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authorLogan Bauman <logan_bauman@brown.edu>2022-05-07 15:58:27 -0400
committerLogan Bauman <logan_bauman@brown.edu>2022-05-07 15:58:27 -0400
commit00991837cc0bbb62b98ab3024ea795a18cf2dde8 (patch)
treec01379070ee0d3ac105dc0c08692e1de9a37b7be
parent266708a7fdcc0c9de5e64970c69e1722cb76e5b6 (diff)
hi
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-rw-r--r--hyperparameters.py4
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diff --git a/hyperparameters.py b/hyperparameters.py
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+++ 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