RomanSteinberg

import numpy as np
import pandas as pd
import scipy.stats as ss
import scipy

def get_bootstrap_samples(data, n_samples):
    indices = np.random.randint(0, len(data), (n_samples, len(data)))
    samples = data[indices]
    return samples

RomanSteinberg

Borders for float features generated
0:	learn 70.50475406	test 40.44999897	bestTest 40.44999897		total: 47.1ms	remaining: 424ms
1:	learn 65.63694777	test 53.45852055	bestTest 40.44999897		total: 47.6ms	remaining: 191ms
2:	learn 57.7842572	test 100.5151111	bestTest 40.44999897		total: 48.1ms	remaining: 112ms
3:	learn 55.30496775	test 128.7490654	bestTest 40.44999897		total: 48.6ms	remaining: 72.8ms
4:	learn 52.89929452	test 136.0044652	bestTest 40.44999897		total: 49ms	remaining: 49ms
5:	learn 51.9437037	test 150.0426779	bestTest 40.44999897		total: 49.6ms	remaining: 33ms
6:	learn 51.70084336	test 158.4656055	bestTest 40.44999897		total: 50.1ms	remaining: 21.5ms
7:	learn 50.61418096	test 162.2118236	bestTest 40.44999897		total: 50.6ms	remaining: 12.6ms
8:	learn 50.49985141	test 165.7670929	bestTest 40.44999897		total: 51.1ms	remaining: 5.67ms

RomanSteinberg

Introduction

I assume you make clean system install, no previous Nvidia installations. Other case, try the following steps on your own risk:

• sudo apt-get purge 'nvidia*'
• rm ~/.Xauthority

Pre-step

Switch off the PC, remove video card

RomanSteinberg

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# import tensorflow as tf

import tensorflow as tf, numpy as np
from tensorflow import nn
from tensorflow import keras as ke
from tensorflow.examples.tutorials.mnist import input_data

RomanSteinberg

def gpu_check():
    import os
    os.environ['CUDA_VISIBLE_DEVICES'] = '/device/gpu:0'
    import tensorflow as tf

    from datetime import datetime

    shape = (10, 10)
    device_name = "/gpu:0"
    with tf.device(device_name):

RomanSteinberg

# -*- coding: utf-8 -*-

import numpy as np
from glob import glob
import tensorflow.contrib.keras as K
from skimage.util import view_as_windows, pad

resnet = K.applications.resnet50
image = K.preprocessing.image

RomanSteinberg

import tensorflow as tf
from glob import glob

folder = './data/*.jpg'

def create_graph():
    filename_queue = tf.train.string_input_producer(list(glob(folder)))

    reader = tf.TextLineReader()
    key, value = reader.read(filename_queue)

RomanSteinberg

import tensorflow as tf
from imageio import imsave, imread


def read_and_preproc():
    inp_img_op = tf.placeholder(tf.float32, shape=[None, None, 3])
    image_size_before_crop, IMG_HEIGHT, IMG_WIDTH = 286, 256, 256
    # Preprocessing:
    out = tf.image.resize_images(inp_img_op, [image_size_before_crop, image_size_before_crop])
    out = tf.random_crop(out, [IMG_HEIGHT, IMG_WIDTH, 3])

RomanSteinberg

function fk(n) {
	return (n*(n + 1)) / 2;
}

var data = readline().split(' ').map(function(x) { return parseInt(x); });
var n = data[0];
var m = data[1];
var k = data[2];

var nL = k - 1;

RomanSteinberg

const Promise = require('bluebird');
const mongoose = require('mongoose');
const _ = require('lodash');

const Answer = require('./answer');
const GooGl = require('./goo-gl');
const Meta = require('./meta');
const Recommendations = require('./recommendations');

const config = require('../config');