def check_negative(self, xp, func_name, input, eps, nout):
# Should be differentiable
func = getattr(self, '_func_{}'.format(func_name))
grad_outputs = [
xp.random.uniform(-1, 1, input.shape).astype(input.dtype)
for _ in range(nout)]
def f():
return func(input) * nout
try:
gradient_check.numerical_grad(
f, (input,), grad_outputs, eps=eps,
detect_nondifferentiable=True)
except gradient_check.NondifferentiableError as e:
raise AssertionError(
'Function `{}` is expected to be differentiable, '
'but determined to be non-differentiable.\n\n'
'eps: {}\n'
'input: {}\n'
'xp: {}\n\n'
'{}: {}'
''.format(
func_name, eps, input, xp.__name__,
e.__class__.__name__, e))
def check_backward(self, x_data, W_data, b_data, y_grad):
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
b = None if b_data is None else chainer.Variable(b_data)
y = functions.convolution_2d(x,
W,
b,
stride=self.stride,
pad=self.pad,
use_cudnn=self.use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
if b is None:
f = lambda: func.forward((x.data, W.data))
gx, gW = gradient_check.numerical_grad(f, (x.data, W.data),
(y.grad, ),
eps=1e-2)
else:
f = lambda: func.forward((x.data, W.data, b.data))
gx, gW, gb = gradient_check.numerical_grad(
f, (x.data, W.data, b.data), (y.grad, ), eps=1e-2)
gradient_check.assert_allclose(gx, x.grad)
gradient_check.assert_allclose(gW, W.grad)
if b is not None:
gradient_check.assert_allclose(gb, b.grad)
def check_backward(self, x_data, W_data, b_data, y_grad):
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
if b_data is None:
y = functions.maxout(x, W)
else:
b = chainer.Variable(b_data)
y = functions.maxout(x, W, b)
y.grad = y_grad
y.backward()
func = y.creator
if b_data is None:
f = lambda: func.forward((x.data, W.data))
gx, gW = gradient_check.numerical_grad(f, (x.data, W.data),
(y.grad, ),
eps=1e-2)
else:
f = lambda: func.forward((x.data, W.data, b.data))
gx, gW, gb = gradient_check.numerical_grad(
f, (x.data, W.data, b.data), (y.grad, ), eps=1e-2)
gradient_check.assert_allclose(gx, x.grad, atol=1e-2)
gradient_check.assert_allclose(gW, W.grad, atol=1e-2)
if b_data is not None:
gradient_check.assert_allclose(gb, b.grad, atol=1e-2)
def check_negative(self, xp, func_name, inputs, eps, nout):
# Should be differentiable
func = getattr(self, '_func_{}'.format(func_name))
grad_outputs = [
xp.random.uniform(-1, 1, _.shape).astype(_.dtype) for _ in inputs
]
def f():
return func(*inputs) * nout
try:
gradient_check.numerical_grad(f,
inputs,
grad_outputs,
eps=eps,
detect_nondifferentiable=True)
except gradient_check.NondifferentiableError as e:
raise AssertionError(
'Function `{}` is expected to be differentiable, '
'but determined to be non-differentiable.\n\n'
'eps: {}\n'
'inputs: {}\n'
'xp: {}\n\n'
'{}: {}'
''.format(func_name, eps, inputs, xp.__name__,
e.__class__.__name__, e))
def check_positive(self, xp, func_name, input, eps, nout):
# Should be non-differentiable
func = getattr(self, '_func_{}'.format(func_name))
grad_outputs = [
xp.random.uniform(-1, 1, input.shape).astype(input.dtype)
for _ in range(nout)
]
def f():
return func(input) * nout
try:
gradient_check.numerical_grad(f, (input, ),
grad_outputs,
eps=eps,
detect_nondifferentiable=True)
except gradient_check.NondifferentiableError:
pass
else:
raise AssertionError(
'Function `{}` is expected to be non-differentiable, '
'but determined to be differentiable.\n\n'
'eps: {}\n'
'input: {}\n'
'xp: {}\n'
''.format(func_name, eps, input, xp.__name__))
def check_backward(self, x0_data, x1_data, t_data):
x0 = chainer.Variable(x0_data)
x1 = chainer.Variable(x1_data)
t = chainer.Variable(t_data)
loss = functions.contrastive(x0, x1, t, self.margin)
loss.backward()
self.assertEqual(None, t.grad)
func = loss.creator
f = lambda: func.forward((x0.data, x1.data, t.data))
gx0, = gradient_check.numerical_grad(f, (x0.data, ), (1, ))
gx1, = gradient_check.numerical_grad(f, (x1.data, ), (1, ))
gradient_check.assert_allclose(gx0, x0.grad, rtol=1e-4, atol=1e-4)
gradient_check.assert_allclose(gx1, x1.grad, rtol=1e-4, atol=1e-4)
def check_backward(self, x0_data, x1_data, t_data, use_cudnn=True):
x0 = chainer.Variable(x0_data)
x1 = chainer.Variable(x1_data)
t = chainer.Variable(t_data)
loss = contrastive(x0, x1, t, use_cudnn)
loss.backward()
self.assertEqual(None, t.grad)
func = loss.creator
f = lambda: func.forward((x0.data, x1.data, t.data))
gx0, = gradient_check.numerical_grad(f, (x0.data, ), (1, ))
gx1, = gradient_check.numerical_grad(f, (x1.data, ), (1, ))
gradient_check.assert_allclose(gx0, x0.grad)
gradient_check.assert_allclose(gx1, x1.grad)
def check_backward(self, h_data, x_data, y_grad):
h = chainer.Variable(h_data)
x = chainer.Variable(x_data)
y = self._forward(self.link, h, x)
y.grad = y_grad
y.backward()
def f():
return _gru(self.link, h_data, x_data),
gx, = gradient_check.numerical_grad(f, (x.data,), (y_grad,))
testing.assert_allclose(gx, x.grad, atol=1e-3)
if isinstance(self.link, links.StatelessGRU):
gh, = gradient_check.numerical_grad(f, (h.data,), (y_grad,))
testing.assert_allclose(gh, h.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 = self._forward(self.link, h, x)
y.grad = y_grad
y.backward()
def f():
return _gru(self.link, h_data, x_data),
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
testing.assert_allclose(gx, x.grad, atol=1e-3)
if isinstance(self.link, links.GRU):
gh, = gradient_check.numerical_grad(f, (h.data,), (y.grad,))
testing.assert_allclose(gh, h.grad, atol=1e-3)
def check_different_eps(self, x, y):
def f():
if -1 < x < 1:
return x.copy(),
elif -2 < x < 2:
return 2 * x,
else:
return 0,
gx, = gradient_check.numerical_grad(f, (x, ), (y, ), eps=0.5)
self.assertEqual(gx, 1.)
gx, = gradient_check.numerical_grad(f, (x, ), (y, ), eps=1.5)
self.assertEqual(gx, 2.)
gx, = gradient_check.numerical_grad(f, (x, ), (y, ), eps=2.5)
self.assertEqual(gx, 0.)
def check_backward(self, x0_data, x1_data, t_data, use_cudnn=True):
x0 = chainer.Variable(x0_data)
x1 = chainer.Variable(x1_data)
t = chainer.Variable(t_data)
loss = contrastive(x0, x1, t, use_cudnn)
loss.backward()
self.assertEqual(None, t.grad)
func = loss.creator
f = lambda: func.forward((x0.data, x1.data, t.data))
gx0, = gradient_check.numerical_grad(f, (x0.data,), (1,))
gx1, = gradient_check.numerical_grad(f, (x1.data,), (1,))
gradient_check.assert_allclose(gx0, x0.grad)
gradient_check.assert_allclose(gx1, x1.grad)
def check_different_eps(self, x, y):
def f():
if -1 < x < 1:
return x.copy(),
elif -2 < x < 2:
return 2 * x,
else:
return 0,
gx, = gradient_check.numerical_grad(f, (x,), (y,), eps=0.5)
self.assertEqual(gx, 1.)
gx, = gradient_check.numerical_grad(f, (x,), (y,), eps=1.5)
self.assertEqual(gx, 2.)
gx, = gradient_check.numerical_grad(f, (x,), (y,), eps=2.5)
self.assertEqual(gx, 0.)
def check_backward(self, x0_data, x1_data, t_data):
x0 = chainer.Variable(x0_data)
x1 = chainer.Variable(x1_data)
t = chainer.Variable(t_data)
loss = functions.contrastive(x0, x1, t, self.margin)
loss.backward()
self.assertEqual(None, t.grad)
func = loss.creator
f = lambda: func.forward((x0.data, x1.data, t.data))
gx0, = gradient_check.numerical_grad(f, (x0.data,), (1,))
gx1, = gradient_check.numerical_grad(f, (x1.data,), (1,))
gradient_check.assert_allclose(gx0, x0.grad, rtol=1e-4, atol=1e-4)
gradient_check.assert_allclose(gx1, x1.grad, rtol=1e-4, atol=1e-4)
def check_backward(update, atom_data, adj_data, y_grad):
"""Check gradient of GGNNUpdate.
This function is different from other backward tests.
Because of GRU, reset_state method has to be called explicitly
before gradient calculation.
Args:
update (callable):
atom_data (numpy.ndarray):
adj_data (numpy.ndarray):
y_grad (numpy.ndarray):
"""
atom = chainer.Variable(atom_data)
update.reset_state()
y = update(atom, adj_data)
y.grad = y_grad
y.backward()
def f():
update.reset_state()
return update(atom_data, adj_data).data,
gx, = gradient_check.numerical_grad(f, (atom.data, ), (y.grad, ))
numpy.testing.assert_allclose(cuda.to_cpu(gx),
cuda.to_cpu(atom.grad),
atol=1e-3,
rtol=1e-3)
return gx
def check_backward(update, atom_data, adj_data, y_grad):
# type: (MPNNUpdate, numpy.ndarray, numpy.ndarray, numpy.ndarray) -> None
"""Check gradient of MPNNUpdate.
This function is different from other backward tests.
Because of GRU, reset_state method has to be called explicitly
before gradient calculation.
Args:
update (callable):
atom_data (numpy.ndarray):
adj_data (numpy.ndarray):
y_grad (numpy.ndarray):
"""
atom = chainer.Variable(atom_data)
adj = chainer.Variable(adj_data)
update.reset_state()
y = update(atom, adj)
y.grad = y_grad
y.backward()
def f():
# type: () -> numpy.ndarray
update.reset_state()
return update(atom_data, adj_data).data,
gx, = gradient_check.numerical_grad(f, (atom.data, ), (y.grad, ))
numpy.testing.assert_allclose(gx, atom.grad, atol=1e-3, rtol=1e-3)
def check_backward(readout, atom_data, y_grad):
# type: (Set2Set, numpy.ndarray, numpy.ndarray) -> None
"""Check gradient of Set2Set.
This function is different from other backward tests.
Because of LSTM, reset_state method has to be called explicitly
before gradient calculation.
Args:
readout:
atom_data:
y_grad:
"""
atom = chainer.Variable(atom_data)
readout.reset_state()
y = readout(atom)
y.grad = y_grad
y.backward()
def f():
readout.reset_state()
return readout(atom_data).data,
gx, = gradient_check.numerical_grad(f, (atom.data, ), (y.grad, ))
numpy.testing.assert_allclose(gx, atom.grad, atol=1e-2, rtol=1e-2)
def check_backward(self, t_data, xs_data, l_length, x_length, grad, gx):
xs = tuple(chainer.Variable(x_data) for x_data in xs_data)
t = chainer.Variable(t_data)
loss = functions.connectionist_temporal_classification(
xs,
t,
2,
input_length=chainer.Variable(x_length),
label_length=chainer.Variable(l_length))
loss.grad = grad
loss.backward()
func = loss.creator
xs_data = tuple(x.data for x in xs)
f = lambda: func.forward((
x_length,
l_length,
t.data,
) + xs_data)
gx_0, gx_1, gx_2, gx_3 = gradient_check.numerical_grad(
f, (xs_data), (gx, ))
gradient_check.assert_allclose(xs[0].grad, gx_0, atol=1e-04)
gradient_check.assert_allclose(xs[1].grad, gx_1, atol=1e-04)
gradient_check.assert_allclose(xs[2].grad, gx_2, atol=1e-04)
gradient_check.assert_allclose(xs[3].grad, gx_3, atol=1e-04)
def check_reference(self, x):
# A returned value and an input refers the same memory.
# See issue #488
def func():
return x,
gx, = gradient_check.numerical_grad(func, (x,), (1,))
testing.assert_allclose(cuda.to_cpu(gx), 1)
def check_backward(readout, atom_data, y_grad):
# type: (Set2Set, numpy.ndarray, numpy.ndarray) -> None
"""Check gradient of Set2Set.
This function is different from other backward tests.
Because of LSTM, reset_state method has to be called explicitly
before gradient calculation.
Args:
readout:
atom_data:
y_grad:
"""
atom = chainer.Variable(atom_data)
readout.reset_state()
y = readout(atom)
y.grad = y_grad
y.backward()
def f():
readout.reset_state()
return readout(atom_data).data,
gx, = gradient_check.numerical_grad(f, (atom.data, ), (y.grad, ))
numpy.testing.assert_allclose(cuda.to_cpu(gx),
cuda.to_cpu(atom.grad),
atol=1e-2,
rtol=1e-2)
def check_backward(self, x_data, t_data, y_grad):
x = chainer.Variable(x_data)
t = chainer.Variable(t_data)
W = self.link.W
y = self.link(x, t)
y.grad = y_grad
y.backward()
# fix samples
negative_sampling.NegativeSamplingFunction.samples = y.creator.samples
def f():
return self.link(x, t).data,
gx, gW = gradient_check.numerical_grad(f, (x.data, W.data), (y.grad, ),
eps=1e-2)
del negative_sampling.NegativeSamplingFunction.samples # clean up
gradient_check.assert_allclose(cuda.to_cpu(gx),
cuda.to_cpu(x.grad),
atol=1.e-4)
gradient_check.assert_allclose(cuda.to_cpu(gW),
cuda.to_cpu(W.grad),
atol=1.e-4)
def check_reference(self, x):
# A returned value and an input refers the same memory.
# See issue #488
def func():
return x,
gx, = gradient_check.numerical_grad(func, (x,), (1,))
testing.assert_allclose(cuda.to_cpu(gx), 1)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.link(x)
y.grad = y_grad
y.backward()
f = lambda: (self.link(x).data,)
gx, gW = gradient_check.numerical_grad(
f, (x.data, self.link.W.data), (y.grad,), eps=1e-2)
if not self.nobias:
gb, = gradient_check.numerical_grad(
f, (self.link.b.data,), (y.grad,), eps=1e-2)
gradient_check.assert_allclose(gx, x.grad)
gradient_check.assert_allclose(gW, self.link.W.grad)
if not self.nobias:
gradient_check.assert_allclose(gb, self.link.b.grad)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.link(x)
y.grad = y_grad
y.backward()
f = lambda: (self.link(x).data,)
gW, = gradient_check.numerical_grad(f, (self.link.W.data,), (y.grad,))
gradient_check.assert_allclose(gW, self.link.W.grad)
def check_backward(self, x_data, gy_data, use_cudnn=True):
x = Variable(x_data)
y = tanh(x, use_cudnn=use_cudnn)
y.grad = gy_data
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,))
assert_allclose(gx, x.grad)
def check_backward(self, x_data, gy_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.tanh(x, use_cudnn=use_cudnn)
y.grad = gy_data
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = gradient_check.numerical_grad(f, (x.data, ), (y.grad, ))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, x_data, gy_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.tanh(x, use_cudnn=use_cudnn)
y.grad = gy_data
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.link(x)
y.grad = y_grad
y.backward()
f = lambda: (self.link(x).data, )
gW, = gradient_check.numerical_grad(f, (self.link.W.data, ),
(y.grad, ))
gradient_check.assert_allclose(gW, self.link.W.grad)
def check_backward(self, x_data, gy_data, use_cudnn=True):
x = Variable(x_data)
y = tanh(x, use_cudnn=use_cudnn)
y.grad = gy_data
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,))
assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.link(x)
y.grad = y_grad
y.backward()
f = lambda: (self.link(x).data, )
gx, gW = gradient_check.numerical_grad(f, (x.data, self.link.W.data),
(y.grad, ),
eps=1e-2)
if not self.nobias:
gb, = gradient_check.numerical_grad(f, (self.link.b.data, ),
(y.grad, ),
eps=1e-2)
gradient_check.assert_allclose(gx, x.grad)
gradient_check.assert_allclose(gW, self.link.W.grad)
if not self.nobias:
gradient_check.assert_allclose(gb, self.link.b.grad)
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = self.func(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gW, = numerical_grad(f, (func.W,), (y.grad,))
assert_allclose(gW, func.gW)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.func(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gW, = gradient_check.numerical_grad(f, (func.W, ), (y.grad, ))
gradient_check.assert_allclose(gW, func.gW)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.expand_dims(x, self.axis)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x_data,))
gx, = gradient_check.numerical_grad(f, (x_data,), (y_grad,))
gradient_check.assert_allclose(cuda.to_cpu(x.grad),
cuda.to_cpu(gx))
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = leaky_relu(x, slope=self.slope)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = numerical_grad(f, (x.data, ), (y.grad, ))
assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.leaky_relu(x, slope=self.slope)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.max_pooling_2d(x, 3, stride=2, pad=1, cover_all=self.cover_all, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(cuda.to_cpu(gx), cuda.to_cpu(x.grad))
def check_backward(self, op, x_data, y_grad):
x = chainer.Variable(x_data)
y = op(x, self.value)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = gradient_check.numerical_grad(f, (x.data, ), (y.grad, ))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad, axis=None, keepdims=False):
x = chainer.Variable(x_data)
y = functions.max(x, axis=axis, keepdims=keepdims)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data.copy(),))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,), eps=1e-5)
gradient_check.assert_allclose(gx, x.grad, rtol=1e-3, atol=1e-3)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = self.link(x)
y.grad = y_grad
y.backward()
f = lambda: (self.link(x).data, )
if self.initial_bias is None:
gx, gW = gradient_check.numerical_grad(
f, (x.data, self.link.W.data),
(y.grad, ), eps=1e-4)
else:
gx, gW, gb = gradient_check.numerical_grad(
f, (x.data, self.link.W.data, self.link.b.data),
(y.grad, ), eps=1e-4)
gradient_check.assert_allclose(gx, x.grad, atol=1e-2)
gradient_check.assert_allclose(gW, self.link.W.grad, atol=1e-2)
if self.initial_bias is not None:
gradient_check.assert_allclose(gb, self.link.b.grad, atol=1e-2)
def check_backward(self, op, x_data, y_grad):
x = Variable(x_data)
y = op(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = numerical_grad(f, (x.data, ), (y.grad, ))
assert_allclose(gx, x.grad)
def check_backward(self, random_array, random_grad_array):
x = chainer.Variable(random_array)
y = functions.depth2space(x, 2)
y.grad = random_grad_array
y.backward()
def func():
return (functions.depth2space(x, 2).data,)
gx, = gradient_check.numerical_grad(func, (x.data,), (y.grad,))
testing.assert_allclose(x.grad, gx, rtol=0.0001)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.local_response_normalization(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,), eps=1)
gradient_check.assert_allclose(gx, x.grad, atol=1e-3)
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = Variable(x_data)
y = average_pooling_2d(x, 3, stride=2, pad=1, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = numerical_grad(f, (x.data, ), (y.grad, ), eps=1e-2)
assert_allclose(to_cpu(gx), to_cpu(x.grad))
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = local_response_normalization(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,), eps=1)
assert_allclose(gx, x.grad, atol=1e-3)
def check_backward(self, data, grad):
x = chainer.Variable(data)
bx = functions.broadcast_to(x, self.out_shape)
func = bx.creator
f = lambda: func.forward((data,))
bx.grad = grad
bx.backward()
gx, = gradient_check.numerical_grad(f, (data,), (bx.grad,))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, op, x_data, y_grad):
x = Variable(x_data)
y = op(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,))
assert_allclose(gx, x.grad)
def check_backward(self, op, x_data, y_grad):
x = chainer.Variable(x_data)
y = op(x, self.value)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(gx, x.grad)
def test_backward_cpu(self):
x = Variable(np.random.randn(3, 2).astype(np.float32))
y = F.relu(x)
y.grad = np.random.randn(3, 2).astype(np.float32)
y.backward()
def f():
return F.relu(x).data,
gx, = gradient_check.numerical_grad(f, (x.data, ), (y.grad, ))
testing.assert_allclose(gx, x.grad)
def check_backward(self, x_data, W_data, b_data, y_grad):
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
b = None if b_data is None else chainer.Variable(b_data)
y = functions.linear(x, W, b)
y.grad = y_grad
y.backward()
func = y.creator
if b_data is None:
f = lambda: func.forward((x.data, W.data))
gx, gW = gradient_check.numerical_grad(f, (x.data, W.data), (y.grad,), eps=1e-2)
else:
f = lambda: func.forward((x.data, W.data, b.data))
gx, gW, gb = gradient_check.numerical_grad(f, (x.data, W.data, b.data), (y.grad,), eps=1e-2)
gradient_check.assert_allclose(gx, x.grad)
gradient_check.assert_allclose(gW, W.grad)
if b_data is not None:
gradient_check.assert_allclose(gb, b.grad)
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = Variable(x_data)
y = average_pooling_2d(x, 3, stride=2, pad=1, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,), eps=1e-2)
assert_allclose(to_cpu(gx), to_cpu(x.grad))
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.swapaxes(x, self.axis1, self.axis2)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data.copy(),))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,), eps=1e-5)
gradient_check.assert_allclose(gx, x.grad, rtol=1e-5)
def check_backward(self, c_data, h_data, x_data, y_grad):
x = chainer.Variable(x_data)
y = self._forward(self.link, x)
y.grad = y_grad
y.backward()
def f():
c, y = _peephole(self.link, c_data, h_data, x_data)
return y,
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(gx, x.grad, atol=1e-3)
def check_backward(self, c_data, h_data, x_data, y_grad):
x = chainer.Variable(x_data)
y = self._forward(self.link, x)
y.grad = y_grad
y.backward()
def f():
c, y = _peephole(self.link, c_data, h_data, x_data)
return y,
gx, = gradient_check.numerical_grad(f, (x.data,), (y_grad,))
testing.assert_allclose(gx, x.grad, atol=1e-3)
def _check_backward(e1, e2, y_grad, link, bias):
e1 = chainer.Variable(e1)
e2 = chainer.Variable(e2)
y = link(e1, e2)
y.grad = y_grad
y.backward()
f = lambda: (link(e1, e2).data, )
ge1, ge2, gW = gradient_check.numerical_grad(
f, (e1.data, e2.data, link.W.data), (y.grad, ), eps=1e-2)
gradient_check.assert_allclose(ge1, e1.grad, rtol=1e-3)
gradient_check.assert_allclose(ge2, e2.grad, rtol=1e-3)
gradient_check.assert_allclose(gW, link.W.grad, rtol=1e-3)
if bias:
gV1, gV2, gb = gradient_check.numerical_grad(
f, (link.V1.data, link.V2.data, link.b.data), (y.grad, ), eps=1e-2)
gradient_check.assert_allclose(gV1, link.V1.grad, rtol=1e-3)
gradient_check.assert_allclose(gV2, link.V2.grad, rtol=1e-3)
gradient_check.assert_allclose(gb, link.b.grad, rtol=1e-3)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.leaky_relu(x, slope=self.slope)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, = gradient_check.numerical_grad(f, (x.data, ), (y.grad, ))
gradient_check.assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = leaky_relu(x, slope=self.slope)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = numerical_grad(f, (x.data,), (y.grad,))
assert_allclose(gx, x.grad)
def check_backward(self, x_data, y_grad, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.spatial_pyramid_pooling_2d(x, self.pyramid_height, self.pooling_class, use_cudnn=use_cudnn)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
gradient_check.assert_allclose(cuda.to_cpu(gx), cuda.to_cpu(x.grad), atol=1e-04)
def check_backward(self, x_data, y_grad):
x = chainer.Variable(x_data)
y = functions.dropout(x, self.ratio)
creator = y.creator
y.grad = y_grad
y.backward()
def f():
y = _dropout(x_data, creator)
return y,
gx, = gradient_check.numerical_grad(f, (x_data, ), (y.grad, ), eps=0.1)
testing.assert_allclose(gx, x.grad, **self.check_backward_options)