Types | Operation | parameter settings |
Improved pulse codingmodule | Spilking Separable Convs_1 Spilking Separable Convs_2 |
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Spilking Separable Convs_1 | Depthwise Convs | Conv2d, out_channels = 3, kernel_size = (3,3), padding = 1, groups = 3 |
Pointwise Convs | Conv2d, out_channels = 128, kernel_size = (1,1), padding = 0, groups = 1 | |
BatchNorm2d | out_channels = 128 | |
neuron.IFNode | out_channels = 128 | |
Spilking Separable Convs_2 | Depthwise Convs | Conv2d, out_channels = 128, kernel_size = (3,3), padding = 1, groups = 3 |
Pointwise Convs | Conv2d, out_channels = 128, kernel_size = (1,1), padding = 0, groups = 1 | |
BatchNorm2d | out_channels = 128 | |
neuron.IFNode | out_channels = 128 | |
| ECABlock | out_channels = 128 |
PulseMaxpool2d | out_channels = 128, kernel_size = (4,1), padding = 0 | |
RUL prediction module | Transformer-Encoder MLP |
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Transformer-Encoder | Transformer-Encoder_1 |
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Transformer-Encoder_2 |
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Transformer-Encoder_1 | Multi-head Attention | Input_dim = 3, d_model = 128, heads = 4, dropout = 0.1 |
Add & Norm | d_model = 128, Num_layers = 2 | |
Feed Forward | dim_feedforward = 4* input_dim | |
Add & Norm | d_model = 128, Num_layers = 2 | |
Transformer-Encoder_2 | Multi-head Attention | Input_dim = 3, d_model = 128, heads = 4, dropout = 0.1 |
Add & Norm | d_model=128, Num_layers = 2 | |
Feed Forward | dim_feedforward = 4* input_dim | |
Add & Norm | d_model = 128, Num_layers = 2 | |
PulseMaxpool2d | out_channels = 128, kernel_size = (8,1), padding = 0 | |
MLP | LINEAR_1 | Input = 32 * n; Ouput = 1 |
Training | Epoch = 1000 | batch_size = 32; Optimizers = SGD; lr = 0.001 |