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+12317,11 @@ "node": ">=18" } }, + "node_modules/whatwg-fetch": { + "version": "3.6.20", + "resolved": "https://registry.npmjs.org/whatwg-fetch/-/whatwg-fetch-3.6.20.tgz", + "integrity": "sha512-EqhiFU6daOA8kpjOWTL0olhVOF3i7OrFzSYiGsEMB8GcXS+RrzauAERX65xMeNWVqxA6HXH2m69Z9LaKKdisfg==" + }, "node_modules/whatwg-mimetype": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/whatwg-mimetype/-/whatwg-mimetype-4.0.0.tgz", @@ -10913,6 +12419,11 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/which-module": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/which-module/-/which-module-2.0.1.tgz", + "integrity": "sha512-iBdZ57RDvnOR9AGBhML2vFZf7h8vmBjhoaZqODJBFWHVtKkDmKuHai3cx5PgVMrX5YDNp27AofYbAwctSS+vhQ==" + }, "node_modules/which-typed-array": { "version": "1.1.15", "resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.15.tgz", diff --git a/package.json b/package.json index 24de612cc..b326fdd9d 100644 --- a/package.json +++ b/package.json @@ -44,6 +44,7 @@ "lunr-languages": "^1.14.0", "lzma": "^2.3.2", "marked": "^4.0.15", + "ml4f": "file:ml4f-1.10.1.tgz", "mobile-drag-drop": "^2.3.0-rc.2", "react": "^18.0.0", "react-dom": "^18.0.0", diff --git a/src/App.tsx b/src/App.tsx index 359f2ec9c..4769cc445 100644 --- a/src/App.tsx +++ b/src/App.tsx @@ -30,7 +30,7 @@ import SettingsProvider from "./settings/settings"; import BeforeUnloadDirtyCheck from "./workbench/BeforeUnloadDirtyCheck"; import { SelectionProvider } from "./workbench/use-selection"; import Workbench from "./workbench/Workbench"; -import MlDataProvider from "./documentation/ml/use-model-data"; +import ModelDataProvider from "./documentation/ml/use-model-data"; const isMockDeviceMode = () => // We use a cookie set from the e2e tests. Avoids having separate test and live builds. @@ -77,7 +77,7 @@ const App = () => { - + @@ -93,7 +93,7 @@ const App = () => { - + diff --git a/src/documentation/ml/ml.ts b/src/documentation/ml/ml.ts index c345ec8dd..230e354d2 100644 --- a/src/documentation/ml/ml.ts +++ b/src/documentation/ml/ml.ts @@ -1,7 +1,7 @@ import * as tf from "@tensorflow/tfjs"; -import { LayersModel, SymbolicTensor } from "@tensorflow/tfjs"; -import { FilterType, MlSettings, ActionData } from "./training-data"; +import { LayersModel } from "@tensorflow/tfjs"; import { Filters, determineFilter } from "./data-functions"; +import { ActionData, FilterType, MlSettings } from "./training-data"; export const Axes = { X: "x", @@ -38,16 +38,15 @@ export const createModel = async ( settings.includedFilters.size * settings.includedAxes.length, ]; - const input = tf.input({ shape: inputShape }); - const normalizer = tf.layers.batchNormalization().apply(input); - const dense = tf.layers - .dense({ units: 16, activation: "relu" }) - .apply(normalizer); - const softmax = tf.layers - .dense({ units: numberOfClasses, activation: "softmax" }) - .apply(dense) as SymbolicTensor; + const model = new tf.Sequential({ + layers: [ + tf.layers.inputLayer({ inputShape }), + tf.layers.batchNormalization(), + tf.layers.dense({ units: 16, activation: "relu" }), + tf.layers.dense({ units: numberOfClasses, activation: "softmax" }), + ], + }); - const model = new tf.LayersModel({ inputs: input, outputs: softmax }); model.compile({ loss: "categoricalCrossentropy", optimizer: tf.train.sgd(0.5), @@ -121,46 +120,33 @@ export const trainModel = async ( }; // Added files to the initial project to enable model class name retention and methods to simulate the model running on the device -export const modelModule = `# A mockup of a machine learning micropython module +export const modelModule = ( + actionNames: string +) => `# A mockup of a machine learning micropython module from microbit import * import random -import mlreader - def get_class_names(): - namesList = mlreader.read_class_names() - return namesList + action_names = ${actionNames} + return action_names def current_action(): - list = mlreader.read_class_names() - sizeList = len(list) - 1 - rnd = random.randrange(sizeList) - return list[rnd] + action_names = get_class_names() + if len(action_names): + return random.choice(action_names) def is_action(action): - list = mlreader.read_class_names() - if action in list: - sizeList = len(list) - 1 - rnd = random.randrange(sizeList) - if action == list[rnd]: - return True - else: - return False - else: - return False + action_names = get_class_names() + if len(action_names) and action in action_names: + return action == random.choice(action_names) + return False def was_action(action): - list = mlreader.read_class_names() - if action in list: - sizeList = len(list) - 1 - rnd = random.randrange(sizeList) - if action == list[rnd]: - return True - else: - return False - else: - return False + action_names = get_class_names() + if len(action_names) and action in action_names: + return action == random.choice(action_names) + return False `; diff --git a/src/documentation/ml/use-model-data.tsx b/src/documentation/ml/use-model-data.tsx index 1500aa4a9..f834c570b 100644 --- a/src/documentation/ml/use-model-data.tsx +++ b/src/documentation/ml/use-model-data.tsx @@ -1,7 +1,7 @@ import { ReactNode, createContext, useContext, useState } from "react"; import { ActionData } from "./training-data"; -type ModelDataContextValue = [ActionData[], (mlData: ActionData[]) => void]; +type ModelDataContextValue = [ActionData[], (modelData: ActionData[]) => void]; const ModelDataContext = createContext( undefined @@ -15,7 +15,7 @@ export const useModelData = (): ModelDataContextValue => { return modelData; }; -const MlDataProvider = ({ children }: { children: ReactNode }) => { +const ModelDataProvider = ({ children }: { children: ReactNode }) => { const [modelData, setModelData] = useState([]); return ( @@ -24,4 +24,4 @@ const MlDataProvider = ({ children }: { children: ReactNode }) => { ); }; -export default MlDataProvider; +export default ModelDataProvider; diff --git a/src/project/project-actions.tsx b/src/project/project-actions.tsx index 452008c02..b762faeb6 100644 --- a/src/project/project-actions.tsx +++ b/src/project/project-actions.tsx @@ -74,7 +74,8 @@ import WebUSBErrorDialog from "../workbench/connect-dialogs/WebUSBErrorDialog"; import reconnectWebm from "../workbench/connect-dialogs/reconnect.webm"; import reconnectMp4 from "../workbench/connect-dialogs/reconnect.mp4"; import { ActionData } from "../documentation/ml/training-data"; -import { modelModule } from "../documentation/ml/ml"; +import { modelModule, trainModel } from "../documentation/ml/ml"; +import { compileModel } from "ml4f"; /** * Distinguishes the different ways to trigger the load action. @@ -118,7 +119,7 @@ export class ProjectActions { private intl: IntlShape, private logging: Logging, private client: LanguageServerClient | undefined, - private setMlData: (mlData: ActionData[]) => void + private setModelData: (modelData: ActionData[]) => void ) {} private get project(): DefaultedProject { @@ -264,16 +265,19 @@ export class ProjectActions { const file = files[0]; const json = await readFileAsText(file); const data = JSON.parse(json) as ActionData[]; - this.setMlData(data); - const actionNames = data.map((action) => action.name).join(" "); - await Promise.all([ - this.fs.write( - "namesOfClasses.txt", - actionNames, - VersionAction.INCREMENT - ), - await this.fs.write("model.py", modelModule, VersionAction.INCREMENT), - ]); + this.setModelData(data); + const actionNames = data.map((action) => action.name); + await this.fs.write( + "model.py", + modelModule(JSON.stringify(actionNames)), + VersionAction.INCREMENT + ); + const model = await trainModel(data); + const result = compileModel(model, {}); + const modelAsHexString = Array.from(result.machineCode, (i) => + i.toString(16).padStart(2, "0") + ).join(""); + console.log(modelAsHexString); } };