boolean value of tensor with more than one value is ambiguous

1 
def runSim(pa, pb, ca_type,cb_type,g_ca_pa,g_ca_pb,g_cb_pa,g_cb_pb):

    pa, pb, ca_type,cb_type,g_ca_pa,g_ca_pb,g_cb_pa,g_cb_pb = Variable(pa), Variable(pb), Variable(ca_type),Variable(cb_type),Variable(g_ca_pa),Variable(g_ca_pb),Variable(g_cb_pa),Variable(g_cb_pb)
    optimizer = optim.SGD(net.parameters(), lr=0.01,
                          momentum=0.01)
    BATCH_SIZE = 64
    EPOCH = 10

    torch_dataset = Data.TensorDataset(pa, pb, ca_type,cb_type,g_ca_pa,g_ca_pb,g_cb_pa,g_cb_pb)

    loader = Data.DataLoader(
        dataset=torch_dataset, 
        batch_size=BATCH_SIZE, 
        shuffle=True, num_workers=2,)
    iteration = 0
    # start training
    for epoch in range(EPOCH):
        print("EPOCH:",epoch)
        for step, (batch_pa, batch_pb, batch_ca_type,batch_cb_type,batch_g_ca_pa,batch_g_ca_pb,batch_g_cb_pa,batch_g_cb_pb) in enumerate(loader): # for each training step
            #batch_x1,  batch_x2, batch_f1, batch_f2 = batch_x1.to(device), batch_x2.to(device), batch_f1.to(device), batch_f2.to(device)


            #b_pa = Variable(batch_pa).to(device)
            #b_pb = Variable(batch_pb).to(device)
            #b_ca_type = Variable(batch_ca_type).to(device)
            #b_cb_type = Variable(batch_cb_type).to(device)
            #b_g_ca_pa = Variable(batch_g_ca_pa).to(device)
            #b_g_ca_pb = Variable(batch_g_ca_pb).to(device)
            #b_g_cb_pa = Variable(batch_g_cb_pa).to(device)
            #b_g_cb_pb = Variable(batch_g_cb_pb).to(device)



            b_pa = batch_pa.to(device)
            b_pb = batch_pb.to(device)
            b_ca_type = batch_ca_type.to(device)
            b_cb_type = batch_cb_type.to(device)
            b_g_ca_pa = batch_g_ca_pa.to(device)
            b_g_ca_pb = batch_g_ca_pb.to(device)
            b_g_cb_pa = batch_g_cb_pa.to(device)
            b_g_cb_pb = batch_g_cb_pb.to(device)
            
            prediction_1 = (b_g_ca_pa + b_ca_type*net(b_pa)).to(device)     # input x and predict based on x
            prediction_2 = (b_g_ca_pb + b_ca_type*net(b_pb)).to(device)
            prediction_3 = (b_g_cb_pa + b_cb_type*net(b_pa)).to(device)
            prediction_4 = (b_g_cb_pb + b_cb_type*net(b_pb)).to(device)

            loss = deepci_loss(prediction_1, prediction_2,prediction_3,prediction_4)     # must be (1. nn output, 2. target)
            if iteration%30 == 0:
                print(loss)
            iteration +=1
            optimizer.zero_grad()   # clear gradients for next train
            loss.backward()         # backpropagation, compute gradients
            optimizer.step()        # apply gradients
        #print(calculate_misclassification_error(example_data_val, example_targets_val, net))
    return (test_error(net))
2

I cannot see which line of code is raising the error, but the issue is raised if you e.g. try to use an if condition using multiple values as seen here:

x = torch.tensor([True, False])
if x:
    print("true")
# RuntimeError: Boolean value of Tensor with more than one value is ambiguous

Use .all() or e.g. .any() instead:

if x.any():
    print("any")
# any
3

I saw the error!! Thanks!!