add node_modules/ to .gitignore

.gitignore

@@ -123,6 +123,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+node_modules./
 
 # Spyder project settings
 .spyderproject
@@ -158,4 +159,6 @@ cython_debug/
 # our project specific stuff
 MetaAugment/__pycache__/main.cpython-38.pyc
 
-**/mnist/
+**/mnist/
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+
+**/.DS_Store
\ No newline at end of file

docs/source/usage/autoaugment_helperclass.rst

@@ -18,7 +18,7 @@ AutoAugment paper:
 
 .. code-block::
 
-    my_policy = subpolicies1 = [
+    my_policy = [
                         (("Invert", 0.8, None), ("Contrast", 0.2, 6)),
                         (("Rotate", 0.7, 2), ("Invert", 0.8, None)),
                         (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),

tutorials/how_use_aalearner.py

-# You can run this in the main directory by typing:
-# python -m tutorials.how_use_aalearner
-
-import MetaAugment.autoaugment_learners as aal
-import MetaAugment.child_networks as cn
-
-import torchvision.datasets as datasets
-import torchvision
-
-
-# Defining our problem setting:
-# In other words, specifying the dataset and the child network
-train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train',
-                                train=True, download=True, transform=None)
-test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', 
-                        train=False, download=True, transform=torchvision.transforms.ToTensor())
-child_network = cn.lenet
-
-
-# NOTE: It is important not to type:
-#   child_network = cn.lenet()
-# We need the ``child_network`` variable to be a ``type``, not a ``nn.Module``
-# because the ``child_network`` will be called multiple times to initialize a 
-# ``nn.Module`` of its architecture multiple times: once every time we need to 
-# train a different network to evaluate a different policy.
-
-
-# Using the random search learner to evaluate randomly generated policies
-rs_agent = aal.randomsearch_learner()
-rs_agent.learn(train_dataset, test_dataset, child_network, toy_flag=True)
-
-
-# Viewing the results
-# ``.history`` is a list containing all the policies tested and the respective
-# accuracies obtained when trained using them
-print(rs_agent.history)
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