63fd068988
* multiple functions for version 0.0 * lightining update * minor changes * data pinn loss added --------- Co-authored-by: Nicola Demo <demo.nicola@gmail.com> Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-3-125.WIFIeduroamSTUD.units.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.station> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@192.168.1.38>
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import torch
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import torch.nn as nn
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from ..utils import check_consistency
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class Network(torch.nn.Module):
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def __init__(self, model, extra_features=None):
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super().__init__()
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# check model consistency
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check_consistency(model, nn.Module, 'torch model')
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self._model = model
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# check consistency and assign extra fatures
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if extra_features is None:
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self._extra_features = []
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else:
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for feat in extra_features:
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check_consistency(feat, nn.Module, 'extra features')
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self._extra_features = nn.Sequential(*extra_features)
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# check model works with inputs
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# TODO
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def forward(self, x):
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"""
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Forward method for Network class. This class
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implements the standard forward method, and
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it adds the possibility to pass extra features.
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:param torch.tensor x: input of the network
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:return torch.tensor: output of the network
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"""
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# extract features and append
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for feature in self._extra_features:
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x = x.append(feature(x))
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# perform forward pass
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return self._model(x)
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@property
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def model(self):
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return self._model
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@property
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def extra_features(self):
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return self._extra_features |