TensorFlow学习－－GoogLeNet实现
GoogLeNet结构
Google Inception Net通常被称为Google Inception V1,在ILSVRC-2014比赛中由论文＜Going deeper with convolutions＞提出．
Inception V1有22层，比AlexNet的8层和VGGNet的19层还要深．参数量（500万）仅有AlexNet参数量（6000万）的1/12，但准确率远胜于AlexNet的准确率．
Inception V1降低参数量的目的：
1. 参数越多模型越庞大，需要模型学习的数据量就越大，且高质量的数据非常昂贵．
2. 参数越多，消耗的计算资源越多．
Inception V1网络的特点：
1. 模型层数更深（22层），表达能力更强．
2. 去除最后的全连接层，用全局平均池化层（即将图片尺寸变为1x1）来代替它．（借鉴了NIN）
3. 使用Inception Module提高了参数利用效率．
Inception V3中的种类：
Inception Module结构图：
1x1卷积可以跨通道组织信息，提高网络的表达能力，同时可以对输出通道升维和降维．Inception Module的4个分支都用到了1x1卷积来进行降维．第一个分支对输入进行了1x1卷积;第二个分支先使用1x1卷积然后连接3x3卷积，相当于进行了两次特征变换;第三个分支先使用了1x1卷积，然后使用5x5卷积;第四个分支是3x3最大池化后用1x1卷积．1x1卷积用很小的计算量就能增加一层特征变换和非线性化．Filter concatenation层将1×1/3×3/5×5的卷积结果连接起来（在输出通道数这个维度上聚合）．这样防止了层数增多带来的计算资源的需求，使网络的宽度和深度均可扩大．
将高度相关的节点连接在一起，形成稀疏网络：
网络模型如图：
GoogLeNet性能测试
import tensorflow as tf
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
# 生成默认参数
def inception_v3_arg_scope(weight_decay=0.00004, # L2正则weight_decay
stddev=0.1, # 标准差
batch_norm_var_collection='moving_vars'):
batch_norm_params = {
'decay': 0.9997,
'epsilon':0.001,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
'variables_collections':{
'beta': None,
'gamma': None,
'moving_mean': [batch_norm_var_collection],
'moving_variance': [batch_norm_var_collection],
}
}
# 提供了新的范围名称scope name
# 对slim.conv2d和slim.fully_connected两个函数的参数自动赋值
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope(
[slim.conv2d], # 对卷积层的参数赋默认值
weights_initializer=tf.truncated_normal_initializer(stddev=stddev), # 权重初始化器
activation_fn=tf.nn.relu, # 激活函数用ReLU
normalizer_params=batch_norm_params) as sc: # 标准化器参数用batch_norm_params
return sc
# inputs为输入图片数据的tensor(299x299x3),scope为包含了函数默认参数的环境
def inception_v3_base(inputs, scope=None):
# 保存某些关键节点
end_points = {}
# 定义InceptionV3的网络结构
with tf.variable_scope(scope, 'InceptionV3', [inputs]):
# 设置卷积/最大池化/平均池化的默认步长为1,padding模式为VALID
# 设置Inception模块组的默认参数
with slim.arg_scope([slim.conv2d, # 创建卷积层
slim.max_pool2d, # 输出的通道数
slim.avg_pool2d], # 卷积核尺寸
stride=1, # 步长
padding='VALID'): # padding模式
# 经3个3x3的卷积层后,输入数据(299x299x3)变为(35x35x192),空间尺寸降低,输出通道增加
net = slim.conv2d(inputs, 32, [3,3], stride=2, scope='Conv2d_1a_3x3')
net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
# 设置卷积/最大池化/平均池化的默认步长为1,padding模式为SAME
# 步长为1,padding模式为SAME,所以图像尺寸不会变,仍为35x35
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
# 设置Inception Moduel名称为Mixed_5b
with tf.variable_scope('Mixed_5b'):
# 第1个分支：64输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：48输出通道的1x1卷积,连接64输出通道的5x5卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1, 1], scope='Con2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
# 第3个分支：64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
# 第4个分支：3x3的平均池化,连接32输出通道的1x1卷积
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
# 4个分支输出通道数之和＝64+64+96+32＝256,输出tensor为35x35x256
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第1个Inception模块组的第2个Inception Module
with tf.variable_scope('Mixed_5c'):
# 第1个分支：64输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：48输出通道的1x1卷积,连接64输出通道的5x5卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0c_5x5')
# 第3个分支：64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
# 第4个分支：3x3的平均池化,连接64输出通道的1x1卷积
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸为35x35x288
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第1个Inception模块组的第3个Inception Module
with tf.variable_scope('Mixed_5d'):
# 第1个分支：64输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：48输出通道的1x1卷积,连接64输出通道的5x5卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
# 第3个分支：64输出通道的1x1卷积,连接两个96输出通道的3x3卷积
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
# 第4个分支：3x3的平均池化,连接64输出通道的1x1卷积
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸为35x35x288
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第2个Inception模块组
with tf.variable_scope('Mixed_6a'):
# 第1个分支：3x3卷积,步长为2,padding模式为VALID,因此图像被压缩为17x17
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 384, [3, 3], stride=2 , padding='VALID', scope='Conv2d_1a_1x1')
# 第2个分支：64输出通道的1x1卷积,连接2个96输出通道的3x3卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
# 步长为2,padding模式为VALID,因此图像被压缩为17x17
branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')
# 第3个分支：3x3的最大池化层,步长为2,padding模式为VALID,因此图像被压缩为17x17x256
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
net = tf.concat([branch_0, branch_1, branch_2], 3)
# 第2个Inception模块组,包含5个Inception Module
with tf.variable_scope('Mixed_6b'):
# 第1个分支：192输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：128输出通道的1x1卷积,接128输出通道的1x7卷积,接192输出通道的7x1卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸=17x17x(192+192+192+192)=17x17x768
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 经过一个Inception Module输出tensor尺寸不变,但特征相当于被精炼类一遍
# 第3个Inception模块组
with tf.variable_scope('Mixed_6c'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸为17x17x768
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第4个Inception模块组
with tf.variable_scope('Mixed_6d'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸为17x17x768
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第5个Inception模块组
with tf.variable_scope('Mixed_6e'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸为17x17x768
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 将Mixed_6e存储于end_points中
end_points['Mixed_6e'] = net
# 第3个Inception模块
# 第1个Inception模块组
with tf.variable_scope('Mixed_7a'):
# 第1个分支：192输出通道的1x1卷积,接320输出通道的3x3卷积 步长为2
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv2d_0a_3x3')
# 第2个分支：4个卷积层
with tf.variable_scope('Branch_1'):
# 192输出通道的1x1卷积
branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
# 192输出通道的1x7卷积
branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
# 192输出通道的7x1卷积
branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
# 192输出通道的3x3卷积 步长为2,输出8x8x192
branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
# 第3个分支：3x3的最大池化层,输出8x8x768
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
# 输出tensor尺寸：8x8x(320+192+768)=8x8x1280,尺寸缩小,通道数增加
net = tf.concat([branch_0, branch_1, branch_2], 3)
# 第2个Inception模块组
with tf.variable_scope('Mixed_7b'):
# 第1个分支：320输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：384输出通道的1x1卷积
# 分支内拆分为两个分支：384输出通道的1x3卷积+384输出通道的3x1卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat([
slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
# 第3个分支：448输出通道的1x1卷积,接384输出通道的3x3卷积,分支内拆分为两个分支
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
# 分支内拆分为两个分支：384输出通道的1x3卷积+384输出通道的3x1卷积
branch_2 = tf.concat([
slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
# 第4个分支：3x3的平均池化层,接192输出通道的1x1卷积,输出8x8x768
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸：8x8x(320+768+768+192)=8x8x2048
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 第3个Inception模块组
with tf.variable_scope('Mixed_7c'):
# 第1个分支：320输出通道的1x1卷积
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
# 第2个分支：384输出通道的1x1卷积
# 分支内拆分为两个分支：384输出通道的1x3卷积+384输出通道的3x1卷积
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat([
slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
# 第3个分支：448输出通道的1x1卷积,接384输出通道的3x3卷积,分支内拆分为两个分支
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
# 分支内拆分为两个分支：384输出通道的1x3卷积+384输出通道的3x1卷积
branch_2 = tf.concat([
slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
# 第4个分支：3x3的平均池化层,接192输出通道的1x1卷积,输出8x8x768
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
# 输出tensor尺寸：8x8x(320+768+768+192)=8x8x2048
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
return net, end_points
# 全局平均池化
def inception_v3(inputs,
num_classes=1000, # 最后分类数量
is_training=True, # 是否是训练过程的标志
dropout_keep_prob=0.8, # Dropout保留节点的比例
prediction_fn=slim.softmax,# 进行分类的函数
spatial_squeeze=True, # 是否对输出进行squeeze操作,即去除维数为1的维度
reuse=None, # tf.variable_scope的reuse默认值
scope='InceptionV3'): # tf.variable_scope的scope默认值
with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
net, end_points = inception_v3_base(inputs, scope=scope)
# 设置卷积/最大池化/平均池化的默认步长为1,padding模式为SAME
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
aux_logits = end_points['Mixed_6e']
# 辅助分类节点
with tf.variable_scope('AuxLogits'):
# 5x5的平均池化,步长设为3,padding模式设为VALID
aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_1a_5x5')
aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv2d_1b_1x1')
aux_logits = slim.conv2d(aux_logits,768, [5, 5], weights_initializer=trunc_normal(0.01), padding='VALID', scope='Conv2d_2a_5x5')
aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, weights_initializer=trunc_normal(0.001), scope='Conv2d_2b_1x1')
if spatial_squeeze:
# 进行squeeze操作,去除维数为1的维度
aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
# 处理正常的分类预测
with tf.variable_scope('Logits'):
# 8x8的平均池化层
net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8')
# Dropout层
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
end_points['PreLogits'] = net
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn= None, normalizer_fn=None,scope='Conv2d_1c_1x1')
if spatial_squeeze:
# 进行squeeze操作,去除维数为1的维度
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
# 辅助节点
end_points['Logits'] = logits
# 利用Softmax对结果进行分类预测
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
import math
from datetime import datetime
import time
# 评估每轮计算占用的时间
# 输入TensorFlow的Session,需要测评的算子target,测试的名称info_string
def time_tensorflow_run(session, target, info_string):
# 定义预热轮数(忽略前10轮,不考虑显存加载等因素的影响)
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)
# 持续时间
duration = time.time()- start_time
if i >= num_steps_burn_in:
# 只考量10轮迭代之后的计算时间
if not i % 10:
print '%s: step %d, duration = %.3f' % (datetime.now().strftime('%X'), i - num_steps_burn_in, duration)
# 记录总时间
total_duration += duration
total_duration_squared += duration * duration
# 计算每轮迭代的平均耗时mn,和标准差sd
mn = total_duration / num_batches
vr = total_duration_squared / num_batches - mn * mn
sd = math.sqrt(vr)
# 打印出每轮迭代耗时
print '%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now().strftime('%X'), info_string, num_batches, mn, sd)
# Inception V3运行性能测试
if __name__ == '__main__':
batch_size = 32
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception_v3_arg_scope()):
# 传入inputs获取logits,end_points
logits, end_points = inception_v3(inputs, is_training=False)
# 初始化
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
num_batches = 100
# 测试Inception V3的forward性能
time_tensorflow_run(sess, logits, 'Forward')输出：
08:44:25: step 0, duration = 12.335
08:46:28: step 10, duration = 12.286
08:48:37: step 20, duration = 12.260
08:50:40: step 30, duration = 12.259
08:52:42: step 40, duration = 12.275
08:54:47: step 50, duration = 12.415
08:56:53: step 60, duration = 12.824
08:59:38: step 70, duration = 17.708
09:03:55: step 80, duration = 29.921
09:08:59: step 90, duration = 37.042
09:14:36: Forward across 100 steps, 18.237 +/- 8.856 sec / batchGoogLeNet相关连接
论文连接：Going deeper with convolutions
Large Scale Visual Recognition Challenge 2014 (ILSVRC2014)