# -*- coding: utf-8 -*- """ @Time ： 2021/8/12 上午9:10 @Auth ： 陈伟峰 @File ：read_data.py @phone: 15882085601 @IDE ：PyCharm @Motto：ABC(Always Be Coding)""" import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data# 输入数据 minst = input_data.read_data_sets("./data/",one_hot=True)# 定义网络的超参数learning_rate = 0.001 training_iters = 200000 batch_size =128 display_step = 10# 定义网路的参数n_input = 784 n_classes = 10 dropout = 0.75# 输入占位符x = tf.placeholder(tf.float32,[None,n_input]) y = tf.placeholder(tf.float32,[None,n_classes]) keep_prob = tf.placeholder(tf.float32) # dropoutdef conv2d(name,x,w,b,strides=1):x = tf.nn.conv2d(x,w,strides=[1,strides,strides,1],padding="SAME") x = tf.nn.bias_add(x,b) # relu激活函数 result = tf.nn.relu(x, name=name) return result# 定义池化层 def maxpool2d(name,x,k=2):result = tf.nn.max_pool(x,ksize=[1,k,k,1],strides=[1,k,k,1],padding='SAME',name=name) return result# 规范化操作 def norm(name,l_input,lsize=4):result = tf.nn.lrn(l_input,lsize,bias=1.0,alpha=0.001/9.0,beta=0.75,name=name)return result# 定义所有的网络参数weights = {"wc1":tf.Variable(tf.random_normal([11,11,1,96])), "wc2": tf.Variable(tf.random_normal([5,5, 96, 256])), "wc3": tf.Variable(tf.random_normal([3, 3, 256, 384])), "wc4": tf.Variable(tf.random_normal([3, 3, 384, 384])), "wc5": tf.Variable(tf.random_normal([3, 3, 384, 256])), "wd1": tf.Variable(tf.random_normal([2*2*256,4096])), "wd2": tf.Variable(tf.random_normal([4096,4096])), "out": tf.Variable(tf.random_normal([4096,10])), } biases = {"bc1":tf.Variable(tf.random_normal([96])), "bc2":tf.Variable(tf.random_normal([256])), "bc3":tf.Variable(tf.random_normal([384])), "bc4":tf.Variable(tf.random_normal([384])), "bc5":tf.Variable(tf.random_normal([256])), "bd1":tf.Variable(tf.random_normal([4096])), "bd2":tf.Variable(tf.random_normal([4096])), "out":tf.Variable(tf.random_normal([n_classes])), }# 定义整个网络 def alex_net(x,weights,biases,dropout): # reshape x = tf.reshape(x,shape=[-1,28,28,1])# 第一层卷积 conv1 = conv2d('conv1',x,weights['wc1'],biases['bc1']) # 下采样 pool1 = maxpool2d('pool1',conv1,k=2) # 规范化 norm1 = norm("morm1",pool1,lsize=4)# 第二层卷积 conv2 = conv2d('conv2', norm1, weights['wc2'], biases['bc2']) # 下采样 pool2 = maxpool2d('pool2', conv2, k=2) # 规范化 norm2 = norm("morm2", pool2, lsize=4)# 第三层卷积 conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3']) # 下采样 pool3 = maxpool2d('pool3', conv3, k=2) # 规范化 norm3 = norm("morm3", pool3, lsize=4)# 第四层卷积 conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4']) conv5 = conv2d('conv5',conv4, weights['wc5'], biases['bc5'])# 下采样 pool5 = maxpool2d('pool5', conv5, k=2) # 规范化 norm5 = norm("morm5", pool5, lsize=4)fc1 = tf.reshape(norm5,[-1,weights["wd1"].get_shape().as_list()[0]])fc1 = tf.add(tf.matmul(fc1,weights['wd1']),biases['bd1']) fc1 = tf.nn.relu(fc1) # dropout fc1 = tf.nn.dropout(fc1,dropout)# 全连接2 fc2 =tf.reshape(fc1, [-1, weights["wd2"].get_shape().as_list()[0]]) fc2 = tf.add(tf.matmul(fc2, weights['wd2']), biases['bd2']) fc2 = tf.nn.relu(fc2) # dropout fc2 = tf.nn.dropout(fc2, dropout)# 输出层 out = tf.add(tf.matmul(fc2,weights["out"]),biases['out']) return out# 构建模型定义损失函数 pred = alex_net(x,weights,biases,keep_prob) # 定义损失函数 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# 评估函数 correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))# 初始化变量 if __name__ == '__main__':init = tf.global_variables_initializer()with tf.Session() as sess: with tf.device("gpu:0"): sess.run(init) # 开始训练 step = 1 while step*batch_size