tensorflow 全连接神经网络识别mnist数据
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。
#!/usr/bin/env python 3.6 #_*_coding:utf-8 _*_ #@Time :2020/2/12 15:34 #@Author :hujinzhou #@FileName: mnist.py #@Software: PyCharm import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data import matplotlib.pyplot as plt import numpy as np from time import time mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)#通过tensorflow下载mnist数据集 """图片的显示""" def plot_image(image): 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, # 所以显示的时候应该reshape成28*28 plt.show() """查看多项数训练数据images与labels""" 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 fig=plt.gcf() fig.set_size_inches(25,25)#设置显示尺寸 if num>25:num=25 for i in range(0,num): ax=plt.subplot(5,5,i+1)#一次显示多个子图 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 title="label="+str(np.argmax(labels[idx]))#设置图像的title,将onehot码转为数值码 """如果有预测的prediction,则重新写title""" if len(prediction)>0: title+=",predict="+str(prediction[idx]) ax.set_title(title,fontsize=10) 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) idx+=1 plt.show() """构造多层感知机""" """自己构造感知机""" # def layer(output_dim, input_dim, inputs, activation=None): # W = tf.Variable(tf.random_normal([input_dim, output_dim])) # b = tf.Variable(tf.random_normal([1, output_dim])) # XWb = tf.matmul(inputs, W) + b # if activation is None: # outputs = XWb # else: # outputs = activation(XWb) # return outputs """采用tf包来构造感知机""" x = tf.placeholder("float", [None, 784]) h1=tf.layers.dense(inputs=x,units=256,activation=tf.nn.relu) # h1 = layer(output_dim=256, input_dim=784, # inputs=x, activation=tf.nn.relu) y_predict = tf.layers.dense(inputs=h1,units=10,activation=None) y_label = tf.placeholder("float", [None, 10]) loss_function = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2 (logits=y_predict, labels=y_label))#计算损失值 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 correct_prediction = tf.equal(tf.argmax(y_label, 1), tf.argmax(y_predict, 1))#相等为1,不想等为0,统计正确的个数 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#精度等于正确个数除以总数 """训练过程""" train_epoch=30 batch_size=100 loss_list=[];epoch_list=[];accuracy_list=[] starttime=time() sess=tf.Session() sess.run(tf.global_variables_initializer()) for epoch in range(train_epoch): for i in range(550): batch_x, batch_y = mnist.train.next_batch(batch_size) sess.run(optimizer, feed_dict={x: batch_x, y_label: batch_y})#使用55000的训练集进行优化 loss, acc = sess.run([loss_function, accuracy], feed_dict={x: mnist.validation.images, y_label: mnist.validation.labels})#验证集进行验证 epoch_list.append(epoch); loss_list.append(loss) accuracy_list.append(acc) print("Train Epoch:", ‘%02d‘ % (epoch + 1), "Loss=", 87 "{:.9f}".format(loss), " Accuracy=", acc) duration = time() - starttime print("The process has taken;{:.10f}".format(duration)) fig2=plt.gcf() fig2.set_size_inches(4,2)#设置显示尺寸 plt.plot(epoch_list,loss_list,label="loss") plt.ylabel(‘loss‘) plt.xlabel(‘epoch‘) plt.legend([‘loss‘],loc=‘upper left‘) plt.show() plt.plot(epoch_list,accuracy_list,label=‘acc‘) plt.show() # sess=tf.Session() # init = tf.global_variables_initializer() # sess.run(init) #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 print("acc:",sess.run(accuracy,feed_dict={x:mnist.test.images,y_label:mnist.test.labels})) pre_result=sess.run(tf.argmax(y_predict,1),feed_dict={x:mnist.test.images}) plot_images_labels_prediction(mnist.test.images,mnist.test.labels,pre_result,0,25) sess.close()
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