clean up fine tune code

fine_tune/success_fine_tune.py

@@ -246,8 +246,6 @@ def train_model(network, criterion, optimizer, scheduler, trainLoader, valLoader
     plt.figure()
     plt.xlabel('epoch')
     plt.ylabel('loss scores')
-    #     axes = plt.gca()
-    #     axes.set_ylim([1.7,2.0])
     plt.plot(xrange(n_epochs), train_loss_arr)
     plt.plot(xrange(n_epochs), valid_loss_arr)
 
@@ -270,45 +268,45 @@ def train_model(network, criterion, optimizer, scheduler, trainLoader, valLoader
     plt.savefig(model_name+'/acctop5.png')
 
 
-# vgg16 = models.vgg16(pretrained = True)
-# for param in vgg16.parameters():
-#     param.requires_grad = False
-# vgg16.classifier = nn.Sequential(
-#     nn.Linear(25088, 4096), 
-#     nn.ReLU(), 
-#     nn.Dropout(0.5),
-#     nn.Linear(4096, 4096),
-#     nn.ReLU(), 
-#     nn.Dropout(0.5),
-#     nn.Linear(4096, len(idx_to_info))
-# )
-
-# optimizer = optim.SGD(vgg16.classifier.parameters(), lr = 0.001)
-# scheduler = LambdaLR(optimizer, lambda e: 1 if e < 200/2 else 0.1)
-# criterion = nn.CrossEntropyLoss()
-
-# print 'VGG16 models loaded'
-# train_model(vgg16, criterion, optimizer, scheduler, trainLoader, valLoader, n_epochs = 200, model_name='vgg16')
-
-
-# resnet18 = models.resnet18(pretrained=True)
-# for param in resnet18.parameters():
-#     param.requires_grad = False
-# resnet18.fc = nn.Sequential(
-#     nn.Linear(512, 512), 
-#     nn.ReLU(), 
-#     nn.Dropout(0.5),
-#     nn.Linear(512, 512),
-#     nn.ReLU(), 
-#     nn.Dropout(0.5),
-#     nn.Linear(512, len(idx_to_info))
-# )
-# optimizer = optim.SGD(resnet18.fc.parameters(), lr = 0.001)
-# scheduler = LambdaLR(optimizer, lambda e: 1 if e < 200/2 else 0.1)
-# criterion = nn.CrossEntropyLoss()
-
-# print 'resnet18 models loaded'
-# train_model(resnet18, criterion, optimizer, scheduler, trainLoader, valLoader, n_epochs = 200, model_name='resnet18')
+vgg16 = models.vgg16(pretrained = True)
+for param in vgg16.parameters():
+    param.requires_grad = False
+vgg16.classifier = nn.Sequential(
+    nn.Linear(25088, 4096), 
+    nn.ReLU(), 
+    nn.Dropout(0.5),
+    nn.Linear(4096, 4096),
+    nn.ReLU(), 
+    nn.Dropout(0.5),
+    nn.Linear(4096, len(idx_to_info))
+)
+
+optimizer = optim.SGD(vgg16.classifier.parameters(), lr = 0.001)
+scheduler = LambdaLR(optimizer, lambda e: 1 if e < 200/2 else 0.1)
+criterion = nn.CrossEntropyLoss()
+
+print 'VGG16 models loaded'
+train_model(vgg16, criterion, optimizer, scheduler, trainLoader, valLoader, n_epochs = 200, model_name='vgg16')
+
+
+resnet18 = models.resnet18(pretrained=True)
+for param in resnet18.parameters():
+    param.requires_grad = False
+resnet18.fc = nn.Sequential(
+    nn.Linear(512, 512), 
+    nn.ReLU(), 
+    nn.Dropout(0.5),
+    nn.Linear(512, 512),
+    nn.ReLU(), 
+    nn.Dropout(0.5),
+    nn.Linear(512, len(idx_to_info))
+)
+optimizer = optim.SGD(resnet18.fc.parameters(), lr = 0.001)
+scheduler = LambdaLR(optimizer, lambda e: 1 if e < 200/2 else 0.1)
+criterion = nn.CrossEntropyLoss()
+
+print 'resnet18 models loaded'
+train_model(resnet18, criterion, optimizer, scheduler, trainLoader, valLoader, n_epochs = 200, model_name='resnet18')
 
 resnet152 = models.resnet152(pretrained=True)
 for param in resnet152.parameters():