From abe9ca849135f800db40896e1964c8c78cf4fd50 Mon Sep 17 00:00:00 2001 From: cs20s030 <89025294+cs20s030@users.noreply.github.com> Date: Thu, 17 Oct 2024 11:51:57 -0400 Subject: [PATCH] readme changes --- dan/README.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/dan/README.md b/dan/README.md index abb5bfe..d332b7a 100755 --- a/dan/README.md +++ b/dan/README.md @@ -108,7 +108,7 @@ Here is an example run that does this. INFO:root:[Epoch 0060] Dev Accuracy: 0.375 Loss: 1.336877 INFO:root:[Epoch 0070] Dev Accuracy: 0.500 Loss: 1.325287 - ... snip ... + ... snip ... INFO:root:[Epoch 0960] Dev Accuracy: 0.750 Loss: 0.007144 INFO:root:[Epoch 0970] Dev Accuracy: 0.750 Loss: 0.006988 @@ -127,10 +127,11 @@ Your network needs to use the layers defined in the constructor: * `linear1 = nn.Linear` * `linear2 = nn.Linear` -Between `linear1` and `linear2` you need to have a non-linear activation (the -unit tests assume ReLU). You *may* have a dropout anywhere you like in the -network, but it must use the `nn_dropout` so we can turn it off for -deterministic testing. +Between `linear1` and `linear2` (but not after `linear2`) you need to have a +non-linear activation (the unit tests assume ReLU). You *may* have a dropout +anywhere you like in the network, but it must use the `nn.Dropout` so we can +turn it off for deterministic testing. Please ensure not to have ReLU after linear2 + Loss function --------------- @@ -162,7 +163,7 @@ Coding: 1. Understand the structure of the code. 2. Understand the vectorize funtion. 3. Write DAN model initialization `__init__` of `DanModel`: replace `self.network = None` with a real network. -4. Write model forward function. +4. Write `DanModel` forward function. 5. Write the model training/testing function in `batch_step`. We don't have unit tests for this part, but to get reasonable performance, it's necessary to get it correct. 6. Write the evaluation code `number_errors` that counts up how many examples you got right. @@ -217,8 +218,8 @@ What to turn in ---------------- 1. Submit your `dan_guesser.py` file and `parameter.py` file (if you change any defaults) -2. Submit an analysis PDF document if you did any of the extra credit. This document should contain: - * An explanation of what you did +2. Submit an analysis PDF document as `analysis.pdf` if you did any of the extra credit. This document should contain: + * Provide an explanation of your work, including details on the type of hyper-parameter tuning you performed. Please follow this with a detailed explanation. For example, you may include graphs such as Accuracy vs. Hyper-parameter values to illustrate the results. * Your results on the full dataset (should be your accuracy given a given number of answer ... bigger the better) * The full command line invocation and example outputs @@ -226,4 +227,4 @@ What to turn in Grading -------------- -To get full points on this assignment, you'll need to have an implementation that can get perfect on the `mini-dev` dataset when trained on the `mini-train` dataset. +To get full points on this assignment, you'll need to have an implementation that can get perfect on the `mini-dev` dataset when trained on the `mini-train` dataset. \ No newline at end of file