• 下载MNIST数据集脚本input_data源码


    # Copyright 2015 Google Inc. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    # http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    """Functions for downloading and reading MNIST data."""
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    import gzip
    import os
    import tensorflow.python.platform
    import numpy
    from six.moves import urllib
    from six.moves import xrange # pylint: disable=redefined-builtin
    import tensorflow as tf
    SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
    def maybe_download(filename, work_directory):
    """Download the data from Yann's website, unless it's already here."""
    if not os.path.exists(work_directory):
    os.mkdir(work_directory)
    filepath = os.path.join(work_directory, filename)
    if not os.path.exists(filepath):
    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
    return filepath
    def _read32(bytestream):
    dt = numpy.dtype(numpy.uint32).newbyteorder('>')
    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
    def extract_images(filename):
    """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
    print('Extracting', filename)
    with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2051:
    raise ValueError(
    'Invalid magic number %d in MNIST image file: %s' %
    (magic, filename))
    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
    def dense_to_one_hot(labels_dense, num_classes=10):
    """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
    def extract_labels(filename, one_hot=False):
    """Extract the labels into a 1D uint8 numpy array [index]."""
    print('Extracting', filename)
    with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2049:
    raise ValueError(
    'Invalid magic number %d in MNIST label file: %s' %
    (magic, filename))
    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)
    return labels
    class DataSet(object):
    def __init__(self, images, labels, fake_data=False, one_hot=False,
    dtype=tf.float32):
    """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
    `[0, 1]`.
    """
    dtype = tf.as_dtype(dtype).base_dtype
    if dtype not in (tf.uint8, tf.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)
    assert images.shape[3] == 1
    images = images.reshape(images.shape[0],
    images.shape[1] * images.shape[2])
    if dtype == tf.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 labels(self):
    return self._labels
    @property
    def num_examples(self):
    return self._num_examples
    @property
    def epochs_completed(self):
    return self._epochs_completed
    def next_batch(self, batch_size, fake_data=False):
    """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
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
    # Finished epoch
    self._epochs_completed += 1
    # Shuffle the data
    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
    assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end]
    def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
    class DataSets(object):
    pass
    data_sets = DataSets()
    if fake_data:
    def fake():
    return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
    data_sets.train = fake()
    data_sets.validation = fake()
    data_sets.test = fake()
    return data_sets
    TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
    TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
    TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
    TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
    VALIDATION_SIZE = 5000
    local_file = maybe_download(TRAIN_IMAGES, train_dir)
    train_images = extract_images(local_file)
    local_file = maybe_download(TRAIN_LABELS, train_dir)
    train_labels = extract_labels(local_file, one_hot=one_hot)
    local_file = maybe_download(TEST_IMAGES, train_dir)
    test_images = extract_images(local_file)
    local_file = maybe_download(TEST_LABELS, train_dir)
    test_labels = extract_labels(local_file, one_hot=one_hot)
    validation_images = train_images[:VALIDATION_SIZE]
    validation_labels = train_labels[:VALIDATION_SIZE]
    train_images = train_images[VALIDATION_SIZE:]
    train_labels = train_labels[VALIDATION_SIZE:]
    data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
    data_sets.validation = DataSet(validation_images, validation_labels,
    dtype=dtype)
    data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
    return data_sets

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  • 原文地址:https://www.cnblogs.com/shouhutian/p/9799141.html
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