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<?xml version="1.0" encoding="UTF-8"?> | |
<project version="4"> | |
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.2 (C:\Program Files\Anaconda3\python.exe)" project-jdk-type="Python SDK" /> | |
</project> |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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# 원문링크: http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/ | |
############################################################## | |
# 라이브러리 import | |
############################################################## | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import tensorflow as tf | |
import math |
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# Lab 10 MNIST and Deep learning CNN | |
''' | |
모두의딥러닝 lab-11-2 기반으로 짜보았습니다. | |
생각한 컨셉은 다음과 같습니다. | |
1. training을 돌려서 accuracy가 70%가 넘으면 멈춘다 | |
2. 예측이 틀린 train set의 label을 +10 해준다 - 별도로 분류해보겠다는 것이었습니다. | |
3. 모든 Weights, biases 를 초기화 한 다음 다시 training 한다 | |
4. 이렇게 학습시킨 네트웍으로 test set을 검증한다 - 물론 검증시 10보다 큰 예측은 -10 해준다 |
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#!/bin/bash | |
#this is for tmux session initialize set script | |
SESSION_NAME="spacosa" | |
# check if session already exist | |
if(tmux has-session -t $SESSION_NAME 2> /dev/null); then | |
echo "Session $SESSION_NAME exists" | |
else | |
echo "Session $SESSION_NAME not exist. Create one" | |
tmux -2 new-session -s ${SESSION_NAME} -d # -2 means 256 color |
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// code from Hwang Hae Yeon of My Facebook Friend | |
// Count light from the photo of night scenary | |
import numpy as np | |
import argparse | |
import cv2 | |
def is_contour (c): | |
peri = cv2.arcLength(c, True) | |
approx = cv2.approxPolyDP(c, 0.02 * peri, True) |
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#!/bin/bash | |
c=1; d=1; mkdir -p dir_${d} | |
for file in * | |
do | |
if [ $c -eq 3001 ] | |
then | |
d=$(( d + 1 )); c=0; mkdir -p dir_${d} | |
fi |
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#!/bin/bash | |
c=0; d=0; mkdir -p data${d} | |
for file in ./test/* | |
do | |
if [ $c -eq 10000 ] | |
then | |
d=$(( d + 1 )); c=0; mkdir -p data${d} | |
fi |
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