All Versions
10
Latest Version
Avg Release Cycle
51 days
Latest Release
1978 days ago

Changelog History

  • v1.5.3 Changes

    November 20, 2018

    ➕ Additions

    Data Set API

    The DataSet family of classes helps the user to create and transform potentially large number of data instances.
    👉 Users can create and perform complex transformations on data sets, using the DataPipe API or simple Scala functions.

    import \_root\_.io.github.mandar2812.dynaml.probability.\_import \_root\_.io.github.mandar2812.dynaml.pipes.\_import io.github.mandar2812.dynaml.tensorflow.\_val random\_numbers = GaussianRV(0.0, 1.0) :\* GaussianRV(1.0, 2.0) //Create a data set.val dataset1 = dtfdata.dataset(random\_numbers.iid(10000).draw) val filter\_gr\_zero = DataPipe[(Double, Double), Boolean]( c =\> c.\_1 \> 0d && c.\_2 \> 0d) //Filter elementsval data\_gr\_zero = dataset1.filter(filter\_gr\_zero) val abs\_func: (Double, Double) =\> (Double, Double) = (c: (Double, Double)) =\> (math.abs(c.\_1), math.abs(c.\_2)) //Map elementsval data\_abs= dataset1.map(abs\_func)

    Find out more about the DataSet API and its capabilities in the user guide.

    Tensorflow Integration

    📦 Package dynaml.tensorflow

    Batch Normalisation

    Batch normalisation is used to standardize activations of convolutional layers and
    to speed up training of deep neural nets.

    Usage

    import io.github.mandar2812.dynaml.tensorflow.\_val bn = dtflearn.batch\_norm("BatchNorm1")

    Inception v2

    The Inception architecture, proposed by Google is an important
    building block of convolutional neural network architectures used in vision applications.

    📄 inception

    In a subsequent paper, the authors introduced optimizations in the Inception
    architecture, known colloquially as Inception v2.

    In Inception v2, larger convolutions (i.e. 3 x 3 and 5 x 5) are implemented in a factorized manner
    to reduce the number of parameters to be learned. For example the 3 x 3 convolution is expressed as a
    combination of 1 x 3 and 3 x 1 convolutions.

    📄 inception

    Similarly the 5 x 5 convolutions can be expressed a combination of two 3 x 3 convolutions

    📄 inception2

    DynaML now offers the Inception cell as a computational layer.

    Usage

    import io.github.mandar2812.dynaml.pipes.\_import io.github.mandar2812.dynaml.tensorflow.\_import org.platanios.tensorflow.api.\_//Create an RELU activation, given a string name/identifier.val relu\_act = DataPipe(tf.learn.ReLU(\_)) //Learn 10 filters in each branch of the inception cellval filters = Seq(10, 10, 10, 10) val inception\_cell = dtflearn.inception\_unit( channels = 3, num\_filters = filters, relu\_act, //Apply batch normalisation after each convolution use\_batch\_norm = true)(layer\_index = 1)

    Dynamical Systems: Continuous Time RNN

    Continuous time recurrent neural networks (CTRNN) are an important class of recurrent neural networks. They enable
    the modelling of non-linear and potentially complex dynamical systems of multiple variables, with feedback.

    ➕ Added CTRNN layer: dtflearn.ctrnn

    ➕ Added CTRNN layer with inferable time step: dtflearn.dctrnn.

    ➕ Added a projection layer for CTRNN based models dtflearn.ts_linear.

    Training Stopping Criteria

    Create common and simple training stop criteria such as.

    Stop after fixed number of iterations dtflearn.max_iter_stop(100000)

    Stop after change in value of loss goes below a threshold. dtflearn.abs_loss_change_stop(0.0001)

    Stop after change in relative value of loss goes below a threshold. dtflearn.rel_loss_change_stop(0.001)

    🏗 Neural Network Building Blocks

    • Added helper method dtlearn.build_tf_model() for training tensorflow models/estimators.

    Usage

    import io.github.mandar2812.dynaml.tensorflow.\_import org.platanios.tensorflow.api.\_import org.platanios.tensorflow.data.image.MNISTLoaderimport ammonite.ops.\_val tempdir = home/"tmp"val dataSet = MNISTLoader.load( java.nio.file.Paths.get(tempdir.toString()) )val trainImages = tf.data.TensorSlicesDataset(dataSet.trainImages)val trainLabels = tf.data.TensorSlicesDataset(dataSet.trainLabels)val trainData = trainImages.zip(trainLabels) .repeat() .shuffle(10000) .batch(256) .prefetch(10)// Create the MLP model.val input = tf.learn.Input( UINT8, Shape( -1, dataSet.trainImages.shape(1), dataSet.trainImages.shape(2)) )val trainInput = tf.learn.Input(UINT8, Shape(-1))val architecture = tf.learn.Flatten("Input/Flatten") \>\> tf.learn.Cast("Input/Cast", FLOAT32) \>\> tf.learn.Linear("Layer\_0/Linear", 128) \>\> tf.learn.ReLU("Layer\_0/ReLU", 0.1f) \>\> tf.learn.Linear("Layer\_1/Linear", 64) \>\> tf.learn.ReLU("Layer\_1/ReLU", 0.1f) \>\> tf.learn.Linear("Layer\_2/Linear", 32) \>\> tf.learn.ReLU("Layer\_2/ReLU", 0.1f) \>\> tf.learn.Linear("OutputLayer/Linear", 10)val trainingInputLayer = tf.learn.Cast("TrainInput/Cast", INT64)val loss = tf.learn.SparseSoftmaxCrossEntropy("Loss/CrossEntropy") \>\> tf.learn.Mean("Loss/Mean") \>\> tf.learn.ScalarSummary("Loss/Summary", "Loss")val optimizer = tf.train.AdaGrad(0.1)// Directory in which to save summaries and checkpointsval summariesDir = java.nio.file.Paths.get( (tempdir/"mnist\_summaries").toString() )val (model, estimator) = dtflearn.build\_tf\_model( architecture, input, trainInput, trainingInputLayer, loss, optimizer, summariesDir, dtflearn.max\_iter\_stop(1000), 100, 100, 100)(trainData)
    • 🏗 Build feedforward layers and feedforward layer stacks easier.

    Usage

    import io.github.mandar2812.dynaml.tensorflow.\_import org.platanios.tensorflow.api.\_//Create a single feedforward layerval layer = dtflearn.feedforward(num\_units = 10, useBias = true)(id = 1)//Create a stack of feedforward layersval net\_layer\_sizes = Seq(10, 5, 3) val stack = dtflearn.feedforward\_stack( (i: Int) =\> dtflearn.Phi("Act\_"+i), FLOAT64)( net\_layer\_sizes)

    3D Graphics

    📦 Package dynaml.graphics

    Create 3d plots of surfaces, for a use case, see the jzydemo.sc and tf_wave_pde.sc

    Library Organisation

    • ✂ Removed the dynaml-notebook module.

    🐛 Bug Fixes

    • 🛠 Fixed bug related to scalar method of VectorField, innerProdDouble and other inner product implementations.

    👌 Improvements and Upgrades

    • ⬆️ Bumped up Ammonite version to 1.1.0
    • RegressionMetrics and RegressionMetricsTF now also compute Spearman rank correlation as
      🐎 one of the performance metrics.

  • v1.5.3-beta.2 Changes

    May 27, 2018

    ➕ Additions

    3D Graphics

    📦 Package dynaml.graphics

    Create 3d plots of surfaces, for a use case, see the jzydemo.sc and tf_wave_pde.sc

    Tensorflow Utilities

    📦 Package dynaml.tensorflow

    Training Stopping Criteria

    Create common and simple training stop criteria such as.

    • Stop after fixed number of iterations dtflearn.max_iter_stop(100000)
    • Stop after change in value of loss goes below a threshold. dtflearn.abs_loss_change_stop(0.0001)
    • Stop after change in relative value of loss goes below a threshold. dtflearn.rel_loss_change_stop(0.001)

    🏗 Neural Network Building Blocks

    • Added helper method dtlearn.build_tf_model() for training tensorflow models/estimators.

    Usage

    val dataSet = MNISTLoader.load(java.nio.file.Paths.get(tempdir.toString())) val trainImages = tf.data.TensorSlicesDataset(dataSet.trainImages) val trainLabels = tf.data.TensorSlicesDataset(dataSet.trainLabels) val trainData = trainImages.zip(trainLabels) .repeat() .shuffle(10000) .batch(256) .prefetch(10) // Create the MLP model.val input = tf.learn.Input(UINT8, Shape(-1, dataSet.trainImages.shape(1), dataSet.trainImages.shape(2))) val trainInput = tf.learn.Input(UINT8, Shape(-1)) val architecture = tf.learn.Flatten("Input/Flatten") \>\> tf.learn.Cast("Input/Cast", FLOAT32) \>\> tf.learn.Linear("Layer\_0/Linear", 128) \>\> tf.learn.ReLU("Layer\_0/ReLU", 0.1f) \>\> tf.learn.Linear("Layer\_1/Linear", 64) \>\> tf.learn.ReLU("Layer\_1/ReLU", 0.1f) \>\> tf.learn.Linear("Layer\_2/Linear", 32) \>\> tf.learn.ReLU("Layer\_2/ReLU", 0.1f) \>\> tf.learn.Linear("OutputLayer/Linear", 10) val trainingInputLayer = tf.learn.Cast("TrainInput/Cast", INT64) val loss = tf.learn.SparseSoftmaxCrossEntropy("Loss/CrossEntropy") \>\> tf.learn.Mean("Loss/Mean") \>\> tf.learn.ScalarSummary("Loss/Summary", "Loss") val optimizer = tf.train.AdaGrad(0.1) // Directory in which to save summaries and checkpointsval summariesDir = java.nio.file.Paths.get((tempdir/"mnist\_summaries").toString()) val (model, estimator) = dtflearn.build\_tf\_model( architecture, input, trainInput, trainingInputLayer, loss, optimizer, summariesDir, dtflearn.max\_iter\_stop(1000), 100, 100, 100)(trainData)
    • 🏗 Build feedforward layers and feedforward layer stacks easier.

    Usage

    //Create a single feedforward layerval layer = dtflearn.feedforward(num\_units = 10, useBias = true)(id = 1)//Create a stack of feedforward layersval stack = dtflearn.feedforward\_stack( (i: Int) =\> dtflearn.Phi("Act\_"+i), FLOAT64)( net\_layer\_sizes.tail)

    📦 Package dynaml.tensorflow.layers

    Dynamical Systems: Continuous Time RNN

    • ➕ Added CTRNN layer with inferable time step: DynamicTimeStepCTRNN.
    • ➕ Added a projection layer for CTRNN based models FiniteHorizonLinear.

    Activations

    • ➕ Added cumulative gaussian distribution function as an activation map dtflearn.Phi("actName").
    • ➕ Added generalised logistic function as an activation map dtflearn.GeneralizedLogistic("actName")

    🐛 Bug Fixes

    • 🛠 Fixed bug related to scalar method of VectorField, innerProdDouble and other inner product implementations.

    👌 Improvements and Upgrades

    • ⬆️ Bumped up Ammonite version to 1.1.0

  • v1.5.3-beta.1 Changes

    March 09, 2018

    ➕ Additions

    Tensorflow Utilities

    📦 Package dynaml.tensorflow

    The dtfpipe object is created to house data pipelines and workflows around tensorflow primitives.

    • dtfpipe.gaussian_standardization performs Gaussian Scaling of the data and returns GaussianScalerTF objects, one each for the input and output data.
    • dtfpipe.minmax_standardization performs [0, 1] scaling of the features and ouputs, returning MinMaxScalerTF objects.

    Usage

    import io.github.mandar2812.dynaml.tensorflow.\_import org.platanios.tensorflow.api.\_val (inputs, outputs): (Tensor, Tensor) = ...val (scaledData, (features\_scaler, targets\_scaler)) = dtfpipe.gaussian\_standardization(inputs, outputs)

    📦 Package dynaml.tensorflow.utils

    📦 Package dynaml.tensorflow.layers

    Dynamical Systems: Continuous Time RNN

    The continuous time recurrent neural network; CTRNN, when discretised for a finite time horizon is represented as the computational layer FiniteTimeCTRNN.

    📦 Package dynaml.tensorflow.learn

    • ➕ Added MVTimeSeriesLoss which helps quantify the average L2 loss over a finite time slice of a multivariate time series.

  • v1.5.2 Changes

    March 05, 2018

    ➕ Additions

    Tensorflow Integration

    📦 Package dynaml.tensorflow

    📦 The dtf package object houses utility functions related to tensorflow primitives. Currently supports creation of tensors from arrays.

    import io.github.mandar2812.dynaml.tensorflow.\_import org.platanios.tensorflow.api.\_//Create a FLOAT32 Tensor of shape (2, 2), i.e. a square matrixval mat = dtf.tensor\_f32(2, 2)(1d, 2d, 3d, 4d) //Create a random 2 \* 3 matrix with independent standard normal entriesval rand\_mat = dtf.random(FLOAT32, 2, 3)( GaussianRV(0d, 1d) \> DataPipe((x: Double) =\> x.toFloat) ) //Multiply matricesval prod = mat.matmul(rand\_mat) println(prod.summarize())val another\_rand\_mat = dtf.random(FLOAT32, 2, 3)( GaussianRV(0d, 1d) \> DataPipe((x: Double) =\> x.toFloat) )//Stack tensors vertically, i.e. row wiseval vert\_tensor = dtf.stack(Seq(rand\_mat, another\_rand\_mat), axis = 0)//Stack vectors horizontally, i.e. column wiseval horz\_tensor = dtf.stack(Seq(rand\_mat, another\_rand\_mat), axis = 1)

    🏗 The dtflearn package object deals with basic neural network building blocks which are often needed while constructing prediction architectures.

    //Create a simple neural architecture with one convolutional layer //followed by a max pool and feedforward layer val net = tf.learn.Cast("Input/Cast", FLOAT32) \>\> dtflearn.conv2d\_pyramid(2, 3)(4, 2)(0.1f, true, 0.6F) \>\> tf.learn.MaxPool("Layer\_3/MaxPool", Seq(1, 2, 2, 1), 1, 1, SamePadding) \>\> tf.learn.Flatten("Layer\_3/Flatten") \>\> dtflearn.feedforward(256)(id = 4) \>\> tf.learn.ReLU("Layer\_4/ReLU", 0.1f) \>\> dtflearn.feedforward(10)(id = 5)

    Library Organisation

    • ➕ Added dynaml-repl and dynaml-notebook modules to repository.

    DynaML Server

    • DynaML ssh server now available (only in Local mode)

      $ ./target/universal/stage/bin/dynaml --server

    To login to the server open a separate shell and type, (when prompted for password, just press ENTER)

    $ ssh repl@localhost -p22222

    Basis Generators

    • Legrendre polynomial basis generators

    🛠 Bugfixes

    • Acceptance rule of HyperParameterMCMC and related classes.

    🔄 Changes

    • 🖨 Increased pretty printing to screen instead of logging.

    Cleanup

    📦 Package dynaml.models.svm

    • 📦 Removal of deprecated model classes from svm package

  • v1.5.2-beta.4 Changes

    February 16, 2018

    ➕ Additions

    • ➕ Added MetricsTF top level class for calculating metrics from tensorflow objects
    • ➕ Added dtflearn object for housing common neural net building blocks

  • v1.5.2-beta.3 Changes

    February 07, 2018

    🐛 Bug Fix Beta Release

    Module dynaml-repl

    • 🛠 Fixed Router code in DynaMLRepl so script arguments are passed correctly.

  • v1.5.2-beta.2 Changes

    January 26, 2018

    ➕ Additions

    • ➕ Added dynaml-repl and dynaml-notebook modules to repository.

    📦 Package dynaml.tensorflow

    • ➕ Added dtf package object for utility functions related to tensorflow primitives. Currently supports creation of tensors from arrays.

    Cleanup

    📦 Package dynaml.models.svm

    • 📦 Removal of deprecated model classes from svm package

  • v1.5.2-beta.1 Changes

    November 10, 2017

    ➕ Additions

    • Tensorflow (beta) support now live, thanks to the tensorflow_scala project! Try it out in:
    • Legrendre polynomial basis generators

    • DynaML ssh server now available

      $ ./target/universal/stage/bin/dynaml --server

    To login to the server open a separate shell and type

    $ ssh repl@localhost -p22222

    🛠 Bugfixes

    • Acceptance rule of HyperParameterMCMC and related classes.

    🔄 Changes

    • 🖨 Increased pretty printing to screen instead of logging.

  • v1.5.1 Changes

    September 20, 2017

    ➕ Additions

    📦 Package dynaml.probability.distributions

    • ➕ Added Kumaraswamy distribution, an alternative to the Beta distribution.
    • ➕ Added Erlang distribution, a special case of the Gamma distribution.

    📦 Package dynaml.analysis

    ➕ Added Radial Basis Function generators.

    • Gaussian
    • Inverse Multi-Quadric
    • Multi-Quadric

    - Matern Half-Integer

    ➕ Added an inner product space implementation for Tuple2

    🐛 Bug Fixes

    📦 Package dynaml.kernels

    • 🛠 Fixed bug concerning hyper-parameter blocking in CompositeCovariance and its children.

    📦 Package dynaml.probability.distributions

    • 🛠 Fixed calculation error for normalisation constant of multivariate T and Gaussian family.

  • v1.5 Changes

    August 15, 2017

    ➕ Additions

    📦 Package dynaml.algebra

    ➕ Added support for dual numbers.

    //Zero Dualval zero = DualNumber.zero[Double] val dnum = DualNumber(1.5, -1.0) val dnum1 = DualNumber(-1.5, 1.0) //Algebraic operations: multiplication and addition/subtractiondnum1\*dnum2 dnum1 - dnum dnum\*zero

    📦 Package dynaml.probability

    • ➕ Added support for mixture distributions and mixture random variables. MixtureRV, ContinuousDistrMixture for random variables and MixtureDistribution for constructing mixtures of breeze distributions.

    📦 Package dynaml.optimization

    • ➕ Added ModelTuner[T, T1] trait as a super trait to GlobalOptimizer[T]
    • GridSearch and CoupledSimulatedAnnealing now extend AbstractGridSearch and AbstractCSA respectively.
    • ➕ Added ProbGPMixtureMachine: constructs a mixture model after a CSA or grid search routine by calculating the mixture probabilities of members of the final hyper-parameter ensemble.

    Stochastic Mixture Models

    📦 Package dynaml.models

    • ➕ Added StochasticProcessMixtureModelas top level class for stochastic mixture models.
    • ➕ Added GaussianProcessMixture: implementation of gaussian process
      mixture models.
    • ➕ Added MVTMixture: implementation of mixture model over
      multioutput matrix T processes.

    Kulback-Leibler Divergence

    📦 Package dynaml.probability

    • ➕ Added method KL() to probability package object, to calculate
      the Kulback Leibler divergence between two continuous random
      variables backed by breeze distributions.

    Adaptive Metropolis Algorithms.

    AdaptiveHyperParameterMCMC which
    adapts the exploration covariance with each sample.

    HyperParameterSCAM adapts
    the exploration covariance for each hyper-parameter independently.

    Splines and B-Spline Generators

    📦 Package dynaml.analysis

    Cubic Spline Interpolation Kernels

    📦 Package dynaml.kernels

    Gaussian Process Models for Linear Partial Differential Equations

    Based on a legacy ICML 2003 paper by Graepel. DynaML now ships with capability of performing PDE forward and inverse inference using the Gaussian Process API.

    📦 Package dynaml.models.gp

    • GPOperatorModel: models a quantity of interest which is governed by a linear PDE in space and time.

    📦 Package dynaml.kernels

    LinearPDEKernel: The core kernel primitive accepted by the GPOperatorModel class.

    GenExpSpaceTimeKernel: a kernel of the exponential family which can serve as a handy base kernel for LinearPDEKernel class.

    Basis Function Gaussian Processes

    👍 DynaML now supports GP models with explicitly incorporated basis
    functions as linear mean/trend functions.

    📦 Package dynaml.models.gp

    🌲 Log Gaussian Processes

    👌 Improvements

    📦 Package dynaml.probability

    • 🔄 Changes to RandomVarWithDistr: made type parameter Dist covariant.
    • Reform to IIDRandomVar hierarchy.

    📦 Package dynaml.probability.mcmc

    • 🐛 Bug-fixes to the HyperParameterMCMC class.

    General

    • DynaML now ships with Ammonite v1.0.0.

    🛠 Fixes

    📦 Package dynaml.optimization

    • Corrected energy calculation in CoupledSimulatedAnnealing; added
      🌲 log likelihood due to hyper-prior.

    📦 Package dynaml.optimization

    • Corrected energy calculation in CoupledSimulatedAnnealing; added
      🌲 log likelihood due to hyper-prior.