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ML.NET para clasificación de imágenes
- 1. #CumbreVirtual2021 @VirtualDEVShow Cumbre Virtual 2021 @VirtualDEVShow CUMBRE VIRTUAL Aprende – Comparte – Conecta ¡Juguemos piedra, papel y tijeras con ML.NET! Luis Beltrán
- 4. @VirtualDEVShow #CumbreVirtual2021 Cumbre Virtual 2021 @VirtualDEVShow Deep Learning
- 6. Inteligencia Artificial ¿Es un pan o no? if(esImagenDePerfil) { if(esRectangular) { if(tieneFormaCircular) { if(tieneOrejas) { return false; } else if (tieneOjos) { return false; } else { return true; } } } }
- 7. Machine Learning Machine Learning es una rama de la IA que se enfoca en construir aplicaciones que aprenden de los datos y mejoran su precisión con el paso del tiempo sin ser explícitamente programadas para ello. Datos históricos Nuevos datos Algoritmos de Machine Learning Modelo de Machine Learning Predicción de Datos Entrenamiento Predicción
- 8. Machine Learning ¿Es un pan o no? Pan No Pan
- 9. Deep Learning Deep learning es un área del Machine Learning relacionada con algoritmos inspirados por la estructura y función del cerebro llamada redes neuronales artificiales. Es excepcionalmente efectiva para descubrir patrones. Los algoritmos aprenden a través de una jerarquía multicapa. Si proporcionas al sistema toneladas de información, comenzará a comprender y responder de manera útil.
- 10. Deep Learning tiene un proceso de aprendizaje de características multi- etapa incorporado que identifica representaciones jerárquicas enriquecidas (patrones). Low-level features Mid-level features Output (e.g. exterior, interior) High-level features Trainable Classifier
- 11. Imagen Texto Cada módulo de Deep Learning transforma su representación de entrada en una de nivel superior, de forma similar a la corteza cerebral del ser humano. Low Level Features Mid Level Features Output High Level Features Trainable Classifier Input
- 12. Capas de Convolución Filtro 1 1 1 1 1 1 0.015686 0.015686 0.011765 0.015686 0.015686 0.015686 0.015686 0.964706 0.988235 0.964706 0.866667 0.031373 0.023529 0.007843 0.007843 0.741176 1 1 0.984314 0.023529 0.019608 0.015686 0.015686 0.015686 0.011765 0.101961 0.972549 1 1 0.996078 0.996078 0.996078 0.058824 0.015686 0.019608 0.513726 1 1 1 0.019608 0.015686 0.015686 0.015686 0.007843 0.011765 1 1 1 0.996078 0.031373 0.015686 0.019608 1 0.011765 0.015686 0.733333 1 1 0.996078 0.019608 0.019608 0.015686 0.015686 0.011765 0.984314 1 1 0.988235 0.027451 0.015686 0.007843 0.007843 1 0.352941 0.015686 0.823529 1 1 0.988235 0.019608 0.019608 0.015686 0.015686 0.019608 1 1 0.980392 0.015686 0.015686 0.015686 0.015686 0.996078 1 0.996078 0.015686 0.913726 1 1 0.996078 0.019608 0.019608 0.019608 0.019608 1 1 0.984314 0.015686 0.015686 0.015686 0.015686 0.952941 1 1 0.992157 0.019608 0.913726 1 1 0.988235 0.019608 0.019608 0.019608 0.039216 0.996078 1 0.015686 0.015686 0.015686 0.015686 0.996078 1 1 1 0.007843 0.019608 0.898039 1 1 0.988235 0.019608 0.015686 0.019608 0.968628 0.996078 0.980392 0.027451 0.015686 0.019608 0.980392 0.972549 1 1 1 0.019608 0.043137 0.905882 1 1 1 0.015686 0.035294 0.968628 1 1 0.023529 1 0.792157 0.996078 1 1 0.980392 0.992157 0.039216 0.023529 1 1 1 1 1 0.992157 0.992157 1 1 0.984314 0.015686 0.015686 0.858824 0.996078 1 0.992157 0.501961 0.019608 0.019608 0.023529 0.996078 0.992157 1 1 1 0.933333 0.003922 0.996078 1 0.988235 1 0.992157 1 1 1 0.988235 1 1 1 1 0.015686 0.74902 1 1 0.984314 0.019608 0.019608 0.031373 0.984314 0.023529 0.015686 0.015686 1 1 1 0 0.003922 0.027451 0.980392 1 0.019608 0.023529 1 1 1 0.019608 0.019608 0.564706 0.894118 0.019608 0.015686 0.015686 1 1 1 0.015686 0.015686 0.015686 0.05098 1 0.015686 0.015686 1 1 1 0.047059 0.019608 0.992157 0.007843 0.011765 0.011765 0.015686 1 1 1 0.015686 0.019608 0.996078 0.023529 0.996078 0.019608 0.015686 0.243137 1 1 0.976471 0.035294 1 0.003922 0.011765 0.011765 0.015686 1 1 1 0.988235 0.988235 1 0.003922 0.015686 0.019608 0.019608 0.027451 1 1 0.992157 0.223529 0.662745 0.011765 0.011765 0.011765 0.015686 1 1 1 0.015686 0.023529 0.996078 0.011765 0.011765 0.015686 0.015686 0.011765 1 1 1 1 0.035294 0.011765 0.011765 0.011765 0.015686 1 1 1 0.015686 0.015686 0.964706 0.003922 0.996078 0.007843 0.019608 0.011765 0.054902 1 1 0.988235 0.007843 0.011765 0.011765 0.015686 0.011765 1 1 1 0.015686 0.015686 0.015686 0.023529 1 0.007843 0.007843 0.015686 0.015686 0.960784 1 0.490196 0.015686 0.015686 0.015686 0.007843 0.027451 1 1 1 0.011765 0.011765 0.043137 1 1 0.023529 0.003922 0.007843 0.023529 0.980392 0.976471 0.039216 0.019608 0.007843 0.019608 0.015686 1 1 1 1 1 1 1 1 1 0 1 0 1 -4 1 0 1 0 Input Image Convoluted Image
- 13. Convolución Input Image Convolved Image (Feature Map) a b c d e f g h i j k l m n o p w1 w2 w3 w4 Filter h1 h2 ℎ1 = 𝑓 𝑎 ∗ 𝑤1 + 𝑏 ∗ 𝑤2 + 𝑒 ∗ 𝑤3 + 𝑓 ∗ 𝑤4 ℎ2 = 𝑓 𝑏 ∗ 𝑤1 + 𝑐 ∗ 𝑤2 + 𝑓 ∗ 𝑤3 + 𝑔 ∗ 𝑤4
- 14. Características: De bajo a alto nivel Input Image Layer 1 Feature Map Layer 2 Feature Map w1 w2 w3 w4 w5 w6 w7 w8 Filter 1 Filter 2
- 15. Pooling Max pooling: informa la salida máxima dentro de una sección. Average pooling: informa la salida promedio dentro de una sección. 1 3 5 3 4 2 3 1 3 1 1 3 0 1 0 4 MaxPool with 2X2 filter with stride of 2 Input Matrix Output Matrix 4 5 3 4
- 16. Red Neuronal de Convolución Arquitectura de Extracción de Características 64 64 128 128 256 256 256 512 512 512 512 512 512 Filter Max Pool Fully Connected Layers Living Room Bed Room Kitchen Bathroom Outdoor Maxpool Output Vector
- 17. @VirtualDEVShow #CumbreVirtual2021 Cumbre Virtual 2021 @VirtualDEVShow ML.NET
- 18. ML.NET es un framework para hacer ML a la medida Ciclo de vida de Machine Learning Datos Históricos Prueba & Evalua Compila & Entrena Modelo ML Archivo .ZIP (Entrenado ) Prepara Datos, Compila y Entrena un modelo de ML ML.NET API (Aplicación .NET de Consola, etc.) ML.NET Model Builder (Herramienta UI Desktop) Tareas de ML, Transformaciones de Datos, Learners/Algoritmos ML.NET API (Aplicación .NET) API para ejecutar el modelo nuget nuget Aplicación de Negocio (Web/Servicio/Desktop/Móvil) ML model file Aplicación .NET Predicción: Ejecuta/consume el modelo de ML ? Datos en tiempo real del usuario ML.NET API para consumir el modelo Ejecuta & Predice .NET .NET BD Archivos CSV etc
- 19. ¿Qué puedes hacer con ML.NET?
- 20. La mayoría de escenarios Microsoft.ML Escenarios de predicción y detección de anomalías Microsoft.ML.TimeSeries Recomendadores Microsoft.ML.Recommender Cargar bases de datos System.Data.SqlClient Consumo de modelos ONNX Microsoft.ML.ONNXTransformer Microsoft.ML.ImageAnalytics para detección de objetos Consumo de modelos de TensorFlow Microsoft.ML.TensorFlow SciSharp.TensorFlow.Redist Microsoft.ML.ImageAnalytics para clasificación de imágenes Entrenar modelos personalizados de clasificación de imagenes Microsoft.ML.Vision Microsoft.ML.ImageAnalytics SciSharp.TensorFlow.Redist
- 21. MLContext MLContext es el punto de inicio para todas las operaciones de ML.NET y proporciona mecanismos para la creación de componentes de: • Preparación de datos • Ingeniería de características • Entrenamiento • Predicción • Evaluación de modelos • Bitácoras • Control de ejecución • Inicialización
- 22. Data Schema IDataView • Es el conjunto de columnas, sus nombres, tipos y otras anotaciones. • Antes de cargar datos, debes definir cómo se verá el esquema de datos (nombres y tipos de columna) • Utiliza definiciones de clase para definir esquemas IDV Dataset Definición de esquema de clases IDataView Label SepalLength SepalWidth PetalLength PetalWidth Iris-setosa 5.1 3.5 1.4 0.2 Iris- versicolor 7.0 3.2 4.7 1.4 Iris-setosa 4.9 3.0 1.5 0.1 …
- 24. Modelo de ML.NET Un archivo zip serializado que contiene esquemas de datos, transformaciones de datos y algoritmos. Model o de ML.NE T MLModel.zip Desktop Web Móvil
- 25. Entrenando modelos de Deep Learning usando ML.NET
- 26. @VirtualDEVShow #CumbreVirtual2021 Cumbre Virtual 2021 @VirtualDEVShow Demo
- 27. Input Model Solution structure Nuget Packages Output Model (Prediction)
- 28. Main libraries Paths PrepareSet: Loading input images for training, validation, and testing
- 29. Display information to the Console Input data (images)
- 31. Main program Loading data for supervised learning (images include tags) Training and Validation sets Load pipeline: Images loaded in memory Training options: ImageClassificationTrainer chosen, based on the InceptionV3 architecture Training pipeline: Trying to predict a category Both pipelines are combined
- 32. Perform training Model precision is validated using validation dataset Model Metrics calculated Test the classification model using the new images Prepare new images for validation Export the model Consume the model
- 33. ConsumingModel Load a previously trained classification model and prepare test images that were not used before in the training and validation stages ClassifyImages: Test the model with new images
- 35. Validation Precision metrics Model validation results: Image Actual damage class Prediction
- 36. Image classification results ML Model exported as zip file
- 37. Tu plataforma para construir lo que necesitas .NET Desktop Web Cloud Mobile Gaming IoT AI
- 38. COMPILERS LANGUAGES RUNTIME COMPONENTS LIBRARIES INFRASTRUCTURE .NET STANDARD TOOLS VISUAL STUDIO CODE CLI VISUAL STUDIO VISUAL STUDIO FOR MAC WEB CLOUD MOBILE GAMING IoT AI DESKTOP
- 40. Unable to find an entry point named 'TF_StringEncodedSize' in DLL 'tensorflow' “I think ml.net support tensorflow 2.3.1 not yet support 2.4, so you must download SciSharp.TensorFlow.Redist 2.3.1” https://github.com/dotnet/machinelearning-samples/issues/880
- 41. @VirtualDEVShow #CumbreVirtual2021 Cumbre Virtual 2021 @VirtualDEVShow Q & A
- 42. ¡Gracias por tu atención! Luis Beltrán Tomás Bata University in Zlín Tecnológico Nacional de México en Celaya About Me: https://about.me/luis-beltran
Notas del editor
- La capacidad de una computadora para realizar tareas comúnmente asociadas con seres inteligentes (razonar, descubrir significado, generalizar, aprender de experiencias pasadas). Normalmente comienza como un sistema basado en reglas o lógica. Las técnicas tradicionales de IA pueden ser difíciles de escalar.
- Low-level features are minor details of the image, like lines or dots, that can be picked up by, say, a convolutional filter (for really low-level things) or (for more abstract things like edges). High-level features are built on top of low-level features to detect objects and larger shapes in the image. Convolutional neural networks use both types of features: the first couple convolutional layers will learn filters for finding lines, dots, curves etc. while the later layers will learn to recognize common objects and shapes.
- Convolution is a general purpose filter effect for images In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image. In terms of an image, a high-frequency image is the one where the intensity of the pixels changes by a large amount, whereas a low-frequency image is the one where the intensity is almost uniform. Usually, an image has both high and low frequency components. The high-frequency components correspond to the edges of an object because at the edges the rate of change of intensity of pixel values is high.
- Convolution is a simple mathematical operation which is fundamental to many common image processing operators. Convolution provides a way of `multiplying together' two arrays of numbers, generally of different sizes, but of the same dimensionality, to produce a third array of numbers of the same dimensionality.
- 3. Now when you apply a set of filters on top of that (pass it through the 2nd conv. layer), the output will be activations that represent higher-level features. Types of these features could be semicircles (a combination of a curve and straight edge) or squares (a combination of several straight edges). As you go through the network and go through more conv. layers, you get activation maps that represent more and more complex features.
- Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively. A pooling layer is a new layer added after the convolutional layer. Specifically, after a nonlinearity (e.g. ReLU) h
- http://www.laurencemoroney.com/rock-paper-scissors-dataset/