1619460537
#repository pattern #asp.net core mvc 5 #entity framework core #core #mvc #asp.net
1602560783
In this article, we’ll discuss how to use jQuery Ajax for ASP.NET Core MVC CRUD Operations using Bootstrap Modal. With jQuery Ajax, we can make HTTP request to controller action methods without reloading the entire page, like a single page application.
To demonstrate CRUD operations – insert, update, delete and retrieve, the project will be dealing with details of a normal bank transaction. GitHub repository for this demo project : https://bit.ly/33KTJAu.
Sub-topics discussed :
In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N).
From new project window, Select Asp.Net Core Web Application_._
Once you provide the project name and location. Select Web Application(Model-View-Controller) and uncheck HTTPS Configuration. Above steps will create a brand new ASP.NET Core MVC project.
Let’s create a database for this application using Entity Framework Core. For that we’ve to install corresponding NuGet Packages. Right click on project from solution explorer, select Manage NuGet Packages_,_ From browse tab, install following 3 packages.
Now let’s define DB model class file – /Models/TransactionModel.cs.
public class TransactionModel
{
[Key]
public int TransactionId { get; set; }
[Column(TypeName ="nvarchar(12)")]
[DisplayName("Account Number")]
[Required(ErrorMessage ="This Field is required.")]
[MaxLength(12,ErrorMessage ="Maximum 12 characters only")]
public string AccountNumber { get; set; }
[Column(TypeName ="nvarchar(100)")]
[DisplayName("Beneficiary Name")]
[Required(ErrorMessage = "This Field is required.")]
public string BeneficiaryName { get; set; }
[Column(TypeName ="nvarchar(100)")]
[DisplayName("Bank Name")]
[Required(ErrorMessage = "This Field is required.")]
public string BankName { get; set; }
[Column(TypeName ="nvarchar(11)")]
[DisplayName("SWIFT Code")]
[Required(ErrorMessage = "This Field is required.")]
[MaxLength(11)]
public string SWIFTCode { get; set; }
[DisplayName("Amount")]
[Required(ErrorMessage = "This Field is required.")]
public int Amount { get; set; }
[DisplayFormat(DataFormatString = "{0:MM/dd/yyyy}")]
public DateTime Date { get; set; }
}
C#Copy
Here we’ve defined model properties for the transaction with proper validation. Now let’s define DbContextclass for EF Core.
#asp.net core article #asp.net core #add loading spinner in asp.net core #asp.net core crud without reloading #asp.net core jquery ajax form #asp.net core modal dialog #asp.net core mvc crud using jquery ajax #asp.net core mvc with jquery and ajax #asp.net core popup window #bootstrap modal popup in asp.net core mvc. bootstrap modal popup in asp.net core #delete and viewall in asp.net core #jquery ajax - insert #jquery ajax form post #modal popup dialog in asp.net core #no direct access action method #update #validation in modal popup
1650636000
Port of deeplearning4j to clojure
Contact info
If you have any questions,
NOT YET RELEASED TO CLOJARS
If using Maven add the following repository definition to your pom.xml:
<repository>
<id>clojars.org</id>
<url>http://clojars.org/repo</url>
</repository>
With Leiningen:
n/a
With Maven:
n/a
<dependency>
<groupId>_</groupId>
<artifactId>_</artifactId>
<version>_</version>
</dependency>
All functions for creating dl4j objects return code by default
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
(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))"]
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
(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))
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 {}}})
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
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))
(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
dl4j Spark usage
How it is done in dl4clj
(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))
(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))
Coming soon
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
1591611780
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.
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#repository pattern #asp.net core mvc 5 #entity framework core #core #mvc #asp.net
1602564619
User registration and authentication are mandatory in any application when you have little concern about privacy. Hence all most all application development starts with an authentication module. In this article, we will discuss the quickest way to use **ASP.NET Core Identity for User Login and Registration **in a new or existing MVC application.
Sub-topics discussed :
ASP.NET Core Identity is an API, which provides both user interface(UI) and functions for user authentication, registration, authorization, etc. Modules/ APIs like this will really be helpful and fasten the development process. It comes with ASP.NET Core Framework and used in many applications before. Which makes the API more dependable and trustworthy.
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First of all, I will create a brand new ASP.NET Core MVC application without any authentication selected. We could add ASP.NET Core Identity later into the project.
In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N). From new project window, select ASP.NET Core Web Application.
Once you provide the project name and location. A new window will be opened as follows, Select _Web Application(Model-View-Controller), _uncheck _HTTPS Configuration _and DO NOT select any authentication method. Above steps will create a brand new ASP.NET Core MVC project.
#asp.net core article #asp.net core #add asp.net core identity to existing project #asp.net core identity in mvc #asp.net core mvc login and registration #login and logout in asp.net core