Using JS Object References in Blazor JS Wrapping

With .NET 5 Blazor WebAssembly also got a lot of great improvements. Among these are IJSObjectReferences (JavaScript Object References). These often get referenced in the changelogs and articles as JavaScript Isolation. The name indicates that this can only be used to isolate JavaScript sources similar to CSS Isolation. But IJSObjectReference can do so much more than just isolation. In this article, we will show what IJSObjectReference is and how it can be used. This article is part of our Blazor JS Wrapping series.

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 Using JS Object References in Blazor  JS Wrapping
Sasha  Lee

Sasha Lee

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Dl4clj: Clojure Wrapper for Deeplearning4j.

dl4clj

Port of deeplearning4j to clojure

Contact info

If you have any questions,

  • my email is will@yetanalytics.com
  • I'm will_hoyt in the clojurians slack
  • twitter is @FeLungz (don't check very often)

TODO

  • update examples dir
  • finish README
    • add in examples using Transfer Learning
  • finish tests
    • eval is missing regression tests, roc tests
    • nn-test is missing regression tests
    • spark tests need to be redone
    • need dl4clj.core tests
  • revist spark for updates
  • write specs for user facing functions
    • this is very important, match isnt strict for maps
    • provides 100% certianty of the input -> output flow
    • check the args as they come in, dispatch once I know its safe, test the pure output
  • collapse overlapping api namespaces
  • add to core use case flows

Features

Stable Features with tests

  • Neural Networks DSL
  • Early Stopping Training
  • Transfer Learning
  • Evaluation
  • Data import

Features being worked on for 0.1.0

  • Clustering (testing in progress)
  • Spark (currently being refactored)
  • Front End (maybe current release, maybe future release. Not sure yet)
  • Version of dl4j is 0.0.8 in this project. Current dl4j version is 0.0.9
  • Parallelism
  • Kafka support
  • Other items mentioned in TODO

Features being worked on for future releases

  • NLP
  • Computational Graphs
  • Reinforement Learning
  • Arbiter

Artifacts

NOT YET RELEASED TO CLOJARS

  • fork or clone to try it out

If using Maven add the following repository definition to your pom.xml:

<repository>
  <id>clojars.org</id>
  <url>http://clojars.org/repo</url>
</repository>

Latest release

With Leiningen:

n/a

With Maven:

n/a

<dependency>
  <groupId>_</groupId>
  <artifactId>_</artifactId>
  <version>_</version>
</dependency>

Usage

Things you need to know

All functions for creating dl4j objects return code by default

  • All of these functions have an option to return the dl4j object
    • :as-code? = false
  • This because all builders require the code representation of dl4j objects
    • this requirement is not going to change
  • INDarray creation fns default to objects, this is for convenience
    • :as-code? is still respected

API functions return code when all args are provided as code

API functions return the value of calling the wrapped method when args are provided as a mixture of objects and code or just objects

The tests are there to help clarify behavior, if you are unsure of how to use a fn, search the tests

  • for questions about spark, refer to the spark section bellow

Example of obj/code duality

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]))

;; as code (the default)

(l/dense-layer-builder
 :activation-fn :relu
 :learning-rate 0.006
 :weight-init :xavier
 :layer-name "example layer"
 :n-in 10
 :n-out 1)

;; =>

(doto
 (org.deeplearning4j.nn.conf.layers.DenseLayer$Builder.)
 (.nOut 1)
 (.activation (dl4clj.constants/value-of {:activation-fn :relu}))
 (.weightInit (dl4clj.constants/value-of {:weight-init :xavier}))
 (.nIn 10)
 (.name "example layer")
 (.learningRate 0.006))

;; as an object

(l/dense-layer-builder
 :activation-fn :relu
 :learning-rate 0.006
 :weight-init :xavier
 :layer-name "example layer"
 :n-in 10
 :n-out 1
 :as-code? false)

;; =>

#object[org.deeplearning4j.nn.conf.layers.DenseLayer 0x69d7d160 "DenseLayer(super=FeedForwardLayer(super=Layer(layerName=example layer, activationFn=relu, weightInit=XAVIER, biasInit=NaN, dist=null, learningRate=0.006, biasLearningRate=NaN, learningRateSchedule=null, momentum=NaN, momentumSchedule=null, l1=NaN, l2=NaN, l1Bias=NaN, l2Bias=NaN, dropOut=NaN, updater=null, rho=NaN, epsilon=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, gradientNormalization=null, gradientNormalizationThreshold=NaN), nIn=10, nOut=1))"]

General usage examples

Importing data

Loading data from a file (here its a csv)


(ns my.ns
 (:require [dl4clj.datasets.input-splits :as s]
           [dl4clj.datasets.record-readers :as rr]
           [dl4clj.datasets.api.record-readers :refer :all]
           [dl4clj.datasets.iterators :as ds-iter]
           [dl4clj.datasets.api.iterators :refer :all]
           [dl4clj.helpers :refer [data-from-iter]]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; file splits (convert the data to records)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def poker-path "resources/poker-hand-training.csv")
;; this is not a complete dataset, it is just here to sever as an example

(def file-split (s/new-filesplit :path poker-path))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers, (read the records created by the file split)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def csv-rr (initialize-rr! :rr (rr/new-csv-record-reader :skip-n-lines 0 :delimiter ",")
                                 :input-split file-split))

;; lets look at some data
(println (next-record! :rr csv-rr :as-code? false))
;; => #object[java.util.ArrayList 0x2473e02d [1, 10, 1, 11, 1, 13, 1, 12, 1, 1, 9]]
;; this is our first line from the csv


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers dataset iterators (turn our writables into a dataset)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
                 :record-reader csv-rr
                 :batch-size 1
                 :label-idx 10
                 :n-possible-labels 10))

;; we use our record reader created above
;; we want to see one example per dataset obj returned (:batch-size = 1)
;; we know our label is at the last index, so :label-idx = 10
;; there are 10 possible types of poker hands so :n-possible-labels = 10
;; you can also set :label-idx to -1 to use the last index no matter the size of the seq

(def other-rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
                       :record-reader csv-rr
                       :batch-size 1
                       :label-idx -1
                       :n-possible-labels 10))

(str (next-example! :iter rr-ds-iter :as-code? false))
;; =>
;;===========INPUT===================
;;[1.00, 10.00, 1.00, 11.00, 1.00, 13.00, 1.00, 12.00, 1.00, 1.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00]


;; and to show that :label-idx = -1 gives us the same output

(= (next-example! :iter rr-ds-iter :as-code? false)
   (next-example! :iter other-rr-ds-iter :as-code? false)) ;; => true

INDArrays and Datasets from clojure data structures


(ns my.ns
  (:require [nd4clj.linalg.factory.nd4j :refer [vec->indarray matrix->indarray
                                                indarray-of-zeros indarray-of-ones
                                                indarray-of-rand vec-or-matrix->indarray]]
            [dl4clj.datasets.new-datasets :refer [new-ds]]
            [dl4clj.datasets.api.datasets :refer [as-list]]
            [dl4clj.datasets.iterators :refer [new-existing-dataset-iterator]]
            [dl4clj.datasets.api.iterators :refer :all]
            [dl4clj.datasets.pre-processors :as ds-pp]
            [dl4clj.datasets.api.pre-processors :refer :all]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; INDArray creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;;TODO: consider defaulting to code

;; can create from a vector

(vec->indarray [1 2 3 4])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x269df212 [1.00, 2.00, 3.00, 4.00]]

;; or from a matrix

(matrix->indarray [[1 2 3 4] [2 4 6 8]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x20aa7fe1
;; [[1.00, 2.00, 3.00, 4.00], [2.00, 4.00, 6.00, 8.00]]]


;; will fill in spareness with zeros

(matrix->indarray [[1 2 3 4] [2 4 6 8] [10 12]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x8b7796c
;;[[1.00, 2.00, 3.00, 4.00],
;; [2.00, 4.00, 6.00, 8.00],
;; [10.00, 12.00, 0.00, 0.00]]]

;; can create an indarray of all zeros with specified shape
;; defaults to :rows = 1 :columns = 1

(indarray-of-zeros :rows 3 :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x6f586a7e
;;[[0.00, 0.00],
;; [0.00, 0.00],
;; [0.00, 0.00]]]

(indarray-of-zeros) ;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xe59ffec 0.00]

;; and if only one is supplied, will get a vector of specified length

(indarray-of-zeros :rows 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2899d974 [0.00, 0.00]]

(indarray-of-zeros :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xa5b9782 [0.00, 0.00]]

;; same considerations/defaults for indarray-of-ones and indarray-of-rand

(indarray-of-ones :rows 2 :columns 3)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x54f08662 [[1.00, 1.00, 1.00], [1.00, 1.00, 1.00]]]

(indarray-of-rand :rows 2 :columns 3)
;; all values are greater than 0 but less than 1
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2f20293b [[0.85, 0.86, 0.13], [0.94, 0.04, 0.36]]]



;; vec-or-matrix->indarray is built into all functions which require INDArrays
;; so that you can use clojure data structures
;; but you still have the option of passing existing INDArrays

(def example-array (vec-or-matrix->indarray [1 2 3 4]))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x5c44c71f [1.00, 2.00, 3.00, 4.00]]

(vec-or-matrix->indarray example-array)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x607b03b0 [1.00, 2.00, 3.00, 4.00]]

(vec-or-matrix->indarray (indarray-of-rand :rows 2))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x49143b08 [0.76, 0.92]]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def ds-with-single-example (new-ds :input [1 2 3 4]
                                    :output [0.0 1.0 0.0]))

(as-list :ds ds-with-single-example :as-code? false)
;; =>
;; #object[java.util.ArrayList 0x5d703d12
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00]]]

(def ds-with-multiple-examples (new-ds
                                :input [[1 2 3 4] [2 4 6 8]]
                                :output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))

(as-list :ds ds-with-multiple-examples :as-code? false)
;; =>
;;#object[java.util.ArrayList 0x29c7a9e2
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00],
;;===========INPUT===================
;;[2.00, 4.00, 6.00, 8.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 1.00]]]

;; we can create a dataset iterator from the code which creates datasets
;; and set the labels for our outputs (optional)

(def ds-with-multiple-examples
  (new-ds
   :input [[1 2 3 4] [2 4 6 8]]
   :output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))

;; iterator
(def training-rr-ds-iter
  (new-existing-dataset-iterator
   :dataset ds-with-multiple-examples
   :labels ["foo" "baz" "foobaz"]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set normalization
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; this gathers statistics on the dataset and normalizes the data
;; and applies the transformation to all dataset objects in the iterator
(def train-iter-normalized
  (c/normalize-iter! :iter training-rr-ds-iter
                     :normalizer (ds-pp/new-standardize-normalization-ds-preprocessor)
                     :as-code? false))

;; above returns the normalized iterator
;; to get fit normalizer

(def the-normalizer
  (get-pre-processor train-iter-normalized))

Model configuration

Creating a neural network configuration with singe and multiple layers

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.conf.distributions :as dist]
            [dl4clj.nn.conf.input-pre-processor :as pp]
            [dl4clj.nn.conf.step-fns :as s-fn]))

;; nn/builder has 3 types of args
;; 1) args which set network configuration params
;; 2) args which set default values for layers
;; 3) args which set multi layer network configuration params

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; single layer nn configuration
;; here we are setting network configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(nn/builder :optimization-algo :stochastic-gradient-descent
            :seed 123
            :iterations 1
            :minimize? true
            :use-drop-connect? false
            :lr-score-based-decay-rate 0.002
            :regularization? false
            :step-fn :default-step-fn
            :layers {:dense-layer {:activation-fn :relu
                                   :updater :adam
                                   :adam-mean-decay 0.2
                                   :adam-var-decay 0.1
                                   :learning-rate 0.006
                                   :weight-init :xavier
                                   :layer-name "single layer model example"
                                   :n-in 10
                                   :n-out 20}})

;; there are several options within a nn-conf map which can be configuration maps
;; or calls to fns
;; It doesn't matter which option you choose and you don't have to stay consistent
;; the list of params which can be passed as config maps or fn calls will
;; be enumerated at a later date

(nn/builder :optimization-algo :stochastic-gradient-descent
            :seed 123
            :iterations 1
            :minimize? true
            :use-drop-connect? false
            :lr-score-based-decay-rate 0.002
            :regularization? false
            :step-fn (s-fn/new-default-step-fn)
            :build? true
            ;; dont need to specify layer order, theres only one
            :layers (l/dense-layer-builder
                    :activation-fn :relu
                    :updater :adam
                    :adam-mean-decay 0.2
                    :adam-var-decay 0.1
                    :dist (dist/new-normal-distribution :mean 0 :std 1)
                    :learning-rate 0.006
                    :weight-init :xavier
                    :layer-name "single layer model example"
                    :n-in 10
                    :n-out 20))

;; these configurations are the same

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; multi-layer configuration
;; here we are also setting layer defaults
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; defaults will apply to layers which do not specify those value in their config

(nn/builder
 :optimization-algo :stochastic-gradient-descent
 :seed 123
 :iterations 1
 :minimize? true
 :use-drop-connect? false
 :lr-score-based-decay-rate 0.002
 :regularization? false
 :default-activation-fn :sigmoid
 :default-weight-init :uniform

 ;; we need to specify the layer order
 :layers {0 (l/activation-layer-builder
             :activation-fn :relu
             :updater :adam
             :adam-mean-decay 0.2
             :adam-var-decay 0.1
             :learning-rate 0.006
             :weight-init :xavier
             :layer-name "example first layer"
             :n-in 10
             :n-out 20)
          1 {:output-layer {:n-in 20
                            :n-out 2
                            :loss-fn :mse
                            :layer-name "example output layer"}}})

;; specifying multi-layer config params

(nn/builder
 ;; network args
 :optimization-algo :stochastic-gradient-descent
 :seed 123
 :iterations 1
 :minimize? true
 :use-drop-connect? false
 :lr-score-based-decay-rate 0.002
 :regularization? false

 ;; layer defaults
 :default-activation-fn :sigmoid
 :default-weight-init :uniform

 ;; the layers
 :layers {0 (l/activation-layer-builder
             :activation-fn :relu
             :updater :adam
             :adam-mean-decay 0.2
             :adam-var-decay 0.1
             :learning-rate 0.006
             :weight-init :xavier
             :layer-name "example first layer"
             :n-in 10
             :n-out 20)
          1 {:output-layer {:n-in 20
                            :n-out 2
                            :loss-fn :mse
                            :layer-name "example output layer"}}}
 ;; multi layer network args
 :backprop? true
 :backprop-type :standard
 :pretrain? false
 :input-pre-processors {0 (pp/new-zero-mean-pre-pre-processor)
                        1 {:unit-variance-processor {}}})

Configuration to Trained models

Multi Layer models

(ns my.ns
  (:require [dl4clj.datasets.iterators :as iter]
            [dl4clj.datasets.input-splits :as split]
            [dl4clj.datasets.record-readers :as rr]
            [dl4clj.optimize.listeners :as listener]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.multilayer.multi-layer-network :as mln]
            [dl4clj.nn.api.model :refer [init! set-listeners!]]
            [dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
            [dl4clj.datasets.api.record-readers :refer [initialize-rr!]]
            [dl4clj.eval.api.eval :refer [get-stats get-accuracy]]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; nn-conf -> multi-layer-network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123 :iterations 1 :regularization? true

   ;; setting layer defaults
   :default-activation-fn :relu :default-l2 7.5e-6
   :default-weight-init :xavier :default-learning-rate 0.0015
   :default-updater :nesterovs :default-momentum 0.98

   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}

   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def multi-layer-network (c/model-from-conf nn-conf))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; local cpu training with dl4j pre-built iterators
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; lets use the pre-built Mnist data set iterator

(def train-mnist-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-mnist-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

;; and lets set a listener so we can know how training is going

(def score-listener (listener/new-score-iteration-listener :print-every-n 5))

;; and attach it to our model

;; TODO: listeners are broken, look into log4j warnning
(def mln-with-listener (set-listeners! :model multi-layer-network
                                       :listeners [score-listener]))

(def trained-mln (mln/train-mln-with-ds-iter! :mln mln-with-listener
                                              :iter train-mnist-iter
                                              :n-epochs 15
                                              :as-code? false))

;; training happens because :as-code? = false
;; if it was true, we would still just have a data structure
;; we now have a trained model that has seen the training dataset 15 times
;; time to evaluate our model

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;Create an evaluation object
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def eval-obj (evaluate-classification :mln trained-mln
                                       :iter test-mnist-iter))

;; always remember that these objects are stateful, dont use the same eval-obj
;; to eval two different networks
;; we trained the model on a training dataset.  We evaluate on a test set

(println (get-stats :evaler eval-obj))
;; this will print the stats to standard out for each feature/label pair

;;Examples labeled as 0 classified by model as 0: 968 times
;;Examples labeled as 0 classified by model as 1: 1 times
;;Examples labeled as 0 classified by model as 2: 1 times
;;Examples labeled as 0 classified by model as 3: 1 times
;;Examples labeled as 0 classified by model as 5: 1 times
;;Examples labeled as 0 classified by model as 6: 3 times
;;Examples labeled as 0 classified by model as 7: 1 times
;;Examples labeled as 0 classified by model as 8: 2 times
;;Examples labeled as 0 classified by model as 9: 2 times
;;Examples labeled as 1 classified by model as 1: 1126 times
;;Examples labeled as 1 classified by model as 2: 2 times
;;Examples labeled as 1 classified by model as 3: 1 times
;;Examples labeled as 1 classified by model as 5: 1 times
;;Examples labeled as 1 classified by model as 6: 2 times
;;Examples labeled as 1 classified by model as 7: 1 times
;;Examples labeled as 1 classified by model as 8: 2 times
;;Examples labeled as 2 classified by model as 0: 3 times
;;Examples labeled as 2 classified by model as 1: 2 times
;;Examples labeled as 2 classified by model as 2: 1006 times
;;Examples labeled as 2 classified by model as 3: 2 times
;;Examples labeled as 2 classified by model as 4: 3 times
;;Examples labeled as 2 classified by model as 6: 3 times
;;Examples labeled as 2 classified by model as 7: 7 times
;;Examples labeled as 2 classified by model as 8: 6 times
;;Examples labeled as 3 classified by model as 2: 4 times
;;Examples labeled as 3 classified by model as 3: 990 times
;;Examples labeled as 3 classified by model as 5: 3 times
;;Examples labeled as 3 classified by model as 7: 3 times
;;Examples labeled as 3 classified by model as 8: 3 times
;;Examples labeled as 3 classified by model as 9: 7 times
;;Examples labeled as 4 classified by model as 2: 2 times
;;Examples labeled as 4 classified by model as 3: 1 times
;;Examples labeled as 4 classified by model as 4: 967 times
;;Examples labeled as 4 classified by model as 6: 4 times
;;Examples labeled as 4 classified by model as 7: 1 times
;;Examples labeled as 4 classified by model as 9: 7 times
;;Examples labeled as 5 classified by model as 0: 2 times
;;Examples labeled as 5 classified by model as 3: 6 times
;;Examples labeled as 5 classified by model as 4: 1 times
;;Examples labeled as 5 classified by model as 5: 874 times
;;Examples labeled as 5 classified by model as 6: 3 times
;;Examples labeled as 5 classified by model as 7: 1 times
;;Examples labeled as 5 classified by model as 8: 3 times
;;Examples labeled as 5 classified by model as 9: 2 times
;;Examples labeled as 6 classified by model as 0: 4 times
;;Examples labeled as 6 classified by model as 1: 3 times
;;Examples labeled as 6 classified by model as 3: 2 times
;;Examples labeled as 6 classified by model as 4: 4 times
;;Examples labeled as 6 classified by model as 5: 4 times
;;Examples labeled as 6 classified by model as 6: 939 times
;;Examples labeled as 6 classified by model as 7: 1 times
;;Examples labeled as 6 classified by model as 8: 1 times
;;Examples labeled as 7 classified by model as 1: 7 times
;;Examples labeled as 7 classified by model as 2: 4 times
;;Examples labeled as 7 classified by model as 3: 3 times
;;Examples labeled as 7 classified by model as 7: 1005 times
;;Examples labeled as 7 classified by model as 8: 2 times
;;Examples labeled as 7 classified by model as 9: 7 times
;;Examples labeled as 8 classified by model as 0: 3 times
;;Examples labeled as 8 classified by model as 2: 3 times
;;Examples labeled as 8 classified by model as 3: 2 times
;;Examples labeled as 8 classified by model as 4: 4 times
;;Examples labeled as 8 classified by model as 5: 3 times
;;Examples labeled as 8 classified by model as 6: 2 times
;;Examples labeled as 8 classified by model as 7: 4 times
;;Examples labeled as 8 classified by model as 8: 947 times
;;Examples labeled as 8 classified by model as 9: 6 times
;;Examples labeled as 9 classified by model as 0: 2 times
;;Examples labeled as 9 classified by model as 1: 2 times
;;Examples labeled as 9 classified by model as 3: 4 times
;;Examples labeled as 9 classified by model as 4: 8 times
;;Examples labeled as 9 classified by model as 6: 1 times
;;Examples labeled as 9 classified by model as 7: 4 times
;;Examples labeled as 9 classified by model as 8: 2 times
;;Examples labeled as 9 classified by model as 9: 986 times

;;==========================Scores========================================
;; Accuracy:        0.9808
;; Precision:       0.9808
;; Recall:          0.9807
;; F1 Score:        0.9807
;;========================================================================

;; can get the stats that are printed via fns in the evaluation namespace
;; after running eval-model-whole-ds

(get-accuracy :evaler evaler-with-stats) ;; => 0.9808

Model Tuning

Early Stopping (controlling training)

it is recommened you start here when designing models

using dl4clj.core


(ns my.ns
  (:require [dl4clj.earlystopping.termination-conditions :refer :all]
            [dl4clj.earlystopping.model-saver :refer [new-in-memory-saver]]
            [dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :as iter]
            [dl4clj.core :as c]))

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123
   :iterations 1
   :regularization? true

   ;; setting layer defaults
   :default-activation-fn :relu
   :default-l2 7.5e-6
   :default-weight-init :xavier
   :default-learning-rate 0.0015
   :default-updater :nesterovs
   :default-momentum 0.98

   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}

   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def train-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

(def invalid-score-condition (new-invalid-score-iteration-termination-condition))

(def max-score-condition (new-max-score-iteration-termination-condition
                          :max-score 20.0))

(def max-time-condition (new-max-time-iteration-termination-condition
                         :max-time-val 10
                         :max-time-unit :minutes))

(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
                                     :max-n-epoch-no-improve 5))

(def target-score-condition (new-best-score-epoch-termination-condition
                             :best-expected-score 0.009))

(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))

(def in-mem-saver (new-in-memory-saver))

(def trained-mln
;; defaults to returning the model
  (c/train-with-early-stopping
   :nn-conf nn-conf
   :training-iter train-mnist-iter
   :testing-iter test-mnist-iter
   :eval-every-n-epochs 1
   :iteration-termination-conditions [invalid-score-condition
                                      max-score-condition
                                      max-time-condition]
   :epoch-termination-conditions [score-doesnt-improve-condition
                                  target-score-condition
                                  max-number-epochs-condition]
   :save-last-model? true
   :model-saver in-mem-saver
   :as-code? false))

(def model-evaler
  (evaluate-classification :mln trained-mln :iter test-mnist-iter))

(println (get-stats :evaler model-evaler))
  • explicit, step by step way of doing this
(ns my.ns
  (:require [dl4clj.earlystopping.early-stopping-config :refer [new-early-stopping-config]]
            [dl4clj.earlystopping.termination-conditions :refer :all]
            [dl4clj.earlystopping.model-saver :refer [new-in-memory-saver new-local-file-model-saver]]
            [dl4clj.earlystopping.score-calc :refer [new-ds-loss-calculator]]
            [dl4clj.earlystopping.early-stopping-trainer :refer [new-early-stopping-trainer]]
            [dl4clj.earlystopping.api.early-stopping-trainer :refer [fit-trainer!]]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.multilayer.multi-layer-network :as mln]
            [dl4clj.utils :refer [load-model!]]
            [dl4clj.datasets.iterators :as iter]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; start with our network config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123 :iterations 1 :regularization? true
   ;; setting layer defaults
   :default-activation-fn :relu :default-l2 7.5e-6
   :default-weight-init :xavier :default-learning-rate 0.0015
   :default-updater :nesterovs :default-momentum 0.98
   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}
   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def mln (c/model-from-conf nn-conf))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; the training/testing data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def train-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we are going to need termination conditions
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; these allow us to control when we exit training

;; this can be based off of iterations or epochs

;; iteration termination conditions

(def invalid-score-condition (new-invalid-score-iteration-termination-condition))

(def max-score-condition (new-max-score-iteration-termination-condition
                          :max-score 20.0))

(def max-time-condition (new-max-time-iteration-termination-condition
                         :max-time-val 10
                         :max-time-unit :minutes))

;; epoch termination conditions

(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
                                     :max-n-epoch-no-improve 5))

(def target-score-condition (new-best-score-epoch-termination-condition :best-expected-score 0.009))

(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we also need a way to save our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; can be in memory or to a local directory

(def in-mem-saver (new-in-memory-saver))

(def local-file-saver (new-local-file-model-saver :directory "resources/tmp/readme/"))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; set up your score calculator
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def score-calcer (new-ds-loss-calculator :iter test-iter
                                          :average? true))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; termination conditions
;; a way to save our model
;; a way to calculate the score of our model on the dataset

(def early-stopping-conf
  (new-early-stopping-config
   :epoch-termination-conditions [score-doesnt-improve-condition
                                  target-score-condition
                                  max-number-epochs-condition]
   :iteration-termination-conditions [invalid-score-condition
                                      max-score-condition
                                      max-time-condition]
   :eval-every-n-epochs 5
   :model-saver local-file-saver
   :save-last-model? true
   :score-calculator score-calcer))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping trainer from our data, model and early stopping conf
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def es-trainer (new-early-stopping-trainer :early-stopping-conf early-stopping-conf
                                            :mln mln
                                            :iter train-iter))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; fit and use our early stopping trainer
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def es-trainer-fitted (fit-trainer! es-trainer :as-code? false))

;; when the trainer terminates, you will see something like this
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO  Completed training epoch 14
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO  New best model: score = 0.005225599372851298,
;;                                                   epoch = 14 (previous: score = 0.018243224899038346, epoch = 7)
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Hit epoch termination condition at epoch 14.
;;                                           Details: BestScoreEpochTerminationCondition(0.009)

;; and if we look at the es-trainer-fitted object we see

;;#object[org.deeplearning4j.earlystopping.EarlyStoppingResult 0x5ab74f27 EarlyStoppingResult
;;(terminationReason=EpochTerminationCondition,details=BestScoreEpochTerminationCondition(0.009),
;; bestModelEpoch=14,bestModelScore=0.005225599372851298,totalEpochs=15)]

;; and our model has been saved to /resources/tmp/readme/bestModel.bin
;; there we have our model config, model params and our updater state

;; we can then load this model to use it or continue refining it

(def loaded-model (load-model! :path "resources/tmp/readme/bestModel.bin"
                               :load-updater? true))

Transfer Learning (freezing layers)


;; TODO: need to write up examples

Spark Training

dl4j Spark usage

How it is done in dl4clj

  • Uses dl4clj.core
    • This example uses a fn which takes care of most steps for you
      • allows you to pass args as code bc the fn accounts for the multiple spark contexts issue encountered when everything is just a data structure

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.spark.masters.param-avg :as master]
            [dl4clj.spark.data.java-rdd :refer [new-java-spark-context
                                                java-rdd-from-iter]]
            [dl4clj.spark.api.dl4j-multi-layer :refer [eval-classification-spark-mln
                                                       get-spark-context]]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def mln-conf
  (nn/builder
   :optimization-algo :stochastic-gradient-descent
   :default-learning-rate 0.006
   :layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
            1 {:output-layer
               {:loss-fn :negativeloglikelihood
                :n-in 2 :n-out 3
                :activation-fn :soft-max
                :weight-init :xavier}}}
   :backprop? true
   :backprop-type :standard))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def training-master
  (master/new-parameter-averaging-training-master
   :build? true
   :rdd-n-examples 10
   :n-workers 4
   :averaging-freq 10
   :batch-size-per-worker 2
   :export-dir "resources/spark/master/"
   :rdd-training-approach :direct
   :repartition-data :always
   :repartition-strategy :balanced
   :seed 1234
   :save-updater? true
   :storage-level :none))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, spark context
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def your-spark-context
  (new-java-spark-context :app-name "example app"))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, training data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def iris-iter
  (new-iris-data-set-iterator
   :batch-size 1
   :n-examples 5))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, spark mln
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def fitted-spark-mln
  (c/train-with-spark :spark-context your-spark-context
                      :mln-conf mln-conf
                      :training-master training-master
                      :iter iris-iter
                      :n-epochs 1
                      :as-code? false))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, use spark context from spark-mln to create rdd
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; TODO: eliminate this step

(def our-rdd
  (let [sc (get-spark-context fitted-spark-mln :as-code? false)]
    (java-rdd-from-iter :spark-context sc
                        :iter iris-iter)))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 6, evaluation model and print stats (poor performance of model expected)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def eval-obj
  (eval-classification-spark-mln
   :spark-mln fitted-spark-mln
   :rdd our-rdd))

(println (get-stats :evaler eval-obj))

  • this example demonstrates the dl4j workflow
    • NOTE: unlike the previous example, this one requires dl4j objects to be used
      • this is becaues spark only wants you to have one spark context at a time
(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.spark.masters.param-avg :as master]
            [dl4clj.spark.data.java-rdd :refer [new-java-spark-context java-rdd-from-iter]]
            [dl4clj.spark.dl4j-multi-layer :as spark-mln]
            [dl4clj.spark.api.dl4j-multi-layer :refer [fit-spark-mln!
                                                       eval-classification-spark-mln]]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def mln-conf
  (nn/builder
   :optimization-algo :stochastic-gradient-descent
   :default-learning-rate 0.006
   :layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
            1 {:output-layer
               {:loss-fn :negativeloglikelihood
                :n-in 2 :n-out 3
                :activation-fn :soft-max
                :weight-init :xavier}}}
   :backprop? true
   :as-code? false
   :backprop-type :standard))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, create a training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; not all options specified, but most are

(def training-master
  (master/new-parameter-averaging-training-master
   :build? true
   :rdd-n-examples 10
   :n-workers 4
   :averaging-freq 10
   :batch-size-per-worker 2
   :export-dir "resources/spark/master/"
   :rdd-training-approach :direct
   :repartition-data :always
   :repartition-strategy :balanced
   :seed 1234
   :as-code? false
   :save-updater? true
   :storage-level :none))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, create a Spark Multi Layer Network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def your-spark-context
  (new-java-spark-context :app-name "example app" :as-code? false))

;; new-java-spark-context will turn an existing spark-configuration into a java spark context
;; or create a new java spark context with master set to "local[*]" and the app name
;; set to :app-name


(def spark-mln
  (spark-mln/new-spark-multi-layer-network
   :spark-context your-spark-context
   :mln mln-conf
   :training-master training-master
   :as-code? false))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, load your data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; one way is via a dataset-iterator
;; can make one directly from a dataset (iterator data-set)
;; see: nd4clj.linalg.dataset.api.data-set and nd4clj.linalg.dataset.data-set
;; we are going to use a pre-built one

(def iris-iter
  (new-iris-data-set-iterator
   :batch-size 1
   :n-examples 5
   :as-code? false))

;; now lets convert the data into a javaRDD

(def our-rdd
  (java-rdd-from-iter :spark-context your-spark-context
                      :iter iris-iter))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, fit and evaluate the model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def fitted-spark-mln
  (fit-spark-mln!
   :spark-mln spark-mln
   :rdd our-rdd
   :n-epochs 1))
;; this fn also has the option to supply :path-to-data instead of :rdd
;; that path should point to a directory containing a number of dataset objects

(def eval-obj
  (eval-classification-spark-mln
   :spark-mln fitted-spark-mln
   :rdd our-rdd))
;; we would want to have different testing and training rdd's but here we are using
;; the data we trained on

;; lets get the stats for how our model performed

(println (get-stats :evaler eval-obj))

Terminology

Coming soon

Packages to come back to:

Implement ComputationGraphs and the classes which use them

NLP

Parallelism

TSNE

UI


Author: yetanalytics
Source Code: https://github.com/yetanalytics/dl4clj
License: BSD-2-Clause License

#machine-learning #deep-learning 

Arvel  Parker

Arvel Parker

1591611780

How to Find Ulimit For user on Linux

How can I find the correct ulimit values for a user account or process on Linux systems?

For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS

We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II)
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]

Archie  Powell

Archie Powell

1658181600

Tensorflex: Tensorflow Bindings for The Elixir Programming Language

Tensorflex

The paper detailing Tensorflex was presented at NeurIPS/NIPS 2018 as part of the MLOSS workshop. The paper can be found here

Contents

How to run

  • You need to have the Tensorflow C API installed. Look here for details.
  • You also need the C library libjpeg. If you are using Linux or OSX, it should already be present on your machine, otherwise be sure to install (brew install libjpeg for OSX, and sudo apt-get install libjpeg-dev for Ubuntu).
  • Simply add Tensorflex to your list of dependencies in mix.exs and you are good to go!:
{:tensorflex, "~> 0.1.2"}

In case you want the latest development version use this:

{:tensorflex, github: "anshuman23/tensorflex"}

Documentation

Tensorflex contains three main structs which handle different datatypes. These are %Graph, %Matrix and %Tensor. %Graph type structs handle pre-trained graph models, %Matrix handles Tensorflex 2-D matrices, and %Tensor handles Tensorflow Tensor types. The official Tensorflow documentation is present here and do note that this README only briefly discusses Tensorflex functionalities.

read_graph/1:

Used for loading a Tensorflow .pb graph model in Tensorflex.

Reads in a pre-trained Tensorflow protobuf (.pb) Graph model binary file.

Returns a tuple {:ok, %Graph}.

%Graph is an internal Tensorflex struct which holds the name of the graph file and the binary definition data that is read in via the .pb file.

get_graph_ops/1:

Used for listing all the operations in a Tensorflow .pb graph.

Reads in a Tensorflex %Graph struct obtained from read_graph/1.

Returns a list of all the operation names (as strings) that populate the graph model.

create_matrix/3:

Creates a 2-D Tensorflex matrix from custom input specifications.

Takes three input arguments: number of rows in matrix (nrows), number of columns in matrix (ncols), and a list of lists of the data that will form the matrix (datalist).

Returns a %Matrix Tensorflex struct type.

matrix_pos/3:

Used for accessing an element of a Tensorflex matrix.

Takes in three input arguments: a Tensorflex %Matrix struct matrix, and the row (row) and column (col) values of the required element in the matrix. Both row and col here are NOT zero indexed.

Returns the value as float.

size_of_matrix/1:

Used for obtaining the size of a Tensorflex matrix.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {nrows, ncols} where nrows represents the number of rows of the matrix and ncols represents the number of columns of the matrix.

append_to_matrix/2:

Appends a single row to the back of a Tensorflex matrix.

Takes a Tensorflex %Matrix matrix as input and a single row of data (with the same number of columns as the original matrix) as a list of lists (datalist) to append to the original matrix.

Returns the extended and modified %Matrix struct matrix.

matrix_to_lists/1:

Converts a Tensorflex matrix (back) to a list of lists format.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a list of lists representing the data stored in the matrix.

NOTE: If the matrix contains very high dimensional data, typically obtained from a function like load_csv_as_matrix/2, then it is not recommended to convert the matrix back to a list of lists format due to a possibility of memory errors.

float64_tensor/2, float32_tensor/2, int32_tensor/2:

Creates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor from Tensorflex matrices containing the values and dimensions specified.

Takes two arguments: a %Matrix matrix (matrix1) containing the values the tensor should have and another %Matrix matrix (matrix2) containing the dimensions of the required tensor.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor/1, float32_tensor/1, int32_tensor/1, string_tensor/1:

Creates a TF_DOUBLE, TF_FLOAT, TF_INT32, or TF_STRING constant value one-dimensional tensor from the input value specified.

Takes in a float, int or string value (depending on function) as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor_alloc/1, float32_tensor_alloc/1, int32_tensor_alloc/1:

Allocates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor of specified dimensions.

This function is generally used to allocate output tensors that do not hold any value data yet, but will after the session is run for Inference. Output tensors of the required dimensions are allocated and then passed to the run_session/5 function to hold the output values generated as predictions.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the potential tensor data and type.

tensor_datatype/1:

Used to get the datatype of a created tensor.

Takes in a %Tensor struct tensor as input.

Returns a tuple {:ok, datatype} where datatype is an atom representing the list of Tensorflow TF_DataType tensor datatypes. Click here to view a list of all possible datatypes.

load_image_as_tensor/1:

Loads JPEG images into Tensorflex directly as a TF_UINT8 tensor of dimensions image height x image width x number of color channels.

This function is very useful if you wish to do image classification using Convolutional Neural Networks, or other Deep Learning Models. One of the most widely adopted and robust image classification models is the Inception model by Google. It makes classifications on images from over a 1000 classes with highly accurate results. The load_image_as_tensor/1 function is an essential component for the prediction pipeline of the Inception model (and for other similar image classification models) to work in Tensorflex.

Reads in the path to a JPEG image file (.jpg or .jpeg).

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the tensor data and type. Here the created Tensor is a uint8 tensor (TF_UINT8).

NOTE: For now, only 3 channel RGB JPEG color images can be passed as arguments. Support for grayscale images and other image formats such as PNG will be added in the future.

loads_csv_as_matrix/2:

Loads high-dimensional data from a CSV file as a Tensorflex 2-D matrix in a super-fast manner.

The load_csv_as_matrix/2 function is very fast-- when compared with the Python based pandas library for data science and analysis' function read_csv on the test.csv file from MNIST Kaggle data (source), the following execution times were obtained:

  • read_csv: 2.549233 seconds
  • load_csv_as_matrix/2: 1.711494 seconds

This function takes in 2 arguments: a path to a valid CSV file (filepath) and other optional arguments opts. These include whether or not a header needs to be discarded in the CSV, and what the delimiter type is. These are specified by passing in an atom :true or :false to the header: key, and setting a string value for the delimiter: key. By default, the header is considered to be present (:true) and the delimiter is set to ,.

Returns a %Matrix Tensorflex struct type.

run_session/5:

Runs a Tensorflow session to generate predictions for a given graph, input data, and required input/output operations.

This function is the final step of the Inference (prediction) pipeline and generates output for a given set of input data, a pre-trained graph model, and the specified input and output operations of the graph.

Takes in five arguments: a pre-trained Tensorflow graph .pb model read in from the read_graph/1 function (graph), an input tensor with the dimensions and data required for the input operation of the graph to run (tensor1), an output tensor allocated with the right dimensions (tensor2), the name of the input operation of the graph that needs where the input data is fed (input_opname), and the output operation name in the graph where the outputs are obtained (output_opname). The input tensor is generally created from the matrices manually or using the load_csv_as_matrix/2 function, and then passed through to one of the tensor creation functions. For image classification the load_image_as_tensor/1 can also be used to create the input tensor from an image. The output tensor is created using the tensor allocation functions (generally containing alloc at the end of the function name).

Returns a List of Lists (similar to the matrix_to_lists/1 function) containing the generated predictions as per the output tensor dimensions.

add_scalar_to_matrix/2:

Adds scalar value to matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

subtract_scalar_from_matrix/2:

Subtracts scalar value from matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

multiply_matrix_with_scalar/2:

Multiplies scalar value with matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

divide_matrix_by_scalar/2:

Divides matrix values by scalar.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

add_matrices/2:

Adds two matrices of same dimensions together.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

subtract_matrices/2:

Subtracts matrix2 from matrix1.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

tensor_to_matrix/1:

Converts the data stored in a 2-D tensor back to a 2-D matrix.

Takes in a single argument as a %Tensor tensor (any TF_Datatype).

Returns a %Matrix 2-D matrix.

NOTE: Tensorflex doesn't currently support 3-D matrices, and therefore tensors that are 3-D (such as created using the load_image_as_tensor/1 function) cannot be converted back to a matrix, yet. Support for 3-D matrices will be added soon.

Examples

Examples are generally added in full description on my blog here. A blog post covering how to do classification on the Iris Dataset is present here.


INCEPTION CNN MODEL EXAMPLE:

Here we will briefly touch upon how to use the Google V3 Inception pre-trained graph model to do image classficiation from over a 1000 classes. First, the Inception V3 model can be downloaded here: http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz

After unzipping, see that it contains the graphdef .pb file (classify_image_graphdef.pb) which contains our graph definition, a test jpeg image that should identify/classify as a panda (cropped_panda.pb) and a few other files I will detail later.

Now for running this in Tensorflex first the graph is loaded:

iex(1)> {:ok, graph} = Tensorflex.read_graph("classify_image_graph_def.pb")
2018-07-29 00:48:19.849870: W tensorflow/core/framework/op_def_util.cc:346] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
{:ok,
 %Tensorflex.Graph{
   def: #Reference<0.2597534446.2498625538.211058>,
   name: "classify_image_graph_def.pb"
 }}

Then the cropped_panda image is loaded using the new load_image_as_tensor function:

iex(2)> {:ok, input_tensor} = Tensorflex.load_image_as_tensor("cropped_panda.jpg")
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_uint8,
   tensor: #Reference<0.2597534446.2498625538.211093>
 }}

Then create the output tensor which will hold out output vector values. For the inception model, the output is received as a 1008x1 tensor, as there are 1008 classes in the model:

iex(3)> out_dims = Tensorflex.create_matrix(1,2,[[1008,1]])
%Tensorflex.Matrix{
  data: #Reference<0.2597534446.2498625538.211103>,
  ncols: 2,
  nrows: 1
}

iex(4)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(out_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.2597534446.2498625538.211116>
 }}

Then the output results are read into a list called results. Also, the input operation in the Inception model is DecodeJpeg and the output operation is softmax:

iex(5)> results = Tensorflex.run_session(graph, input_tensor, output_tensor, "DecodeJpeg", "softmax")
2018-07-29 00:51:13.631154: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
[
  [1.059142014128156e-4, 2.8240500250831246e-4, 8.30648496048525e-5,
   1.2982363114133477e-4, 7.32232874725014e-5, 8.014426566660404e-5,
   6.63459359202534e-5, 0.003170756157487631, 7.931600703159347e-5,
   3.707312498590909e-5, 3.0997329304227605e-5, 1.4232713147066534e-4,
   1.0381334868725389e-4, 1.1057958181481808e-4, 1.4321311027742922e-4,
   1.203602587338537e-4, 1.3130248407833278e-4, 5.850398520124145e-5,
   2.641105093061924e-4, 3.1629020668333396e-5, 3.906813799403608e-5,
   2.8646905775531195e-5, 2.2863158665131778e-4, 1.2222197256051004e-4,
   5.956588938715868e-5, 5.421260357252322e-5, 5.996063555357978e-5,
   4.867801326327026e-4, 1.1005574924638495e-4, 2.3433618480339646e-4,
   1.3062104699201882e-4, 1.317620772169903e-4, 9.388553007738665e-5,
   7.076268957462162e-5, 4.281177825760096e-5, 1.6863139171618968e-4,
   9.093972039408982e-5, 2.611844101920724e-4, 2.7584232157096267e-4,
   5.157176201464608e-5, 2.144951868103817e-4, 1.3628098531626165e-4,
   8.007588621694595e-5, 1.7929042223840952e-4, 2.2831936075817794e-4,
   6.216531619429588e-5, 3.736453436431475e-5, 6.782123091397807e-5,
   1.1538144462974742e-4, ...]
]

Finally, we need to find which class has the maximum probability and identify it's label. Since results is a List of Lists, it's better to read in the nested list. Then we need to find the index of the element in the new list which as the maximum value. Therefore:

iex(6)> max_prob = List.flatten(results) |> Enum.max
0.8849328756332397

iex(7)> Enum.find_index(results |> List.flatten, fn(x) -> x == max_prob end)
169

We can thus see that the class with the maximum probability predicted (0.8849328756332397) for the image is 169. We will now find what the 169 label corresponds to. For this we can look back into the unzipped Inception folder, where there is a file called imagenet_2012_challenge_label_map_proto.pbtxt. On opening this file, we can find the string class identifier for the 169 class index. This is n02510455 and is present on Line 1556 in the file. Finally, we need to match this string identifier to a set of identification labels by referring to the file imagenet_synset_to_human_label_map.txt file. Here we can see that corresponding to the string class n02510455 the human labels are giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (Line 3691 in the file).

Thus, we have correctly identified the animal in the image as a panda using Tensorflex!


RNN LSTM SENTIMENT ANALYSIS MODEL EXAMPLE:

A brief idea of what this example entails:

  • The Recurrent Neural Network utilizes Long-Short-Term-Memory (LSTM) cells for holding the state for the data flowing in through the network
  • In this example, we utilize the LSTM network for sentiment analysis on movie reviews data in Tensorflex. The trained models are originally created as part of an online tutorial (source) and are present in a Github repository here.

To do sentiment analysis in Tensorflex however, we first need to do some preprocessing and prepare the graph model (.pb) as done multiple times before in other examples. For that, in the examples/rnn-lstm-example directory there are two scripts: freeze.py and create_input_data.py. Prior to explaining the working of these scripts you first need to download the original saved models as well as the datasets:

  • For the model, download from here and then store all the 4 model files in the examples/rnn-lstm-example/model folder
  • For the dataset, download from here. After decompressing, we do not need all the files, just the 2 numpy binaries wordsList.npy and wordVectors.npy. These will be used to encode our text data into UTF-8 encoding for feeding our RNN as input.

Now, for the Python two scripts: freeze.py and create_input_data.py:

  • freeze.py: This is used to create our pb model from the Python saved checkpoints. Here we will use the downloaded Python checkpoints' model to create the .pb graph. Just running python freeze.py after putting the model files in the correct directory will do the trick. In the same ./model/ folder, you will now see a file called frozen_model_lstm.pb. This is the file which we will load into Tensorflex. In case for some reason you want to skip this step and just get the loaded graph here is a Dropbox link
  • create_input_data.py: Even if we can load our model into Tensorflex, we also need some data to do inference on. For that, we will write our own example sentences and convert them (read encode) to a numeral (int32) format that can be used by the network as input. For that, you can inspect the code in the script to get an understanding of what is happening. Basically, the neural network takes in an input of a 24x250 int32 (matrix) tensor created from text which has been encoded as UTF-8. Again, running python create_input_data.py will give you two csv files (one indicating positive sentiment and the other a negative sentiment) which we will later load into Tensorflex. The two sentences converted are:
    • Negative sentiment sentence: That movie was terrible.
    • Positive sentiment sentence: That movie was the best one I have ever seen.

Both of these get converted to two files inputMatrixPositive.csv and inputMatrixNegative.csv (by create_input_data.py) which we load into Tensorflex next.

Inference in Tensorflex: Now we do sentiment analysis in Tensorflex. A few things to note:

  • The input graph operation is named Placeholder_1
  • The output graph operation is named add and is the eventual result of a matrix multiplication. Of this obtained result we only need the first row
  • Here the input is going to be a integer valued matrix tensor of dimensions 24x250 representing our sentence/review
  • The output will have 2 columns, as there are 2 classes-- for positive and negative sentiment respectively. Since we will only be needing only the first row we will get our result in a 1x2 vector. If the value of the first column is higher than the second column, then the network indicates a positive sentiment otherwise a negative sentiment. All this can be observed in the original repository in a Jupyter notebook here: ```elixir iex(1)> {:ok, graph} = Tensorflex.read_graph "examples/rnn-lstm-example/model/frozen_model_lstm.pb" {:ok, %Tensorflex.Graph{ def: #Reference<0.713975820.1050542081.11558>, name: "examples/rnn-lstm-example/model/frozen_model_lstm.pb" }}

iex(2)> Tensorflex.get_graph_ops graph ["Placeholder_1", "embedding_lookup/params_0", "embedding_lookup", "transpose/perm", "transpose", "rnn/Shape", "rnn/strided_slice/stack", "rnn/strided_slice/stack_1", "rnn/strided_slice/stack_2", "rnn/strided_slice", "rnn/stack/1", "rnn/stack", "rnn/zeros/Const", "rnn/zeros", "rnn/stack_1/1", "rnn/stack_1", "rnn/zeros_1/Const", "rnn/zeros_1", "rnn/Shape_1", "rnn/strided_slice_2/stack", "rnn/strided_slice_2/stack_1", "rnn/strided_slice_2/stack_2", "rnn/strided_slice_2", "rnn/time", "rnn/TensorArray", "rnn/TensorArray_1", "rnn/TensorArrayUnstack/Shape", "rnn/TensorArrayUnstack/strided_slice/stack", "rnn/TensorArrayUnstack/strided_slice/stack_1", "rnn/TensorArrayUnstack/strided_slice/stack_2", "rnn/TensorArrayUnstack/strided_slice", "rnn/TensorArrayUnstack/range/start", "rnn/TensorArrayUnstack/range/delta", "rnn/TensorArrayUnstack/range", "rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3", "rnn/while/Enter", "rnn/while/Enter_1", "rnn/while/Enter_2", "rnn/while/Enter_3", "rnn/while/Merge", "rnn/while/Merge_1", "rnn/while/Merge_2", "rnn/while/Merge_3", "rnn/while/Less/Enter", "rnn/while/Less", "rnn/while/LoopCond", "rnn/while/Switch", "rnn/while/Switch_1", "rnn/while/Switch_2", "rnn/while/Switch_3", ...]

First we will try for positive sentiment:
```elixir
iex(3)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixPositive.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13138>,
  ncols: 250,
  nrows: 24
}

iex(4)> input_dims = Tensorflex.create_matrix(1,2,[[24,250]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13575>,
  ncols: 2,
  nrows: 1
}

iex(5)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals, input_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.14434>
 }}

iex(6)> output_dims = Tensorflex.create_matrix(1,2,[[24,2]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.14870>,
  ncols: 2,
  nrows: 1
}

iex(7)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(output_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.713975820.1050542081.15363>
 }}

We only need the first row, the rest do not indicate anything:

iex(8)> [result_pos | _ ] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [4.483788013458252, -1.273943305015564],
  [-0.17151066660881042, -2.165886402130127],
  [0.9569928646087646, -1.131581425666809],
  [0.5669126510620117, -1.3842089176177979],
  [-1.4346938133239746, -4.0750861167907715],
  [0.4680981934070587, -1.3494354486465454],
  [1.068990707397461, -2.0195648670196533],
  [3.427264451980591, 0.48857203125953674],
  [0.6307879686355591, -2.069119691848755],
  [0.35061028599739075, -1.700657844543457],
  [3.7612719535827637, 2.421398878097534],
  [2.7635951042175293, -0.7214710116386414],
  [1.146680235862732, -0.8688814640045166],
  [0.8996094465255737, -1.0183486938476563],
  [0.23605018854141235, -1.893072247505188],
  [2.8790698051452637, -0.37355837225914],
  [-1.7325369119644165, -3.6470277309417725],
  [-1.687785029411316, -4.903762340545654],
  [3.6726789474487305, 0.14170047640800476],
  [0.982108473777771, -1.554244875907898],
  [2.248904228210449, 1.0617655515670776],
  [0.3663095533847809, -3.5266385078430176],
  [-1.009346604347229, -2.901120901107788],
  [3.0659966468811035, -1.7605335712432861]
]

iex(9)> result_pos
[4.483788013458252, -1.273943305015564]

Thus we can clearly see that the RNN predicts a positive sentiment. For a negative sentiment, next:

iex(10)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixNegative.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.16780>,
  ncols: 250,
  nrows: 24
}

iex(11)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals,input_dims)
{:ok,              
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.16788>
 }}

iex(12)> [result_neg|_] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [0.7635725736618042, 10.895986557006836],
  [2.205151319503784, -0.6267685294151306],
  [3.5995595455169678, -0.1240251287817955],
  [-1.6063352823257446, -3.586883068084717],
  [1.9608432054519653, -3.084211826324463],
  [3.772461414337158, -0.19421455264091492],
  [3.9185996055603027, 0.4442034661769867],
  [3.010765552520752, -1.4757057428359985],
  [3.23650860786438, -0.008513949811458588],
  [2.263028144836426, -0.7358709573745728],
  [0.206748828291893, -2.1945853233337402],
  [2.913491725921631, 0.8632720708847046],
  [0.15935257077217102, -2.9757845401763916],
  [-0.7757357358932495, -2.360766649246216],
  [3.7359719276428223, -0.7668198347091675],
  [2.2896337509155273, -0.45704856514930725],
  [-1.5497230291366577, -4.42919921875],
  [-2.8478822708129883, -5.541027545928955],
  [1.894787073135376, -0.8441318273544312],
  [0.15720489621162415, -2.699129819869995],
  [-0.18114641308784485, -2.988100051879883],
  [3.342879056930542, 2.1714375019073486],
  [2.906526565551758, 0.18969044089317322],
  [0.8568912744522095, -1.7559258937835693]
]
iex(13)> result_neg
[0.7635725736618042, 10.895986557006836]

Thus we can clearly see that in this case the RNN indicates negative sentiment! Our model works!

Pull Requests Made


Author: anshuman23
Source code: https://github.com/anshuman23/tensorflex
License: Apache-2.0 license

#elixir #tensorflow #machine-learning 

systemctl List All Failed Units/Services on Linux

Is there is a command to print list all failed units or services when using systemd on Linux? Can you tell me the systemctl command to list all failed services on Linux?

This quick tutorial explains how to find/list all failed systemd services/units on Linux operating systems using the systemctl command.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]