import numpy
import chainer
from chainer import cuda
from chainer.functions.loss import black_out
from chainer import link
from chainer.utils import walker_alias
[docs]class BlackOut(link.Link):
"""BlackOut loss layer.
.. seealso:: :func:`~chainer.functions.black_out` for more detail.
Args:
in_size (int): Dimension of input vectors.
counts (int list): Number of each identifiers.
sample_size (int): Number of negative samples.
Attributes:
W (~chainer.Variable): Weight parameter matrix.
"""
def __init__(self, in_size, counts, sample_size):
vocab_size = len(counts)
super(BlackOut, self).__init__(W=(vocab_size, in_size))
p = numpy.array(counts, dtype=numpy.float32)
self.sampler = walker_alias.WalkerAlias(p)
self.sample_size = sample_size
def to_cpu(self):
super(BlackOut, self).to_cpu()
self.sampler.to_cpu()
def to_gpu(self, device=None):
with cuda._get_device(device):
super(BlackOut, self).to_gpu()
self.sampler.to_gpu()
def __call__(self, x, t):
"""Computes the loss value for given input and ground truth labels.
Args:
x (~chainer.Variable): Input of the weight matrix multiplication.
t (~chainer.Variable): Batch of ground truth labels.
Returns:
~chainer.Variable: Loss value.
"""
batch_size = x.shape[0]
if hasattr(self, 'sample_data'):
# for test
sample_data = self.sample_data
else:
shape = (batch_size, self.sample_size)
sample_data = self.sampler.sample(shape)
samples = chainer.Variable(sample_data)
return black_out.black_out(x, t, self.W, samples)