From 1de0f23de30ff045555f141b5221502286dddca4 Mon Sep 17 00:00:00 2001
From: Max Ramsay King <maxramsayking@gmail.com>
Date: Wed, 27 Apr 2022 15:40:42 +0100
Subject: [PATCH] corrected megapolicy
---
.../autoaugment_learners/aa_learner.py | 51 +++++++------------
1 file changed, 18 insertions(+), 33 deletions(-)
diff --git a/MetaAugment/autoaugment_learners/aa_learner.py b/MetaAugment/autoaugment_learners/aa_learner.py
index 00d38aab..83782991 100644
--- a/MetaAugment/autoaugment_learners/aa_learner.py
+++ b/MetaAugment/autoaugment_learners/aa_learner.py
@@ -407,38 +407,23 @@ class aa_learner:
return accuracy
- # def demo_plot(self, train_dataset, test_dataset, child_network_architecture, n=5):
- # """
- # I made this to plot a couple of accuracy graphs to help manually tune my gradient
- # optimizer hyperparameters.
+ def get_mega_policy(self, number_policies):
+ """
+ Produces a mega policy, based on the n best subpolicies (evo learner)/policies
+ (other learners)
- # Saves a plot of `n` training accuracy graphs overlapped.
- # """
- # acc_lists = []
-
- # # This is dummy code
- # # test out `n` random policies
- # for _ in range(n):
- # policy = self._generate_new_policy()
-
- # pprint(policy)
- # reward, acc_list = self._test_autoaugment_policy(policy,
- # child_network_architecture,
- # train_dataset,
- # test_dataset,
- # logging=True)
-
- # self.history.append((policy, reward))
- # acc_lists.append(acc_list)
-
- # for acc_list in acc_lists:
- # plt.plot(acc_list)
- # plt.title('I ran 5 random policies to see if there is any sign of \
- # catastrophic failure during training. If there are \
- # any lines which reach significantly lower (>10%) \
- # accuracies, you might want to tune the hyperparameters')
- # plt.xlabel('epoch')
- # plt.ylabel('accuracy')
- # plt.show()
- # plt.savefig('training_graphs_without_policies')
\ No newline at end of file
+ Args:
+ number_policies -> int: Number of (sub)policies to be included in the mega
+ policy
+
+ Returns:
+ megapolicy -> [subpolicy, subpolicy, ...]
+ """
+ inter_pol = sorted(self.history, key=lambda x: x[1], reverse = True)[:number_policies]
+
+ megapol = []
+ for pol in inter_pol:
+ megapol += pol[0]
+
+ return megapol
--
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