10k-labels-idx1-ubyte.gz'
|
||||
|
||||
local_file = _maybe_download(train_images_file, train_dir,
|
||||
source_url + train_images_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
train_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(train_labels_file, train_dir,
|
||||
source_url + train_labels_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
train_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
local_file = _maybe_download(test_images_file, train_dir,
|
||||
source_url + test_images_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
test_images = _extract_images(f)
|
||||
|
||||
local_file = _maybe_download(test_labels_file, train_dir,
|
||||
source_url + test_labels_file)
|
||||
with gfile.Open(local_file, 'rb') as f:
|
||||
test_labels = _extract_labels(f, one_hot=one_hot)
|
||||
|
||||
if not 0 <= validation_size <= len(train_images):
|
||||
raise ValueError(
|
||||
'Validation size should be between 0 and {}. Received: {}.'.format(
|
||||
len(train_images), validation_size))
|
||||
|
||||
validation_images = train_images[:validation_size]
|
||||
validation_labels = train_labels[:validation_size]
|
||||
train_images = train_images[validation_size:]
|
||||
train_labels = train_labels[validation_size:]
|
||||
|
||||
options = dict(dtype=dtype, reshape=reshape, seed=seed)
|
||||
|
||||
train = _DataSet(train_images, train_labels, **options)
|
||||
validation = _DataSet(validation_images, validation_labels, **options)
|
||||
test = _DataSet(test_images, test_labels, **options)
|
||||
|
||||
return _Datasets(train=train, validation=validation, test=test)
|
||||
|
||||
Reference in New Issue
Block a user