def test_bias(self):
img = sail.random.uniform(0, 1, (64, 4, 60, 60))
lay = sail.modules.Conv2D(4, 32, 3, 1, padding_mode="same", use_bias=True)
y = lay(img)
self.assert_eq(y.shape, (64, 32, 60, 60))
z = sail.sum(y)
z.backward()
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(self):
img = sail.random.uniform(0, 1, (64, 4, 60, 60))
lay = sail.modules.MaxPool2D(2)#(2,2))
y = lay(img)
self.assert_eq(y.shape, (64, 4, 30, 30))
z = sail.sum(y)
z.backward()
def test_sum(self, rq):
times = []
for c in choices:
arr1 = np.random.uniform(0, 1, (c["shape"]))
x1 = sail.Tensor(arr1, requires_grad=rq)
x3 = sail.sum(x1, c['axis'], keepdims=c['keepdims'])
arr3 = np.sum(arr1, c["axis"], keepdims=c['keepdims'])
self.assert_eq(x3.shape, c["result_shape"])
self.assert_eq_np_sail(arr3, x3, 1e-7)
self.assert_eq(x3.requires_grad, rq)
return
sail.log(tensor) -> Tensor
Returns natural log of `tensor`
.. math::
\text{out} = ln(\text{tensor})
Args:
tensor (Tensor): Input data
Examples:
>>> a = sail.random.uniform(1, 2, (20, 3))
>>> b = sail.log(a)
"""
add_docstring(sail.log, descr)
descr = r"""
sail.sum(tensor, axis=None, keepdims=False) -> Tensor
Returns the sum of `tensor` over specified axis.
.. note::
If ``axis < 0``, then the axis that will be computed over is ``tensor.ndim + axis``.
Args:
tensor (Tensor): Input data
axis (int or tuple of ints, optional): If provided, then `axis` represents the axis to be summed over. If `axis` is a tuple, then the axes provided will be summed over
keepdims (boolean, optional): If True, then the axes that are reduced will be replaced with 1, otherwise, those axes will be removed
Examples:
>>> x = sail.random.uniform(0, 1, (12, 32, 4, 5))
>>> y = sail.sum(x, 1, True)
>>> y.shape
def forward(a):
c = sail.modules.ReLU()(a)
d = sail.sum(c)
return d
def forward(a):
c = sail.modules.Softmax()(a)
d = sail.sum(c)
return d
def forward(a):
c = sail.clip(a, 4, 6)
d = sail.sum(c)
return d
def forward(a, b, c):
d = sail.addmm(a, b, c)
e = sail.sum(d)
return e
def forward(a):
c = -a
d = sail.sum(c)
return d
def forward(a):
c = sail.exp(a)
d = sail.sum(c)
return d
def forward(a, b):
c = sail.power(a, b)
d = sail.sum(c)
return d
def forward(a, b):
c = a / b
d = sail.sum(c)
return d
def forward(a, b):
c = sail.add(a, b)
d = sail.sum(c)
return d
def forward(a, b):
c = sail.matmul(a, b)
d = sail.sum(c)
return d
def forward(a):
y = sail.sum(a)
return y
def forward(a):
c = sail.log(a)
d = sail.sum(c)
return d