baseline code

MetaAugment/autoaugment_learners/aa_learner.py

@@ -240,11 +240,11 @@ class aa_learner:
         accuracy = train_child_network(child_network, 
                                     train_loader, 
                                     test_loader, 
-                                    sgd = optim.SGD(child_network.parameters(), lr=1e-1),
+                                    sgd = optim.SGD(child_network.parameters(), lr=3e-1),
                                     # sgd = optim.Adadelta(child_network.parameters(), lr=1e-2),
                                     cost = nn.CrossEntropyLoss(),
                                     max_epochs = 3000000, 
-                                    early_stop_num = 10, 
+                                    early_stop_num = 15, 
                                     logging = logging,
                                     print_every_epoch=True)
         

MetaAugment/autoaugment_learners/baseline.py

@@ -3,6 +3,7 @@ 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)
@@ -15,7 +16,15 @@ 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))]
 
-aalearner.test_autoaugment_policy(null_policy, child_network(), train_dataset, test_dataset, 
+
+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

MetaAugment/child_networks/bad_lenet.py

 import torch.nn as nn
 
 
-class Bad_LeNet(nn.Module):
-    # 1. I reduced the channel sizes of the convolutional layers
-    # 2. I reduced the number of fully ocnnected layers from 3 to 2
-    # 
-    # no. of weights: 25*2 + 25*2*4 + 16*4*10 = 250+640 = 890
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 2, 5)
-        self.relu1 = nn.ReLU()
-        self.pool1 = nn.MaxPool2d(2)
-        self.conv2 = nn.Conv2d(2, 4, 5)
-        self.relu2 = nn.ReLU()
-        self.pool2 = nn.MaxPool2d(2)
-        self.fc1 = nn.Linear(16*4,  10)
-        self.relu3 = nn.ReLU()
+# class Bad_LeNet(nn.Module):
+#     # 1. I reduced the channel sizes of the convolutional layers
+#     # 2. I reduced the number of fully ocnnected layers from 3 to 2
+#     # 
+#     # no. of weights: 25*2 + 25*2*4 + 16*4*10 = 250+640 = 890
+#     def __init__(self):
+#         super().__init__()
+#         self.conv1 = nn.Conv2d(1, 2, 5)
+#         self.relu1 = nn.ReLU()
+#         self.pool1 = nn.MaxPool2d(2)
+#         self.conv2 = nn.Conv2d(2, 4, 5)
+#         self.relu2 = nn.ReLU()
+#         self.pool2 = nn.MaxPool2d(2)
+#         self.fc1 = nn.Linear(16*4,  10)
+#         self.relu3 = nn.ReLU()
 
 
-    def forward(self, x):
-        y = self.conv1(x)
-        y = self.relu1(y)
-        y = self.pool1(y)
-        y = self.conv2(y)
-        y = self.relu2(y)
-        y = self.pool2(y)
-        y = y.view(y.shape[0], -1)
-        y = self.fc1(y)
-        y = self.relu3(y)
-        return y
+#     def forward(self, x):
+#         y = self.conv1(x)
+#         y = self.relu1(y)
+#         y = self.pool1(y)
+#         y = self.conv2(y)
+#         y = self.relu2(y)
+#         y = self.pool2(y)
+#         y = y.view(y.shape[0], -1)
+#         y = self.fc1(y)
+#         y = self.relu3(y)
+#         return y
 
 class Bad_LeNet(nn.Module):
     # 1. I reduced the channel sizes of the convolutional layers

bad_lenet_baseline.txt

+0.4399999976158142
+0.550000011920929