skyer9

#!/bin/sh
# ------------------------------------------------------------------------------ 
# SOME INFOS : fairly standard (debian) init script.
#                 Note that node doesn't create a PID file (hence --make-pidfile) 
#                 has to be run in the background (hence --background)
#                 and NOT as root (hence --chuid)
#
# MORE INFOS :  INIT SCRIPT       http://www.debian.org/doc/debian-policy/ch-opersys.html#s-sysvinit
#               INIT-INFO RULES   http://wiki.debian.org/LSBInitScripts
#               INSTALL/REMOVE    http://www.debian-administration.org/articles/28

skyer9

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

skyer9

# -*- coding: utf-8 -*-
"""Sequence-to-sequence model with an attention mechanism."""

# original code
# https://gist.github.com/pannous/b3f8ab944a85b33e694de21c6ded029e

# see https://www.tensorflow.org/versions/r0.10/tutorials/seq2seq/index.html
# compare https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py
from __future__ import print_function
import numpy as np

skyer9

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout


batch_size = 128
num_classes = 10

skyer9

x_train = [1., 2., 3., 4.]
y_train = [1.1, 2.1, 3.1, 4.1]

W, b = 123, 45
learning_rate = 0.05

for epoch in range(0, 200):
    for j in range(0, len(x_train)):
        cal_y = W * x_train[j] + b

skyer9

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np

x_data = [[0, 0, 0],
          [1, 0, 0],
          [0, 1, 0],
          [0, 0, 1],
          [1, 1, 0],

skyer9

# https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np

skyer9

# https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np

skyer9

package kr.co.episode.muglangguide.data.remote;

import android.content.Context;

import org.apache.http.conn.ssl.SSLSocketFactory;

import java.io.IOException;
import java.io.InputStream;
import java.net.URL;
import java.security.KeyManagementException;

skyer9

1. 마켓 플레이스에서 SFTP 다운받기

2. ctrl+shift+p 를 누른 후, sftp: config 를 입력한다.

3. 설정을 아래와 같이 수정한다.

{
    "context": "./sftp/searver_a",
    "protocol": "sftp",
    "host": "13.XXX.42.109",