modified the CNN to get better results

source/NN/model.py

@@ -10,19 +10,15 @@ class Conv_Neural_Network_Model(nn.Module):
         self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
         self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
         self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
-        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
-        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
-        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
 
-        self.fc1 = nn.Linear(128*12*12,hidden_layer1)
+        self.fc1 = nn.Linear(64*25*25,hidden_layer1)
         self.fc2 = nn.Linear(hidden_layer1,hidden_layer2)
         self.out = nn.Linear(hidden_layer2,num_classes)
 
     def forward(self, x):
         x = self.pool1(F.relu(self.conv1(x)))
-        x = self.pool2(F.relu(self.conv2(x)))
-        x = self.pool3(F.relu(self.conv3(x)))
-        x = x.view(-1, 128*12*12) #<----flattening the image
+        x = self.pool1(F.relu(self.conv2(x)))
+        x = x.view(-1, 64*25*25) #<----flattening the image
         x = self.fc1(x)
         x = torch.relu(x)
         x = self.fc2(x)

source/NN/neural_network.py

@@ -29,7 +29,7 @@ test_set = datasets.ImageFolder(root='resources/test', transform=data_transforme
 
 #function for training model
 def train(model, dataset, iter=100, batch_size=64):
-    optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
     criterion = nn.NLLLoss()
     train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
     model.train()
@@ -60,13 +60,13 @@ model = Conv_Neural_Network_Model()
 model.to(device)
 
 #loading the already saved model:
-# model.load_state_dict(torch.load('model.pth'))
-# model.eval()
+model.load_state_dict(torch.load('CNN_model.pth'))
+model.eval()
 
-#training the model:
+# #training the model:
 # train(model, train_set)
 # print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
-# torch.save(model.state_dict(), 'model.pth')
+# torch.save(model.state_dict(), 'CNN_model.pth')
 
 
 
@@ -98,7 +98,7 @@ def guess_image(model, image_tensor):
 # print(f"The predicted image is: {prediction}")
 
 #TEST - loading the image and getting results:
-testImage_path = 'resources/images/plant_photos/pexels-justus-menke-3490295-5213970.jpg'
+testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
 testImage = Image.open(testImage_path)
 testImage = data_transformer(testImage)
 testImage = testImage.unsqueeze(0)