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
\(0.5* (\theta - x)^T * Q * (\theta - x)\)
xcoming from the quadratic data set, i.e., zero-mean normal.
- batch_size (int) -- Batch size to use.
- weight_decay (float) -- No weight decay (L2-regularization) is used in this
test problem. Defaults to
Noneand any input here is ignored.
The DeepOBS data set class for the quadratic test problem.
A tensorflow operation initializing the test problem for the training phase.
A tensorflow operation initializing the test problem for evaluating on training data.
A tensorflow operation initializing the test problem for evaluating on test data.
A tf.Tensor of shape (batch_size, ) containing the per-example loss values.
A scalar tf.Tensor containing a regularization term. Will always be
0.0since no regularizer is used.