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 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 tanh(x):
"""
Tanh Activation Function
"""
return R.div(R.sub(R.exp(x), R.exp(R.mul(R.minus_one(), x))),
R.add(R.exp(x), R.exp(R.mul(R.minus_one(), x))))
def fit(self, X, y):
n_samples, n_features = X.shape
y_ = y
# y_ = R.where(y <= 0, -1, 1)
self.w = R.Tensor(np.zeros(n_features))
self.b = Scalar(0)
for epoch in range(self.n_iters):
print("Epoch: ", epoch)
for idx, x_i in enumerate(X):
x_i = Tensor(x_i)
y_i = Tensor([y_[idx]])
val = y_i * (R.dot(x_i, self.w) - self.b)
condition = R.greater_equal(val, Scalar(1))
while condition.status != 'computed':
pass
if condition():
self.w = self.w - self.lr * (Scalar(2) * self.lambda_param * self.w)
else:
self.w = self.w - self.lr * (Scalar(2) * self.lambda_param * self.w - R.mul(x_i, y_i))
self.b = self.b - (self.lr * y_i)
