def f(h, x):
# As forward computation is executed multiple times in
# check_double_backward, use a fixed flag.
xp_str = 'numpy' if xp is numpy else 'cupy'
with mock.patch('{}.random.rand'.format(xp_str),
return_value=flag_x) as mock_rand:
y = functions.zoneout(h, x, self.ratio)
mock_rand.assert_called_once_with(*x.shape)
return y * y
def f(h, x):
# As forward computation is executed multiple times in
# check_double_backward, use a fixed flag.
xp_str = 'numpy' if xp is numpy else 'cupy'
with mock.patch(
'{}.random.rand'.format(xp_str),
return_value=flag_x) as mock_rand:
y = functions.zoneout(h, x, self.ratio)
mock_rand.assert_called_once_with(*x.shape)
return y
def check_forward(self, h_data, x_data):
h = chainer.Variable(h_data)
x = chainer.Variable(x_data)
h_next = functions.zoneout(h, x, self.ratio)
if self.ratio == 0:
h_next_expect = x_data
elif self.ratio == 1:
h_next_expect = h_data
else:
h_next_expect = _zoneout(h_data, x_data, h_next.creator)
testing.assert_allclose(h_next.data, h_next_expect)
def check_forward(self, h_data, x_data):
h = chainer.Variable(h_data)
x = chainer.Variable(x_data)
h_next = functions.zoneout(h, x, self.ratio)
if self.ratio == 0:
h_next_expect = x_data
elif self.ratio == 1:
h_next_expect = h_data
else:
h_next_expect = _zoneout(h_data, x_data, h_next.creator)
testing.assert_allclose(h_next.data, h_next_expect)
def check_backward(self, h_data, x_data, y_grad):
h = chainer.Variable(h_data)
x = chainer.Variable(x_data)
y = functions.zoneout(h, x, self.ratio)
d = {'creator': y.creator}
y.grad = y_grad
y.backward()
def f():
creator = d['creator']
y = _zoneout(h_data, x_data, creator)
return y,
gh, gx, = gradient_check.numerical_grad(f, (h.data, x.data,),
(y.grad,))
testing.assert_allclose(gh, h.grad, atol=1e-3)
testing.assert_allclose(gx, x.grad, atol=1e-3)
def check_backward(self, h_data, x_data, y_grad):
h = chainer.Variable(h_data)
x = chainer.Variable(x_data)
y = functions.zoneout(h, x, self.ratio)
d = {'creator': y.creator}
y.grad = y_grad
y.backward()
def f():
creator = d['creator']
y = _zoneout(h_data, x_data, creator)
return y,
gh, gx, = gradient_check.numerical_grad(f, (h.data, x.data,),
(y_grad,))
testing.assert_allclose(gh, h.grad, atol=1e-3)
testing.assert_allclose(gx, x.grad, atol=1e-3)
