"""
Some common objective functions (for use with the ``objective`` argument in
`.Simulator.train` or `.Simulator.loss`).
"""
import tensorflow as tf
from nengo_dl.compat import tf_math
[docs]def mse(outputs, targets):
"""
Compute Mean Squared Error between given outputs and targets.
If any values in ``targets`` are ``nan``, that will be treated as
zero error for those elements.
Parameters
----------
outputs : ``tf.Tensor``
Output values from a Probe in a network.
targets : ``tf.Tensor``
Target values for a Probe in a network.
Returns
-------
mse : ``tf.Tensor``
Tensor representing the mean squared error.
"""
targets = tf.where(tf_math.is_nan(targets), outputs, targets)
return tf.reduce_mean(input_tensor=tf.square(targets - outputs))
[docs]class Regularize:
"""
An objective function to apply regularization penalties.
Parameters
----------
order : int or str
Order of the regularization norm (e.g. ``1`` for L1 norm, ``2`` for
L2 norm). See https://www.tensorflow.org/api_docs/python/tf/norm for
a full description of the possible values for this parameter.
axis : int or None
The axis of the probed signal along which to compute norm. If None
(the default), the signal is flattened and the norm is computed across
the resulting vector. Note that these are only the axes with respect
to the output on a single timestep (i.e. batch/time dimensions are not
included).
weight : float
Scaling weight to apply to regularization penalty.
Notes
-----
The mean will be computed across all the non-``axis`` dimensions after
computing the norm (including batch/time) in order to compute the overall
objective value.
"""
def __init__(self, order=2, axis=None, weight=None):
self.order = order
self.axis = axis
self.weight = weight
def __call__(self, x):
if self.axis is None:
if x.get_shape().ndims > 3:
# flatten signal (keeping batch/time dimension)
x = tf.reshape(x, tf.concat([tf.shape(input=x)[:2], (-1,)], axis=0))
axis = 2
else:
axis = self.axis + 2
output = tf.reduce_mean(
input_tensor=tf.norm(tensor=x, axis=axis, ord=self.order)
)
if self.weight is not None:
output *= self.weight
return output
