codekansas

from keras.optimizers import SGD, Adagrad, RMSprop, Adadelta, Adam, Adamax
import keras.backend as K

DEFAULT_NOISE = 0.05


def ballpark_gradient(gradient, noise):
    return [g * K.random_normal(shape=K.shape(g), mean=1.0, std=noise) for g in gradient]

codekansas

[>....................]how compare home insurance
Predicted: ([ 0.39381921])in order to calculate your annual premium for home insurance it be necessary answer some basic question about the risk and obtain a quote basic characteristic of the home will include the year build , square footage , type of roof , foundation type , number of bathroom , style of home , prior claim and identify information of the owner you may obtain a quote and buy home insurance online use an online link in multiple state ; it may be use by escrow / closing professional , mortgage professional , real estate agent directly consumer an exemplary online experience for all WEBSITELINK
Expected: ([ 0.38000157])start by talk to your Insurance Professional it be important not only compare coverage offering and policy extension and limitation , but also the company that be offer this protection proposal and suggest coverage shall be base upon the result of a careful Reconstruction Analysis which will determine the amount of recommend covera

codekansas

// reference: http://codeforces.com/contest/697/problem/C

import java.util.HashMap;
import java.util.Map;
import java.util.Scanner;

public class Codeforces697ProblemC {
	public static void main(String... args) {
		Scanner s = new Scanner(System.in);
		

codekansas

class FastScanner {
	BufferedReader br;
	StringTokenizer st;
	
	public FastScanner() {
		br = new BufferedReader(new InputStreamReader(System.in));
	}
	
	String next() {
		while (st == null || !st.hasMoreElements()) {

codekansas

from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

codekansas

from __future__ import print_function

import theano
import theano.tensor as T
import numpy as np
import time

X = theano.shared(value=np.asarray([[0, 1], [1, 0], [0, 0], [1, 1]]), name='X')
y = theano.shared(value=np.asarray([[0], [0], [1], [1]]), name='y')
rng = np.random.RandomState(1234)

codekansas

from __future__ import print_function

from time import sleep
import os

type_to_rep = {
    'empty': '.',
    'head': 'H',
    'tail': 'T',
    'conductor': '=',