Fairly newbie to Pytorch & neural nets world, so bear with me.

Below is a code snippet from a binary classification being done using a simple 3 layer network :

```
n_input_dim = X_train.shape[1]
n_hidden = 100 # Number of hidden nodes
n_output = 1 # Number of output nodes = for binary classifier
# Build the network
model = nn.Sequential(
nn.Linear(n_input_dim, n_hidden),
nn.ELU(),
nn.Linear(n_hidden, n_output),
nn.Sigmoid())
x_tensor = torch.from_numpy(X_train.values).float()
tensor([[ -1.0000, -1.0000, -1.0000, ..., -99.0000, -99.0000, -99.0000],
[ -1.0000, -1.0000, -1.0000, ..., 0.1538, 5.0000, 0.1538],
[ -1.0000, -1.0000, -1.0000, ..., -99.0000, 6.0000, 0.2381],
...,
[ -1.0000, -1.0000, -1.0000, ..., -99.0000, -99.0000, -99.0000],
[ -1.0000, -1.0000, -1.0000, ..., -99.0000, -99.0000, -99.0000],
[ -1.0000, -1.0000, -1.0000, ..., -99.0000, -99.0000, -99.0000]])
y_tensor = torch.from_numpy(Y_train).float()
tensor([0., 0., 1., ..., 0., 0., 0.])
#Loss Computation
loss_func = nn.BCELoss()
#Optimizer
learning_rate = 0.0001
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train_loss = []
iters = 500
for i in range(iters):
y_pred = model(x_tensor)
loss = loss_func(y_pred, y_tensor)
print " Loss in iteration :"
print (i, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss.append(loss.item())
```

In the above case , what i’m not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1). Is this way of loss computation fine in Classification problem in pytorch? Shouldn’t loss be computed between two probabilities set ideally ? If this is fine , then does loss function , BCELoss over here , scales the input in some manner ?

Also , x tensor is ranging for all sort of values.Do i need to scale it before feeding into the the model network ?