Source code for gluonnlp.model.parameter

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"""Building blocks and utility for models."""
__all__ = ['WeightDropParameter']

from mxnet import nd, gluon

[docs]class WeightDropParameter(gluon.Parameter): """A Container holding parameters (weights) of Blocks and performs dropout. Parameters ---------- parameter : Parameter The parameter which drops out. rate : float, default 0.0 Fraction of the input units to drop. Must be a number between 0 and 1. Dropout is not applied if dropout_rate is 0. mode : str, default 'training' Whether to only turn on dropout during training or to also turn on for inference. Options are 'training' and 'always'. axes : tuple of int, default () Axes on which dropout mask is shared. """ def __init__(self, parameter, rate=0.0, mode='training', axes=()): p = parameter self._deferred_init = p._deferred_init super(WeightDropParameter, self).__init__(, grad_req=p.grad_req, shape=p._shape, dtype=p.dtype, lr_mult=p.lr_mult, wd_mult=p.wd_mult, init=p.init, allow_deferred_init=p._allow_deferred_init, differentiable=p._differentiable) self._rate = rate self._mode = mode self._axes = axes self._var = p._var self._data = p._data self._grad = p._grad self._ctx_list = p._ctx_list self._ctx_map = p._ctx_map self._trainer = p._trainer
[docs] def data(self, ctx=None): """Returns a copy of this parameter on one context. Must have been initialized on this context before. Parameters ---------- ctx : Context Desired context. Returns ------- NDArray on ctx """ d = self._check_and_get(self._data, ctx) if self._rate: d = nd.Dropout(d, self._rate, self._mode, self._axes) return d
def __repr__(self): s = 'WeightDropParameter {name} (shape={shape}, dtype={dtype}, rate={rate}, mode={mode})' return s.format(, shape=self.shape, dtype=self.dtype, rate=self._rate, mode=self._mode)