garage.torch.modules.mlp_module module

MLP Module.

class MLPModule(input_dim, output_dim, hidden_sizes, hidden_nonlinearity=<sphinx.ext.autodoc.importer._MockObject object>, hidden_w_init=<sphinx.ext.autodoc.importer._MockObject object>, hidden_b_init=<sphinx.ext.autodoc.importer._MockObject object>, output_nonlinearity=None, output_w_init=<sphinx.ext.autodoc.importer._MockObject object>, output_b_init=<sphinx.ext.autodoc.importer._MockObject object>, layer_normalization=False)[source]

Bases: garage.torch.modules.multi_headed_mlp_module.MultiHeadedMLPModule

MLP Model.

A Pytorch module composed only of a multi-layer perceptron (MLP), which maps real-valued inputs to real-valued outputs.

Parameters:
  • input_dim (int) – Dimension of the network input.
  • output_dim (int) – Dimension of the network output.
  • hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means this MLP consists of two hidden layers, each with 32 hidden units.
  • hidden_nonlinearity (callable or torch.nn.Module) – Activation function for intermediate dense layer(s). It should return a torch.Tensor. Set it to None to maintain a linear activation.
  • hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
  • hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
  • output_nonlinearity (callable or torch.nn.Module) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation.
  • output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
  • output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
  • layer_normalization (bool) – Bool for using layer normalization or not.
forward(input_value)[source]

Forward method.

Parameters:input_value (torch.Tensor) – Input values with (N, *, input_dim) shape.
Returns:Output value
Return type:torch.Tensor