TensorFlow 操作簡介

在tensorflow中定義常數

import tensorflow as tf

a = tf.constant(1)
b = tf.constant(2)
sess = tf.session(
)sess.run(a)
sess.run(b)

with tf.session(

)as sess:
print
("addition matrix: %i"
% sess.run(a + b)
)print
("multiplication matrix: %i"
% sess.run(a * b)
)

a = tf.placeholder(tf.int16)

b = tf.placeholder(tf.int16)
add = tf.add(a, b)
mul = tf.multiply(a, b)
with tf.session(
)as sess:
print
("addition with variables: %i"
% sess.run(add, feed_dict=))
print
("multiplication with variables: %i"
% sess.run(mul, feed_dict =
))

matrix1 = tf.constant([[

3.,3
.]])
matrix2 = tf.constant([[
2.],
[2.]
])mul = tf.matmul(matrix1, matrix2)
sess = tf.session(
)print
(sess.run(mul)
)

from __feature__ import absolute_import, division, print_function

import tensorflow as tf
import numpy as np
print
("setting edge model..."
)tf.enable_edger_execution(
)tf.contrib.eager
print
("define constant eager"
)a = tf.constant(2)
print
("a = %i"
% a)
b = tf.constant(3)
print
("b = %i"
% b)
print
("running operations, without tf.session"
)c = a + b
print
("a + b = % i"
% c)
d = a * b
print
("a * b = % i"
% d)
print
("mixing operations with tensors and numpy arrays"
)a = tf.constant([[
2.,1
.],[
1.,0
.]], dtype = tf.float32)
print
("tensor: \n a = %s"
% a)
b = np.array([[
3.,0
.],[
5.,1
.]], dtype = np.float32)
print
("numpyarray: \b b = %s"
% b)
print
("running operations, without tf.session"
)c = a + b
print
("a + b = %s"
% c)
d = a * b
print
("a * b = %s"
% d)
print
("iterate through tensor 'a' :"
)for i in
range
(a.shape[0]
):for j in
range
(a.shape[1]
):print
(a[i]
[j])

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