取自于TensorFlow实战
''' 本文代码中的AlexNet不是完整的，书中也只给了卷积网络的构建，全连接层是博主自己加的，后面会分享完整的AlexNet 因为设备运算速度原因，只计算前向和反向的计算时间。 AlexNet使用了五个卷积层，各卷积层使用了relu、LRN，maxpool等trick 每层的卷积核大小和卷积步长需要经验和实践尝试 LRN层现在很少使用了，因为效果不是很明显，可以不加 maxpool层有ksize=[1,3,3,1],strides=[1,2,2,1],有重叠，可以增加特征的丰富性 ''' from datetime import datetime import tensorflow as tf import math import time #设置TensorFlow的CPU运算优先级 import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' #设置训练参数 batch_size=128 num_batches=100 #节点信息打印 def print_activation(t): print(t.op.name,' ',t.get_shape().as_list()) #网络构建 def inference(images): #参数信息列表 parameters=[] #第一卷积层 with tf.name_scope('conv1') as scope: #卷积核[11,11,3,64]，卷积步长[1,4,4,1] kernel=tf.Variable(tf.truncated_normal([11,11,3,64],dtype=tf.float32,stddev=1e-1),name='weights') conv=tf.nn.conv2d(images,kernel,[1,4,4,1],padding='SAME') #加偏置 biases=tf.Variable(tf.constant(0,shape=[64],dtype=tf.float32),trainable=True,name='biases') bias=tf.nn.bias_add(conv,biases) #整流线性激活 conv1=tf.nn.relu(bias,name=scope) #参数存入参数列表 parameters+=[kernel,biases] #LRN标准化，池化，pool1节点信息显示 lrn1=tf.nn.lrn(conv1,4,bias=1,alpha=0.001/9,beta=0.75,name='lrn1') pool1=tf.nn.max_pool(lrn1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID',name='pool1') print_activation(pool1) #第二卷积层，同上，卷积核[5, 5, 64, 192]，卷积步长[1,1,1,1,1] with tf.name_scope('conv2') as scope: kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0, shape=[192], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activation(conv2) lrn2 = tf.nn.lrn(conv2, 4, bias=1, alpha=0.001 / 9, beta=0.75, name='lrn2') pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool2') print_activation(pool2) #第三卷积层卷积核[3, 3, 192, 384]，卷积步长[1, 1, 1, 1]，不做LRN和池化 with tf.name_scope('conv3') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0, shape=[384], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv3 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activation(conv3) # 第四卷积层，卷积核[3, 3, 384, 256]，卷积步长[1, 1, 1, 1]，不做LRN和池化 with tf.name_scope('conv4') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0, shape=[256], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv4 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activation(conv4) # 第五卷积层同上，卷积核[3, 3, 256, 256]，卷积步长[1, 1, 1, 1]，池化 with tf.name_scope('conv5') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0, shape=[256], dtype=tf.float32), trainable=True, name='biases') bias = tf.nn.bias_add(conv, biases) conv5 = tf.nn.relu(bias, name=scope) parameters += [kernel, biases] print_activation(conv5) pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool5') print_activation(pool5) # 第一全连接层，一维化、线性、激活 with tf.name_scope('fc1') as scope: reshape = tf.reshape(pool5, [batch_size, -1],name='reshape') dim = reshape.get_shape()[1].value w1 = tf.Variable(tf.truncated_normal([dim,4096], dtype=tf.float32, stddev=1e-1), name='weights') b1 = tf.Variable(tf.constant(0.1, shape=[4096],dtype=tf.float32), trainable=True, name='biases') fc1 = tf.nn.relu(tf.matmul(reshape, w1) + b1,name=scope) parameters += [w1, b1] print_activation(fc1) # 第二全连接层，线性、激活 with tf.name_scope('fc2') as scope: w2 = tf.Variable(tf.truncated_normal([4096,4096], dtype=tf.float32, stddev=1e-1), name='weights') b2 = tf.Variable(tf.constant(0.1, shape=[4096],dtype=tf.float32), trainable=True, name='biases') fc2 = tf.nn.relu(tf.matmul(fc1, w2) + b2,name=scope) parameters += [w2, b2] print_activation(fc2) # 第三全连接层、线性，激活函数为softmax with tf.name_scope('fc3') as scope: w3 = tf.Variable(tf.truncated_normal([4096,1000], dtype=tf.float32, stddev=1e-1), name='weights') b3 = tf.Variable(tf.constant(0.1, shape=[1000],dtype=tf.float32), trainable=True, name='biases') fc3 = tf.nn.softmax(tf.matmul(fc2, w3) + b3,name=scope) parameters += [w3, b3] print_activation(fc3) #返回结果和参数列表 return fc3,parameters #预计算节点计算耗时函数 def time_tensorflow_run(session,target,info_string): num_steps_burn_in=10 total_duration=0 total_duration_squared=0 for i in range(num_batches+num_steps_burn_in): start_time=time.time() _=session.run(target) duration=time.time()-start_time if i>num_steps_burn_in: if not i%10: print('{:s}:step{:d},duration={:.3f}'.format(datetime.now(),i-num_steps_burn_in,duration)) total_duration+=duration total_duration_squared+=duration*duration mn=total_duration/num_batches vr=total_duration_squared/num_batches-mn*mn sd=math.sqrt(vr) print('{:s}:{:s} across {:d} steps,{:.3f}+/-{:.3f} sec/batch'.format(datetime.now(),info_string,num_batches,mn,sd)) #计算耗时 def run_benckmark(): with tf.Graph().as_default(): #加载训练数据和网络 image_size=224 images=tf.Variable(tf.random_normal([batch_size,image_size,image_size,3],dtype=tf.float32,stddev=0.1)) fc3,parameters=inference(images) #初始化 init=tf.global_variables_initializer() sess=tf.Session() sess.run(init) #计算前向耗时 time_tensorflow_run(sess,fc3,'Forward') #定义损失函数，计算反向耗时 objective=tf.nn.l2_loss(fc3) grad=tf.gradients(objective,parameters) time_tensorflow_run(sess,grad,'Forward-backward') run_benckmark() 文章来源: 部分AlexNet构建