leechanwoo

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leechanwoo

import UIKit
import AVFoundation

import SwiftUI




// Image Extraction
protocol FrameExtractorDelegate: class {

leechanwoo

                                                                                   X 
<figure>
<figcaption>Listen to the T-Rex:</figcaption>
<audio
controls
src="https://s3.ap-northeast-2.amazonaws.com/audio-streaming-test/2018-11-30+                                                               %E1%84%86%E1%85%A6%E1%86%AB%E1%84%90%E1%85%A1%E1%86%AF%E1%84%80%E1%85%AE%E1%84%8C%E1%85%A9%E1%84%83%E1%85%A2+                                       1%E1%84%87%E1%85%AE(%E1%84%90%E1%85%A9%E1%86%BC%E1%84%92%E1%85%A1%E1%86%B8).mp3">
Your browser does not support the
<code>audio</code> element.
</audio>
figure>

leechanwoo

{
  "audio": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ]
}

leechanwoo

# python for loop
for d in Dataset:
	d = preprocessing(d)
	write_tfrecord(d)

# python map function
itr = map(preprocessing, Dataset)
for i in itr:
	write_tfrecord(i)

leechanwoo

import tensorflow as tf


def input_fn():
    dataset = tf.data.TFRecordDataset('dataset_path')
    dataset = dataset.batch(10)
    dataset = dataset.shuffle(6666)
    dataset = dataset.repeat(10)

leechanwoo

import csv
import os

hand_path = '/Users/chanwoo/Documents/sources/IEMOCAP_full_release/Session1/dialog/MOCAP_hand/'
label_path = '/Users/chanwoo/Documents/sources/IEMOCAP_full_release/Session1/dialog/EmoEvaluation/'
filename = 'Ses01F_impro01.txt'

with open(os.path.join(hand_path, filename), 'r') as f:
    raw_text = f.read()

leechanwoo

leechanwoo

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

tf.reset_default_graph()

data = [[i for i in range(10)] for _ in range(1000)]
noise = np.random.randn(1000, 10)

real_data = tf.placeholder(tf.float32, shape=[None, 10])

leechanwoo

import tensorflow as tf
import numpy as np

tf.reset_default_graph()

x = [[i for i in range(10)] for _ in range(1000)]
z = np.random.randn(1000, 10)

real_data = tf.placeholder(tf.float32, shape=[None, 10])
noise = tf.placeholder(tf.float32, shape=[None, 10])