Sushrut Ashtikar novasush

## push.js
var apn = require("apn");
var Firebase = require("firebase");

var service = new apn.connection({ production: false }); // true for production pipeline

// Create a reference to the push notification queue
var pushRef = new Firebase("<your-firebase>.firebaseio.com/notificationQueue");
// listen for items added to the queue
pushRef.on("child_added", function(snapshot) {

## TensorflowFromBrowser.html
<!DOCTYPE html>
<html>
<head>
	<title>Training a model on browser</title>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>

	<script lang="js">
        async function doTraining(model){
            const history =
                  await model.fit(xs, ys,

## Rock_Paper_Scissor.ipynb

      
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                novasush
                / Rock_Paper_Scissor.ipynb
            
            
              Last active
              March 6, 2020 15:42
            
              
                A simple python based rock paper scissor game
              
          
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## setup.py
import multiprocessing

import tensorflow as tf
import tensorflow_datasets as tfds

from os import getcwd

## create_model.py
def create_model():
    input_layer = tf.keras.layers.Input(shape=(224, 224, 3))
    base_model = tf.keras.applications.MobileNetV2(input_tensor=input_layer,
                                                   weights='imagenet',
                                                   include_top=False)
    base_model.trainable = False
    x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
    x = tf.keras.layers.Dense(2, activation='softmax')(x)

    model = tf.keras.models.Model(inputs=input_layer, outputs=x)

## naive_approach.py
dataset_name = 'cats_vs_dogs'
filePath = f"{getcwd()}/../tmp2"
dataset, info = tfds.load(name=dataset_name, split=tfds.Split.TRAIN, with_info=True, data_dir=filePath)

print(info.version)

def preprocess(features):
    image = features['image']
    image = tf.image.resize(image, (224, 224))
    image = image / 255.0

## creating_dataset.py


file_pattern = f'{getcwd()}/../tmp2/{dataset_name}/{info.version}/{dataset_name}-train.tfrecord*'
files = tf.data.Dataset.list_files(file_pattern)

## parallelize_extraction.py
# Parallelize the extraction of the stored TFRecords of
# the cats_vs_dogs dataset by using the interleave operation with
# cycle_length = 4 and the number of parallel calls set to tf.data.experimental.AUTOTUNE.
train_dataset = files.interleave(tf.data.TFRecordDataset,
                               cycle_length=4,
                               num_parallel_calls=tf.data.experimental.AUTOTUNE)

## read_tfrecord.py
def read_tfrecord(serialized_example):

    # Create the feature description dictionary
    feature_description = {
        'image': tf.io.FixedLenFeature((), tf.string, ""),
        'label': tf.io.FixedLenFeature((), tf.int64, -1),
    }
    # Parse the serialized_example and decode the image
    example = tf.io.parse_single_example(serialized_example, feature_description)
    image = tf.io.decode_jpeg(example['image'], channels=3)

## parallelize_transformation.py
# Get the number of CPU cores.
cores = multiprocessing.cpu_count()

print(cores)

# Parallelize the transformation of the train_dataset by using
# the map operation with the number of parallel calls set to
# the number of CPU cores.
train_dataset = train_dataset.map(read_tfrecord,num_parallel_calls=cores)
	var apn = require("apn");
	var Firebase = require("firebase");

	var service = new apn.connection({ production: false }); // true for production pipeline

	// Create a reference to the push notification queue
	var pushRef = new Firebase("<your-firebase>.firebaseio.com/notificationQueue");
	// listen for items added to the queue
	pushRef.on("child_added", function(snapshot) {
	<!DOCTYPE html>
	<html>
	<head>
	<title>Training a model on browser</title>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>

	<script lang="js">
	async function doTraining(model){
	const history =
	await model.fit(xs, ys,
	import multiprocessing

	import tensorflow as tf
	import tensorflow_datasets as tfds

	from os import getcwd
	def create_model():
	input_layer = tf.keras.layers.Input(shape=(224, 224, 3))
	base_model = tf.keras.applications.MobileNetV2(input_tensor=input_layer,
	weights='imagenet',
	include_top=False)
	base_model.trainable = False
	x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
	x = tf.keras.layers.Dense(2, activation='softmax')(x)

	model = tf.keras.models.Model(inputs=input_layer, outputs=x)
	dataset_name = 'cats_vs_dogs'
	filePath = f"{getcwd()}/../tmp2"
	dataset, info = tfds.load(name=dataset_name, split=tfds.Split.TRAIN, with_info=True, data_dir=filePath)

	print(info.version)

	def preprocess(features):
	image = features['image']
	image = tf.image.resize(image, (224, 224))
	image = image / 255.0


	file_pattern = f'{getcwd()}/../tmp2/{dataset_name}/{info.version}/{dataset_name}-train.tfrecord*'
	files = tf.data.Dataset.list_files(file_pattern)
	# Parallelize the extraction of the stored TFRecords of
	# the cats_vs_dogs dataset by using the interleave operation with
	# cycle_length = 4 and the number of parallel calls set to tf.data.experimental.AUTOTUNE.
	train_dataset = files.interleave(tf.data.TFRecordDataset,
	cycle_length=4,
	num_parallel_calls=tf.data.experimental.AUTOTUNE)
	def read_tfrecord(serialized_example):

	# Create the feature description dictionary
	feature_description = {
	'image': tf.io.FixedLenFeature((), tf.string, ""),
	'label': tf.io.FixedLenFeature((), tf.int64, -1),
	}
	# Parse the serialized_example and decode the image
	example = tf.io.parse_single_example(serialized_example, feature_description)
	image = tf.io.decode_jpeg(example['image'], channels=3)
	# Get the number of CPU cores.
	cores = multiprocessing.cpu_count()

	print(cores)

	# Parallelize the transformation of the train_dataset by using
	# the map operation with the number of parallel calls set to
	# the number of CPU cores.
	train_dataset = train_dataset.map(read_tfrecord,num_parallel_calls=cores)