31:[] 2f:{"title":"My TensorFlowJS Playground","cells":[{"id":528,"value":"md`## My TensorFlowJS Playground`","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"tf = require('@tensorflow/tfjs')","pinned":true,"hidden":false,"mode":"ojs"},{"id":0,"value":"tex`y = ax^3 + bx^2 + cx + d + \\mathcal{N}(0, \\sigma^2)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3,"value":"viewof coeff = {\n const form = html``;\n form.value = [form.a, form.b, form.c, form.d].map(i => i.valueAsNumber);\n [form.a, form.b, form.c, form.d].forEach((d, i) => {\n d.oninput = () => form.value[i] = d.valueAsNumber;\n });\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"viewof sd = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"md`\na = ${coeff[0]}, \nb = ${coeff[1]}, \nc = ${coeff[2]}, \nd = ${coeff[3]}, \nsd = ${sd}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"viewof trigger = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"inputData = {\n const data = tf.tidy(() => {\n const numPoints = 200;\n const [a, b, c, d] = coeff.map(i => tf.scalar(i)); // a = tf.scalar(coeff[0]), and so on\n\n const x = tf.randomUniform([numPoints], -1, 1);\n const y = a.mul(x.pow(tf.scalar(3)))\n .add(b.mul(x.square()))\n .add(c.mul(x))\n .add(d)\n .add(tf.randomNormal([numPoints], 0, sd));\n\n return {x, y}; \n });\n yield data;\n \n // This cell is re-evaluated and creates new tensors as we interact with the sliders to adjust parameters.\n // We need to manually dispose them to avoid memory leak. \n // See: https://beta.observablehq.com/@nsthorat/clean-up-deeplearn-js-tensor-generator-cells\n try {\n yield invalidation;\n } finally {\n data.x.dispose();\n data.y.dispose();\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":228,"value":"plot(inputData.x, inputData.y, null)","pinned":false,"hidden":false,"mode":"ojs"},{"id":354,"value":"// Loss: Mean Squared Error (MSE)\nfunction loss(predictions, labels) {\n return tf.tidy(() => predictions.sub(labels).square().mean());\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"cubicPredictions = {\n const predictions = tf.tidy(() => {\n // Model parameters: \n const a = tf.variable(tf.zeros([1]));\n const b = tf.variable(tf.zeros([1]));\n const c = tf.variable(tf.zeros([1]));\n const d = tf.variable(tf.zeros([1]));\n\n // Model: f(x) = a * x^3 + b * x^2 + c * x + d\n const f = (x) => \n a.mul(x.pow(tf.scalar(3)))\n .add(b.mul(x.square()))\n .add(c.mul(x))\n .add(d);\n\n // Optimizer: Stochastic Gradient Descent (SGD)\n const optimizer = tf.train.sgd(0.8);\n let mse = null;\n\n // Training Loop\n for (let i = 0; i < numIterations; i++) {\n optimizer.minimize(() => loss(f(inputData.x), inputData.y)); \n }\n\n const predTensor = f(inputData.x); \n \n // tf.tidy() will not clean up variables\n [a, b, c, d].forEach(x => { \n x.dispose(); \n });\n\n return predTensor; \n });\n yield predictions; \n \n try {\n yield invalidation;\n } finally {\n predictions.dispose();\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":241,"value":"viewof numIterations = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":244,"value":"md`${numIterations} iterations`","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"cubicLoss = {\n const mseTensor = loss(cubicPredictions, inputData.y);\n const mse = mseTensor.dataSync()[0];\n mseTensor.dispose();\n return md`Mean Squared Error: ${mse}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":248,"value":"plot(inputData.x, inputData.y, cubicPredictions)","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"dnnPredictions = {\n const dnn = tf.sequential({\n name: 'dnn',\n layers: [\n tf.layers.dense({units: 10, inputShape: [1], activation: 'tanh'}),\n tf.layers.dense({units: 5, activation: 'tanh'}),\n tf.layers.dense({units: 5, activation: 'tanh'}),\n tf.layers.dense({units: 1, activation: 'tanh'})\n ]\n });\n dnn.compile({loss: 'meanSquaredError', optimizer: 'Adam'});\n \n const history = await dnn.fit(inputData.x, inputData.y);\n \n const predictions = tf.tidy(() => \n dnn.predict(inputData.x.expandDims(1))\n );\n yield predictions;\n \n try {\n yield invalidation;\n } finally {\n predictions.dispose();\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":404,"value":"dnnLoss = {\n const mseTensor = tf.tidy(() => loss(tf.tensor(dnnPredictions.dataSync()), inputData.y));\n const mse = mseTensor.dataSync()[0];\n mseTensor.dispose();\n return md`Mean Squared Error: ${mse}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"plot(inputData.x, inputData.y, dnnPredictions)","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"WTF = {\n let totalCubic = 0; \n let totalDNN = 0; \n const cubic = cubicPredictions.dataSync(); \n const dnn = dnnPredictions.dataSync();\n const y = inputData.y.dataSync(); \n for(let i=0; i<200; i++) {\n totalCubic += Math.pow(cubic[i] - y[i], 2);\n totalDNN += Math.pow(dnn[i] - y[i], 2);\n }\n return {cubic: totalCubic / 200.0, dnn: totalDNN / 200.0}\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":534,"value":"dnnLossV1 = {\n const mseTensor = tf.tidy(() => loss(tf.tensor(dnnPredictions.dataSync()), inputData.y));\n const mse = mseTensor.dataSync()[0];\n mseTensor.dispose();\n return md`Mean Squared Error: ${mse}`;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":536,"value":"dnnLossV2 = {\n const mseTensor = tf.tidy(() => loss(dnnPredictions, inputData.y));\n const mse = mseTensor.dataSync()[0];\n mseTensor.dispose();\n return md`Mean Squared Error: ${mse}`;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":538,"value":"dnnLossV3 = {\n const mseTensor = tf.tidy(() => loss(tf.tensor(dnnPredictions.dataSync()), tf.tensor(inputData.y.dataSync())));\n const mse = mseTensor.dataSync()[0];\n mseTensor.dispose();\n return md`Mean Squared Error: ${mse}`;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":20,"value":"numTensors = tf.memory().numTensors","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"vegalite = require('@observablehq/vega-lite')","pinned":false,"hidden":false,"mode":"ojs"},{"id":45,"value":"plot = async (xTensor, yTensor, predTensor) => {\n const x = await xTensor.data();\n const y = await yTensor.data(); \n const pred = predTensor ? await predTensor.data() : null; \n \n const values = [...x].map((xVal, i) => { // Spread operator: [...x] = Array.from(x)\n return predTensor ? \n {\"x\": xVal, \"y\": y[i], \"pred\": pred[i]} :\n {\"x\": xVal, \"y\": y[i]};\n });\n \n return vegalite({\n width: 400,\n height: 300,\n \"data\": { values },\n \"layer\": [{\n \"mark\": \"circle\",\n \"encoding\": {\n \"x\": {\"field\": \"x\", \"type\": \"quantitative\"},\n \"y\": {\"field\": \"y\", \"type\": \"quantitative\"}\n }\n }, {\n \"mark\": \"line\",\n \"encoding\": {\n \"x\": {\"field\": \"x\", \"type\": \"quantitative\"},\n \"y\": {\"field\": \"pred\", \"type\": \"quantitative\"} \n }\n }],\n \"config\": {\n \"circle\": {\"color\": \"#1E4865\", \"size\": 50, \"opacity\": 0.6},\n \"line\": {\"color\": \"#F1AD46\", \"strokeWidth\": 4, \"opacity\": 0.6}\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":101,"value":"html`\n`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}