def __call__(self, x, soft_labels=False, temperature=None, train=False):
if self.mlp:
act = self.mlp_activation
x_in = act(self.W_mlp * x + self.b_mlp)
else:
x_in = x
logits = self.W*x_in + self.b
if soft_labels and temperature:
# calculate the soft labels smoothed with the temperature
# see Distilling the Knowledge in a Neural Network
elems = dynet.exp(logits / temperature)
return dynet.cdiv(elems, dynet.sum_elems(elems))
if self.act:
return self.act(logits)
return logits
def __call__(self, x, soft_labels=False, temperature=None):
if self.mlp:
W_mlp = dynet.parameter(self.W_mlp)
b_mlp = dynet.parameter(self.b_mlp)
act = self.mlp_activation
x_in = act(W_mlp * x + b_mlp)
else:
x_in = x
# from params to expressions
W = dynet.parameter(self.W)
b = dynet.parameter(self.b)
logits = (W * x_in + b) + dynet.scalarInput(1e-15)
if soft_labels and temperature:
# calculate the soft labels smoothed with the temperature
# see Distilling the Knowledge in a Neural Network
elems = dynet.exp(logits / temperature)
return dynet.cdiv(elems, dynet.sum_elems(elems))
return self.act(logits)
def __call__(self, x, soft_labels=False, temperature=None, train=False):
if self.mlp:
W_mlp = dynet.parameter(self.W_mlp)
b_mlp = dynet.parameter(self.b_mlp)
act = self.mlp_activation
x_in = act(W_mlp * x + b_mlp)
else:
x_in = x
# from params to expressions
W = dynet.parameter(self.W)
b = dynet.parameter(self.b)
logits = W*x_in + b
if soft_labels and temperature:
# calculate the soft labels smoothed with the temperature
# see Distilling the Knowledge in a Neural Network
elems = dynet.exp(logits / temperature)
return dynet.cdiv(elems, dynet.sum_elems(elems))
if self.act:
return self.act(logits)
return logits
def cosine(self, e1, e2):
return dynet.cdiv(
dynet.dot_product(e1, e2),
(dynet.cmult(dynet.squared_norm(e1), dynet.squared_norm(e2))))