"""Bernoulli MLP Regressor based on MLP with Normalized Inputs."""
from dowel import tabular
import numpy as np
import tensorflow as tf
from garage import make_optimizer
from garage.tf.distributions import Bernoulli
from garage.tf.misc import tensor_utils
from garage.tf.models import NormalizedInputMLPModel
from garage.tf.optimizers import ConjugateGradientOptimizer, LbfgsOptimizer
from garage.tf.regressors.regressor import StochasticRegressor
[docs]class BernoulliMLPRegressor(StochasticRegressor):
"""Fits data to a Bernoulli distribution, parameterized by an MLP.
Args:
input_shape (tuple[int]): Input shape of the training data. Since an
MLP model is used, implementation assumes flattened inputs. The
input shape of each data point should thus be of shape (x, ).
output_dim (int): Output dimension of the model.
name (str): Model name, also the variable scope.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for the network. 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. Default is Glorot uniform initializer.
hidden_b_init (Callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor. Default is zero initializer.
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. Default is Glorot uniform initializer.
output_b_init (Callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor. Default is zero initializer.
optimizer (garage.tf.Optimizer): Optimizer for minimizing the negative
log-likelihood. Defaults to LbsgsOptimizer
optimizer_args (dict): Arguments for the optimizer. Default is None,
which means no arguments.
tr_optimizer (garage.tf.Optimizer): Optimizer for trust region
approximation. Defaults to ConjugateGradientOptimizer.
tr_optimizer_args (dict): Arguments for the trust region optimizer.
Default is None, which means no arguments.
use_trust_region (bool): Whether to use trust region constraint.
max_kl_step (float): KL divergence constraint for each iteration.
normalize_inputs (bool): Bool for normalizing inputs or not.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
input_shape,
output_dim,
name='BernoulliMLPRegressor',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.sigmoid,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
optimizer=None,
optimizer_args=None,
tr_optimizer=None,
tr_optimizer_args=None,
use_trust_region=True,
max_kl_step=0.01,
normalize_inputs=True,
layer_normalization=False):
super().__init__(input_shape, output_dim, name)
self._use_trust_region = use_trust_region
self._max_kl_step = max_kl_step
self._normalize_inputs = normalize_inputs
with tf.compat.v1.variable_scope(self._name, reuse=False) as vs:
self._variable_scope = vs
optimizer_args = optimizer_args or dict()
tr_optimizer_args = tr_optimizer_args or dict()
if optimizer is None:
self._optimizer = make_optimizer(LbfgsOptimizer,
**optimizer_args)
else:
self._optimizer = make_optimizer(optimizer, **optimizer_args)
if tr_optimizer is None:
self._tr_optimizer = make_optimizer(ConjugateGradientOptimizer,
**tr_optimizer_args)
else:
self._tr_optimizer = make_optimizer(tr_optimizer,
**tr_optimizer_args)
self._first_optimized = False
self.model = NormalizedInputMLPModel(
input_shape,
output_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,
layer_normalization=layer_normalization)
self._dist = Bernoulli(output_dim)
self._network = None
self._initialize()
def _initialize(self):
input_var = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
self._input_shape)
with tf.compat.v1.variable_scope(self._variable_scope):
self._network = self.model.build(input_var)
ys_var = tf.compat.v1.placeholder(dtype=tf.float32,
name='ys',
shape=(None, self._output_dim))
old_prob_var = tf.compat.v1.placeholder(dtype=tf.float32,
name='old_prob',
shape=(None,
self._output_dim))
y_hat = self._network.y_hat
old_info_vars = dict(p=old_prob_var)
info_vars = dict(p=y_hat)
mean_kl = tf.reduce_mean(
self._dist.kl_sym(old_info_vars, info_vars))
loss = -tf.reduce_mean(
self._dist.log_likelihood_sym(ys_var, info_vars))
predicted = y_hat >= 0.5
self._f_predict = tensor_utils.compile_function([input_var],
predicted)
self._f_prob = tensor_utils.compile_function([input_var], y_hat)
self._optimizer.update_opt(loss=loss,
target=self,
inputs=[input_var, ys_var])
self._tr_optimizer.update_opt(
loss=loss,
target=self,
inputs=[input_var, ys_var, old_prob_var],
leq_constraint=(mean_kl, self._max_kl_step))
[docs] def fit(self, xs, ys):
"""Fit with input data xs and label ys.
Args:
xs (numpy.ndarray): Input data.
ys (numpy.ndarray): Label of input data.
"""
if self._normalize_inputs:
# recompute normalizing constants for inputs
self._network.x_mean.load(np.mean(xs, axis=0, keepdims=True))
self._network.x_std.load(np.std(xs, axis=0, keepdims=True) + 1e-8)
if self._use_trust_region and self._first_optimized:
# To use trust region constraint and optimizer
old_prob = self._f_prob(xs)
inputs = [xs, ys, old_prob]
optimizer = self._tr_optimizer
else:
inputs = [xs, ys]
optimizer = self._optimizer
loss_before = optimizer.loss(inputs)
tabular.record('{}/LossBefore'.format(self._name), loss_before)
optimizer.optimize(inputs)
loss_after = optimizer.loss(inputs)
tabular.record('{}/LossAfter'.format(self._name), loss_after)
tabular.record('{}/dLoss'.format(self._name), loss_before - loss_after)
self._first_optimized = True
[docs] def predict(self, xs):
"""Predict ys based on input xs.
Args:
xs (numpy.ndarray): Input data of shape (samples, input_dim)
Return:
numpy.ndarray: The deterministic predicted ys (one hot vectors)
of shape (samples, output_dim)
"""
return self._f_predict(xs)
[docs] def sample_predict(self, xs):
"""Do a Bernoulli sampling given input xs.
Args:
xs (numpy.ndarray): Input data of shape (samples, input_dim)
Returns:
numpy.ndarray: The stochastic sampled ys
of shape (samples, output_dim)
"""
p = self._f_prob(xs)
return self._dist.sample(dict(p=p))
[docs] def predict_log_likelihood(self, xs, ys):
"""Log likelihood of ys given input xs.
Args:
xs (numpy.ndarray): Input data of shape (samples, input_dim)
ys (numpy.ndarray): Output data of shape (samples, output_dim)
Returns:
numpy.ndarray: The log likelihood of shape (samples, )
"""
p = self._f_prob(xs)
return self._dist.log_likelihood(ys, dict(p=p))
[docs] def log_likelihood_sym(self, x_var, y_var, name=None):
"""Build a symbolic graph of the log-likelihood.
Args:
x_var (tf.Tensor): Input tf.Tensor for the input data.
y_var (tf.Tensor): Input tf.Tensor for the one hot label of data.
name (str): Name of the new graph.
Return:
tf.Tensor: Output of the symbolic log-likelihood graph.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
prob, _, _ = self.model.build(x_var, name=name).outputs
return self._dist.log_likelihood_sym(y_var, dict(p=prob))
# pylint: disable=unused-argument
[docs] def dist_info_sym(self, input_var, state_info_vars=None, name=None):
"""Build a symbolic graph of the distribution parameters.
Args:
input_var (tf.Tensor): Input tf.Tensor for the input data.
state_info_vars (dict): a dictionary whose values should contain
information about the state of the regressor at the time it
received the input.
name (str): Name of the new graph.
Return:
dict[tf.Tensor]: Output of the symbolic graph of the distribution
parameters.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
prob, _, _ = self.model.build(input_var, name=name).outputs
return dict(prob=prob)
@property
def recurrent(self):
"""bool: If this module has a hidden state."""
return False
@property
def vectorized(self):
"""bool: If this module supports vectorization input."""
return True
@property
def distribution(self):
"""garage.tf.distributions.DiagonalGaussian: Distribution."""
return self._dist
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_predict']
del new_dict['_f_prob']
del new_dict['_network']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()