# Weird result when run with the autograd.record

Hi!

I have some experiences with tensorflow and is new to mxnet.

I define a hybrid net which is composed of a 3D convolution net (SampleNet) and a pre-trained PSP net.

I use the following codes to fixed the parameters in the PSP net.

``````def net(input)
trainer = gluon.Trainer(self.sample_net.collect_params(),'sgd',{'learning_rate':0.0001})
The command `autograd.record` puts your network in training mode, which means that layers such as dropout, BatchNorm will behave differently than in inference mode. By default, the output of a network is in `autograd.predict_mode`. Therefore when you comment out the line `with autograd.record` your network will use summary statistics (for BatchNorm) and average of the activations (dropout, instead of killing a random sample of them). Hope this helps.