NeuralNetwork/learning_results.txt

@@ -8,3 +8,24 @@ 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
+
+Epoch:  1 Train Loss: 42 Train Accuracy: 0.6428571428571429
+Epoch:  2 Train Loss: 11 Train Accuracy: 0.8306693306693307
+Epoch:  3 Train Loss: 3 Train Accuracy: 0.8921078921078921
+Epoch:  4 Train Loss: 2 Train Accuracy: 0.8891108891108891
+Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
+Epoch:  6 Train Loss: 0 Train Accuracy: 0.952047952047952
+Epoch:  7 Train Loss: 0 Train Accuracy: 0.9545454545454546
+Epoch:  8 Train Loss: 0 Train Accuracy: 0.9655344655344655
+Epoch:  9 Train Loss: 0 Train Accuracy: 0.9815184815184815
+Epoch:  10 Train Loss: 0 Train Accuracy: 0.9805194805194806
+Epoch:  11 Train Loss: 0 Train Accuracy: 0.9855144855144855
+Epoch:  12 Train Loss: 0 Train Accuracy: 0.989010989010989
+Epoch:  13 Train Loss: 0 Train Accuracy: 0.9925074925074925
+Epoch:  14 Train Loss: 0 Train Accuracy: 0.9915084915084915
+Epoch:  15 Train Loss: 0 Train Accuracy: 0.9885114885114885
+Epoch:  16 Train Loss: 0 Train Accuracy: 0.994005994005994
+Epoch:  17 Train Loss: 0 Train Accuracy: 0.997002997002997
+Epoch:  18 Train Loss: 0 Train Accuracy: 0.9965034965034965
+Epoch:  19 Train Loss: 0 Train Accuracy: 0.999000999000999
+Epoch:  20 Train Loss: 0 Train Accuracy: 1.0

NeuralNetwork/neural_network_learning.py

@@ -21,7 +21,7 @@ optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
 #loss function
 criterion = nn.CrossEntropyLoss()
 
-num_epochs = 10
+num_epochs = 20
 # train_size = len(glob.glob(images_path+'*.jpg'))
 train_size = 2002