PyTorch custom loss function : <p>How should a custom loss function be implemented ? Using below code is causing error : </p>

<pre><code>import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
import torch.utils.data as data_utils
import torch.nn as nn
import torch.nn.functional as F

num_epochs = 20

x1 = np.array([0,0])
x2 = np.array([0,1])
x3 = np.array([1,0])
x4 = np.array([1,1])

num_epochs = 200

class cus2(torch.nn.Module):

    def __init__(self):
        super(cus2,self).__init__()

    def forward(self, outputs, labels):
        # reshape labels to give a flat vector of length batch_size*seq_len
        labels = labels.view(-1)  

        # mask out 'PAD' tokens
        mask = (labels &gt;= 0).float()

        # the number of tokens is the sum of elements in mask
        num_tokens = int(torch.sum(mask).data[0])

        # pick the values corresponding to labels and multiply by mask
        outputs = outputs[range(outputs.shape[0]), labels]*mask

        # cross entropy loss for all non 'PAD' tokens
        return -torch.sum(outputs)/num_tokens


x = torch.tensor([x1,x2,x3,x4]).float()

y = torch.tensor([0,1,1,0]).long()

train = data_utils.TensorDataset(x,y)
train_loader = data_utils.DataLoader(train , batch_size=2 , shuffle=True)

device = 'cpu'

input_size = 2
hidden_size = 100 
num_classes = 2

learning_rate = .0001

class NeuralNet(nn.Module) : 
    def __init__(self, input_size, hidden_size, num_classes) : 
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size , hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size , num_classes)

    def forward(self, x) : 
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

for i in range(0 , 1) :

        model = NeuralNet(input_size, hidden_size, num_classes).to(device)

        criterion = nn.CrossEntropyLoss()
#         criterion = Regress_Loss()
#         criterion = cus2()
        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

        total_step = len(train_loader)
        for epoch in range(num_epochs) : 
            for i,(images , labels) in enumerate(train_loader) : 
                images = images.reshape(-1 , 2).to(device)
                labels = labels.to(device)

                outputs = model(images)
                loss = criterion(outputs , labels)

                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
#                 print(loss)

        outputs = model(x)

        print(outputs.data.max(1)[1])
</code></pre>

<p>makes perfect predictions on training data : </p>

<pre><code>tensor([0, 1, 1, 0])
</code></pre>

<p>Using a custom loss function from <a href="https://cs230-stanford.github.io/pytorch-nlp.html#writing-a-custom-loss-function" rel="noreferrer">https://cs230-stanford.github.io/pytorch-nlp.html#writing-a-custom-loss-function</a> : </p>

<p><a href="https://i.stack.imgur.com/Eh3bZ.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Eh3bZ.png" alt="enter image description here"></a></p>

<p>is implemented in above code as <code>cus2</code></p>

<p>Un-commenting code <code>#         criterion = cus2()</code> to use this loss function returns : </p>

<pre><code>tensor([0, 0, 0, 0])
</code></pre>

<p>A warning is also returned : </p>

<blockquote>
  <p>UserWarning: invalid index of a 0-dim tensor. This will be an error in
  PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python
  number</p>
</blockquote>

<p>I've not implemented the custom loss function correctly ?</p>
