leechanwoo

integer = 10
floating_point = 10.5
string1 = ‘hello python!’
string2 = “hello python”

print( integer )
print( floating_point )
print( string1 )
print( string2 )

leechanwoo

import tensorflow as tf

const = tf.constant("hello world")
print(const)

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
x = tf.placeholder(tf.int32)

a = tf.Variable(5)
b = tf.Variable(2)

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

samples = 1000
data = [[float(i)*0.01] for i in range(-samples, samples)]
label = [[1 if i[0] > 2.5 else 0] for i in data]

x = tf.placeholder(tf.float32)
y_ = tf.placeholder(tf.float32)

leechanwoo

import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

samples = 1000
up = [i for i in range(10)]
down = [9-i for i in range(10)]
data = [up if i%2 == 0 else down for i in range(samples)]
label = [[1] if i%2 == 0 else [0] for i in range(samples)]

leechanwoo

import tensorflow as tf

samples = 1000
up = [i for i in range(10)]
down = [9-i for i in range(10)]
data = [up if i%2 == 0 else down for i in range(samples)]
label = [[1] if i%2 == 0 else [0] for i in range(samples)]

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

leechanwoo

Train Dataset0
https://drive.google.com/open?id=0B1UrJwDGWri2NHYtaDJUMEJrdHc

Label preprocessed
https://drive.google.com/open?id=0B1UrJwDGWri2UG1kV1JNSTBETTQ

cuDNN update
\\70.12.107.50\Users\student\Download\cudnn-8.0-windows10-x64-v5.1\cuda\       <<< copy
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\         <<< paste

leechanwoo

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

files = "dataset/test_dataset_png/"
file_dir = os.path.join(os.getcwd(), files)
filenames = [os.path.join(file_dir, f) for f in os.listdir(file_dir)]

filename_queue = tf.train.string_input_producer(filenames)

leechanwoo

dataset = tf.contrib.data.TextLineDataset(labelname).batch(10)
itr = dataset.make_one_shot_iterator()

batch = itr.get_next()

with tf.Session() as sess:
    print(sess.run(batch))

leechanwoo

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

images = "dataset/test_dataset_png/"
image_dir = os.path.join(os.getcwd(), images)
imagenames = [os.path.join(image_dir, f) for f in os.listdir(image_dir)]

label = "dataset/test_dataset_csv/label.csv"