`garage.tf.embeddings`¶

Embeddings.

class Encoder(name)¶

Bases: garage.np.embeddings.Encoder, garage.tf.models.Module

Inheritance diagram of garage.tf.embeddings.Encoder

Base class for encoders in TensorFlow.

spec¶

Input and output space.

Type:	garage.InOutSpec

input_dim¶

Dimension of the encoder input.

Type:	int

output_dim¶

Dimension of the encoder output (embedding).

Type:	int

name¶

Name of this module.

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]

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:	garage.tf.embeddings.encoder.Encoder

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.

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

Inheritance diagram of garage.tf.embeddings.StochasticEncoder

Base class for stochastic encoders in TensorFlow.

distribution¶

Embedding distribution.

Type:	object