"""GaussianMLPEncoder."""
import numpy as np
import tensorflow as tf
from garage.tf.embeddings import StochasticEncoder
from garage.tf.models import GaussianMLPModel, StochasticModule
[docs]class GaussianMLPEncoder(StochasticEncoder, StochasticModule):
"""GaussianMLPEncoder with GaussianMLPModel.
An embedding that contains a MLP to make prediction based on
a gaussian distribution.
Args:
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.
"""
def __init__(self,
embedding_spec,
name='GaussianMLPEncoder',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
learn_std=True,
adaptive_std=False,
std_share_network=False,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=tf.nn.tanh,
std_output_nonlinearity=None,
std_parameterization='exp',
layer_normalization=False):
super().__init__(name)
self._embedding_spec = embedding_spec
self._hidden_sizes = hidden_sizes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._learn_std = learn_std
self._adaptive_std = adaptive_std
self._std_share_network = std_share_network
self._init_std = init_std
self._min_std = min_std
self._max_std = max_std
self._std_hidden_sizes = std_hidden_sizes
self._std_hidden_nonlinearity = std_hidden_nonlinearity
self._std_output_nonlinearity = std_output_nonlinearity
self._std_parameterization = std_parameterization
self._layer_normalization = layer_normalization
self._latent_dim = embedding_spec.output_space.flat_dim
self._input_dim = embedding_spec.input_space.flat_dim
self._network = None
self._f_dist = None
self.model = GaussianMLPModel(
output_dim=self._latent_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
adaptive_std=adaptive_std,
std_share_network=std_share_network,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_hidden_sizes=std_hidden_sizes,
std_hidden_nonlinearity=std_hidden_nonlinearity,
std_output_nonlinearity=std_output_nonlinearity,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization,
name='GaussianMLPModel')
self._initialize()
def _initialize(self):
"""Initialize encoder."""
embedding_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, None,
self._input_dim),
name='default_encoder')
with tf.compat.v1.variable_scope(self._name) as vs:
self._variable_scope = vs
self._network = self.model.build(embedding_input)
self._f_dist = tf.compat.v1.get_default_session().make_callable(
[
self._network.dist.sample(), self._network.mean,
self._network.log_std
],
feed_list=[embedding_input])
[docs] def build(self, embedding_input, name=None):
"""Build encoder.
Args:
embedding_input (tf.Tensor) : Embedding input.
name (str): Name of the model, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Distribution.
tf.tensor: Mean.
tf.Tensor: Log of standard deviation.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
return self.model.build(embedding_input, name=name)
@property
def spec(self):
"""garage.InOutSpec: Specification of input and output."""
return self._embedding_spec
@property
def input_dim(self):
"""int: Dimension of the encoder input."""
return self._embedding_spec.input_space.flat_dim
@property
def output_dim(self):
"""int: Dimension of the encoder output (embedding)."""
return self._embedding_spec.output_space.flat_dim
@property
def vectorized(self):
"""bool: If this module supports vectorization input."""
return True
[docs] def get_latent(self, input_value):
"""Get a sample of embedding for the given input.
Args:
input_value (numpy.ndarray): Tensor to encode.
Returns:
numpy.ndarray: An embedding sampled from embedding distribution.
dict: Embedding distribution information.
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.
"""
flat_input = self._embedding_spec.input_space.flatten(input_value)
sample, mean, log_std = self._f_dist(np.expand_dims([flat_input], 1))
sample = self._embedding_spec.output_space.unflatten(
np.squeeze(sample, 1)[0])
mean = self._embedding_spec.output_space.unflatten(
np.squeeze(mean, 1)[0])
log_std = self._embedding_spec.output_space.unflatten(
np.squeeze(log_std, 1)[0])
return sample, dict(mean=mean, log_std=log_std)
[docs] def get_latents(self, input_values):
"""Get samples of embedding for the given inputs.
Args:
input_values (numpy.ndarray): Tensors to encode.
Returns:
numpy.ndarray: Embeddings sampled from embedding distribution.
dict: Embedding distribution information.
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.
"""
flat_input = self._embedding_spec.input_space.flatten_n(input_values)
samples, means, log_stds = self._f_dist(np.expand_dims(flat_input, 1))
samples = self._embedding_spec.output_space.unflatten_n(
np.squeeze(samples, 1))
means = self._embedding_spec.output_space.unflatten_n(
np.squeeze(means, 1))
log_stds = self._embedding_spec.output_space.unflatten_n(
np.squeeze(log_stds, 1))
return samples, dict(mean=means, log_std=log_stds)
@property
def distribution(self):
"""Encoder distribution.
Returns:
tfp.Distribution.MultivariateNormalDiag: Encoder distribution.
"""
return self._network.dist
@property
def input(self):
"""tf.Tensor: Input to encoder network."""
return self._network.input
@property
def latent_mean(self):
"""tf.Tensor: Predicted mean of a Gaussian distribution."""
return self._network.mean
@property
def latent_std_param(self):
"""tf.Tensor: Predicted std of a Gaussian distribution."""
return self._network.log_std
[docs] def clone(self, name):
"""Return a clone of the encoder.
Args:
name (str): Name of the newly created encoder. It has to be
different from source encoder if cloned under the same
computational graph.
Returns:
garage.tf.embeddings.encoder.Encoder: Newly cloned encoder.
"""
new_encoder = self.__class__(
embedding_spec=self._embedding_spec,
name=name,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
learn_std=self._learn_std,
adaptive_std=self._adaptive_std,
std_share_network=self._std_share_network,
init_std=self._init_std,
min_std=self._min_std,
max_std=self._max_std,
std_hidden_sizes=self._std_hidden_sizes,
std_hidden_nonlinearity=self._std_hidden_nonlinearity,
std_output_nonlinearity=self._std_output_nonlinearity,
std_parameterization=self._std_parameterization,
layer_normalization=self._layer_normalization)
return new_encoder
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_dist']
del new_dict['_network']
return new_dict
def __setstate__(self, state):
"""Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
"""
super().__setstate__(state)
self._initialize()