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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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BetaPyTorch中的.view()函数是一个用于改变张量形状的方法。它类似于NumPy中的.reshape()函数，可以通过重新排列张量的维度来改变其形状，而不改变张量的数据。在深度学习中，.view()函数常用于调整输入数据的形状以适应模型的输入要求，或者在网络层之间传递数据时进行形状的转换。
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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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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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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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Beta这篇文章主要介绍了对pytorch中x = x.view(x.size(0), -1) 的理解说明，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 在pytorch的CNN代码中经常会看到 x.view(x.size(0), -1) 首先，在pytorch中的view()函数就是用来改变tensor的形状的，例如将2行3列的tensor变为1行6列，其中-1表示会自适应的调整 ...
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Betanumpy.ndarray.view() helps to get a new view of array with the same data. Syntax: ndarray.view(dtype=None, type=None) Parameters: dtype : Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. type : Python type, optional Returns : ndarray or matrix. Code #1: Python3
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