class deepobs.tensorflow.testproblems.quadratic_deep.quadratic_deep(batch_size, weight_decay=None)[source]

DeepOBS test problem class for a stochastic quadratic test problem 100dimensions. The 90 % of the eigenvalues of the Hessian are drawn from theinterval $$(0.0, 1.0)$$ and the other 10 % are from $$(30.0, 60.0)$$ simulating an eigenspectrum which has been reported for Deep Learning https://arxiv.org/abs/1611.01838.

This creatis a loss functions of the form

$$0.5* (\theta - x)^T * Q * (\theta - x)$$

with Hessian Q and "data" x coming from the quadratic data set, i.e., zero-mean normal.

Parameters: 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.
dataset

The DeepOBS data set class for the quadratic 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.