I am learning Logistic Regression within Pytorch and to better understand I am defining a custom CrossEntropyLoss as below:

```
def softmax(x):
exp_x = torch.exp(x)
sum_x = torch.sum(exp_x, dim=1, keepdim=True)
return exp_x/sum_x
def log_softmax(x):
return torch.exp(x) - torch.sum(torch.exp(x), dim=1, keepdim=True)
def CrossEntropyLoss(outputs, targets):
num_examples = targets.shape[0]
batch_size = outputs.shape[0]
outputs = log_softmax(outputs)
outputs = outputs[range(batch_size), targets]
return - torch.sum(outputs)/num_examples
```

I also make my own logistic regression (to predict FashionMNIST) as below:

```
input_dim = 784 # 28x28 FashionMNIST data
output_dim = 10
w_init = np.random.normal(scale=0.05, size=(input_dim,output_dim))
w_init = torch.tensor(w_init, requires_grad=True).float()
b = torch.zeros(output_dim)
def my_model(x):
bs = x.shape[0]
return x.reshape(bs, input_dim) @ w_init + b
```

To validate my custom crossentropyloss, I compared it with nn.CrossEntropyLoss from Pytorch by applying it on FashionMNIST data as below:

```
criterion = nn.CrossEntropyLoss()
for X, y in trn_fashion_dl:
outputs = my_model(X)
my_outputs = softmax(outputs)
my_ce = CrossEntropyLoss(my_outputs, y)
pytorch_ce = criterion(outputs, y)
print (f'my custom cross entropy: {my_ce.item()}\npytorch cross entroopy: {pytorch_ce.item()}')
break
```

My question is toward the results my_ce (my cross entropy) vs pytorch_ce (pytorch cross entropy) where they are different:

```
my custom cross entropy: 9.956839561462402
pytorch cross entroopy: 2.378990888595581
```