From e77377191fc6344d307146e74dade8aeb3789369 Mon Sep 17 00:00:00 2001 From: cjon256 <3659487+cjon256@users.noreply.github.com> Date: Thu, 24 Aug 2023 18:23:28 -0400 Subject: [PATCH] 04_mnist_basics.ipynb mean() argument When mean() takes a tuple argument it treats it as a list of axes to reduce, not as a range of axes. --- 04_mnist_basics.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04_mnist_basics.ipynb b/04_mnist_basics.ipynb index 675bb5b..846743f 100644 --- a/04_mnist_basics.ipynb +++ b/04_mnist_basics.ipynb @@ -2239,7 +2239,7 @@ "\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", + "Finally, our function calls `mean((-1,-2))`. The tuple `(-1,-2)` represents a list 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", "We'll be learning lots more about broadcasting throughout this book, especially in <>, and will be practicing it regularly too.\n", "\n",