def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.weight = jt.zeros((num_features))
self.bias = jt.zeros((num_features))
init.uniform_(self.weight, 0, 1)
init.constant_(self.bias, 0.0)
def test_uniform(self):
a = init.uniform(5, "float32")
assert ((a > 0) & (a < 1)).all()
a = init.uniform((2, 3), low=-1, high=1)
assert ((a > -1) & (a < 1)).all()
linear = nn.Linear(2, 2)
init.uniform_(linear.weight)
assert (linear.weight > 0).all()
linear.weight.uniform_()
assert (linear.weight > 0).all()
def uniform(size, var):
if var is not None:
bound = 1.0 / math.sqrt(size)
init.uniform_(var, -bound, bound)
def glorot(var):
if var is not None:
stdv = math.sqrt(6.0 / (var.size(-2) + var.size(-1)))
init.uniform_(var, -stdv, stdv)
def kaiming_uniform(var, fan, a):
if var is not None:
bound = math.sqrt(6 / ((1 + a**2) * fan))
init.uniform_(var, -bound, bound)
def XavierFill(tensor):
"""Caffe2 XavierFill Implementation"""
size = reduce(operator.mul, tensor.shape, 1)
fan_in = size / tensor.shape[0]
scale = math.sqrt(3 / fan_in)
return init.uniform_(tensor, -scale, scale)
def uniform_(x,l,r):
x = jt.array([x])
init.uniform_(x,l,r)
return x.numpy().item()
