def _input_grad(self) -> ndarray:
# if "single_class", "un-normalize" probabilities before returning gradient:
if self.single_class:
return unnormalize(self.softmax_preds - self.target)
else:
return (self.softmax_preds - self.target) / self.prediction.shape[0]
def _input_grad(self) -> ndarray:
prob_grads = []
batch_size = self.softmax_preds.shape[0]
num_features = self.softmax_preds.shape[1]
for n in range(batch_size):
exp_ratio = exp_ratios(self.prediction[n] - np.max(self.prediction[n]))
jacobian = np.zeros((num_features, num_features))
for f1 in range(num_features): # p index
for f2 in range(num_features): # SCE index
if f1 == f2:
jacobian[f1][f2] = (
self.softmax_preds[n][f1] - self.target[n][f1])
else:
jacobian[f1][f2] = (
-(self.target[n][f2]-1) * exp_ratio[f1][f2] + self.target[n][f2] + self.softmax_preds[n][f1] - 1)
prob_grads.append(jacobian.sum(axis=1))
if self.single_class:
return unnormalize(np.stack(prob_grads))
else:
return np.stack(prob_grads)
