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VGG网络像素值计算
这是VGG的网络:
下面算一下每一层的像素值计算:
输入:224*224*3
1. conv3 - 64(卷积核的数量):kernel size:3 stride:1 pad:1
像素:(224-3+2*1)/1+1=224 224*224*64
参数: (3*3*3)*64 =1728
2. conv3 - 64:kernel size:3 stride:1 pad:1
像素: (224-3+1*2)/1+1=224 224*224*64
参数: (3*3*64)*64 =36864
3. pool2 kernel size:2 stride:2 pad:0
像素: (224-2)/2 = 112 112*112*64
参数: 0
4.conv3-128:kernel size:3 stride:1 pad:1
像素: (112-3+2*1)/1+1 = 112 112*112*128
参数: (3*3*64)*128 =73728
5.conv3-128:kernel size:3 stride:1 pad:1
像素: (112-3+2*1)/1+1 = 112 112*112*128
参数: (3*3*128)*128 =147456
6.pool2: kernel size:2 stride:2 pad:0
像素: (112-2)/2+1=56 56*56*128
参数:0
7.conv3-256: kernel size:3 stride:1 pad:1
像素: (56-3+2*1)/1+1=56 56*56*256
参数:(3*3*128)*256=294912
8.conv3-256: kernel size:3 stride:1 pad:1
像素: (56-3+2*1)/1+1=56 56*56*256
参数:(3*3*256)*256=589824
9.conv3-256: kernel size:3 stride:1 pad:1
像素: (56-3+2*1)/1+1=56 56*56*256
参数:(3*3*256)*256=589824
10.pool2: kernel size:2 stride:2 pad:0
像素:(56 - 2)/2+1=28 28*28*256
参数:0
11. conv3-512:kernel size:3 stride:1 pad:1
像素:(28-3+2*1)/1+1=28 28*28*512
参数:(3*3*256)*512 = 1179648
12. conv3-512:kernel size:3 stride:1 pad:1
像素:(28-3+2*1)/1+1=28 28*28*512
参数:(3*3*512)*512 = 2359296
13. conv3-512:kernel size:3 stride:1 pad:1
像素:(28-3+2*1)/1+1=28 28*28*512
参数:(3*3*512)*512 = 2359296
14.pool2: kernel size:2 stride:2 pad:0
像素:(28-2)/2+1=14 14*14*512
参数: 0
15. conv3-512:kernel size:3 stride:1 pad:1
像素:(14-3+2*1)/1+1=14 14*14*512
参数:(3*3*512)*512 = 2359296
16. conv3-512:kernel size:3 stride:1 pad:1
像素:(14-3+2*1)/1+1=14 14*14*512
参数:(3*3*512)*512 = 2359296
17. conv3-512:kernel size:3 stride:1 pad:1
像素:(14-3+2*1)/1+1=14 14*14*512
参数:(3*3*512)*512 = 2359296
18.pool2:kernel size:2 stride:2 pad:0
像素:(14-2)/2+1=7 7*7*512
参数:0
19.FC: 4096 neurons
像素:1*1*4096
参数:7*7*512*4096 = 102760448
20.FC: 4096 neurons
像素:1*1*4096
参数:4096*4096 = 16777216
21.FC:1000 neurons
像素:1*1*1000
参数:4096*1000=4096000
总共参数数量大约138M左右。
本文主要工作计算了一下VGG网络各层的输出像素以及所需参数,作为一个理解CNN的练习,VGG网络的特点是利用小的尺寸核代替大的卷积核,然后把网络做深,举个例子,VGG把alexnet最开始的一个7*7的卷积核用3个3*3的卷积核代替,其感受野是一样。关于感受野的计算可以参照另一篇博文。
AlexNet最开始的7*7的卷积核的感受野是:7*7
VGG第一个卷积核的感受野:3*3
第二个卷积核的感受野:(3-1)*1+3=5
第三个卷积核的感受野:(5-1)*1+3=7
可见三个3*3卷积核和一个7*7卷积核的感受野是一样的,但是3*3卷积核可以把网络做的更深。VGGNet不好的一点是它耗费更多计算资源,并且使用了更多的参数,导致更多的内存占用。
Tensorflow实现
代码参考:《Tensorflow实践》——黄文坚
from datetime import datetime import tensorflow as tf import math import time batch_size = 32 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): # 输入的通道数 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
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:
这里我没有使用GPU加速,所以速度比较缓慢。