evo docs

autoaug/autoaugment_learners/EvoLearner.py

@@ -10,28 +10,85 @@ import autoaug.controller_networks as cont_n
 
 
 class EvoLearner(AaLearner):
-    """evo algorithm
+    """Evolutionary Strategy learner
     
-    long explanatino here
+    This learner generates neural networks that predict optimal augmentation
+    policies. Hence, there is no backpropagation or gradient descent. Instead,
+    training is done by randomly changing weights of the 'parent' networks, where
+    parents are determined by their ability to produce policies that 
+    increase the accuracy of the child network.
 
     Args:
-        AaLearner (_type_): _description_
+        AaLearner: 
+            Base class of all-auto augmentation learners.
     
-        
+        sp_num: int, default 5
+            Number of subpolicies to keep in the final policy
+
+        p_bins: int, default 1
+            Number of probability bins for the controller network.
+
+        m_bins: int, default 1
+            Number of magnitude bins for the controller network
+
+        discrete_p_m: bool, default False
+            Boolean value to set if there are discrete or continuous
+            probability and mangitude bins (if False; p_bins, m_bins = 1)
+
+        exclude_method: list, default []
+            List of augmentations to be excluded from the search space
+
+        (Child Network Args)
+
+        learning_rate: float, default 1e-6
+            Learning rate of the child network
+
+        max_epochs: float, default float('inf')
+            Theoretical maximum number of epochs that the child network 
+            can be trained on 
+
+        early_stop_num: int, default 20
+            Criteria for early stopping. I.e. if the network has not improved 
+            after early_stop_num iterations, the training is stopped
+
+        batch_size: int, default 8
+            Batch size for the datasets
+
+        toy_size: float, default 1
+            If a toy dataset is created, it will be of size toy_size compared
+            to the original dataset
+
+        (Evolutionary learner specific settings)
+
+        num_solutions: int, default 5
+            Number of offspring spawned at each generation of the algorithm 
+
+        num_parents_mating: int, default 3
+            Number of networks chosen as parents for the next generation of networks 
+
+        controller: Torch Network, default cont_n.EvoController
+            Controller network for the evolutionary algorithm
+
+
     See Also
     --------
 
 
     Notes
     -----
+    The Evolutionary algorithm runs in generations, and so batches of child networks
+    are trained at specific time intervals.
 
 
     References
     ----------
+
     
 
     Examples
     --------
+    from autoaug.autoaugment_learners.EvlLearner import EvoLearner
+    evo_learner = EvoLearner()
 
 
     """

autoaug/autoaugment_learners/GenLearner.py

@@ -195,6 +195,7 @@ class Genetic_learner(AaLearner):
                 policy = [self.gen_random_subpol()]
             else:
                 policy = self.bin_to_subpol(random.choice(self.generate_children()))
+            print("Policy: ", policy)
             
             reward = self._test_autoaugment_policy(policy,
                                                 child_network_architecture,

extract_gen.py

+import pickle 
+
+with open('genetic_logs.pkl', 'rb') as f:
+    y = pickle.load(f)
+
+accs = [x[1] for x in y]
+
+print("accs: ", accs)
+print("len accs: ", len(accs))
+
+with open("genetic_accs.txt", "w") as output:
+    output.write(str(accs))
\ No newline at end of file

genetic_accs.txt

+[0.6100000143051147, 0.7799999713897705, 0.6700000166893005, 0.1599999964237213, 0.7200000286102295, 0.7400000095367432, 0.550000011920929, 0.5600000023841858, 0.6000000238418579, 0.550000011920929, 0.699999988079071, 0.3700000047683716, 0.699999988079071, 0.7699999809265137, 0.6800000071525574, 0.38999998569488525, 0.7400000095367432, 0.5799999833106995, 0.3700000047683716, 0.4699999988079071, 0.7099999785423279, 0.6399999856948853, 0.6200000047683716, 0.6200000047683716, 0.6899999976158142, 0.7099999785423279, 0.1599999964237213, 0.49000000953674316, 0.7099999785423279, 0.3700000047683716, 0.699999988079071, 0.6299999952316284, 0.75, 0.6700000166893005, 0.6600000262260437, 0.7400000095367432, 0.6299999952316284, 0.5099999904632568, 0.5099999904632568, 0.1599999964237213, 0.6700000166893005, 0.38999998569488525, 0.6600000262260437, 0.5699999928474426, 0.5799999833106995, 0.3400000035762787, 0.5400000214576721, 0.6100000143051147, 0.09000000357627869, 0.75, 0.7900000214576721, 0.6800000071525574, 0.1599999964237213, 0.5099999904632568, 0.6800000071525574, 0.5, 0.6299999952316284, 0.7300000190734863, 0.6399999856948853, 0.7400000095367432, 0.5799999833106995, 0.7699999809265137, 0.6399999856948853, 0.7099999785423279, 0.49000000953674316, 0.30000001192092896, 0.7200000286102295, 0.46000000834465027, 0.6200000047683716, 0.4399999976158142, 0.1599999964237213, 0.23000000417232513, 0.6899999976158142, 0.1599999964237213, 0.7300000190734863, 0.7099999785423279, 0.800000011920929, 0.46000000834465027, 0.7099999785423279, 0.7300000190734863, 0.2199999988079071, 0.6700000166893005, 0.7099999785423279, 0.4000000059604645, 0.5400000214576721, 0.4300000071525574, 0.75, 0.1599999964237213, 0.6399999856948853, 0.6399999856948853, 0.6499999761581421, 0.7900000214576721, 0.7400000095367432, 0.6399999856948853, 0.5899999737739563, 0.7099999785423279, 0.5199999809265137, 0.5, 0.7099999785423279, 0.6299999952316284]
\ No newline at end of file

ttest_gen.py

@@ -15,14 +15,29 @@ import pickle
 #                                 train=True, download=True, transform=None)
 # test_dataset = datasets.MNIST(root='./datasets/mnist/test', 
 #                         train=False, download=True, transform=torchvision.transforms.ToTensor())
-train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+# train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+#                         train=True, download=True, transform=None)
+# test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+#                         train=False, download=True,
+#                         transform=torchvision.transforms.ToTensor())
+
+
+train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
                         train=True, download=True, transform=None)
-test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
-                        train=False, download=True,
+test_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                        train=False, download=True, 
                         transform=torchvision.transforms.ToTensor())
+
 child_network_architecture = cn.lenet
 # child_network_architecture = cn.lenet()
 
+child_network_architecture = cn.LeNet(
+                                    img_height=32,
+                                    img_width=32,
+                                    num_labels=10,
+                                    img_channels=3
+                                    )
+
 agent = Genetic_learner(
                             sp_num=2,
                             toy_size=0.01,