garage.tf.embeddings.gaussian_mlp_encoder module

GaussianMLPEncoder.

class GaussianMLPEncoder(embedding_spec, name='GaussianMLPEncoder', hidden_sizes=(32, 32), hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, 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=<function tanh>, std_output_nonlinearity=None, std_parameterization='exp', layer_normalization=False)[source]

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

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.
build(embedding_input, name=None)[source]

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
clone(name)[source]

Return a clone of the encoder.

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
distribution

Encoder distribution.

Returns:Encoder distribution.
Return type:tfp.Distribution.MultivariateNormalDiag
get_latent(input_value)[source]

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)[source]

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.
input

Input to encoder network.

Type:tf.Tensor
input_dim

Dimension of the encoder input.

Type:int
latent_mean

Predicted mean of a Gaussian distribution.

Type:tf.Tensor
latent_std_param

Predicted std of a Gaussian distribution.

Type:tf.Tensor
output_dim

Dimension of the encoder output (embedding).

Type:int
spec

Specification of input and output.

Type:garage.InOutSpec
vectorized

If this module supports vectorization input.

Type:bool