wookayin

import static org.hamcrest.CoreMatchers.*;
import static org.junit.Assert.*;

import java.net.URI;

import org.junit.Test;
import org.springframework.web.util.UriComponentsBuilder;

public class URIComponentsCannotHandleRelativeUri {

wookayin

<style>
/* from http://sanographix.github.io/css3-countdown/demo/ */

dl.discharge .number-wrapper {
    margin: 5px;
    height: 100%;
    font-size: 68px;
    -moz-box-shadow: 0 5px 8px #000000;
    -webkit-box-shadow: 0 5px 8px #000000;
    box-shadow: 0 5px 8px #000000;

wookayin

package org.springframework.issues;

import static org.hamcrest.Matchers.*;
import static org.junit.Assert.*;

import java.net.SocketTimeoutException;

import org.apache.http.conn.ConnectTimeoutException;
import org.junit.Test;
import org.springframework.http.client.HttpComponentsClientHttpRequestFactory;

wookayin

2014-09-28 11:59:00 +0900

./configure
--with-features=huge
--enable-multibyte
--with-macarchs=x86_64
--enable-perlinterp
--enable-rubyinterp
--enable-tclinterp
--with-ruby-command=/System/Library/Frameworks/Ruby.framework/Versions/Current/usr/bin/ruby

wookayin

def im_drawbox(window, color='r', class_name='', score=0.0):
    """
    Add a detection window (bounding box) into the current plot image.

    window: tuple-like of length 4 (xmin, ymin, xmax, ymax)
    """
    import matplotlib.pyplot as plt
    xmin, ymin, xmax, ymax = window
    coords = (ymin, xmin), ymax - ymin, xmax - xmin
    currentAxis = plt.gca()

wookayin

# use the following snippet in your ipython notebook shell
import argparse
import tensorflow as tf

tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues()
tf.app.flags._global_parser = argparse.ArgumentParser()

wookayin

brew install poppler

# 현재 디렉토리의 모든 *.pdf 패턴을 page1 만 추출해서 page1.*.pdf 로 저장
for source_pdf in ./*.pdf; do
    pdfseparate -f 1 -l 1 ${source_pdf} page1.${source_pdf}
done
pdfunite page1.* page1.merged.pdf

wookayin

# in a courtesy of Caffe's filter visualization example
# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
    '''
    Take an array of shape (N, H, W) or (N, H, W, C)
    and visualize each (H, W) image in a grid style (height x width).
    '''
    if normalize:
        data -= data.min()

wookayin

import tensorflow as tf
import tensorflow.contrib.layers as layers
from datetime import datetime

# MNIST input data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

def multilayer_perceptron(x):
    fc1 = layers.fully_connected(x, 256, activation_fn=tf.nn.relu, scope='fc1')

wookayin

Displaying Configurations in TensorBoard

This is a simple demonstration of displaying training configuration in a TensorBoard, using text summaries.

Screenshot