使用 Kears 实现ResNet-34 CNN

让我们开始实现ResNet-34.
一、创建一个ResidualUnit层。

from functools import partial
import tensorflow as tf
from tensorflow import keras



DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, strides=1,
                        padding="SAME", use_bias=False)

class ResidualUnit(keras.layers.Layer):
    def __init__(self, filters, strides=1, activation="relu", **kwargs):
        super().__init__(**kwargs)
        self.activation = keras.activations.get(activation)
        self.main_layers = [
            DefaultConv2D(filters, strides=strides),
            keras.layers.BatchNormalization(),
            self.activation,
            DefaultConv2D(filters),
            keras.layers.BatchNormalization()]
        self.skip_layers = []
        if strides > 1:
            self.skip_layers = [
                DefaultConv2D(filters, kernel_size=1, strides=strides),
                keras.layers.BatchNormalization()]

    def call(self, inputs):
        Z = inputs
        for layer in self.main_layers:
            Z = layer(Z)
        skip_Z = inputs
        for layer in self.skip_layers:
            skip_Z = layer(skip_Z)
        return self.activation(Z + skip_Z)

由上，在构造函数中，我们创建了所需要的所有层：
主要层、跳过层（当步幅大于1时需要）。
在call（）方法中，我们使输入经过主要层、跳过层，然后添加输出层并应用激活函数。

二、用Sequential 模型来构建ResNet-34.
这个模型 实际上是一个 非常长的 层序列。
现在有了上面的ResidualUnit类，我们可以将每个残差单元是为一个层。

model = keras.models.Sequential()
model.add(DefaultConv2D(64, kernel_size=7, strides=2,
                        input_shape=[224, 224, 3]))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation("relu"))
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2, padding="SAME"))
prev_filters = 64
for filters in [64] * 3 + [128] * 4 + [256] * 6 + [512] * 3:
    strides = 1 if filters == prev_filters else 2
    model.add(ResidualUnit(filters, strides=strides))
    prev_filters = filters
model.add(keras.layers.GlobalAvgPool2D())
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(10, activation="softmax"))

这里将ResidualUnit 层 加到模型的循环：
前3个RU（残差单元） 具有64个滤波器，然后余下4个有128个。以此类推。
当滤波器的数量与上一个RU层相同时，将步幅设置为1，否则为2.
然后添加ResidualUnit ，最后更新prev_filters.

三、别忘了实例化模型。

model.summary()

结果：

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param # 
=================================================================
 conv2d (Conv2D)             (None, 112, 112, 64)      9408      
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 64)     256       
 ormalization)                                                   
                                                                 
 activation (Activation)     (None, 112, 112, 64)      0         
                                                                 
 max_pooling2d (MaxPooling2D  (None, 56, 56, 64)       0         
 )                                                               
                                                                 
 residual_unit (ResidualUnit  (None, 56, 56, 64)       74240     
 )                                                               
                                                                 
 residual_unit_1 (ResidualUn  (None, 56, 56, 64)       74240     
 it)                                                             
                                                                 
 residual_unit_2 (ResidualUn  (None, 56, 56, 64)       74240     
 it)                                                             
                                                                 
 residual_unit_3 (ResidualUn  (None, 28, 28, 128)      230912    
 it)                                                             
                                                                 
 residual_unit_4 (ResidualUn  (None, 28, 28, 128)      295936    
 it)                                                             
                                                                 
 residual_unit_5 (ResidualUn  (None, 28, 28, 128)      295936    
 it)                                                             
                                                                 
 residual_unit_6 (ResidualUn  (None, 28, 28, 128)      295936    
 it)                                                             
                                                                 
 residual_unit_7 (ResidualUn  (None, 14, 14, 256)      920576    
 it)                                                             
                                                                 
 residual_unit_8 (ResidualUn  (None, 14, 14, 256)      1181696   
 it)                                                             
                                                                 
 residual_unit_9 (ResidualUn  (None, 14, 14, 256)      1181696   
 it)                                                             
                                                                 
 residual_unit_10 (ResidualU  (None, 14, 14, 256)      1181696   
 nit)                                                            
                                                                 
 residual_unit_11 (ResidualU  (None, 14, 14, 256)      1181696   
 nit)                                                            
                                                                 
 residual_unit_12 (ResidualU  (None, 14, 14, 256)      1181696   
 nit)                                                            
                                                                 
 residual_unit_13 (ResidualU  (None, 7, 7, 512)        3676160   
 nit)                                                            
                                                                 
 residual_unit_14 (ResidualU  (None, 7, 7, 512)        4722688   
 nit)                                                            
                                                                 
 residual_unit_15 (ResidualU  (None, 7, 7, 512)        4722688   
 nit)                                                            
                                                                 
 global_average_pooling2d (G  (None, 512)              0         
 lobalAveragePooling2D)                                          
                                                                 
 flatten (Flatten)           (None, 512)               0         
                                                                 
 dense (Dense)               (None, 10)                5130      
                                                                 
=================================================================
Total params: 21,306,826
Trainable params: 21,289,802
Non-trainable params: 17,024
_________________________________________________________________

参考：
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 作者： Aurelien Geron(法语) , 又 O Reilly 出版， 书号 978-1-492-03264-9。