class BNN:
"""Neural network models which model aleatoric uncertainty (and possibly epistemic uncertainty
with ensembling).
"""
def __init__(self, params):
"""Initializes a class instance.
Arguments:
params (DotMap): A dotmap of model parameters.
.name (str): Model name, used for logging/use in variable scopes.
Warning: Models with the same name will overwrite each other.
.num_networks (int): (optional) The number of networks in the ensemble. Defaults to 1.
Ignored if model is being loaded.
.model_dir (str/None): (optional) Path to directory from which model will be loaded, and
saved by default. Defaults to None.
.load_model (bool): (optional) If True, model will be loaded from the model directory,
assuming that the files are generated by a model of the same name. Defaults to False.
.sess (tf.Session/None): The session that this model will use.
If None, creates a session with its own associated graph. Defaults to None.
"""
self.name = get_required_argument(params, 'name', 'Must provide name.')
self.model_dir = params.get('model_dir', None)
if params.get('sess', None) is None:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.4
self._sess = tf.Session(config=config)
else:
self._sess = params.get('sess')
# Instance variables
self.finalized = False
self.layers, self.max_logvar, self.min_logvar = [], None, None
self.decays, self.optvars, self.nonoptvars = [], [], []
self.end_act, self.end_act_name = None, None
self.scaler = None
# Training objects
self.optimizer = None
self.sy_train_in, self.sy_train_targ = None, None
self.train_op, self.mse_loss = None, None
# Prediction objects
self.sy_pred_in2d, self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac = None, None, None
self.sy_pred_mean2d, self.sy_pred_var2d = None, None
self.sy_pred_in3d, self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac = None, None, None
if params.get('load_model', False):
if self.model_dir is None:
raise ValueError(
"Cannot load model without providing model directory.")
self._load_structure()
self.num_nets, self.model_loaded = self.layers[
0].get_ensemble_size(), True
print("Model loaded from %s." % self.model_dir)
else:
self.num_nets = params.get('num_networks', 1)
self.model_loaded = False
if self.num_nets == 1:
print("Created a neural network with variance predictions.")
else:
print(
"Created an ensemble of %d neural networks with variance predictions."
% (self.num_nets))
@property
def is_probabilistic(self):
return True
@property
def is_tf_model(self):
return True
@property
def sess(self):
return self._sess
###################################
# Network Structure Setup Methods #
###################################
def add(self, layer):
"""Adds a new layer to the network.
Arguments:
layer: (layer) The new layer to be added to the network.
If this is the first layer, the input dimension of the layer must be set.
Returns: None.
"""
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if len(self.layers) == 0 and layer.get_input_dim() is None:
raise ValueError("Must set input dimension for the first layer.")
if self.model_loaded:
raise RuntimeError("Cannot add layers to a loaded model.")
layer.set_ensemble_size(self.num_nets)
if len(self.layers) > 0:
layer.set_input_dim(self.layers[-1].get_output_dim())
self.layers.append(layer.copy())
def pop(self):
"""Removes and returns the most recently added layer to the network.
Returns: (layer) The removed layer.
"""
if len(self.layers) == 0:
raise RuntimeError("Network is empty.")
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if self.model_loaded:
raise RuntimeError("Cannot remove layers from a loaded model.")
return self.layers.pop()
def finalize(self, optimizer, optimizer_args=None, *args, **kwargs):
"""Finalizes the network.
Arguments:
optimizer: (tf.train.Optimizer) An optimizer class from those available at tf.train.Optimizer.
optimizer_args: (dict) A dictionary of arguments for the __init__ method of the chosen optimizer.
Returns: None
"""
if len(self.layers) == 0:
raise RuntimeError("Cannot finalize an empty network.")
if self.finalized:
raise RuntimeError("Can only finalize a network once.")
optimizer_args = {} if optimizer_args is None else optimizer_args
self.optimizer = optimizer(**optimizer_args)
# Add variance output.
self.layers[-1].set_output_dim(2 * self.layers[-1].get_output_dim())
# Remove last activation to isolate variance from activation function.
self.end_act = self.layers[-1].get_activation()
self.end_act_name = self.layers[-1].get_activation(as_func=False)
self.layers[-1].unset_activation()
# Construct all variables.
with self.sess.as_default():
with tf.variable_scope(self.name):
self.scaler = TensorStandardScaler(
self.layers[0].get_input_dim())
self.max_logvar = tf.Variable(
np.ones([1, self.layers[-1].get_output_dim() // 2]) / 2.,
dtype=tf.float32,
name="max_log_var")
self.min_logvar = tf.Variable(
-np.ones([1, self.layers[-1].get_output_dim() // 2]) * 10.,
dtype=tf.float32,
name="min_log_var")
for i, layer in enumerate(self.layers):
with tf.variable_scope("Layer%i" % i):
layer.construct_vars()
self.decays.extend(layer.get_decays())
self.optvars.extend(layer.get_vars())
self.optvars.extend([self.max_logvar, self.min_logvar])
self.nonoptvars.extend(self.scaler.get_vars())
# Set up training
with tf.variable_scope(self.name):
self.optimizer = optimizer(**optimizer_args)
self.sy_train_in = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="training_inputs")
self.sy_train_targ = tf.placeholder(
dtype=tf.float32,
shape=[
self.num_nets, None, self.layers[-1].get_output_dim() // 2
],
name="training_targets")
train_loss = tf.reduce_sum(
self._compile_losses(self.sy_train_in,
self.sy_train_targ,
inc_var_loss=True))
train_loss += tf.add_n(self.decays)
# regularization to ensure max_logvar not grow too much beyond training distribution
# and min_logvar not drop below training distribution
train_loss += 0.01 * tf.reduce_sum(
self.max_logvar) - 0.01 * tf.reduce_sum(self.min_logvar)
self.mse_loss = self._compile_losses(self.sy_train_in,
self.sy_train_targ,
inc_var_loss=False)
self.train_op = self.optimizer.minimize(train_loss,
var_list=self.optvars)
# Initialize all variables
self.sess.run(
tf.variables_initializer(self.optvars + self.nonoptvars +
self.optimizer.variables()))
# Set up prediction
with tf.variable_scope(self.name):
self.sy_pred_in2d = tf.placeholder(
dtype=tf.float32,
shape=[None, self.layers[0].get_input_dim()],
name="2D_training_inputs")
self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac = \
self.create_prediction_tensors(self.sy_pred_in2d, factored=True)
self.sy_pred_mean2d = tf.reduce_mean(self.sy_pred_mean2d_fac,
axis=0)
self.sy_pred_var2d = tf.reduce_mean(self.sy_pred_var2d_fac, axis=0) + \
tf.reduce_mean(tf.square(self.sy_pred_mean2d_fac - self.sy_pred_mean2d), axis=0)
self.sy_pred_in3d = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="3D_training_inputs")
self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac = \
self.create_prediction_tensors(self.sy_pred_in3d, factored=True)
# Load model if needed
# self.optimizer.variables() no need to save and load
if self.model_loaded:
with self.sess.as_default():
params_dict = loadmat(
os.path.join(self.model_dir, "%s.mat" % self.name))
all_vars = self.nonoptvars + self.optvars
for i, var in enumerate(all_vars):
var.load(params_dict[str(i)])
self.finalized = True
#################
# Model Methods #
#################
def train(self,
inputs,
targets,
batch_size=32,
epochs=100,
hide_progress=False,
holdout_ratio=0.0,
max_logging=5000):
"""Trains/Continues network training
Arguments:
inputs (np.ndarray): Network inputs in the training dataset in rows.
targets (np.ndarray): Network target outputs in the training dataset in rows corresponding
to the rows in inputs.
batch_size (int): The minibatch size to be used for training.
epochs (int): Number of epochs (full network passes that will be done.
hide_progress (bool): If True, hides the progress bar shown at the beginning of training.
Returns: None
"""
def shuffle_rows(arr):
idxs = np.argsort(np.random.uniform(size=arr.shape), axis=-1)
return arr[np.arange(arr.shape[0])[:, None], idxs]
# Split into training and holdout sets
num_holdout = min(int(inputs.shape[0] * holdout_ratio), max_logging)
# shuffle np.range(inputs.shape[0])
permutation = np.random.permutation(inputs.shape[0])
inputs, holdout_inputs = inputs[permutation[num_holdout:]], inputs[
permutation[:num_holdout]]
targets, holdout_targets = targets[permutation[num_holdout:]], targets[
permutation[:num_holdout]]
holdout_inputs = np.tile(holdout_inputs[None], [self.num_nets, 1, 1])
holdout_targets = np.tile(holdout_targets[None], [self.num_nets, 1, 1])
with self.sess.as_default():
self.scaler.fit(inputs)
#range bewteen 0-inuputs.shape[0]
# [ensemble size, train_sample_size]
idxs = np.random.randint(inputs.shape[0],
size=[self.num_nets, inputs.shape[0]])
if hide_progress:
epoch_range = range(epochs)
else:
epoch_range = trange(epochs,
unit="epoch(s)",
desc="Network training")
for _ in epoch_range:
for batch_num in range(int(np.ceil(idxs.shape[-1] / batch_size))):
batch_idxs = idxs[:, batch_num * batch_size:(batch_num + 1) *
batch_size]
self.sess.run(self.train_op,
feed_dict={
self.sy_train_in: inputs[batch_idxs],
self.sy_train_targ: targets[batch_idxs]
})
idxs = shuffle_rows(idxs)
if not hide_progress:
if holdout_ratio < 1e-12:
epoch_range.set_postfix({
"Training loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
})
})
else:
epoch_range.set_postfix({
"Training loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
}),
"Holdout loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in: holdout_inputs,
self.sy_train_targ: holdout_targets
})
})
def predict(self, inputs, factored=False, *args, **kwargs):
"""Returns the distribution predicted by the model for each input vector in inputs.
Behavior is affected by the dimensionality of inputs and factored as follows:
inputs is 2D, factored=True: Each row is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of shape
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector j.
inputs is 2D, factored=False: Each row is treated as an input vector.
Returns a mean of shape [batch_size, output_dim] and variance of shape
[batch_size, output_dim], where aggregation is performed as described in the paper.
inputs is 3D, factored=True/False: Each row in the last dimension is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of sha
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector [i, j].
Arguments:
inputs (np.ndarray): An array of input vectors in rows. See above for behavior.
factored (bool): See above for behavior.
"""
if len(inputs.shape) == 2:
if factored:
return self.sess.run(
[self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac],
feed_dict={self.sy_pred_in2d: inputs})
else:
return self.sess.run([self.sy_pred_mean2d, self.sy_pred_var2d],
feed_dict={self.sy_pred_in2d: inputs})
else:
return self.sess.run(
[self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac],
feed_dict={self.sy_pred_in3d: inputs})
def create_prediction_tensors(self,
inputs,
factored=False,
*args,
**kwargs):
"""See predict() above for documentation.
"""
factored_mean, factored_variance = self._compile_outputs(inputs)
if inputs.shape.ndims == 2 and not factored:
# DS
# [ensemble_size, nparticle, dO] -> [nparticle, dO]
mean = tf.reduce_mean(factored_mean, axis=0)
variance = tf.reduce_mean(tf.square(factored_mean - mean), axis=0) + \
tf.reduce_mean(factored_variance, axis=0)
return mean, variance
# TS1, TSInf
# [ensemble_size, nparticle / ensemble_size, dO]
return factored_mean, factored_variance
def save(self, savedir=None):
"""Saves all information required to recreate this model in two files in savedir
(or self.model_dir if savedir is None), one containing the model structuure and the other
containing all variables in the network.
savedir (str): (Optional) Path to which files will be saved. If not provided, self.model_dir
(the directory provided at initialization) will be used.
"""
if not self.finalized:
raise RuntimeError()
model_dir = self.model_dir if savedir is None else savedir
# Write structure to file
with open(os.path.join(model_dir, "%s.nns" % self.name), "w+") as f:
for layer in self.layers[:-1]:
f.write("%s\n" % repr(layer))
last_layer_copy = self.layers[-1].copy()
last_layer_copy.set_activation(self.end_act_name)
last_layer_copy.set_output_dim(last_layer_copy.get_output_dim() //
2)
f.write("%s\n" % repr(last_layer_copy))
# Save network parameters (including scalers) in a .mat file
var_vals = {}
for i, var_val in enumerate(
self.sess.run(self.nonoptvars + self.optvars)):
var_vals[str(i)] = var_val
savemat(os.path.join(model_dir, "%s.mat" % self.name), var_vals)
def _load_structure(self):
"""Uses the saved structure in self.model_dir with the name of this network to initialize
the structure of this network.
"""
structure = []
with open(os.path.join(self.model_dir, "%s.nns" % self.name),
"r") as f:
for line in f:
kwargs = {
key: val
for (key, val) in
[argval.split("=") for argval in line[3:-2].split(", ")]
}
kwargs["input_dim"] = int(kwargs["input_dim"])
kwargs["output_dim"] = int(kwargs["output_dim"])
kwargs["weight_decay"] = None if kwargs[
"weight_decay"] == "None" else float(
kwargs["weight_decay"])
kwargs["activation"] = None if kwargs[
"activation"] == "None" else kwargs["activation"][1:-1]
kwargs["ensemble_size"] = int(kwargs["ensemble_size"])
structure.append(FC(**kwargs))
self.layers = structure
#######################
# Compilation methods #
#######################
def _compile_outputs(self, inputs, ret_log_var=False):
"""Compiles the output of the network at the given inputs.
If inputs is 2D, returns a 3D tensor where output[i] is the output of the ith network in the ensemble.
If inputs is 3D, returns a 3D tensor where output[i] is the output of the ith network on the ith input matrix.
Arguments:
inputs: (tf.Tensor) A tensor representing the inputs to the network
ret_log_var: (bool) If True, returns the log variance instead of the variance.
Returns: (tf.Tensors) The mean and variance/log variance predictions at inputs for each network
in the ensemble.
"""
dim_output = self.layers[-1].get_output_dim()
cur_out = self.scaler.transform(inputs)
for layer in self.layers:
cur_out = layer.compute_output_tensor(cur_out)
mean = cur_out[:, :, :dim_output // 2]
if self.end_act is not None:
mean = self.end_act(mean)
# notice the gradient flows here through max_logvar and min_logvar
# equal as exp(max_logvar) * exp(logvar) / exp(max_logvar) + exp(logvar)
logvar = self.max_logvar - tf.nn.softplus(
self.max_logvar - cur_out[:, :, dim_output // 2:])
# equal as exp(logvar) + exp(min_var)
logvar = self.min_logvar + tf.nn.softplus(logvar - self.min_logvar)
if ret_log_var:
return mean, logvar
else:
return mean, tf.exp(logvar)
def _compile_losses(self, inputs, targets, inc_var_loss=True):
"""Helper method for compiling the loss function.
The loss function is obtained from the log likelihood, assuming that the output
distribution is Gaussian, with both mean and (diagonal) covariance matrix being determined
by network outputs.
Arguments:
inputs: (tf.Tensor) A tensor representing the input batch
targets: (tf.Tensor) The desired targets for each input vector in inputs.
inc_var_loss: (bool) If True, includes log variance loss.
Returns: (tf.Tensor) A tensor representing the loss on the input arguments.
"""
mean, log_var = self._compile_outputs(inputs, ret_log_var=True)
inv_var = tf.exp(-log_var)
if inc_var_loss:
# max log liklihood in for Gaussian
mse_losses = tf.reduce_mean(tf.reduce_mean(
tf.square(mean - targets) * inv_var, axis=-1),
axis=-1)
var_losses = tf.reduce_mean(tf.reduce_mean(log_var, axis=-1),
axis=-1)
total_losses = mse_losses + var_losses
else:
total_losses = tf.reduce_mean(tf.reduce_mean(tf.square(mean -
targets),
axis=-1),
axis=-1)
return total_losses
class NN:
"""Neural network models which cannot capture aleatoric uncertainty (but possibly epistemic uncertainty
with ensembling).
"""
def __init__(self, params):
"""Initializes a class instance.
Arguments:
params (DotMap): A dotmap of model parameters.
.name (str): Model name, used for logging/use in variable scopes.
Warning: Models with the same name will overwrite each other.
.num_networks (int): (optional) The number of networks in the ensemble. Defaults to 1.
Ignored if model is being loaded.
.model_dir (str/None): (optional) Path to directory from which model will be loaded, and
saved by default. Defaults to None.
.load_model (bool): (optional) If True, model will be loaded from the model directory,
assuming that the files are generated by a model of the same name. Defaults to False.
.sess (tf.Session/None): The session that this model will use.
If None, creates a session with its own associated graph. Defaults to None.
"""
self.name = get_required_argument(params, 'name', 'Must provide name.')
self.model_dir = params.get('model_dir', None)
if params.get('sess', None) is None:
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
self._sess = tf.Session(config=config)
else:
self._sess = params.get('sess')
# Instance variables
self.finalized = False
self.layers, self.decays, self.optvars, self.nonoptvars = [], [], [], []
self.scaler = None
# Training objects
self.optimizer = None
self.sy_train_in, self.sy_train_targ = None, None
self.train_op, self.mse_loss = None, None
# Prediction objects
self.sy_pred_in2d, self.sy_pred_mean2d_fac = None, None
self.sy_pred_mean2d, self.sy_pred_var2d = None, None
self.sy_pred_in3d, self.sy_pred_mean3d_fac = None, None
if params.get('load_model', False):
if self.model_dir is None:
raise ValueError(
"Cannot load model without providing model directory.")
self._load_structure()
self.num_nets, self.model_loaded = self.layers[
0].get_ensemble_size(), True
print("Model loaded from %s." % self.model_dir)
else:
self.num_nets = params.get('num_networks', 1)
self.model_loaded = False
if self.num_nets == 1:
print("Created a neural network without variance predictions.")
else:
print(
"Created an ensemble of %d neural networks without variance predictions."
% (self.num_nets))
@property
def is_probabilistic(self):
return True if self.num_nets > 1 else False
@property
def is_tf_model(self):
return True
@property
def sess(self):
return self._sess
###################################
# Network Structure Setup Methods #
###################################
def add(self, layer):
"""Adds a new layer to the network.
Arguments:
layer: (layer) The new layer to be added to the network.
If this is the first layer, the input dimension of the layer must be set.
Returns: None.
"""
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if len(self.layers) == 0 and layer.get_input_dim() is None:
raise ValueError("Must set input dimension for the first layer.")
if self.model_loaded:
raise RuntimeError("Cannot add layers to a loaded model.")
layer.set_ensemble_size(self.num_nets)
if len(self.layers) > 0:
layer.set_input_dim(self.layers[-1].get_output_dim())
self.layers.append(layer.copy())
def pop(self):
"""Removes and returns the most recently added layer to the network.
Returns: (layer) The removed layer.
"""
if len(self.layers) == 0:
raise RuntimeError("Network is empty.")
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if self.model_loaded:
raise RuntimeError("Cannot remove layers from a loaded model.")
return self.layers.pop()
def finalize(self,
optimizer,
optimizer_args=None,
suffix="",
*args,
**kwargs):
"""Finalizes the network.
Arguments:
optimizer: (tf.train.Optimizer) An optimizer class from those available at tf.train.Optimizer.
optimizer_args: (dict) A dictionary of arguments for the __init__ method of the chosen optimizer.
Returns: None
"""
if len(self.layers) == 0:
raise RuntimeError("Cannot finalize an empty network.")
if self.finalized:
raise RuntimeError("Can only finalize a network once.")
optimizer_args = {} if optimizer_args is None else optimizer_args
self.optimizer = optimizer(**optimizer_args)
# Construct all variables.
with self.sess.as_default():
with tf.variable_scope(self.name):
self.scaler = TensorStandardScaler(
self.layers[0].get_input_dim(), suffix)
for i, layer in enumerate(self.layers):
with tf.variable_scope(("Layer%i" + suffix) % i):
layer.construct_vars()
self.decays.extend(layer.get_decays())
self.optvars.extend(layer.get_vars())
self.nonoptvars.extend(self.scaler.get_vars())
# Setup training
with tf.variable_scope(self.name):
self.optimizer = optimizer(**optimizer_args)
self.sy_train_in = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="training_inputs")
self.sy_train_targ = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[-1].get_output_dim()],
name="training_targets")
train_loss = tf.reduce_sum(
self._compile_losses(self.sy_train_in, self.sy_train_targ))
train_loss += tf.add_n(self.decays)
self.mse_loss = self._compile_losses(self.sy_train_in,
self.sy_train_targ)
self.train_op = self.optimizer.minimize(train_loss,
var_list=self.optvars)
# Initialize all variables
self.sess.run(
tf.variables_initializer(self.optvars + self.nonoptvars +
self.optimizer.variables()))
# Setup prediction
with tf.variable_scope(self.name):
self.sy_pred_in2d = tf.placeholder(
dtype=tf.float32,
shape=[None, self.layers[0].get_input_dim()],
name="2D_training_inputs")
self.sy_pred_mean2d_fac = self.create_prediction_tensors(
self.sy_pred_in2d, factored=True)[0]
self.sy_pred_mean2d = tf.reduce_mean(self.sy_pred_mean2d_fac,
axis=0)
self.sy_pred_var2d = tf.reduce_mean(
tf.square(self.sy_pred_mean2d_fac - self.sy_pred_mean2d),
axis=0)
self.sy_pred_in3d = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="3D_training_inputs")
self.sy_pred_mean3d_fac = \
self.create_prediction_tensors(self.sy_pred_in3d, factored=True)[0]
# Load model if needed
if self.model_loaded:
with self.sess.as_default():
params_dict = loadmat(
os.path.join(self.model_dir, "%s.mat" % self.name))
all_vars = self.nonoptvars + self.optvars
for i, var in enumerate(all_vars):
var.load(params_dict[str(i)])
self.finalized = True
#################
# Model Methods #
#################
def train(self,
inputs,
targets,
batch_size=32,
epochs=100,
hide_progress=False,
holdout_ratio=0.0,
max_logging=5000):
def shuffle_rows(arr):
idxs = np.argsort(np.random.uniform(size=arr.shape), axis=-1)
return arr[np.arange(arr.shape[0])[:, None], idxs]
# Split into training and holdout sets
num_holdout = min(int(inputs.shape[0] * holdout_ratio), max_logging)
permutation = np.random.permutation(inputs.shape[0])
inputs, holdout_inputs = inputs[permutation[num_holdout:]], inputs[
permutation[:num_holdout]]
targets, holdout_targets = targets[permutation[num_holdout:]], targets[
permutation[:num_holdout]]
holdout_inputs = np.tile(holdout_inputs[None], [self.num_nets, 1, 1])
holdout_targets = np.tile(holdout_targets[None], [self.num_nets, 1, 1])
with self.sess.as_default():
self.scaler.fit(inputs)
idxs = np.random.randint(inputs.shape[0],
size=[self.num_nets, inputs.shape[0]])
if hide_progress:
epoch_range = range(epochs)
else:
epoch_range = trange(epochs,
unit="epoch(s)",
desc="Network training")
for _ in epoch_range:
for batch_num in range(int(np.ceil(idxs.shape[-1] / batch_size))):
batch_idxs = idxs[:, batch_num * batch_size:(batch_num + 1) *
batch_size]
self.sess.run(self.train_op,
feed_dict={
self.sy_train_in: inputs[batch_idxs],
self.sy_train_targ: targets[batch_idxs]
})
idxs = shuffle_rows(idxs)
if not hide_progress:
if holdout_ratio < 1e-12:
epoch_range.set_postfix({
"Current loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
}),
})
else:
epoch_range.set_postfix({
"Current loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
}),
"Holdout loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in: holdout_inputs,
self.sy_train_targ: holdout_targets
})
})
def predict(self, inputs, factored=False, *args, **kwargs):
"""Returns the distribution predicted by the model for each input vector in inputs.
Behavior is affected by the dimensionality of inputs and factored as follows:
inputs is 2D, factored=True: Each row is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of shape
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector j.
inputs is 2D, factored=False: Each row is treated as an input vector.
Returns a mean of shape [batch_size, output_dim] and variance of shape
[batch_size, output_dim], where aggregation is performed as described in the paper.
inputs is 3D, factored=True/False: Each row in the last dimension is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of sha
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector [i, j].
Arguments:
inputs (np.ndarray): An array of input vectors in rows. See above for behavior.
factored (bool): See above for behavior.
"""
if len(inputs.shape) == 2:
if factored:
mean = self.sess.run(self.sy_pred_mean2d_fac,
feed_dict={self.sy_pred_in2d: inputs})
return mean, None
else:
return self.sess.run([self.sy_pred_mean2d, self.sy_pred_var2d],
feed_dict={self.sy_pred_in2d: inputs})
else:
mean = self.sess.run(self.sy_pred_mean3d_fac,
feed_dict={self.sy_pred_in3d: inputs})
return mean, None
def create_prediction_tensors(self,
inputs,
factored=False,
*args,
**kwargs):
"""See predict() above for documentation.
"""
factored_mean = self._compile_outputs(inputs)
if inputs.shape.ndims == 2 and not factored:
mean = tf.reduce_mean(factored_mean, axis=0)
variance = tf.reduce_mean(tf.square(factored_mean - mean), axis=0)
return mean, variance
return factored_mean, None
def save(self, savedir=None):
"""Saves all information required to recreate this model in two files in savedir
(or self.model_dir if savedir is None), one containing the model structuure and the other
containing all variables in the network.
savedir (str): (Optional) Path to which files will be saved. If not provided, self.model_dir
(the directory provided at initialization) will be used.
"""
if not self.finalized:
raise RuntimeError()
model_dir = self.model_dir if savedir is None else savedir
# Write structure to file
with open(os.path.join(model_dir, "%s.nns" % self.name), "w+") as f:
for layer in self.layers:
f.write("%s\n" % repr(layer))
# Save network parameters (including scalers) in a .mat file
var_vals = {}
for i, var_val in enumerate(
self.sess.run(self.nonoptvars + self.optvars)):
var_vals[str(i)] = var_val
savemat(os.path.join(model_dir, "%s.mat" % self.name), var_vals)
def _load_structure(self):
"""Uses the saved structure in self.model_dir with the name of this network to initialize
the structure of this network.
"""
structure = []
with open(os.path.join(self.model_dir, "%s.nns" % self.name),
"r") as f:
for line in f:
kwargs = {
key: val
for (key, val) in
[argval.split("=") for argval in line[3:-2].split(", ")]
}
kwargs["input_dim"] = int(kwargs["input_dim"])
kwargs["output_dim"] = int(kwargs["output_dim"])
kwargs["weight_decay"] = None if kwargs[
"weight_decay"] == "None" else float(
kwargs["weight_decay"])
kwargs["activation"] = None if kwargs[
"activation"] == "None" else kwargs["activation"][1:-1]
kwargs["ensemble_size"] = int(kwargs["ensemble_size"])
structure.append(FC(**kwargs))
self.layers = structure
#######################
# Compilation methods #
#######################
def _compile_outputs(self, inputs):
cur_out = self.scaler.transform(inputs)
for layer in self.layers:
cur_out = layer.compute_output_tensor(cur_out)
return cur_out
def _compile_losses(self, inputs, targets):
mean = self._compile_outputs(inputs)
return tf.reduce_mean(tf.reduce_mean(tf.square(mean - targets) / 2,
axis=-1),
axis=-1)
class BNN:
"""Neural network models which model aleatoric uncertainty (and possibly epistemic uncertainty
with ensembling).
"""
def __init__(self, params):
"""Initializes a class instance.
Arguments:
params (DotMap): A dotmap of model parameters.
.name (str): Model name, used for logging/use in variable scopes.
Warning: Models with the same name will overwrite each other.
.num_networks (int): (optional) The number of networks in the ensemble. Defaults to 1.
Ignored if model is being loaded.
.model_dir (str/None): (optional) Path to directory from which model will be loaded, and
saved by default. Defaults to None.
.load_model (bool): (optional) If True, model will be loaded from the model directory,
assuming that the files are generated by a model of the same name. Defaults to False.
.sess (tf.Session/None): The session that this model will use.
If None, creates a session with its own associated graph. Defaults to None.
"""
self.name = get_required_argument(params, 'name', 'Must provide name.')
self.model_dir = params.get('model_dir', None)
if params.get('sess', None) is None:
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
self._sess = tf.Session(config=config)
else:
self._sess = params.get('sess')
# Instance variables
self.finalized = False
self.layers, self.max_logvar, self.min_logvar = [], None, None
self.decays, self.optvars, self.nonoptvars = [], [], []
self.end_act, self.end_act_name = None, None
self.scaler = None
# Training objects
self.optimizer = None
self.sy_train_in, self.sy_train_targ = None, None
self.train_op, self.mse_loss = None, None
# Prediction objects
self.sy_pred_in2d, self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac = None, None, None
self.sy_pred_mean2d, self.sy_pred_var2d = None, None
self.sy_pred_in3d, self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac = None, None, None
if params.get('load_model', False):
if self.model_dir is None:
raise ValueError(
"Cannot load model without providing model directory.")
self._load_structure()
self.num_nets, self.model_loaded = self.layers[
0].get_ensemble_size(), True
print("Model loaded from %s." % self.model_dir)
else:
self.num_nets = params.get('num_networks', 1)
self.model_loaded = False
if self.num_nets == 1:
print("Created a neural network with variance predictions.")
else:
print(
"Created an ensemble of %d neural networks with variance predictions."
% (self.num_nets))
@property
def is_probabilistic(self):
return True
@property
def is_tf_model(self):
return True
@property
def sess(self):
return self._sess
###################################
# Network Structure Setup Methods #
###################################
def add(self, layer):
"""Adds a new layer to the network.
Arguments:
layer: (layer) The new layer to be added to the network.
If this is the first layer, the input dimension of the layer must be set.
Returns: None.
"""
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if len(self.layers) == 0 and layer.get_input_dim() is None:
raise ValueError("Must set input dimension for the first layer.")
if self.model_loaded:
raise RuntimeError("Cannot add layers to a loaded model.")
layer.set_ensemble_size(self.num_nets)
if len(self.layers) > 0:
layer.set_input_dim(self.layers[-1].get_output_dim())
self.layers.append(layer.copy())
def pop(self):
"""Removes and returns the most recently added layer to the network.
Returns: (layer) The removed layer.
"""
if len(self.layers) == 0:
raise RuntimeError("Network is empty.")
if self.finalized:
raise RuntimeError(
"Cannot modify network structure after finalizing.")
if self.model_loaded:
raise RuntimeError("Cannot remove layers from a loaded model.")
return self.layers.pop()
def finalize(self, optimizer, optimizer_args=None, *args, **kwargs):
"""Finalizes the network.
Arguments:
optimizer: (tf.train.Optimizer) An optimizer class from those available at tf.train.Optimizer.
optimizer_args: (dict) A dictionary of arguments for the __init__ method of the chosen optimizer.
Returns: None
"""
if len(self.layers) == 0:
raise RuntimeError("Cannot finalize an empty network.")
if self.finalized:
raise RuntimeError("Can only finalize a network once.")
optimizer_args = {} if optimizer_args is None else optimizer_args
self.optimizer = optimizer(**optimizer_args)
out_dim = self.layers[-1].get_output_dim()
# Add variance output.
self.layers[-1].set_output_dim(2 * out_dim)
# Remove last activation to isolate variance from activation function.
self.end_act = self.layers[-1].get_activation()
self.end_act_name = self.layers[-1].get_activation(as_func=False)
self.layers[-1].unset_activation()
self.recalibrator = RecalibrationLayer(out_dim)
self.cal_vars = self.recalibrator.get_vars()
# Construct all variables.
with self.sess.as_default():
with tf.variable_scope(self.name):
self.scaler = TensorStandardScaler(
self.layers[0].get_input_dim())
self.max_logvar = tf.Variable(
np.ones([1, self.layers[-1].get_output_dim() // 2]) / 2.,
dtype=tf.float32,
name="max_log_var")
self.min_logvar = tf.Variable(
-np.ones([1, self.layers[-1].get_output_dim() // 2]) * 10.,
dtype=tf.float32,
name="min_log_var")
for i, layer in enumerate(self.layers):
with tf.variable_scope("Layer%i" % i):
layer.construct_vars()
self.decays.extend(layer.get_decays())
self.optvars.extend(layer.get_vars())
self.optvars.extend([self.max_logvar, self.min_logvar])
self.nonoptvars.extend(self.scaler.get_vars())
# Set up training
with tf.variable_scope(self.name):
self.optimizer = optimizer(**optimizer_args)
self.sy_train_in = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="training_inputs")
self.sy_train_targ = tf.placeholder(
dtype=tf.float32,
shape=[
self.num_nets, None, self.layers[-1].get_output_dim() // 2
],
name="training_targets")
train_loss = tf.reduce_sum(
self._compile_losses(self.sy_train_in,
self.sy_train_targ,
inc_var_loss=True))
train_loss += tf.add_n(self.decays)
train_loss += 0.01 * tf.reduce_sum(
self.max_logvar) - 0.01 * tf.reduce_sum(self.min_logvar)
self.mse_loss = self._compile_losses(self.sy_train_in,
self.sy_train_targ,
inc_var_loss=False)
self.train_op = self.optimizer.minimize(train_loss,
var_list=self.optvars)
with tf.variable_scope('calibration'):
self.sy_cdf_in = tf.placeholder(
dtype=tf.float32,
shape=[None, self.recalibrator.get_output_dim()],
name="training_inputs_cdf")
self.sy_cdf_true = tf.placeholder(
dtype=tf.float32,
shape=[None, self.recalibrator.get_output_dim()],
name="training_targets_cdf")
self.cal_optimizer = tf.train.AdamOptimizer(learning_rate=5e-2)
cdf_pred = self.recalibrator(self.sy_cdf_in, activation=False)
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.sy_cdf_true, logits=cdf_pred)
self.cal_loss = tf.reduce_mean(tf.reduce_mean(cross_entropy,
axis=-1),
axis=-1)
self.cal_train_op = self.cal_optimizer.minimize(
self.cal_loss, var_list=self.cal_vars)
# Initialize all variables
self.sess.run(
tf.variables_initializer(self.optvars + self.nonoptvars +
self.optimizer.variables() +
self.cal_vars +
self.cal_optimizer.variables()))
# Set up prediction
with tf.variable_scope(self.name):
self.sy_pred_in2d = tf.placeholder(
dtype=tf.float32,
shape=[None, self.layers[0].get_input_dim()],
name="2D_training_inputs")
self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac = \
self.create_prediction_tensors(self.sy_pred_in2d, factored=True)
self.sy_pred_mean2d = tf.reduce_mean(self.sy_pred_mean2d_fac,
axis=0)
self.sy_pred_var2d = tf.reduce_mean(self.sy_pred_var2d_fac, axis=0) + \
tf.reduce_mean(tf.square(self.sy_pred_mean2d_fac - self.sy_pred_mean2d), axis=0)
self.sy_pred_in3d = tf.placeholder(
dtype=tf.float32,
shape=[self.num_nets, None, self.layers[0].get_input_dim()],
name="3D_training_inputs")
self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac = \
self.create_prediction_tensors(self.sy_pred_in3d, factored=True)
# Load model if needed
if self.model_loaded:
with self.sess.as_default():
params_dict = loadmat(
os.path.join(self.model_dir, "%s.mat" % self.name))
all_vars = self.nonoptvars + self.optvars + self.cal_vars
for i, var in enumerate(all_vars):
var.load(params_dict[str(i)])
self.finalized = True
#################
# Model Methods #
#################
def calibrate(self,
inputs,
targets,
batch_size=32,
epochs=1000,
hide_progress=False,
holdout_ratio=0.0,
max_logging=5000):
"""Calibrates network post-training
Arguments:
inputs (np.ndarray): Network inputs in the training dataset in rows.
targets (np.ndarray): Network target outputs in the training dataset in rows corresponding
to the rows in inputs.
batch_size (int): The minibatch size to be used for training.
epochs (int): Number of epochs (full network passes that will be done.
hide_progress (bool): If True, hides the progress bar shown at the beginning of training.
Returns: None
"""
with self.sess.as_default():
self.scaler.fit(inputs)
all_mus, all_vars = self.predict(inputs)
all_ys = targets
train_x = np.zeros_like(all_ys)
train_y = np.zeros_like(all_ys)
for d in range(all_mus.shape[1]):
mu = all_mus[:, d]
var = all_vars[:, d]
ys = all_ys[:, d]
cdf_pred = norm.cdf(ys, loc=mu, scale=np.sqrt(var))
cdf_true = np.array(
[np.sum(cdf_pred < p) / len(cdf_pred) for p in cdf_pred])
train_x[:, d] = cdf_pred
train_y[:, d] = cdf_true
epochs = 200
if hide_progress:
epoch_range = range(epochs)
else:
epoch_range = trange(epochs,
unit="epoch(s)",
desc="Calibration training")
def iterate_minibatches(inp, targs, batchsize, shuffle=True):
assert inp.shape[0] == targs.shape[0]
indices = np.arange(inp.shape[0])
if shuffle:
np.random.shuffle(indices)
last_idx = 0
for curr_idx in range(0, inp.shape[0] - batchsize + 1, batchsize):
curr_batch = indices[curr_idx:curr_idx + batchsize]
last_idx = curr_idx + batchsize
yield inp[curr_batch], targs[curr_batch]
if inp.shape[0] % batchsize != 0:
last_batch = indices[last_idx:]
yield inp[last_batch], targs[last_batch]
for _ in epoch_range:
for x_batch, y_batch in iterate_minibatches(
train_x, train_y, batch_size):
self.sess.run(self.cal_train_op,
feed_dict={
self.sy_cdf_in: x_batch,
self.sy_cdf_true: y_batch
})
if not hide_progress:
epoch_range.set_postfix({
"Training loss(es)":
self.sess.run(self.cal_loss,
feed_dict={
self.sy_cdf_in: train_x,
self.sy_cdf_true: train_y
})
})
def save_calibration_info(self, inputs, targets, save_dir, calibrate=True):
all_mus, all_vars = self.predict(inputs)
all_ys = targets
all_cdfs_pred = norm.cdf(all_ys, loc=all_mus, scale=np.sqrt(all_vars))
all_cdfs_pred_cal = self.sess.run(
self.recalibrator(all_cdfs_pred)) if calibrate else []
# Save network parameters (including scalers) in a .mat file
save_vals = {
'all_mus': all_mus,
'all_vars': all_mus,
'all_ys': targets,
'all_cdfs_pred': all_cdfs_pred,
'all_cdfs_pred_cal': all_cdfs_pred_cal
}
savemat(os.path.join(save_dir, "calib_logs.mat"), save_vals)
def plot_calibration(self, inputs, targets, save_dir):
all_mus, all_vars = self.predict(inputs)
all_ys = targets
timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
all_cdfs_pred = norm.cdf(all_ys, loc=all_mus, scale=np.sqrt(all_vars))
all_cdfs_pred_cal = self.sess.run(self.recalibrator(all_cdfs_pred))
for d in range(all_mus.shape[1]):
mu = all_mus[:, d]
var = all_vars[:, d]
ys = all_ys[:, d]
cdf_pred = all_cdfs_pred[:, d]
cdf_pred_cal = all_cdfs_pred_cal[:, d]
cal_ps = np.linspace(0, 1, num=30)
cdf_emp = [np.sum(cdf_pred < p) / len(cdf_pred) for p in cal_ps]
cdf_emp_cal = [
np.sum(cdf_pred_cal < p) / len(cdf_pred_cal) for p in cal_ps
]
plt.close('all')
plt.figure()
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0))
ax1.set_title('Calibration Curve BNN (dim={}, t={})'.format(
d, timestamp))
ax1.set_xlabel('Expected confidence level')
ax1.set_ylabel('Observed confidence level')
ax1.plot(cal_ps, cdf_emp, "s-", label='Uncalibrated')
ax1.plot(cal_ps, cdf_emp_cal, "s-", label='Calibrated')
ax1.plot(cal_ps, cal_ps, alpha=0.6, color='gray')
ax1.legend()
ax2.hist(cdf_pred,
range=(0, 1),
bins=10,
label='Uncalibrated',
histtype="step",
lw=2)
ax2.hist(cdf_pred_cal,
range=(0, 1),
bins=10,
label='Calibrated',
histtype="step",
lw=2)
ax2.set_xlabel("Probability of predicted value")
ax2.set_ylabel("Count")
ax2.legend(loc="upper left", ncol=2)
plt.tight_layout()
print('Saving dim={}'.format(d))
plt.savefig(
os.path.join(save_dir,
'cal_{}_dim_{}.png'.format(timestamp, d)))
def sample_predictions(self, means, var, calibrate=True):
"""
Input shape of mean and var is N x d where N
is batch size and d is size of state space dimension
"""
if not calibrate:
return means + tf.random_normal(
shape=tf.shape(means), mean=0, stddev=1) * tf.sqrt(var)
ps = tf.random.uniform(shape=means.shape)
ps = self.recalibrator.inv_call(ps, activation=True)
ps = tf.clip_by_value(ps, 1e-6, 1 - 1e-6)
dist = tfp.distributions.Normal(loc=means, scale=tf.sqrt(var))
ret = dist.quantile(ps)
return ret
def train(self,
inputs,
targets,
batch_size=32,
epochs=100,
hide_progress=False,
holdout_ratio=0.0,
max_logging=5000):
"""Trains/Continues network training
Arguments:
inputs (np.ndarray): Network inputs in the training dataset in rows.
targets (np.ndarray): Network target outputs in the training dataset in rows corresponding
to the rows in inputs.
batch_size (int): The minibatch size to be used for training.
epochs (int): Number of epochs (full network passes that will be done.
hide_progress (bool): If True, hides the progress bar shown at the beginning of training.
Returns: None
"""
def shuffle_rows(arr):
idxs = np.argsort(np.random.uniform(size=arr.shape), axis=-1)
return arr[np.arange(arr.shape[0])[:, None], idxs]
# Split into training and holdout sets
num_holdout = min(int(inputs.shape[0] * holdout_ratio), max_logging)
permutation = np.random.permutation(inputs.shape[0])
inputs, holdout_inputs = inputs[permutation[num_holdout:]], inputs[
permutation[:num_holdout]]
targets, holdout_targets = targets[permutation[num_holdout:]], targets[
permutation[:num_holdout]]
holdout_inputs = np.tile(holdout_inputs[None], [self.num_nets, 1, 1])
holdout_targets = np.tile(holdout_targets[None], [self.num_nets, 1, 1])
with self.sess.as_default():
self.scaler.fit(inputs)
idxs = np.random.randint(inputs.shape[0],
size=[self.num_nets, inputs.shape[0]])
if hide_progress:
epoch_range = range(epochs)
else:
epoch_range = trange(epochs,
unit="epoch(s)",
desc="Network training")
for _ in epoch_range:
for batch_num in range(int(np.ceil(idxs.shape[-1] / batch_size))):
batch_idxs = idxs[:, batch_num * batch_size:(batch_num + 1) *
batch_size]
self.sess.run(self.train_op,
feed_dict={
self.sy_train_in: inputs[batch_idxs],
self.sy_train_targ: targets[batch_idxs]
})
idxs = shuffle_rows(idxs)
if not hide_progress:
if holdout_ratio < 1e-12:
epoch_range.set_postfix({
"Training loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
})
})
else:
epoch_range.set_postfix({
"Training loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in:
inputs[idxs[:, :max_logging]],
self.sy_train_targ:
targets[idxs[:, :max_logging]]
}),
"Holdout loss(es)":
self.sess.run(self.mse_loss,
feed_dict={
self.sy_train_in: holdout_inputs,
self.sy_train_targ: holdout_targets
})
})
def predict(self, inputs, factored=False, *args, **kwargs):
"""Returns the distribution predicted by the model for each input vector in inputs.
Behavior is affected by the dimensionality of inputs and factored as follows:
inputs is 2D, factored=True: Each row is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of shape
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector j.
inputs is 2D, factored=False: Each row is treated as an input vector.
Returns a mean of shape [batch_size, output_dim] and variance of shape
[batch_size, output_dim], where aggregation is performed as described in the paper.
inputs is 3D, factored=True/False: Each row in the last dimension is treated as an input vector.
Returns a mean of shape [ensemble_size, batch_size, output_dim] and variance of sha
[ensemble_size, batch_size, output_dim], where N(mean[i, j, :], diag([i, j, :])) is the
predicted output distribution by the ith model in the ensemble on input vector [i, j].
Arguments:
inputs (np.ndarray): An array of input vectors in rows. See above for behavior.
factored (bool): See above for behavior.
"""
if len(inputs.shape) == 2:
if factored:
return self.sess.run(
[self.sy_pred_mean2d_fac, self.sy_pred_var2d_fac],
feed_dict={self.sy_pred_in2d: inputs})
else:
return self.sess.run([self.sy_pred_mean2d, self.sy_pred_var2d],
feed_dict={self.sy_pred_in2d: inputs})
else:
return self.sess.run(
[self.sy_pred_mean3d_fac, self.sy_pred_var3d_fac],
feed_dict={self.sy_pred_in3d: inputs})
def create_prediction_tensors(self,
inputs,
factored=False,
*args,
**kwargs):
"""See predict() above for documentation.
"""
factored_mean, factored_variance = self._compile_outputs(inputs)
if inputs.shape.ndims == 2 and not factored:
mean = tf.reduce_mean(factored_mean, axis=0)
variance = tf.reduce_mean(tf.square(factored_mean - mean), axis=0) + \
tf.reduce_mean(factored_variance, axis=0)
return mean, variance
return factored_mean, factored_variance
def save(self, savedir=None):
"""Saves all information required to recreate this model in two files in savedir
(or self.model_dir if savedir is None), one containing the model structuure and the other
containing all variables in the network.
savedir (str): (Optional) Path to which files will be saved. If not provided, self.model_dir
(the directory provided at initialization) will be used.
"""
if not self.finalized:
raise RuntimeError()
model_dir = self.model_dir if savedir is None else savedir
# Write structure to file
with open(os.path.join(model_dir, "%s.nns" % self.name), "w+") as f:
for layer in self.layers[:-1]:
f.write("%s\n" % repr(layer))
last_layer_copy = self.layers[-1].copy()
last_layer_copy.set_activation(self.end_act_name)
last_layer_copy.set_output_dim(last_layer_copy.get_output_dim() //
2)
f.write("%s\n" % repr(last_layer_copy))
# Save network parameters (including scalers) in a .mat file
var_vals = {}
for i, var_val in enumerate(
self.sess.run(self.nonoptvars + self.optvars + self.cal_vars)):
var_vals[str(i)] = var_val
savemat(os.path.join(model_dir, "%s.mat" % self.name), var_vals)
def _load_structure(self):
"""Uses the saved structure in self.model_dir with the name of this network to initialize
the structure of this network.
"""
structure = []
with open(os.path.join(self.model_dir, "%s.nns" % self.name),
"r") as f:
for line in f:
kwargs = {
key: val
for (key, val) in
[argval.split("=") for argval in line[3:-2].split(", ")]
}
kwargs["input_dim"] = int(kwargs["input_dim"])
kwargs["output_dim"] = int(kwargs["output_dim"])
kwargs["weight_decay"] = None if kwargs[
"weight_decay"] == "None" else float(
kwargs["weight_decay"])
kwargs["activation"] = None if kwargs[
"activation"] == "None" else kwargs["activation"][1:-1]
kwargs["ensemble_size"] = int(kwargs["ensemble_size"])
structure.append(FC(**kwargs))
self.layers = structure
#######################
# Compilation methods #
#######################
def _compile_outputs(self, inputs, ret_log_var=False):
"""Compiles the output of the network at the given inputs.
If inputs is 2D, returns a 3D tensor where output[i] is the output of the ith network in the ensemble.
If inputs is 3D, returns a 3D tensor where output[i] is the output of the ith network on the ith input matrix.
Arguments:
inputs: (tf.Tensor) A tensor representing the inputs to the network
ret_log_var: (bool) If True, returns the log variance instead of the variance.
Returns: (tf.Tensors) The mean and variance/log variance predictions at inputs for each network
in the ensemble.
"""
dim_output = self.layers[-1].get_output_dim()
cur_out = self.scaler.transform(inputs)
for layer in self.layers:
cur_out = layer.compute_output_tensor(cur_out)
mean = cur_out[:, :, :dim_output // 2]
if self.end_act is not None:
mean = self.end_act(mean)
logvar = self.max_logvar - tf.nn.softplus(
self.max_logvar - cur_out[:, :, dim_output // 2:])
logvar = self.min_logvar + tf.nn.softplus(logvar - self.min_logvar)
if ret_log_var:
return mean, logvar
else:
return mean, tf.exp(logvar)
def _compile_losses(self, inputs, targets, inc_var_loss=True):
"""Helper method for compiling the loss function.
The loss function is obtained from the log likelihood, assuming that the output
distribution is Gaussian, with both mean and (diagonal) covariance matrix being determined
by network outputs.
Arguments:
inputs: (tf.Tensor) A tensor representing the input batch
targets: (tf.Tensor) The desired targets for each input vector in inputs.
inc_var_loss: (bool) If True, includes log variance loss.
Returns: (tf.Tensor) A tensor representing the loss on the input arguments.
"""
mean, log_var = self._compile_outputs(inputs, ret_log_var=True)
inv_var = tf.exp(-log_var)
if inc_var_loss:
mse_losses = tf.reduce_mean(tf.reduce_mean(
tf.square(mean - targets) * inv_var, axis=-1),
axis=-1)
var_losses = tf.reduce_mean(tf.reduce_mean(log_var, axis=-1),
axis=-1)
total_losses = mse_losses + var_losses
else:
total_losses = tf.reduce_mean(tf.reduce_mean(tf.square(mean -
targets),
axis=-1),
axis=-1)
return total_losses