class Residual(nn.Module): def __init__(self,in_c,out_c): super(Residual,self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), nn.Conv2d(in_channels = out_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), ) self.botneck = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pool = nn.MaxPool2d(kernel_size = 2,stride = 2) def forward(self,x): x_prim = x x = self.conv(x) x = self.botneck(x_prim) + x x = self.pool(x) return x改为tensorflow模型

以下是使用TensorFlow实现的Residual Block类的代码：

import tensorflow as tf

class Residual(tf.keras.Model):
    def __init__(self, in_c, out_c):
        super(Residual, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(out_c, 3, padding='same', activation='relu')
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.conv2 = tf.keras.layers.Conv2D(out_c, 3, padding='same', activation='relu')
        self.bn2 = tf.keras.layers.BatchNormalization()
        self.botneck = tf.keras.layers.Conv2D(out_c, 1)
        self.pool = tf.keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2))
        
    def call(self, x):
        x_prim = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.botneck(x_prim) + x
        x = self.pool(x)
        return x

这个类与PyTorch实现的Residual Block类非常相似，唯一的区别是使用了TensorFlow的卷积层和批归一化层，并且在调用函数时需要使用call()函数而不是forward()函数。