Fully Connected Network#

class physicsnemo.models.mlp.fully_connected.FullyConnected(*args, **kwargs)[source]#

Bases: Module

A densely-connected MLP architecture

Parameters:

  • in_features (int, optional) – Size of input features, by default 512

  • layer_size (int, optional) – Size of every hidden layer, by default 512

  • out_features (int, optional) – Size of output features, by default 512

  • num_layers (int, optional) – Number of hidden layers, by default 6

  • activation_fn (Union[str, List[str]], optional) – Activation function to use, by default ‘silu’

  • skip_connections (bool, optional) – Add skip connections every 2 hidden layers, by default False

  • adaptive_activations (bool, optional) – Use an adaptive activation function, by default False

  • weight_norm (bool, optional) – Use weight norm on fully connected layers, by default False

  • weight_fact (bool, optional) – Use weight factorization on fully connected layers, by default False

Example

>>> model = physicsnemo.models.mlp.FullyConnected(in_features=32, out_features=64)
>>> input = torch.randn(128, 32)
>>> output = model(input)
>>> output.size()
torch.Size([128, 64])
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