model = Sequential() model.add(Dense(units=32, activation='relu', input_dim=5)) model.add(Dense(units=16, activation='relu')) model.add(Dense(units=8, activation='relu')) model.add(Dense(units=16, activation='sigmoid')) model.summary()

这是一个基于Keras的神经网络模型的定义和摘要。模型包含了4个密集连接层(Dense)。第一个层有32个隐藏单元，激活函数为ReLU，输入维度为5。第二个和第三个层都有16个隐藏单元，激活函数为ReLU。最后一个层有16个隐藏单元，激活函数为Sigmoid。

以下是模型的摘要信息：

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 32)                192       
_________________________________________________________________
dense_1 (Dense)              (None, 16)                528       
_________________________________________________________________
dense_2 (Dense)              (None, 8)                 136       
_________________________________________________________________
dense_3 (Dense)              (None, 16)                144       
=================================================================
Total params: 1,000
Trainable params: 1,000
Non-trainable params: 0
_________________________________________________________________