之前没有学过tensorflow，所以使用tensorflow来对mnist数据进行识别，采用最简单的全连接神经网络，第一层是784,（输入层）,隐含层是256，输出层是10
，相关注释卸载程序中。
1 #!/usr/bin/env python 3.6
2 #_*_coding:utf-8 _*_
3 #@Time :2020/2/12 15:34
4 #@Author :hujinzhou
5 #@FileName: mnist.py
6
7 #@Software: PyCharm
8 import tensorflow as tf
9 import tensorflow.examples.tutorials.mnist.input_data as input_data
10 import matplotlib.pyplot as plt
11 import numpy as np
12 from time import time
13 mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)#通过tensorflow下载mnist数据集
14 """图片的显示"""
15 def plot_image(image):
16 plt.imshow(image.reshape(28,28),cmap=‘binary‘)#tensorflow中的数据是将图片平铺成一列的存储，
17 # 所以显示的时候应该reshape成28*28
18 plt.show()
19 """查看多项数训练数据images与labels"""
20 def plot_images_labels_prediction(images,labels,prediction,idx,num):#idx表示要显示的第idx个图像从idx~idx+25
21 fig=plt.gcf()
22 fig.set_size_inches(25,25)#设置显示尺寸
23 if num>25:num=25
24 for i in range(0,num):
25 ax=plt.subplot(5,5,i+1)#一次显示多个子图
26 ax.imshow(np.reshape(images[idx],(28,28)),cmap=‘binary‘)#将第idx个图像数据reshape成28*28的numpy并显示
27 title="label="+str(np.argmax(labels[idx]))#设置图像的title，将onehot码转为数值码
28 """如果有预测的prediction，则重新写title"""
29 if len(prediction)>0:
30 title+=",predict="+str(prediction[idx])
31 ax.set_title(title,fontsize=10)
32 ax.set_xticks([]);ax.set_yticks([])#设置xy轴为空，如果不设置则会有标度（像素值）
33 idx+=1
34 plt.show()
35
36
37 """构造多层感知机"""
38 """自己构造感知机"""
39 # def layer(output_dim, input_dim, inputs, activation=None):
40 # W = tf.Variable(tf.random_normal([input_dim, output_dim]))
41 # b = tf.Variable(tf.random_normal([1, output_dim]))
42 # XWb = tf.matmul(inputs, W) + b
43 # if activation is None:
44 # outputs = XWb
45 # else:
46 # outputs = activation(XWb)
47 # return outputs
48
49 """采用tf包来构造感知机"""
50 x = tf.placeholder("float", [None, 784])
51 h1=tf.layers.dense(inputs=x,units=256,activation=tf.nn.relu)
52 # h1 = layer(output_dim=256, input_dim=784,
53 # inputs=x, activation=tf.nn.relu)
54 y_predict = tf.layers.dense(inputs=h1,units=10,activation=None)
55 y_label = tf.placeholder("float", [None, 10])
56 loss_function = tf.reduce_mean(
57 tf.nn.softmax_cross_entropy_with_logits_v2
58 (logits=y_predict,
59 labels=y_label))#计算损失值
60 optimizer = tf.train.AdamOptimizer(learning_rate=0.001) 61 .minimize(loss_function)#使用优化器反向传播，使得损失量为最小
62 correct_prediction = tf.equal(tf.argmax(y_label, 1),
63 tf.argmax(y_predict, 1))#相等为1，不想等为0，统计正确的个数
64 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#精度等于正确个数除以总数
65 """训练过程"""
66 train_epoch=30
67 batch_size=100
68 loss_list=[];epoch_list=[];accuracy_list=[]
69 starttime=time()
70
71
72 sess=tf.Session()
73 sess.run(tf.global_variables_initializer())
74 for epoch in range(train_epoch):
75 for i in range(550):
76 batch_x, batch_y = mnist.train.next_batch(batch_size)
77 sess.run(optimizer, feed_dict={x: batch_x, y_label: batch_y})#使用55000的训练集进行优化
78
79 loss, acc = sess.run([loss_function, accuracy],
80 feed_dict={x: mnist.validation.images,
81 y_label: mnist.validation.labels})#验证集进行验证
82
83 epoch_list.append(epoch);
84 loss_list.append(loss)
85 accuracy_list.append(acc)
86 print("Train Epoch:", ‘%02d‘ % (epoch + 1), "Loss=", 87 "{:.9f}".format(loss), " Accuracy=", acc)
88 duration = time() - starttime
89 print("The process has taken；{:.10f}".format(duration))
90 fig2=plt.gcf()
91 fig2.set_size_inches(4,2)#设置显示尺寸
92 plt.plot(epoch_list,loss_list,label="loss")
93
94 plt.ylabel(‘loss‘)
95 plt.xlabel(‘epoch‘)
96 plt.legend([‘loss‘],loc=‘upper left‘)
97 plt.show()
98 plt.plot(epoch_list,accuracy_list,label=‘acc‘)
99 plt.show()
100 # sess=tf.Session()
101 # init = tf.global_variables_initializer()
102 # sess.run(init)
103 #注意这个地方，不可以重新设置sess，不可以重新开启回话，重新开启会错误
104 print("acc:",sess.run(accuracy,feed_dict={x:mnist.test.images,y_label:mnist.test.labels}))
105
106 pre_result=sess.run(tf.argmax(y_predict,1),feed_dict={x:mnist.test.images})
107 plot_images_labels_prediction(mnist.test.images,mnist.test.labels,pre_result,0,25)
108 sess.close()
原文地址：https://www.cnblogs.com/hujinzhou/p/guobao_2020_2_13.html
时间： 2024-08-18 12:03:31