Functional and Cell

Operator Functional Interface

nn Subnet Interface

Cell Training State Change

Some Tensor operations in neural networks do not behave the same during training and inference, e.g., nn.Dropout performs random dropout during training but not during inference, and nn.BatchNorm requires updating the mean and var variables during training and fixing their values unchanged during inference. So we can set the state of the neural network through the Cell.set_train interface.

When set_train is set to True, the neural network state is train, and the default value of set_train interface is True:

   net.set_train()
   print(net.phase)

   train

When set_train is set to False, the neural network state is predict:

   net.set_train()
   print(net.phase)

   predict