在PyTorch中,我们可以通过定义torch.autograd.Function和实现forward 和backward函数的子类来轻松定义自己的autograd运算符。然后,我们可以通过构造实例并像调用函数一样调用新的autograd运算符,并传递包含输入数据的张量。 在此示例中,我们定义了自己的自定义autograd函数来执行ReLU非线性,并使用它来实现我们的两层网络: imp...
如果要实现类似 BN 滑动平均的操作,在 forward 函数中要使用原地(inplace)操作给滑动平均赋值。 classBN(torch.nn.Module) def__init__(self): ... self.register_buffer('running_mean', torch.zeros(num_features)) defforward(self, X): ... self.running_mean ...
Pytorch学习之源码理解:pytorch/examples/mnists from__future__importprint_functionimportargparseimporttorchimporttorch.nnasnnimporttorch.nn.functionalasFimporttorch.optimasoptimfromtorchvisionimportdatasets, transformsfromtorch.optim.lr_schedulerimportStepLRclassNet(nn.Module):def__init__(self):super(Net, self...
AI代码解释 #include<vector>std::vector<at::Tensor>lltm_forward(at::Tensor input,at::Tensor weights,at::Tensor bias,at::Tensor old_h,at::Tensor old_cell){autoX=at::cat({old_h,input},/*dim=*/1);auto gate_weights=at::addmm(bias,X,weights.transpose(0,1));auto gates=gate_weights....
We redefine ReLU and achieve the forward pass and backward pass. 这里自定义了 ReLU函数的前馈和反馈过程 importtorchclassMyReLU(torch.autograd.Function):""" We can implement our own custom autograd Functions by subclassing torch.autograd.Function and implementing the forward and backward passes ...
defforward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) ...
pytorch实现的loss function 1.均方损失函数 2. 交叉熵损失函数 3、自定义损失函数 1、关于nn.Module与nn.Functional的区别 2、自定义损失函数 神经网络主要实现分类以及回归预测两类问题 对于分类,主要讲述二分类交叉熵和多分类交叉熵函数,对于回归问题,主要讲述均方损失函数,而对于一些回归问题,需要根据特殊情况自定义...
调用forward将向正在运行ParameterServer的节点发送一个 RPC ,以调用参数服务器的forward函数,并返回对应于模型输出的结果Tensor。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 class TrainerNet(nn.Module): ... def forward(self, x): model_output = remote_method( ParameterServer.forward, self.param...
The MNIST HOGWILD example and the PyTorch data loader are good examples of how to use torch multiprocessing. Advanced Topics Defining new autograd functions Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors ...
Changetorch.Tensor.new_tensor()to be on the given Tensor's device by default (#144958) This function was always creating the new Tensor on the "cpu" device and will now use the same device as the current Tensor object. This behavior is now consistent with other.new_*methods. ...