second program launch with better accuracy (#27) from neural_network_python into master
Reviewed-on: #27
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@ -7,4 +7,25 @@ Epoch: 6 Train Loss: 0 Train Accuracy: 0.9545454545454546
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Epoch: 7 Train Loss: 0 Train Accuracy: 0.972027972027972
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Epoch: 7 Train Loss: 0 Train Accuracy: 0.972027972027972
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Epoch: 8 Train Loss: 0 Train Accuracy: 0.9820179820179821
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Epoch: 8 Train Loss: 0 Train Accuracy: 0.9820179820179821
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Epoch: 9 Train Loss: 0 Train Accuracy: 0.994005994005994
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Epoch: 9 Train Loss: 0 Train Accuracy: 0.994005994005994
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Epoch: 10 Train Loss: 0 Train Accuracy: 0.9945054945054945
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Epoch: 10 Train Loss: 0 Train Accuracy: 0.9945054945054945
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Epoch: 1 Train Loss: 42 Train Accuracy: 0.6428571428571429
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Epoch: 2 Train Loss: 11 Train Accuracy: 0.8306693306693307
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Epoch: 3 Train Loss: 3 Train Accuracy: 0.8921078921078921
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Epoch: 4 Train Loss: 2 Train Accuracy: 0.8891108891108891
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Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
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Epoch: 6 Train Loss: 0 Train Accuracy: 0.952047952047952
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Epoch: 7 Train Loss: 0 Train Accuracy: 0.9545454545454546
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Epoch: 8 Train Loss: 0 Train Accuracy: 0.9655344655344655
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Epoch: 9 Train Loss: 0 Train Accuracy: 0.9815184815184815
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Epoch: 10 Train Loss: 0 Train Accuracy: 0.9805194805194806
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Epoch: 11 Train Loss: 0 Train Accuracy: 0.9855144855144855
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Epoch: 12 Train Loss: 0 Train Accuracy: 0.989010989010989
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Epoch: 13 Train Loss: 0 Train Accuracy: 0.9925074925074925
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Epoch: 14 Train Loss: 0 Train Accuracy: 0.9915084915084915
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Epoch: 15 Train Loss: 0 Train Accuracy: 0.9885114885114885
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Epoch: 16 Train Loss: 0 Train Accuracy: 0.994005994005994
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Epoch: 17 Train Loss: 0 Train Accuracy: 0.997002997002997
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Epoch: 18 Train Loss: 0 Train Accuracy: 0.9965034965034965
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Epoch: 19 Train Loss: 0 Train Accuracy: 0.999000999000999
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Epoch: 20 Train Loss: 0 Train Accuracy: 1.0
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@ -21,7 +21,7 @@ optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
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#loss function
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#loss function
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criterion = nn.CrossEntropyLoss()
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criterion = nn.CrossEntropyLoss()
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num_epochs = 10
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num_epochs = 20
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# train_size = len(glob.glob(images_path+'*.jpg'))
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# train_size = len(glob.glob(images_path+'*.jpg'))
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train_size = 2002
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train_size = 2002
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NeuralNetwork/older_best_model.pth
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NeuralNetwork/older_best_model.pth
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