图像识别与卷积神经网络

6.1 图像识别问题简介及经典数据集

CIFAR 数据集就是一个影响力很大的图像分类数据集。CIFAR数据集分为了CIFAR-10 和 CIFAR-100 两个问题，它们都是图像词典项目( Visual Dictionary ) 中 800 万张图片的一个子集。CIFAR数据集中的图片为32×32的彩色图片。

CIFAR-10 问题收集了来自 10 个不一样种类的 60000 张图片。每张图片大小固定且仅含一个种类的实体。与MNIST相比，最大区别是图片由黑白变成彩色，且分类难度更高。

不管是 MNIST 数据集仍是 CIFAR 数据集，相比真实环境下的图像识别问题，有 2 个最大的问题。第一，现实生活中的图片分辨率要远高于 32 × 32，并且图像的分辨率也不会是固定的。第二，现实生活中的物体类别不少，不管是 10 种仍是 100 种都远远不够，并且一张图片中不会只出现一个种类的物体。

ImageNet很大程度上解决了这两个问题，更加贴近真实环境下的图像识别问题。

ImageNet是一个基于WordNet的大型图像数据库。有将近1500万图片被关联到了WordNet的大约2000个名词同义词集上。每个与ImageNet相关的WordNet同义词集都表明了现实世界中的一个实体，能够被认为是分类问题中的一个类别。一张图片中可能有多个同义词集所表明的实体。

ILSVRC2012图像分类数据集是ImageNet的子集，包含了来自1000个类别的120万张图片，其中每张图片只属于一个类别。图片是直接从网上爬取的，因此图片的大小从几千字节到几百万字节不等。

top-N正确率是指图像识别算法给出前N个答案中有一个是正确的几率。在图像分类问题上，不少学术论文都将前N个答案的正确率做为比较的方法，其中N的取值通常为3或5。

 

6.2 卷积神经网络简介

在全链接神经网络中，每相邻两层之间的节点都有边相连，因而通常会将每一层全链接层中的节点组织成一列，这样方便显示链接结构。而对于卷积神经网络，相邻两层之间只有部分节点相连，通常会将每一层卷积层的节点组织成一个三维矩阵。虽然直观上差别很大，实际上总体架构很是类似，并且输入输出、训练流程也基本一致。两者惟一的区别在于神经网络中相邻两层的链接方式。

    使用全链接神经网络处理图像的最大问题在于全链接层的参数太多，参数增多除了致使计算速度减慢，还很容易致使过拟合问题。卷积神经网络的目的就是为了减小参数个数。

卷积神经网络中前几层中每一个节点只和上一层中的部分节点相连。

卷积神经网络的五种结构：

　　1.输入层：一张图片的像素矩阵，长×宽×深度(色道)

　　2.卷积层：卷积层中每一个节点的输入只是上一层神经网络的一小块，这个小块的经常使用大小有3×3或5×5。卷积层试图将神经网络中的每一小块进行更深刻地分析从而获得抽象程度更高的特征。通常来讲，经过卷积层处理过的节点矩阵深度会增长。

　　3.池化层：不改变三维矩阵的深度，但能够缩小矩阵的大小。池化操做能够认为是将一张分辨率较高的图片转化为分辨率较低的图片。经过池化层，能够进一步缩小最后全链接层中节点的个数，从而达到减小整个神经网络中参数的目的。

　　4.全链接层：通过几轮卷积层和池化层的处理以后，能够认为图像中的信息已经被抽象成了信息含量更高的特征。在卷积层和池化层完成自动图像特征提取以后，仍然须要全链接层完成分类任务。

　　5.Softmax层：用于分类问题。经过Softmax层，能够获得当前样例属于不一样种类的几率分布状况。

 

6.3 卷积神经网络经常使用结构

卷积层神经网络结构中最重要的部分是过滤器(filter)或者内核(kernel)，过滤器能够把当前层神经网络上的一个子节点矩阵转化为下一层神经网络上的一个单位节点矩阵，即长宽为1，深度不限的节点矩阵。

过滤器所处理的节点矩阵的长和宽都是人工指定的，这个节点矩阵的尺寸也被称为过滤器的尺寸。经常使用尺寸有3×3或5×5。由于过滤器处理的矩阵深度和当前层神经网络节点矩阵的深度是一致的，因此虽然节点矩阵是三维的，但过滤器的尺寸只需指定两个维度。

过滤器中另一个须要人工指定的设置是处理获得的单位节点矩阵的深度，称为过滤器的深度。

(局部)过滤器的前向传播过程就是经过左侧小矩阵中的节点计算出右侧单位节点矩阵中节点的过程。与全链接层相似，也是权重和偏置项。如图6-8

(总体)卷积层的前向传播过程就是经过将一个过滤器从神经网络当前层的左上角移动到右下角，而且在移动中计算每个对应的单位矩阵获得的。如图6-10

过滤器每移动一次，能够计算出一个值(当深度为 k 时会计算出 k 个值)，将这些数值拼接成一个新的矩阵，就完成了卷积层前向传播的过程。

当过滤器的大小不为 1×1 时，卷积层前向传播获得的矩阵的尺寸要小于当前层矩阵的尺寸。

为了不尺寸的变化，能够在当前层矩阵的边界上加入全0填充。如图6-11

除了使用全0填充，还能够经过设置过滤器移动的步长来调整结果矩阵的大小。图6-12显示了当移动步长为2且使用全0填充时，卷积层前向传播的过程。

输出层大小的肯定：

　　使用全0填充时，向上取整

　　　　out_length =in_length / stride_length

　　　　out_width = in_width / stride_width

　　不使用全0填充时，向上取整

　　　　out_length = (in_length - filter_length +1) / stride_length

　　　　out_width = (in_width - filter_width + 1) / stride_width

卷积神经网络有一个很是重要的性质就是每个卷积层中使用的过滤器中的参数相同，这可使得图像上的内容不受位置的影响。以mnist手写体数字识别为例，不管数字“1”出如今左上角仍是右下角，图片的种类都是不变的。并且，共享每一个卷积层中过滤器的参数能够巨幅减小神经网络上的参数。以CIFAR-10问题为例，输入层矩阵的维度为32×32×3，假设卷积层使用的过滤器尺寸为5×5，深度为16，那么这个卷积层的参数个数为5*5*3*16+16=1216个(能够想象为输入层为5*5*三、输出层为16*1的全链接层)。并且，参数个数只与过滤器的尺寸、深度以及当前层节点矩阵的深度有关，而与图片大小无关，这使得卷积神经网络能够很好地扩展到更大的图像数据上。

经过tensorflow实现卷积层的前向传播，

 1 x = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name='x-input')
 2 # shape分别为过滤器尺寸、当前层深度、过滤器深度
 3 filter_weight = tf.get_variable(
 4     'weights', shape=[5, 5, 3, 16], initializer=tf.truncated_normal_initializer(stddev=0.1)
 5 )
 6 biases = tf.get_variable(
 7     'biases', shape=[16], initializer=tf.constant_initializer(0.1)  # shape为过滤器深度
 8 )
 9 # 第一个输入为当前层的节点矩阵，该矩阵为四维矩阵，第一个维度对应一个输入batch, 后三个维度对应一个节点矩阵(长*宽*深)
10 # 第二个输入为卷积层的权重，也就是过滤器
11 # 第三个输入为不一样维度上的步长，长度为4的数组，要求第一维度和第四维度必定为1, 由于卷积层的步长只对矩阵的长和宽有效
12 # 第四个输入为padding, 取值能够为SAME或VALID
13 conv = tf.nn.conv2d(
14     x, filter_weight, strides=[1, 1, 1, 1], padding='SAME'
15 )
16 # print(conv.shape)  # (?, 32, 32, 16)  # 深度变成16, 根据公式，使用全0填充时为32, 不使用时为28
17 
18 # 不能直接使用加法，由于矩阵上不一样位置的节点都须要加上一样的偏置项。
19 # 例如图6-13所示, 虽然下一层神经网络的大小为 2×2, 可是偏置项只有一个数(由于深度为1), 而2×2矩阵中的每个值都须要加上这个偏置项。
20 bias = tf.nn.bias_add(conv, biases)
21 actived_conv = tf.nn.relu(bias)
22 
23 # 注意区分输入的四个维度、权重的四个维度、步长的四个维度。

6.3.2 池化层

池化层主要用于减少矩阵的尺寸，从而减小最后全链接层中的参数。使用池化层既能够加快计算速度也有防止过拟合问题的做用。

池化层的前向传播过程也是经过移动一个相似过滤器的结构完成的。但池化层过滤器中的计算不是节点的加权和，而是采用更简单的最大值或平均值运算。使用最大值操做的池化层称为最大池化层，使用平均值操做的池化层称为平均池化层。

与卷积层的过滤器相似，池化层的过滤器也须要人工设定过滤器的尺寸、是否使用全0填充以及过滤器移动的步长等。卷积层和池化层中过滤器的移动方式是类似的，惟一的区别在于卷积层使用的过滤器是横跨整个深度的，而池化层使用的过滤器只影响一个深度上的节点。因此池化层的过滤器除了在长和宽上移动，还须要在深度上移动。

卷积层，深度为3，3个相加

池化层，深度为2，分别处理

 

经过tensorflow实现池化层的前向传播，

1 # 第一个输入为当前层节点矩阵(四维矩阵)
2 # 第二个为过滤器尺寸，长度为4的数组，第一维度和第四维度必须为1，这意味着过滤器不可跨不一样输入样例和节点矩阵深度，使用最多的是[1,2,2,1]或[1,3,3,1]  # 与卷积层不一样
3 # 第三个输入为步长，长度为4的数组，第一维度和第四维度必须为1，这意味着池化层不能减小节点矩阵的深度或输入样例的个数。
4 # 第四个输入为padding
5 pool = tf.nn.max_pool(actived_conv, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')

卷积层和池化层的最大不一样在于过滤器：[5, 5, 3, 16]  [1, 3, 3, 1]

 

6.4 经典卷积网络模型

6.4.1 LeNet-5模型

第一个成功应用于数字识别问题的卷积神经网络。

LetNet-5模型接受的输入层大小为三维矩阵(长×宽×深)。

参数个数远远小于链接个数，但卷积层的链接个数？？没搞懂为啥还要加1。

只有全链接层的权重须要加入正则化。

relu和dropout不在最后一层使用。

  1 # mnist_inference.py
  2 
  3 import tensorflow as tf
  4 
  5 IMAGE_SIZE = 28
  6 NUM_CHANNELS = 1  # 黑白
  7 NUM_LABELS = 10
  8 
  9 # 第一层卷积层的尺寸和深度
 10 CONV1_SIZE = 5
 11 CONV1_DEEP = 32
 12 # 第二层卷积层的尺寸和深度
 13 CONV2_SIZE = 5
 14 CONV2_DEEP = 64
 15 # 全链接层的节点个数
 16 FC_SIZE = 512
 17 
 18 def get_weight_variable(shape, regularizer):
 19     weights = tf.get_variable('weight', shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
 20     if regularizer:
 21         tf.add_to_collection('losses', regularizer(weights))
 22     return weights
 23 
 24 def inference(input_tensor, train, regularizer):
 25     with tf.variable_scope('layer1-conv1'):
 26         # 输入层为28×28×1，尺寸为5×5，深度为32，步长为1，输出层为28×28×32
 27         conv1_weights = get_weight_variable([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP], None)
 28         conv1_biases = tf.get_variable('bias', [CONV1_DEEP], initializer=tf.constant_initializer(0.0))
 29         conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
 30         relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
 31 
 32     with tf.name_scope('layer2-pool1'):
 33         # 输出层为14*14*32
 34         pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 35 
 36     with tf.variable_scope('layer3-conv2'):
 37         # 尺寸为5*5，深度为64，输出层为14*14*64
 38         conv2_weights = get_weight_variable([CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP], None)
 39         conv2_biases = tf.get_variable('bias', [CONV2_DEEP], initializer=tf.constant_initializer(0.0))
 40         conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
 41         relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
 42 
 43     with tf.name_scope('layer4-pool2'):
 44         # 输出层为7*7*64
 45         pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 46 
 47     # 全链接层的输入格式为特征向量，这就须要将三维矩阵拉直成一维向量。
 48     pool_shape = pool2.get_shape().as_list()  # 包含一个batch
 49     nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]  # 3136
 50     reshaped = tf.reshape(pool2, [pool_shape[0], nodes])
 51 
 52     # dropout在训练时会随机将部分节点的输出改成0。dropout方法能够进一步提高模型可靠性并防止过拟合，dropout过程只在训练时使用。
 53     with tf.variable_scope('layer5-fc1'):
 54         # 只有全链接层的权重须要加入正则化
 55         fc1_weights = get_weight_variable([nodes, FC_SIZE], regularizer)
 56         fc1_biases = tf.get_variable('bias', shape=[FC_SIZE], initializer=tf.constant_initializer(0.1))
 57         fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
 58         if train:
 59             fc1 = tf.nn.dropout(fc1, 0.5)
 60 
 61     with tf.variable_scope('layer6-fc2'):
 62         fc2_weights = get_weight_variable([FC_SIZE, NUM_LABELS], regularizer)
 63         fc2_biases = tf.get_variable('bias', shape=[NUM_LABELS], initializer=tf.constant_initializer(0.1))
 64         logit = tf.matmul(fc1, fc2_weights) + fc2_biases
 65 
 66     # relu和dropout不在最后一层使用。  后面会使用sparse_softmax_cross_entropy_with_logits计算交叉熵。
 67     return logit
 68 
 69 
 72 # mnist_train.py
 73 
 74 #!coding:utf8
 75 import tensorflow as tf
 76 from tensorflow.examples.tutorials.mnist import input_data
 77 import mnist_inference
 78 import os
 79 import numpy as np
 80 
 81 BATCH_SIZE = 100
 82 
 83 LEARNING_RATE_BASE = 0.8
 84 LEARNING_RATE_DECAY = 0.99
 85 REGULARIZATION_RATE = 0.0001  # lambda
 86 TRAINING_STEPS = 30000
 87 MOVING_AVERAGE_DACAY = 0.99
 88 
 89 MODEL_SAVE_PATH = '/home/yangxl/files/save_model'
 90 MODEL_NAME = 'conv2d.ckpt'
 91 
 92 
 93 def train(mnist):
 94     # 由于从池化层到全链接层要进行reshape，因此不能为shape[0]不能为None。
 95     x = tf.placeholder(tf.float32, [BATCH_SIZE, mnist_inference.IMAGE_SIZE, mnist_inference.IMAGE_SIZE, mnist_inference.NUM_CHANNELS], 'x-input')
 96     y_ = tf.placeholder(tf.float32, [BATCH_SIZE, mnist_inference.NUM_LABELS], 'y-input')
 97 
 98     # 正则化
 99     from tensorflow.contrib.layers import l2_regularizer
100     regularizer = l2_regularizer(REGULARIZATION_RATE)
101 
102     y = mnist_inference.inference(x, True, regularizer)
103 
104     global_step = tf.Variable(0, trainable=False)
105 
106     # 滑动平均
107     variables_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DACAY, global_step)
108     variables_averages_op = variables_averages.apply(tf.trainable_variables())
109     # 互斥分类问题；
110     # 由于标准答案是一个长度为10的一维数组，而该函数须要提供的是一个正确答案的数字，因此须要使用tf.argmax 函数来获得正确答案对应的类别编号。
111     cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
112     cross_entropy_mean = tf.reduce_mean(cross_entropy)
113     loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
114 
115     learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE,
116                                                LEARNING_RATE_DECAY, staircase=True)
117     train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step)
118     with tf.control_dependencies([train_step, variables_averages_op]):
119         train_op = tf.no_op(name='train')
120 
121     saver = tf.train.Saver()
122 
123     with tf.Session() as sess:
124         tf.global_variables_initializer().run()
125 
126         for i in range(TRAINING_STEPS):
127             xs, ys = mnist.train.next_batch(BATCH_SIZE)  # xs.shape=(100, 784)
128             reshaped_xs = np.reshape(xs, [BATCH_SIZE, mnist_inference.IMAGE_SIZE, mnist_inference.IMAGE_SIZE, mnist_inference.NUM_CHANNELS])
129             _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})
130 
131             if i % 1000 == 0:
132                 print('after %d training steps, loss on training batch is %g ' % (i, loss_value))
133                 saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
134 
135 
136 def main(argv=None):
137     mnist = input_data.read_data_sets('/home/yangxl/files/mnist', one_hot=True)
138     import time
139     # print('start...', int(time.time()))
140     train(mnist)
141     # print(int(time.time()))
142 
143 
144 if __name__ == '__main__':
145     tf.app.run()
146 
147 
150 # mnist_eval.py
151 
152 #!coding:utf8
153 import tensorflow as tf
154 from tensorflow.examples.tutorials.mnist import input_data
155 import mnist_inference
156 import mnist_train
157 import time
158 import numpy as np
159 
160 # 每10秒加载一次最新模型，并在测试数据上测试最新模型的正确率。
161 EVAL_INTERVAL_SECS = 60
162 
163 def evaluate(mnist):
164     x = tf.placeholder(tf.float32, [mnist.test.num_examples, mnist_inference.IMAGE_SIZE, mnist_inference.IMAGE_SIZE, mnist_inference.NUM_CHANNELS], 'x-input')
165     y_ = tf.placeholder(tf.float32, [mnist.test.num_examples, mnist_inference.NUM_LABELS], 'y-input')
166 
167     y = mnist_inference.inference(x, False, None)
168 
169     correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
170     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
171 
172     # 滑动平均
173     variables_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DACAY)
174     variables_to_restore = variables_averages.variables_to_restore()
175 
176     saver = tf.train.Saver(variables_to_restore)  # 训练时须要保存滑动平均模型，验证时才能加载到。
177 
178     while True:
179         with tf.Session() as sess:
180             reshape_x = np.reshape(mnist.test.images, [-1, 28, 28, 1])
181             validate_feed = {x: reshape_x, y_: mnist.test.labels}
182 
183             # 经过checkpoint文件自动找到目录中最新模型的文件名
184             ckpt = tf.train.get_checkpoint_state(mnist_train.MODEL_SAVE_PATH)
185             if ckpt and ckpt.model_checkpoint_path:
186                 saver.restore(sess, ckpt.model_checkpoint_path)
187 
188                 global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
189                 accuracy_score = sess.run(accuracy, feed_dict=validate_feed)
190                 print('after %s training steps, validation accuracy = %g ' % (global_step, accuracy_score))
191             else:
192                 print('No checkpoint file found')
193                 return
194         time.sleep(EVAL_INTERVAL_SECS)
195 
196 
197 def main(argv=None):
198     mnist = input_data.read_data_sets('/home/yangxl/files/mnist', one_hot=True)
199     print('start...')
200     evaluate(mnist)
201 
202 
203 if __name__ == '__main__':
204     tf.app.run()

代码么问题，损失函数不固定，准确率大约为0.117，应该大约为99.4%才对啊。

一种卷积神经网络架构不能解决全部问题。好比，LeNet-5模型就没法很好地处理相似ImageNet这样比较大的图像数据集。

如下正则表达式公式总结了一些经典的用于图片分类问题的卷积神经网络架构：输入层 --> (卷积层+ --> 池化层?)+ --> 全链接层+

大部分卷积神经网络中通常最多连续使用三层卷积层。

在多轮卷积层和池化层以后，卷积神经网络在输出以前通常会通过1~2个全链接层。

在过滤器的深度上，大部分卷积神经网络都采用逐层递增的方式。

6.4.2 Inception-v3模型

LeNet-5模型中，不一样卷积层经过串联的方式链接在一块儿，而Inception-v3模型中，inception结构是将不一样卷积层经过并联的方式结合在一块儿。

在6.4.1中提到了一个卷积层可使用边长为一、3或5的过滤器，那么如何在这些边长中选择呢？inception模型给出了一个方案，那就是同时使用全部不一样尺寸的过滤器，而后再将获得的矩阵拼接起来。

虽然过滤器的尺寸不一样，但若是全部的过滤器都使用全0填充而且步长为1，那么前向传播获得的结果矩阵的长和宽都与输入矩阵一致。这样通过不一样过滤器处理的结果矩阵能够拼接成一个更深的矩阵。

Inception-v3 模型总共有46 层(图中方框里的层数)，由 11 个(图中方框) Inception 模块组成。在 Inception-v3 模型中有 96 个卷积层。

inception-v3模型的代码和slim库，

  1 import tensorflow as tf
  2 import tensorflow.contrib.slim as slim
  3 
  4 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
  5 
  6 def inception_v3_base(inputs,
  7                       final_endpoint='Mixed_7c',
  8                       min_depth=16,
  9                       depth_multiplier=1.0,
 10                       scope=None):
 11     end_points = {}
 12 
 13     if depth_multiplier <= 0:
 14         raise ValueError('depth_multiplier is not greater than zero.')
 15     depth = lambda d: max(int(d * depth_multiplier), min_depth)
 16 
 17     with tf.variable_scope(scope, 'InceptionV3', [inputs]):
 18         # arg_scope用于设置默认的参数取值
 19         with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
 20                        stride=1,
 21                        padding='VALID'):
 22             # 299 × 299 × 3
 23             end_point = 'Conv2d_1a_3x3'  # 字母数字下划线，乘号用x代替
 24             # 不使用全0填充
 25             net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
 26             end_points[end_point] = net
 27             if end_point == final_endpoint:
 28                 return net, end_points
 29             # 149 × 149 × 32
 30             end_point = 'Conv2d_2a_3x3'
 31             # 不使用全0填充，步长为1
 32             net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
 33             end_points[end_point] = net
 34             if end_point == final_endpoint:
 35                 return net, end_points
 36             # 147 × 147 × 32
 37             end_point = 'Conv2d_2b_3x3'
 38             net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
 39             end_points[end_point] = net
 40             if end_point == final_endpoint:
 41                 return net, end_points
 42             # 147 × 147 × 64
 43             end_point = 'MaxPool_3a_3x3'
 44             net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
 45             end_points[end_point] = net
 46             if end_point == final_endpoint:
 47                 return net, end_points
 48             # 73 × 73 × 64
 49             end_point = 'Conv2d_3b_1x1'
 50             net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
 51             end_points[end_point] = net
 52             if end_point == final_endpoint:
 53                 return net, end_points
 54             # 73 × 73 × 80
 55             end_point = 'Conv2d_4a_3x3'
 56             net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
 57             end_points[end_point] = net
 58             if end_point == final_endpoint:
 59                 return net, end_points
 60             # 71 × 71 × 192
 61             end_point = 'MaxPool_5a_3x3'
 62             net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
 63             end_points[end_point] = net
 64             if end_point == final_endpoint:
 65                 return net, end_points
 66             # 35 × 35 × 192
 67 
 68         # Inception blocks
 69         with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
 70                             stride=1,
 71                             padding='SAME'):
 72             # mixed: 35 × 35 × 256
 73             end_point = 'Mixed_5b'
 74             with tf.variable_scope(end_point):
 75                 with tf.variable_scope('Branch_0'):
 76                     branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
 77                 with tf.variable_scope('Branch_1'):
 78                     branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
 79                     branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_1x1')
 80                 with tf.variable_scope('Branch_2'):
 81                     branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
 82                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_1x1')
 83                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_1x1')
 84                 with tf.variable_scope('Branch_3'):
 85                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
 86                     branch_3 = slim.conv2d(branch_3, depth(32), [1, 1], scope='Conv2d_0b_1x1')
 87                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 88             end_points[end_point] = net
 89             if end_point == final_endpoint:
 90                 return net, end_points
 91 
 92             # mixed_1: 35 × 35 × 288
 93             end_point = 'Mixed_5c'
 94             with tf.variable_scope(end_point):
 95                 with tf.variable_scope('Branch_0'):
 96                     branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
 97                 with tf.variable_scope('Branch_1'):
 98                     branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
 99                     branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv_1_0c_1x1')
100                 with tf.variable_scope('Branch_2'):
101                     branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
102                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3')
103                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3')
104                 with tf.variable_scope('Branch_3'):
105                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
106                     branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1')
107                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
108             end_points[end_point] = net
109             if end_point == final_endpoint:
110                 return net, end_points
111 
112             # mixed_2: 35 × 35 × 288
113             end_point = 'Mixed_5d'
114             with tf.variable_scope(end_point):
115                 with tf.variable_scope('Branch_0'):
116                     branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
117                 with tf.variable_scope('Branch_1'):
118                     branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
119                     branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5')
120                 with tf.variable_scope('Branch_2'):
121                     branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
122                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3')
123                     branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0c_3x3')
124                 with tf.variable_scope('Branch_3'):
125                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
126                     branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], scope='Conv2d_0b_1x1')
127                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
128             end_points[end_point] = net
129             if end_point == final_endpoint:
130                 return net, end_points
131 
132             # mixed_3: 17 × 17 × 768
133             end_point = 'Mixed_6a'
134             with tf.variable_scope(end_point):
135                 with tf.variable_scope('Branch_0'):
136                     branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')
137                 with tf.variable_scope('Branch_1'):
138                     branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
139                     branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
140                     branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')
141                 with tf.variable_scope('Branch_2'):
142                     branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
143                 net = tf.concat([branch_0, branch_1, branch_2], 3)
144             end_points[end_point] = net
145             if end_point == final_endpoint:
146                 return net, end_points
147 
148             # mixed_4: 17 x 17 x 768.
149             end_point = 'Mixed_6b'
150             with tf.variable_scope(end_point):
151                 with tf.variable_scope('Branch_0'):
152                     branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
153                 with tf.variable_scope('Branch_1'):
154                     branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
155                     branch_1 = slim.conv2d(branch_1, depth(128), [1, 7], scope='Conv2d_0b_1x7')  # 输出层大小不变，即便过滤器长宽不一样
156                     branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
157                 with tf.variable_scope('Branch_2'):
158                     branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
159                     branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], scope='Conv2d_0b_7x1')
160                     branch_2 = slim.conv2d(branch_2, depth(128), [1, 7], scope='Conv2d_0c_1x7')
161                     branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], scope='Conv2d_0d_7x1')
162                     branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
163                 with tf.variable_scope('Branch_3'):
164                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
165                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
166                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
167             end_points[end_point] = net
168             if end_point == final_endpoint:
169                 return net, end_points
170 
171             # mixed_5: 17 x 17 x 768.
172             end_point = 'Mixed_6c'
173             with tf.variable_scope(end_point):
174                 with tf.variable_scope('Branch_0'):
175                     branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
176                 with tf.variable_scope('Branch_1'):
177                     branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
178                     branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7')
179                     branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
180                 with tf.variable_scope('Branch_2'):
181                     branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
182                     branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1')
183                     branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7')
184                     branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1')
185                     branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
186                 with tf.variable_scope('Branch_3'):
187                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
188                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
189                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
190             end_points[end_point] = net
191             if end_point == final_endpoint:
192                 return net, end_points
193 
194             # mixed_6: 17 x 17 x 768.
195             end_point = 'Mixed_6d'
196             with tf.variable_scope(end_point):
197                 with tf.variable_scope('Branch_0'):
198                     branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
199                 with tf.variable_scope('Branch_1'):
200                     branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
201                     branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], scope='Conv2d_0b_1x7')
202                     branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
203                 with tf.variable_scope('Branch_2'):
204                     branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
205                     branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0b_7x1')
206                     branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], scope='Conv2d_0c_1x7')
207                     branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], scope='Conv2d_0d_7x1')
208                     branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
209                 with tf.variable_scope('Branch_3'):
210                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
211                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
212                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
213             end_points[end_point] = net
214             if end_point == final_endpoint:
215                 return net, end_points
216 
217             # mixed_7: 17 x 17 x 768.
218             end_point = 'Mixed_6e'
219             with tf.variable_scope(end_point):
220                 with tf.variable_scope('Branch_0'):
221                     branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
222                 with tf.variable_scope('Branch_1'):
223                     branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
224                     branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7')
225                     branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
226                 with tf.variable_scope('Branch_2'):
227                     branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
228                     branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], scope='Conv2d_0b_7x1')
229                     branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0c_1x7')
230                     branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], scope='Conv2d_0d_7x1')
231                     branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], scope='Conv2d_0e_1x7')
232                 with tf.variable_scope('Branch_3'):
233                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
234                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
235                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
236             end_points[end_point] = net
237             if end_point == final_endpoint:
238                 return net, end_points
239 
240             # mixed_8: 8 x 8 x 1280.
241             end_point = 'Mixed_7a'
242             with tf.variable_scope(end_point):
243                 with tf.variable_scope('Branch_0'):
244                     branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
245                     branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
246                 with tf.variable_scope('Branch_1'):
247                     branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
248                     branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], scope='Conv2d_0b_1x7')
249                     branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], scope='Conv2d_0c_7x1')
250                     branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
251                 with tf.variable_scope('Branch_2'):
252                     branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
253                 net = tf.concat([branch_0, branch_1, branch_2], 3)
254             end_points[end_point] = net
255             if end_point == final_endpoint:
256                 return net, end_points
257 
258             # mixed_9: 8 x 8 x 2048.
259             end_point = 'Mixed_7b'
260             with tf.variable_scope(end_point):
261                 with tf.variable_scope('Branch_0'):
262                     branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
263                 with tf.variable_scope('Branch_1'):
264                     branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
265                     branch_1 = tf.concat(
266                         [
267                             slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
268                             slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')
269                         ],
270                         3)
271                 with tf.variable_scope('Branch_2'):
272                     branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
273                     branch_2 = slim.conv2d(branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
274                     branch_2 = tf.concat(
275                         [
276                             slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
277                             slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')
278                         ],
279                         3)
280                 with tf.variable_scope('Branch_3'):
281                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
282                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
283                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
284             end_points[end_point] = net
285             if end_point == final_endpoint:
286                 return net, end_points
287 
288             # mixed_10: 8 x 8 x 2048.
289             end_point = 'Mixed_7c'
290             with tf.variable_scope(end_point):
291                 with tf.variable_scope('Branch_0'):
292                     branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
293                 with tf.variable_scope('Branch_1'):
294                     branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
295                     branch_1 = tf.concat(
296                         [
297                             slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
298                             slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')
299                         ],
300                         3)
301                 with tf.variable_scope('Branch_2'):
302                     branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
303                     branch_2 = slim.conv2d(branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
304                     branch_2 = tf.concat(
305                         [
306                             slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
307                             slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')
308                         ],
309                         3)
310                 with tf.variable_scope('Branch_3'):
311                     branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
312                     branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
313                 net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
314             end_points[end_point] = net
315             if end_point == final_endpoint:
316                 return net, end_points
317         raise ValueError('Unknown final endpoint %s' % final_endpoint)
318 
319 
320 # 源文件中该函数放在了后面也能调用，why??
321 def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
322     '''
323     Define kernel size which is automatically reduced for small input.
324 
325     If the shape of the input images is unknown at graph construction time this
326     function assumes that the input images are is large enough.
327 
328     '''
329     shape = input_tensor.get_shape().as_list()  # [?, 5, 5, 128]
330     if shape[1] is None or shape[2] is None:
331         kernel_size_out = kernel_size
332     else:
333         kernel_size_out = [min(shape[1], kernel_size[0]), min(shape[2], kernel_size[1])]
334     return kernel_size_out
335 
336 
337 def incepiton_v3(inputs,
338                  num_classes=1000,
339                  is_training=True,
340                  dropout_keep_prob=0.8,
341                  min_depth=16,
342                  depth_multiplier=1.0,
343                  prediction_fn=slim.softmax,
344                  spatial_squeeze=True,
345                  reuse=None,
346                  scope='InceptionV3'):
347     if depth_multiplier <= 0:
348         raise ValueError('depth_multiplier is not greater than zero.')
349     depth = lambda d: max(int(d * depth_multiplier), min_depth)
350 
351     with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
352         with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
353             net, end_points = inception_v3_base(inputs, scope=scope, min_depth=min_depth, depth_multiplier=depth_multiplier)
354             # Auxiliary Head logits
355             # 这一部分是作啥的??
356             with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
357                                 stride=1,
358                                 padding='SAME'):
359                 aux_logits = end_points['Mixed_6e']  # mixed_7: 17 x 17 x 768
360                 with tf.variable_scope('AuxLogits'):
361                     # 5 × 5 × 768
362                     aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_1a_5x5')
363                     # 5 × 5 × 128
364                     aux_logits = slim.conv2d(aux_logits, depth(128), [1, 1], scope='Conv2d_1b_1x1')
365 
366                     # shape of feature map before the final layer.
367                     kernel_size = _reduced_kernel_size_for_small_input(aux_logits, [5, 5])
368                     # 1 × 1 × 768  输入层大小与过滤器尺寸相同，按照公式计算就没问题
369                     aux_logits = slim.conv2d(aux_logits, depth(768), kernel_size, padding='VALID', weights_initializer=trunc_normal(0.01), scope='Conv2d_2a_{}x{}'.format(*kernel_size))
370                     # 1 × 1 × 1000
371                     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')
372                     if spatial_squeeze:
373                         # (?, 1000)
374                         aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
375                     end_points['AuxLogits'] = aux_logits
376 
377             # final pooling and prediction
378             with tf.variable_scope('Logits'):
379                 kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
380                 # 1 × 1 × 2048
381                 net = slim.avg_pool2d(net, kernel_size, padding='VALID', scope='AvgPool_1a_{}x{}'.format(*kernel_size))
382                 # 1 × 1 × 2048
383                 # 这里竟然有一个dropout方法??
384                 net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
385                 end_points['Predictions'] = net
386                 slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='Conv2d_1c_1x1')
387                 if spatial_squeeze:
388                     # (?, 2048)
389                     logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
390             end_points['Logits'] = logits
391             end_points['Predictions'] = slim.softmax(logits, scope='Predictions')
392 
393     return logits, end_points
394 
395 
396 # 在迁移中，定义模型时会用到
397 def inception_v3_arg_scope(weight_decay=0.00004,
398                            batch_norm_var_collection='moving_vars',
399                            batch_norm_decay=0.9997,
400                            batch_norm_epsilon=0.001,
401                            updates_collections=tf.GraphKeys.UPDATE_OPS,
402                            use_fused_batchnorm=True):
403     """Defines the default InceptionV3 arg scope.
404     Returns:
405     An `arg_scope` to use for the inception v3 model.
406     """
407     batch_norm_params = {
408         # Decay for the moving averages.
409         'decay': batch_norm_decay,
410         # epsilon to prevent 0s in variance.
411         'epsilon': batch_norm_epsilon,
412         # collection containing update_ops.
413         'updates_collections': updates_collections,
414         # Use fused batch norm if possible.
415         'fused': use_fused_batchnorm,
416         # collection containing the moving mean and moving variance.
417         'variables_collections': {
418             'beta': None,
419             'gamma': None,
420             'moving_mean': [batch_norm_var_collection],
421             'moving_variance': [batch_norm_var_collection],
422         }
423     }
424 
425     # Set weight_decay for weights in Conv and FC layers.
426     with slim.arg_scope([slim.conv2d, slim.fully_connected], weights_regularizer=slim.l2_regularizer(weight_decay)):
427         with slim.arg_scope(
428                 [slim.conv2d],
429                 weights_initializer=slim.variance_scaling_initializer(),
430                 activation_fn=tf.nn.relu,
431                 normalizer_fn=slim.batch_norm,
432                 normalizer_params=batch_norm_params) as sc:
433             return sc
434 
435 
436 inputs = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name='X')
437 # inception_v3_base(inputs)
438 incepiton_v3(inputs)

输入层为299*299*3的三维矩阵。

 

6.5 卷积神经网络迁移学习

迁移学习，就是将一个问题上训练好的模型经过简单的调整使其适用于一个新的问题。

利用 ImageNet 数据集上训练好的 Inception-v3 模型来解决一个新的图像分类问题 。能够保留训练好的 Inception-v3 模型中全部卷积层的参数，只是替换最后一层全链接层。在最后这一层全链接层以前的网络层称之为瓶颈层( bottleneck ) 。瓶颈层指的是一层。

通常来讲，在数据足够的状况下，迁移学习的效果不如彻底从新训练。

迁移学习处理

处理文件样例，须要在2核8g上才能执行

 1 import os
 2 import glob
 3 import tensorflow as tf
 4 import numpy as np
 5 
 6 INPUT_DATA = '/home/yangxl/flower_photos'  # 输入文件
 7 OUTPUT_DATA = '/home/yangxl/flower_processed_data.npy'  # 输出文件
 8 
 9 VALIDATION_PERCENTAGE = 10
10 TEST_PERCENTAGE = 10
11 
12 def create_image_lists(sess, testing_percentage, validation_percentage):
13     sub_dirs = [x[0] for x in os.walk(INPUT_DATA)]  # 当前目录和子目录
14     # print(sub_dirs)
15     is_root_dir = True
16 
17     # 初始化各个数据集
18     training_images = []
19     training_labels = []
20     testing_images = []
21     testing_labels = []
22     validation_images = []
23     validation_labels = []
24     current_labels = 0
25 
26     # 读取全部子目录
27     for sub_dir in sub_dirs:
28         if is_root_dir:  # 把第一个排除了
29             is_root_dir = False
30             continue
31 
32         # 获取一个子目录中全部的图片文件
33         extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
34         file_list = []
35         dir_name = os.path.basename(sub_dir)  # '/'最后面的部分
36         print(dir_name)
37         for extension in extensions:
38             file_glob = os.path.join(INPUT_DATA, dir_name, '*.' + extension)
39             file_list.extend(glob.glob(file_glob))  # glob.glob返回一个匹配该模式的列表, glob和os配合使用来操做文件
40         if not file_list:
41             continue
42 
43         # 处理图片数据
44         for file_name in file_list:
45             image_raw_data = tf.gfile.GFile(file_name, 'rb').read()  # 二进制数据
46             image = tf.image.decode_jpeg(image_raw_data)  # tensor, dtype=uint8  333×500×3   色道0~255
47             if image.dtype != tf.float32:
48                 image = tf.image.convert_image_dtype(image, dtype=tf.float32)  # 色道值0～1
49             image = tf.image.resize_images(image, [299, 299])
50             image_value = sess.run(image)  # numpy.ndarray
51 
52             # 随机划分数据集
53             chance = np.random.randint(100)
54             if chance < validation_percentage:
55                 validation_images.append(image_value)
56                 validation_labels.append(current_labels)
57             elif chance < validation_percentage + testing_percentage:
58                 testing_images.append(image_value)
59                 testing_labels.append(current_labels)
60             else:
61                 training_images.append(image_value)
62                 training_labels.append(current_labels)
63         current_labels += 1
64 
65     # 将训练数据随机打乱以得到更好的训练效果， 将数据打乱，但仍保持training_images和training_labels的对应关系。
66     state = np.random.get_state()
67     np.random.shuffle(training_images)
68     np.random.set_state(state)
69     np.random.shuffle(training_labels)
70 
71     print("it's time to return")
72     return np.asarray([training_images, training_labels,
73                        validation_images, validation_labels,
74                        testing_images, testing_labels])
75 
76 def main():
77     with tf.Session() as sess:
78         processed_data = create_image_lists(sess, TEST_PERCENTAGE, VALIDATION_PERCENTAGE)
79         # 经过numpy格式保存处理后的数据
80         np.save(OUTPUT_DATA, processed_data)
81 
82 if __name__ == '__main__':
83     main()

 

获取交叉熵更简便的方式：

tf.losses.softmax_cross_entropy(tf.one_hot(labels, N_CLASSES), logits, weights=1.0)
train_step = tf.train.RMSPropOptimizer(LEARNING_RATE).minimize(tf.losses.get_total_loss())

迁移学习示例，

  1 #!coding:utf8
  2 
  3 import tensorflow as tf
  4 import numpy as np
  5 import tensorflow.contrib.slim as slim
  6 
  7 # 加载inception-v3模型
  8 import tensorflow.contrib.slim.python.slim.nets.inception_v3 as inception_v3
  9 
 10 INPUT_DATA = '/home/yangxl/files/flower_processed_data.npy'
 11 
 12 TRAIN_FILE = '/home/yangxl/files/save_model'
 13 CKPT_FILE = '/home/yangxl/files/inception_v3.ckpt'
 14 
 15 LEARNING_RATE = 0.0001
 16 STEPS = 300
 17 BATCH = 32
 18 N_CLASSES = 5  # 5种花
 19 
 20 CHECKPOINT_EXCLUDE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
 21 TRAINABLE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
 22 
 23 # 获取全部须要从训练好的模型中加载的参数
 24 def get_tuned_variables():
 25     exclusions = [scope.strip() for scope in CHECKPOINT_EXCLUDE_SCOPES.split(',')]
 26     variables_to_restore = []
 27 
 28     # 过滤参数
 29     for var in slim.get_model_variables():  # 先定义了inception-v3模型，以后才会有变量
 30         excluded = False
 31         for exclusion in exclusions:
 32             if var.op.name.startswith(exclusion):
 33                 excluded = True
 34                 break
 35         if not excluded:
 36             variables_to_restore.append(var)
 37     return variables_to_restore
 38 
 39 # 获取全部须要训练的变量列表
 40 def get_trainable_variables():
 41     scopes = [scope.strip() for scope in TRAINABLE_SCOPES.split(',')]
 42     variables_to_train = []
 43     for scope in scopes:
 44         variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)  # 对scope进行正则匹配
 45         variables_to_train.append(variables)
 46     return variables_to_train
 47 
 48 def main(arg=None):
 49     processed_data = np.load(INPUT_DATA)
 50     training_images = processed_data[0]
 51     n_training_example = len(training_images)
 52     training_labels = processed_data[1]
 53     validation_images = processed_data[2]
 54     validation_labels = processed_data[3]
 55     testing_images = processed_data[4]
 56     testing_labels = processed_data[5]
 57     print('%d training examples, %s validation examples and %d tseting examples.' % (n_training_example, len(validation_labels), len(testing_labels)))
 58 
 59     images = tf.placeholder(tf.float32, [None, 299, 299, 3], name='input_images')
 60     labels = tf.placeholder(tf.int64, [None], name='labels')  # 5种花
 61 
 62     # 定义inception-v3模型，由于谷歌给出的只有模型参数取值，因此这里须要在这个代码中定义inception-v3的结构。
 63     with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
 64         # inception_v3.inception_v3_arg_scope()是一个包含两个键的字典。嵌套的arg_scope函数返回的字典会整合到一块儿。
 65         # inception_v3.inception_v3函数里的一些函数可能会使用字典中的参数。
 66         logits, _ = inception_v3.inception_v3(images, num_classes=N_CLASSES)
 67 
 68     # 获取须要训练的变量
 69     trainable_variables = get_trainable_variables()
 70     # print('==', len(trainable_variables), trainable_variables)
 71     '''
 72     [[<tf.Variable 'InceptionV3/Logits/Conv2d_1c_1x1/weights:0' shape=(1, 1, 2048, 5) dtype=float32_ref>, 
 73       <tf.Variable 'InceptionV3/Logits/Conv2d_1c_1x1/biases:0' shape=(5,) dtype=float32_ref>], 
 74      [<tf.Variable 'InceptionV3/AuxLogits/Conv2d_1b_1x1/weights:0' shape=(1, 1, 768, 128) dtype=float32_ref>, 
 75       <tf.Variable 'InceptionV3/AuxLogits/Conv2d_1b_1x1/BatchNorm/beta:0' shape=(128,) dtype=float32_ref>, 
 76       <tf.Variable 'InceptionV3/AuxLogits/Conv2d_2a_5x5/weights:0' shape=(5, 5, 128, 768) dtype=float32_ref>, 
 77       <tf.Variable 'InceptionV3/AuxLogits/Conv2d_2a_5x5/BatchNorm/beta:0' shape=(768,) dtype=float32_ref>, 
 78       <tf.Variable 'InceptionV3/AuxLogits/Conv2d_2b_1x1/weights:0' shape=(1, 1, 768, 5) dtype=float32_ref>, 
 79       <tf.Variable 'InceptionV3/AuxLogits/Conv2d_2b_1x1/biases:0' shape=(5,) dtype=float32_ref>]]
 80 
 81     '''
 82     # 定义损失函数。在模型定义的时候已经将正则化损失加入损失集合了。
 83     tf.losses.softmax_cross_entropy(tf.one_hot(labels, N_CLASSES), logits, weights=1.0)
 84 
 85     # 定义训练过程
 86     train_step = tf.train.RMSPropOptimizer(LEARNING_RATE).minimize(tf.losses.get_total_loss())
 87 
 88     # 计算正确率
 89     with tf.name_scope('evaluation'):
 90         correct_prediction = tf.equal(tf.argmax(logits, 1), labels)
 91         evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 92 
 93     # 定义加载模型的函数。返回一个回调函数callback，执行callback(sess)就会加载get_tuned_variables()变量列表到当前图。
 94     load_fn = slim.assign_from_checkpoint_fn(CKPT_FILE, get_tuned_variables(), ignore_missing_vars=True)
 95 
 96     # 定义保存新的训练好的模型的函数
 97     saver = tf.train.Saver()
 98 
 99     with tf.Session() as sess:
100         # 初始化没有加载进来的变量。这个过程必定要在模型加载以前，不然初始化过程会将已经加载好的变量从新赋值。
101         tf.global_variables_initializer().run()
102         # 加载已经训练好的模型
103         print('Loading tuned variables from %s' % CKPT_FILE)
104         load_fn(sess)
105 
106         start = 0
107         end = BATCH
108         for i in range(STEPS):
109             sess.run(train_step, feed_dict={
110                 images: training_images[start: end],
111                 labels: training_labels[start: end]
112             })
113 
114             # 输出日志
115             if i % 30 == 0 or i + 1 == STEPS:
116                 saver.save(sess, TRAIN_FILE, global_step=i)
117                 validation_accuracy = sess.run(evaluation_step, feed_dict={
118                     images: validation_images, labels: validation_labels
119                 })
120                 print('Step %d: Validation accuracy = %.1f%%' % (i, validation_accuracy * 100.0))
121 
122             start = end
123             if start == n_training_example:
124                 start = 0
125             end = start + BATCH
126             if end > n_training_example:
127                 end = n_training_example
128             test_accuracy = sess.run(evaluation_step, feed_dict={
129                 images: testing_images, labels: testing_labels
130             })
131             print('Final test accuracy = %.1f%%' % (test_accuracy * 100.0))
132 
133 if __name__ == '__main__':
134     tf.app.run()

执行过程：

代码执行了12个小时，可是top命令中的TIME+显示只有300多分钟，why??

执行过程当中，`load average`至关高，可是进程的CPU、MEM使用率很低，多是CPU执行了内存和swap之间的调度，really??