def forward(self, inputs, device):
x, = inputs
r = self.r.astype(x.dtype)
r = device.send(r)
with chainer.using_config('train', self.train):
y = functions.rrelu(x, l=self.l, u=self.u, r=r)
return y,
def forward(self, inputs, device):
x, = inputs
r = self.r.astype(x.dtype)
r = device.send(r)
with chainer.using_config('train', self.train):
y = functions.rrelu(x, l=self.l, u=self.u, r=r)
return y,
def f(x):
return functions.rrelu(
x,
self.l,
self.u,
r=r.astype(x.dtype) # check_backward casts only x
)
def check_forward(self, x_data):
x = chainer.Variable(x_data)
xp = backend.get_array_module(x)
with chainer.using_config('train', self.train):
y, r = functions.rrelu(x, l=self.l, u=self.u, return_r=True)
self.assertEqual(y.data.dtype, self.dtype)
expected = xp.where(x_data >= 0, x_data, x_data * r)
testing.assert_allclose(expected, y.data, **self.check_forward_options)
def check_forward(self, x_data):
x = chainer.Variable(x_data)
xp = cuda.get_array_module(x)
with chainer.using_config('train', self.train):
y, r = functions.rrelu(x, l=self.l, u=self.u, return_r=True)
self.assertEqual(y.data.dtype, self.dtype)
expected = xp.where(x_data >= 0, x_data, x_data * r)
testing.assert_allclose(
expected, y.data, **self.check_forward_options)
def _check(self):
r = self.r if self.specify_r else None
with chainer.using_config('tarin', self.train):
out, out_r = functions.rrelu(
self.x, self.l, self.u, r=r, return_r=True)
assert isinstance(out_r, type(out.array))
if r is None:
assert out_r.shape == out.array.shape
else:
if chainer.config.train:
assert out_r is r
def prediction(self, x):
x = Variable(x)
ecfp = self.build_ecfp(x)
fcfp = self.build_fcfp(x)
ecfp_fcfp = F.concat((ecfp, fcfp), axis=1)
h1 = self.attention_layer1(ecfp_fcfp)
#h2 = self.attention_layer2(h1)
attentions_1 = F.rrelu(self.attention_layer2(h1))
attentions_2 = F.rrelu(self.attention_layer3(h1))
attentions = F.concat((attentions_1, attentions_2), axis=1)
attentions = F.softmax(attentions)
attentions = F.split_axis(attentions, 2, 1)
attention_ecfp = attentions[0]
attention_fcfp = attentions[1]
attentioned_ecfc = F.concat(
(attention_ecfp * ecfp, attention_fcfp * fcfp), axis=1)
#attentioned_ecfc = F.concat((attention_ecfp * ecfp, attention_fcfp * fcfp),axis=1)
pred = self.dnn(attentioned_ecfc)
return pred, attention_ecfp, attention_fcfp
def f(x):
return functions.rrelu(x, self.l, self.u, r=r)
def f(x):
return functions.rrelu(x, self.l, self.u, r=r)