卷积神经网络应用于tensorflow手写数字识别（第三版）
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("F:\TensorflowProject\MNIST_data",one_hot=True) #每个批次大小 batch_size = 100 #计算一共有多少个批次 n_batch = mnist.train.num_examples //batch_size #初始化权值 def weight_variable(shape): initial = tf.truncated_normal(shape,stddev=0.1) #初始化一个截断的正态分布 return tf.Variable(initial) #初始化偏值 def bias_variable(shape): initial = tf.constant(0.1,shape=shape) return tf.Variable(initial) #卷积层 def conv2d(x,W): #x input tensor of shape '[batch,in_height,in_width,in_channels]' #W filter/kernel tensor of shape [filter_height,filter_width,in_channels,out_channels] #strides[0] = strides[3] = 1, strides[1]代表x方向的步长，strides[2]代表y方向的步长 #padding:A string from :SAME 或者 VALID return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') #池化层 def max_pool_2x2(x): #ksize[1,x,y,1] return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') #定义两个placeholder x = tf.placeholder(tf.float32,[None,784]) #28*28 y = tf.placeholder(tf.float32,[None,10]) #设置x的格式为4D向量 [batch,in_height,in_width,in_chanels] x_image = tf.reshape(x,[-1,28,28,1]) #初始化第一个卷积层的权值和偏值 W_conv1 = weight_variable([5,5,1,32]) b_conv1 = bias_variable([32]) #把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) #max-pooling，经过池化计算得到一个结果 #初始化第二个卷积层的权值和偏置值 W_conv2 = weight_variable([5,5,32,64]) b_conv2 = bias_variable([64]) #把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) #max-pooling #28*28的图片第一次卷积后还是28*28，第一次池化后为14*14 #第二次卷积后是14*14，第二次池化后为7*7 #上面步骤完成以后得到64张7*7的平面 #初始化第一个全连接层的权值 W_fc1 = weight_variable([7*7*64,1024]) #上一步有 7*7*64个神经元，全连接层有1024个神经元 b_fc1 = bias_variable([1024]) #1024个节点 #把池化层2的输出扁平化为1维 h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64]) #求第一个全连接层的输出 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1) #keep_prob标识神经元输出概率 keep_prob =tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob) #初始化第二个全连接层 W_fc2 = weight_variable([1024,10]) b_fc2 = bias_variable([10]) #计算输出 prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2) #交叉熵代价函数 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) #使用AdamOptimizer进行优化 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #用布尔列表存放结果 correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) #求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch in range(21): for batch 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,keep_prob:0.7}) test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) print("Iter "+str(epoch)+" ,Testing Accuracy = "+str(test_acc))
##############运行结果
Iter 0 ,Testing Accuracy = 0.9552 Iter 1 ,Testing Accuracy = 0.9743 Iter 2 ,Testing Accuracy = 0.9796 Iter 3 ,Testing Accuracy = 0.9807 Iter 4 ,Testing Accuracy = 0.9849 Iter 5 ,Testing Accuracy = 0.9863 Iter 6 ,Testing Accuracy = 0.9859 Iter 7 ,Testing Accuracy = 0.9885 Iter 8 ,Testing Accuracy = 0.9887 Iter 9 ,Testing Accuracy = 0.9894 Iter 10 ,Testing Accuracy = 0.9907 Iter 11 ,Testing Accuracy = 0.991 Iter 12 ,Testing Accuracy = 0.9903 Iter 13 ,Testing Accuracy = 0.992 Iter 14 ,Testing Accuracy = 0.9904 Iter 15 ,Testing Accuracy = 0.9915 Iter 16 ,Testing Accuracy = 0.9903 Iter 17 ,Testing Accuracy = 0.9912 Iter 18 ,Testing Accuracy = 0.9917 Iter 19 ,Testing Accuracy = 0.9912 Iter 20 ,Testing Accuracy = 0.992
