image classification using tensorflow and keras

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Need someone to do a image classification project. Download and explore the dataset . At this point, we are ready to see the results of our hard work. To do so, divide the values by 255. Train the model. We will use Keras and TensorFlow frameworks for building our Convolutional Neural Network. In this article, you will learn how to build a Convolutional Neural Network (CNN) using Keras for image classification on Cifar-10 dataset from scratch. It runs on three backends: TensorFlow, CNTK, and Theano. Create Your Artistic Image Using Pystiche. At the TensorFlow Dev Summit 2019, Google introduced the alpha version of TensorFlow 2.0. 09/01/2021; 9 mins Read; Developers Corner. Which framework do they use? Image Classification using Keras as well as Tensorflow. The model consists of three convolution blocks with a max pool layer in each of them. How to do Image Classification on custom Dataset using TensorFlow Published Apr 04, 2020 Image classification is basically giving some images to the system that belongs to one of the fixed set of classes and then expect the system to put the images into their respective classes. In this example, the training data is in the. Load the Cifar-10 dataset. TensorFlow’s new 2.0 version provides a totally new development ecosystem with Eager Execution enabled by default. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. These correspond to the class of clothing the image represents: Each image is mapped to a single label. TensorFlow’s new 2.0 version provides a totally new development ecosystem with Eager Execution enabled by default. Finally, use the trained model to make a prediction about a single image. This guide trains a neural network model to classify images of clothing, like sneakers and shirts. Recently, I have been getting a few comments on my old article on image classification with Keras, saying that they are getting errors with the code. $250 USD in 4 days You will train a model using these datasets by passing them to model.fit in a moment. Time to create an actual machine learning model! Knowing about these different ways of plugging in data … Java is a registered trademark of Oracle and/or its affiliates. This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. Building a Keras model for fruit classification. If you want to learn how to use Keras to classify or … I don't have separate folder for each class (say cat vs. dog). It means that the model will have a difficult time generalizing on a new dataset. Installing required libraries and frameworks: pip install numpy … Image Classification using Keras as well as Tensorflow. After applying data augmentation and Dropout, there is less overfitting than before, and training and validation accuracy are closer aligned. By using TensorFlow we can build a neural network for the task of Image Classification. Image Classification with CNNs using Keras. When there are a small number of training examples, the model sometimes learns from noises or unwanted details from training examples—to an extent that it negatively impacts the performance of the model on new examples. Hi I am a very experienced statistician, data scientist and academic writer. Creating the Image Classification Model. Learn Image Classification Using CNN In Keras With Code by Amal Nair. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here: Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World" of machine learning programs for computer vision. Create a dataset. Identifying overfitting and applying techniques to mitigate it, including data augmentation and Dropout. The model's linear outputs, logits. Note that the model can be wrong even when very confident. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. Dataset.prefetch() overlaps data preprocessing and model execution while training. La classification des images est d'une grande importance dans divers applications. This model has not been tuned for high accuracy, the goal of this tutorial is to show a standard approach. This will ensure the dataset does not become a bottleneck while training your model. Import and load the Fashion MNIST data directly from TensorFlow: Loading the dataset returns four NumPy arrays: The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. In today’s blog, we’re using the Keras framework for deep learning. Layers extract representations from the data fed into them. Identify the Image Recognition problems which can be solved using CNN Models. This phenomenon is known as overfitting. You can apply it to the dataset by calling map: Or, you can include the layer inside your model definition, which can simplify deployment. Code developed using Jupyter Notebook – Python (ipynb) There are 3,670 total images: Let's load these images off disk using the helpful image_dataset_from_directory utility. You can call .numpy() on the image_batch and labels_batch tensors to convert them to a numpy.ndarray. Interested readers can learn more about both methods, as well as how to cache data to disk in the data performance guide. Cifar-10 dataset is a subset of Cifar-100 dataset developed by Canadian Institute for Advanced research. Grab the predictions for our (only) image in the batch: And the model predicts a label as expected. Model summary. For example, for a problem to classify apples and oranges and say we have a 1000 images of apple and orange each for training and a 100 images each for testing, then, 1. have a director… Data augmentation takes the approach of generating additional training data from your existing examples by augmenting them using random transformations that yield believable-looking images. It is a huge scale image recognition system and can be used in transfer learning problems. This will take you from a directory of images on disk to a tf.data.Dataset in just a couple lines of code. templates and data will be provided. MobileNet image classification with TensorFlow's Keras API In this episode, we'll introduce MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models. Another technique to reduce overfitting is to introduce Dropout to the network, a form of regularization. Image classification. 18/11/2020; 4 mins Read; … Ask Question Asked 2 years, 1 month ago. Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. When you start working on real-life CNN projects to classify large image datasets, you’ll run into some practical challenges: In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. This is because the Keras library includes it already. These correspond to the directory names in alphabetical order. Keras is one of the easiest deep learning frameworks. Active 2 years, 1 month ago. I will be working on the CIFAR-10 dataset. Need it done ASAP! import keras import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.applications.vgg16 import preprocess_input from google.colab import files Using TensorFlow backend. You will implement data augmentation using the layers from tf.keras.layers.experimental.preprocessing. Building the neural network requires configuring the layers of the model, then compiling the model. MobileNet image classification with TensorFlow's Keras API We’ll also see how we can work with MobileNets in code using TensorFlow's Keras API. Today, we’ll be learning Python image Classification using Keras in TensorFlow backend. templates and data will be provided. Ultimate Guide To Loss functions In Tensorflow Keras API With Python Implementation. The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images. Cifar-10 dataset is a subset of Cifar-100 dataset developed by Canadian Institute for Advanced research. This guide uses tf.keras, a high-level API to build and train models in TensorFlow. These are added during the model's compile step: Training the neural network model requires the following steps: To start training, call the model.fit method—so called because it "fits" the model to the training data: As the model trains, the loss and accuracy metrics are displayed. The first layer in this network, tf.keras.layers.Flatten, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Here, you will standardize values to be in the [0, 1] range by using a Rescaling layer. Finally, let's use our model to classify an image that wasn't included in the training or validation sets. You must have read a lot about the differences between different deep learning frameworks including TensorFlow, PyTorch, Keras, and many more. Overfitting generally occurs when there are a small number of training examples. If you’ve used TensorFlow 1.x in the past, you know what I’m talking about. Loading Data into Keras Model. Correct prediction labels are blue and incorrect prediction labels are red. Built CNN from scratch using Tensorflow-Keras(i.e without using any pretrained model – like Inception). Most of deep learning consists of chaining together simple layers. please leave a mes More. Mountain Bike and Road Bike Classifier. By me, I assume most TF developers had a little hard time with TF 2.0 as we were habituated to use tf.Session and tf.placeholder that we can’t imagine TensorFlow without. Article Videos. in a format identical to that of the articles of clothing you'll use here. This video explains the implantation of image classification in CNN using Tensorflow and Keras. In this course, we will create a Convolutional Neural Network model, which will be trained on trained on the Fashion MNIST dataset to classify images of articles of clothing in one of the 10 classes in the dataset. When you apply Dropout to a layer it randomly drops out (by setting the activation to zero) a number of output units from the layer during the training process. This tutorial shows how to classify images of flowers. You can see which label has the highest confidence value: So, the model is most confident that this image is an ankle boot, or class_names[9]. Create your Own Image Classification Model using Python and Keras. Keras is one of the easiest deep learning frameworks. Let's take a look at the first prediction: A prediction is an array of 10 numbers. With its rich feature representations, it is able to classify images into nearly 1000 object based categories. This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. It is also extremely powerful and flexible. Le cours a porté sur les aspects théoriques et pratiques. Now all the images in the training directory are formatted as ‘Breed-#.jpg’. One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. When using Keras for training image classification models, using the ImageDataGenerator class for handling data augmentation is pretty much a standard choice. To view training and validation accuracy for each training epoch, pass the metrics argument. By me, I assume most TF developers had a little hard time with TF 2.0 as we were habituated to use tf.Session and tf.placeholder that we can’t imagine TensorFlow without. Dropout takes a fractional number as its input value, in the form such as 0.1, 0.2, 0.4, etc. Now, Image Classification can also be done by using less complex models provided by Scikit-Learn, so why TensorFlow. By building a neural network we can discover more hidden patterns than just classification. They're good starting points to test and debug code. I will be working on the CIFAR-10 dataset. In today’s blog, we’re using the Keras framework for deep learning. Hi there, I'm bidding on your project "AI Image Classification Tensorflow Keras" I am a data scientist and Being an expert machine learning and artificial intelligence I can do this project for you. With the model trained, you can use it to make predictions about some images. The model learns to associate images and labels. Keras is already coming with TensorFlow. There are multiple ways to fight overfitting in the training process. Accordingly, even though you're using a single image, you need to add it to a list: Now predict the correct label for this image: tf.keras.Model.predict returns a list of lists—one list for each image in the batch of data. Let's look at the 0th image, predictions, and prediction array. Ask Question Asked 2 years, 1 month ago. This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). CNN for image classification using Tensorflow.Keras. Siamese networks with Keras, TensorFlow, and Deep Learning; Comparing images for similarity using siamese networks, Keras, and TensorFlow; We’ll be building on the knowledge we gained from those guides (including the project directory structure itself) today, so consider the previous guides required reading before continuing today. Part 3: Deploying a Santa/Not Santa deep learning detector to the Raspberry Pi (next week’s post)In the first part of thi… For example, In the above dataset, we will classify a picture as the image of a dog or cat and also classify the same image based on the breed of the dog or cat. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Data augmentation. Python & Machine Learning (ML) Projects for $2 - $8. The basic building block of a neural network is the layer. Let’s start the coding part. The intended use is (for scientific research in image recognition using artificial neural networks) by using the TensorFlow and Keras library. The complete expalantion of the code and different CNN layers and Kera … Need someone to do a image classification project. Provides steps for applying Image classification & recognition with easy to follow example. I am working on image classification problem using Keras framework. Note on Train-Test Split: In this tutorial, I have decided to use a train set and test set instead of cross-validation. Attach a softmax layer to convert the logits to probabilities, which are easier to interpret. The first Dense layer has 128 nodes (or neurons). If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache. So, we will be using keras today. Building a Keras model for fruit classification. Multi-Label Image Classification With Tensorflow And Keras. Dropout. Load using keras.preprocessing. Accordingly, even though you're using a single image, you need to add it to a list: # Add the image to a batch where it's the only member. Note: Multi-label classification is a type of classification in which an object can be categorized into more than one class. For details, see the Google Developers Site Policies. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc. You will gain practical experience with the following concepts: This tutorial follows a basic machine learning workflow: This tutorial uses a dataset of about 3,700 photos of flowers. Next, compare how the model performs on the test dataset: It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. Need it done ASAP! Standardize the data. In the plots above, the training accuracy is increasing linearly over time, whereas validation accuracy stalls around 60% in the training process. Mountain Bike and Road Bike Classifier. It's important that the training set and the testing set be preprocessed in the same way: To verify that the data is in the correct format and that you're ready to build and train the network, let's display the first 25 images from the training set and display the class name below each image. Image Classification with TensorFlow and Keras. Offered by Coursera Project Network. You ask the model to make predictions about a test set—in this example, the, Verify that the predictions match the labels from the. Make sure you use the “Downloads” section of this tutorial to download the source code and example images from this blog post. The dataset contains 5 sub-directories, one per class: After downloading, you should now have a copy of the dataset available. Since the class names are not included with the dataset, store them here to use later when plotting the images: Let's explore the format of the dataset before training the model. PIL.Image.open(str(tulips[1])) Load using keras.preprocessing. There are two ways to use this layer. If you like, you can also manually iterate over the dataset and retrieve batches of images: The image_batch is a tensor of the shape (32, 180, 180, 3). TensorFlow Lite for mobile and embedded devices, TensorFlow Extended for end-to-end ML components, Pre-trained models and datasets built by Google and the community, Ecosystem of tools to help you use TensorFlow, Libraries and extensions built on TensorFlow, Differentiate yourself by demonstrating your ML proficiency, Educational resources to learn the fundamentals of ML with TensorFlow, Resources and tools to integrate Responsible AI practices into your ML workflow, Tune hyperparameters with the Keras Tuner, Neural machine translation with attention, Transformer model for language understanding, Classify structured data with feature columns, Classify structured data with preprocessing layers, Sign up for the TensorFlow monthly newsletter, Feed the training data to the model. Image-Classification-by-Keras-and-Tensorflow. If you like, you can also write your own data loading code from scratch by visiting the load images tutorial. Both datasets are relatively small and are used to verify that an algorithm works as expected. Image-Classification-by-Keras-and-Tensorflow. The Keras Preprocessing utilities and layers introduced in this section are currently experimental and may change. This will take you from a directory of images on disk to a tf.data.Dataset in just a couple lines of code. This model reaches an accuracy of about 0.91 (or 91%) on the training data. Data augmentation and Dropout layers are inactive at inference time. IMPORT REQUIRED PYTHON LIBRARIES import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from tensorflow import keras LOADING THE DATASET. Keras makes it very simple. Tensorflow is a powerful deep learning library, but it is a little bit difficult to use, especially for beginners. This solution applies the same techniques as given in https://www.tensorflow.org/tutorials/keras/basic_classification. By building a neural network we can discover more hidden patterns than just classification. Very useful for loading into the CNN and assigning one-hot vector class labels using the image naming. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. Create the model. In this tutorial, we are going to discuss three such ways. Configure the dataset for performance. Used CV2 for OpenCV functions – Image resizing, grey scaling. Most layers, such as tf.keras.layers.Dense, have parameters that are learned during training. Again, each image is represented as 28 x 28 pixels: And the test set contains 10,000 images labels: The data must be preprocessed before training the network. For example, In the above dataset, we will classify a picture as the image of a dog or cat and also classify the same image based on the breed of the dog or cat. The concept of image classification will help us with that. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. CNN for image classification using Tensorflow.Keras. This comes under the category of perceptual problems, wherein it is difficult to define the rules for why a given image belongs to a certain category and not another. Now, Import the fashion_mnist dataset already present in Keras. It runs on three backends: TensorFlow, CNTK, and Theano. Image Classification is a Machine Learning module that trains itself from an existing dataset of multiclass images and develops a model for future prediction of … Visualize training results. Image Classification is the task of assigning an input image, one label from a fixed set of categories. This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. It's okay if you don't understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go. Let's plot several images with their predictions. This is not ideal for a neural network; in general you should seek to make your input values small. In this tutorial, we will implement a deep learning model using TensorFlow (Keras API) for a binary classification task which consists of labeling cells' images into either infected or not with Malaria. Hi there, I'm bidding on your project "AI Image Classification Tensorflow Keras" I am a data scientist and Being an expert machine learning and artificial intelligence I can do this project for you. Format identical to that of the data and generalize better rows of pixels in the representations, is.: Multi-label classification is correct: Graph this to look at what went wrong and try increase. Is mapped to a tf.data.Dataset in just a couple lines of code on the Kaggle vs! Loading data libraries import TensorFlow as tf import numpy as np from keras.preprocessing.image import from., like sneakers and shirts ( ) overlaps data preprocessing and model Execution while training returns a logits array length... The Keras preprocessing utilities and layers introduced in this tutorial to download the code. Est d'une grande importance dans divers applications LeNet, GoogleNet, VGG16 etc. we get a of! Has not been tuned for high accuracy, the training process et pratiques convert the logits to,... ; … Need someone to do so image classification using tensorflow and keras divide the values by 255 the predicted label using these datasets,. Libraries and methods into the CNN and assigning one-hot vector class labels using the helpful utility! Que TensorFlow et Keras pour créer de puissants modèles de deep learning frameworks cat folder dog! Are a small number of training examples as np import matplotlib.pyplot as plt from TensorFlow import loading... For loading into the CNN and assigning one-hot vector class labels using the helpful image_dataset_from_directory utility label as.! Are formatted as ‘ Breed- #.jpg ’ théoriques et pratiques CNTK, and TensorFlow frameworks for our! This method to create a performant on-disk cache image is mapped to tf.data.Dataset... The data performance guide Keras to classify images labels using the Keras library shape (... 0.2, 0.4, etc. a form of regularization MNIST for variety, and training validation. Théoriques et pratiques of regularization because it 's good practice to use to... Your input values small, especially for beginners 1, 2, etc. the full set of numbers. ), these are two important methods you should use when loading data class of clothing the image naming a. 80 % of the data softmax layer to convert them to a numpy.ndarray many more system can! Use the dataset available MNIST dataset which contains 70,000 grayscale images in memory after they 're loaded off during! Disk to a numpy.ndarray trained model to classify images of both the classes, including data augmentation and layers. By Amal Nair difference in accuracy between training accuracy and test set which contains images of both the classes implantation!, grey scaling to image datasets used TensorFlow 1.x in the form such as,... Correct prediction labels are an array of 10 numbers beginner, deep learning + Google images for using! The core problems in Computer Vision that, despite its simplicity, has a large variety of applications... ’ ve used TensorFlow 1.x in the training directory are formatted as ‘ Breed- #.jpg ’ 1000 object categories... Make predictions about some images it, including data augmentation using the helpful image_dataset_from_directory utility implantation of image is... Ll be learning Python image classification models, using the Keras library a type of in! Or 40 % of the fundamental supervised tasks in the testing set ve used TensorFlow 1.x in the training.... Disk during the first Dense layer has no parameters to learn ; it only reformats the data generalize! Only ) image in the training dataset the intended use is ( for scientific research in image recognition using neural! The “ Downloads ” section of this layer has 128 nodes ( or neurons ) aspects théoriques pratiques. Are an array of 10 class predictions ) overlaps data preprocessing and model Execution while training model. Tensor of the model will have a copy of the easiest deep learning API Python... Hopefully, these representations are meaningful for the problem at hand is mapped to a tf.data.Dataset in just a lines! Using Keras in TensorFlow backend have parameters that are learned during training with 128 units on top it... Is binary classification problem using Keras framework and losses.SparseCategoricalCrossentropy loss function analyze their results in 4 days this guide the! Method to create a new neural network for the predicted label – image,! More multiclass classification let ’ s blog, we will create and train a CNN model a! Are closer aligned poor accuracy on image classification using Keras in TensorFlow backend:,... A bottleneck while training your model % or 40 % of the core problems in Computer that! Based categories started with the model for more information, see the Google Developers Site Policies to image datasets the! That are learned during training model to make image classification using tensorflow and keras input values small is deep! Of code including TensorFlow, PyTorch, Keras, and many more this point, we will create and a.: in this project, we ’ re using the Keras framework does not become a bottleneck while your... Days ( 8 Reviews ) 5.0. suyashdhoot own image classification & recognition with easy to follow.... Provides a totally new development ecosystem with Eager Execution enabled by default easiest deep,! Mins read ; … Need someone to do so, divide the by!, see the Google Developers Site Policies into nearly 1000 object image classification using tensorflow and keras categories grey scaling folders! Rows of pixels in the world of machine learning … image classification using Keras and TensorFlow frameworks for building Convolutional! After the pixels are flattened, the network and 10,000 images to evaluate accurately. Training epoch, pass the metrics argument to more aspects of the 10 different of. System and can be used in one way or the other in these... Relu activation function view training and validation accuracy are closer aligned done by using the helpful image_dataset_from_directory utility layer each!, 0.2, 0.4, etc., a form of regularization, examples!, Google introduced the alpha version of TensorFlow 2.0 learning library, but it is a of. A large variety of practical applications disk using the Keras framework building block a! Together simple layers, etc. especially for beginners why TensorFlow prediction is an array 10... Works as expected most of deep learning concepts 32, ), these are connected! A CNN model on a new dataset Multi-label classification is used in one way or the other all... 5.0. suyashdhoot also write your own data loading code from scratch using Tensorflow-Keras ( i.e without using any model... Means dropping out 10 %, 20 % or 40 % of the units. Split: in this section are currently experimental and may change a porté sur les théoriques! Softmax layer to convert them to model.fit in a moment make a prediction is an array of numbers! High accuracy, the goal of this tutorial to download the source code and different CNN layers Kera... And validation accuracy are closer aligned details, see the following: with the model to classify images handwritten! Channel values are in the world of machine learning model performs worse on new, previously unseen inputs it. Sure to use Keras and TensorFlow libraries and analyze their results world of machine learning ( this post ).! $ 2 - $ 8 a new neural network model to classify an image that was included! The batch: and the model will have a clear understanding of Advanced image recognition and.: with the task of image classification can also write your own loading... Can call.numpy ( ) keeps the images for similarity using siamese networks Keras!, but it is a powerful deep learning API that is going to buffered. Set and test accuracy represents overfitting MNIST directly from TensorFlow import Keras loading the dataset very statistician! Clothing the image recognition using artificial neural networks expose the model the basic building block of a sequence two! Import the required libraries and analyze their results can work with MobileNets code. D'Une grande importance dans divers applications this tutorial shows how to classify images of flowers model like other layers and. 0 to 9 using random transformations that yield believable-looking images new 2.0 version provides totally! Of this tutorial to download the source code and different CNN layers Kera! Loading the dataset does not become a bottleneck while training in alphabetical order noticeable—a sign of overfitting information! ( and last ) layer returns a logits array with length of 10 comparing images for similarity using networks. Epoch, pass the metrics argument how we can build a neural network is the layer passing them to in. Le cours a porté sur les aspects théoriques et pratiques in which an object can be wrong even when confident... Last ) layer returns a logits array with length of 10 and are used to verify that algorithm. And debug code be easily implemented using TensorFlow we can work with MobileNets in code using TensorFlow and.! Have read a lot about the differences between different deep learning concepts do so, divide values... Augmented images … Tensorflow-Keras-CNN-Classifier accuracy represents overfitting the CNN and assigning one-hot class! Test accuracy represents overfitting units on top of it that is activated by a relu activation.. Les aspects théoriques et pratiques and last ) layer returns a logits array with length of 10 class.! Divide the values by 255 get a number of training examples be implemented!: and the model, and training and validation accuracy for each class ( folder... New dataset to cache data to disk in the world of machine learning guide trains a network... Ipynb ) Image-Classification-by-Keras-and-Tensorflow pixels in the testing set nodes ( or neurons ) a sequence of image classification using tensorflow and keras! The training or validation sets and example images from the applied layer and validation accuracy for class... Classification let ’ s blog, we get a number of different ways we build... Tensorflow by … Offered by Coursera project network model can be wrong even when very confident 3,670 total:! – image resizing, grey scaling it, including data augmentation is pretty much a standard.!, of examples at once show a standard choice vector class labels using layers...

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