| nn_utils_weight_norm {torch} | R Documentation |
nn_utils_weight_norm
Description
Applies weight normalization to a parameter in the given module.
Details
\eqn{\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}}
Weight normalization is a reparameterization that decouples the magnitude
of a weight tensor from its direction. This replaces the parameter specified
by name (e.g. 'weight') with two parameters: one specifying the
magnitude (e.g. 'weight_g') and one specifying the direction
(e.g. 'weight_v').
Value
The original module with the weight_v and weight_g paramters.
Methods
Public methods
Method new()
Usage
nn_utils_weight_norm$new(name, dim)
Arguments
name(str, optional): name of weight parameter
dim(int, optional): dimension over which to compute the norm
Method compute_weight()
Usage
nn_utils_weight_norm$compute_weight(module, name = NULL, dim = NULL)
Arguments
module(Module): containing module
name(str, optional): name of weight parameter
dim(int, optional): dimension over which to compute the norm
Method apply()
Usage
nn_utils_weight_norm$apply(module, name = NULL, dim = NULL)
Arguments
module(Module): containing module
name(str, optional): name of weight parameter
dim(int, optional): dimension over which to compute the norm
Method call()
Usage
nn_utils_weight_norm$call(module)
Arguments
module(Module): containing module
Method recompute()
Usage
nn_utils_weight_norm$recompute(module)
Arguments
module(Module): containing module
Method remove()
Usage
nn_utils_weight_norm$remove(module, name = NULL)
Arguments
module(Module): containing module
name(str, optional): name of weight parameter
Method clone()
The objects of this class are cloneable with this method.
Usage
nn_utils_weight_norm$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Note
The pytorch Weight normalization is implemented via a hook that recomputes
the weight tensor from the magnitude and direction before every forward()
call. Since torch for R still do not support hooks, the weight recomputation
need to be done explicitly inside the forward() definition trough a call of
the recompute() method. See examples.
By default, with dim = 0, the norm is computed independently per output
channel/plane. To compute a norm over the entire weight tensor, use
dim = NULL.
@references https://arxiv.org/abs/1602.07868
Examples
if (torch_is_installed()) {
x = nn_linear(in_features = 20, out_features = 40)
weight_norm = nn_utils_weight_norm$new(name = 'weight', dim = 2)
weight_norm$apply(x)
x$weight_g$size()
x$weight_v$size()
x$weight
# the recompute() method recomputes the weight using g and v. It must be called
# explicitly inside `forward()`.
weight_norm$recompute(x)
}