Version 0.9.5

ChangeLog update

• Added jupyter notebooks with a guided tour through TATi.
• Added Dockerfile for putting TATi in docker container.
• Added analysis capability for ensemble averaging.
• FIX: SGLD formula in userguide was incorrect.
• Added GradientDescent optimizer with Barzilai-Borwein learning rate picker.
• Accumulation of norms of gradients, noise, momentum, ... is made optional
  and can be fully switched off for performance reasons.
• Optimizers have been moved into distinct module/folder.
• several smaller fixes preventing the distributed tarball from compiling.
• small runtime improvements.
• FIX: time_per_nth_step column in run info file/dataframe was wrong when
  using HMC.

Version 0.9.4

ChangeLog update

• Simulation has get_losses() and get_activations() with list of valid names.
• TrajectoryData's dataframes' columns now all have correct dtype.
• TATiLossFunctionSampler may resample trajectory in subspace.
• Refactored TATiLossFunctionSampler extensively, introduced SamplingModes.
• TATi can now be installed as PyPI (wheel) package, i.e. pip install tati.
• smaller fixes to userguide.
• FIX: Rewite of Model class broke check for present nn in Simulation.

Version 0.9.3

ChangeLog update

• DOCU: Improved and update userguide.
• DOCU: Code documentation now follows Google Style (no longer ReST) and has
  API documentation.
• Introduced a general test threshold to account for numerical inaccuracy of
  parallel reduction on GPU-assisted hardware.
• Fully refactored model class: ModelState, MultiLayerPerceptron,
  InputPipelineFactory.
• Configure now checks presence of required python packages.
• added full copyright notes, added code check tests on this.
• extracted grid-based sampling from TATiLossFunctionSampler.
• TATiAnalyser can perform covariance and Integrated Autocorrelation Time (IAT)
  analysis.
• Refactored TATiAnalyser into several operation modes such that these are
  easy-to-use and accessible from Python interfaces.

Version 0.9.2

ChangeLog update

• added fully tested Hamiltonian Monte Carlo method with first and second order time integrator (Euler and Leapfrog), following [Neal, 2011].
• added Ensemble Quasi Newton scheme for all samplers, tested on simple Gaussian mixture model and MNIST single-layer perceptron.
• for checking virial theorem, average moment of inertia is written to averages file.
• allowing "0" in option hidden_dimension.
• trajectory can be written w.r.t to subspace spanned by vectors in new option directions_file.
• summaries now write memory and cputime usage for debugging bottlenecks.
• DOCU: re-added accidentally dropped reference section on simulation module.
• FIX: TATiOptimizer always writes last step to files.
• FIX: update of parameters caused no update of simulation's evaluation cache.
• FIX: time_per_nth_step was using process_time instead of time, i.e. accumulated time over all processes.
• FIX: Boolean parameters were not used from cmd-line.
• FIX: Assigning weights from dataframes took ages. Now works also for multiple walkers.
• FIX: parse_parameters_file was not working in TATi.simulation.
• tested on TF version up to 1.10.

sha256: 182418765305da71563bb737f68d2a3ce7bcd0744af11e8ea3506ec9cf0f06b0 thermodynamicanalyticstoolkit-0.9.tar.bz2

Version 0.9

ChangeLog update

• added simulation module, an easy-to-use python interface to loss manifold sampling for neural networks.
• large rewrite of userguide, now in asciidoc.
• added programmer's guide
• added roadmap.
• option types are checked in python interface.
• tensorflow up to 1.8 supported.
• improved input pipeline (and thereby overall) performance.
• several smaller fixes.
• HMC is removed temporarily till being fully validated.

sha1: a25214752556e4699b7af9c011cdaa23bdd47286 thermodynamicanalyticstoolkit-0.9.tar.bz2

Version 0.8

ChangeLog update

• introducing replicated neural networks to allow for multiple walkers that
  proceed in parallel on individual trajectories with the ability to exchange
  information, e.g. for Ensemble Quasi Newton method.
• Updated package dependencies and funding notes in README.
• Added python interface that allows to use neural network as a general
  function depending parameters and with a gradient.
• FIX: TATiExplorer could still experience dead-locks.
• Docbook now also supports non-standard fop and xsltproc installation paths.
• FIX: scipy.sparse's linalg module was not loaded correctly for certain scipy
  versions.
• added Covariance Controlled Adaptive Langevin (CCAdL) as sampler, untested.
• added option burnin to drop initial set of steps from accumulated averages
• added option progress to display a progress bar with time estimate
• added option summaries_path to write summaries for TensorBoard on demand
• FIX: accuracy was not calculated correctly for multi class classification.
• added testsuite section on tensorflow (non-)capabilities.

sha1: 80fa208ddae2b274aebed065daccba3c39dfd51f thermodynamicanalyticstoolkit-0.8.tar.bz2

Version 0.7

ChangeLog Update

• renamed from DataDrivenSampler (DDS) to Thermodynamic Analytics Toolkit
  (TATi)
• added (vectorized) hessian and gradient nodes to allow easy access through
  numpy arrays
• sampler, optimizer, lossfunctionsampler, and inputspacesampler may now parse
  parameters from a given CSV file through a single cmd-line option
• Explorer can now run parallel processes each sampling or training along a
  independent trajectory
• FIX: Sampler module's names were inconsistent
• FIX: sqlite3 presence check was broken

sha1: b3eca5fe66b71c644935b1c2cd75308dc4d857b3 thermodynamicanalyticstoolkit-0.7.tar.bz2

Version 0.6

ChangeLog update

• supporting now up to tensorflow 1.6
• added DDSExplorer for exploring loss landscapes, picking minima long the way
• prints replaced by logging expressions and verbose cmdline statment supported
• FIX: rejection_rate in HMC fixed
• tensorflow computations can now be done with a given basetype.
• returned to default tf.float32 as tensorflow basetype. tf.float64 seems to
  be broken to some extent suggested from sampler's convergence plots
• FIX: SGLD was not resetting aggregated values in run info
• LossFunctionSampler can now fix partial set of parameters and to values
  obtained from (minima, trajectory) file
• FIX: Picking input columns (e.g. "sin(x1)") was broken to some extent
• LossFunctionSampler and InputSpaceSampler can now interpret CSV files of
  arbitrary type (they pick out the columns they need)
• updated userguide significantly
• some fixes to changed dependent python packages related to Ubuntu 16.04

sha1: 346348be9678b5b5cf43fa96d7a81bb4249f3a7d datadrivensampler-0.6.tar.bz2

Version 0.5

ChangeLog update

• may read TFRecords (as well as CSV) files
• added example for MNIST optimization
• input pipelines now depend on tf.Dataset framework. Either in-memory for
  smaller datasets or file
• priors have been added for BAOAB and HMC
• version now always gives a git hash (of the commit)
• Python API can feed its own in-memory dataset for sampling or optimization

sha1: 2fb81ff09fb1ca6f2181fd349953589b22fc289d datadrivensampler-0.5.tar.bz2

Version 0.3

ChangeLog update

• added HamiltonianMonteCarlo sampler
• added BAOAB sampler
• parameters can be fixed in optimization or sampling (also loss function
  sampling)
• FIX: trajectories are now correctly written also for networks with hidden
  layers
• diffusion map analysis can now use python package pydiffmap (and is
  recommended due to optimal epsilon choice)
• all cmdline examples in userguide are also fully tested
• added input space sampler to see classification boundaries of network
• we no longer generate datasets in memory but parse from CSV files
• added DatasetWriter to produce CSV for old in-memory datasets

sha1: c1a663adb4ead4e5989cff57c16e3bf530b0ecc4 datadrivensampler-0.3.tar.bz2