Created
November 1, 2019 05:20
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| class AttendDiagnose(nn.Module): | |
| def __init__(self, number_measurements, filter_number): | |
| super().__init__() | |
| self.d_model = filter_number*number_measurements | |
| self.embedding_conv = nn.Conv1d(number_measurements, filter_number*number_measurements, 1) | |
| self.pe = PositionalEncoding(filter_number*number_measurements) | |
| # embed_dim and attention_heads | |
| self.masked_attn = nn.modules.activation.MultiheadAttention(filter_number*number_measurements, 8) | |
| self.norm = nn.modules.normalization.LayerNorm(self.d_model) | |
| self.final_layer = nn.Linear(self.d_model, 1) | |
| def forward(self, X): | |
| x = self.embedding_conv(X) | |
| x= x.transpose(1,2) | |
| x = self.pe(x) | |
| x = self.masked_attn(x, x, x)[0] | |
| x = self.norm(x) | |
| x = self.final_layer(x) | |
| print(x.shape) | |
| def positional_encoding(X): | |
| # TODO implement | |
| pass |
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