Rishit Dagli Rishit-dagli

## import_tensorflow2.0.py
try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass

import tensorflow as tf

## fashion_mnist import.py
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

## Callback_class.py
class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs = {}):
    if logs.get('loss') < 0.7:
      print("\n Low loss so cancelling the training")
      self.model.stop_training = True


## excercise2_solution.py
def train_mnist():
    # Please write your code only where you are indicated.
    # please do not remove # model fitting inline comments.

    # YOUR CODE SHOULD START HERE
    class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if(logs.get('acc')>0.99):
                print("/nReached 99% accuracy so cancelling training!")
                self.model.stop_training = True

## Excercise 3 Solution.py
def train_mnist_conv():
    # Please write your code only where you are indicated.
    # please do not remove model fitting inline comments.

    # YOUR CODE STARTS HERE
    class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if(logs.get('acc')>0.998):
                print("/n Reached 99.8% accuracy so cancelling training!")
                self.model.stop_training = True

## monster_ex.java
public class Monster{

 private String name;
 private final int MAX_HEALTH = 100;
 private static int curr_health = 100;
 private double dmg_inf;
 private double dmg_taken;

 public String getName() {
	 return name;

## projector_config_imdb.json
{
  "embeddings": [
    {
      "tensorName": "My tensor",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://raw.githubusercontent.com/.../tensors.tsv",
      "metadataPath": "https://raw.githubusercontent.com/.../optional.metadata.tsv"

## index.html
<!DOCTYPE html>
<html>
<head>
  <title>TensorFlow.js Tutorial</title>

  <!-- Import TensorFlow.js -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
  <!-- Import tfjs-vis -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js"></script>

## script.js
/**
 * Get the car data reduced to just the variables we are interested
 * and cleaned of missing data.
 */
async function getData() {
  const carsDataReq = await fetch('https://storage.googleapis.com/tfjs-tutorials/carsData.json');
  const carsData = await carsDataReq.json();
  const cleaned = carsData.map(car => ({
    mpg: car.Miles_per_Gallon,
    horsepower: car.Horsepower,

## script.js
async function run() {
  // Load and plot the original input data that we are going to train on.
  const data = await getData();
  const values = data.map(d => ({
    x: d.horsepower,
    y: d.mpg,
  }));

  tfvis.render.scatterplot(
    {name: 'Horsepower v MPG'},
	try:
	# %tensorflow_version only exists in Colab.
	%tensorflow_version 2.x
	except Exception:
	pass

	import tensorflow as tf
	mnist = tf.keras.datasets.fashion_mnist
	(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
	class myCallback(tf.keras.callbacks.Callback):
	def on_epoch_end(self, epoch, logs = {}):
	if logs.get('loss') < 0.7:
	print("\n Low loss so cancelling the training")
	self.model.stop_training = True
	def train_mnist():
	# Please write your code only where you are indicated.
	# please do not remove # model fitting inline comments.

	# YOUR CODE SHOULD START HERE
	class myCallback(tf.keras.callbacks.Callback):
	def on_epoch_end(self, epoch, logs={}):
	if(logs.get('acc')>0.99):
	print("/nReached 99% accuracy so cancelling training!")
	self.model.stop_training = True
	def train_mnist_conv():
	# Please write your code only where you are indicated.
	# please do not remove model fitting inline comments.

	# YOUR CODE STARTS HERE
	class myCallback(tf.keras.callbacks.Callback):
	def on_epoch_end(self, epoch, logs={}):
	if(logs.get('acc')>0.998):
	print("/n Reached 99.8% accuracy so cancelling training!")
	self.model.stop_training = True
	public class Monster{

	private String name;
	private final int MAX_HEALTH = 100;
	private static int curr_health = 100;
	private double dmg_inf;
	private double dmg_taken;

	public String getName() {
	return name;
	{
	"embeddings": [
	{
	"tensorName": "My tensor",
	"tensorShape": [
	1000,
	50
	],
	"tensorPath": "https://raw.githubusercontent.com/.../tensors.tsv",
	"metadataPath": "https://raw.githubusercontent.com/.../optional.metadata.tsv"
	<!DOCTYPE html>
	<html>
	<head>
	<title>TensorFlow.js Tutorial</title>

	<!-- Import TensorFlow.js -->
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
	<!-- Import tfjs-vis -->
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js"></script>
	/**
	* Get the car data reduced to just the variables we are interested
	* and cleaned of missing data.
	*/
	async function getData() {
	const carsDataReq = await fetch('https://storage.googleapis.com/tfjs-tutorials/carsData.json');
	const carsData = await carsDataReq.json();
	const cleaned = carsData.map(car => ({
	mpg: car.Miles_per_Gallon,
	horsepower: car.Horsepower,
	async function run() {
	// Load and plot the original input data that we are going to train on.
	const data = await getData();
	const values = data.map(d => ({
	x: d.horsepower,
	y: d.mpg,
	}));

	tfvis.render.scatterplot(
	{name: 'Horsepower v MPG'},