- Dataset - MNIST dataset; 60k train data, 10k test data
Classification report: precision recall f1-score support
0 0.97 0.99 0.98 980
1 0.96 1.00 0.98 1135
2 0.98 0.97 0.97 1032
3 0.96 0.97 0.96 1010
4 0.98 0.97 0.97 982
5 0.97 0.96 0.96 892
6 0.98 0.99 0.98 958
7 0.96 0.96 0.96 1028
8 0.99 0.94 0.96 974
9 0.96 0.96 0.96 1009
accuracy 0.97 10000
macro avg 0.97 0.97 0.97 10000 weighted avg 0.97 0.97 0.97 10000
Confusion matrix: array([[ 974, 1, 1, 0, 0, 1, 2, 1, 0, 0], [ 0, 1133, 2, 0, 0, 0, 0, 0, 0, 0], [ 10, 9, 996, 2, 0, 0, 0, 13, 2, 0], [ 0, 2, 4, 976, 1, 13, 1, 7, 3, 3], [ 1, 6, 0, 0, 950, 0, 4, 2, 0, 19], [ 6, 1, 0, 11, 2, 859, 5, 1, 3, 4], [ 5, 3, 0, 0, 3, 3, 944, 0, 0, 0], [ 0, 21, 5, 0, 1, 0, 0, 991, 0, 10], [ 8, 2, 4, 16, 8, 11, 3, 4, 914, 4], [ 4, 5, 2, 8, 9, 2, 1, 8, 2, 968]], dtype=int64)
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Linear Classification report: precision recall f1-score support
0 0.94 0.97 0.96 980 1 0.96 0.99 0.97 1135 2 0.90 0.93 0.91 1032 3 0.89 0.93 0.91 1010 4 0.92 0.94 0.93 982 5 0.91 0.89 0.90 892 6 0.96 0.93 0.95 958 7 0.95 0.92 0.93 1028 8 0.91 0.88 0.90 974 9 0.93 0.89 0.91 1009accuracy 0.93 10000 macro avg 0.93 0.93 0.93 10000 weighted avg 0.93 0.93 0.93 10000
Confusion matrix: array([[ 953, 0, 6, 2, 1, 8, 6, 2, 1, 1], [ 0, 1118, 7, 2, 0, 1, 2, 1, 4, 0], [ 9, 12, 956, 11, 9, 4, 5, 5, 18, 3], [ 7, 1, 15, 940, 0, 17, 1, 6, 19, 4], [ 3, 2, 18, 1, 927, 0, 3, 6, 3, 19], [ 7, 6, 7, 40, 5, 791, 12, 1, 20, 3], [ 14, 3, 17, 1, 9, 19, 892, 0, 3, 0], [ 2, 8, 23, 14, 11, 2, 0, 945, 2, 21], [ 11, 7, 10, 29, 8, 23, 8, 6, 860, 12], [ 9, 7, 6, 11, 38, 5, 0, 23, 12, 898]], dtype=int64)
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Radial(rbf) Classification report: precision recall f1-score support
0 0.98 0.99 0.98 980 1 0.99 0.99 0.99 1135 2 0.95 0.97 0.96 1032 3 0.97 0.97 0.97 1010 4 0.97 0.96 0.97 982 5 0.96 0.96 0.96 892 6 0.98 0.97 0.97 958 7 0.93 0.96 0.94 1028 8 0.96 0.95 0.96 974 9 0.97 0.94 0.95 1009accuracy 0.97 10000 macro avg 0.97 0.97 0.97 10000 weighted avg 0.97 0.97 0.97 10000
Confusion matrix: array([[ 967, 0, 2, 1, 0, 3, 3, 2, 2, 0], [ 0, 1125, 5, 0, 0, 1, 2, 0, 2, 0], [ 5, 1, 996, 2, 2, 0, 1, 15, 9, 1], [ 0, 0, 3, 980, 1, 7, 0, 12, 7, 0], [ 0, 0, 13, 0, 945, 2, 3, 7, 2, 10], [ 2, 0, 2, 11, 1, 857, 6, 5, 6, 2], [ 6, 2, 0, 0, 4, 8, 927, 6, 5, 0], [ 1, 6, 13, 3, 3, 0, 0, 989, 0, 13], [ 3, 0, 6, 5, 6, 10, 3, 12, 926, 3], [ 4, 5, 6, 11, 13, 2, 0, 21, 3, 944]], dtype=int64)
Classification report: precision recall f1-score support
0 1.00 0.99 0.99 980
1 0.99 0.99 0.99 1135
2 0.99 0.99 0.99 1032
3 0.99 1.00 0.99 1010
4 1.00 0.99 1.00 982
5 0.99 0.99 0.99 892
6 1.00 0.99 0.99 958
7 0.98 1.00 0.99 1028
8 0.99 1.00 0.99 974
9 0.99 0.99 0.99 1009
accuracy 0.99 10000
macro avg 0.99 0.99 0.99 10000 weighted avg 0.99 0.99 0.99 10000
Confusion matrix: array([[ 974, 0, 2, 0, 0, 0, 0, 1, 3, 0], [ 0, 1129, 0, 2, 0, 0, 0, 3, 1, 0], [ 0, 0, 1025, 0, 0, 0, 0, 7, 0, 0], [ 0, 0, 1, 1005, 0, 3, 0, 0, 1, 0], [ 0, 0, 0, 0, 977, 0, 0, 0, 0, 5], [ 1, 1, 0, 7, 0, 880, 2, 1, 0, 0], [ 3, 3, 2, 1, 2, 1, 944, 0, 2, 0], [ 0, 1, 0, 0, 0, 0, 0, 1025, 1, 1], [ 0, 0, 1, 0, 0, 0, 0, 2, 970, 1], [ 0, 1, 0, 0, 2, 2, 0, 7, 1, 996]], dtype=int64)
Classification Report precision recall f1-score support
0 1.00 0.99 0.99 980
1 0.99 0.99 0.99 1135
2 0.99 0.99 0.99 1032
3 0.99 1.00 0.99 1010
4 1.00 0.99 1.00 982
5 0.99 0.99 0.99 892
6 1.00 0.99 0.99 958
7 0.98 1.00 0.99 1028
8 0.99 1.00 0.99 974
9 0.99 0.99 0.99 1009
accuracy 0.99 10000
macro avg 0.99 0.99 0.99 10000 weighted avg 0.99 0.99 0.99 10000
Confusion matrix: array([[ 968, 0, 1, 0, 0, 2, 4, 1, 4, 0], [ 0, 1124, 2, 3, 0, 2, 2, 0, 1, 1], [ 6, 0, 998, 8, 2, 0, 4, 8, 6, 0], [ 0, 0, 9, 976, 0, 6, 0, 9, 8, 2], [ 1, 0, 3, 0, 957, 0, 4, 0, 2, 15], [ 3, 0, 0, 8, 3, 863, 6, 2, 4, 3], [ 6, 3, 1, 0, 2, 4, 939, 0, 3, 0], [ 1, 3, 17, 1, 1, 0, 0, 995, 1, 9], [ 3, 0, 6, 8, 5, 5, 4, 5, 928, 10], [ 5, 6, 2, 10, 11, 4, 1, 4, 3, 963]], dtype=int64)
Final Accuracy: tensor(92.4600)
I'm not sure how to show the classification report and confusion matrix from this, but I'll try to figure it out Used pytorch for it from a tutorial, will change it to tensorflow.