| 42 | raise ValueError(f"{name} not a known positional encoding.") |
| 43 | |
| 44 | def get_neural_network(name, input_dim, hparams=None): |
| 45 | if name == "linear": |
| 46 | return nn.Linear(input_dim, hparams['num_classes']) |
| 47 | elif name == "siren": |
| 48 | return NN.SirenNet( |
| 49 | dim_in=input_dim, |
| 50 | dim_hidden=hparams['dim_hidden'], |
| 51 | num_layers=hparams['num_layers'], |
| 52 | dim_out=hparams['num_classes'], |
| 53 | dropout=hparams['dropout'] if "dropout" in hparams.keys() else False |
| 54 | ) |
| 55 | elif name == "fcnet": |
| 56 | return NN.FCNet( |
| 57 | num_inputs=input_dim, |
| 58 | num_classes=hparams['num_classes'], |
| 59 | dim_hidden=hparams['dim_hidden'] |
| 60 | ) |
| 61 | else: |
| 62 | raise ValueError(f"{name} not a known neural networks.") |
| 63 | |
| 64 | def get_param(hparams, key, default=False): |
| 65 | """ |