Tensorflow 实现MINIST数据集多分类问题

tensorflow 入门程序 MINIST数据集

tensorflow是采用计算图的方式，先把所有的计算都用计算图描述出来，
然后将定义的所有计算放到外面计算，大大提高了效率

下载minists数据集

import tensorflow.examples.tutorials.mnist.input_datamnist = read_data_sets("MNIST_data/", one_hot=True)from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport tempfileimport numpyfrom six.moves import urllibfrom six.moves import xrange # pylint: disable=redefined-builtinimport tensorflow as tffrom tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

#读入minist数据集,读取二进制文件，这里为读入一张图片查看一下

import structimport numpy as npimport matplotlib.pyplot as pltdef minist_read_plot1(filename):        binfile = open(filename , 'rb')    buf = binfile.read()    index = 0    magic, numImages , numRows , numColumns = struct.unpack_from('>IIII' , buf , index)    index += struct.calcsize('>IIII')       #'>IIII'是说使用大端法读取4个unsinged int32    #然后读取一个图片测试是否读取成功    im = struct.unpack_from('>784B' ,buf, index)    index += struct.calcsize('>784B')    im = np.array(im)    im = im.reshape(28,28)    ##显示图片    fig = plt.figure()    plotwindow = fig.add_subplot(111)    plt.imshow(im , cmap='gray')    plt.show()    #'>784B'的意思就是用大端法读取784个unsigned byte；显示结果为5

filename = 'train-images.idx3-ubyte'minist_read_plot1(filename)

###读取MINIST所有数据的函数,输出images, labels都是ndarray类型

def load_mnist(path, kind='train'):    """Load MNIST data from `path`"""    labels_path = os.path.join(path,                               '%s-labels.idx1-ubyte'                               % kind)    images_path = os.path.join(path,                               '%s-images.idx3-ubyte'                               % kind)    with open(labels_path, 'rb') as lbpath:        magic, n = struct.unpack('>II',                                 lbpath.read(8))        labels = np.fromfile(lbpath,                             dtype=np.uint8)    with open(images_path, 'rb') as imgpath:        magic, num, rows, cols = struct.unpack('>IIII',                                               imgpath.read(16))        images = np.fromfile(imgpath,                             dtype=np.uint8).reshape(len(labels), 784)    return images, labels

filepath='your path'images, labels = load_mnist(filepath, kind='train')test_images, test_labels = load_mnist(filepath, kind='t10k')

#使用tensorflow训练之前，首先对数据进行处理
# 1.将图片像素值 0-255 之间的值转化为 [0,1]
# 2.对标签数据进行one-hot编码，比如1-->[1,0,0,0,0,0,0,0,0,0]，不过，one-hot对于softmax没有什么多大意义，不是必要选择
#另外，由于图像数据是[60000,748]的矩阵，放弃了图像的结构信息，不利于分类，
#但是这里，只是学习tensorflow的用法，不涉及算法的优化

from sklearn import preprocessingenc = preprocessing.OneHotEncoder() a = np.array([1,3,4,6,5,2,9,0,8,7])enc.fit(a)    #one-hot编码首先要学习该变量整数编码label_trans = enc.transform(labels.reshape(-1,1)).toarray()test_labels = enc.transform(test_labels.reshape(-1,1)).toarray()images_trans = images/255  #转化为[0,1]之间，防止指数溢出test_images = test_images/255

##使用tensorflow训练模型，采用算法 softmax regression

import tensorflow as tfx = tf.placeholder(tf.float32, [None, 784])#搭建模型，variable是可变的张量W = tf.Variable(tf.zeros([784,10]))b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x,W) + b)#损失函数占位符，交叉熵y_ = tf.placeholder("float", [None,10])cross_entropy = -tf.reduce_sum(y_*tf.log(y))#计算,梯度下降求解train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)#初始化变量init = tf.initialize_all_variables()#现在我们可以在一个Session里面启动我们的模型，并且初始化变量：sess = tf.Session()sess.run(init)

#该循环的每个步骤中，我们都会随机抓取训练数据中的100个批处理数据点，
#然后我们用这些数据点作为参数替换之前的占位符来运行train_step
##定义取消批量数据的函数，每个样本只提取一次，从头开始直到结束,按一行作为一个样本

#要求为100的整数倍def next_batch(data_set,batch_size,from_size):    if batch_size%100==0:        return data_set[from_size:from_size+batch_size,:]tmp = 0for i in range(600):    if tmp<=len(images_trans):        batch_xs = next_batch(images_trans, 100, tmp)        batch_ys = next_batch(label_trans,100,tmp)        tmp += 100        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

#统计训练集准确率

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

#测试集效果

print(sess.run(accuracy, feed_dict={x: test_images, y_: test_labels}))结果为：0.9031