self.layer2 = Linear(hidden_features, out_features) # 定义前向传播函数 forward defforward(self, x): # 首先将输入 x 传入第一层,得到隐层输出 x = self.layer1(x) # 对隐层输出应用 sigmoid 激活函数 x = t.sigmoid(x) # 将激活后的输出传入第二层,得到最终输出 returnself.layer2(x) # 创...
(1,6,kernel_size=5,padding=2),nn.Sigmoid(), nn.AvgPool2D(kernel_size=2,stride=2), nn.Conv2D(6,16,kernel_size=5),nn.Sigmoid(), nn.AvgPool2D(kernel_size=2,stride=2), nn.Flatten(), nn.Linear(16*5*5,120),nn.Sigmoid(), nn.Linear(120,84),nn.Sigmoid(), nn.Linear(84,10)...
MPSCnnNeuronHardSigmoid MPSCnnNeuronHardSigmoidNode MPSCnnNeuronLinear MPSCnnNeuronLinearNode MPSCnnNeuronLogarithm MPSCnnNeuronLogarithmNode MPSCnnNeuronNode MPSCnnNeuronPower MPSCnnNeuronPowerNode MPSCnnNeuronPReLU MPSCnnNeuronPReLUNode MPSCnnNeuronReLU MPSCnnNeuronReLun MPSCnnNeuronReLunNode MPSCnnNeuronR...
6, kernel_size=(5, 5), stride=(1, 1))#(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))#(fc1): Linear(in_features=400, out_features=120, bias=True)#(fc2): Linear(in_features=120, out_features=84, bias=True)#(fc3): Linear(in_features=84, out_features=10...
nn.Linear(120, 84), nn.Linear(84, n_class)) def forward(self, x): out = self.conv(x) out = out.view(out.size(0), 400) # 400 = 16 x 5 x 5 out = self.fc(out) return out model = Cnn(1, 10) # 图片大小是28x28, 10是数据的种类 ...
1.关于非线性转化方程(non-linear transformation function) sigmoid函数(S 曲线)用来作为 activation function: 形状大致为S型,y的取值在【0,1】之间,当x趋近于无穷小时,y趋近于0;当x无穷大时,y趋近于1;当x=0时,y=0.5;y值在0-1之间有一个渐变过程,这是神经网络中一个非常有用的性质。
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/nn/modules/activation.py at main · pytorch/pytorch
local LinearWeightNorm, parent = torch.class('nn.LinearWeightNorm', 'nn.Linear') function LinearWeightNorm:__init(inputSize, outputSize, bias, eps) nn.Module.__init(self) -- Skip nn.Linear constructor local bias = ((bias == nil) and true) or bias self.eps = eps or 1e-16 self....
\text{Swish}(x_i) = x_i * Sigmoid(x_i) \f] * \f$ x_i \f$ is the input. */ ACTIVATION_TYPE_SWISH = 18, /** * Gaussian error linear unit (GELU) activation function. * The GELU function is defined as follows: \f[ GELU(x_i) = x_i*P(X < x_i)...
多层感知机的网络结构如图所示。它由两个全连接层组成,采用sigmoid函数作为激活函数(图中没有画出)。 image.png classPerceptron(nn.Module):def__init__(self,in_features,hidden_features,out_features):nn.Module.__init__(self)self.layer1=Linear(in_features,hidden_features)# 此处的Linear是前面自定义的...