用tensorflow2写GCNet的代码

以下是使用TensorFlow 2实现GCNet的代码：

import tensorflow as tf

class GCBlock(tf.keras.layers.Layer):
    def __init__(self, channels, reduction=16):
        super(GCBlock, self).__init__()
        self.channels = channels
        self.reduction = reduction
        self.global_avg_pool = tf.keras.layers.GlobalAvgPool2D()
        self.fc1 = tf.keras.layers.Dense(units=self.channels // self.reduction, activation='relu')
        self.fc2 = tf.keras.layers.Dense(units=self.channels, activation='sigmoid')
    
    def call(self, inputs):
        x = self.global_avg_pool(inputs)
        x = self.fc1(x)
        x = self.fc2(x)
        x = tf.reshape(x, [-1, self.channels, 1, 1])
        return inputs * x

def GCNet(inputs):
    x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(inputs)
    x = GCBlock(channels=64)(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
    x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(x)
    x = GCBlock(channels=128)(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
    x = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(x)
    x = GCBlock(channels=256)(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(units=128, activation='relu')(x)
    outputs = tf.keras.layers.Dense(units=10, activation='softmax')(x)
    return outputs

inputs = tf.keras.layers.Input(shape=(28, 28, 1))
outputs = GCNet(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)

在上面的代码中，我们定义了一个GCBlock类，该类实现了GCNet中的GC模块。然后我们定义了一个GCNet函数，该函数使用Conv2D、GCBlock、MaxPooling2D和Dense等层来构建GCNet模型。最后，我们使用Keras的Model类将输入和输出包装在一起以创建完整的模型。

请注意，此代码仅用于演示目的，并可能需要进行调整以适合您的数据集和任务。