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  1. from .data import Data
  2. from typing import List, \
  3. Callable
  4. from .trainprep import PreparedData
  5. import torch
  6. from .convlayer import DecagonLayer
  7. from .input import OneHotInputLayer
  8. from types import FunctionType
  9. from .declayer import DecodeLayer
  10. from .batch import PredictionsBatch
  11. class Model(torch.nn.Module):
  12. def __init__(self, prep_d: PreparedData,
  13. layer_dimensions: List[int] = [32, 64],
  14. keep_prob: float = 1.,
  15. rel_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
  16. layer_activation: Callable[[torch.Tensor], torch.Tensor] = torch.nn.functional.relu,
  17. dec_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
  18. **kwargs) -> None:
  19. super().__init__(**kwargs)
  20. if not isinstance(prep_d, PreparedData):
  21. raise TypeError('prep_d must be an instance of PreparedData')
  22. if not isinstance(layer_dimensions, list):
  23. raise TypeError('layer_dimensions must be a list')
  24. keep_prob = float(keep_prob)
  25. if not isinstance(rel_activation, FunctionType):
  26. raise TypeError('rel_activation must be a function')
  27. if not isinstance(layer_activation, FunctionType):
  28. raise TypeError('layer_activation must be a function')
  29. if not isinstance(dec_activation, FunctionType):
  30. raise TypeError('dec_activation must be a function')
  31. self.prep_d = prep_d
  32. self.layer_dimensions = layer_dimensions
  33. self.keep_prob = keep_prob
  34. self.rel_activation = rel_activation
  35. self.layer_activation = layer_activation
  36. self.dec_activation = dec_activation
  37. self.seq = None
  38. self.build()
  39. def build(self):
  40. in_layer = OneHotInputLayer(self.prep_d)
  41. last_output_dim = in_layer.output_dim
  42. seq = [ in_layer ]
  43. for dim in self.layer_dimensions:
  44. conv_layer = DecagonLayer(input_dim = last_output_dim,
  45. output_dim = [ dim ] * len(self.prep_d.node_types),
  46. data = self.prep_d,
  47. keep_prob = self.keep_prob,
  48. rel_activation = self.rel_activation,
  49. layer_activation = self.layer_activation)
  50. last_output_dim = conv_layer.output_dim
  51. seq.append(conv_layer)
  52. dec_layer = DecodeLayer(input_dim = last_output_dim,
  53. data = self.prep_d,
  54. keep_prob = self.keep_prob,
  55. activation = self.dec_activation)
  56. seq.append(dec_layer)
  57. seq = torch.nn.Sequential(*seq)
  58. self.seq = seq
  59. def forward(self, _):
  60. return self.seq(None)