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
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