[PYTHON] Einführung in TensorFlow - Zusammenfassung von vier Regeln und grundlegenden mathematischen Funktionen

Ich habe die mathematikbezogenen Funktionen zusammengefasst, die hauptsächlich in TensorFlow verwendet werden.

Rechenzeichen

Anwendungsbeispiel)

vim arithmetic_operators.py

import tensorflow as tf

def add(j, k):
   _j = tf.constant(j)
   _k = tf.constant(k)
   result = tf.add(_j, _k)
   return result

def sub(j,k):
   _j = tf.constant(j)
   _k = tf.constant(k)
   result = tf.sub(_j,_k)
   return result

def mul(j,k):
   _j = tf.constant(j)
   _k = tf.constant(k)
   result = tf.mul(_j,_k)
   return result

def mod(j,k):
   _j = tf.constant(j)
   _k = tf.constant(k)
   result = tf.mod(_j,_k)
   return result

def div(j,k):
   _j = tf.constant(j)
   _k = tf.constant(k)
   result = tf.div(_j,_k)
   return result


with tf.Session() as sess:
     result = sess.run([mod(10,3)]) #10 %3 = 1
     result2 = sess.run([mul(5,4)]) #5 x 4 = 20
     result3 = sess.run([sub(10,6)]) #10 - 6 = 4
     result4 = sess.run([add(5,6)]) #5 + 6 =11
     result5 = sess.run([div(11.,7.)]) #11 / 7 =　1.5714285
     print result
     print result2
     print result3
     print result4
     print result5

Ergebnis

python arithmetic_operators.py
[1]
[20]
[4]
[11]
[1.5714285]

Grundlegende mathematische Funktionen

Verwenden Sie im Beispiel mit Quadrat die folgende Formel.

y=x2+b

Anwendungsbeispiel) vim square_test.py

import tensorflow as tf

def x2_plus_b(x, b):
    _x = tf.constant(x)
    _b = tf.constant(b)
    result = tf.square(_x)
    result = tf.add(result, _b)
    return result

with tf.Session() as sess:
    result = sess.run([x2_plus_b(2.0,3.0)])
    print result

Ergebnis

python square_test.py

[7.0]

Klicken Sie hier für weitere mathematische Funktionen (http://mirai-tec.hatenablog.com/entry/2016/02/22/001459). Ich habe auf den Blog verwiesen.

vim basic_math_fun.py

import tensorflow as tf

sess = tf.InteractiveSession()

################
# tf.add_n
################
a = tf.constant([1., 2.])
b = tf.constant([3., 4.])
c = tf.constant([5., 6.])
tf_addn = tf.add_n([a, b, c])
print "tf.add_n"
print sess.run(tf_addn)

# output:

# tf.add_n
# [  9.  12.]

################
# tf.abs
################
x = tf.constant([[-1., 2.], [3., -4.]])
tf_abs = tf.abs(x)
print "tf.abs"
print sess.run(tf_abs)

# output:

# tf.abs
# [[ 1.  2.]
#  [ 3.  4.]]

################
# tf.neg
################
x = tf.constant([[-1., 2.], [3., -4.]])
tf_neg = tf.neg(x)
print "tf.neg"
print sess.run(tf_neg)

# output:

# tf.neg
# [[ 1. -2.]
#  [-3.  4.]]

################
# tf.sign
################
x = tf.constant([[-1., 2.], [3., -4.]])
tf_sign = tf.sign(x)
print "tf.sign"
print sess.run(tf_sign)

# output:

# tf.sign
# [[-1.  1.]
#  [ 1. -1.]]

################
# tf.inv
################
x = tf.constant([[-1., 2.], [3., -4.]])
tf_inv = tf.inv(x)
print "tf.inv"
print sess.run(tf_inv)

# output:

# tf.inv
# [[-1.          0.5       ]
#  [ 0.33333334 -0.25      ]]

################
# tf.square
################
x = tf.constant([[-1., 2.], [3., -4.]])
tf_square = tf.square(x)
print "tf.square"
print sess.run(tf_square)

# output:

# tf.square
# [[  1.   4.]
#  [  9.  16.]]

################
# tf.round
################
x = tf.constant([0.9, 2.5, 2.3, -4.4])
tf_round = tf.round(x)
print "tf.round"
print sess.run(tf_round)

# output:

# tf.round
# [ 1.  3.  2. -4.]

################
# tf.sqrt
################
x = tf.constant([[1., 2.], [3., 4.]])
tf_sqrt = tf.sqrt(x)
print "tf.sqrt"
print sess.run(tf_sqrt)

# output:

# tf.sqrt
# [[ 0.99999994  1.41421342]
#  [ 1.73205078  1.99999988]]

################
# tf.rsqrt
################
x = tf.constant([[1., 2.], [3., 4.]])
tf_rsqrt = tf.rsqrt(x)
print "tf.rsqrt"
print sess.run(tf_rsqrt)

# output:

# tf.rsqrt
# [[ 0.99999994  0.70710671]
# [ 0.57735026  0.49999997]]

################
# tf.pow
################
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf_pow = tf.pow(x, y)
print "tf.pow"
print sess.run(tf_pow)

# output:

# tf.pow
# [[  256 65536]
#  [    9    27]]

################
# tf.exp
################
x = tf.constant([[1., 2.], [3., 4.]])
tf_exp = tf.exp(x)
print "tf.exp"
print sess.run(tf_exp)

# output:

# tf.exp
# [[  2.71828175   7.38905621]
#  [ 20.08553696  54.59815216]]

################
# tf.log
################
x = tf.constant([[1., 2.], [3., 4.]])
tf_log = tf.log(x)
print "tf.log"
print sess.run(tf_log)

# output:

# tf.log
# [[ 0.          0.69314718]
#  [ 1.09861231  1.38629436]]

################
# tf.ceil
################
x = tf.constant([[1.1, 2.2], [3.3, 4.4]])
tf_ceil = tf.ceil(x)
print "tf.ceil"
print sess.run(tf_ceil)

# output:

# tf.ceil
# [[ 2.  3.]
#  [ 4.  5.]]

################
# tf.floor
################
x = tf.constant([[1.1, 2.2], [3.3, 4.4]])
tf_floor = tf.floor(x)
print "tf.floor"
print sess.run(tf_floor)

# output:

# tf.floor
# [[ 1.  2.]
#  [ 3.  4.]]

################
# tf.maximum
################
x = tf.constant([[2, 8], [3, 12]])
y = tf.constant([[4, 10], [1, 9]])
tf_maximum = tf.maximum(x, y)
print "tf.maximum"
print sess.run(tf_maximum)

# output:

# tf.maximum
# [[ 4 10]
#  [ 3 12]]

################
# tf.minimum
################
x = tf.constant([[2, 8], [3, 12]])
y = tf.constant([[4, 10], [1, 9]])
tf_minimum = tf.minimum(x, y)
print "tf.minimum"
print sess.run(tf_minimum)

# output:

# tf.minimum
# [[2 8]
#  [1 9]]

################
# tf.cos
################
x = tf.constant([[2., 8.], [3., 12.]])
tf_cos = tf.cos(x)
print "tf.cos"
print sess.run(tf_cos)

# output:

# tf.cos
# [[-0.41614681 -0.14550003]
#  [-0.9899925   0.84385395]]

################
# tf.sin
################
x = tf.constant([[2., 8.], [3., 12.]])
tf_sin = tf.sin(x)
print "tf.sin"
print sess.run(tf_sin)

# output:

# tf.sin
# [[ 0.90929741  0.98935825]
#  [ 0.14112    -0.53657293]]

sess.close()

Referenz: https://www.tensorflow.org/versions/r0.9/api_docs/python/math_ops.html http://dev.classmethod.jp/machine-learning/tensorflow-math/ http://mirai-tec.hatenablog.com/entry/2016/02/22/001459