MNIST的结果是0-9，常用softmax函数进行分类，输出结果。

softmax函数常用于分类，定义如下：

​ $$softmax(x_i)=\frac{exp(x_i)}{\sum_jexp(x)}$$

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# coding: utf-8

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data



#载入数据集

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



#每个批次的大小

batch_size = 100

#计算一共有多少个批次

n_batch = mnist.train.num_examples // batch_size

print( mnist.train.num_examples)

#定义两个placeholder

x = tf.placeholder(tf.float32,[None,784])#输入

y = tf.placeholder(tf.float32,[None,10])#输出



#创建一个简单的神经网络

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

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

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



#二次代价函数

loss = tf.reduce_mean(tf.square(y-prediction))

#使用梯度下降法

train_step = tf.train.GradientDescentOptimizer(3).minimize(loss)

#train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量

init = tf.global_variables_initializer()



#结果存放在一个布尔型列表中

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置

#求准确率

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#布尔转成float32，然后求平均值



with tf.Session() as sess:

    sess.run(init)

    for epoch in range(100):

        for batch in range(n_batch):

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

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

        

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

        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

        if acc > 0.99:

            break

            

#输出

# Iter 0,Testing Accuracy 0.9074

# Iter 1,Testing Accuracy 0.9168

# Iter 2,Testing Accuracy 0.9221

# Iter 3,Testing Accuracy 0.9223

# Iter 4,Testing Accuracy 0.9237

# Iter 5,Testing Accuracy 0.9248

# Iter 6,Testing Accuracy 0.9246

# Iter 7,Testing Accuracy 0.925

# Iter 8,Testing Accuracy 0.9257

# Iter 9,Testing Accuracy 0.9278

# Iter 10,Testing Accuracy 0.9268

# Iter 11,Testing Accuracy 0.928

# Iter 12,Testing Accuracy 0.9269

# Iter 13,Testing Accuracy 0.9288

# Iter 14,Testing Accuracy 0.9273

# Iter 15,Testing Accuracy 0.9282

# Iter 16,Testing Accuracy 0.9301

# Iter 17,Testing Accuracy 0.9287

# Iter 18,Testing Accuracy 0.9297

# Iter 19,Testing Accuracy 0.9296

# Iter 20,Testing Accuracy 0.9285

# Iter 21,Testing Accuracy 0.9286

# Iter 22,Testing Accuracy 0.9288

# Iter 23,Testing Accuracy 0.9285

# Iter 24,Testing Accuracy 0.9311

# Iter 25,Testing Accuracy 0.9298

# Iter 26,Testing Accuracy 0.9294

# Iter 27,Testing Accuracy 0.9299

# Iter 28,Testing Accuracy 0.9298

# Iter 29,Testing Accuracy 0.9298

# Iter 30,Testing Accuracy 0.9307

# Iter 31,Testing Accuracy 0.9305

# Iter 32,Testing Accuracy 0.9291

# Iter 33,Testing Accuracy 0.9295

# Iter 34,Testing Accuracy 0.9289

# Iter 35,Testing Accuracy 0.9301

# Iter 36,Testing Accuracy 0.93

# Iter 37,Testing Accuracy 0.9293

# Iter 38,Testing Accuracy 0.93

# Iter 39,Testing Accuracy 0.9298

# Iter 40,Testing Accuracy 0.9299

# Iter 41,Testing Accuracy 0.9304

# Iter 42,Testing Accuracy 0.9303

# Iter 43,Testing Accuracy 0.93

# Iter 44,Testing Accuracy 0.9308

# Iter 45,Testing Accuracy 0.9296

# Iter 46,Testing Accuracy 0.9291

# Iter 47,Testing Accuracy 0.9306

# Iter 48,Testing Accuracy 0.9311

# Iter 49,Testing Accuracy 0.9301

# Iter 50,Testing Accuracy 0.93

# Iter 51,Testing Accuracy 0.9301

# Iter 52,Testing Accuracy 0.9306

# Iter 53,Testing Accuracy 0.9303

# Iter 54,Testing Accuracy 0.9307

# Iter 55,Testing Accuracy 0.9295

# Iter 56,Testing Accuracy 0.9313

# Iter 57,Testing Accuracy 0.9295

# Iter 58,Testing Accuracy 0.9303

# Iter 59,Testing Accuracy 0.9299

# Iter 60,Testing Accuracy 0.9286

# Iter 61,Testing Accuracy 0.9301

# Iter 62,Testing Accuracy 0.9303

# Iter 63,Testing Accuracy 0.9289

# Iter 64,Testing Accuracy 0.9301

# Iter 65,Testing Accuracy 0.9296

# Iter 66,Testing Accuracy 0.9303

# Iter 67,Testing Accuracy 0.9313

# Iter 68,Testing Accuracy 0.9301

# Iter 69,Testing Accuracy 0.9312

# Iter 70,Testing Accuracy 0.9294

# Iter 71,Testing Accuracy 0.9283

# Iter 72,Testing Accuracy 0.9295

# Iter 73,Testing Accuracy 0.9305

# Iter 74,Testing Accuracy 0.929

# Iter 75,Testing Accuracy 0.9315

# Iter 76,Testing Accuracy 0.9306

# Iter 77,Testing Accuracy 0.9288

# Iter 78,Testing Accuracy 0.9312

# Iter 79,Testing Accuracy 0.9309

# Iter 80,Testing Accuracy 0.9298

# Iter 81,Testing Accuracy 0.9293

# Iter 82,Testing Accuracy 0.9295

# Iter 83,Testing Accuracy 0.9292

# Iter 84,Testing Accuracy 0.9291

# Iter 85,Testing Accuracy 0.9294

# Iter 86,Testing Accuracy 0.9298

# Iter 87,Testing Accuracy 0.9296

# Iter 88,Testing Accuracy 0.9301

# Iter 89,Testing Accuracy 0.9306

# Iter 90,Testing Accuracy 0.9297

# Iter 91,Testing Accuracy 0.9307

# Iter 92,Testing Accuracy 0.9289

# Iter 93,Testing Accuracy 0.931

# Iter 94,Testing Accuracy 0.9301

# Iter 95,Testing Accuracy 0.9302

# Iter 96,Testing Accuracy 0.9297

# Iter 97,Testing Accuracy 0.9299

# Iter 98,Testing Accuracy 0.9317

# Iter 99,Testing Accuracy 0.9297