refactor UCB1_JC_py.py

MetaAugment/UCB1_JC_py.py

@@ -20,106 +20,8 @@ from matplotlib import pyplot as plt
 from numpy import save, load
 from tqdm import trange
 
-
-# In[2]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class LeNet(nn.Module):
-    def __init__(self, img_height, img_width, num_labels, img_channels):
-        super().__init__()
-        self.conv1 = nn.Conv2d(img_channels, 6, 5)
-        self.relu1 = nn.ReLU()
-        self.pool1 = nn.MaxPool2d(2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.relu2 = nn.ReLU()
-        self.pool2 = nn.MaxPool2d(2)
-        self.fc1 = nn.Linear(int((((img_height-4)/2-4)/2)*(((img_width-4)/2-4)/2)*16), 120)
-        self.relu3 = nn.ReLU()
-        self.fc2 = nn.Linear(120, 84)
-        self.relu4 = nn.ReLU()
-        self.fc3 = nn.Linear(84, num_labels)
-        self.relu5 = 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)
-        y = self.fc2(y)
-        y = self.relu4(y)
-        y = self.fc3(y)
-        y = self.relu5(y)
-        return y
-
-
-# In[3]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class EasyNet(nn.Module):
-    def __init__(self, img_height, img_width, num_labels, img_channels):
-        super().__init__()
-        self.fc1 = nn.Linear(img_height*img_width*img_channels, 2048)
-        self.relu1 = nn.ReLU()
-        self.fc2 = nn.Linear(2048, num_labels)
-        self.relu2 = nn.ReLU()
-
-    def forward(self, x):
-        y = x.view(x.shape[0], -1)
-        y = self.fc1(y)
-        y = self.relu1(y)
-        y = self.fc2(y)
-        y = self.relu2(y)
-        return y
-
-
-# In[4]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class SimpleNet(nn.Module):
-    def __init__(self, img_height, img_width, num_labels, img_channels):
-        super().__init__()
-        self.fc1 = nn.Linear(img_height*img_width*img_channels, num_labels)
-        self.relu1 = nn.ReLU()
-
-    def forward(self, x):
-        y = x.view(x.shape[0], -1)
-        y = self.fc1(y)
-        y = self.relu1(y)
-        return y
-
-
-# In[5]:
-
-
-"""Make toy dataset"""
-
-def create_toy(train_dataset, test_dataset, batch_size, n_samples):
-    
-    # shuffle and take first n_samples %age of training dataset
-    shuffle_order_train = np.random.RandomState(seed=100).permutation(len(train_dataset))
-    shuffled_train_dataset = torch.utils.data.Subset(train_dataset, shuffle_order_train)
-    indices_train = torch.arange(int(n_samples*len(train_dataset)))
-    reduced_train_dataset = data_utils.Subset(shuffled_train_dataset, indices_train)
-
-    # shuffle and take first n_samples %age of test dataset
-    shuffle_order_test = np.random.RandomState(seed=1000).permutation(len(test_dataset))
-    shuffled_test_dataset = torch.utils.data.Subset(test_dataset, shuffle_order_test)
-    indices_test = torch.arange(int(n_samples*len(test_dataset)))
-    reduced_test_dataset = data_utils.Subset(shuffled_test_dataset, indices_test)
-
-    # push into DataLoader
-    train_loader = torch.utils.data.DataLoader(reduced_train_dataset, batch_size=batch_size)
-    test_loader = torch.utils.data.DataLoader(reduced_test_dataset, batch_size=batch_size)
-
-    return train_loader, test_loader
+from MetaAugment.child_networks import *
+from MetaAugment.main import create_toy
 
 
 # In[6]:

MetaAugment/main.py

@@ -19,7 +19,7 @@ def create_toy(train_dataset, test_dataset, batch_size, n_samples, seed=100):
     reduced_train_dataset = torch.utils.data.Subset(shuffled_train_dataset, indices_train)
     
     # shuffle and take first n_samples %age of test dataset
-    shuffle_order_test = np.random.RandomState(seed=seed).permutation(len(test_dataset))
+    shuffle_order_test = np.random.RandomState(seed=10*seed).permutation(len(test_dataset))
     shuffled_test_dataset = torch.utils.data.Subset(test_dataset, shuffle_order_test)
 
     big = 1 # how much bigger is the test set