# 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
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""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 numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
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_classeslabels_one_hot = numpy.zeros((num_labels, num_classes))labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return 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):if fake_data:self._num_examples = 10000else: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] == 1images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])# Convert from [0, 255] -> [0.0, 1.0].images = images.astype(numpy.float32)images = numpy.multiply(images, 1.0 / 255.0)self._images = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef next_batch(self, batch_size, fake_data=False):"""Return the next `batch_size` examples from this data set."""if fake_data:fake_image = [1.0 for _ in xrange(784)]fake_label = 0return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]start = self._index_in_epochself._index_in_epoch += batch_sizeif self._index_in_epoch > self._num_examples:# Finished epochself._epochs_completed += 1# Shuffle the dataperm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self._images[perm]self._labels = self._labels[perm]# Start next epochstart = 0self._index_in_epoch = batch_sizeassert batch_size <= self._num_examplesend = self._index_in_epochreturn self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False):class DataSets(object):passdata_sets = DataSets()if fake_data:data_sets.train = DataSet([], [], fake_data=True)data_sets.validation = DataSet([], [], fake_data=True)data_sets.test = DataSet([], [], fake_data=True)return data_setsTRAIN_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 = 5000local_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)data_sets.validation = DataSet(validation_images, validation_labels)data_sets.test = DataSet(test_images, test_labels)return data_sets

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 11 23:10:15 2018@author: zhuang
"""import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 12 11:43:37 2018@author: zhuang
"""
import input_data
import tensorflow as tfmnist = 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)# 计算交叉熵
y_ = tf.placeholder("float",[None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))#梯度下降算法，以0.01的学习率更新参数
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()sess = tf.Session()
sess.run(init)#训练模型1000次
for i in range(1000):batch_xs,batch_ys = mnist.train.next_batch(100)sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})#评估模型
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})