In [35]:

    

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline  
print(tf.__version__)
print(np.__version__)


    

    




1.2.0
1.13.0

In [9]:

    

txt = tf.constant("Hello")


    

In [10]:

    

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


    

    




b'Hello'

In [11]:

    

with tf.Session() as sess:
    print(str(sess.run(txt), encoding = "utf-8"))


    

    




Hello

In [21]:

    

def make_dataset():
    num_points = 1000
    vectors_set = []
    
    for i in range(num_points):
        x1 = np.random.normal(0.0,0.55)
        y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0,0.03)
        vectors_set.append([x1,y1])
    
    x_data = [v[0] for v in vectors_set]
    y_data = [v[1] for v in vectors_set]
    
    return x_data, y_data


    

In [ ]:

    




    

In [22]:

    

x_data, y_data = make_dataset()


    

In [49]:

    

plt.plot(x_data, y_data, 'yo', alpha=0.5)
# plt.legend()
plt.show()


    

In [39]:

    

np.random.seed(379)
x_data = np.random.normal(0, 0.55, 1000) # 0을 기준으로 표준편차 0.55인 정규분포를 만듬 1000개만듬
y_data = x_data * 0.1 + np.random.normal(0.3,0.03, 1000)


    

In [41]:

    

plt.plot(x_data,y_data,'ro',alpha=0.5)
plt.show()


    

In [45]:

    

temp = np.linspace(0,1000,8)


    

In [46]:

    

temp = np.floor(temp)


    

In [47]:

    

print(temp)


    

    




[    0.   142.   285.   428.   571.   714.   857.  1000.]

In [ ]:

    

y_data =