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torch.optim

.. automodule:: torch.optim

How to use an optimizer

To use :mod:`torch.optim` you have to construct an optimizer object, that will hold the current state and will update the parameters based on the computed gradients.

Constructing it

To construct an :class:`Optimizer` you have to give it an iterable containing the parameters (all should be :class:`~torch.autograd.Variable` s) to optimize. Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc.

Note

If you need to move a model to GPU via .cuda(), please do so before constructing optimizers for it. Parameters of a model after .cuda() will be different objects with those before the call.

In general, you should make sure that optimized parameters live in consistent locations when optimizers are constructed and used.

Example:

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
optimizer = optim.Adam([var1, var2], lr=0.0001)

Per-parameter options

:class:`Optimizer` s also support specifying per-parameter options. To do this, instead of passing an iterable of :class:`~torch.autograd.Variable` s, pass in an iterable of :class:`dict` s. Each of them will define a separate parameter group, and should contain a params key, containing a list of parameters belonging to it. Other keys should match the keyword arguments accepted by the optimizers, and will be used as optimization options for this group.

Note

You can still pass options as keyword arguments. They will be used as defaults, in the groups that didn't override them. This is useful when you only want to vary a single option, while keeping all others consistent between parameter groups.

For example, this is very useful when one wants to specify per-layer learning rates:

optim.SGD([
                {'params': model.base.parameters()},
                {'params': model.classifier.parameters(), 'lr': 1e-3}
            ], lr=1e-2, momentum=0.9)

This means that model.base's parameters will use the default learning rate of 1e-2, model.classifier's parameters will use a learning rate of 1e-3, and a momentum of 0.9 will be used for all parameters.

Taking an optimization step

All optimizers implement a :func:`~Optimizer.step` method, that updates the parameters. It can be used in two ways:

optimizer.step()

This is a simplified version supported by most optimizers. The function can be called once the gradients are computed using e.g. :func:`~torch.autograd.Variable.backward`.

Example:

for input, target in dataset:
    optimizer.zero_grad()
    output = model(input)
    loss = loss_fn(output, target)
    loss.backward()
    optimizer.step()

optimizer.step(closure)

Some optimization algorithms such as Conjugate Gradient and LBFGS need to reevaluate the function multiple times, so you have to pass in a closure that allows them to recompute your model. The closure should clear the gradients, compute the loss, and return it.

Example:

for input, target in dataset:
    def closure():
        optimizer.zero_grad()
        output = model(input)
        loss = loss_fn(output, target)
        loss.backward()
        return loss
    optimizer.step(closure)

Algorithms

.. autoclass:: Optimizer
    :members:

.. autoclass:: Adadelta
    :members:

.. autoclass:: Adagrad
    :members:

.. autoclass:: Adam
    :members:

.. autoclass:: AdamW
    :members:

.. autoclass:: SparseAdam
    :members:

.. autoclass:: Adamax
    :members:

.. autoclass:: ASGD
    :members:

.. autoclass:: LBFGS
    :members:

.. autoclass:: RMSprop
    :members:

.. autoclass:: Rprop
    :members:

.. autoclass:: SGD
    :members:

How to adjust learning rate

:mod:`torch.optim.lr_scheduler` provides several methods to adjust the learning rate based on the number of epochs. :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` allows dynamic learning rate reducing based on some validation measurements.

Learning rate scheduling should be applied after optimizer's update; e.g., you should write your code this way:

>>> scheduler = ...
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

Warning

Prior to PyTorch 1.1.0, the learning rate scheduler was expected to be called before the optimizer's update; 1.1.0 changed this behavior in a BC-breaking way. If you use the learning rate scheduler (calling scheduler.step()) before the optimizer's update (calling optimizer.step()), this will skip the first value of the learning rate schedule. If you are unable to reproduce results after upgrading to PyTorch 1.1.0, please check if you are calling scheduler.step() at the wrong time.

.. autoclass:: torch.optim.lr_scheduler.LambdaLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.MultiplicativeLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.StepLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.MultiStepLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.ExponentialLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.CosineAnnealingLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.ReduceLROnPlateau
    :members:

.. autoclass:: torch.optim.lr_scheduler.CyclicLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.OneCycleLR
    :members:

.. autoclass:: torch.optim.lr_scheduler.CosineAnnealingWarmRestarts
    :members: