TensorFlow—多层感知器—MNIST手写数字识别


  1 import tensorflow as tf
  2 import tensorflow.examples.tutorials.mnist.input_data as input_data
  3 import matplotlib.pyplot as plt
  4 import numpy as np
  5 mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)           #下载据数
  6 print('train images:',mnist.train.images.shape,                       #查看数据
  7      'labels:',mnist.train.labels.shape)
  8 print('validation images:',mnist.validation.images.shape,
  9      'labels:',mnist.validation.labels.shape)
 10 print('test images:',mnist.test.images.shape,
 11      'labels:',mnist.test.labels.shape
 12 #定义显示多项图像的函数
 13 def plot_images_labels_prediction_3(images,labels,prediction,idx,num=10):
 14     fig=plt.gcf()
 15     fig.set_size_inches(12,14)
 16     if num>25:num=25
 17     for i in range(0,num):
 18         ax=plt.subplot(5,5,i+1)
 19         ax.imshow(np.reshape(images[idx],(28,28)),cmap='binary')
 20         title='lable='+str(np.argmax(labels[idx]))
 21         if len(prediction)>0:
 22             title+=",prediction="+str(prediction[idx])
 23         ax.set_title(title,fontsize=10)
 24         ax.set_xticks([]);ax.set_yticks([])
 25         idx+=1
 26     plt.show()
 27 
 28 plot_images_labels_prediction_3(mnist.train.images,mnist.train.labels,[],0)
 29 #定义layer函数，构建多层感知器模型
 30 def layer(output_dim,input_dim,inputs,activation=None):
 31     W=tf.Variable(tf.random_normal([input_dim,output_dim]))
 32     b=tf.Variable(tf.random_normal([1,output_dim]))
 33     XWb=tf.matmul(inputs,W)+b
 34     if activation is None:
 35         outputs=XWb
 36     else:
 37         outputs=activation(XWb)
 38     return outputs
 39 #建立输入层
 40 x=tf.placeholder("float",[None,784])
 41 #建立隐藏层
 42 h1=layer(output_dim=256,input_dim=784,inputs=x,
 43        activation=tf.nn.relu)
 44 #建立输出层
 45 y_predict=layer(output_dim=10,input_dim=256,inputs=h1,
 46         activation=None)
 47 y_label=tf.placeholder("float",[None,10])
 48 #定义损失函数
 49 loss_function=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits
 50                              (logits=y_predict,
 51                               labels=y_label))
 52 #定义优化器
 53 optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss_function)
 54 #计算每一项数据是否预测正确
 55 correct_prediction=tf.equal(tf.argmax(y_label,1),
 56                             tf.argmax(y_predict,1))
 57 #计算预测正确结果的平均值
 58 accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
 59 #1、定义训练参数
 60 trainEpochs=15                                   #设置执行15个训练周期
 61 batchSize=100                                    #每一批次项数为100
 62 totalBatchs=int(mnist.train.num_examples/batchSize)        #计算每个训练周期
 63 loss_list=[];epoch_list=[];accuracy_list=[]      #初始化训练周期、误差、准确率
 64 from time import time                            #导入时间模块
 65 startTime=time()                                 #开始计算时间
 66 sess=tf.Session()                                #建立Session
 67 sess.run(tf.global_variables_initializer())       #初始化TensorFlow global 变量
 68 #2、进行训练
 69 for epoch in range(trainEpochs):
 70     for i in range(totalBatchs):
 71         batch_x,batch_y=mnist.train.next_batch(batchSize)  #使用mnist.train.next_batch方法读取批次数据，传入参数batchSize是100
 72         sess.run(optimizer,feed_dict={x:batch_x,
 73                                       y_label:batch_y})    #执行批次训练
 74     loss,acc=sess.run([loss_function,accuracy],            #使用验证数据计算准确率
 75                       feed_dict={x:mnist.validation.images,
 76                                  y_label:mnist.validation.labels})
 77     epoch_list.append(epoch);                              #加入训练周期列表
 78     loss_list.append(loss)                                 #加入误差列表
 79     accuracy_list.append(acc)                              #加入准确率列表
 80     print("Train Epoch:",'%02d' % (epoch+1),"Loss=",
 81           "{:.9f}".format(loss),"Accuracy=",acc)
 82 duration=time()-startTime
 83 print("Train Finished takes:",duration)                    #计算并显示全部训练所需时间
 84 #画出误差执行结果
 85 
 86 fig=plt.gcf()
 87 fig.set_size_inches(4,2)
 88 plt.plot(epoch_list,loss_list,label='loss')
 89 plt.ylabel('loss')
 90 plt.xlabel('epoch')
 91 plt.legend(['loss'],loc='upper left')
 92 #画出准确率执行结果
 93 plt.plot(epoch_list,accuracy_list,label="accuracy")
 94 fig=plt.gcf()
 95 fig.set_size_inches(4,2)
 96 plt.ylim(0.8,1)
 97 plt.ylabel('accuracy')
 98 plt.xlabel('epoch')
 99 plt.legend()
100 plt.show()
101 #评估模型准确率
102 print("accuracy:",sess.run(accuracy,
103                            feed_dict={x:mnist.test.images,
104                                       y_label:mnist.test.labels}))
105 #进行预测
106 #1.执行预测
107 prediction_result=sess.run(tf.argmax(y_predict,1),
108                            feed_dict={x:mnist.test.images})
109 #2.预测结果
110 print(prediction_result[:10])
111 #3.显示前10项预测结果
112 plot_images_labels_prediction_3(mnist.test.images,
113                               mnist.test.labels,
114                               prediction_result,0)