首先要在對應的目錄下安裝好手寫數字識別數據集。

編寫代碼如下所示：

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("F:/anaconda/workspace/Data/MNIST_data",one_hot=True)

#設置每個批次的大小,一次運算100張圖片

batch_size = 100

#計算共有多少批次

n_batch = mnist.train.num_examples // batch_size

#創建兩個placeholder

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

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

#創建簡單的神經網絡

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

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

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

#二次代價函數

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

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)

#初始化變量

init = tf.global_variables_initializer()

#結果存放在一個布爾類型列表中 argmax:返回一位張量中的最大值所在的位置(概率最大的位置)

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

#計算準確率 cast:把true轉化為1.0,false轉化為0.0

accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:

sess.run(init)

for epoch in range(21):

for bach 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})

#計算準確率

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

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

代價 函數可以更換，本文使用了兩種代價函數，一個是二次代價函數另一個是交叉熵代價函數，進行20次訓練后的準確率為：

#交叉熵

Iter 0Testing Accuracy 0.8666

Iter 1Testing Accuracy 0.8774

Iter 2Testing Accuracy 0.8841

Iter 3Testing Accuracy 0.8874

Iter 4Testing Accuracy 0.8895

Iter 5Testing Accuracy 0.893

Iter 6Testing Accuracy 0.8944

Iter 7Testing Accuracy 0.8971

Iter 8Testing Accuracy 0.8972

Iter 9Testing Accuracy 0.8968

Iter 10Testing Accuracy 0.8996

Iter 11Testing Accuracy 0.8998

Iter 12Testing Accuracy 0.9011

Iter 13Testing Accuracy 0.9014

Iter 14Testing Accuracy 0.9009

Iter 15Testing Accuracy 0.9014

Iter 16Testing Accuracy 0.9016

Iter 17Testing Accuracy 0.9021

Iter 18Testing Accuracy 0.9032

Iter 19Testing Accuracy 0.9034

Iter 20Testing Accuracy 0.903

#二次代價函數

Iter 0Testing Accuracy 0.8175

Iter 1Testing Accuracy 0.8515

Iter 2Testing Accuracy 0.8639

Iter 3Testing Accuracy 0.8709

Iter 4Testing Accuracy 0.8769

Iter 5Testing Accuracy 0.8809

Iter 6Testing Accuracy 0.8844

Iter 7Testing Accuracy 0.8865

Iter 8Testing Accuracy 0.8896

Iter 9Testing Accuracy 0.8907

Iter 10Testing Accuracy 0.8921

Iter 11Testing Accuracy 0.8933

Iter 12Testing Accuracy 0.8947

Iter 13Testing Accuracy 0.8962

Iter 14Testing Accuracy 0.8965

Iter 15Testing Accuracy 0.897

Iter 16Testing Accuracy 0.8985

Iter 17Testing Accuracy 0.8989

Iter 18Testing Accuracy 0.8994

Iter 19Testing Accuracy 0.8999

Iter 20Testing Accuracy 0.9005

看起來兩者的差距并不是很大。在這里的代價函數和優化器自己可以調整。

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