Address typos in tfjs-examples repo (#1303)

Author: gaikwadrahul8

addition-rnn-webworker/index.html

@@ -70,7 +70,7 @@ <h1>TensorFlow.js: Addition RNN in web worker</h1>
         like <span class=out-example>7</span>.
       </p>
       <p>
-        This example generates its own training data programatically.
+        This example generates its own training data programmatically.
       </p>
     </section>
 

addition-rnn/index.html

@@ -66,7 +66,7 @@ <h1>TensorFlow.js: Addition RNN</h1>
         like <span class=out-example>7</span>.
       </p>
       <p>
-        This example generates it's own training data programatically.
+        This example generates it's own training data programmatically.
       </p>
     </section>
 

cart-pole/index.js

@@ -207,7 +207,7 @@ class PolicyNetwork {
 
       // Get the probability of the leftward action.
       const leftProb = tf.sigmoid(logits);
-      // Probabilites of the left and right actions.
+      // Probabilities of the left and right actions.
       const leftRightProbs = tf.concat([leftProb, tf.sub(1, leftProb)], 1);
       const actions = tf.multinomial(leftRightProbs, 1, null, true);
       return [logits, actions];
@@ -269,7 +269,7 @@ export class SaveablePolicyNetwork extends PolicyNetwork {
   }
 
   /**
-   * Load the model fom IndexedDB.
+   * Load the model from IndexedDB.
    *
    * @returns {SaveablePolicyNetwork} The instance of loaded
    *   `SaveablePolicyNetwork`.

cart-pole/ui.js

@@ -87,7 +87,7 @@ export function onGameEnd(gameCount, totalGames) {
 }
 
 /**
- * A function invokved at the end of a training iteration.
+ * A function invoked at the end of a training iteration.
  *
  * @param {number} iterationCount A count of how many iterations has completed
  *   so far in the current round of training.

chrome-extension/src/content.js

@@ -164,7 +164,7 @@ function clickHandler(mouseEvent) {
 }
 
 function loadImageAndSendDataBack(src, sendResponse) {
-  // Load image (with crossOrigin set to anonymouse so that it can be used in a
+  // Load image (with crossOrigin set to anonymous so that it can be used in a
   // canvas later).
   const img = new Image();
   img.crossOrigin = 'anonymous';

custom-layer/README.md

@@ -5,7 +5,7 @@ This example demonstrates how to write a custom layer for tfjs-layers.
 We build a custom activation layer called 'Antirectifier' which outputs two
 channels for each input, one with just the positive signal, and one with just
 the negative signal.  The example shows exercises the `call` and
-`computeOutputShape` overides of the `Layer`.
+`computeOutputShape` overrides of the `Layer`.
 
 [See this example
 live!](https://storage.googleapis.com/tfjs-examples/custom-layer/dist/index.html)

custom-layer/index.html

@@ -55,7 +55,7 @@ <h1>TensorFlow.js: Working with Custom Layers</h1>
         We build a custom activation layer called 'Antirectifier' which outputs two
         channels for each input, one with just the positive signal, and one with just
         the negative signal. The example shows exercises the <code>call</code> and
-        <code>computeOutputShape</code> overides of the <code>Layer</code>.
+        <code>computeOutputShape</code> overrides of the <code>Layer</code>.
       </p>
     </section>
 

data-csv/index.js

@@ -39,7 +39,7 @@ const SUNY_CSV_URL =
 
 /**
  * Builds a CSV Dataset object using the URL specified in the UI.  Then iterates
- * over all eleemnts in that dataset to count them.  Updates the UI accordingly.
+ * over all elements in that dataset to count them.  Updates the UI accordingly.
  */
 async function countRowsHandler() {
   const url = ui.getQueryElement().value;

data-generator/game.js

@@ -41,7 +41,7 @@ export const GAME_STATE = {
   // Constants defining the range of card values.
   min_card_value: 1,
   max_card_value: 13,
-  // Controls how many cards per hand.  Controlable from a UI element.
+  // Controls how many cards per hand.  Controllable from a UI element.
   num_cards_per_hand: 3
 };
 

data-generator/index.html

@@ -123,9 +123,9 @@ <h3>TensorFlow.js: tfjs-data</h3>
   This demo is meant to illustrate how to train a model using a tf.data.Dataset
   constructed from a generator using <pre>tf.data.generator()</pre>.
   In this case, we will predict the likelihood of
-  winning a game. The game itself is described below, and implmented in game.js.
-  The game exports a function we will use as a "generator" function to create a
-  dataset. We will then fit a model to this dataset using
+  winning a game. The game itself is described below, and implemented in game.
+  js.The game exports a function we will use as a "generator" function to
+  create a dataset. We will then fit a model to this dataset using
   <pre>model.fitDataset()</pre>, with the goal of predicting
   the "win" state from features calculated from Player 1's hand.  The output
   logit value will be larger for hands which the model considers more likely

electron/image_classifier.js

@@ -39,7 +39,7 @@ export class ImageClassifier {
    * Perform classification on a batch of image tensors.
    *
    * @param {tf.Tensor} images Batch image tensor of shape
-   *   `[numExamples, height, width, channels]`. The values of `heigth`,
+   *   `[numExamples, height, width, channels]`. The values of `height`,
    *   `width` and `channel` must match the underlying MobileNetV2 model
    *   (default: 224, 224, 3).
    * @param {number} topK How many results with top probability / logit values

fashion-mnist-vae/model.js

@@ -184,7 +184,7 @@ function vaeLoss(inputs, outputs) {
     const zLogVar = outputs[2];
 
     // First we compute a 'reconstruction loss' terms. The goal of minimizing
-    // tihs term is to make the model outputs match the input data.
+    // this term is to make the model outputs match the input data.
     const reconstructionLoss =
         tf.losses.meanSquaredError(inputs, decoderOutput).mul(originalDim);
 

fashion-mnist-vae/train.js

@@ -67,7 +67,7 @@ import('./data.js').then(exports => {
   // instead of the more typical model.fit. We thus need to define an optimizer
   // and manage batching the data ourselves.
 
-  // Cteate the optimizer
+  // Create the optimizer
   const optimizer = tf.train.adam();
 
   // Group the data into batches.