garage.tf.embeddings.encoder
¶
Encoders in TensorFlow.
- class Encoder(name)¶
Bases:
garage.np.embeddings.Encoder
,garage.tf.models.Module
Base class for encoders in TensorFlow.
- abstract property spec¶
Input and output space.
- Type
- property 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]
- property 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]
- get_latent(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(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(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
- reset(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.
- terminate()¶
Clean up operation.
- get_trainable_vars()¶
Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_global_vars()¶
Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_regularizable_vars()¶
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()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- flat_to_params(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.
- abstract property spec¶
Input and output space.
- Type
- property 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]
- property 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(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.
- reset(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.
- terminate()¶
Clean up operation.
- get_trainable_vars()¶
Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_global_vars()¶
Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_regularizable_vars()¶
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()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- flat_to_params(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]