Div, Grad, Curl and All That: An Informal Text on Vector Calculu...

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需积分: 34 59 下载量 53 浏览量 更新于2023-05-21 1 收藏 11.92MB PDF 举报
Div, Grad, Curl and All That: An Informal Text on Vector Calculus，非常好的高等数学，向量分析教材。

PyTorch中 tensor.detach() 和 tensor.data 的区别详解

2020-09-18 上传
今天小编就为大家分享一篇PyTorch中 tensor.detach() 和 tensor.data 的区别详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

Vector Calculus

2018-11-25 上传
Vector Calculus Michael Corral Page：222 This book covers calculus in two and three variables. It is suitable for a one-semester course, normally known as “Vector Calculus”, “Multivariable Calculus”, or simply “Calculus III”. The prerequisites are the standard courses in single-variable calculus (a.k.a. Calculus I and II).

def ObjFun(x,y,beta): # 目标函数 """ Logistic regression loss function :param beta: model parameter vector :param x: feature matrix :param y: label vector :return: loss value """ n = x.shape[0] p = x.shape[1] pred = 1 / (1 + np.exp(-np.dot(x, beta))) pred = np.clip(pred, 1e-15, 1 - 1e-15) # 将预测值限制在一个很小的区间内 ObjVal = -np.sum(y * np.log(pred) + (1 - y) * np.log(1 - pred)) / n return ObjVal

2023-06-06 上传
:param beta: model parameter vector :param x: feature matrix :param y: label vector :return: gradient vector """ n = x.shape[0] pred = 1 / (1 + np.exp(-np.dot(x, beta))) pred = np.clip(pred, ...

with torch.no_grad(): IndentationError: unexpected indent

2023-08-19 上传
with torch.no_grad(): cosine = F.linear(F.normalize(input), F.normalize(self.weight)) phi = cosine - self.margin one_hot = input.new_zeros(cosine.size()) one_hot.scatter_(1, label.view(-1, 1)....

RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.

2023-08-11 上传
- *1* [yolov5训练报错： a view of a leaf Variable that requires grad is being used in an in-place operation](https://blog.csdn.net/J_oshua/article/details/131822629)[target="_blank" data-report-click=...

def compute_irm_penalty(losses, dummy): g1 = grad(losses[0::2].mean(), dummy, create_graph=True)[0] g2 = grad(losses[1::2].mean(), dummy, create_graph=True)[0] return (g1 * g2).sum()中的dummy是什么意思

2023-07-08 上传
通过使用 `grad` 函数计算 `losses` 中每个奇数索引和偶数索引对应的损失的平均值的梯度，并将两个梯度相乘，最后对结果求和，从而得到 `IRM` 惩罚项。 这个惩罚项可以用于训练模型以鼓励模型在不同的数据分布上...

with torch.no_grad():

2023-05-18 上传
`with torch.no_grad()` is a context manager in PyTorch that disables gradient computation. This means that any operations that are performed within this context will not have their gradients computed ...

with torch.set_grad_enabled(enable_grad):

2023-04-02 上传
This is a context manager in PyTorch that enables or disables gradient computation. When `enable_grad` is set to `True`, any computation performed within the context of this manager that involves ...

x.requires_grad = True RuntimeError: you can only change requires_grad flags of leaf variables.为什么

2023-06-03 上传
这个错误通常表示你试图修改一个计算图中的非叶子节点的 `requires_grad` 属性。在 PyTorch 中，只有叶子节点才可以修改 `requires_grad` 属性。 在你的代码中，如果 `x` 是一个非叶子节点，那么你就无法通过 `x....

现在我们有一个要优化的网络，让我们看看PyTorch的优化器类。为了实现一个优化器，我们需要实现step()函数来实际更新参数。帮我按照上面的要求补充下面的代码 class GD(Optimizer): def __init__(self, params, lr=0.2) -> None: super().__init__(params, {'lr': lr}) self.lr = lr def step(self): # do not consider the next steps for gradient calculations with torch.no_grad(): # iter over all parameters for p in self.param_groups[0]['params']: # if the gradient is set, update it if p.grad is not None: # update parameters # hint: in torch each function (e.g. Tensor.add()) has an inplace variant # which modifies the tensor inplace: Tensor.add_() ...

2023-06-12 上传
Tensor.add()) has an inplace variant # which modifies the tensor inplace: Tensor.add_() p.add_(self.lr * (-p.grad)) # clear gradients p.grad.zero_() ``` 这段代码中，我们定义了一个名为`GD`的类来...
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