CNN.rar_CNN_cnn 手写_tensorflow cnn

#CNN,数据保存和复原 import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_DATA',one_hot = True) def computer_accuracy(v_xs,v_ys): global prediction y_pre = sess.run(prediction,feed_dict={xs:v_xs,keep_prob:0.6}) correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1)) accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:0.6}) return result def weight_variable(shape): initial = tf.truncated_normal(shape,stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1,shape=shape) return tf.Variable(initial) def conv2d(x,W): #卷积层运行方式 #strides 步长(1（必定），1（x步长），1（y步长），1（必定）) return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') def max_pool_2x2(x): #池化层运行方式 return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') keep_prob=tf.placeholder(tf.float32) #dropout的 xs=tf.placeholder(tf.float32,[None,784],name='x_input') #输入数据 ys=tf.placeholder(tf.float32,[None,10],name='y_input') #输出数据 #784=28*28(分辨率)，1个通道,-1数量（全部） x_image=tf.reshape(xs,[-1,28,28,1]) #转化为图片 #定义卷积层1(5*5的大小，insize 1,outsize 32) 32个卷积核 W_conv1 = weight_variable([5,5,1,32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output 28*28*32 h_pool1 = max_pool_2x2(h_conv1) #output 14*14*32 #定义卷积层2(5*5的大小，insize 32,outsize 128) W_conv2 = weight_variable([5,5,32,128]) b_conv2 = bias_variable([128]) h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)#output 14*14*64 h_pool2 = max_pool_2x2(h_conv2) #output 7*7*64 # func1全连接层（7*7*128个输入数据，512个神经元） W_fc1 = weight_variable([7*7*128,512]) b_fc1 = bias_variable([512]) #将卷积结果平面化 h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*128]) #进入全连接层 h_fcl = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1) h_fcl_drop = tf.nn.dropout(h_fcl,keep_prob) #func2全连接层（512个输入数据，64个神经元） W_fc2 = weight_variable([512,64]) b_fc2 = bias_variable([64]) h_fc2 = tf.nn.relu(tf.matmul(h_fcl_drop,W_fc2)+b_fc2) h_fc2_drop = tf.nn.dropout(h_fc2,keep_prob) #func3全连接层（64个输入数据，10个神经元） W_fc3 = weight_variable([64,10]) b_fc3 = bias_variable([10]) prediction = tf.nn.softmax(tf.matmul(h_fc2_drop,W_fc3)+b_fc3) #loss函数，交叉熵 cross_entropy =tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) #训练方法 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #保存数据 saver = tf.train.Saver() sess=tf.Session() saver.restore(sess,"my_data/1.ckpt") for i in range(1000): batch_xs,batch_ys=mnist.train.next_batch(100) sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.6}) if i%50==0: print(computer_accuracy(mnist.test.images,mnist.test.labels)) saver.save(sess,"my_data/1.ckpt") print("saved")