'best_model.pth' neural network learning model + fixes

NeuralNetwork/learning_results.txt

@@ -0,0 +1,10 @@
+Epoch:  1 Train Loss: 65 Train Accuracy: 0.5754245754245755
+Epoch:  2 Train Loss: 25 Train Accuracy: 0.7457542457542458
+Epoch:  3 Train Loss: 8 Train Accuracy: 0.8431568431568431
+Epoch:  4 Train Loss: 2 Train Accuracy: 0.9010989010989011
+Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
+Epoch:  6 Train Loss: 0 Train Accuracy: 0.9545454545454546
+Epoch:  7 Train Loss: 0 Train Accuracy: 0.972027972027972
+Epoch:  8 Train Loss: 0 Train Accuracy: 0.9820179820179821
+Epoch:  9 Train Loss: 0 Train Accuracy: 0.994005994005994
+Epoch:  10 Train Loss: 0 Train Accuracy: 0.9945054945054945

NeuralNetwork/neural_network_learning.py

@@ -22,7 +22,8 @@ optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
 criterion = nn.CrossEntropyLoss()
 
 num_epochs = 10
-train_size = len(glob.glob(images_path, '*.jpg'))
+# train_size = len(glob.glob(images_path+'*.jpg'))
+train_size = 2002
 
 go_to_accuracy = 0.0
 for epoch in range(num_epochs):
@@ -50,10 +51,10 @@ for epoch in range(num_epochs):
     train_accuracy = train_accuracy/train_size
     train_loss = train_loss/train_size
 
+    model.eval()
+
     print('Epoch:  '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
 
     if train_accuracy > go_to_accuracy:
         go_to_accuracy= train_accuracy
-        torch.save(model.state_dict(), "best_model.pth") 
-
-
+        torch.save(model.state_dict(), "best_model.pth") 

NeuralNetwork/src/data_model.py

@@ -9,35 +9,35 @@ class DataModel(nn.Module):
 
         # convolution
         self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
-        #shape (256, 12, 244x244)
+        #shape (256, 12, 224x224)
 
         # batch normalization
         self.bn1 = nn.BatchNorm2d(num_features=12)
-        #shape (256, 12, 244x244)
+        #shape (256, 12, 224x224)
         self.reul1 = nn.ReLU()
 
         self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
         # reduce image size by factor 2
         # pooling window moves by 2 pixels at a time instead of 1
-        # shape (256, 12, 122x122)
+        # shape (256, 12, 112x112)
 
 
 
         self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
         self.bn2 = nn.BatchNorm2d(num_features=24)
         self.reul2 = nn.ReLU()
-        # shape (256, 24, 122x122)
+        # shape (256, 24, 112x112)
 
         self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
-        #shape (256, 48, 122x122)
+        #shape (256, 48, 112x112)
         self.bn3 = nn.BatchNorm2d(num_features=48)
-        #shape (256, 48, 122x122)
+        #shape (256, 48, 112x112)
         self.reul3 = nn.ReLU()
 
         # connected layer
-        self.fc = nn.Linear(in_features=48*122*122, out_features=num_objects)
+        self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
 
-        def forward(self, input):
+    def forward(self, input):
             output = self.conv1(input)
             output = self.bn1(output)
             output = self.reul1(output)
@@ -51,8 +51,11 @@ class DataModel(nn.Module):
             output = self.bn3(output)
             output = self.reul3(output)
 
-            # output shape matrix (256, 48, 122x122)
-            output = output.view(-1, 48*122*122)
+            # output shape matrix (256, 48, 112x112)
+            #print(output.shape)
+            #print(self.fc.weight.shape)
+
+            output = output.view(-1, 48*112*112)
             output = self.fc(output)
             
             return output

NeuralNetwork/src/torchvision_resize_dataset.py

@@ -1,3 +1,4 @@
+import glob
 import pathlib
 import torchvision.transforms as transforms
 from torchvision.datasets import ImageFolder
@@ -27,4 +28,4 @@ combined_dataset = ImageFolder(images_path, transform=transform)
 path=pathlib.Path(images_path)
 classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
 
-# print(classes)
+# print(classes)