用TensorFlow实现ResNeXt和DenseNet，超简单!

图：pixabay

原文来源：GitHub

「机器人圈」编译：嗯~阿童木呀、多啦A亮

ResNeXt-Tensorflow

使用Cifar10数据集的ResNeXt在Tensorflow上的实现。

如果你想查看原作者的代码，请参考此链接https://github.com/facebookresearch/ResNeXt

要求

? Tensorflow 1.x

? Python 3.x

? tflearn（如果你觉得全局平均池比较容易，你应该安装tflearn）

架构比较

ResNet

ResNeXt

? 我实施了（b）

? （b）是分割 + 变换（Bottleneck）+ 拼接 + 转换 + 合并

问题

什么是“分割”？

def split_layer(self, input_x, stride, layer_name):

with tf.name_scope(layer_name) :

layers_split = list()

for i in range(cardinality) :

splits = self.transform_layer(input_x, stride=stride, scope=layer_name + '_splitN_' + str(i))

layers_split.append(splits)

return Concatenation(layers_split)

? 基数是指你想要分割多少次

什么是“变换”？

def transform_layer(self, x, stride, scope):

with tf.name_scope(scope) :

x = conv_layer(x, filter=depth, kernel=[1,1], stride=stride, layer_name=scope+'_conv1')

x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

x = Relu(x)

x = conv_layer(x, filter=depth, kernel=[3,3], stride=1, layer_name=scope+'_conv2')

x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')

x = Relu(x)

return x

什么是“转换”？

def transition_layer(self, x, out_dim, scope):

with tf.name_scope(scope):

x = conv_layer(x, filter=out_dim, kernel=[1,1], stride=1, layer_name=scope+'_conv1')

x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

return x

结果比较（ResNet、DenseNet、ResNet）

相关作品

? DenseNet （https://github.com/taki0112/Densenet-Tensorflow）

参考

分类数据集结果（http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html）

Densenet-Tensorflow

使用Cifar10、MNIST数据集的Densenet（https://arxiv.org/abs/1608.06993）在Tensorflow上的实现。

? 实现本论文的代码是Densenet.py。

? 有一些细微的区别是，我用的是AdamOptimizer。

如果你想查看原作者的代码或其他实现，请参考此链接（https://github.com/liuzhuang13/DenseNet）

要求

? Tensorflow 1.x

? Python 3.x

? tflearn（如果你对全局平均池（global average pooling）能够很好地掌握，那你就应该安装tflearn。

但是，我使用的是tf.layers将它实现的，因此你也不必过分担心。

问题

我使用tf.contrib.layers.batch_norm

defBatch_Normalization(x, training, scope):

witharg_scope([batch_norm],

scope=scope,

updates_collections=None,

decay=0.9,

center=True,

scale=True,

zero_debias_moving_mean=True) :

returntf.cond(training,

lambda :batch_norm(inputs=x, is_training=training, reuse=None),

lambda :batch_norm(inputs=x, is_training=training, reuse=True))

理念

什么是全局平均池（global average pooling）？

defGlobal_Average_Pooling(x, stride=1) :

width=np.shape(x)[1]

height=np.shape(x)[2]

pool_size= [width, height]

return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride)

# The stride value does not matter

如果你使用的是tflearn，请参考此链接（http://tflearn.org/layers/conv/#global-average-pooling）

defGlobal_Average_Pooling(x):

returntflearn.layers.conv.global_avg_pool(x, name='Global_avg_pooling')

什么是密集连接（Dense Connectivity）？

什么是Densenet架构？

池化减少了特征映射的大小

特征映射大小在每个块内匹配

defDense_net(self, input_x):

x =conv_layer(input_x, filter=2*self.filters, kernel=[7,7], stride=2, layer_name='conv0')

x =Max_Pooling(x, pool_size=[3,3], stride=2)

x =self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')

x =self.transition_layer(x, scope='trans_1')

x =self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')

x =self.transition_layer(x, scope='trans_2')

x =self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')

x =self.transition_layer(x, scope='trans_3')

x =self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')

x =Batch_Normalization(x, training=self.training, scope='linear_batch')

x =Relu(x)

x =Global_Average_Pooling(x)

x = Linear(x)

return x

什么是密集块（Dense Block）？

defdense_block(self, input_x, nb_layers, layer_name):

withtf.name_scope(layer_name):

layers_concat=list()

layers_concat.append(input_x)

x =self.bottleneck_layer(input_x, scope=layer_name+'_bottleN_'+str(0))

layers_concat.append(x)

for i inrange(nb_layers-1):

x =Concatenation(layers_concat)

x =self.bottleneck_layer(x, scope=layer_name+'_bottleN_'+str(i +1))

layers_concat.append(x)

return x

什么是Bottleneck 层？

defbottleneck_layer(self, x, scope):

withtf.name_scope(scope):

x =Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

x =Relu(x)

x =conv_layer(x, filter=4*self.filters, kernel=[1,1], layer_name=scope+'_conv1')

x =Drop_out(x, rate=dropout_rate, training=self.training)

x =Batch_Normalization(x, training=self.training, scope=scope+'_batch2')

x =Relu(x)

x =conv_layer(x, filter=self.filters, kernel=[3,3], layer_name=scope+'_conv2')

x =Drop_out(x, rate=dropout_rate, training=self.training)

return x

什么是转换层（Transition Layer）？

deftransition_layer(self, x, scope):

withtf.name_scope(scope):

x =Batch_Normalization(x, training=self.training, scope=scope+'_batch1')

x =Relu(x)

x =conv_layer(x, filter=self.filters, kernel=[1,1], layer_name=scope+'_conv1')

x =Drop_out(x, rate=dropout_rate, training=self.training)

x =Average_pooling(x, pool_size=[2,2], stride=2)

return x

结构比较（CNN、ResNet、DenseNet）

结果

? （MNIST）最高测试精度为99.2％（该结果不使用dropout）。

? 密集块层数固定为4个。

for i inrange(self.nb_blocks) :

# original : 6 -> 12 -> 48

x =self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))

x =self.transition_layer(x, scope='trans_'+str(i))

CIFAR-10

CIFAR-100

Image Net