Model |
L
|
N
|
p
| Train | Validation | Test |
---|
 |  |  |  | Cost | Acc.% | Cost | Acc.% | Cost | Acc.% |
---|
FConn | 6 | 256 | 5.77 | 0.977 | 58.93 | 1.038 | 56.19 | 1.043 | 55.80 |
CNN3x3 | 6 | 256 | 6.68 | 0.726 | 71.10 | 0.740 | 70.39 | 0.746 | 70.37 |
C1-LSTM | 4 | 256 | 6.53 | 0.788 | 67.58 | 0.877 | 64.82 | 0.886 | 64.04 |
C2-LSTM
|
6
|
256
|
6.59
|
0.651
|
74.43
|
0.726
|
71.48
|
0.733
|
70.98
|
- The model column refers to the network architecture, L and N are the number of hidden layers and nodes in each layer (the detailed function of these parameters in each structure can be found in Section 3.3). p is a base-10 logarithmic measure of the number of parameters. The value of the cost or loss function and the clasiffication accuracy is included for the training, validation and test subsets. The best model in terms of validation cost is highlighted in italics