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if you are wondering what x.view(-1) does it flattens the vector. Why? Because it has to construct a new view with only 1 dimension and infer the dimension -- so it flattens it. In addition it seems this operation avoids the very nasty bugs .resize() brings since the order of the elements seems to be respected.
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Beta特殊用法view(-1) ... view函数的语法如下： ```python new_tensor = tensor.view(*shape) ``` 其中，tensor是待改变形状的张量，shape是目标形状，可以是一个元组或多个整数。值得注意的是，新张量的元素个数必须与原始张量的元素个数相同，否则会报错。
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Betaview. viewもよく使われる関数です。 1つ目の引数に-1を入れることで、2つ目の引数で指定した値にサイズ数を自動的に調整してくれます。 Tensorの要素数が指定したサイズ数に合わない（割り切れない）場合、エラーになります。
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Beta在使用pytorch定义神经网络时，经常会看到类似如下的.view()用法，这里对其用法做出讲解与演示。一、普通用法 (手动调整size) view()相当于reshape、resize，重新调整Tensor的形状。import torch a1 = torch.arang…
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BetaCreates a 1-dimensional Tensor from an object that implements the Python buffer protocol. zeros. Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size. ... Returns a 1-dimensional view of each input tensor with zero dimensions. atleast_2d. Returns a 2-dimensional view of each input tensor with ...
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Beta1. view() tensor.view()方法可以调整tensor的形状，但必须保证调整前后元素总数一致。view不会修改自身的数据，返回的新tensor与原tensor共享内存，即更改一个，另一个也随之改变。在实际应用中，可能经常需要添加或者减少某一维度，这是sequeeze()和unsequeeze()这两个方法就派上了用场。
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Beta在pytorch当中，我们经常会用到x.view()方法来进行数据维度的变化，但是这个方法具体该如何使用呢？ 下面我来记录一下笔记： 一.按照传入数字使数据维度进行转换 首先，我们可以传入我们想要的维度，然后按照传入的数字对数据进行维度变化。比如，x.view()当中可以放入列表或者是单个数字，比如
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Beta文章浏览阅读3.6k次，点赞5次，收藏14次。.view(1,-1)将多维张量转换为行向量，而.view(-1,1)转换为列向量，两种方法均保持张量元素总数不变。它们根据原始张量的形状和指定的维度自动推断另一个维度，常用于数据重塑。
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Betaview()函数是用于对Tensor(张量)进行形状变化的函数，如一个Tensor的size是3x2，可以通过view()函数将其形状转为2x3。但是需要注意的是进行操作的张量必须是contiguous()的，即在在内存中连续的。（不连续可以使用tensor.contiguous()操作转为连续）。一、view()函数基本操作 函数定义：view(*args) [Ps:...
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BetaPython中的函数不仅仅是一种实用工具，更是贯穿于各类编程场景的核心构件。无论是在函数式编程、装饰器设计、GUI编程、Web开发、异步任务处理，还是数据预处理和机器学习等领域，偏函数都能助力开发者简化代码结构、增强代码可读性和可维护性，进而提升整体编程效率。
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