def __init__(self,
input_shape,
output_dim,
name='BernoulliMLPRegressorWithModel',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.sigmoid,
output_w_init=tf.glorot_uniform_initializer(),
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.variable_scope(self._name, reuse=False) as vs:
self._variable_scope = vs
if optimizer_args is None:
optimizer_args = dict()
if tr_optimizer_args is None:
tr_optimizer_args = dict()
if optimizer is None:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
if tr_optimizer is None:
tr_optimizer = ConjugateGradientOptimizer(**tr_optimizer_args)
else:
tr_optimizer = tr_optimizer(**tr_optimizer_args)
self._optimizer = optimizer
self._tr_optimizer = tr_optimizer
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._initialize()
def __init__(self,
input_shape,
output_dim,
name='CategoricalMLPRegressor',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
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
if optimizer_args is None:
optimizer_args = dict()
if tr_optimizer_args is None:
tr_optimizer_args = 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._initialize()
def __init__(self,
input_shape,
output_dim,
name='ContinuousMLPRegressor',
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(),
optimizer=None,
optimizer_args=None,
normalize_inputs=True):
super().__init__(input_shape, output_dim, name)
self._normalize_inputs = normalize_inputs
with tf.compat.v1.variable_scope(self._name, reuse=False) as vs:
self._variable_scope = vs
if optimizer_args is None:
optimizer_args = dict()
if optimizer is None:
self._optimizer = make_optimizer(LbfgsOptimizer,
**optimizer_args)
else:
self._optimizer = make_optimizer(optimizer, **optimizer_args)
self.model = NormalizedInputMLPModel(
input_shape=input_shape,
output_dim=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)
self._network = None
self._initialize()
class CategoricalMLPRegressorWithModel(StochasticRegressor2):
"""
CategoricalMLPRegressor with garage.tf.models.NormalizedInputMLPModel.
A class for performing regression (or classification, really) by fitting
a Categorical distribution to the outputs. Assumes that the output will
always be a one hot vector
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 tanh 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 softmax 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='CategoricalMLPRegressorWithModel',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
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
if optimizer_args is None:
optimizer_args = dict()
if tr_optimizer_args is None:
tr_optimizer_args = dict()
if optimizer is None:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
if tr_optimizer is None:
tr_optimizer = ConjugateGradientOptimizer(**tr_optimizer_args)
else:
tr_optimizer = tr_optimizer(**tr_optimizer_args)
self._optimizer = optimizer
self._tr_optimizer = tr_optimizer
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._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.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.model.networks['default'].y_hat
old_info_vars = dict(prob=old_prob_var)
info_vars = dict(prob=y_hat)
self._dist = Categorical(self._output_dim)
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 = tf.one_hot(tf.argmax(y_hat, axis=1),
depth=self._output_dim)
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,
network_output=[y_hat],
inputs=[input_var, ys_var])
self._tr_optimizer.update_opt(
loss=loss,
target=self,
network_output=[y_hat],
inputs=[input_var, ys_var, old_prob_var],
leq_constraint=(mean_kl, self._max_kl_step))
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.model.networks['default'].x_mean.load(
np.mean(xs, axis=0, keepdims=True))
self.model.networks['default'].x_std.load(
np.std(xs, axis=0, keepdims=True))
if self._use_trust_region:
# 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
def predict(self, xs):
"""
Predict ys based on input xs.
Args:
xs (numpy.ndarray): Input data.
Return:
The predicted ys (one hot vectors).
"""
return self._f_predict(xs)
def predict_log_likelihood(self, xs, ys):
"""
Predict log likelihood of output based on input xs and labels ys.
Args:
xs (numpy.ndarray): Input data.
ys (numpy.ndarray): Input labels in one hot representation.
Return:
The predicted log likelihoods.
"""
prob = self._f_prob(xs)
return self._dist.log_likelihood(ys, dict(prob=prob))
def dist_info_sym(self, x_var, name=None):
"""
Symbolic graph of the distribution.
Args:
x_var (tf.Tensor): Input tf.Tensor for the input data.
name (str): Name of the new graph.
Return:
tf.Tensor output of the symbolic graph of the distribution.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
prob, _, _ = self.model.build(x_var, name=name)
return dict(prob=prob)
def log_likelihood_sym(self, x_var, y_var, name=None):
"""
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.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
prob, _, _ = self.model.build(x_var, name=name)
return self._dist.log_likelihood_sym(y_var, dict(prob=prob))
def get_params_internal(self, **args):
"""Get the params, which are the trainable variables."""
return self._variable_scope.trainable_variables()
def __getstate__(self):
"""Object.__getstate__."""
new_dict = super().__getstate__()
del new_dict['_f_predict']
del new_dict['_f_prob']
del new_dict['_dist']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__."""
super().__setstate__(state)
self._initialize()
class ContinuousMLPRegressor(Regressor):
"""Fits continuously-valued data to an MLP model.
Args:
input_shape (tuple[int]): Input shape of the training data.
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 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.
optimizer (garage.tf.Optimizer): Optimizer for minimizing the negative
log-likelihood.
optimizer_args (dict): Arguments for the optimizer. Default is None,
which means no arguments.
normalize_inputs (bool): Bool for normalizing inputs or not.
"""
def __init__(self,
input_shape,
output_dim,
name='ContinuousMLPRegressor',
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(),
optimizer=None,
optimizer_args=None,
normalize_inputs=True):
super().__init__(input_shape, output_dim, name)
self._normalize_inputs = normalize_inputs
with tf.compat.v1.variable_scope(self._name, reuse=False) as vs:
self._variable_scope = vs
if optimizer_args is None:
optimizer_args = dict()
if optimizer is None:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
self._optimizer = optimizer
self.model = NormalizedInputMLPModel(
input_shape=input_shape,
output_dim=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)
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._name) as vs:
self._variable_scope = vs
self.model.build(input_var)
ys_var = tf.compat.v1.placeholder(dtype=tf.float32,
name='ys',
shape=(None, self._output_dim))
y_hat = self.model.networks['default'].y_hat
loss = tf.reduce_mean(tf.square(y_hat - ys_var))
self._f_predict = tensor_utils.compile_function([input_var], y_hat)
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[ys_var],
)
optimizer_args['inputs'] = [input_var, ys_var]
with tf.name_scope('update_opt'):
self._optimizer.update_opt(**optimizer_args)
def fit(self, xs, ys):
"""Fit with input data xs and label ys.
Args:
xs (numpy.ndarray): Input data.
ys (numpy.ndarray): Output labels.
"""
if self._normalize_inputs:
# recompute normalizing constants for inputs
self.model.networks['default'].x_mean.load(
np.mean(xs, axis=0, keepdims=True))
self.model.networks['default'].x_std.load(
np.std(xs, axis=0, keepdims=True) + 1e-8)
inputs = [xs, ys]
loss_before = self._optimizer.loss(inputs)
tabular.record('{}/LossBefore'.format(self._name), loss_before)
self._optimizer.optimize(inputs)
loss_after = self._optimizer.loss(inputs)
tabular.record('{}/LossAfter'.format(self._name), loss_after)
tabular.record('{}/dLoss'.format(self._name), loss_before - loss_after)
def predict(self, xs):
"""Predict y based on input xs.
Args:
xs (numpy.ndarray): Input data.
Return:
numpy.ndarray: The predicted ys.
"""
return self._f_predict(xs)
def predict_sym(self, xs, name=None):
"""Build a symbolic graph of the model prediction.
Args:
xs (tf.Tensor): Input tf.Tensor for the input data.
name (str): Name of the new graph.
Return:
tf.Tensor: Output of the symbolic prediction graph.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
y_hat, _, _ = self.model.build(xs, name=name)
return y_hat
@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
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_predict']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): unpickled state.
"""
super().__setstate__(state)
self._initialize()
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
if optimizer_args is None:
optimizer_args = dict()
if tr_optimizer_args is None:
tr_optimizer_args = dict()
if optimizer is None:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
if tr_optimizer is None:
tr_optimizer = ConjugateGradientOptimizer(**tr_optimizer_args)
else:
tr_optimizer = tr_optimizer(**tr_optimizer_args)
self._optimizer = optimizer
self._tr_optimizer = tr_optimizer
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._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.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.model.networks['default'].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))
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.model.networks['default'].x_mean.load(
np.mean(xs, axis=0, keepdims=True))
self.model.networks['default'].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
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)
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))
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))
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)
return self._dist.log_likelihood_sym(y_var, dict(p=prob))
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)
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.model.networks['default'].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']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()