How to use dqs.torch.loss.NegativeLoglikelihood
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In this tutorial, we demonstrate how to use ``dqs.torch.loss.CensoredNegativeLoglikelihood``.
The source code used in this tutorial is available at `github <https://github.com/IBM/dqs/blob/main/sample/torch/survival_analysis_cnll.py>`_.

You can construct a neural network model by using the following code.

.. code-block:: python

    boundaries = torch.linspace(0.0, 1.0, 5)
    dist = DistributionLinear(boundaries)
    loss_fn = NegativeLogLikelihood(dist, boundaries)
    mlp = MLP(3, 4)

In this example, the knots of probability distributions are specified as the ``boundaries`` tensor.
The DistributionLinear ``dist`` is used to convert the output of a neural network into a probability distribution, and this is given as an input of the loss function.

The MLP model ``mlp`` can be trained by using a training loop like this, where ``data_x`` contains feature vectors, ``data_y`` contains event times or censored times, and ``data_e`` contains uncensored (True) or not (False).

.. code-block:: python

    # train model
    optimizer = torch.optim.Adam(mlp.parameters(), lr=0.01)
    for epoch in range(100):
        pred = mlp(data_x)
        loss = loss_fn.loss(pred, data_y, data_e)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        print('epoch=%d, loss=%f' % (epoch,loss))