# -*- coding: utf-8 -*-
"""A simple 2D Noisy Rosenbrock Loss Function."""
import tensorflow as tf
from ..datasets.two_d import two_d
from .testproblem import TestProblem
[docs]class two_d_rosenbrock(TestProblem):
r"""DeepOBS test problem class for a stochastic version of the\
two-dimensional Rosenbrock function as the loss function.
Using the deterministic `Rosenbrock function
<https://en.wikipedia.org/wiki/Rosenbrock_function>`_ and adding stochastic
noise of the form
:math:`u \cdot x + v \cdot y`
where ``x`` and ``y`` are normally distributed with mean ``0.0`` and
standard deviation ``1.0`` we get a loss function of the form
:math:`(1 - u)^2 + 100 \cdot (v - u^2)^2 + u \cdot x + v \cdot y`
Args:
batch_size (int): Batch size to use.
weight_decay (float): No weight decay (L2-regularization) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
Attributes:
dataset: The DeepOBS data set class for the two_d stochastic test problem.
train_init_op: A tensorflow operation initializing the test problem for the
training phase.
train_eval_init_op: A tensorflow operation initializing the test problem for
evaluating on training data.
test_init_op: A tensorflow operation initializing the test problem for
evaluating on test data.
losses: A tf.Tensor of shape (batch_size, ) containing the per-example loss
values.
regularizer: A scalar tf.Tensor containing a regularization term.
Will always be ``0.0`` since no regularizer is used.
"""
def __init__(self, batch_size, weight_decay=None):
"""Create a new 2D Rosenbrock Test Problem instance.
Args:
batch_size (int): Batch size to use.
weight_decay (float): No weight decay (L2-regularization) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
"""
super(two_d_rosenbrock, self).__init__(batch_size, weight_decay)
if weight_decay is not None:
print(
"WARNING: Weight decay is non-zero but no weight decay is used",
"for this model."
)
[docs] def set_up(self):
"""Sets up the stochastic two-dimensional Rosenbrock test problem.
Using ``-0.5`` and ``1.5`` as a starting point for the weights ``u``
and ``v``.
"""
self.dataset = two_d(self._batch_size)
self.train_init_op = self.dataset.train_init_op
self.train_eval_init_op = self.dataset.train_eval_init_op
self.test_init_op = self.dataset.test_init_op
x, y = self.dataset.batch
# Set starting point
starting_point = [-0.5, 1.5]
# Set model weights
u = tf.get_variable(
"weight",
shape=(),
initializer=tf.constant_initializer(starting_point[0]))
v = tf.get_variable(
"bias",
shape=(),
initializer=tf.constant_initializer(starting_point[1]))
self.losses = (1 - u)**2 + 100 * (v - u**2)**2 + u * x + v * y
self.regularizer = tf.losses.get_regularization_loss()