def setUp(self):
x_shape = (self.batch_size, self.in_size)
self.x = numpy.random.uniform(-1, 1, x_shape).astype(numpy.float32)
self.t = numpy.random.randint(len(self.count),
size=self.batch_size).astype(numpy.int32)
self.link = links.BlackOut(self.in_size, self.count, self.n_samples)
self.w = numpy.random.uniform(-1, 1, self.link.W.data.shape)
self.link.W.data[:] = self.w
def __init__(self, vocab_size, embed_size, hidden_size, counts, sample_size=200):
super(Decoder, self).__init__(
ye=L.EmbedID(vocab_size, embed_size, ignore_label=-1),
eh=L.Linear(embed_size, 4 * hidden_size),
hh=L.Linear(hidden_size, 4 * hidden_size),
wc=L.Linear(hidden_size, hidden_size),
wh=L.Linear(hidden_size, hidden_size),
fy=L.BlackOut(hidden_size, counts=counts, sample_size=sample_size),
test_out=L.Linear(hidden_size, vocab_size),
)
self.fy.W.data = self.test_out.W.data
def setUp(self):
self._config_user = chainer.using_config('dtype', self.dtype)
self._config_user.__enter__()
x_shape = (self.batch_size, self.in_size)
self.x = numpy.random.uniform(-1, 1, x_shape).astype(self.dtype)
self.t = numpy.random.randint(len(self.count),
size=self.batch_size).astype(numpy.int32)
self.link = links.BlackOut(self.in_size, self.count, self.n_samples)
self.w = numpy.random.uniform(-1, 1, self.link.W.data.shape)
self.link.W.data[:] = self.w
if self.dtype == numpy.float16:
self.check_forward_options = {'atol': 5e-3}
else:
self.check_forward_options = {'atol': 1e-4}