flatten(input, start_dim=0, end_dim=-1) → Tensor # Parameters:input (Tensor) – 输入为Tensor #start_dim (int) – 展平的开始维度 #end_dim (int) – 展平的最后维度 #example #一个3x2x2的三维张量 >>> t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9,...
flatten(x, 1) # flatten all dimensions except batch return x 接下来,我们定义一个非常标准的交叉熵损失函数。这个损失函数将是我们讨论的主要焦点。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def log_softmax(x): return x - x.exp().sum(-1).log().unsqueeze(-1) def weighted_nll(...
lstm.flatten_parameters() outputs, (hidden, cell) = self.lstm(x) predictions = self.fc(hidden[-1]) return predictions # 超参数 input_size = 10 # 特征数量 hidden_size = 50 # LSTM隐藏层大小 num_layers = 2 # LSTM层数 output_size = 3 # 输出大小,对应3种资产的权重 sequence_length = ...
importtorchfrom torchimportnnimporttorch.nn.functional as FclassModel_Seq(nn.Module):"""通过继承基类nn.Module来构建模型"""def__init__(self,in_dim,n_hidden_1,n_hidden_2,out_dim):super(Model_Seq,self).__init__()self.flatten=nn.Flatten()self.linea...
二、实现一个常用类Flatten类 Flatten就是将2D的特征图压扁为1D的特征向量,用于全连接层的输入。 #Flatten继承ModuleclassFlatten(nn.Module):#构造函数,没有什么要做的def__init__(self):#调用父类构造函数super(Flatten, self).__init__()#实现forward函数defforward(self, input):#保存batch维度,后面的维度...
简单只需要nn.DataParallel(model)但是如果在GPU上使用而且模型较大可能会遇到一个warning RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(...
(x) x = torch.flatten(x, 1) x = self.classifier(x) return x # Lightweight neural network class to be used as student: class LightNN(nn.Module): def __init__(self, num_classes=10): super(LightNN, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 16, kernel_...
torch.manual_seed(0)#Learning rate.lr = 0.003#Defining the VGG16 sequential model.vgg16=models.vgg16()vgg_layers_list=list(vgg16.children())[:-1]vgg_layers_list.append(nn.Flatten())vgg_layers_list.append(nn.Linear(25088,4096))vgg_layers_list.append(nn.ReLU())vgg_layers_list.append(...
optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9) 我们还将训练数据分成训练集和验证集。因此,我们在 80%的数据上进行训练,并在剩余的 20%上计算验证损失。我们可以配置通过训练和测试集的批处理大小。 使用DataParallel 添加(多)GPU 支持 ...
x=x.flatten(1) logits=self.linear_relu_stack(x) returnlogits 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. ...