tensorflow 1d resnet18

在 TensorFlow 中实现 1D 的 ResNet-18 模型可以通过以下步骤完成：

1. 导入所需的库和模块：

import tensorflow as tf
from tensorflow.keras import layers

2. 定义 ResNet-18 的基本残差块（Residual Block）：

class ResidualBlock(layers.Layer):
    def __init__(self, filters, strides=1, activation='relu', **kwargs):
        super(ResidualBlock, self).__init__(**kwargs)
        self.conv1 = layers.Conv1D(filters, 3, strides=strides, padding='same')
        self.bn1 = layers.BatchNormalization()
        self.conv2 = layers.Conv1D(filters, 3, padding='same')
        self.bn2 = layers.BatchNormalization()
        self.activation = layers.Activation(activation)
        self.shortcut = tf.keras.Sequential()
        
        if strides > 1:
            self.shortcut.add(layers.Conv1D(filters, 1, strides=strides))
            self.shortcut.add(layers.BatchNormalization())

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = self.activation(x)
        x = self.conv2(x)
        x = self.bn2(x)
        
        shortcut = self.shortcut(inputs)
        x = layers.add([x, shortcut])
        x = self.activation(x)
        return x

3. 构建 ResNet-18 模型：

def ResNet18(input_shape, num_classes):
    inputs = tf.keras.Input(shape=input_shape)
    
    x = layers.Conv1D(64, 7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling1D(3, strides=2, padding='same')(x)

    x = ResidualBlock(64, strides=1)(x)
    x = ResidualBlock(64, strides=1)(x)
    x = ResidualBlock(128, strides=2)(x)
    x = ResidualBlock(128, strides=1)(x)
    x = ResidualBlock(256, strides=2)(x)
    x = ResidualBlock(256, strides=1)(x)
    x = ResidualBlock(512, strides=2)(x)
    x = ResidualBlock(512, strides=1)(x)

    x = layers.GlobalAveragePooling1D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)
    model = tf.keras.Model(inputs=inputs, outputs=x)
    return model

4. 创建模型实例：

model = ResNet18(input_shape=(input_length, input_channels), num_classes=num_classes)

5. 编译和训练模型：

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))