tensorflow2 一维残差网络代码

以下是一个简单的一维残差网络的TensorFlow 2代码示例：

import tensorflow as tf

class ResNet(tf.keras.Model):
    def __init__(self, num_classes):
        super(ResNet, self).__init__()
        self.conv1 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.conv2 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')
        self.bn2 = tf.keras.layers.BatchNormalization()
        self.conv3 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', padding='same')
        self.bn3 = tf.keras.layers.BatchNormalization()
        self.pool1 = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)
        
        self.conv4 = tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')
        self.bn4 = tf.keras.layers.BatchNormalization()
        self.conv5 = tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')
        self.bn5 = tf.keras.layers.BatchNormalization()
        self.conv6 = tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', padding='same')
        self.bn6 = tf.keras.layers.BatchNormalization()
        self.pool2 = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)
        
        self.conv7 = tf.keras.layers.Conv1D(filters=256, kernel_size=3, activation='relu', padding='same')
        self.bn7 = tf.keras.layers.BatchNormalization()
        self.conv8 = tf.keras.layers.Conv1D(filters=256, kernel_size=3, activation='relu', padding='same')
        self.bn8 = tf.keras.layers.BatchNormalization()
        self.conv9 = tf.keras.layers.Conv1D(filters=256, kernel_size=3, activation='relu', padding='same')
        self.bn9 = tf.keras.layers.BatchNormalization()
        self.pool3 = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)
        
        self.flatten = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(units=512, activation='relu')
        self.dense2 = tf.keras.layers.Dense(units=num_classes, activation='softmax')
        
    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = tf.keras.layers.add([x, inputs])
        x = tf.keras.activations.relu(x)
        x = self.pool1(x)
        
        shortcut = x
        
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = self.conv6(x)
        x = self.bn6(x)
        x = tf.keras.layers.add([x, shortcut])
        x = tf.keras.activations.relu(x)
        x = self.pool2(x)
        
        shortcut = x
        
        x = self.conv7(x)
        x = self.bn7(x)
        x = self.conv8(x)
        x = self.bn8(x)
        x = self.conv9(x)
        x = self.bn9(x)
        x = tf.keras.layers.add([x, shortcut])
        x = tf.keras.activations.relu(x)
        x = self.pool3(x)
        
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dense2(x)
        
        return x

这个ResNet模型由9个卷积层和3个池化层组成。在每个卷积层之后，都有一个批量归一化层。在每个残差块中，我们将输入添加到输出并对其进行ReLU激活。最后，我们将扁平层和两个密集层添加到模型中，以获得最终的分类输出。