move notebooks into /notebooks

MetaAugment/autoaugment_learners/baseline.py

-import MetaAugment.child_networks as cn
-from pprint import pprint
-import torchvision.datasets as datasets
-import torchvision
-from MetaAugment.autoaugment_learners.aa_learner import aa_learner
-import pickle
-
-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.bad_lenet
-
-aalearner = aa_learner(discrete_p_m=True)
-
-# this policy is same as identity function, because probabaility and magnitude are both zero
-null_policy = [(("Contrast", 0.0, 0.0), ("Contrast", 0.0, 0.0))]
-
-
-with open('bad_lenet_baseline.txt', 'w') as file:
-    file.write('')
-
-for _ in range(100):
-    acc = aalearner.test_autoaugment_policy(null_policy, child_network(), train_dataset, test_dataset, 
-                                toy_flag=True, logging=False)
-    with open('bad_lenet_baseline.txt', 'a') as file:
-        file.write(str(acc))
-        file.write('\n')
-
-pprint(aalearner.history)
\ No newline at end of file