我正在使用PyTorch代码在无监督设置中对自定义损失函数进行训练。然而，在训练阶段的五个时期内，损失不会下降并保持不变。请参阅下面的训练代码片段：

X = np.load(<data path>) #Load dataset which is a numpy array of N points with some dimension each.
num_samples, num_features = X.shape

gmm = GaussianMixture(n_components=num_classes, covariance_type='spherical')
gmm.fit(X)
z_gmm = gmm.predict(X)

R_gmm = gmm.predict_proba(X)
pre_R = Variable(torch.log(torch.from_numpy(R_gmm + 1e-8)).type(dtype), requires_grad=True)
R = torch.nn.functional.softmax(pre_R)

F = torch.stack(Variable(torch.from_numpy(X).type(dtype), requires_grad=True))
U = Variable(torch.from_numpy(gmm.means_).type(dtype), requires_grad=False)

z_pred = torch.max(R, 1)[1]

distances = torch.sum(((F.unsqueeze(1) - U) ** 2), dim=2)
custom_loss = torch.sum(R * distances) / num_samples

learning_rate = 1e-3
opt_train= torch.optim.Adam([train_var], lr = learning_rate)
U = torch.div(torch.mm(torch.t(R), F), torch.sum(R, dim=0).unsqueeze(1)) #In place assignment with a formula over variables and hence no gradient update is needed.

for epoch in range(max_epochs+1):
    running_loss = 0.0
    for i in range(stepSize):
    # zero the parameter gradients
    opt_train.zero_grad()

    # forward + backward + optimize
    loss = custom_loss
    loss.backward(retain_graph=True)
    opt_train.step()
    running_loss += loss.data[0]

if epoch % 25 == 0:
    print(epoch, loss.data[0]) # OR running_loss also gives the same values.
    running_loss = 0.0

O/P: 0 5.8993988037109375 25 5.8993988037109375 50 5.8993988037109375 75 5.8993988037109375 100 5.8993988037109375
我在培训中错过了什么吗？我遵循了这个example/tutorial。在这方面的任何帮助和指点将不胜感激。