From ac7f2f3369ba987b9df6856a639d27ec88bdd774 Mon Sep 17 00:00:00 2001
From: Logan Bauman <logan_bauman@brown.edu>
Date: Thu, 5 May 2022 00:04:26 -0400
Subject: hi

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From e81d626e4554a81aafab96ed4de277982a449d33 Mon Sep 17 00:00:00 2001
From: Logan Bauman <logan_bauman@brown.edu>
Date: Sat, 7 May 2022 12:01:31 -0400
Subject: hi

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From 00991837cc0bbb62b98ab3024ea795a18cf2dde8 Mon Sep 17 00:00:00 2001
From: Logan Bauman <logan_bauman@brown.edu>
Date: Sat, 7 May 2022 15:58:27 -0400
Subject: hi

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diff --git a/hyperparameters.py b/hyperparameters.py
index 6c82a745..a15d04ac 100644
--- a/hyperparameters.py
+++ b/hyperparameters.py
@@ -9,14 +9,14 @@ Number of epochs. If you experiment with more complex networks you
 might need to increase this. Likewise if you add regularization that
 slows training.
 """
-num_epochs = 7000
+num_epochs = 10000
 
 """
 A critical parameter that can dramatically affect whether training
 succeeds or fails. The value for this depends significantly on which
 optimizer is used. Refer to the default learning rate parameter
 """
-learning_rate = 2e-3
+learning_rate = .002
 
 momentum = 0.01
 
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