From c17050f663298cf920b19368add6c54ec713b4e2 Mon Sep 17 00:00:00 2001
From: Ulrich <ulrich.kerzel@rwth-aachen.de>
Date: Fri, 22 Mar 2024 10:36:22 +0100
Subject: [PATCH] add ref to paper about conv. arithmetic
---
datascienceintro/solutions/Solution_PyTorch_MNIST.ipynb | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/datascienceintro/solutions/Solution_PyTorch_MNIST.ipynb b/datascienceintro/solutions/Solution_PyTorch_MNIST.ipynb
index e16faf1..79e3bcf 100644
--- a/datascienceintro/solutions/Solution_PyTorch_MNIST.ipynb
+++ b/datascienceintro/solutions/Solution_PyTorch_MNIST.ipynb
@@ -208,7 +208,7 @@
"\n",
"***Hint*** \\\n",
"One of the tricky things to evaluate is the output size after pooling layers.\n",
- "This [blog](https://androidkt.com/calculate-output-size-convolutional-pooling-layers-cnn/) has a nice illustrated intuition behind the formulae for convolutional and maxpool layers. This [repository](https://github.com/tuttelikz/conv_output_size) has a collection of helper functions.\n",
+ "This [blog](https://androidkt.com/calculate-output-size-convolutional-pooling-layers-cnn/) has a nice illustrated intuition behind the formulae for convolutional and maxpool layers. This [repository](https://github.com/tuttelikz/conv_output_size) has a collection of helper functions, there is a pre-print [A guide to convolution arithmetic for deep learning](https://arxiv.org/pdf/1603.07285.pdf) to discuss this in more detail.\n",
"\n",
"For convolutional layers: \\\n",
"```#output = Floor( ( #input - #filter + 2* #padding)/stride + 1)```\n",
--
GitLab