garage.tf.embeddings.gaussian_mlp_encoder

GaussianMLPEncoder.

class GaussianMLPEncoder(embedding_spec, name='GaussianMLPEncoder', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), learn_std=True, adaptive_std=False, std_share_network=False, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=tf.nn.tanh, std_output_nonlinearity=None, std_parameterization='exp', layer_normalization=False)

Bases: garage.tf.embeddings.StochasticEncoder, garage.tf.models.StochasticModule

Inheritance diagram of garage.tf.embeddings.gaussian_mlp_encoder.GaussianMLPEncoder

GaussianMLPEncoder with GaussianMLPModel.

An embedding that contains a MLP to make prediction based on a gaussian distribution.

Parameters

  • embedding_spec (garage.InOutSpec) – Encoder specification.

  • name (str) – Model name, also the variable scope.

  • hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.

  • hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.

  • hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.

  • output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.

  • output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.

  • learn_std (bool) – Is std trainable.

  • adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.

  • std_share_network (bool) – Boolean for whether mean and std share the same network.

  • init_std (float) – Initial value for std.

  • std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.

  • min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.

  • max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.

  • std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • std_output_nonlinearity (callable) – Nonlinearity for output layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • std_parameterization (str) –

    How the std should be parametrized. There are a few options: - exp: the logarithm of the std will be stored, and applied a

    exponential transformation

    • softplus: the std will be computed as log(1+exp(x))

  • layer_normalization (bool) – Bool for using layer normalization or not.

property spec

Specification of input and output.

Type

garage.InOutSpec

property input_dim

Dimension of the encoder input.

Type

int

property output_dim

Dimension of the encoder output (embedding).

Type

int

property vectorized

If this module supports vectorization input.

Type

bool

property distribution

Encoder distribution.

Returns

Encoder distribution.

Return type

tfp.Distribution.MultivariateNormalDiag

property input

Input to encoder network.

Type

tf.Tensor

property latent_mean

Predicted mean of a Gaussian distribution.

Type

tf.Tensor

property latent_std_param

Predicted std of a Gaussian distribution.

Type

tf.Tensor

property name

Name of this module.

Type

str

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.

Parameters

  • embedding_input (tf.Tensor) – Embedding input.

  • name (str) – Name of the model, which is also the name scope.

Returns

Distribution. tf.tensor: Mean. tf.Tensor: Log of standard deviation.

Return type

tfp.distributions.MultivariateNormalDiag

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

garage.tf.embeddings.encoder.Encoder

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]