TensorFlow实战学习笔记（14）------VGGNet

一、VGGNet：5段卷积【每段有2~3个卷积层+最大池化层】【每段过滤器个数：64-128-256-512-512】

每段的2~3个卷积层串联在一起的作用：

2个3×3的卷积层串联的效果相当于一个5×5的卷积层，即一个像素会跟周围5×5的像素产生关联。【28*28的输入经过一次5*5得到24*24，s=1,p=0,（28-5）/1 + 1 = 24。而28*28经过2个3*3也可以得到24*24.】

3个3×3的卷积层串联的效果相当于一个7×7的卷积层，

• 好处一：3个3×3的卷积层串联拥有的餐数量比1个7×7的参数量少。只是后者的：（3×3×3）/ （7 × 7） = 55 %。
• 好处二：3个3×3的卷积层拥有比1个7×7的卷积层更多的线性变换（如，前者可以使用三次Relu函数，后者只有一次），使得CNN对特征的学习能力更强。

VGG探索了卷积神经网络的深度与其性能之间的关系，反复堆叠3×3的小型卷积核和2×2的最大池化层，构筑了16~19层深度的卷积神经网络。

二、VGG训练的技巧：

1. 先训练级别A的简单网络，再复用A网络的权重来初始化后面的几个复杂模型，这样训练收敛的速度更快。
2. 在预测时，VGG采用Multi-Scale的方法，将图像scale到一个尺寸Q，并将图片输入卷积网络计算。然后在最后一个卷积层使用滑窗的方式进行分类预测，将不同窗口的分类结果平均，再将不同尺寸Q的结果平均得到最后结果。提高数据利用率和预测准确率
3. 采用了Multi-scale做数据增强，防止过拟合

三、代码：

#加载模块
from datetime import datetime
import math
import time
import tensorflow as tf

#定义函数：卷积层、池化层、全连接层
#conv_op用来创建卷积层
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:
        w = 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,w,strides = [1,dh,dw,1],padding = 'SAME')
        b = tf.Variable(tf.constant(0.0,shape = [n_out] , dtype = tf.float32),trainable = True , name = 'b')
        z = tf.nn.bias_add(conv,b)
        activation = tf.nn.relu(z,name = scope)
        p+=[w,b]
        return activation

#用来创建全连接层
def fc_op(input_op,name,n_out,p):
    n_in = input_op.get_shape()[-1].value
    with tf.name_scope(name) as scope:
        w = tf.get_variable(scope+'w',shape = [n_in,n_out],dtype = tf.float32,
                            initializer= tf.contrib.layers.xavier_initializer())
        b = tf.Variable(tf.constant(0.1,shape = [n_out],dtype = tf.float32),name = 'b')
        activation = tf.nn.relu_layer(input_op,w,b,name = scope)
        p += [w,b]
        return activation

#用来创建池化层
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)

#建立VGG模型
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

#时间差
def time_tensorflow_run(session,target,feed,info_string):

        num_steps_burn_in=10

        total_duration=0.0

        total_duration_squared=0.0

        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:

                        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

        vr=total_duration_squared/num_batches-mn*mn

        sd=math.sqrt(vr)

        print('%s:%s across %d steps,%.3f +/- %.3f sec / batch' % (datetime.now(),info_string,num_batches,mn,sd))

#预测
def run_benchmark():

        with tf.Graph().as_default():

                image_size=224

                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)

                predictions,softmax,fc8,p=inference_op(images,keep_prob)

                init=tf.global_variables_initializer()

                sess=tf.Session()

                sess.run(init)

                time_tensorflow_run(sess,predictions,{keep_prob:1.0},"Forward")

                objective=tf.nn.l2_loss(fc8)

                grad=tf.gradients(objective,p)

                time_tensorflow_run(sess,grad,{keep_prob:0.5},"Forward-backward")

#训练
batch_size=32

num_batches=100

run_benchmark()