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implementation in pure TypeScript with GPU support.","homepage":"https://github.com/DrA1ex/mind-net.js","keywords":["ml","machine-learning","deep-learning","neural-network","neural-network-engine","gan","generative-adversarial-network","artificial-intelligence","classification","regression","image-processing","unsupervised-learning","supervised-learning","vae","variational-autoencoder"],"repository":{"type":"git","url":"git+https://github.com/DrA1ex/mind-net.js.git"},"author":{"name":"DrA1ex"},"bugs":{"url":"https://github.com/DrA1ex/mind-net.js/issues"},"license":"BSD-3","readme":"# mind-net.js\n\nSimple to use neural network implementation in pure TypeScript with GPU support.\n\n[](https://badge.fury.io/js/mind-net.js) [](https://github.com/DrA1ex/mind-net.js/actions/workflows/tests.yml) [](https://github.com/DrA1ex/mind-net.js/actions/workflows/jekyll-gh-pages.yml)\n\n
\n\n
\n\n\n\n## About\nmind-net.js is a fast and lightweight library that offers the necessary tools to train and execute neural networks. By using mind-net.js, developers can conveniently create and explore neural networks, gaining practical knowledge in the domain of machine learning.\n\n**Note:** This library is primarily intended for small to medium-sized projects or educational purposes. It may not be suitable for high-performance or large-scale applications.\n## Installation \n\n```bash\nnpm install mind-net.js\n```\n\n## Get Started\n#### Approximation of the XOR function\n```javascript\nimport MindNet from \"mind-net.js\";\n\nconst network = new MindNet.Models.Sequential(\"rmsprop\");\n\nnetwork.addLayer(new MindNet.Layers.Dense(2));\nnetwork.addLayer(new MindNet.Layers.Dense(4));\nnetwork.addLayer(new MindNet.Layers.Dense(1));\n\nnetwork.compile();\n\nconst input = [[0, 0], [0, 1], [1, 0], [1, 1]];\nconst expected = [[0], [1], [1], [0]];\nfor (let i = 0; i < 20000; i++) {\n network.train(input, expected);\n}\n\nconsole.log(network.compute([1, 0])); // 0.99\n```\n\n### Use in browser:\n\n1. Install packages and build bundle:\n```bash\n# Step 1: Install the required packages\nnpm install esbuild --save-dev\n\n# Option 1: Building the single file bundle\n# (Assuming your entry file is index.js)\n# This command creates bundle.js as the output bundle\nnpx esbuild index.js --bundle --format=esm --outfile=bundle.js\n\n# Option 2: Building the bundle with the worker script for ParallelModelWrapper\n# Use this command if you want to include the worker script\n# (Assuming your entry file is index.js)\n# This command creates a bundle directory with the built bundle set\nesbuild index=index.js parallel.worker=node_modules/mind-net.js/parallel.worker.js --bundle --splitting --format=esm --outdir=./bundle\n```\n\n2. Import the bundle script in your HTML:\n```html\n\n\n\n\n\n```\n\n## Table of Contents\n- [Examples](#examples)\n - [Approximation of distance function](#Approximation-of-distance-function)\n - [Generative Adversarial network (GAN) for Colorful Cartoon generation with Autoencoder filtering](#generative-adversarial-network-gan-for-colorful-cartoon-generation-with-autoencoder-filtering)\n - [Multithreading](#Multithreading)\n - [GPU](#gpu)\n - [Saving/Loading model](#savingloading-model)\n - [Configuration of Training dashboard](#Configuration-of-Training-dashboard)\n- [Benchmark](#benchmark)\n - [CPU Benchmark](#cpu-benchmark-v133)\n - [GPU Benchmark](#gpu-benchmark-core-v141-gpu-binding-v101)\n- [Examples source code](#Examples-source-code)\n- [Demo](#demo)\n - [Sequential demo](#Sequential-demo)\n - [Generative-adversarial Network demo](#Generative-adversarial-Network-demo)\n - [Cartoonify image](#generating-cartoon-portrait-from-given-image-link)\n- [Datasets](#Datasets-used-in-examples)\n\n\n## Examples\n\n#### Approximation of distance function\n```javascript\nimport MindNet, {Matrix} from \"mind-net.js\";\n\nconst optimizer = new MindNet.Optimizers.AdamOptimizer({lr: 0.01, decay: 1e-3});\nconst loss = new MindNet.Loss.MeanSquaredErrorLoss({k: 500});\n\nconst network = new MindNet.Models.Sequential(optimizer, loss);\n\nnetwork.addLayer(new MindNet.Layers.Dense(2));\n\nfor (const size of [64, 64]) {\n network.addLayer(new MindNet.Layers.Dense(size, {\n activation: \"leakyRelu\", weightInitializer: \"xavier\", options: {\n l2WeightRegularization: 1e-5,\n l2BiasRegularization: 1e-5,\n }\n }));\n}\n\nnetwork.addLayer(new MindNet.Layers.Dense(1, {\n activation: \"linear\", weightInitializer: \"xavier\"\n}));\n\nnetwork.compile();\n\n\nconst MaxNumber = 10;\nconst nextFn = () => [Math.random() * MaxNumber, Math.random() * MaxNumber];\nconst realFn = (x, y) => Math.sqrt(x * x + y * y);\n\nconst Input = Matrix.fill(nextFn, 1000);\nconst Expected = Input.map(([x, y]) => [realFn(x, y)]);\n\nconst TestInput = Input.splice(0, Input.length / 10);\nconst TestExpected = Expected.splice(0, TestInput.length);\n\n// Training should take about 100-200 epochs\nfor (let i = 0; i < 300; i++) {\n network.train(Input, Expected, {epochs: 10, batchSize: 64});\n\n const {loss, accuracy} = network.evaluate(TestInput, TestExpected);\n console.log(`Epoch ${network.epoch}. Loss: ${loss}. Accuracy: ${accuracy.toFixed(2)}`);\n\n if (loss < 1e-4) {\n console.log(`Training complete. Epoch: ${network.epoch}`);\n break;\n }\n}\n\nconst [x, y] = nextFn();\nconst real = realFn(x, y);\nconst [result] = network.compute([x, y]);\nconsole.log(`sqrt(${x.toFixed(2)} ** 2 + ${y.toFixed(2)} ** 2) = ${result.toFixed(2)} (real: ${real.toFixed(2)})`);\n```\n\n### Generative Adversarial network (GAN) for Colorful Cartoon generation with Autoencoder filtering\n\n\n\nGenerated images grid (20x20): [link](https://github.com/DrA1ex/mind-net.js/assets/1194059/8866fc09-823b-4a13-be8e-de81d4fbafd5)\n\n\n```javascript\n// Full code see in ./examples/src/cartoon_colorful_example.js\n\n// Fetch dataset\nconst DatasetUrl = \"https://github.com/DrA1ex/mind-net.js/files/12396106/cartoon-2500-28.zip\";\nconst zipData = await fetch(DatasetUrl).then(r => r.arrayBuffer());\n\n\n// Loading the dataset from the zip file\nconst trainData = (await DatasetUtils.loadDataset(zipData));\n\n// Creating grayscale Autoencoder training data from the RGB training data\nconst gsTrainData = grayscaleDataset(trainData, 3);\n\n// ... Create generator and discriminator models\n\n// Creating the generative adversarial model\nconst ganModel = new GenerativeAdversarialModel(generator, discriminator, createOptimizer(), loss);\n\n// Creating the autoencoder (AE) model\nconst ae = new SequentialModel(createOptimizer(), \"mse\");\n// ... add AE layers and compile\n\n// Declare filtering function\nfunction _filterWithAE(input) { /* ... */ }\n\n// Train loop\nfor (let i = 0; i < epochs; i++) {\n console.log(\"Epoch:\", ganModel.ganChain.epoch + 1);\n \n // Train epoch\n ganModel.train(trainData, {batchSize});\n ae.train(gsTrainData, gsTrainData, {batchSize});\n\n // Save generated image\n await ImageUtils.saveSnapshot(generator, ganModel.ganChain.epoch, {label: \"generated\", channel: 3});\n \n // Save filtered image\n await ImageUtils.saveImageGrid((x, y) => _filterWithAE(ImageUtils.InputCache.get(generator)[`${x},${y}`]),\n `./out/filtered_${ae.epoch.toString().padStart(6, \"0\")}.png`, imageSize, 10, 3);\n}\n```\n\n### Multithreading\n```javascript\nimport {SequentialModel, Dense, ParallelModelWrapper} from \"mind-net.js\";\n\n// Create and configure model\nconst network = new SequentialModel();\nnetwork.addLayer(new Dense(2));\nnetwork.addLayer(new Dense(64, {activation: \"leakyRelu\"}));\nnetwork.addLayer(new Dense(1, {activation: \"linear\"}));\nnetwork.compile();\n\n// Define the input and expected output data\nconst input = [[1, 2], [3, 4], [5, 6]];\nconst expected = [[3], [7], [11]];\n\n// Create and initialize wrapper\nconst parallelism = 4;\nconst pModel = new ParallelModelWrapper(network, parallelism);\nawait pModel.init();\n\n// Train model\nawait pModel.train(input, expected);\n\n// Compute predictions\nconst predictions = await pModel.compute(input);\n\n// Terminate workers\nawait pModel.terminate();\n```\n\n### GPU\n\n1. Install the binding\n```shell\nnpm install @mind-net.js/gpu\n```\n\n_Optionally_, if you encounter any build issues, you can add overrides for the gl package by modifying your package.json configuration as follows:\n```javascript\n{\n //...\n \"overrides\": {\n \"gl\": \"^6.0.2\"\n }\n}\n```\n\n2. Use imported binding\n```javascript\nimport {SequentialModel, Dense} from \"mind-net.js\";\nimport {GpuModelWrapper} from \"@mind-net.js/gpu\";\n\nconst network = new SequentialModel();\nnetwork.addLayer(new Dense(2));\nnetwork.addLayer(new Dense(64, {activation: \"leakyRelu\"}));\nnetwork.addLayer(new Dense(1, {activation: \"linear\"}));\nnetwork.compile();\n\n// Define the input and expected output data\nconst input = [[1, 2], [3, 4], [5, 6]];\nconst expected = [[3], [7], [11]];\n\n// Create GPU wrapper\nconst batchSize = 128; // Note: batchSize specified only when creating the wrapper\nconst gpuWrapper = new GpuModelWrapper(network, batchSize);\n\n// Train model\ngpuWrapper.train(input, expected);\n\n// Compute predictions\nconst predictions = gpuWrapper.compute(input);\n\n// Free resources\ngpuWrapper.destroy();\n```\n\n### Saving/Loading model\n```javascript\nimport {SequentialModel, Dense, ModelSerialization, BinarySerializer, TensorType} from \"mind-net.js\";\n\n// Create and configure model\nconst network = new SequentialModel();\nnetwork.addLayer(new Dense(2));\nnetwork.addLayer(new Dense(64, {activation: \"leakyRelu\"}));\nnetwork.addLayer(new Dense(1, {activation: \"linear\"}));\nnetwork.compile();\n\n// Save model\nconst savedModel = ModelSerialization.save(network);\nconsole.log(savedModel);\n\n// Load model\nconst loadedModel = ModelSerialization.load(savedModel);\n\n// Save model in binary representation and reduce weights precision to Float32\nconst binaryModel = BinarySerializer.save(network, TensorType.F32);\nconsole.log(`Model size: ${binaryModel.byteLength}`);\n\n// Load binary model\nconst loadedFromBinary = BinarySerializer.load(binaryModel);\n\n```\n\n### Configuration of Training dashboard\n```javascript\nimport {SequentialModel, AdamOptimizer, Dense, TrainingDashboard, Matrix} from \"mind-net.js\";\n\n// Create and configure model\nconst network = new SequentialModel(new AdamOptimizer({lr: 0.0005, decay: 1e-3, beta: 0.5}));\nnetwork.addLayer(new Dense(2));\nnetwork.addLayer(new Dense(64, {activation: \"leakyRelu\"}));\nnetwork.addLayer(new Dense(64, {activation: \"leakyRelu\"}));\nnetwork.addLayer(new Dense(1, {activation: \"linear\"}));\nnetwork.compile();\n\n// Define the input and expected output data\nconst input = Matrix.fill(() => [Math.random(), Math.random()], 500);\nconst expected = input.map(([x, y]) => [Math.cos(Math.PI * x) + Math.sin(-Math.PI * y)]);\n\n// Define the test data\nconst tInput = input.splice(0, Math.floor(input.length / 10));\nconst tExpected = expected.splice(0, tInput.length);\n\n// Optionally configure dashboard size\nconst dashboardOptions = {width: 100, height: 20};\n\n// Create a training dashboard to monitor the training progress\nconst dashboard = new TrainingDashboard(network, tInput, tExpected, dashboardOptions);\n\n// Train the network\nfor (let i = 0; i <= 150; i++) {\n // Train over data\n network.train(input, expected, {progress: false});\n\n // Update the dashboard\n dashboard.update();\n\n // Print the training metrics every 5 iterations\n if (i % 5 === 0) dashboard.print();\n}\n```\n\n\n\n\n## Benchmark\n\n### CPU Benchmark (v1.3.3)\n\n**Full-sized dataset (5 iterations), CPU only, Prediction Speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total Time (s) | Speed compared to Worker |\n|----------------------|--------------|-------------|------------------|---------------------------|\n| mind-net.js (Worker) | 149.6 | 9.182 | 0.7481 | Baseline |\n| mind-net.js | 744.4 | 2.652 | 3.7222 | ~397.77% Slower |\n| Tensorflow.js | 929 | 0.762 | 4.6448 | ~520.88% Slower |\n| Brain.js | 1024.9 | 1.819 | 5.1247 | ~585.05% Slower |\n| Tensorflow (Native) | 74.8 | 11.46 | 0.3742 | ~49.91% Faster |\n\n**Single-sample dataset (10,000 iterations), CPU only, Prediction Speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total Time (s) | Speed compared to Worker |\n|----------------------|--------------|-------------|------------------|---------------------------|\n| mind-net.js (Worker) | 0.4 | 474.086 | 3.662 | Baseline |\n| mind-net.js | 0.4 | 69.923 | 3.7037 | - |\n| Tensorflow.js | 0.6 | 74.901 | 5.8835 | ~60% Slower |\n| Brain.js | 0.5 | 78.524 | 5.2406 | ~43% Slower |\n| Tensorflow (Native) | 0.8 | 86.92 | 8.2383 | ~124% Slower |\n\n**Full-sized dataset (5 iterations), CPU only, Train Speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total Time (s) | Speed compared to Worker |\n|----------------------|--------------|-------------|------------------|---------------------------|\n| mind-net.js (Worker) | 464 | 4.132 | 2.3202 | Baseline |\n| mind-net.js | 2345.2 | 3.452 | 11.7263 | ~405.4% Slower |\n| Tensorflow.js | 2826.3 | 0.794 | 14.1317 | ~509.63% Slower |\n| Brain.js | 2703.6 | 0.309 | 13.518 | ~483.05% Slower |\n| Tensorflow (Native) | 109 | 7.124 | 0.5451 | ~76% Faster |\n\n**Single-sample dataset (10,000 iterations), CPU only, Train Speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total Time (s) | Speed compared to Worker |\n|----------------------|--------------|-------------|------------------|---------------------------|\n| mind-net.js (Worker) | 2.9 | 99.576 | 29.1533 | Baseline |\n| mind-net.js | 3 | 498.137 | 29.7619 | ~3.45% Slower |\n| Tensorflow.js | 8.5 | 827.866 | 84.7745 | ~192.86% Slower |\n| Brain.js | 1.5 | 1129.554 | 15.4283 | ~48.28% Faster |\n| Tensorflow (Native) | 3.5 | 166.625 | 34.9394 | ~19.8% Slower |\n\nComparison with different dataset sizes: [link](https://docs.google.com/spreadsheets/d/e/2PACX-1vQhyMUNaJj1-9JhKrMHIIhj5fjzoVue1b0Lrke8UhEkhNqHqVJ9s1uRK6ceQkdrloia2OPqUlWNdEzr/pubchart?oid=360497977&format=interactive)\n\nYou can find benchmark script at: [/examples/src/benchmark.js](/examples/src/benchmark.js)\n\n### GPU Benchmark (Core v1.4.1, GPU binding v1.0.1)\n\n**Full-sized dataset (10 iterations), GPU only, Prediction speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total time (s) | Speed comparison |\n|---------------------------|----------------|--------------|----------------|------------------|\n| mind-net.js | 239.7 | 19.9932 | 2.3967 | Baseline |\n| Tensorflow.js (native) | 98.7 | 9.5713 | 0.9869 | ~58.82% Faster |\n| Brain.js | 2629 | 6.0515 | 26.29 | ~991.46% Slower |\n\n**Full-sized dataset (10 iterations), GPU only, Train speed:**\n\n| Library | Mean Time (ms) | Variance (%) | Total time (s) | Speed comparison |\n|-------------------------|----------------|--------------|----------------|------------------|\n| mind-net.js | 677.4 | 5.6329 | 6.7735 | Baseline |\n| Tensorflow.js (native) | 216.5 | 4.3513 | 2.1646 | ~68.02% Faster |\n| Brain.js | 3849.8 | 4.8699 | 38.4984 | ~468.94% Slower |\n\nYou can find benchmark script at: [/examples/src/benchmark.js](/examples/src/benchmark_gpu.js)\n\n## Examples source code\n\nSee examples [here](examples/)\n\nTo run examples, follow these steps:\n```shell\n# Go to examples folder\ncd ./examples\n\n# Install packages\nnpm install\n\n# Run example\nnode ./src/cartoon_colorful_example.js\n```\n\n# Demo\n\n## Sequential demo\n### Classification of 2D space from set of points with different type ([link](https://dra1ex.github.io/mind-net.js/demo1/))\n\n\n\n\n\n#### Training dashboard (browser console)\n\n\n\n#### Controls:\n- To place **T1** point do _Left click_ or select **T1** switch\n- To place **T2** point do _Right click_ or _Option(Alt) + Left click_ or select **T2** switch\n- To retrain model from scratch click refresh button\n- To clear points click delete button\n- To Export/Import point set click export/import button \n\n**Source code**: [src/app/pages/demo1](/src/app/pages/demo1)\n\n## Generative-adversarial Network demo\n### Generating images by unlabeled sample data ([link](https://dra1ex.github.io/mind-net.js/demo2/))\n\n**DISCLAIMER**: The datasets used in this example have been deliberately simplified, and the hyperparameters have been selected for the purpose of demonstration to showcase early results. It is important to note that the quality of the outcomes may vary and is dependent on the size of the model and chosen hyperparameters.\n\n\n\n\n\n\n\n\n\n**Source code**: [src/app/pages/demo2](/src/app/pages/demo2)\n\n## Prediction demo\n### Generating cartoon portrait from given image ([link](https://dra1ex.github.io/mind-net.js/demo3/))\n\n\n\n\n## Datasets used in examples\n\n_Black & White:_\n- [mnist-500-16.zip](https://github.com/DrA1ex/mind-net.js/files/7082675/mnist-16.zip) ([source](https://www.kaggle.com/competitions/digit-recognizer))\n- [check-mark-10-16.zip](https://github.com/DrA1ex/mind-net.js/files/7082841/check-mark-16.zip) (original)\n- [fashion-mnist-300-28.zip](https://github.com/DrA1ex/mind-net.js/files/12293875/fashion-mnist-dataset.zip) ([source](https://www.kaggle.com/datasets/zalando-research/fashionmnist))\n- [mnist-60000-28.zip](https://github.com/DrA1ex/mind-net.js/files/12456697/mnist-10000-28.zip) ([source](https://www.kaggle.com/competitions/digit-recognizer))\n\n_Colorful:_\n- [cartoon-500-28.zip](https://github.com/DrA1ex/mind-net.js/files/12394478/cartoon-500-28.zip) ([source](https://google.github.io/cartoonset/))\n- [cartoon-2500-28.zip](https://github.com/DrA1ex/mind-net.js/files/12407792/cartoon-2500-28.zip) ([source](https://google.github.io/cartoonset/))\n- [cartoon-2500-64.zip](https://github.com/DrA1ex/mind-net.js/files/12398103/cartoon-2500-64.zip) ([source](https://google.github.io/cartoonset/))\n- [doomguy-36-28.zip](https://github.com/DrA1ex/mind-net.js/files/12574918/doomguy-36-28.zip)\n\n\n\n## License\nThis project is licensed under the BSD 3 License. See the [LICENSE](LICENSE) file for more information.\n","readmeFilename":"README.md"}
![\"star\"](\"https://user-images.githubusercontent.com/1194059/128631442-0a0350df-d5b1-4ac2-b3d0-030e341f68a3.png\")
\n\n#### Training dashboard (browser console)\n\n