pred = output.argmax(1)
for i,n in enumerate(pred):
pos = onehot and target[i].argmax(0) or int(target[i])
self.conf[pos][n] += 1
def value(self):
After Change
if np.ndim(predicted) != 1:
assert predicted.shape[1] == self.k, \
"number of predictions does not match size of confusion matrix"
predicted = np.argmax(predicted, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
"predicted values are not between 1 and k"
onehot_target = np.ndim(target) != 1
if onehot_target:
assert target.shape[1] == self.k, \
"Onehot target does not match size of confusion matrix"
assert (target >= 0).all() and (target <= 1).all(), \
"in one-hot encoding, target values should be 0 or 1"
assert (target.sum(1) == 1).all(), \
"multi-label setting is not supported"
target = np.argmax(target, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
"predicted values are not between 1 and k"
// hack for bincounting 2 arrays together
x = predicted + self.k * target
bincount_2d = np.bincount(x.astype(np.int32),
minlength=self.k ** 2)
assert bincount_2d.size == self.k ** 2
conf = bincount_2d.reshape((self.k, self.k))
