leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt

tf.logging.set_verbosity(tf.logging.INFO)
tf.reset_default_graph()

def parser(serialized_example):
    features = {
        'age': tf.FixedLenFeature([1], tf.int64),
        'img': tf.FixedLenFeature([61*49], tf.int64)

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt

tf.logging.set_verbosity(tf.logging.INFO)
tf.reset_default_graph()

def parser(serialized_example):
    features = {
        'age': tf.FixedLenFeature([1], tf.int64),
        'img': tf.FixedLenFeature([61*49], tf.int64)

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt

tf.reset_default_graph()
tf.logging.set_verbosity(tf.logging.INFO)

import tensorflow as tf
import matplotlib.pyplot as plt

tf.reset_default_graph()

leechanwoo

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

tf.reset_default_graph()

samples = 1000
times = [1e-2*i for i in range(samples+1)]
sin = np.sin(times[:-1])
sin_next = np.sin(times[1:])

leechanwoo

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

tf.reset_default_graph()

samples = 10000
times = [1e-2*float(i) for i in range(samples+1)]
sin = np.sin(times[:-1])
sin_next = np.sin(times[1:])

leechanwoo

import tensorflow as tf

tf.reset_default_graph()

def parser(serialized_example):
    features = {
        'age': tf.FixedLenFeature([1], tf.int64),
        'img': tf.FixedLenFeature([61*49], tf.int64)
    }
    

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt

tf.reset_default_graph()

def parser(serialized_example):
    features = {
        'age': tf.FixedLenFeature([1], tf.int64),
        'img': tf.FixedLenFeature([61*49], tf.int64)
    }

leechanwoo

import os

import tensorflow as tf
import matplotlib.pyplot as plt

tf.reset_default_graph()

img_dir = './tfrecord_dataset/images_png/'
label_filename = './tfrecord_dataset/label_csv/label.csv'
img_names = sorted([os.path.join(img_dir, n) for n in os.listdir(img_dir)])

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt
import os

tf.reset_default_graph()
image_dir = './tfrecord_dataset/images_png/
label_name = './tfrecord_dataset/label_csv/label.csv'
image_names = [os.path.join(image_dir, n) for n in os.listdir(image_dir)]

img_name_queue = tf.train.string_input_producer(image_names, seed=7777)

leechanwoo

import tensorflow as tf
import numpy as np
import csv

samples = 1000
test_samples = 100
train_dataset_dir = './ffnn_dataset/train_dataset.csv'
test_dataset_dir = './ffnn_dataset/test_dataset.csv'

def write_dataset(samples, test_samples, train_dir, test_dir):