tensorflow 1D resnet18 代码

抱歉，ResNet-18 是一个用于图像分类的经典网络结构，通常用于处理二维图像数据。它不适用于一维数据（如时间序列或信号数据）。如果你想使用 ResNet-18 处理一维数据，你可以将输入数据扩展为二维数据输入网络。下面是一个使用 TensorFlow 实现的示例代码：

import tensorflow as tf

def resnet_block(inputs, filters, kernel_size, strides):
    x = tf.keras.layers.Conv1D(filters, kernel_size, strides=strides, padding='same')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    
    x = tf.keras.layers.Conv1D(filters, kernel_size, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    
    if strides > 1:
        shortcut = tf.keras.layers.Conv1D(filters, 1, strides=strides, padding='same')(inputs)
        shortcut = tf.keras.layers.BatchNormalization()(shortcut)
    else:
        shortcut = inputs
    
    x = tf.keras.layers.Add()([x, shortcut])
    x = tf.keras.layers.ReLU()(x)
    
    return x

def create_resnet18(input_shape, num_classes):
    inputs = tf.keras.layers.Input(shape=input_shape)
    
    x = tf.keras.layers.Conv1D(64, 7, strides=2, padding='same')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    x = tf.keras.layers.MaxPooling1D(3, strides=2, padding='same')(x)
    
    x = resnet_block(x, 64, 3, strides=1)
    x = resnet_block(x, 64, 3, strides=1)
    
    x = resnet_block(x, 128, 3, strides=2)
    x = resnet_block(x, 128, 3, strides=1)
    
    x = resnet_block(x, 256, 3, strides=2)
    x = resnet_block(x, 256, 3, strides=1)
    
    x = resnet_block(x, 512, 3, strides=2)
    x = resnet_block(x, 512, 3, strides=1)
    
    x = tf.keras.layers.GlobalAveragePooling1D()(x)
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
    
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

# 定义输入形状和类别数
input_shape = (100, 1) # 这里假设输入是长度为100的一维信号数据
num_classes = 10

# 创建ResNet-18模型
model = create_resnet18(input_shape, num_classes)

# 打印模型结构
model.summary()

请确保你已经导入了 TensorFlow 库，并且安装了对应的版本。你可以根据自己的需要进行修改和调整。