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还在纠结垃圾分类问题？带你用Python感受ImageNet冠军模型SENet的强大

百家作者：AI100 2019-07-07 14:33:35

作者 |?beyondma

转载自CSDN博客

本月1日起，上海正式开始了“史上最严“垃圾分类的规定，扔错垃圾最高可罚200元。全国其它46个城市也要陆续步入垃圾分类新时代。各种被垃圾分类逼疯的段子在社交媒体上层出不穷。

其实从人工智能的角度看垃圾分类就是图像处理中图像分类任务的一种应用，而这在2012年以来的ImageNet图像分类任务的评比中，SENet模型以top-5测试集回归2.25%错误率的成绩可谓是技压群雄，堪称目前最强的图像分类器。

? ? ? ?? ? ? ?

笔者刚刚还到SENet的创造者momenta公司的网站上看了一下，他们最新的方向已经是3D物体识别和标定了，效果如下：

? ? ? ?

可以说他们提出的SENet进行垃圾图像处理是完全没问题的。

Senet简介

Senet的是由momenta和牛津大学共同提出的一种基于挤压(squeeze）和激励（Excitation）的模型，每个模块通过“挤压”操作嵌入来自全局感受野的信息，并且通过“激励”操作选择性地诱导响应增强。我们可以看到历年的ImageNet冠军基本都是在使用加大模型数量和连接数量的方式来提高精度，而Senet在这种”大力出奇迹”的潮流下明显是一股清流。其论文地址如下：http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf

其具体原理说明如下：

Sequeeze：对 C×H×W 进行 global average pooling，得到 1×1×C 大小的特征图，这个特征图可以理解为具有全局感受野。翻译论文原文来说：将每个二维的特征通道变成一个实数，这个实数某种程度上具有全局的感受野，并且输出的维度和输入的特征通道数相匹配。它表征着在特征通道上响应的全局分布，而且使得靠近输入的层也可以获得全局的感受野。

Excitation ：使用一个全连接神经网络，对 Sequeeze 之后的结果做一个非线性变换。它的机制一个类似于循环神经网络中的门。通过参数 w 来为每个特征通道生成权重，其中参数 w 被学习用来显式地建模特征通道间的相关性。

特征重标定：使用 Excitation 得到的结果作为权重，乘到输入特征上。将Excitation输出的权重可以认为是特征通道的重要性反应，逐通道加权到放到先前的特征上，完成对原始特征的重标定。

其模型架构如下：

SENet 构造非常简单，而且很容易被部署，不需要引入新的函数或者层。其caffe模型可以通过百度下载（https://pan.baidu.com/s/1o7HdfAE?errno=0&errmsg=Auth%20Login%20Sucess&&bduss=&ssnerror=0&traceid=）

Senet的运用

如果读者布署有caffe那么直接下载刚刚的模型直接load进来就可以使用了。如果没有装caffe而装了tensorflow也没关系，我们刚刚说了SENet没有引入新的函数和层，很方便用tensorflow实现。

下载图像集：经笔者各方查找发现了这个数据集，虽然不大也没有发挥出SENet的优势，不过也方便使用：

https://raw.githubusercontent.com/garythung/trashnet/master/data/dataset-resized.zip

建立SENet模型：使用tensorflow建立的模型在github上也有开源项目了，网址如下：https://github.com/taki0112/SENet-Tensorflow，只是他使用的是Cifar10数据集，不过这也没关系，只需要在gitclone以下将其cifar10.py中的prepare_data函数做如下修改即可。

 1def?prepare_data():
 2????print("======Loading?data======")
 3????download_data()
 4????data_dir?=?'e:/test/'
 5????#data_dir?=?'./cifar-10-batches-py'#改为你的文件侠
 6????image_dim?=?image_size?*?image_size?*?img_channels
 7????#meta?=?unpickle(data_dir?+?'/batches.meta')#本数据集不使用meta文件分类，故需要修改
 8????label_names?=?['cardboard','glass','metal','trash','paper','plastic']
 9????label_count?=?len(label_names)
10????#train_files?=?['data_batch_%d'?%?d?for?d?in?range(1,?6)]
11????train_files?=?[data_dir+s?for?s?in?label_names]#改为
12????train_data,?train_labels?=?load_data(train_files,?data_dir,?label_count)
13????test_data,?test_labels?=?load_data(['test_batch'],?data_dir,?label_count)
14
15????print("Train?data:",?np.shape(train_data),?np.shape(train_labels))
16????print("Test?data?:",?np.shape(test_data),?np.shape(test_labels))
17????print("======Load?finished======")
18
19????print("======Shuffling?data======")
20????indices?=?np.random.permutation(len(train_data))
21????train_data?=?train_data[indices]
22????train_labels?=?train_labels[indices]
23????print("======Prepare?Finished======")
24
25????return?train_data,?train_labels,?test_data,?test_labels

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其最主要的建模代码如下，其主要工作就是将SENet的模型结构实现一下即可：

  1import?tensorflow?as?tf
  2from?tflearn.layers.conv?import?global_avg_pool
  3from?tensorflow.contrib.layers?import?batch_norm,?flatten
  4from?tensorflow.contrib.framework?import?arg_scope
  5from?cifar10?import?*
  6import?numpy?as?np
  7
  8weight_decay?=?0.0005
  9momentum?=?0.9
 10
 11init_learning_rate?=?0.1
 12
 13reduction_ratio?=?4
 14
 15batch_size?=?128
 16iteration?=?391
 17#?128?*?391?~?50,000
 18
 19test_iteration?=?10
 20
 21total_epochs?=?100
 22
 23def?conv_layer(input,?filter,?kernel,?stride=1,?padding='SAME',?layer_name="conv",?activation=True):
 24????with?tf.name_scope(layer_name):
 25????????network?=?tf.layers.conv2d(inputs=input,?use_bias=True,?filters=filter,?kernel_size=kernel,?strides=stride,?padding=padding)
 26????????if?activation?:
 27????????????network?=?Relu(network)
 28????????return?network
 29
 30def?Fully_connected(x,?units=class_num,?layer_name='fully_connected')?:
 31????with?tf.name_scope(layer_name)?:
 32????????return?tf.layers.dense(inputs=x,?use_bias=True,?units=units)
 33
 34def?Relu(x):
 35????return?tf.nn.relu(x)
 36
 37def?Sigmoid(x):
 38????return?tf.nn.sigmoid(x)
 39
 40def?Global_Average_Pooling(x):
 41????return?global_avg_pool(x,?name='Global_avg_pooling')
 42
 43def?Max_pooling(x,?pool_size=[3,3],?stride=2,?padding='VALID')?:
 44????return?tf.layers.max_pooling2d(inputs=x,?pool_size=pool_size,?strides=stride,?padding=padding)
 45
 46def?Batch_Normalization(x,?training,?scope):
 47????with?arg_scope([batch_norm],
 48???????????????????scope=scope,
 49???????????????????updates_collections=None,
 50???????????????????decay=0.9,
 51???????????????????center=True,
 52???????????????????scale=True,
 53???????????????????zero_debias_moving_mean=True)?:
 54????????return?tf.cond(training,
 55???????????????????????lambda?:?batch_norm(inputs=x,?is_training=training,?reuse=None),
 56???????????????????????lambda?:?batch_norm(inputs=x,?is_training=training,?reuse=True))
 57
 58def?Concatenation(layers)?:
 59????return?tf.concat(layers,?axis=3)
 60
 61def?Dropout(x,?rate,?training)?:
 62????return?tf.layers.dropout(inputs=x,?rate=rate,?training=training)
 63
 64def?Evaluate(sess):
 65????test_acc?=?0.0
 66????test_loss?=?0.0
 67????test_pre_index?=?0
 68????add?=?1000
 69
 70????for?it?in?range(test_iteration):
 71????????test_batch_x?=?test_x[test_pre_index:?test_pre_index?+?add]
 72????????test_batch_y?=?test_y[test_pre_index:?test_pre_index?+?add]
 73????????test_pre_index?=?test_pre_index?+?add
 74
 75????????test_feed_dict?=?{
 76????????????x:?test_batch_x,
 77????????????label:?test_batch_y,
 78????????????learning_rate:?epoch_learning_rate,
 79????????????training_flag:?False
 80????????}
 81
 82????????loss_,?acc_?=?sess.run([cost,?accuracy],?feed_dict=test_feed_dict)
 83
 84????????test_loss?+=?loss_
 85????????test_acc?+=?acc_
 86
 87????test_loss?/=?test_iteration?#?average?loss
 88????test_acc?/=?test_iteration?#?average?accuracy
 89
 90????summary?=?tf.Summary(value=[tf.Summary.Value(tag='test_loss',?simple_value=test_loss),
 91????????????????????????????????tf.Summary.Value(tag='test_accuracy',?simple_value=test_acc)])
 92
 93????return?test_acc,?test_loss,?summary
 94
 95class?SE_Inception_resnet_v2():
 96????def?__init__(self,?x,?training):
 97????????self.training?=?training
 98????????self.model?=?self.Build_SEnet(x)
 99
100????def?Stem(self,?x,?scope):
101????????with?tf.name_scope(scope)?:
102????????????x?=?conv_layer(x,?filter=32,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_conv1')
103????????????x?=?conv_layer(x,?filter=32,?kernel=[3,3],?padding='VALID',?layer_name=scope+'_conv2')
104????????????block_1?=?conv_layer(x,?filter=64,?kernel=[3,3],?layer_name=scope+'_conv3')
105
106????????????split_max_x?=?Max_pooling(block_1)
107????????????split_conv_x?=?conv_layer(block_1,?filter=96,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv1')
108????????????x?=?Concatenation([split_max_x,split_conv_x])
109
110????????????split_conv_x1?=?conv_layer(x,?filter=64,?kernel=[1,1],?layer_name=scope+'_split_conv2')
111????????????split_conv_x1?=?conv_layer(split_conv_x1,?filter=96,?kernel=[3,3],?padding='VALID',?layer_name=scope+'_split_conv3')
112
113????????????split_conv_x2?=?conv_layer(x,?filter=64,?kernel=[1,1],?layer_name=scope+'_split_conv4')
114????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=64,?kernel=[7,1],?layer_name=scope+'_split_conv5')
115????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=64,?kernel=[1,7],?layer_name=scope+'_split_conv6')
116????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=96,?kernel=[3,3],?padding='VALID',?layer_name=scope+'_split_conv7')
117
118????????????x?=?Concatenation([split_conv_x1,split_conv_x2])
119
120????????????split_conv_x?=?conv_layer(x,?filter=192,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv8')
121????????????split_max_x?=?Max_pooling(x)
122
123????????????x?=?Concatenation([split_conv_x,?split_max_x])
124
125????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
126????????????x?=?Relu(x)
127
128????????????return?x
129
130????def?Inception_resnet_A(self,?x,?scope):
131????????with?tf.name_scope(scope)?:
132????????????init?=?x
133
134????????????split_conv_x1?=?conv_layer(x,?filter=32,?kernel=[1,1],?layer_name=scope+'_split_conv1')
135
136????????????split_conv_x2?=?conv_layer(x,?filter=32,?kernel=[1,1],?layer_name=scope+'_split_conv2')
137????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=32,?kernel=[3,3],?layer_name=scope+'_split_conv3')
138
139????????????split_conv_x3?=?conv_layer(x,?filter=32,?kernel=[1,1],?layer_name=scope+'_split_conv4')
140????????????split_conv_x3?=?conv_layer(split_conv_x3,?filter=48,?kernel=[3,3],?layer_name=scope+'_split_conv5')
141????????????split_conv_x3?=?conv_layer(split_conv_x3,?filter=64,?kernel=[3,3],?layer_name=scope+'_split_conv6')
142
143????????????x?=?Concatenation([split_conv_x1,split_conv_x2,split_conv_x3])
144????????????x?=?conv_layer(x,?filter=384,?kernel=[1,1],?layer_name=scope+'_final_conv1',?activation=False)
145
146????????????x?=?x*0.1
147????????????x?=?init?+?x
148
149????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
150????????????x?=?Relu(x)
151
152????????????return?x
153
154????def?Inception_resnet_B(self,?x,?scope):
155????????with?tf.name_scope(scope)?:
156????????????init?=?x
157
158????????????split_conv_x1?=?conv_layer(x,?filter=192,?kernel=[1,1],?layer_name=scope+'_split_conv1')
159
160????????????split_conv_x2?=?conv_layer(x,?filter=128,?kernel=[1,1],?layer_name=scope+'_split_conv2')
161????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=160,?kernel=[1,7],?layer_name=scope+'_split_conv3')
162????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=192,?kernel=[7,1],?layer_name=scope+'_split_conv4')
163
164????????????x?=?Concatenation([split_conv_x1,?split_conv_x2])
165????????????x?=?conv_layer(x,?filter=1152,?kernel=[1,1],?layer_name=scope+'_final_conv1',?activation=False)
166????????????#?1154
167????????????x?=?x?*?0.1
168????????????x?=?init?+?x
169
170????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
171????????????x?=?Relu(x)
172
173????????????return?x
174
175????def?Inception_resnet_C(self,?x,?scope):
176????????with?tf.name_scope(scope)?:
177????????????init?=?x
178
179????????????split_conv_x1?=?conv_layer(x,?filter=192,?kernel=[1,1],?layer_name=scope+'_split_conv1')
180
181????????????split_conv_x2?=?conv_layer(x,?filter=192,?kernel=[1,?1],?layer_name=scope?+?'_split_conv2')
182????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=224,?kernel=[1,?3],?layer_name=scope?+?'_split_conv3')
183????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=256,?kernel=[3,?1],?layer_name=scope?+?'_split_conv4')
184
185????????????x?=?Concatenation([split_conv_x1,split_conv_x2])
186????????????x?=?conv_layer(x,?filter=2144,?kernel=[1,1],?layer_name=scope+'_final_conv2',?activation=False)
187????????????#?2048
188????????????x?=?x?*?0.1
189????????????x?=?init?+?x
190
191????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
192????????????x?=?Relu(x)
193
194????????????return?x
195
196????def?Reduction_A(self,?x,?scope):
197????????with?tf.name_scope(scope)?:
198????????????k?=?256
199????????????l?=?256
200????????????m?=?384
201????????????n?=?384
202
203????????????split_max_x?=?Max_pooling(x)
204
205????????????split_conv_x1?=?conv_layer(x,?filter=n,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv1')
206
207????????????split_conv_x2?=?conv_layer(x,?filter=k,?kernel=[1,1],?layer_name=scope+'_split_conv2')
208????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=l,?kernel=[3,3],?layer_name=scope+'_split_conv3')
209????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=m,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv4')
210
211????????????x?=?Concatenation([split_max_x,?split_conv_x1,?split_conv_x2])
212
213????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
214????????????x?=?Relu(x)
215
216????????????return?x
217
218????def?Reduction_B(self,?x,?scope):
219????????with?tf.name_scope(scope)?:
220????????????split_max_x?=?Max_pooling(x)
221
222????????????split_conv_x1?=?conv_layer(x,?filter=256,?kernel=[1,1],?layer_name=scope+'_split_conv1')
223????????????split_conv_x1?=?conv_layer(split_conv_x1,?filter=384,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv2')
224
225????????????split_conv_x2?=?conv_layer(x,?filter=256,?kernel=[1,1],?layer_name=scope+'_split_conv3')
226????????????split_conv_x2?=?conv_layer(split_conv_x2,?filter=288,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv4')
227
228????????????split_conv_x3?=?conv_layer(x,?filter=256,?kernel=[1,1],?layer_name=scope+'_split_conv5')
229????????????split_conv_x3?=?conv_layer(split_conv_x3,?filter=288,?kernel=[3,3],?layer_name=scope+'_split_conv6')
230????????????split_conv_x3?=?conv_layer(split_conv_x3,?filter=320,?kernel=[3,3],?stride=2,?padding='VALID',?layer_name=scope+'_split_conv7')
231
232????????????x?=?Concatenation([split_max_x,?split_conv_x1,?split_conv_x2,?split_conv_x3])
233
234????????????x?=?Batch_Normalization(x,?training=self.training,?scope=scope+'_batch1')
235????????????x?=?Relu(x)
236
237????????????return?x
238
239????def?Squeeze_excitation_layer(self,?input_x,?out_dim,?ratio,?layer_name):
240????????with?tf.name_scope(layer_name)?:
241
242
243????????????squeeze?=?Global_Average_Pooling(input_x)
244
245????????????excitation?=?Fully_connected(squeeze,?units=out_dim?/?ratio,?layer_name=layer_name+'_fully_connected1')
246????????????excitation?=?Relu(excitation)
247????????????excitation?=?Fully_connected(excitation,?units=out_dim,?layer_name=layer_name+'_fully_connected2')
248????????????excitation?=?Sigmoid(excitation)
249
250????????????excitation?=?tf.reshape(excitation,?[-1,1,1,out_dim])
251????????????scale?=?input_x?*?excitation
252
253????????????return?scale
254
255????def?Build_SEnet(self,?input_x):
256????????input_x?=?tf.pad(input_x,?[[0,?0],?[32,?32],?[32,?32],?[0,?0]])
257????????#?size?32?->?96
258????????print(np.shape(input_x))
259????????#?only?cifar10?architecture
260
261????????x?=?self.Stem(input_x,?scope='stem')
262
263????????for?i?in?range(5)?:
264????????????x?=?self.Inception_resnet_A(x,?scope='Inception_A'+str(i))
265????????????channel?=?int(np.shape(x)[-1])
266????????????x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_A'+str(i))
267
268????????x?=?self.Reduction_A(x,?scope='Reduction_A')
269
270????????channel?=?int(np.shape(x)[-1])
271????????x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_A')
272
273????????for?i?in?range(10)??:
274????????????x?=?self.Inception_resnet_B(x,?scope='Inception_B'+str(i))
275????????????channel?=?int(np.shape(x)[-1])
276????????????x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_B'+str(i))
277
278????????x?=?self.Reduction_B(x,?scope='Reduction_B')
279
280????????channel?=?int(np.shape(x)[-1])
281????????x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_B')
282
283????????for?i?in?range(5)?:
284????????????x?=?self.Inception_resnet_C(x,?scope='Inception_C'+str(i))
285????????????channel?=?int(np.shape(x)[-1])
286????????????x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_C'+str(i))
287
288
289????????#?channel?=?int(np.shape(x)[-1])
290????????#?x?=?self.Squeeze_excitation_layer(x,?out_dim=channel,?ratio=reduction_ratio,?layer_name='SE_C')
291
292????????x?=?Global_Average_Pooling(x)
293????????x?=?Dropout(x,?rate=0.2,?training=self.training)
294????????x?=?flatten(x)
295
296????????x?=?Fully_connected(x,?layer_name='final_fully_connected')
297????????return?x
298
299
300train_x,?train_y,?test_x,?test_y?=?prepare_data()
301train_x,?test_x?=?color_preprocessing(train_x,?test_x)
302
303
304#?image_size?=?32,?img_channels?=?3,?class_num?=?10?in?cifar10
305x?=?tf.placeholder(tf.float32,?shape=[None,?image_size,?image_size,?img_channels])
306label?=?tf.placeholder(tf.float32,?shape=[None,?class_num])
307
308training_flag?=?tf.placeholder(tf.bool)
309
310
311learning_rate?=?tf.placeholder(tf.float32,?name='learning_rate')
312
313logits?=?SE_Inception_resnet_v2(x,?training=training_flag).model
314cost?=?tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label,?logits=logits))
315
316l2_loss?=?tf.add_n([tf.nn.l2_loss(var)?for?var?in?tf.trainable_variables()])
317optimizer?=?tf.train.MomentumOptimizer(learning_rate=learning_rate,?momentum=momentum,?use_nesterov=True)
318train?=?optimizer.minimize(cost?+?l2_loss?*?weight_decay)
319
320correct_prediction?=?tf.equal(tf.argmax(logits,?1),?tf.argmax(label,?1))
321accuracy?=?tf.reduce_mean(tf.cast(correct_prediction,?tf.float32))
322
323saver?=?tf.train.Saver(tf.global_variables())
324
325with?tf.Session()?as?sess:
326????ckpt?=?tf.train.get_checkpoint_state('./model')
327????if?ckpt?and?tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
328????????saver.restore(sess,?ckpt.model_checkpoint_path)
329????else:
330????????sess.run(tf.global_variables_initializer())
331
332????summary_writer?=?tf.summary.FileWriter('./logs',?sess.graph)
333
334????epoch_learning_rate?=?init_learning_rate
335????for?epoch?in?range(1,?total_epochs?+?1):
336????????if?epoch?%?30?==?0?:
337????????????epoch_learning_rate?=?epoch_learning_rate?/?10
338
339????????pre_index?=?0
340????????train_acc?=?0.0
341????????train_loss?=?0.0
342
343????????for?step?in?range(1,?iteration?+?1):
344????????????if?pre_index?+?batch_size?< ?50000:
345????????????????batch_x?=?train_x[pre_index:?pre_index?+?batch_size]
346????????????????batch_y?=?train_y[pre_index:?pre_index?+?batch_size]
347????????????else:
348????????????????batch_x?=?train_x[pre_index:]
349????????????????batch_y?=?train_y[pre_index:]
350
351????????????batch_x?=?data_augmentation(batch_x)
352
353????????????train_feed_dict?=?{
354????????????????x:?batch_x,
355????????????????label:?batch_y,
356????????????????learning_rate:?epoch_learning_rate,
357????????????????training_flag:?True
358????????????}
359
360????????????_,?batch_loss?=?sess.run([train,?cost],?feed_dict=train_feed_dict)
361????????????batch_acc?=?accuracy.eval(feed_dict=train_feed_dict)
362
363????????????train_loss?+=?batch_loss
364????????????train_acc?+=?batch_acc
365????????????pre_index?+=?batch_size
366
367
368????????train_loss?/=?iteration?#?average?loss
369????????train_acc?/=?iteration?#?average?accuracy
370
371????????train_summary?=?tf.Summary(value=[tf.Summary.Value(tag='train_loss',?simple_value=train_loss),
372??????????????????????????????????????????tf.Summary.Value(tag='train_accuracy',?simple_value=train_acc)])
373
374????????test_acc,?test_loss,?test_summary?=?Evaluate(sess)
375
376????????summary_writer.add_summary(summary=train_summary,?global_step=epoch)
377????????summary_writer.add_summary(summary=test_summary,?global_step=epoch)
378????????summary_writer.flush()
379
380????????line?=?"epoch:?%d/%d,?train_loss:?%.4f,?train_acc:?%.4f,?test_loss:?%.4f,?test_acc:?%.4f?n"?%?(
381????????????epoch,?total_epochs,?train_loss,?train_acc,?test_loss,?test_acc)
382????????print(line)
383
384????????with?open('logs.txt',?'a')?as?f:
385????????????f.write(line)
386
387????????saver.save(sess=sess,?save_path='./model/Inception_resnet_v2.ckpt')