resnet一维数据代码

对于一维数据，可以使用一维卷积神经网络（1D Convolutional Neural Network）来构建ResNet。以下是一个示例代码：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, Activation, Add

def resnet_block(inputs, filters, kernel_size, strides):
    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    x = Add()([x, inputs])
    x = Activation('relu')(x)
    return x

def build_resnet(input_shape):
    inputs = Input(shape=input_shape)
    x = Conv1D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = resnet_block(x, filters=64, kernel_size=3, strides=2)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = tf.keras.layers.GlobalAveragePooling1D()(x)
    outputs = tf.keras.layers.Dense(units=num_classes, activation='softmax')(x)

    model = Model(inputs=inputs, outputs=outputs)
    return model

# 定义输入维度和类别数量
input_shape = (100, 1)  # 输入数据的形状，例如 (时间步长, 特征数)
num_classes = 10  # 类别数量

# 构建模型
model = build_resnet(input_shape)
model.summary()