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* Create GAN.py * gan update * Delete train-labels-idx1-ubyte.gz * Update GAN.py * Update GAN.py * Delete GAN.py * Create gan.py * Update gan.py * input_data import file
333 lines
12 KiB
Python
333 lines
12 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functions for downloading and reading MNIST data (deprecated).
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This module and all its submodules are deprecated.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import gzip
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import os
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import numpy
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from six.moves import urllib
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import random_seed
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from tensorflow.python.platform import gfile
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from tensorflow.python.util.deprecation import deprecated
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_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
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# CVDF mirror of http://yann.lecun.com/exdb/mnist/
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DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
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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)[0]
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@deprecated(None, 'Please use tf.data to implement this functionality.')
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def _extract_images(f):
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"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
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Args:
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f: A file object that can be passed into a gzip reader.
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Returns:
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data: A 4D uint8 numpy array [index, y, x, depth].
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Raises:
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ValueError: If the bytestream does not start with 2051.
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"""
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print('Extracting', f.name)
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with gzip.GzipFile(fileobj=f) as bytestream:
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magic = _read32(bytestream)
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if magic != 2051:
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raise ValueError('Invalid magic number %d in MNIST image file: %s' %
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(magic, f.name))
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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
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@deprecated(None, 'Please use tf.one_hot on tensors.')
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def _dense_to_one_hot(labels_dense, num_classes):
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"""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
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labels_one_hot = numpy.zeros((num_labels, num_classes))
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labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
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return labels_one_hot
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@deprecated(None, 'Please use tf.data to implement this functionality.')
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def _extract_labels(f, one_hot=False, num_classes=10):
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"""Extract the labels into a 1D uint8 numpy array [index].
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Args:
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f: A file object that can be passed into a gzip reader.
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one_hot: Does one hot encoding for the result.
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num_classes: Number of classes for the one hot encoding.
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Returns:
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labels: a 1D uint8 numpy array.
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Raises:
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ValueError: If the bystream doesn't start with 2049.
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"""
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print('Extracting', f.name)
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with gzip.GzipFile(fileobj=f) as bytestream:
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magic = _read32(bytestream)
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if magic != 2049:
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raise ValueError('Invalid magic number %d in MNIST label file: %s' %
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(magic, f.name))
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num_items = _read32(bytestream)
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buf = bytestream.read(num_items)
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labels = numpy.frombuffer(buf, dtype=numpy.uint8)
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if one_hot:
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return _dense_to_one_hot(labels, num_classes)
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return labels
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class _DataSet(object):
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"""Container class for a _DataSet (deprecated).
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THIS CLASS IS DEPRECATED.
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"""
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@deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
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' from tensorflow/models.')
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def __init__(self,
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images,
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labels,
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fake_data=False,
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one_hot=False,
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dtype=dtypes.float32,
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reshape=True,
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seed=None):
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"""Construct a _DataSet.
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one_hot arg is used only if fake_data is true. `dtype` can be either
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`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
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`[0, 1]`. Seed arg provides for convenient deterministic testing.
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Args:
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images: The images
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labels: The labels
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fake_data: Ignore inages and labels, use fake data.
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one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
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False).
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dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
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range [0,255]. float32 output has range [0,1].
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reshape: Bool. If True returned images are returned flattened to vectors.
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seed: The random seed to use.
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"""
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seed1, seed2 = random_seed.get_seed(seed)
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# If op level seed is not set, use whatever graph level seed is returned
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numpy.random.seed(seed1 if seed is None else seed2)
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dtype = dtypes.as_dtype(dtype).base_dtype
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if dtype not in (dtypes.uint8, dtypes.float32):
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raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
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dtype)
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if fake_data:
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self._num_examples = 10000
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self.one_hot = one_hot
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else:
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assert images.shape[0] == labels.shape[0], (
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'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
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self._num_examples = images.shape[0]
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# Convert shape from [num examples, rows, columns, depth]
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# to [num examples, rows*columns] (assuming depth == 1)
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if reshape:
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assert images.shape[3] == 1
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images = images.reshape(images.shape[0],
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images.shape[1] * images.shape[2])
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if dtype == dtypes.float32:
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# Convert from [0, 255] -> [0.0, 1.0].
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images = images.astype(numpy.float32)
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images = numpy.multiply(images, 1.0 / 255.0)
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self._images = images
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self._labels = labels
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self._epochs_completed = 0
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self._index_in_epoch = 0
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@property
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def images(self):
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return self._images
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@property
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def labels(self):
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return self._labels
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@property
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def num_examples(self):
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return self._num_examples
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@property
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def epochs_completed(self):
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return self._epochs_completed
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def next_batch(self, batch_size, fake_data=False, shuffle=True):
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"""Return the next `batch_size` examples from this data set."""
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if fake_data:
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fake_image = [1] * 784
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if self.one_hot:
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fake_label = [1] + [0] * 9
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else:
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fake_label = 0
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return [fake_image for _ in xrange(batch_size)
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], [fake_label for _ in xrange(batch_size)]
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start = self._index_in_epoch
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# Shuffle for the first epoch
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if self._epochs_completed == 0 and start == 0 and shuffle:
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perm0 = numpy.arange(self._num_examples)
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numpy.random.shuffle(perm0)
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self._images = self.images[perm0]
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self._labels = self.labels[perm0]
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# Go to the next epoch
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if start + batch_size > self._num_examples:
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# Finished epoch
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self._epochs_completed += 1
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# Get the rest examples in this epoch
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rest_num_examples = self._num_examples - start
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images_rest_part = self._images[start:self._num_examples]
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labels_rest_part = self._labels[start:self._num_examples]
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# Shuffle the data
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if shuffle:
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perm = numpy.arange(self._num_examples)
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numpy.random.shuffle(perm)
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self._images = self.images[perm]
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self._labels = self.labels[perm]
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# Start next epoch
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start = 0
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self._index_in_epoch = batch_size - rest_num_examples
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end = self._index_in_epoch
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images_new_part = self._images[start:end]
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labels_new_part = self._labels[start:end]
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return numpy.concatenate((images_rest_part, images_new_part),
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axis=0), numpy.concatenate(
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(labels_rest_part, labels_new_part), axis=0)
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else:
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self._index_in_epoch += batch_size
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end = self._index_in_epoch
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return self._images[start:end], self._labels[start:end]
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@deprecated(None, 'Please write your own downloading logic.')
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def _maybe_download(filename, work_directory, source_url):
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"""Download the data from source url, unless it's already here.
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Args:
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filename: string, name of the file in the directory.
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work_directory: string, path to working directory.
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source_url: url to download from if file doesn't exist.
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Returns:
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Path to resulting file.
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"""
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if not gfile.Exists(work_directory):
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gfile.MakeDirs(work_directory)
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filepath = os.path.join(work_directory, filename)
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if not gfile.Exists(filepath):
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urllib.request.urlretrieve(source_url, filepath)
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with gfile.GFile(filepath) as f:
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size = f.size()
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print('Successfully downloaded', filename, size, 'bytes.')
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return filepath
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@deprecated(None, 'Please use alternatives such as:'
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' tensorflow_datasets.load(\'mnist\')')
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def read_data_sets(train_dir,
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fake_data=False,
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one_hot=False,
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dtype=dtypes.float32,
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reshape=True,
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validation_size=5000,
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seed=None,
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source_url=DEFAULT_SOURCE_URL):
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if fake_data:
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def fake():
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return _DataSet([], [],
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fake_data=True,
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one_hot=one_hot,
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dtype=dtype,
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seed=seed)
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train = fake()
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validation = fake()
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test = fake()
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return _Datasets(train=train, validation=validation, test=test)
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if not source_url: # empty string check
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source_url = DEFAULT_SOURCE_URL
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train_images_file = 'train-images-idx3-ubyte.gz'
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train_labels_file = 'train-labels-idx1-ubyte.gz'
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test_images_file = 't10k-images-idx3-ubyte.gz'
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test_labels_file = 't10k-labels-idx1-ubyte.gz'
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local_file = _maybe_download(train_images_file, train_dir,
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source_url + train_images_file)
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with gfile.Open(local_file, 'rb') as f:
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train_images = _extract_images(f)
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local_file = _maybe_download(train_labels_file, train_dir,
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source_url + train_labels_file)
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with gfile.Open(local_file, 'rb') as f:
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train_labels = _extract_labels(f, one_hot=one_hot)
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local_file = _maybe_download(test_images_file, train_dir,
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source_url + test_images_file)
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with gfile.Open(local_file, 'rb') as f:
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test_images = _extract_images(f)
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local_file = _maybe_download(test_labels_file, train_dir,
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source_url + test_labels_file)
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with gfile.Open(local_file, 'rb') as f:
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test_labels = _extract_labels(f, one_hot=one_hot)
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if not 0 <= validation_size <= len(train_images):
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raise ValueError(
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'Validation size should be between 0 and {}. Received: {}.'.format(
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len(train_images), validation_size))
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validation_images = train_images[:validation_size]
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validation_labels = train_labels[:validation_size]
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train_images = train_images[validation_size:]
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train_labels = train_labels[validation_size:]
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options = dict(dtype=dtype, reshape=reshape, seed=seed)
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train = _DataSet(train_images, train_labels, **options)
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validation = _DataSet(validation_images, validation_labels, **options)
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test = _DataSet(test_images, test_labels, **options)
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return _Datasets(train=train, validation=validation, test=test)
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