def categorical_hinge(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
neg = R.max(R.elemul(R.sub(R.Scalar(-1), y_true), y_pred))
pos = R.sum(R.elemul(y_true, y_pred))
loss = R.max((R.sub(neg, pos), R.Scalar(1)), R.Scalar(0))
return loss
def log_loss(y_true, y_pred, with_logit=True):
if with_logit:
y_pred = sigmoid(y_pred)
else:
pass
y_pred = R.clip(y_pred, R.epsilon(), R.sub(R.Scalar(1), R.epsilon()))
loss = R.elemul(R.Scalar(-1), R.mean(R.elemul(y_true, R.natlog(y_pred)),
R.elemul((R.sub(R.Scalar(1), y_true)), R.natlog(R.sub(R.Scalar(1), y_pred)))))
return loss
def huber(y_true, y_pred, d):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
d = R.Scalar(d)
x = R.sub(y_true, y_pred)
if R.abs(x) <= d:
return R.elemul(R.Scalar(d), R.elemul(x, x))
if R.abs(x) > d:
return R.add(R.elemul(R.Scalar(d), R.mul(d, d)), R.elemul(d, R.sub(R.abs(x), d)))
def Poisson_loss(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
y_pred = R.clip(y_pred, R.epsilon(), R.Saclar(1) - R.epsilon())
return R.sub(y_pred, R.elemul(y_true, R.natlog(y_pred)))
def KL_div_loss(y_true, y_pred, d):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
y_pred = R.clip(y_pred, R.epsilon(), R.Saclar(1) - R.epsilon())
return R.elemul(y_true, R.natlog(R.div(y_true, y_pred)))
def sparse_cross_entropy(y_true, y_pred, with_logit=True):
if with_logit:
y_pred = softmax(y_pred)
else:
pass
y_pred = R.clip(y_pred, R.epsilon(), R.div(R.Scalar(1), R.epsilon()))
N = y_pred.shape[0]
loss = R.elemul(R.Scalar(-1), R.div(R.sum(R.natlog(y_pred[R.len(y_pred), y_true])), R.Scalar(N)))
return loss
def one_hot_cross_entropy(y_true, y_pred, with_logit=True):
if with_logit:
y_pred = softmax(y_pred)
else:
pass
y_pred = R.clip(y_pred, R.epsilon(), R.div(R.Scalar(1), R.epsilon()))
N = y_pred.shape[0]
loss = R.div(R.elemul(R.Scalar(-1), R.mul(R.sum(y_true, R.natlog(R.add(y_pred, 1e-9))))), R.Scalar(N))
return loss
confusion = R.Tensor(confusion)
if average=='macro':
for i in confusion:
TP ,TN ,FP ,FN = i[0] ,i[1] ,i[2] ,i[3]
Recall = R.div(TP, R.add(TP, FN))
Precision = R.div(TP, R.add(TP, FP))
if Precision == 0 or Recall == 0
or Recall == np.nan or Precision == np.nan:
final.append(0)
else:
F1 = R.div(R.elemul(R.Scalar(2), R.elemul(Recall, Precision)),R.sum(Recall ,Precision))
final.append(F1)
return R.mean(final)
if average=='micro':
confusion = R.Tensor(confusion)
TP = R.sum(confusion ,axis=0)[0]
TN = R.sum(confusion ,axis=0)[1]
FP = R.sum(confusion ,axis=0)[2]
FN = R.sum(confusion ,axis=0)[3]
Recall = R.div(TP, R.add(TP, FN))
Precision = R.div(TP, R.add(TP, FP))
F1 = R.div(R.elemul(R.Scalar(2), R.elemul(Recall, Precision)),R.sum(Recall ,Precision))
