hayunjong83

###################################################################################################
#                                                                                                 #
#    RAG(Retrieval Augmented Generation) example                                                  #
#                                                <hayunjong83@gmail.com>                          #
#                                                                                                 #
# Used:                                                                                           #
#        Application : Streamlit                                                                  #
#        LLM Framework : LangChain                                                                #
#        LLM Related : llama-2 7B, GPT4All(embedding model)                                       #
#                                                                                                 #

hayunjong83

import tensorflow as tf
import pathlib

# Load the MobileNet tf.keras model
model = tf.keras.applications.MobileNetV2(weights="imagenet", input_shape=(224,224,3))

# Convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

hayunjong83

import tensorflow as tf
# Store data for x and y
x = [-1, 0, 1, 2, 3, 4]
y = [-3,-1, 1, 3, 5, 7]

# Create a simple Keras model
model = tf.keras.models.Sequential(
  [tf.keras.layers.Dense(units=1, input_shape=[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=500)

hayunjong83

class Solution {
public:
    int minTimeToVisitAllPoints(vector<vector<int>>& points) {
        int time = 0;
        for(int i = 1; i < points.size() ; i++)
        {
            int x, y, m;
            x = abs(points[i-1][0] - points[i][0]);
            y = abs(points[i-1][1] - points[i][1]);
            m = min(x, y);

hayunjong83

#include <iostream>
#include <cstdio>
#include <helper_cuda.h>
#include <helper_string.h>

#define MAX_DEPTH       16
#define SELECTION_SORT  32

__device__ void selection_sort(unsigned int *data, int left, int right)
{

hayunjong83

#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <helper_cuda.h>
#include <helper_string.h>

__device__ int g_uids = 0;

__device__ void print_info(int depth, int thread, int uid, int parent_uid)
{

hayunjong83

#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void saxpy(int n, float a, float *__restrict__ x, float *__restrict__ y)
{
  int i = blockIdx.x * blockDim.x + threadIdx.x;
  if( i < n )
    y[i] = a * x[i] + y[i];
}

hayunjong83

#include <cublas.h>

int main()
{
  int N = 1 << 16;
  
  int size = N * sizeof(float);
  float *h_x = (float*)malloc(size);
  float *h_y = (float*)malloc(size);
  

hayunjong83

__global__ void saxpy(int n, float a, float *__restrict__ x, float *__restrict__ y)
{
  int i = blockIdx.x * blockDim.x + threadIdx.x;
  if( i < n )
    y[i] = a * x[i] + y[i];
}

int main()
{
  int N = 1 << 16;

hayunjong83

#include <opencv2/opencv.hpp>
#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

int main(int argv, char** argc)
{
  Mat frame;