具有可定制隐藏层的TensorFlow模型的子类定义

import tensorflow as tf

class CustomModel(tf.keras.Model):
    def __init__(self, num_hidden_layers, hidden_layer_size, activation):
        super(CustomModel, self).__init__()
        self.hidden_layers = []
        for _ in range(num_hidden_layers):
            self.hidden_layers.append(tf.keras.layers.Dense(hidden_layer_size, activation=activation))
        self.output_layer = tf.keras.layers.Dense(1, activation='sigmoid')

    def call(self, inputs):
        x = inputs
        for hidden_layer in self.hidden_layers:
            x = hidden_layer(x)
        output = self.output_layer(x)
        return output

# 创建一个具有2个隐藏层，每个隐藏层大小为64，激活函数为ReLU的模型
model = CustomModel(num_hidden_layers=2, hidden_layer_size=64, activation='relu')