solaris33

# coding: utf-8

"""PTB 텍스트 파일을 파싱(parsing)하기 위한 유틸리티들(Utilities)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import os
import sys

solaris33

# coding: utf-8

"""Example / benchmark for building a PTB LSTM model.

Trains the model described in:
(Zaremba, et. al.) Recurrent Neural Network Regularization
http://arxiv.org/abs/1409.2329

There are 3 supported model configurations:
===========================================

solaris33

# -*- coding: utf-8 -*-

"""Inception v3 architecture 모델을 retraining한 모델을 이용해서 이미지에 대한 추론(inference)을 진행하는 예제"""

import numpy as np
import tensorflow as tf

imagePath = '/tmp/test_chartreux.jpg'                                      # 추론을 진행할 이미지 경로
modelFullPath = '/tmp/output_graph.pb'                                      # 읽어들일 graph 파일 경로
labelsFullPath = '/tmp/output_labels.txt'                                   # 읽어들일 labels 파일 경로

solaris33

# -*- coding: utf-8 -*-

"""Inception v3 architecture 모델을 이용한 간단한 Transfer Learning (TensorBoard 포함)

This example shows how to take a Inception v3 architecture model trained on
ImageNet images, and train a new top layer that can recognize other classes of
images.

The top layer receives as input a 2048-dimensional vector for each image. We
train a softmax layer on top of this representation. Assuming the softmax layer

solaris33

/**
 * Log a LogRecord.
 * <p>
 * All the other logging methods in this class call through
 * this method to actually perform any logging.  Subclasses can
 * override this single method to capture all log activity.
 *
 * @param record the LogRecord to be published
 */
public void log(LogRecord record) {

solaris33

abstract class Logger {
    public static int ERR = 3;
    public static int NOTICE = 5;
    public static int DEBUG = 7;
    protected int mask;

    // The next element in the chain of responsibility
    protected Logger next;

    public Logger setNext(Logger log) {

solaris33

# -*- coding: utf-8 -*-

# Convolutional Neural Networks(CNNs)를 이용한 Deep MNIST 분류기(Classifier)

# 절대 임포트 설정 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# 필요한 라이브러리들을 임포트

solaris33

# -*- coding: utf-8 -*-

# MNIST 숫자 분류를 위한 Stacked AutoEncoder 예제 

# 절대 임포트 설정
from __future__ import division, print_function, absolute_import

# 필요한 라이브러리들을 임포트
import tensorflow as tf
import numpy as np

solaris33

    // -----------------------------------
    // 1. Get today's date
    LocalDate today = LocalDate.now(); 
    System.out.println("Today's Local date : " + today); 

    // Output : Today's Local date : 2014-01-14


    // -----------------------------------
    // 2. Get today's year, month, and day

solaris33

// Traditional Method
public static void main(String[] args) {

	List<String> lines = Arrays.asList("apple", "banana", "grape");
	List<String> result = getFilterOutput(lines, "grape");
	for (String temp : result) {
	    System.out.println(temp);    //output : apple, banana
	}

}