In [6]:

    

import tensorflow as tf
import numpy as np


    

In [2]:

    

import csv


    

In [3]:

    

file = open('test.csv')

print(file)


    

    




<_io.TextIOWrapper name='test.csv' mode='r' encoding='UTF-8'>

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reader = csv.reader(file)


    

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for row in reader:
    print(row)


    

    




['1', '2', '3', '4']

In [8]:

    

tensor_1d = np.array([1.3,1,4.0,23.99])


    

In [9]:

    

print(tensor_1d)


    

    




[  1.3    1.     4.    23.99]

In [10]:

    

tensor_1d.ndim


    

    
Out[10]:





1

In [11]:

    

tensor_1d.shape


    

    
Out[11]:





(4,)

In [12]:

    

tensor_1d.dtype


    

    
Out[12]:





dtype('float64')

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tf_tensor = tf.convert_to_tensor(tensor_1d,dtype=tf.float64)


    

In [14]:

    

print(tf_tensor)


    

    




Tensor("Const:0", shape=(4,), dtype=float64)

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sess = tf.Session()


    

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sess.close()


    

In [19]:

    

#with구분을 사용하면 Session의 close() 호출을 자동으로 해준다.
with tf.Session() as sess:
    print(sess.run(tf_tensor))
    print(sess.run(tf_tensor[0]))
    print(sess.run(tf_tensor[2]))


    

    




[  1.3    1.     4.    23.99]
1.3
4.0

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