1. MNIST数据集的下载与导入

由于某些不可名说的原因，教程中的MNIST数据集无法下载打开导致一直出错，现在百度网盘放出下载资源：

百度网盘：链接: https://pan.baidu.com/s/1boOSDYJ 密码: 58kw

有需要的同学可以下载使用。

提取和导入MNIST的代码如下：

# 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

存为“input_data.py”文件即可。至此，MNIST文件的获取和导入即已完成。

2. MNIST手写识别测试

整个导入，训练，验证和测试的代码如下，详细解释可以在上述教程中得到。

import tensorflowas tf

import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder("float", [None, 784])

W = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,W)+b)

y_ = tf.placeholder("float", [None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for iin range(1000):

batch_xs, batch_ys = mnist.train.next_batch(100)

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

执行代码后可得：

训练结果为0.9137，符合教程的结果，测试成功。

MNIST测试集安装及调试（附MNIST数据集百度网盘打包下载）

MNIST测试集安装及调试（附MNIST数据集百度网盘打包下载）

1. MNIST数据集的下载与导入

2. MNIST手写识别测试

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