class torch.nn.Softplus(beta=1, threshold=20) source f(x)=1beta∗log(1+exp(β∗xi)) SoftPlusis a smooth approximation to theReLU functionand can be used to constrain the output of a machine to always be positive. ...
softmax(p, -1) return p.view(p_sz) elif pos_fn == 'exp': return torch.exp(p) elif pos_fn == 'softplus': return F.softplus(p, beta=10) elif pos_fn == 'sigmoid': return F.sigmoid(p) else: print('Undefined positive function!') return ...
The softplus function in torch will return x_i when beta*x_i>threshold.The softplus function in MIL Ops doesn't support the threshold parameter. And I don't find any other MIL op supports the threshold directly.If both beta and threshold are not set to their default values, the else ...
local SoftPlus, parent = torch.class('nn.SoftPlus', 'nn.Module')function SoftPlus:__init(beta) parent.__init(self) self.beta = beta or 1 -- Beta controls sharpness of transfer function self.threshold = 20 -- Avoid floating point issues with exp(x), x>20 endfunction...
所谓激活函数,就是在神经网络的神经元上运行的函数,负责将神经元的输入映射到输出端。常见的激活函数包括Sigmoid、TanHyperbolic(tanh)、ReLu、softplus以及softmax函数。这些函数有一个共同的特点那就是他们都是非线性的函数。那么我们为什么要在神经网络中引入非线性的激活函数呢?解释就是:如果不用激励函数(其实相当于...
prelu(y, torch.ones(1).to(y.device)) sync_if_needed(x) @torch.no_grad() def mish(x: torch.Tensor) -> torch.Tensor: y = x for _ in range(100): return torch.nn.functional.mish(y) sync_if_needed(x) @torch.no_grad() def scalar_mult(x): y = x @@ -376,6 +392,10 @...
def prelu(x: torch.Tensor) -> torch.Tensor: y = x for _ in range(100): y = torch.nn.functional.prelu(y, torch.ones(1).to(y.device)) sync_if_needed(x) @torch.no_grad() def mish(x: torch.Tensor) -> torch.Tensor: y = x for _ in range(100): return torch.nn.functional...