def forward(a, b, d, f):
c = (a / b) + (d * f)
h = sail.exp(c)
g = h - sail.max(h, 1, True) + d - c
i = sail.sum(h)
return i
def test_integer(self):
choices = unary_elementwise_options
times = []
for c in choices:
arr1 = np.random.uniform(0, 1, (c)).astype(np.int32)
x1 = sail.Tensor(arr1)
x3 = sail.exp(x1)
arr3 = np.exp(arr1)
self.assert_eq_np_sail(arr3, x3, eps=1e-6)
return
def test_base(self, rq):
choices = unary_elementwise_options
times = []
for c in choices:
arr1 = np.random.uniform(0, 1, (c))
x1 = sail.Tensor(arr1, requires_grad=rq)
t = time.time()
x3 = sail.exp(x1)
times.append(time.time() - t)
arr3 = np.exp(arr1)
self.assert_eq_np_sail(arr3, x3, eps=1e-6)
self.assert_eq(x3.requires_grad, rq)
return
def test_broadcast(self):
choices = unary_broadcasted_options
times = []
for c in choices:
for i in range(len(c)):
b = list(c)
b[i] = 1
arr1 = np.random.uniform(1, 2, (b))
x1 = sail.Tensor(arr1, requires_grad=False)
x2 = sail.broadcast_to(x1, c)
arr2 = np.broadcast_to(arr1, c)
t = time.time()
x3 = sail.exp(x2)
times.append(time.time() - t)
arr3 = np.exp(arr2)
self.assert_eq_np_sail(arr3, x3, eps=1e-6)
return
.. note::
If tensor shapes do not match, they must be broadcastable to a common shape.
Args:
base (Tensor): Base
exppnent (Tensor): Exponent
Examples:
>>> base = sail.random.uniform(0, 1, (20, 3))
>>> exponent = sail.random.uniform(1, 2, (20, 3))
>>> c = sail.power(base, exponent)
"""
add_docstring(sail.power, descr)
descr = r"""
sail.exp(tensor) -> Tensor
Returns e the power `tensor`.
.. math::
\text{out} = \text{e}^{\text{tensor}}
Args:
tensor (Tensor): The exponent
Examples:
>>> power = sail.random.uniform(1, 2, (20, 3))
>>> b = sail.exp(power)
"""
add_docstring(sail.exp, descr)
def forward(a):
c = sail.exp(a)
d = sail.sum(c)
return d