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
Sequential Model.
- Parameters
name (str) – Model name, also the variable scope.
models (list[garage.tf.models.Model]) – The models to be connected in sequential order.
-
property
input
(self)¶ tf.Tensor: input of the model by default.
-
property
output
(self)¶ tf.Tensor: output of the model by default.
-
property
inputs
(self)¶ tf.Tensor: inputs of the model by default.
-
property
outputs
(self)¶ tf.Tensor: outputs of the model by default.
-
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
- 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.
-
network_output_spec
(self)¶ Network output spec.
-
property
parameters
(self)¶ Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
-
property
name
(self)¶ Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
-
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.
-
property
state_info_specs
(self)¶ State info specification.
- Returns
- keys and shapes for the information related to the
module’s state when taking an action.
- Return type
List[str]
-
property
state_info_keys
(self)¶ State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
-
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]