Performance after convergence, in this case 2 epochs, does not vary. No changes after convergence. Final weight sum is 0. Final Bias is dependent on the learning rate, 0.2
Over 300 Epochs with a learning rate of 0.25, we manage to accomplish a test Accuracy: 75% in 0.6996002197265625 s. Input has the same nodes as the number of cross-cut attributes being analysed. We have three neurons in the hidden layer and the output has a set of unique labels
We attain an accuracy of about 90.5%
- Convolution #1. Input = 32x32x1. Output = 28x28x6 conv2d
- SubSampling #1. Input = 28x28x6. Output = 14x14x6. SubSampling is simply Average Pooling so we use avg_pool
- Convolution #2. Input = 14x14x6. Output = 10x10x16 conv2d
- SubSampling #2. Input = 10x10x16. Output = 5x5x16 avg_pool
- Fully Connected #1. Input = 5x5x16. Output = 120
- Fully Connected #2. Input = 120. Output = 84
- Output 10
suggested activation function is tanh, ReLU observed to have a higher accuracy
Test loss 0.0381, accuracy 98.72%
Completed:244.60259580612183 s
Training set score: 0.996483
Test set score: 0.966400
precision recall f1-score support
0 0.98 0.99 0.98 980
1 0.99 0.99 0.99 1135
2 0.96 0.97 0.96 1032
3 0.95 0.96 0.95 1010
4 0.97 0.97 0.97 982
5 0.97 0.95 0.96 892
6 0.97 0.97 0.97 958
7 0.97 0.96 0.96 1028
8 0.96 0.95 0.95 974
9 0.96 0.96 0.96 1009
Hidden Layers 3
Epochs 25
Train accuracy: 0.99
Val accuracy: 0.9335
Execution time: 26.512019634246826 s
To get the full yolo weights curl -o "./Yolo/data/yolo.weights" -XGET https://pjreddie.com/media/files/yolov3.weights