Add all files, First Commit

README.md

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+# Digit Recognition - #MadeWithTFJS*
+
+\*This project uses [TensorFlow.JS](https://www.tensorflow.org/js).

data.js

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+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+const IMAGE_SIZE = 784;
+const NUM_CLASSES = 10;
+const NUM_DATASET_ELEMENTS = 65000;
+
+const NUM_TRAIN_ELEMENTS = 55000;
+const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;
+
+const MNIST_IMAGES_SPRITE_PATH =
+    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png';
+const MNIST_LABELS_PATH =
+    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8';
+
+/**
+ * A class that fetches the sprited MNIST dataset and returns shuffled batches.
+ *
+ * NOTE: This will get much easier. For now, we do data fetching and
+ * manipulation manually.
+ */
+export class MnistData {
+  constructor() {
+    this.shuffledTrainIndex = 0;
+    this.shuffledTestIndex = 0;
+  }
+
+  async load() {
+    // Make a request for the MNIST sprited image.
+    const img = new Image();
+    const canvas = document.createElement('canvas');
+    const ctx = canvas.getContext('2d');
+    const imgRequest = new Promise((resolve, reject) => {
+      img.crossOrigin = '';
+      img.onload = () => {
+        img.width = img.naturalWidth;
+        img.height = img.naturalHeight;
+
+        const datasetBytesBuffer =
+            new ArrayBuffer(NUM_DATASET_ELEMENTS * IMAGE_SIZE * 4);
+
+        const chunkSize = 5000;
+        canvas.width = img.width;
+        canvas.height = chunkSize;
+
+        for (let i = 0; i < NUM_DATASET_ELEMENTS / chunkSize; i++) {
+          const datasetBytesView = new Float32Array(
+              datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
+              IMAGE_SIZE * chunkSize);
+          ctx.drawImage(
+              img, 0, i * chunkSize, img.width, chunkSize, 0, 0, img.width,
+              chunkSize);
+
+          const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+
+          for (let j = 0; j < imageData.data.length / 4; j++) {
+            // All channels hold an equal value since the image is grayscale, so
+            // just read the red channel.
+            datasetBytesView[j] = imageData.data[j * 4] / 255;
+          }
+        }
+        this.datasetImages = new Float32Array(datasetBytesBuffer);
+
+        resolve();
+      };
+      img.src = MNIST_IMAGES_SPRITE_PATH;
+    });
+
+    const labelsRequest = fetch(MNIST_LABELS_PATH);
+    const [imgResponse, labelsResponse] =
+        await Promise.all([imgRequest, labelsRequest]);
+
+    this.datasetLabels = new Uint8Array(await labelsResponse.arrayBuffer());
+
+    // Create shuffled indices into the train/test set for when we select a
+    // random dataset element for training / validation.
+    this.trainIndices = tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);
+    this.testIndices = tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);
+
+    // Slice the the images and labels into train and test sets.
+    this.trainImages =
+        this.datasetImages.slice(0, IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
+    this.testImages = this.datasetImages.slice(IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
+    this.trainLabels =
+        this.datasetLabels.slice(0, NUM_CLASSES * NUM_TRAIN_ELEMENTS);
+    this.testLabels =
+        this.datasetLabels.slice(NUM_CLASSES * NUM_TRAIN_ELEMENTS);
+  }
+
+  nextTrainBatch(batchSize) {
+    return this.nextBatch(
+        batchSize, [this.trainImages, this.trainLabels], () => {
+          this.shuffledTrainIndex =
+              (this.shuffledTrainIndex + 1) % this.trainIndices.length;
+          return this.trainIndices[this.shuffledTrainIndex];
+        });
+  }
+
+  nextTestBatch(batchSize) {
+    return this.nextBatch(batchSize, [this.testImages, this.testLabels], () => {
+      this.shuffledTestIndex =
+          (this.shuffledTestIndex + 1) % this.testIndices.length;
+      return this.testIndices[this.shuffledTestIndex];
+    });
+  }
+
+  nextBatch(batchSize, data, index) {
+    const batchImagesArray = new Float32Array(batchSize * IMAGE_SIZE);
+    const batchLabelsArray = new Uint8Array(batchSize * NUM_CLASSES);
+
+    for (let i = 0; i < batchSize; i++) {
+      const idx = index();
+
+      const image =
+          data[0].slice(idx * IMAGE_SIZE, idx * IMAGE_SIZE + IMAGE_SIZE);
+      batchImagesArray.set(image, i * IMAGE_SIZE);
+
+      const label =
+          data[1].slice(idx * NUM_CLASSES, idx * NUM_CLASSES + NUM_CLASSES);
+      batchLabelsArray.set(label, i * NUM_CLASSES);
+    }
+
+    const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
+    const labels = tf.tensor2d(batchLabelsArray, [batchSize, NUM_CLASSES]);
+
+    return {xs, labels};
+  }
+}

digit.css

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+.digit-demo-container {
+    background-color: rgb(158, 220, 228);
+    border-radius: 5px;
+    margin: auto;
+   }
+    .digit-demo-container h3 {
+      font-size: 15px;
+      background-color: rgb(0, 0, 0);
+      padding: 20px;
+      color: white;
+      margin: 0px;
+  }
+
+  .flex-two {
+    display: flex;
+    flex-wrap: wrap; 
+  }
+
+  .flex-two div {
+    flex: 1;
+    padding: 5px;
+    text-align: center; 
+  }
+
+  img{
+    height: 82px;
+    width: 293px;
+  }
+
+  #footerText{
+    text-align: center !important;
+  }
+
+  @media screen and (max-width: 640px) {
+      .flex-two div {
+          padding: 5px;           
+          flex: 0 0 100%;
+      } 
+      #footerText{
+        font-size: 20px !important;
+      }
+      img{
+        height: 32px !important;
+        width: 143px !important;
+        vertical-align: bottom;
+      }
+  }
+
+  .canvas-box-wrapper {
+    display: block !important; }
+
+
+  #chart_box {
+    background-color: white;
+    padding: 20px;
+    border-radius: 5px; 
+    max-width: 350px;
+    max-height: 350px;
+  }
+
+  .btn{
+    width: 150px;
+  }
+
+  .coding {
+    background-color: #ffefef !important;
+    font-size: 12px !important;
+    font-family: monospace !important;
+    line-height: 25px;
+    border-radius: 4px !important;
+    padding: 3px 6px !important;
+    color: #d2372a !important;
+    border: 1px solid #fad1d1;
+  }
+
+  @media screen and (max-width: 767px) {
+      #chart_box {
+        padding: 5px; 
+      } 
+  }