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  1. import torch
  2. class FFNN(torch.nn.Module):
  3. def __init__(self, configuration: dict):
  4. super().__init__()
  5. self.hidden_size = configuration["hidden_size"]
  6. self.input_size = configuration["input_size"]
  7. self.output_size = 2
  8. self.hidden_1 = torch.nn.Linear(self.input_size, self.hidden_size)
  9. self.output_layer = torch.nn.Linear(self.hidden_size, 2)
  10. def forward(self, x: torch.Tensor)-> torch.Tensor:
  11. x = self.hidden_1(x)
  12. x = torch.relu(x)
  13. y = self.output_layer(x)
  14. return y
  15. def predict(self, x: torch.Tensor) -> torch.Tensor:
  16. outputs = self.forward(x)
  17. outputs = outputs.softmax(dim=1)
  18. return outputs

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