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