garage.torch.modules.gaussian_mlp_module
¶
GaussianMLPModule.
-
class
GaussianMLPBaseModule
(input_dim, output_dim, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, learn_std=True, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=torch.tanh, std_hidden_w_init=nn.init.xavier_uniform_, std_hidden_b_init=nn.init.zeros_, std_output_nonlinearity=None, std_output_w_init=nn.init.xavier_uniform_, std_parameterization='exp', layer_normalization=False, normal_distribution_cls=Normal)¶ Bases:
torch.nn.Module
Base of GaussianMLPModel.
- Parameters
input_dim (int) – Input dimension of the model.
output_dim (int) – Output dimension of the model.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – 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) – 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.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues (plain value - not log or exponentiated).
max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues (plain value - not log or exponentiated).
std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network.
std_hidden_w_init (callable) – Initializer function for the weight of hidden layer (s).
std_hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s).
std_output_nonlinearity (callable) – Activation function for output dense layer in the std network. It should return a torch.Tensor. Set it to None to maintain a linear activation.
std_output_w_init (callable) – Initializer function for the weight of output dense layer(s) in the std network.
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation.
softplus: the std will be computed as log(1+exp(x)).
layer_normalization (bool) – Bool for using layer normalization or not.
normal_distribution_cls (torch.distribution) – normal distribution class to be constructed and returned by a call to forward. By default, is torch.distributions.Normal.
-
to
(self, *args, **kwargs)¶ Move the module to the specified device.
- Parameters
*args – args to pytorch to function.
**kwargs – keyword args to pytorch to function.
-
forward
(self, *inputs)¶ Forward method.
- Parameters
*inputs – Input to the module.
- Returns
- Independent
distribution.
- Return type
torch.distributions.independent.Independent
-
class
GaussianMLPModule
(input_dim, output_dim, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, learn_std=True, init_std=1.0, min_std=1e-06, max_std=None, std_parameterization='exp', layer_normalization=False, normal_distribution_cls=Normal)¶ Bases:
garage.torch.modules.gaussian_mlp_module.GaussianMLPBaseModule
GaussianMLPModule that mean and std share the same network.
- Parameters
input_dim (int) – Input dimension of the model.
output_dim (int) – Output dimension of the model.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – 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) – 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.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues (plain value - not log or exponentiated).
max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues (plain value - not log or exponentiated).
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation
softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool) – Bool for using layer normalization or not.
normal_distribution_cls (torch.distribution) – normal distribution class to be constructed and returned by a call to forward. By default, is torch.distributions.Normal.
-
to
(self, *args, **kwargs)¶ Move the module to the specified device.
- Parameters
*args – args to pytorch to function.
**kwargs – keyword args to pytorch to function.
-
forward
(self, *inputs)¶ Forward method.
- Parameters
*inputs – Input to the module.
- Returns
- Independent
distribution.
- Return type
torch.distributions.independent.Independent
-
class
GaussianMLPIndependentStdModule
(input_dim, output_dim, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, learn_std=True, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=torch.tanh, std_hidden_w_init=nn.init.xavier_uniform_, std_hidden_b_init=nn.init.zeros_, std_output_nonlinearity=None, std_output_w_init=nn.init.xavier_uniform_, std_parameterization='exp', layer_normalization=False, normal_distribution_cls=Normal)¶ Bases:
garage.torch.modules.gaussian_mlp_module.GaussianMLPBaseModule
GaussianMLPModule which has two different mean and std network.
- Parameters
input_dim (int) – Input dimension of the model.
output_dim (int) – Output dimension of the model.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – 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) – 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.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues (plain value - not log or exponentiated).
max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues (plain value - not log or exponentiated).
std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network.
std_hidden_w_init (callable) – Initializer function for the weight of hidden layer (s).
std_hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s).
std_output_nonlinearity (callable) – Activation function for output dense layer in the std network. It should return a torch.Tensor. Set it to None to maintain a linear activation.
std_output_w_init (callable) – Initializer function for the weight of output dense layer(s) in the std network.
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation
softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool) – Bool for using layer normalization or not.
normal_distribution_cls (torch.distribution) – normal distribution class to be constructed and returned by a call to forward. By default, is torch.distributions.Normal.
-
to
(self, *args, **kwargs)¶ Move the module to the specified device.
- Parameters
*args – args to pytorch to function.
**kwargs – keyword args to pytorch to function.
-
forward
(self, *inputs)¶ Forward method.
- Parameters
*inputs – Input to the module.
- Returns
- Independent
distribution.
- Return type
torch.distributions.independent.Independent
-
class
GaussianMLPTwoHeadedModule
(input_dim, output_dim, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, learn_std=True, init_std=1.0, min_std=1e-06, max_std=None, std_parameterization='exp', layer_normalization=False, normal_distribution_cls=Normal)¶ Bases:
garage.torch.modules.gaussian_mlp_module.GaussianMLPBaseModule
GaussianMLPModule which has only one mean network.
- Parameters
input_dim (int) – Input dimension of the model.
output_dim (int) – Output dimension of the model.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – 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) – 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.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues (plain value - not log or exponentiated).
max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues (plain value - not log or exponentiated).
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation
softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool) – Bool for using layer normalization or not.
normal_distribution_cls (torch.distribution) – normal distribution class to be constructed and returned by a call to forward. By default, is torch.distributions.Normal.
-
to
(self, *args, **kwargs)¶ Move the module to the specified device.
- Parameters
*args – args to pytorch to function.
**kwargs – keyword args to pytorch to function.
-
forward
(self, *inputs)¶ Forward method.
- Parameters
*inputs – Input to the module.
- Returns
- Independent
distribution.
- Return type
torch.distributions.independent.Independent