garage.torch.modules.multi_headed_mlp_module module¶
MultiHeadedMLPModule.
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class
MultiHeadedMLPModule(n_heads, input_dim, output_dims, 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_nonlinearities=None, output_w_inits=<sphinx.ext.autodoc.importer._MockObject object>, output_b_inits=<sphinx.ext.autodoc.importer._MockObject object>, layer_normalization=False)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObjectMultiHeadedMLPModule Model.
A PyTorch module composed only of a multi-layer perceptron (MLP) with multiple parallel output layers which maps real-valued inputs to real-valued outputs. The length of outputs is n_heads and shape of each output element is depend on each output dimension
Parameters: - n_heads (int) – Number of different output layers
- input_dim (int) – Dimension of the network input.
- output_dims (int or list or tuple) – 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 or list or tuple) – 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_nonlinearities (callable or torch.nn.Module or list or tuple) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation. Size of the parameter should be 1 or equal to n_head
- output_w_inits (callable or list or tuple) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor. Size of the parameter should be 1 or equal to n_head
- output_b_inits (callable or list or tuple) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor. Size of the parameter should be 1 or equal to n_head
- layer_normalization (bool) – Bool for using layer normalization or not.