用tensorflow求手写数字的识别准确率 （简单版）

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#载入数据集

mnist = input_data.read_data_sets("F:\\TensorflowProject\\MNIST_data",one_hot=True)

#每个批次的大小，训练时一次100张放入神经网络中训练

batch_size = 100

#计算一共有多少个批次

n_batch = mnist.train.num_examples//batch_size

#定义两个placeholder

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

#0-9十个数字

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

#创建一个神经网络

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))

#交叉熵代价函数

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

#使用梯度下降法

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

train_step = tf.train.AdamOptimizer(0.01).minimize(loss) #1e-2

#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))

#求准确率

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))with tf.Session() as sess:

　　sess.run(init)

　　for epoch in range(21):

　　　　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})

　　　　#测试准确率

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

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

###########运行结果

Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz

Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz

Iter: 0  ,Testing Accuracy  0.9221

Iter: 1  ,Testing Accuracy  0.9133

Iter: 2  ,Testing Accuracy  0.9271

Iter: 3  ,Testing Accuracy  0.9262

Iter: 4  ,Testing Accuracy  0.9299

Iter: 5  ,Testing Accuracy  0.9293

Iter: 6  ,Testing Accuracy  0.9301

Iter: 7  ,Testing Accuracy  0.9299

Iter: 8  ,Testing Accuracy  0.9287

Iter: 9  ,Testing Accuracy  0.9319

Iter: 10  ,Testing Accuracy  0.9317

Iter: 11  ,Testing Accuracy  0.9315

Iter: 12  ,Testing Accuracy  0.9307

Iter: 13  ,Testing Accuracy  0.932

Iter: 14  ,Testing Accuracy  0.9314

Iter: 15  ,Testing Accuracy  0.9316

Iter: 16  ,Testing Accuracy  0.9311

Iter: 17  ,Testing Accuracy  0.9333

Iter: 18  ,Testing Accuracy  0.9318

Iter: 19  ,Testing Accuracy  0.9318

Iter: 20  ,Testing Accuracy  0.9289