mxnet.autograd.record¶

mxnet.autograd.record(train_mode=True)[source]¶

Returns an autograd recording scope context to be used in ‘with’ statement and captures code that needs gradients to be calculated.

Note

When forwarding with train_mode=False, the corresponding backward should also use train_mode=False, otherwise gradient is undefined.

Example:

with autograd.record():
    y = model(x)
    backward([y])
metric.update(...)
optim.step(...)

Parameters: train_mode (bool, default True) – Whether the forward pass is in training or predicting mode. This controls the behavior of some layers such as Dropout, BatchNorm.