TCN代码keras版本，加入残差网络,和卷积层深度

以下是一个带有残差网络和可配置卷积层深度的TCN代码示例：

from keras.layers import Input, Dense, Dropout, Add
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.models import Model

def tcn_resnet(input_shape, num_classes, num_layers=8):
    # Input layer
    inputs = Input(shape=input_shape)

    # Convolutional layers
    x = inputs
    skips = []
    for i in range(num_layers):
        d = 2 ** i
        # Dilated convolution
        res = Conv1D(filters=64, kernel_size=3, strides=1, dilation_rate=d, padding='causal', activation='relu')(x)
        res = Dropout(0.5)(res)
        # Residual connection
        if i > 0:
            x = Add()([x, res])
        else:
            x = res
        skips.append(res)

    # Global Max Pooling layer
    x = GlobalMaxPooling1D()(x)

    # Fully connected layers
    for i in range(2):
        x = Dense(64, activation='relu')(x)
        x = Dropout(0.5)(x)
    outputs = Dense(num_classes, activation='softmax')(x)

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

这个TCN模型包含了一个残差网络，它会在每个卷积层中添加一个残差连接。你可以通过调整`num_layers`参数来配置卷积层的深度。这个模型还包含了两个全连接层，用于最终的分类任务。