The grad is none, please help me.

[yiyi123123132]

I don't know why the grad is none.? this is my net code. CNN+ESN

class CNN_ESN(nn.Module):
def init(self, output_size,channel_num, device,drop_prob=0.5):
super(CNN_ESN, self).init()
self.conv = nn.Sequential(
nn.Conv2d(channel_num, 64, (5, 5), padding='same'),
nn.ReLU(),
nn.Conv2d(64, 128, (4, 4), padding='same'),
nn.ReLU(),
nn.Conv2d(128, 256, (4, 4), padding='same'),
nn.ReLU(),
nn.Conv2d(256, 64, (1, 1), padding='same'),
nn.ReLU(),
nn.MaxPool2d((2, 2)),
nn.Flatten(),
nn.Linear(1024, 512)
).to(device)
self.esn = ESN(4,128,128,output_steps='mean', readout_training='svd').to(device)
self.fc = nn.Linear(128,output_size).to(device)
self.dropout = nn.Dropout(drop_prob)
self.sig = nn.Sigmoid()
self.washout_rate = 0.2
def forward(self, x):
conv_result = self.conv(x).reshape(128,x.shape[0],-1)
conv_result =self.dropout(conv_result)
washout_lst = [int(self.washout_rate * conv_result.size(0))] * conv_result.size(1)
out,hn = self.esn(conv_result,washout_lst)
hn = hn.transpose(1,0)
logit = self.fc(hn).squeeze(1)
return self.sig(logit).squeeze(1)

    for inputs, labels in train_loader: 
        net.train()
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()  
        output = net(inputs) 
        pred = torch.round(output)
        loss = loss_func(output, labels)
        pred_list = [float(i) for i in pred.tolist()]
        for p, l in zip(pred_list, labels.tolist()):
            tr_pre.append(int(p))
            tr_lab.append(l)
        tr_loss.append(loss.item())
        loss.backward()
        optimizer.step()

and I print the grad as below, the 2nd epoch reulst:

conv.0.weight None
conv.0.bias None
conv.2.weight None
conv.2.bias None
conv.4.weight None
conv.4.bias None
conv.6.weight None
conv.6.bias None
conv.10.weight None
conv.10.bias None
esn.reservoir.weight_ih_l0 None
esn.reservoir.weight_hh_l0 None
esn.reservoir.bias_ih_l0 None
esn.readout.weight None
esn.readout.bias None
fc.weight tensor([[ 0.0480, -0.0079, 0.0929, 0.1358, 0.1022, -0.1127, 0.0495, 0.1056,
-0.0923, 0.0720, 0.1122, 0.0139, -0.0619, 0.0796, 0.1433, 0.0295,
0.0884, -0.0504, 0.0305, 0.0264, 0.1352, 0.0467, -0.0607, -0.0363,
-0.0114, -0.1393, -0.0917, 0.0194, 0.1076, -0.0713, -0.0487, 0.0433,
-0.0875, 0.0212, 0.1007, -0.0711, 0.1098, 0.0577, 0.0607, 0.0299,
0.0380, 0.0955, 0.0062, -0.0620, -0.0463, -0.0354, 0.1050, -0.0920,
0.0742, -0.0550, -0.1270, -0.0597, 0.0736, 0.0246, 0.0521, -0.0866,
-0.0065, -0.0764, 0.0087, -0.0810, 0.0551, 0.0999, 0.1078, -0.0082,
-0.0940, -0.0628, -0.0624, 0.0779, -0.0107, -0.0069, 0.0793, -0.0318,
0.0086, -0.1427, 0.0617, 0.0839, -0.0904, 0.0535, 0.0678, -0.0232,
0.0361, -0.0065, -0.0806, 0.0824, -0.0336, 0.0707, -0.1042, -0.0336,
-0.1039, 0.0715, -0.1403, -0.0068, -0.0912, -0.1043, 0.0455, -0.0223,
0.0448, 0.0321, 0.0844, -0.0734, -0.0050, 0.1083, 0.0027, -0.0897,
-0.1176, -0.1059, 0.1006, 0.0873, -0.0715, -0.1130, 0.0707, -0.0015,
0.0257, -0.0536, -0.0769, -0.0671, -0.0329, -0.0666, -0.0631, 0.1100,
-0.1013, -0.0392, -0.1062, 0.1276, -0.0686, -0.1200, -0.0669, 0.0424]])
fc.bias tensor([0.1520])

could tell me the reason?

[yiyi123123132]

net = md.CNN_ESN(output_size,channel_num, device,drop_prob=0.5)