DenseNet中的dense_block模块的实现
运行环境：tensorflow2.1+python3.7
代码如下：
#denseNet121的实现
import tensorflow as tf
from tensorflow.keras.layers import Dense,Conv2D,BatchNormalization,AveragePooling2D,Flatten
from tensorflow.keras.layers import MaxPooling2D,Activation,Concatenate,GlobalAveragePooling2D
from tensorflow.keras import Input,Model
def dense_block(x,blocks):
for i in range(blocks):
x=conv2d_block(x,32)
return x
def conv2d_block(x,grow_rate):
#在tf中，axis是从0开始计算的，3表示channel层
bn_axis=3
x1=BatchNormalization(axis=bn_axis,epsilon=1.0001e-5)(x)
x1=Activation('relu')(x1)
x1=Conv2D(grow_rate*4,(1,1),padding='same')(x1)
x1=BatchNormalization(axis=bn_axis,epsilon=1.0001e-5)(x1)
x1=Activation('relu')(x1)
x1=Conv2D(grow_rate,(3,3),padding='same')(x1)
x=Concatenate()([x,x1])
return x
def transition_block(x,reduction):
bn_axis=3
x=BatchNormalization(axis=bn_axis,epsilon=1.001e-5)(x)
x=Activation('relu')(x)
x=Conv2D(int(x.shape[bn_axis]*reduction),(1,1),padding='same')(x)
x=AveragePooling2D(strides=(2,2))(x)
return x
def denseNet(inputs,blocks):
x=Conv2D(24,(7,7),padding='same',strides=2)(inputs)
x=MaxPooling2D(pool_size=(3,3),strides=2,padding='same')(x)
for i,block in enumerate(blocks):
x=dense_block(x,block)
if i!=len(blocks)-1:
x=transition_block(x,0.5)
x=GlobalAveragePooling2D()(x)
x=Flatten()(x)
x=Dense(1000,activation='relu')(x)
return x
inputs=Input([224,224,3])
blocks=[6,12,24,16]
#x=dense_block(inputs,6,"dense")
#outputs=transition_block(x,0.5,"dense")
outputs=denseNet(inputs,blocks)
model=Model(inputs,outputs,name="densenet")
model.summary()运行结果：
"""
Model: "densenet"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 112, 112, 24) 3552 input_1[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 56, 56, 24) 0 conv2d[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 56, 56, 24) 96 max_pooling2d[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 56, 56, 24) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 56, 56, 128) 3200 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 56, 56, 128) 512 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 56, 56, 128) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 56, 56, 32) 36896 activation_1[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 56, 56, 56) 0 max_pooling2d[0][0]
conv2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 56, 56, 56) 224 concatenate[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 56, 56, 56) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 56, 56, 128) 7296 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 56, 56, 128) 512 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 56, 56, 128) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 56, 56, 32) 36896 activation_3[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 56, 56, 88) 0 concatenate[0][0]
conv2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 56, 56, 88) 352 concatenate_1[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 56, 56, 88) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 56, 56, 128) 11392 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 56, 56, 128) 512 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 56, 56, 128) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 56, 56, 32) 36896 activation_5[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 56, 56, 120) 0 concatenate_1[0][0]
conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 56, 56, 120) 480 concatenate_2[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 56, 56, 120) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 56, 56, 128) 15488 activation_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 56, 56, 128) 512 conv2d_7[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 56, 56, 128) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 56, 56, 32) 36896 activation_7[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 56, 56, 152) 0 concatenate_2[0][0]
conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 56, 56, 152) 608 concatenate_3[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 56, 56, 152) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 56, 56, 128) 19584 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 56, 56, 128) 512 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 56, 56, 128) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 56, 56, 32) 36896 activation_9[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 56, 56, 184) 0 concatenate_3[0][0]
conv2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 56, 56, 184) 736 concatenate_4[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 56, 56, 184) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 56, 56, 128) 23680 activation_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 56, 56, 128) 512 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 56, 56, 128) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 56, 56, 32) 36896 activation_11[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 56, 56, 216) 0 concatenate_4[0][0]
conv2d_12[0][0]
"""在上述结果中的concatenate_5，他的输出是concatenate_4+conv2d_12，也就是dense_block中的密集连接（在这里有所体现，也是核心所作）。
注：input layer之后的7*7卷积层的卷积层数为24，论文中提到应设为2*k
参考资料：
https://blog.csdn.net/weixin_44791964/article/details/105472196