编写一维时序卷积残差网络代码中适用于keras

以下是一个基于Keras的一维时序卷积残差网络代码示例：

from keras.layers import Input, Conv1D, BatchNormalization, Activation, Add, MaxPooling1D, Flatten, Dense
from keras.models import Model

def resnet_block(inputs, filters, kernel_size, padding='same', strides=1):
    # First Conv1D layer
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding=padding, strides=strides)(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # Second Conv1D layer
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding=padding, strides=strides)(x)
    x = BatchNormalization()(x)

    # Shortcut connection
    shortcut = inputs
    if strides != 1 or filters != shortcut.shape[-1]:
        shortcut = Conv1D(filters=filters, kernel_size=1, padding=padding, strides=strides)(shortcut)
        shortcut = BatchNormalization()(shortcut)

    # Add shortcut and residual
    x = Add()([x, shortcut])
    x = Activation('relu')(x)

    return x

def resnet(input_shape, num_classes):
    inputs = Input(shape=input_shape)

    # Initial Conv1D layer
    x = Conv1D(filters=64, kernel_size=7, padding='same', strides=2)(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)

    # ResNet blocks
    x = resnet_block(x, filters=64, kernel_size=3, padding='same', strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, padding='same', strides=1)
    x = resnet_block(x, filters=128, kernel_size=3, padding='same', strides=2)
    x = resnet_block(x, filters=128, kernel_size=3, padding='same', strides=1)
    x = resnet_block(x, filters=256, kernel_size=3, padding='same', strides=2)
    x = resnet_block(x, filters=256, kernel_size=3, padding='same', strides=1)

    # Average pooling and output layers
    x = Flatten()(x)
    x = Dense(num_classes, activation='softmax')(x)

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

在此示例中，我们定义了一个包含6个残差块的一维时序卷积残差网络。每个残差块包含两个卷积层和一个快捷连接，通过调用`resnet_block`函数来实现。在模型定义的最后，我们使用`Flatten`层将输出展平，并使用一个具有softmax激活函数的全连接层作为输出层。