def check_value_check(self, x_data, t_data, use_cudnn):
x = functions.softmax(chainer.Variable(x_data))
t = chainer.Variable(t_data)
if self.valid:
# Check if it throws nothing
cross_entropy(x, t, use_cudnn)
else:
with self.assertRaises(ValueError):
cross_entropy(x, t, use_cudnn)
def check_forward(self, x_data, t_data, use_cudnn=True):
x = functions.softmax(chainer.Variable(x_data))
t = chainer.Variable(t_data)
loss = cross_entropy(
x, t, use_cudnn=use_cudnn, cache_score=self.cache_score)
self.assertEqual(loss.data.shape, ())
self.assertEqual(loss.data.dtype, numpy.float32)
self.assertEqual(hasattr(loss.creator, 'y'), self.cache_score)
loss_value = float(cuda.to_cpu(loss.data))
# Compute expected value
loss_expect = 0.0
count = 0
x = numpy.rollaxis(self.x, 1, self.x.ndim).reshape(
(self.t.size, self.x.shape[1]))
t = self.t.ravel()
for xi, ti in six.moves.zip(x, t):
if ti == -1:
continue
log_z = numpy.ufunc.reduce(numpy.logaddexp, xi)
loss_expect -= (xi - log_z)[ti]
count += 1
if count == 0:
loss_expect = 0.0
else:
loss_expect /= count
self.assertAlmostEqual(loss_expect, loss_value, places=5)