Insert your OFDM code into my CNN

[jasmine202106]

Hi,
sorry to bother you.
i met some problem when using your ofdm code, and want to get your help.
that is, i want to insert your ofdm code into my CNN network that is for camera calibration, but meet gradient vanish.
below is the run result on the terminal. netE is before ofdm channel, netG is after ofdm.
channel parameters: M = 2048 #subcarrier; K= 64 #CP; P = 1; N_pilot = 2; L = 8 and decay = 4
would you please give me some suggestion for such problem?
if no ofdm, the CNN network is ok.
thanks very much.

-->name: netE.conv2.bias -->grad_value: tensor(1.3146, device='cuda:0')
-->name: netE.conv3.weight -->grad_value: tensor(-0.0004, device='cuda:0')
-->name: netE.conv3.bias -->grad_value: tensor(-0.3970, device='cuda:0')
-->name: netE.conv4.weight -->grad_value: tensor(0.0002, device='cuda:0')
-->name: netE.conv4.bias -->grad_value: tensor(-0.0917, device='cuda:0')
-->name: netE.res1_conv1.weight -->grad_value: tensor(-0.0005, device='cuda:0')
-->name: netE.res1_conv1.bias -->grad_value: tensor(-0.0209, device='cuda:0')
-->name: netE.res1_conv2.weight -->grad_value: tensor(-0.0033, device='cuda:0')
-->name: netE.res1_conv2.bias -->grad_value: tensor(-0.0383, device='cuda:0')
-->name: netE.res1_conv3.weight -->grad_value: tensor(0.0007, device='cuda:0')
-->name: netE.res1_conv3.bias -->grad_value: tensor(-0.0154, device='cuda:0')
-->name: netE.res2_conv1.weight -->grad_value: tensor(0.0004, device='cuda:0')
-->name: netE.res2_conv1.bias -->grad_value: tensor(-0.0005, device='cuda:0')
-->name: netE.res2_conv2.weight -->grad_value: tensor(0.0004, device='cuda:0')
-->name: netE.res2_conv2.bias -->grad_value: tensor(-0.0006, device='cuda:0')
-->name: netE.res2_conv3.weight -->grad_value: tensor(4.6024e-05, device='cuda:0')
-->name: netE.res2_conv3.bias -->grad_value: tensor(-7.4400e-05, device='cuda:0')
-->name: netE.res2_skip.weight -->grad_value: tensor(2.7986e-05, device='cuda:0')
-->name: netE.res2_skip.bias -->grad_value: tensor(3.6480e-05, device='cuda:0')
-->name: netE.res3_conv1.weight -->grad_value: tensor(3.5228e-05, device='cuda:0')
-->name: netE.res3_conv1.bias -->grad_value: tensor(6.6632e-05, device='cuda:0')
-->name: netE.res3_conv2.weight -->grad_value: tensor(-2.3665e-05, device='cuda:0')
-->name: netE.res3_conv2.bias -->grad_value: tensor(8.1201e-05, device='cuda:0')
-->name: netE.res3_conv3.weight -->grad_value: tensor(-2.9679e-05, device='cuda:0')
-->name: netE.res3_conv3.bias -->grad_value: tensor(9.4048e-05, device='cuda:0')
-->name: netE.fc1.weight -->grad_value: tensor(4.7012e-05, device='cuda:0')
-->name: netE.fc1.bias -->grad_value: tensor(0.0001, device='cuda:0')
-->name: netE.fc2.weight -->grad_value: tensor(2.7713e-05, device='cuda:0')
-->name: netE.fc2.bias -->grad_value: tensor(0.0001, device='cuda:0')
-->name: netE.fc3.weight -->grad_value: tensor(-0.0030, device='cuda:0')
-->name: netE.fc3.bias -->grad_value: tensor(0.0055, device='cuda:0')
-->name: netG.fc4.weight -->grad_value: tensor(0.3041, device='cuda:0')
-->name: netG.fc4.bias -->grad_value: tensor(-19.7438, device='cuda:0')