Robert Grzelka robgrzel

## py36tf.yml
name: py36tf
channels:
  - engility
  - conda-forge
  - defaults
dependencies:
  - attrs=18.2.0=py_0
  - backcall=0.1.0=py_0
  - bleach=3.0.2=py_0
  - decorator=4.3.0=py_0

## condaenv.txt
# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

## training_set.csv

          
            -0.73481
            0.46286
            -1

            
              -0.71797
              -0.27961
              -1

            
              -0.36226
              0.43877
              -1

            
              0.16178
              0.66627
              -1

            
              0.75976
              0.93611
              -1

            
              0.47294
              0.74
              -1

            
              0.45301
              0.60238
              -1

            
              -0.15869
              -0.48386
              -1

            
              0.35195
              -0.19585
              -1

            
              0.60033
              -0.57328
              -1

## training_set.csv

          
            -0.71797
            -0.27961
            -1

            
              -0.36226
              0.43877
              -1

            
              0.16178
              0.66627
              -1

            
              0.75976
              0.93611
              -1

            
              0.47294
              0.74
              -1

            
              0.45301
              0.60238
              -1

            
              -0.15869
              -0.48386
              -1

            
              0.35195
              -0.19585
              -1

            
              0.60033
              -0.57328
              -1

## main.cpp
#include <iostream>
#include <iomanip>
#include <cstdlib>

#include <thrust/device_vector.h>

/*
 * --- Using Thrust vectors in kernel functions ---
 * This example demonstrates how we can use device vectors (vectors that live
 * in GPU) as arguments to a kernel launched by the CPU.

## onehiddenlayerperceptron.cu
//
//  onehiddenlayerperceptron.cu
//  onehiddenlayerperceptron
//
//  Created by Sergei Bugrov on 8/21/17.
//  Copyright © 2017 Sergei Bugrov. All rights reserved.
//

#include <stdio.h>

## cudnn_convolution_forward.cu
#include <iomanip>
#include <iostream>
#include <cstdlib>
#include <vector>

#include <cuda.h>
#include <cudnn.h>

#define CUDA_CALL(f) { \
  cudaError_t err = (f); \

## gist:d1e79721a898f721bcd279d107663c8b
#include <iostream>
#include <vector>
#include <string>
using std::string;

/*
	These macros define a struct with an implicitly defined Show function that visits each member.
	The trick is that a typedef is split between each STRUCT_MEMBER instance and the preceding macro,
	which allows it to define an empty struct type that identifies the following struct member, while also
	knowing the struct type that the preceding macro is using to identify the current member.

## conv.cu
#include <cudnn.h>
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <opencv2/opencv.hpp>

#define checkCUDNN(expression)                               \
  {                                                          \
    cudnnStatus_t status = (expression);                     \
    if (status != CUDNN_STATUS_SUCCESS) {                    \

## debian-install-zram.sh
#!/bin/bash
#

not_root() {
    echo "ERROR: You have to be root to execute this script"
    exit 1
}

zram_exists() {
    echo "ERROR: /etc/init.d/zram already exists"
	name: py36tf
	channels:
	- engility
	- conda-forge
	- defaults
	dependencies:
	- attrs=18.2.0=py_0
	- backcall=0.1.0=py_0
	- bleach=3.0.2=py_0
	- decorator=4.3.0=py_0
	# For Windows users# Note: <> denotes changes to be made

	#Create a conda environment
	conda create --name <environment-name> python=<version:2.7/3.5>

	#To create a requirements.txt file:
	conda list #Gives you list of packages used for the environment

	conda list -e > requirements.txt #Save all the info about packages to your folder
-0.73481	0.46286	-1
-0.71797	-0.27961	-1
-0.36226	0.43877	-1
0.16178	0.66627	-1
0.75976	0.93611	-1
0.47294	0.74	-1
0.45301	0.60238	-1
-0.15869	-0.48386	-1
0.35195	-0.19585	-1
0.60033	-0.57328	-1
	#include <iostream>
	#include <iomanip>
	#include <cstdlib>

	#include <thrust/device_vector.h>

	/*
	* --- Using Thrust vectors in kernel functions ---
	* This example demonstrates how we can use device vectors (vectors that live
	* in GPU) as arguments to a kernel launched by the CPU.
	//
	// onehiddenlayerperceptron.cu
	// onehiddenlayerperceptron
	//
	// Created by Sergei Bugrov on 8/21/17.
	// Copyright © 2017 Sergei Bugrov. All rights reserved.
	//

	#include <stdio.h>
	#include <iostream>
	#include <vector>
	#include <string>
	using std::string;

	/*
	These macros define a struct with an implicitly defined Show function that visits each member.
	The trick is that a typedef is split between each STRUCT_MEMBER instance and the preceding macro,
	which allows it to define an empty struct type that identifies the following struct member, while also
	knowing the struct type that the preceding macro is using to identify the current member.
	#include <cudnn.h>
	#include <cassert>
	#include <cstdlib>
	#include <iostream>
	#include <opencv2/opencv.hpp>

	#define checkCUDNN(expression) \
	{ \
	cudnnStatus_t status = (expression); \
	if (status != CUDNN_STATUS_SUCCESS) { \
	#!/bin/bash
	#

	not_root() {
	echo "ERROR: You have to be root to execute this script"
	exit 1
	}

	zram_exists() {
	echo "ERROR: /etc/init.d/zram already exists"