torch.nn.Softplus 原型 CLASS torch.nn.Softplus(beta=1, threshold=20) 参数 beta (int) – Softplus里β \betaβ 值, 默认为 1. threshold (int) – 高于这个值恢复为线性函数,默认为 20. 图 代码 AI检测代码解析 import torch import torch.nn as nn m = nn.Softplus() input = torch.randn(4) ...
beta –the β\betaβ value for the Softplus formulation. Default: 1 threshold –values above this revert to a linear function. Default: 20 Input: (N,∗)(N, *)(N,∗) where * means, any number of additional dimensions Output: (N,∗)(N, *)(N,∗) , same shape as the ...
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5)# submodule: Conv2d self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)...
Softplus()激活函数: 绘制激活函数 import torch import numpy as np from torch import nn import matplotlib.pyplot as plt x = torch.linspace(-6, 6, 100) sigmoid = nn.Sigmoid() ysigmoid = sigmoid(x) tanh = nn.Tanh() ytanh = tanh(x) relu = nn.ReLU() yrelu = relu(x) softplus = n...
See Softplus for more details.softmintorch.nn.functional.softmin(input, dim=None, _stacklevel=3, dtype=None) [source] Applies a softmin function. Note that Softmin(x)=Softmax(−x) . See softmax definition for mathematical formula. See Softmin for more details. Parameters input (Tensor)–...
classConditionalModel(nn.Module):defforward(self,x):ifx.sum()>0:returntorch.relu(x)else:returntorch.nn.functional.softplus(x) 如果我们用一个x.sum() > 0的输入来跟踪这个模型,那么跟踪图将只包含ReLU操作。因此,跟踪模型将始终执行ReLU,忽略softplus路径。
.nn.functional.tanhshrink(input) torch.nn.functional.softsign(input) torch.nn.functional.softplus(input, beta=1, threshold=20) torch.nn.functional.softmin(input) torch.nn.functional.softmax(input) torch.nn.functional.softshrink(input, lambd=0.5) torch.nn.functional.log_softmax(input) torch.nn....
4: softplus: Softplus(x)=β1∗log(1+exp(β∗x)) API文档跳转: https://pytorch.org/docs/master/generated/torch.nn.Softplus.html#torch.nn.Softplus 5. maxout f(x) = (w1x+b, w2x+b) 6. LogSigmoid LogSigmoid(x)=log(1+exp(−x)1) API文档跳转:https://pytorch.org/docs/...
torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)对由几个输入平面组成的输入信号应用一维卷积。详细信息和输出形状,查看Conv1d参数: input– 输入张量的形状 (minibatch x in_channels x iW) weight– 过滤器的形状 (out_channels, in_channels, kW) ...
import torch from torch import nn from torch.nn import functional as F class Model(nn.Module): def forward(self, x): return F.softplus(x, beta=2, threshold=2) x = torch.Tensor([0.5, 1.5, 2.5]) y = Model().eval()(x) print(y) # tensor([0.6566, 1.5000, 2.5000])...