跳跃连接代码

跳跃连接（shortcut connection）可以方便神经网络学习较复杂的函数，从而提高模型的性能。下面是一个简单的跳跃连接的代码示例，实现了一个简单的卷积神经网络：

import tensorflow as tf

class ConvNet(tf.keras.Model):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')
        self.conv2 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')
        self.pool1 = tf.keras.layers.MaxPooling2D((2, 2))
        self.conv3 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same')
        self.conv4 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same')
        self.pool2 = tf.keras.layers.MaxPooling2D((2, 2))
        self.conv5 = tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same')
        self.conv6 = tf.keras.layers.Conv2D(1024, (3, 3), activation='relu', padding='same')
        self.pool3 = tf.keras.layers.MaxPooling2D((2, 2))
        self.flatten = tf.keras.layers.Flatten()
        self.fc1 = tf.keras.layers.Dense(1024, activation='relu')
        self.fc2 = tf.keras.layers.Dense(10, activation='softmax')
        
    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.conv2(x)
        skip1 = x
        x = self.pool1(x)
        x = self.conv3(x)
        x = self.conv4(x)
        skip2 = x
        x = self.pool2(x)
        x = self.conv5(x)
        x = self.conv6(x)
        skip3 = x
        x = self.pool3(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = tf.keras.layers.Add()([x, self.fc1(self.flatten(skip3))])
        x = tf.keras.layers.Add()([x, self.fc1(self.flatten(skip2))])
        x = tf.keras.layers.Add()([x, self.fc1(self.flatten(skip1))])
        x = self.fc2(x)
        return x

在上面的代码中，我们使用了跳跃连接来连接了网络中的不同层，从而让网络能够更好地学习复杂的函数。具体来说，我们在第 2、4、6 层卷积层之后分别保存了当前的输出，然后在神经网络的最后一层全连接层中将这些输出与当前的输出进行相加，从而得到最终的输出。这样一来，我们就可以在不影响前向传播的情况下引入更多的信息，从而提高模型的性能。