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#include<opencv2\core\core.hpp>
#include<opencv2\highgui\highgui.hpp>
#define TEST_IMAGE "D:\\opencv.png"

int main(){
	// Open the file.
	cv::Mat img = cv::imread(TEST_IMAGE);

	// Display the image.
	cv::namedWindow("Image:", CV_WINDOW_AUTOSIZE);

chakkritte

1.Install python
https://www.python.org/downloads/release/python-352/

2.To install the CPU-only version of TensorFlow

C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

3.Test your installation

$ python

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name: "CReLU-conv1-4"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 224
  dim: 224
}
layer {
  name: "conv1"

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1.Install Keras

git clone git://github.com/fchollet/keras.git

cd keras

python setup.py develop

2.Demo training data on keras with gpu

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from __future__ import print_function
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils

batch_size = 128
nb_classes = 10
nb_epoch = 20

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#Install tensorflow 1.0 on windows 10

we’re announcing TensorFlow 1.0:
	-It’s faster
	-It’s more flexible
	-It’s more production-ready than ever

#Step 1 install

##CPU-only

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1. Install CUDA v8.0 toolkit --> https://developer.nvidia.com/cuda-downloads
2. Install cuDNN v5.1 for CUDA 8.0 --> https://developer.nvidia.com/rdp/cudnn-download
3. Install GIT 64-bit -> https://git-scm.com/download/win
4. Install Anaconda3-4.2.0  --> https://repo.continuum.io/archive/Anaconda3-4.2.0-Windows-x86_64.exe
5. Open windows command terminal 
	- conda create -n tensorflow
	- activate tensorflow
	- pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.0.1-cp35-cp35m-win_amd64.whl 

6. Validate your installation (Open windows command terminal )

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-- Xception model
-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
-- E. Culurciello, October 2016

require 'nn'
local nClasses = 1000

function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
   local block = nn.Sequential()
   block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))

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pragma solidity ^0.5.0;

// ----------------------------------------------------------------------------
// 'FIXED' 'Example Fixed Supply Token' token contract
//
// Symbol      : FIXED
// Name        : Example Fixed Supply Token
// Total supply: 1,000,000.000000000000000000
// Decimals    : 18
//

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#!/bin/sh

# base image docker
BASE=ufoym/deepo
# name of container
NAME=$1
# directory
DIR=/home/$(whoami)

export UID=$(id -u)