RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)[source]¶
Implements RMSprop algorithm.
For further details regarding the algorithm we refer to lecture notes by G. Hinton. and centered version Generating Sequences With Recurrent Neural Networks. The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective learning rate is thus where is the scheduled learning rate and is the weighted moving average of the squared gradient.
params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional) – learning rate (default: 1e-2)
momentum (float, optional) – momentum factor (default: 0)
alpha (float, optional) – smoothing constant (default: 0.99)
eps (float, optional) – term added to the denominator to improve numerical stability (default: 1e-8)
centered (bool, optional) – if
True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance
weight_decay (float, optional) – weight decay (L2 penalty) (default: 0)
Add a param group to the
This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the
Optimizeras training progresses.
param_group (dict) – Specifies what Tensors should be optimized along with group specific optimization options.
Loads the optimizer state.
state_dict (dict) – optimizer state. Should be an object returned from a call to
Returns the state of the optimizer as a
It contains two entries:
- state - a dict holding current optimization state. Its content
differs between optimizer classes.
- param_groups - a list containing all parameter groups where each
parameter group is a dict
Performs a single optimization step.
closure (callable, optional) – A closure that reevaluates the model and returns the loss.
Sets the gradients of all optimized
torch.Tensors to zero.
set_to_none (bool) – instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests
zero_grad(set_to_none=True)followed by a backward pass,
.grads are guaranteed to be None for params that did not receive a gradient. 3.
torch.optimoptimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether).