https://datawhalechina.github.io/thorough-pytorch/第三章/3.9 优化器.html https://blog.csdn.net/qq_38251616/article/details/125178716 文章链接:https://cloud.tencent.com/developer/article/2304593
self.state_size=state_size #3*state_sizeforinput gate,output gate and candidate cell gate.# input_features+state_size because we will multiplywith[input,h].self.weights=torch.nn.Parameter(torch.Tensor(3*state_size,input_features+state_size))self.bias=torch.nn.Parameter(torch.Tensor(3*state_...
importtorchimporttorch.nnasnnimporttorch.optimasoptimfromtorchvisionimportdatasets,transformsfromtorch.utils.dataimportDataLoader# 定义一个简单的模型classSimpleModel(nn.Module):def__init__(self):super(SimpleModel,self).__init__()self.fc1=nn.Linear(28*28,128)self.relu=nn.ReLU()self.fc2=nn.Linear(...
self.fc = nn.Linear(int(1024 * block.expansion * s), num_classes)这两种计算会出现问题,把模块封装到nn.Sequential居然出现了问题,出现计算结果很低的情况,很蛋疼,正常这个应该是没问题,就是把多个op组到一起。但是测试居然出现问题。关于softmax还是做到模型的外面的好。而且要区分模型里面一些操作也会用到...
(0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.5, inplace=False) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace=True) (5): Dropout(p=0.5, inplace=False) ...
Linear(64, 1) def forward(self, input): x = F.relu(self.liner_1(input)) x = F.relu(self.liner_2(x)) x = F.sigmoid(self.liner_3(x)) return x lr = 0.0001 def get_model(): model = Model() opt = torch.optim.Adam(model.parameters(), lr=lr) return model, opt train_x,...
(),# **函数模块nn.Linear(512,10)# 线性变换模块,输入为[batch_size,512],输出为[batch_size,10])defforward(self,x):x=self.flatten(x)# 从x的第二维开始拉成一维,[64, 1, 28, 28]--->[64, 1*28*28]logits=self.linear_relu_stack(x)returnlogits model=NeuralNetwork().to(device)print(...
(fc): Linear(in_features=512, out_features=1000, bias=True) 1 2 Thus, we must reinitialize model.fc to be a Linear layer with 512 input features and 2 output features with: model.fc = nn.Linear(512, num_classes) 1 2 Alexnet Alexnet was introduced in the paper ImageNet Classifica...
gate_weights = F.linear(X, self.weights, self.bias) # Split the combined gate weight matrix into its components. gates = gate_weights.chunk(3, dim=1) input_gate = F.sigmoid(gates[0]) output_gate = F.sigmoid(gates[1]) # Here we use an ELU instead of the usual tanh. ...
如何生成可参考右边的帮助文档 文章目录 线性回归 1.线性回归简介 2.损失函数 3.优化算法 3.1 正规⽅程 3.2 梯度下降 3.3 梯度下降和正规⽅程的对⽐ 3.4梯度下降法⼤家族 4.⽋拟合和过拟合 4.1 原因以及解决办法 4.2 正则化 线性回归 1.线性回归简介 线性回归(Linear regression)是利⽤回归⽅程(...