Revisions · 1874d747a461534b2314e9f6540f4584

takane revised this on 28 Oct 2022

c7ecd9e

createmltest.swift
// createmltest.swift //　Usage: // swift createmltest.swift <train folder path> <test folder path> // // reference: // https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift // // note: // trainフォルダとtestフォルダをコマンドライン引数で指定 // mlmodelファイルをhomeのDesktopに書き込むように変更 // 20行目の augmentation:[...] の中にオプションを指定。 .rotationはエラーが起こる import CreateML import Foundation // Initializing the properly labeled training data from Resources folder. let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1])) // Initializing the classifier with a training data. let classifier = try! MLImageClassifier(trainingData: trainingData, parameters: MLImageClassifier.ModelParameters(maxIterations:1000, augmentation:[.crop, .blur, .exposure, .noise, .flip])) // Evaluating training & validation accuracies. let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100 let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100 // Initializing the properly labeled testing data from Resources folder. let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2])) // Counting the testing evaluation. let evaluationMetrics = classifier.evaluation(on: testingData) let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100 // Confusion matrix in order to see which labels were classified wrongly. let confusionMatrix = evaluationMetrics.confusion //print("Confusion matrix: \(confusionMatrix)") print(evaluationMetrics) print("Training Accuracy: \(trainingAccuracy)%") print("Validation Accuracy: \(validationAccuracy)%") print("Evaluation Accuracy: \(evaluationAccuracy)%") print("") // Metadata for saving the model. let metadata = MLModelMetadata(author: "S.Takane", shortDescription: "Cats and Dogs", version: "1.0") // Saving the model. Remember to update the path. try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"), metadata: metadata) // createmltest.swift //　Usage: // swift createmltest.swift <train folder path> <test folder path> // // reference: // https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift // // note: // trainフォルダとtestフォルダをコマンドライン引数で指定 // mlmodelファイルをhomeのDesktopに書き込むように変更 // 20行目の augmentation:[...] の中にオプションを指定。 .rotationはエラーが起こる import CreateML import Foundation // Initializing the properly labeled training data from Resources folder. let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1])) // Initializing the classifier with a training data. let classifier = try! MLImageClassifier(trainingData: trainingData, parameters: MLImageClassifier.ModelParameters(maxIterations:1000, augmentation:[.crop, .blur, .exposure, .noise, .flip])) // Evaluating training & validation accuracies. let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100 let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100 // Initializing the properly labeled testing data from Resources folder. let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2])) // Counting the testing evaluation. let evaluationMetrics = classifier.evaluation(on: testingData) let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100 // Confusion matrix in order to see which labels were classified wrongly. let confusionMatrix = evaluationMetrics.confusion print("Confusion matrix: \(confusionMatrix)") print("Training Accuracy: \(trainingAccuracy)%") print("Validation Accuracy: \(validationAccuracy)%") print("Evaluation Accuracy: \(evaluationAccuracy)%") print("") // Metadata for saving the model. let metadata = MLModelMetadata(author: "S.Takane", shortDescription: "Cats and Dogs", version: "1.0") // Saving the model. Remember to update the path. try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"), metadata: metadata)

createmltest.swift

import CreateML
import Foundation

// Initializing the properly labeled training data from Resources folder.
let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1]))

// Initializing the classifier with a training data.
let classifier = try! MLImageClassifier(trainingData: trainingData,
                                        parameters: MLImageClassifier.ModelParameters(maxIterations:1000, augmentation:[.crop, .blur, .exposure, .noise, .flip]))

// Evaluating training & validation accuracies.
let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100
let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100

// Initializing the properly labeled testing data from Resources folder.
let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2]))

// Counting the testing evaluation.
let evaluationMetrics = classifier.evaluation(on: testingData)
let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100

// Confusion matrix in order to see which labels were classified wrongly.
let confusionMatrix = evaluationMetrics.confusion

//print("Confusion matrix: \(confusionMatrix)")
print(evaluationMetrics)
print("Training Accuracy: \(trainingAccuracy)%")
print("Validation Accuracy: \(validationAccuracy)%")
print("Evaluation Accuracy: \(evaluationAccuracy)%")
print("")

// Metadata for saving the model.
let metadata = MLModelMetadata(author: "S.Takane",
                               shortDescription: "Cats and Dogs",
                               version: "1.0")

// Saving the model. Remember to update the path.
try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"),
                     metadata: metadata)

import CreateML
import Foundation

// Initializing the properly labeled training data from Resources folder.
let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1]))

// Initializing the properly labeled testing data from Resources folder.
let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2]))

// Counting the testing evaluation.
let evaluationMetrics = classifier.evaluation(on: testingData)
let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100

// Confusion matrix in order to see which labels were classified wrongly.
let confusionMatrix = evaluationMetrics.confusion

print("Confusion matrix: \(confusionMatrix)")
print("Training Accuracy: \(trainingAccuracy)%")
print("Validation Accuracy: \(validationAccuracy)%")
print("Evaluation Accuracy: \(evaluationAccuracy)%")
print("")

// Metadata for saving the model.
let metadata = MLModelMetadata(author: "S.Takane",
                               shortDescription: "Cats and Dogs",
                               version: "1.0")

// Saving the model. Remember to update the path.
try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"),
                     metadata: metadata)

takane revised this on 11 Oct 2022

96f2725

createmltest.swift
// createmltest.swift //　Usage: // swift createmltest.swift <train folder path> <test folder path> // // reference: // https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift // // note: // trainフォルダとtestフォルダをコマンドライン引数で指定 // mlmodelファイルをhomeのDesktopに書き込むように変更 // 20行目の augmentation:[...] の中にオプションを指定。 .rotationはエラーが起こる import CreateML import Foundation // Initializing the properly labeled training data from Resources folder. let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1])) // Initializing the classifier with a training data. let classifier = try! MLImageClassifier(trainingData: trainingData, parameters: MLImageClassifier.ModelParameters(maxIterations:1000, augmentation:[.crop, .blur, .exposure, .noise, .flip])) // Evaluating training & validation accuracies. let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100 let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100 // Initializing the properly labeled testing data from Resources folder. let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2])) // Counting the testing evaluation. let evaluationMetrics = classifier.evaluation(on: testingData) let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100 // Confusion matrix in order to see which labels were classified wrongly. let confusionMatrix = evaluationMetrics.confusion print("Confusion matrix: \(confusionMatrix)") print("Training Accuracy: \(trainingAccuracy)%") print("Validation Accuracy: \(validationAccuracy)%") print("Evaluation Accuracy: \(evaluationAccuracy)%") print("") // Metadata for saving the model. let metadata = MLModelMetadata(author: "S.Takane", shortDescription: "Cats and Dogs", version: "1.0") // Saving the model. Remember to update the path. try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"), metadata: metadata) // createmltest.swift //　Usage: // swift createmltest.swift <train folder path> <test folder path> // // reference: // https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift // // note: // trainフォルダとtestフォルダをコマンドライン引数で指定 // mlmodelファイルをhomeのDesktopに書き込むように変更 // maxIterations // augmentation import CreateML import Foundation // Initializing the properly labeled training data from Resources folder. let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/train")) // Initializing the classifier with a training data. let classifier = try! MLImageClassifier(trainingData: trainingData, parameters: MLImageClassifier.ModelParameters( maxIterations:500, augmentation:[.noise,.crop,.blur])) // Evaluating training & validation accuracies. let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100 let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100 // Initializing the properly labeled testing data from Resources folder. let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/test")) // Counting the testing evaluation. let evaluationMetrics = classifier.evaluation(on: testingData) let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100 // Confusion matrix in order to see which labels were classified wrongly. let confusionMatrix = evaluationMetrics.confusion print("Confusion matrix: \(confusionMatrix)") // Metadata for saving the model. let metadata = MLModelMetadata(author: "S.Takane", shortDescription: "Cats and Dogs", version: "1.0") // Saving the model. Remember to update the path. try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"), metadata: metadata)

createmltest.swift

import CreateML
import Foundation

// Initializing the properly labeled training data from Resources folder.
let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[1]))

// Initializing the properly labeled testing data from Resources folder.
let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: CommandLine.arguments[2]))

// Counting the testing evaluation.
let evaluationMetrics = classifier.evaluation(on: testingData)
let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100

// Confusion matrix in order to see which labels were classified wrongly.
let confusionMatrix = evaluationMetrics.confusion

// Metadata for saving the model.
let metadata = MLModelMetadata(author: "S.Takane",
                               shortDescription: "Cats and Dogs",
                               version: "1.0")

// Saving the model. Remember to update the path.
try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"),
                     metadata: metadata)

// createmltest.swift
//　Usage: 
//      swift createmltest.swift <train folder path> <test folder path>
//
// reference:
// https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift
//
// note:
// trainフォルダとtestフォルダをコマンドライン引数で指定
// mlmodelファイルをhomeのDesktopに書き込むように変更
//   maxIterations
//   augmentation
 
import CreateML
import Foundation
 
// Initializing the properly labeled training data from Resources folder.
let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/train"))
 
// Initializing the classifier with a training data.
let classifier = try! MLImageClassifier(trainingData: trainingData,
                                        parameters: MLImageClassifier.ModelParameters(
                                            maxIterations:500, 
                                            augmentation:[.noise,.crop,.blur]))
 
// Evaluating training & validation accuracies.
let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100
let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100
 
// Initializing the properly labeled testing data from Resources folder.
let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/test"))
 
// Counting the testing evaluation.
let evaluationMetrics = classifier.evaluation(on: testingData)
let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100
 
// Confusion matrix in order to see which labels were classified wrongly.
let confusionMatrix = evaluationMetrics.confusion
print("Confusion matrix: \(confusionMatrix)")
 
// Metadata for saving the model.
let metadata = MLModelMetadata(author: "S.Takane",
                               shortDescription: "Cats and Dogs",
                               version: "1.0")
 
// Saving the model. Remember to update the path.
try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"),
                     metadata: metadata)

takane revised this on 11 Oct 2022

d533bc1

createmltest.swift
// createmltest.swift //　Usage: // swift createmltest.swift <train folder path> <test folder path> // // reference: // https://www.netguru.com/blog/createml-start-your-adventure-in-machine-learning-with-swift // // note: // trainフォルダとtestフォルダをコマンドライン引数で指定 // mlmodelファイルをhomeのDesktopに書き込むように変更 // maxIterations // augmentation import CreateML import Foundation // Initializing the properly labeled training data from Resources folder. let trainingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/train")) // Initializing the classifier with a training data. let classifier = try! MLImageClassifier(trainingData: trainingData, parameters: MLImageClassifier.ModelParameters( maxIterations:500, augmentation:[.noise,.crop,.blur])) // Evaluating training & validation accuracies. let trainingAccuracy = (1.0 - classifier.trainingMetrics.classificationError) * 100 let validationAccuracy = (1.0 - classifier.validationMetrics.classificationError) * 100 // Initializing the properly labeled testing data from Resources folder. let testingData = MLImageClassifier.DataSource.labeledDirectories(at: URL(fileURLWithPath: "/Users/takane/Desktop/CreateMLTest/CreateMLTest/Data/image/test")) // Counting the testing evaluation. let evaluationMetrics = classifier.evaluation(on: testingData) let evaluationAccuracy = (1.0 - evaluationMetrics.classificationError) * 100 // Confusion matrix in order to see which labels were classified wrongly. let confusionMatrix = evaluationMetrics.confusion print("Confusion matrix: \(confusionMatrix)") // Metadata for saving the model. let metadata = MLModelMetadata(author: "S.Takane", shortDescription: "Cats and Dogs", version: "1.0") // Saving the model. Remember to update the path. try classifier.write(to: URL(fileURLWithPath: NSHomeDirectory()+"/Desktop"), metadata: metadata)

createmltest.swift