garage.tf.models.sequential

Sequential Model.

A model composed of one or more models which are connected sequential, according to the insertion order.

class Sequential(*models, name=None)

Bases: garage.tf.models.model.Model

Inheritance diagram of garage.tf.models.sequential.Sequential

Sequential Model.

Parameters:
input

input of the model by default.

Type:tf.Tensor
output

output of the model by default.

Type:tf.Tensor
inputs

inputs of the model by default.

Type:tf.Tensor
outputs

outputs of the model by default.

Type:tf.Tensor
parameters

Parameters of the model.

Returns:Parameters
Return type:np.ndarray
name

Name (str) of the model.

This is also the variable scope of the model.

Returns:Name of the model.
Return type:str
state_info_specs

State info specification.

Returns:
keys and shapes for the information related to the
module’s state when taking an action.
Return type:List[str]
state_info_keys

State info keys.

Returns:
keys for the information related to the module’s state
when taking an input.
Return type:List[str]
build(self, *inputs, name=None)

Build a Network with the given input(s).

* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *

It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.

Parameters:
  • inputs (list[tf.Tensor]) – Tensor input(s), recommended to be positional arguments, for example, def build(self, state_input, action_input, name=None).
  • name (str) – Name of the model, which is also the name scope of the model.
Raises:

ValueError – When a Network with the same name is already built.
Returns:

Output tensors of the model with the given

inputs.
Return type:

list[tf.Tensor]
network_input_spec(self)

Network input spec.

Returns:List of key(str) for the network inputs.
Return type:list[str]
network_output_spec(self)

Network output spec.

Returns:List of key(str) for the network outputs.
Return type:list[str]
reset(self, do_resets=None)

Reset the module.

This is effective only to recurrent modules. do_resets is effective only to vectoried modules.

For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.

Parameters:do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
terminate(self)

Clean up operation.

get_trainable_vars(self)

Get trainable variables.

Returns:
A list of trainable variables in the current
variable scope.
Return type:List[tf.Variable]
get_global_vars(self)

Get global variables.

Returns:
A list of global variables in the current
variable scope.
Return type:List[tf.Variable]
get_regularizable_vars(self)

Get all network weight variables in the current scope.

Returns:
A list of network weight variables in the
current variable scope.
Return type:List[tf.Variable]
get_params(self)

Get the trainable variables.

Returns:
A list of trainable variables in the current
variable scope.
Return type:List[tf.Variable]
get_param_shapes(self)

Get parameter shapes.

Returns:A list of variable shapes.
Return type:List[tuple]
get_param_values(self)

Get param values.

Returns:
Values of the parameters evaluated in
the current session
Return type:np.ndarray
set_param_values(self, param_values)

Set param values.

Parameters:param_values (np.ndarray) – A numpy array of parameter values.
flat_to_params(self, flattened_params)

Unflatten tensors according to their respective shapes.

Parameters:flattened_params (np.ndarray) – A numpy array of flattened params.
Returns:
A list of parameters reshaped to the
shapes specified.
Return type:List[np.ndarray]