Rishabh Anand rish-16

## model.py
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

n_h1 = 500
n_h2 = 500
n_classes = 10
batch_size = 128

## model.py
import numpy as np
from keras.models import Sequential
from keras.datasets import mnist
from keras.layers import Dense
from keras.utils import np_utils

n_classes = 10
batch_size = 128

(X_train, y_train), (X_test, y_test) = mnist.load_data()

## model.py
import tflearn
from tflearn.layers.core import input_data, fully_connected
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist

X_train, y_train, X_test, y_test = mnist.load_data(one_hot=True)

X_train = X_train.reshape([-1, 1])
X_test = X_test.reshape([-1, 1])

## index.html
<html>
	<head>
		<title>TensorFlow Demo!</title>
		<meta charset='uft-8'>
		<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.8.0"></script>
		<script src="TF.js"></script>
	<head>

	<body>


## TF.js
import * as tf from '@tensorflow/tfjs';

// A sequential model is a container which you can add layers to.
const model = tf.sequential();

// Add a dense layer with 1 output unit.
model.add(tf.layers.dense({units: 10,
                           inputShape: [1],
                           activation='relu'
                          }));

## TF.py
import tflearn
import numpy as np

x = np.array([[1], [2], [3], [4], [5], [6], [7]], dtype=np.float32)
y = np.array([[1], [3], [5], [7], [9], [11], [13]], dtype=np.float32)

n = tflearn.input_data(shape=[None, 1])
n = tflearn.fully_connected(n, 1, activation='softmax')
n = tflearn.regression(n, optimizer=tflearn.SGD(), loss='categorical_crossentropy')

## TF.js
// A sequential model is a container which you can add layers to.
const model = tf.sequential();

// Add a dense layer with 1 output unit.
model.add(tf.layers.dense({units: 1,
                           inputShape: [1],
                           activation: 'softmax'
                          }));

// Specify the loss type and optimizer for training.

## cat_utils.py
import numpy as np
from sklearn.model_selection import train_test_split

def load_cats():
    cats = np.load("./cats.npy")
    print (cats.shape)
    Y = []
    for i in range(cats.shape[0]):
        Y.append([1,0])
    Y = np.array(Y)

## PAgent.py
class PAgent():
    def __init__(self, length):
        self.string = ''.join(random.choice(string.ascii_letters) for  _ in range(length))
        self.fitness = -1

## SAgent.py
class SAgent():
    def __init__(self, in_str):
        self.in_str = in_str
        self.in_str_len = len(in_str)
        self.average_score_fitness = -1
        self.number_successful = 0
        self.population = random.randint(1, 21)
        self.generations = random.randint(1, 5000)
        self.threshold = 90
	import numpy as np
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data

	mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

	n_h1 = 500
	n_h2 = 500
	n_classes = 10
	batch_size = 128
	import numpy as np
	from keras.models import Sequential
	from keras.datasets import mnist
	from keras.layers import Dense
	from keras.utils import np_utils

	n_classes = 10
	batch_size = 128

	(X_train, y_train), (X_test, y_test) = mnist.load_data()
	import tflearn
	from tflearn.layers.core import input_data, fully_connected
	from tflearn.layers.estimator import regression
	import tflearn.datasets.mnist as mnist

	X_train, y_train, X_test, y_test = mnist.load_data(one_hot=True)

	X_train = X_train.reshape([-1, 1])
	X_test = X_test.reshape([-1, 1])
	<html>
	<head>
	<title>TensorFlow Demo!</title>
	<meta charset='uft-8'>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.8.0"></script>
	<script src="TF.js"></script>
	<head>

	<body>
	import * as tf from '@tensorflow/tfjs';

	// A sequential model is a container which you can add layers to.
	const model = tf.sequential();

	// Add a dense layer with 1 output unit.
	model.add(tf.layers.dense({units: 10,
	inputShape: [1],
	activation='relu'
	}));
	import tflearn
	import numpy as np

	x = np.array([[1], [2], [3], [4], [5], [6], [7]], dtype=np.float32)
	y = np.array([[1], [3], [5], [7], [9], [11], [13]], dtype=np.float32)

	n = tflearn.input_data(shape=[None, 1])
	n = tflearn.fully_connected(n, 1, activation='softmax')
	n = tflearn.regression(n, optimizer=tflearn.SGD(), loss='categorical_crossentropy')
	import numpy as np
	from sklearn.model_selection import train_test_split

	def load_cats():
	cats = np.load("./cats.npy")
	print (cats.shape)
	Y = []
	for i in range(cats.shape[0]):
	Y.append([1,0])
	Y = np.array(Y)
	class PAgent():
	def __init__(self, length):
	self.string = ''.join(random.choice(string.ascii_letters) for _ in range(length))
	self.fitness = -1
	class SAgent():
	def __init__(self, in_str):
	self.in_str = in_str
	self.in_str_len = len(in_str)
	self.average_score_fitness = -1
	self.number_successful = 0
	self.population = random.randint(1, 21)
	self.generations = random.randint(1, 5000)
	self.threshold = 90