garage.tf.embeddings
¶
Embeddings.
-
class
Encoder
(name)¶ Bases:
garage.np.embeddings.Encoder
,garage.tf.models.Module
Base class for encoders in TensorFlow.
-
get_latent
(self, input_value)¶ Get a sample of embedding for the given input.
- Parameters
input_value (numpy.ndarray) – Tensor to encode.
- Returns
An embedding sampled from embedding distribution. dict: Embedding distribution information.
- Return type
numpy.ndarray
Note
It returns an embedding and a dict, with keys - mean (numpy.ndarray): Mean of the distribution. - log_std (numpy.ndarray): Log standard deviation of the
distribution.
-
get_latents
(self, input_values)¶ Get samples of embedding for the given inputs.
- Parameters
input_values (numpy.ndarray) – Tensors to encode.
- Returns
Embeddings sampled from embedding distribution. dict: Embedding distribution information.
- Return type
numpy.ndarray
Note
It returns an embedding and a dict, with keys - mean (list[numpy.ndarray]): Means of the distribution. - log_std (list[numpy.ndarray]): Log standard deviations of the
distribution.
-
clone
(self, name)¶ Return a clone of the encoder.
It copies the configuration of the primitive and also the parameters.
- Parameters
name (str) – Name of the newly created encoder. It has to be different from source encoder if cloned under the same computational graph.
- Returns
Newly cloned encoder.
- Return type
-
property
spec
(self)¶ garage.InOutSpec: Input and output space.
-
property
input_dim
(self)¶ int: Dimension of the encoder input.
-
property
output_dim
(self)¶ int: Dimension of the encoder output (embedding).
-
reset
(self, do_resets=None)¶ Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective only to vectoried encoder.
For a vectorized encoder, 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
name
(self)¶ str: Name of this module.
-
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]
-
-
class
StochasticEncoder
(name)¶ Bases:
garage.np.embeddings.StochasticEncoder
,garage.tf.models.StochasticModule
Base class for stochastic encoders in TensorFlow.
-
build
(self, embedding_input, name=None)¶ Build encoder.
After buil, self.distribution is a Gaussian distribution conitioned on embedding_input.
- Parameters
embedding_input (tf.Tensor) – Embedding input.
name (str) – Name of the model, which is also the name scope.
-
property
distribution
(self)¶ object: Embedding distribution.
-
property
spec
(self)¶ garage.InOutSpec: Input and output space.
-
property
input_dim
(self)¶ int: Dimension of the encoder input.
-
property
output_dim
(self)¶ int: Dimension of the encoder output (embedding).
-
reset
(self, do_resets=None)¶ Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective only to vectoried encoder.
For a vectorized encoder, 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
name
(self)¶ str: Name of this module.
-
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]
-