Update.

diff --git a/README.txt b/README.txt
index d5cf6d8..4a5ca30 100644 (file)
--- a/README.txt
+++ b/README.txt
@@ -38,12 +38,12 @@ My home-baked GPT-37M trained with 250k solves this with ~99% success
 At every iteration, we select the GPT with the lowest test accuracy,
 and run one epoch.
 
 At every iteration, we select the GPT with the lowest test accuracy,
 and run one epoch.
 

+* Creating new quizzes
+

 If its test accuracy got higher than 97.5%, it will create new
 quizzes. To do so, it generates a large number of pairs of frames, and
 checks which ones of these quizzes are hard but not too hard, which
 If its test accuracy got higher than 97.5%, it will create new
 quizzes. To do so, it generates a large number of pairs of frames, and
 checks which ones of these quizzes are hard but not too hard, which

-means
-
-[THIS IS THE IMPORTANT BIT]:
+means [THIS IS THE IMPORTANT BIT]:

 
 it can be solved, in both time directions, by all the other GPTs **but
 one**
 
 it can be solved, in both time directions, by all the other GPTs **but
 one**

diff --git a/tasks.py b/tasks.py
index 1254323..ecf0a65 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -101,8 +101,8 @@ class World(Task):
 
         self.batch_size = batch_size
         self.device = device
 
         self.batch_size = batch_size
         self.device = device

-        self.height = 7
-        self.width = 9
+        self.height = 6
+        self.width = 8

 
         self.train_input = world.generate_seq(
             nb_train_samples, height=self.height, width=self.width
 
         self.train_input = world.generate_seq(
             nb_train_samples, height=self.height, width=self.width