EKON 24 ML_community_edition
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The document discusses various resources and APIs related to neural networks, including the TensorFlow library, FANN (Fast Artificial Neural Network), and CAI neural APIs optimized for different instruction sets. It provides installation guidelines, example codes, and links to projects that demonstrate simple image classification and model saving processes. Additionally, it offers sources and links to further materials for developers interested in machine learning and neural networks.
EKON 24 ML_community_edition
- 1. 1 ML Community Edition 10 Writeln 'Hi AI'; 20 Goto 10;
- 2. 2 Agenda EKON 24 • Tensorflow64 Library • FANN (Fast Artificial Neural Network ) • CAI NEURAL API • K-CAI NEURAL API (Jupyter Notebook) • Installation, Optimisation & Sources
- 4. 4 Develop for Multiple Devices https://www.tensorflow.org/
- 5. 5 • unit tensorflow; • • interface • • Const • tensorflow_dll = 'tensorflow64.dll'; • • Type • pTFChar = PAnsiChar; • ppTFChar = ^pTFChar; • size_t = NativeUInt; • int64_t = Int64; • pint64_t = ^int64_t; • ppint64_t = ^pint64_t; • psize_t = ^size_t; • TEnumType = Cardinal; • Float = Single; • pFloat = ^Float; Now Demo: TensorFlowTest64.dproj
- 6. 6 FANN • Fast Artificial Neural Network (FANN) Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. • Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. • Bindings to more than 15 programming languages are available. • https://github.com/libfann/fann
- 7. 7 Delphi Wrapper • Fast Artificial Neural Network Library (FANN) v2.2.0 (Original: https://github.com/libfann/fann) • Fast Artificial Neural Network Library (FANN) v2.2.0 (https://github.com/hatsunearu/fann with FANN_RELU and FANN_LEAKY_RELU) • TensorFlow 1.3.0 → Demo • https://github.com/Laex/Delphi-Artificial-Neural-Network-Library •
- 8. 8 FANN Scripting NN:= TFannNetwork.create(self) with NN do begin Layers.add('2') Layers.add('3') Layers.add('1') LearningRate:= 0.699999988079071100 ConnectionRate:= 1.000 TrainingAlgorithm:= taFANN_TRAIN_RPROP ActivationFunctionHidden:= afFANN_SIGMOID ActivationFunctionOutput:= afFANN_SIGMOID end; C:maXboxEKON24examples814_FANN_XorSample2.pas
- 9. 9 CAI NEURAL API • K-CAI NEURAL API is a Keras based neural network API for machine learning that will allow you to prototype with a lots of possibilities of Tensorflow! Python, Free Pascal and Delphi together in Google Colab, Git or the Community Edition. • https://github.com/joaopauloschuler/neural-api
- 10. 10 CAI NEURAL API II • CAI NEURAL API is a pascal based neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA. This API has been tested under Windows and Linux. • This project is a subproject from a bigger and older project called CAI and is sister to Keras based K-CAI NEURAL API. https://github.com/joaopauloschuler/neural-api
- 11. 11 Colab as Universal Platform Simple Image Classification with any Dataset: this example shows how to create a model and train it with a dataset (samples and features) passed as parameter. Open In Colab https://colab.research.google.com/github/maxkleiner/maXbox/blob/master/Copy_of_simple_image_classification_with_any_dataset.ipynb https://colab.research.google.com/github/maxkleiner/ maXbox/blob/master/Copy_of_simple_image_classificat ion_with_any_dataset.ipynb
- 12. 12 CIFAR-10 Image Classifier This example has interesting aspects to look at: Its source code is very small. Layers are added sequentially. Training hyper-parameters are defined before calling fit method. Model parameters are saved as hdf5 EKONSimpleImageClassifier.nn https://github.com/maxkleiner/maXbox/blob/master/EKON24_SimpleImageClassificationCPU.ipynb and the same in colab.research: https://colab.research.google.com/github/maxkleiner/maXbox/blob/master/EKON24_SimpleImageClassificationCPU.ipynb
- 13. 13 Win 10 Core Optimisation CPU or GPU or TPU?
- 14. 14 Win 32 or 64 API ?
- 15. 15 Save the model • Keras separates the concerns of saving your model architecture and saving your model weights. • Model weights are saved to HDF5 format. This is a grid format that is ideal for storing multi-dimensional arrays of numbers. • •Layer 11 Max Output: 0.812 Min Output: 0.000 TNNetSoftMax 10,1,1 Times: 0.00s 0.00s Parent:10 •Starting Testing. •Epochs: 50 Examples seen:2000000 Test Accuracy: 0.8383 Test Error: 0.4463 Test Loss: 0.4969 Total time: 162.32min •Epoch time: 2.7 minutes. 100 epochs: 4.5 hours. •Epochs: 50. Working time: 2.71 hours. •Finished.
- 16. 16 Save files local • import os • import urllib.request • import tarfile • • if not os.path.isfile('cifar-10-batches-bin/data_batch_1.bin'): • print("Downloading CIFAR-10 Files") • url = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz' • urllib.request.urlretrieve(url, './file.tar') • • tar = tarfile.open("file.tar") • tar.extractall() • tar.close()
- 17. 17 Finally you can get with git • C:maXboxmX399100maxbox4maxbox42maxbox4>git clone https://github.com/joaopau • loschuler/k-neural-api.git k • Cloning into 'k'... • remote: Enumerating objects: 65, done. • remote: Counting objects: 100% (65/65), done. • remote: Compressing objects: 100% (43/43), done. • remote: Total 356 (delta 38), reused 38 (delta 18), pack-reused 291 • Receiving objects: 100% (356/356), 224.47 KiB | 1.57 MiB/s, done. • Resolving deltas: 100% (225/225), done. http://docs.codehaus.org/display/SONAR/Developers%27+Seven+Deadly+Sins
- 18. 18
- 19. 19 Links & Sources • Almost all files: • • https://sourceforge.net/projects/maxbox/files/Examples/EKON/EKON24/ • • https://maxbox4.wordpress.com/blog/ • • https://github.com/maxkleiner/maXbox4/releases •
- 20. 20 May the source be with you! [email protected] www.softwareschule.ch