The problem about the loss backward()

autograd
1 
g_error = generator_loss(x)
g_error.backward()

Then the error showed:
backward() takes 1 positional argument but 2 were given.

The value of g_error is tensor([16.8434], grad_fn=)

I want to know the cause of this problem and the way to solve.

2

Could you post a small executable code snippet?
This would make debugging a bit easier.

3

Thank you very much! Here is the related code.

class VerificationMatcher_ldaloss(nn.Module):
    def __init__(self,te,P,y,lamda,cta,w,mu,sigma,sr,T):
        super(VerificationMatcher_ldaloss,self).__init__()
        self.te=te
        self.P=P
        self.y=y
        self.lamda=lamda
        self.cta=cta
        self.sr=sr
        self.w=w
        self.mu=mu
        self.sigma=sigma
        self.T=T
        #self.beta=beta
        return
    def forward(self,x):
        fwa=extractiVector()#self.y+x,self.sr,self.w,self.mu,self.sigma)
        #print(wa)
        voice=self.y+x
        wa=fwa(voice,self.sr,self.w,self.mu,self.sigma,self.T)
        ta=self.P.float().mm(wa)
        
        ta=torch.t(ta)
        #self.te=torch.t(self.te)
        #print(te)
        #te=torch()
        #print(torch.norm(self.te.float(), 2, 1))
        #print(2)
        cos = nn.CosineSimilarity(dim=0, eps=1e-6)
        #print(self.te)
        loss =1- self.lamda*(cos(self.te[0].float(),ta[0])-self.cta)+torch.norm(x).item()
        print(loss)
        return loss

generator_loss=VerificationMatcher_ldaloss(te=te,P=P,y=y,lamda=lamda,cta=cta,
w=w,mu=mu,sigma=sigma,sr=sr,T=T)
def train_a_gan(G_net, G_optimizer,generator_loss, show_every=250, 
                noise_size=200, num_epochs=1000):
    iter_count = 0
    sample_noise = (torch.rand(1, noise_size) - 0.5) / 0.5
    for epoch in range(num_epochs):
        #sample_noise = (torch.rand(1, noise_size) - 0.5) / 0.5 # -1 ~ 1 的均匀分布
        g_fake_seed = Variable(sample_noise)#.cuda()
        if torch.cuda.is_available():
            g_fake_seed=g_fake_seed.cuda()
        fake_voices = G_net(g_fake_seed) # 生成的假的数据
        
        g_error = generator_loss(fake_voices)
        
        G_optimizer.zero_grad()
        #weights=torch.ones(1)*0.1
        #g_error.backward(weights,retain_graph=True)
        g_error.backward()
        G_optimizer.step() # 优化生成网络
        if (iter_count % show_every == 0):
            print('Iter: {}, G:{:.4}'.format(iter_count,g_error.item()))
            voices_numpy = fake_voices.data.cpu().numpy()
            #show_images(imgs_numpy[0:16])
            #plt.show()
            #print()
        iter_count += 1

train_a_gan(G_net, G_optimizer,generator_loss, show_every=25, noise_size=200, num_epochs=100001)

The error is:
TypeError: backward() takes 1 positional argument but 2 were given
in the line " g_error.backward()"

4

Thanks for the code!
Unfortunately I cannot debug it smoothly, as all arguments (with their shapes) are missing as well as the definition of extractiVector().

One issue in your forward method is, that you are detaching torch.norm(x) by calling item on it, thus it works as a constant value.
The error message is strange given your code, as no arguments are passed to g_error.backward().
Could you try to simplify the code a bit or post all arguments using torch.randn with the corresponding shapes?