gated cnn的python代码

以下是一个简单的示例，展示了如何使用Python实现Gated CNN模型：

import tensorflow as tf
from tensorflow.keras.layers import Conv1D, Multiply, Activation, Input, Dense, Flatten
from tensorflow.keras.models import Model

def gated_cnn_model(input_shape, num_classes):
    # 输入层
    inputs = Input(shape=input_shape)

    # Gated CNN模型
    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(inputs)
    gate = Conv1D(filters=128, kernel_size=3, activation='sigmoid')(inputs)
    gated_conv = Multiply()([conv, gate])

    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(gated_conv)
    gate = Conv1D(filters=128, kernel_size=3, activation='sigmoid')(gated_conv)
    gated_conv = Multiply()([conv, gate])

    flatten = Flatten()(gated_conv)
    dense = Dense(128, activation='relu')(flatten)
    output = Dense(num_classes, activation='softmax')(dense)

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

# 定义输入形状和类别数量
input_shape = (100, 1) # 假设输入数据维度是100维
num_classes = 10 # 假设有10个类别

# 创建并编译模型
model = gated_cnn_model(input_shape, num_classes)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 打印模型结构
model.summary()

这是一个简单的Gated CNN模型的实现代码。你可以根据自己的需求进行修改和扩展。注意，这只是一个示例，具体的实现可能需要根据你的具体问题进行调整。