Updates.

Author: mandar2812

.dockerignore

@@ -5,7 +5,7 @@ import sbtbuildinfo.BuildInfoPlugin.autoImport._
 import org.scoverage.coveralls.Imports.CoverallsKeys._
 
 val mainVersion = "v2.0-SNAPSHOT"
-maintainer := "Mandar Chandorkar <tailhq@gmail.com>"
+maintainer := "Mandar Chandorkar <mandar2812@gmail.com>"
 packageSummary := "Scala Library/REPL for Machine Learning Research"
 packageDescription := "DynaML is a Scala & JVM Machine Learning toolbox for research, education & industry."
 

Dockerfile

@@ -0,0 +1,180 @@
+![3dplot](images/dynaml_logo3_small.png)
+
+# DynaML: ML + JVM + Scala
+
+
+[![Join the chat at https://gitter.im/DynaML/Lobby](https://badges.gitter.im/DynaML/Lobby.svg)](https://gitter.im/DynaML/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Build Status](https://travis-ci.org/transcendent-ai-labs/DynaML.svg?branch=master)](https://travis-ci.org/transcendent-ai-labs/DynaML) [![](https://jitpack.io/v/transcendent-ai-labs/DynaML.svg)](https://jitpack.io/#transcendent-ai-labs/DynaML)
+[![codecov](https://codecov.io/gh/transcendent-ai-labs/DynaML/branch/master/graph/badge.svg)](https://codecov.io/gh/transcendent-ai-labs/DynaML)
+[![status](http://joss.theoj.org/papers/a561bdd3e960c5b0718c67c3f73c6f3b/status.svg)](http://joss.theoj.org/papers/a561bdd3e960c5b0718c67c3f73c6f3b)
+
+------------------
+
+<br/>
+
+DynaML is a Scala & JVM Machine Learning toolbox for research, education & industry.
+
+<br/>
+
+<table>
+ <tr>
+  <th> <img src="images/plot3dsmall.jpeg" alt="Plot3d"> </th> 
+  <th> <img src="images/plots_small.png" alt="Plot2d"> </th>
+ </tr>
+</table>
+
+
+------------------
+
+## Motivation
+
+
+ - __Interactive.__ Don't want to create Maven/sbt project skeletons
+ every time you want to try out ideas? Create and execute [scala worksheets](https://github.com/transcendent-ai-labs/DynaML/blob/master/scripts/randomvariables.sc) 
+ in the DynaML shell. DynaML comes packaged with a customized version of the [Ammonite](http://ammonite.io) REPL, 
+ with *auto-complete*, file operations and scripting capabilities. 
+ 
+ - __End to End.__ Create complex pre-processing pipelines with the [data pipes](https://transcendent-ai-labs.github.io/DynaML/pipes/pipes/) API, 
+ train models ([deep nets](https://github.com/transcendent-ai-labs/DynaML/blob/master/scripts/cifar.sc), [gaussian processes](https://transcendent-ai-labs.github.io/DynaML/core/core_gp/), 
+ [linear models](https://transcendent-ai-labs.github.io/DynaML/core/core_glm/) and more), 
+ optimize over [hyper-parameters](https://transcendent-ai-labs.github.io/DynaML/core/core_opt_global/), 
+ [evaluate](https://transcendent-ai-labs.github.io/DynaML/core/core_model_evaluation/) model predictions and 
+ [visualise](https://transcendent-ai-labs.github.io/DynaML/core/core_graphics/) results.
+ 
+ - __Enterprise Friendly.__ Take advantage of the JVM and Scala ecosystem, use Apache [Spark](https://spark.apache.org) 
+ to write scalable data analysis jobs, [Tensorflow](http://tensorflow.org) for deep learning, all in the same toolbox.
+
+------------------
+
+## Getting Started
+
+### Platform Compatibility
+
+Currently, only *nix and OSX platforms are supported.
+
+DynaML is compatible with Scala `2.11`
+
+### Installation
+
+Easiest way to install DynaML is cloning & compiling from the [github](/) repository. Please take a look at 
+the [installation](https://transcendent-ai-labs.github.io/DynaML/installation/installation/) instructions, 
+to make sure that you have the pre-requisites and to configure your installation.
+
+------------------
+
+## CIFAR in 100 lines
+
+Below is a sample [script](https://github.com/transcendent-ai-labs/DynaML/blob/master/scripts/cifar.sc) where we train a neural network of stacked 
+[Inception](https://arxiv.org/pdf/1409.4842.pdf) cells on the [CIFAR-10](https://en.wikipedia.org/wiki/CIFAR-10)
+image classification task.
+
+```scala
+import ammonite.ops._
+import io.github.tailhq.dynaml.pipes.DataPipe
+import io.github.tailhq.dynaml.tensorflow.data.AbstractDataSet
+import io.github.tailhq.dynaml.tensorflow.{dtflearn, dtfutils}
+import io.github.tailhq.dynaml.tensorflow.implicits._
+import org.platanios.tensorflow.api._
+import org.platanios.tensorflow.api.learn.layers.Activation
+import org.platanios.tensorflow.data.image.CIFARLoader
+import java.nio.file.Paths
+
+
+val tempdir = home/"tmp"
+
+val dataSet = CIFARLoader.load(
+ Paths.get(tempdir.toString()), 
+ CIFARLoader.CIFAR_10)
+
+val tf_dataset = AbstractDataSet(
+ dataSet.trainImages, dataSet.trainLabels, dataSet.trainLabels.shape(0),
+ dataSet.testImages, dataSet.testLabels, dataSet.testLabels.shape(0))
+
+val trainData =
+ tf_dataset.training_data
+ .repeat()
+ .shuffle(10000)
+ .batch(128)
+ .prefetch(10)
+
+
+println("Building the model.")
+val input = tf.learn.Input(
+ UINT8, 
+ Shape(
+ -1, 
+ dataSet.trainImages.shape(1), 
+ dataSet.trainImages.shape(2), 
+ dataSet.trainImages.shape(3))
+)
+
+val trainInput = tf.learn.Input(UINT8, Shape(-1))
+
+val relu_act = DataPipe[String, Activation](tf.learn.ReLU(_))
+
+val architecture = tf.learn.Cast("Input/Cast", FLOAT32) >>
+ dtflearn.inception_unit(
+ channels = 3, Seq.fill(4)(10), 
+ relu_act)(layer_index = 1) >>
+ dtflearn.inception_unit(
+ channels = 40, Seq.fill(4)(5), 
+ relu_act)(layer_index = 2) >>
+ tf.learn.Flatten("Layer_3/Flatten") >>
+ dtflearn.feedforward(256)(id = 4) >>
+ tf.learn.ReLU("Layer_4/ReLU", 0.1f) >>
+ dtflearn.feedforward(10)(id = 5)
+
+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.Adam(0.1)
+
+val summariesDir = Paths.get((tempdir/"cifar_summaries").toString())
+
+val (model, estimator) = dtflearn.build_tf_model(
+ architecture, input, trainInput, trainingInputLayer,
+ loss, optimizer, summariesDir, dtflearn.max_iter_stop(500),
+ 100, 100, 100)(
+ trainData, true)
+
+def accuracy(predictions: Tensor, labels: Tensor): Float =
+ predictions.argmax(1)
+ .cast(UINT8)
+ .equal(labels)
+ .cast(FLOAT32)
+ .mean()
+ .scalar
+ .asInstanceOf[Float]
+
+val (trainingPreds, testPreds): (Option[Tensor], Option[Tensor]) =
+ dtfutils.predict_data[
+ Tensor, Output, DataType, Shape, Output,
+ Tensor, Output, DataType, Shape, Output,
+ Tensor, Tensor](
+ estimator,
+ data = tf_dataset,
+ pred_flags = (true, true),
+ buff_size = 20000)
+
+val (trainAccuracy, testAccuracy) = (
+ accuracy(trainingPreds.get, dataSet.trainLabels),
+ accuracy(testPreds.get, dataSet.testLabels))
+
+print("Train accuracy = ")
+pprint.pprintln(trainAccuracy)
+
+print("Test accuracy = ")
+pprint.pprintln(testAccuracy)
+```
+
+------------------
+
+
+## Support & Community
+
+ - [Gitter](https://gitter.im/DynaML/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+ - [Contributing](https://github.com/transcendent-ai-labs/DynaML/blob/master/CONTRIBUTING.md)
+ - [Code of Conduct](https://github.com/transcendent-ai-labs/DynaML/blob/master/CODE_OF_CONDUCT.md)