Tensorflow2.0学习笔记（八）Resnet

目录

1 退化问题？

 2 ResNet的残差学习单元（Residual Unit）

3 Resnet18

4 Resnet在不同层数时的网络配置

 

1 退化问题？

在不断增加神经网络的深度时，会出现一个退化的问题：

准确率会先上升然后达到饱和，再持续增加网络的深度则会导致准确率下降。

这并不是过拟合的问题，原因是不光在测试集上误差增大，训练集本身误差也会增大。

 2 ResNet的残差学习单元（Residual Unit）

                    

这个Residual block通过shortcut connection实现，通过shortcut将这个block的输入和输出进行加叠，这个简单的加法并不会给网络增加额外的参数和计算量，同时却可以大大增加模型的训练速度、提高训练效果，并且当模型的层数加深时，这个简单的结构能够很好的解决退化问题。

class BasicBlock(layers.Layer):
    def __init__(self, filter_num, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
        self.bn1 = layers.BatchNormalization()
        self.relu = layers.Activation('relu')
        self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
        self.bn2 = layers.BatchNormalization()
        if stride != 1:
            self.downsample = Sequential()
            self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
        else:
            self.downsample = lambda x: x

    def call(self, inputs, training=None):
        out = self.conv1(inputs)
        out = self.bn1(out, training=training)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out, training=training)
        identity = self.downsample(inputs)
        output = layers.add([out, identity])
        output = tf.nn.relu(output)
        return output

3 Resnet18

class ResNet(keras.Model):
    def __init__(self, layer_dims, num_classes=2): 
        super(ResNet, self).__init__()
        self.stem = Sequential([layers.Conv2D(64, (3, 3), strides=(1, 1)),
                                layers.BatchNormalization(),
                                layers.Activation('relu'),
                                layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding='same')
                                ])
        self.layer1 = self.build_resblock(64,  layer_dims[0])
        self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
        self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
        self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)
        self.avgpool = layers.GlobalAveragePooling2D()
        self.fc = layers.Dense(num_classes)

    def call(self, inputs, training=None):
        x = self.stem(inputs, training=training)
        x = self.layer1(x, training=training)
        x = self.layer2(x, training=training)
        x = self.layer3(x, training=training)
        x = self.layer4(x, training=training)
        x = self.avgpool(x)
        x = self.fc(x)
        return x

    def build_resblock(self, filter_num, blocks, stride=1):
        res_blocks = Sequential()
        res_blocks.add(BasicBlock(filter_num, stride))
        for _ in range(1, blocks):
            res_blocks.add(BasicBlock(filter_num, stride=1))
        return res_blocks


def resnet18():
    return ResNet([2, 2, 2, 2])

4 Resnet在不同层数时的网络配置