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Merge pull request #215 from joe-bender/patch-3

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Fix sentence about abs in mnist_distance
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Jeremy Howard 2020-09-03 10:28:59 -07:00 committed by GitHub
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04_mnist_basics.ipynb
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@ -2237,7 +2237,7 @@
"\n",
"This is true of all broadcasting and elementwise operations and functions done in PyTorch. *It's the most important technique for you to know to create efficient PyTorch code.*\n",
"\n",
"Next in `mnist_distance` we see `abs`. You might be able to guess now what this does when applied to a tensor. It applies the method to each individual element in the tensor, and returns a tensor of the results (that is, it applies the method \"elementwise\"). So in this case, we'll get back 1,010 absolute values.\n",
"Next in `mnist_distance` we see `abs`. You might be able to guess now what this does when applied to a tensor. It applies the method to each individual element in the tensor, and returns a tensor of the results (that is, it applies the method \"elementwise\"). So in this case, we'll get back 1,010 matrices of absolute values.\n",
"\n",
"Finally, our function calls `mean((-1,-2))`. The tuple `(-1,-2)` represents a range of axes. In Python, `-1` refers to the last element, and `-2` refers to the second-to-last. So in this case, this tells PyTorch that we want to take the mean ranging over the values indexed by the last two axes of the tensor. The last two axes are the horizontal and vertical dimensions of an image. After taking the mean over the last two axes, we are left with just the first tensor axis, which indexes over our images, which is why our final size was `(1010)`. In other words, for every image, we averaged the intensity of all the pixels in that image.\n",
"\n",