自定义损失功能不会在Pytorch中最小化

Question

我正在使用pytorch代码在无监督的设置中训练自定义损失功能。但是，在训练阶段，损失不会下降，并且在训练阶段保持不变。请参阅下面的培训代码段：

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

我在培训中缺少一些东西吗？我遵循此示例/教程。在这方面的任何帮助和指示都将不胜感激。

Answer 1

尝试自定义损失功能的此结构，并进行必要的更改。通过在代码中编写此语句来使用此损失功能：

criterion = Custom_Loss()

在这里，我显示了一个名为Custom_loss的自定义损失，该损失为输入2种输入X和Y。然后，它重塑x与y相似，最后通过计算重塑x和y之间的L2差来返回损失。这是您经常在培训网络中经常遇到的标准件事。

认为x是形状(5,10(，y将被形状(5,5,10(。因此，我们需要为X添加一个维度，然后沿添加的维度重复以匹配y的尺寸。然后，(x-y(将是形状(5,5,10(。我们将不得不添加所有三个维度，即三个Torch.Sum((才能获得标量。

    class Custom_Loss(torch.nn.Module):
    
    def __init__(self):
        super(Regress_Loss,self).__init__()
        
    def forward(self,x,y):
        y_shape = y.size()[1]
        x_added_dim = x.unsqueeze(1)
        x_stacked_along_dimension1 = x_added_dim.repeat(1,NUM_WORDS,1)
        diff = torch.sum((y - x_stacked_along_dimension1)**2,2)
        totloss = torch.sum(torch.sum(torch.sum(diff)))
        return totloss