脚本宝典收集整理的这篇文章主要介绍了Deep Learning Week1 Notes，脚本宝典觉得挺不错的，现在分享给大家，也给大家做个参考。

1. Tensors

(text{A tensor is a generalized matrix:})

(text{an element of }mathbb{R^3} text{ is a 3-dimension vector, but it's a 1-dimension tensor.})

(large text{The 'dimension' of a tensor is the number of indices.})

2. PyTorch operation

@ (text{ corresponds to matrix/vector or matrix/matrix multiplication.})

* (text{ is component-wise product.})

lstsq:(text{ least square: }mq = y)

>>>y = torch.randn(3)
>>>y
tensor([ 1.3663, -0.5444, -1.7488])
>>>m = torch.randn(3,3)
>>>q = torch.linalg.lstsq(m,y).solution
>>>m@q
tensor([ 1.3663, -0.5444, -1.7488])

3. Data Sharing

>>> a = torch.full((2, 3), 1)
>>> a
tensor([[1, 1, 1],
[1, 1, 1]])
>>> b = a.view(-1)
>>> b
tensor([1, 1, 1, 1, 1, 1])
>>> a[1, 1] = 2
>>> a
tensor([[1, 1, 1],
[1, 2, 1]])
>>> b
tensor([1, 1, 1, 1, 2, 1])
>>> b[0] = 9
>>> a
tensor([[9, 1, 1],
[1, 2, 1]])
>>> b
tensor([9, 1, 1, 1, 2, 1])

(large text{Note: many operations returns a new tensor which shares the same underlying storage as the original tensor, so changing the values of one will change the other as well:}) view, transpose, squeeze, unsqueeze, expand, permute.

4. Einstein summation convention

torch.einsum: (text{Matrix Multiplication})

>>> p = torch.rand(2, 5)
>>> q = torch.rand(5, 4)
>>> torch.einsum('ij,jk->ik', p, q)
tensor([[2.0833, 1.1046, 1.5220, 0.4405],
[2.1338, 1.2601, 1.4226, 0.8641]])
>>> p@q
tensor([[2.0833, 1.1046, 1.5220, 0.4405],
[2.1338, 1.2601, 1.4226, 0.8641]])

(text{Matrix-Vector product:})

w = torch.einsum('ij,j->i', m, v)

(text{Component-wise Product:})

m = torch.einsum('ij,ij->ij', p, q)

(text{Trace:})

v = torch.einsum('ii->i', m)

5. Storage

>>> x = torch.zeros(2, 4)
>>> x.storage()
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
[torch.FloatStorage of size 8]
>>> q = x.storage()
>>> q[4] = 1.0
>>> x
tensor([[ 0., 0., 0., 0.],
[ 1., 0., 0., 0.]])

(large text{The main idea of functions like })view, narrow, transpose, (largetext{ etc. and of operations involving broadcasting is to never replicate data in memory, but to “play” with the offsets and strides of the underlying storage.}) (text{Therefore:})

>>> x = torch.empty(100, 100)
>>> x.t().view(-1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: invalid argument 2: view size is not compatible with
input tensor's size and stride (at least one dimension spans across
two contiguous subspaces). Call .contiguous() before .view()

x.t() (text{ shares the storage with })x, (text{ cannot flatten to 1d}). (text{We can use the function })reshape().

脚本宝典总结

以上是脚本宝典为你收集整理的Deep Learning Week1 Notes全部内容，希望文章能够帮你解决Deep Learning Week1 Notes所遇到的问题。

如果觉得脚本宝典网站内容还不错，欢迎将脚本宝典推荐好友。

本图文内容来源于网友网络收集整理提供，作为学习参考使用，版权属于原作者。
如您有任何意见或建议可联系处理。小编QQ：384754419，请注明来意。