Athony1

input_data.py
"""Functions for downloading and reading MNIST data.""" ...
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08
2019/08

input_data.py

"""Functions for downloading and reading MNIST data."""

#以下三个是从py3中提前获取功能,__future__为py2与py3的兼容库
from __future__ import absolute_import
from __future__ import division #精确除法,即/不为整除
from __future__ import print_function

import gzip
import os
import tensorflow.python.platform
import numpy as np

from six.moves import urllib #下载用的
from six.moves import xrange  #生成器 list(xrange(5)) 等价 range(5)
 # pylint: disable=redefined-builtin

import tensorflow as tf  #导入tf库

SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' #mnist的数据库地址
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):   #如果当前路径下没有work_directory这个文件,便创建这个目录
    os.mkdir(work_directory)

  filepath = os.path.join(work_directory, filename)#将文件目录和文件名组合成一个文件路径

  if not os.path.exists(filepath):#下载数据库
    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)

    statinfo = os.stat(filepath)#查看当前路径目录的大小,并打印出来

    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')

  return filepath

def _read32(bytestream):
  dt = np.dtype(np.uint32).newbyteorder('>') #uint32无符号整数
  return np.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 = np.frombuffer(buf, dtype=np.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 = np.arange(num_labels) * num_classes
  labels_one_hot = np.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 np 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 = np.frombuffer(buf, dtype=np.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(np.float32)
        images = np.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 = np.arange(self._num_examples)
      np.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
最后修改:2019 年 08 月 09 日 03 : 40 PM
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2 条评论

  1. Ai

    虽然看不懂,但还是水一水

    1. Athony1
      @Ai

      就是一个纯下载东西,还有包含转换的一个misit模块

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