def backward(self, values, gradients):
x, y = self.args
dfdx = self.dfdx(values[x], values[y], values[self])
dfdy = self.dfdy(values[x], values[y], values[self])
if x in gradients:
gradients[x] += nphacks.contract_like(dfdx * gradients[self], values[x])
if y in gradients:
gradients[y] += nphacks.contract_like(dfdy * gradients[self], values[y])
def backward(self, values, gradients):
c, x, y = self.args
if x in gradients:
gradients[x] += nphacks.contract_like(c.astype(bool) * gradients[self], values[x])
if y in gradients:
gradients[y] += nphacks.contract_like(numpy.logical_not(c) * gradients[self], values[x])
def backward(self, values, gradients):
x, y = self.args
if x in gradients:
gradients[x] += nphacks.contract_like(gradients[self], values[x])
if y in gradients:
gradients[y] += nphacks.contract_like(gradients[self], values[y])
def backward(self, values, gradients):
x = self.args[0]
dfdx = self.dfdx(values[x], values[self])
if x in gradients:
gradients[x] += nphacks.contract_like(dfdx * gradients[self], values[x])
