def add_docstring_fcn(class_, method, descr):
cls = getattr(sail.optimizers, class_)
add_docstring(getattr(cls, method), descr)
def add_docstring_class(class_, descr):
add_docstring(getattr(sail.optimizers, class_), descr)
def add_docstring_class(class_, descr):
add_docstring(getattr(sail.modules, class_), descr)
def add_docstring_fcn(class_, method, descr):
cls = getattr(sail.modules, class_)
add_docstring(getattr(cls, method), descr)
Tensor `x1` must have at least 2 dimensions
Args:
x1 (Tensor): Input tensor to be filled
gain (float): Scaling parameter
Examples:
>>> x = sail.random.uniform(0, 1, (10))
>>> sail.init.xavier_uniform(x, gain=0.5)
tensor([[-0.06891035 0.24276903 0.33934638 0.15491413]
[0.16111927 0.36402372 0.36035901 0.39027894]
[0.23715521 0.31049126 -0.34743783 -0.39470020]
[0.11064298 -0.00741466 -0.04901789 0.40659216]], shape=(4, 4))
"""
add_docstring(sail.init.xavier_uniform, descr)
descr = r"""
sail.init.xavier_normal(x1, gain=1.0) -> Tensor
Fills the tensor `x1` with values generated from an xavier normal distribution :math:`\mathcal{{N}}(0, \text{std}^2)`
.. math::
\text{std} = \text{gain} * \sqrt{\frac{2}{fan\_in + fan\_out}}
.. note::
Xavier normal method originally described in `Understanding the difficulty of training deep feedforward neural networks <https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_ (Glorot, X. & Bengio, Y., 2010)
.. note::
Tensor `x1` must have at least 2 dimensions
Args:
.. math::
\text{out} = \text{x1} + \text{x2}
.. note::
If tensor shapes do not match, they must be broadcastable to a common shape.
Args:
x1 (Tensor): First Tensor
x2 (Tensor): Second Tensor
Examples:
>>> a = sail.random.uniform(0, 1, (20, 3))
>>> b = sail.random.uniform(14, 15, (20, 3))
>>> c = sail.add(a, b)
"""
add_docstring(sail.add, descr)
descr = r"""
sail.subtract(x1, x2) -> Tensor
Returns the elementwise subtraction of Tensor `x2` from `x1`
.. math::
\text{out} = \text{x1} - \text{x2}
.. note::
If tensor shapes do not match, they must be broadcastable to a common shape.
Args:
x1 (Tensor): First Tensor
x2 (Tensor): Second Tensor
def add_docstring_fcn(method, descr):
add_docstring(getattr(sail.Tensor, method), descr)