VGGnet网络结构详解

 VGG网络结构：

from datetime import datetime import tensorflow as tf import math import time batch_size = 16 num_batches = 100 # 用来创建卷积层并把本层的参数存入参数列表 # input_op:输入的tensor name:该层的名称 kh:卷积层的高 kw:卷积层的宽 n_out:输出通道数，dh:步长的高 dw:步长的宽，p是参数列表 def conv_op(input_op,name,kh,kw,n_out,dh,dw,p): n_in = input_op.get_shape()[-1].value with tf.name_scope(name) as scope: kernel = tf.get_variable(scope + "w",shape=[kh,kw,n_in,n_out],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer_conv2d()) conv = tf.nn.conv2d(input_op, kernel, (1,dh,dw,1),padding='SAME') bias_init_val = tf.constant(0.0, shape=[n_out],dtype=tf.float32) biases = tf.Variable(bias_init_val , trainable=True , name='b') z = tf.nn.bias_add(conv,biases) activation = tf.nn.relu(z,name=scope) p += [kernel,biases] return activation # 定义全连接层 def fc_op(input_op,name,n_out,p): n_in = input_op.get_shape()[-1].value with tf.name_scope(name) as scope: kernel = tf.get_variable(scope+'w',shape=[n_in,n_out],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer_conv2d()) biases = tf.Variable(tf.constant(0.1,shape=[n_out],dtype=tf.float32),name='b') # tf.nn.relu_layer()用来对输入变量input_op与kernel做乘法并且加上偏置b activation = tf.nn.relu_layer(input_op,kernel,biases,name=scope) p += [kernel,biases] return activation # 定义最大池化层 def mpool_op(input_op,name,kh,kw,dh,dw): return tf.nn.max_pool(input_op,ksize=[1,kh,kw,1],strides=[1,dh,dw,1],padding='SAME',name=name) #定义网络结构 def inference_op(input_op,keep_prob): p = [] conv1_1 = conv_op(input_op,name='conv1_1',kh=3,kw=3,n_out=64,dh=1,dw=1,p=p) conv1_2 = conv_op(conv1_1,name='conv1_2',kh=3,kw=3,n_out=64,dh=1,dw=1,p=p) pool1 = mpool_op(conv1_2,name='pool1',kh=2,kw=2,dw=2,dh=2) conv2_1 = conv_op(pool1,name='conv2_1',kh=3,kw=3,n_out=128,dh=1,dw=1,p=p) conv2_2 = conv_op(conv2_1,name='conv2_2',kh=3,kw=3,n_out=128,dh=1,dw=1,p=p) pool2 = mpool_op(conv2_2, name='pool2', kh=2, kw=2, dw=2, dh=2) conv3_1 = conv_op(pool2, name='conv3_1', kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) conv3_2 = conv_op(conv3_1, name='conv3_2', kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) conv3_3 = conv_op(conv3_2, name='conv3_3', kh=3, kw=3, n_out=256, dh=1, dw=1, p=p) pool3 = mpool_op(conv3_3, name='pool3', kh=2, kw=2, dw=2, dh=2) conv4_1 = conv_op(pool3, name='conv4_1', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) conv4_2 = conv_op(conv4_1, name='conv4_2', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) conv4_3 = conv_op(conv4_2, name='conv4_3', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) pool4 = mpool_op(conv4_3, name='pool4', kh=2, kw=2, dw=2, dh=2) conv5_1 = conv_op(pool4, name='conv5_1', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) conv5_2 = conv_op(conv5_1, name='conv5_2', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) conv5_3 = conv_op(conv5_2, name='conv5_3', kh=3, kw=3, n_out=512, dh=1, dw=1, p=p) pool5 = mpool_op(conv5_3, name='pool5', kh=2, kw=2, dw=2, dh=2) shp = pool5.get_shape() flattened_shape = shp[1].value * shp[2].value * shp[3].value resh1 = tf.reshape(pool5,[-1,flattened_shape],name="resh1") fc6 = fc_op(resh1,name="fc6",n_out=4096,p=p) fc6_drop = tf.nn.dropout(fc6,keep_prob,name='fc6_drop') fc7 = fc_op(fc6_drop,name="fc7",n_out=4096,p=p) fc7_drop = tf.nn.dropout(fc7,keep_prob,name="fc7_drop") fc8 = fc_op(fc7_drop,name="fc8",n_out=1000,p=p) softmax = tf.nn.softmax(fc8) predictions = tf.argmax(softmax,1) return predictions,softmax,fc8,p def time_tensorflow_run(session,target,feed,info_string): num_steps_burn_in = 10 # 预热轮数 total_duration = 0.0 # 总时间 total_duration_squared = 0.0 # 总时间的平方和用以计算方差 for i in range(num_batches + num_steps_burn_in): start_time = time.time() _ = session.run(target,feed_dict=feed) duration = time.time() - start_time if i >= num_steps_burn_in: # 只考虑预热轮数之后的时间 if not i % 10: print('%s:step %d,duration = %.3f' % (datetime.now(), i - num_steps_burn_in, duration)) total_duration += duration total_duration_squared += duration * duration mn = total_duration / num_batches # 平均每个batch的时间 vr = total_duration_squared / num_batches - mn * mn # 方差 sd = math.sqrt(vr) # 标准差 print('%s: %s across %d steps, %.3f +/- %.3f sec/batch' % (datetime.now(), info_string, num_batches, mn, sd)) def run_benchmark(): with tf.Graph().as_default(): image_size = 224 # 输入图像尺寸 images = tf.Variable(tf.random_normal([batch_size, image_size, image_size, 3], dtype=tf.float32, stddev=1e-1)) keep_prob = tf.placeholder(tf.float32) prediction,softmax,fc8,p = inference_op(images,keep_prob) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) time_tensorflow_run(sess, prediction,{keep_prob:1.0}, "Forward") # 用以模拟训练的过程 objective = tf.nn.l2_loss(fc8) # 给一个loss grad = tf.gradients(objective, p) # 相对于loss的 所有模型参数的梯度 time_tensorflow_run(sess, grad, {keep_prob:0.5},"Forward-backward") run_benchmark() 

这个代码只是用来模拟训练过程然后评估每轮的计算时间的，结果如下：

2018-11-27 22:05:00.:step 0,duration = 0.320 2018-11-27 22:05:03.:step 10,duration = 0.320 2018-11-27 22:05:06.:step 20,duration = 0.340 2018-11-27 22:05:10.030007:step 30,duration = 0.324 2018-11-27 22:05:13.:step 40,duration = 0.320 2018-11-27 22:05:16.:step 50,duration = 0.320 2018-11-27 22:05:19.:step 60,duration = 0.324 2018-11-27 22:05:22.:step 70,duration = 0.320 2018-11-27 22:05:26.:step 80,duration = 0.328 2018-11-27 22:05:29.:step 90,duration = 0.324 2018-11-27 22:05:32.: Forward across 100 steps, 0.032 +/- 0.097 sec/batch 2018-11-27 22:05:51.071198:step 0,duration = 1.103 2018-11-27 22:06:01.:step 10,duration = 1.083 2018-11-27 22:06:12.:step 20,duration = 1.071 2018-11-27 22:06:23.:step 30,duration = 1.087 2018-11-27 22:06:34.:step 40,duration = 1.083 2018-11-27 22:06:44.:step 50,duration = 1.075 2018-11-27 22:06:55.:step 60,duration = 1.067 2018-11-27 22:07:06.:step 70,duration = 1.067 2018-11-27 22:07:17.:step 80,duration = 1.083 2018-11-27 22:07:28.:step 90,duration = 1.075 2018-11-27 22:07:37.: Forward-backward across 100 steps, 0.108 +/- 0.324 sec/batch

选用3*3卷积核的优点：

1. 使网络结构更深，学习到的特征更多，结果更具有判别性（discriminative）
2. 三层3*3的卷积核比一层7*7的卷积核所需要的参数更少（假设卷积前后的通道数均为C）

 参数和内存占用分析（来源[斯坦福大学CS231课程]课件截图)：

由分析可以看出:

• 前面部分的卷积层占用大量内存
• 后面的三层全连接层占用了大量的参数