tensorflow中常用的計算操作

從tensor的維度上面計算元素之和

tf.reduce_sum(

input_tensor, # 輸入
axis=none, # 表示在哪個維度進行sum操作。
keepdims=none, # 表示是否保留原始資料的維度，false相當於執行完後原始資料就會少乙個維度。
name=none,
reduction_indices=none,
keep_dims=none
)

import tensorflow as tf

x = tf.constant([[
1,1,
1],[
1,1,
1]])
a = tf.reduce_sum(x)
# 修改這裡
b = tf.reduce_sum(x, axis=0)
c = tf.reduce_sum(x, axis=1)
d = tf.reduce_sum(x, keep_dims=
true
)e = tf.reduce_sum(x, keep_dims=
false
)f = tf.reduce_sum(x, axis=[0
,1])
tensors =
[a, b, c, d, e, f]
with tf.session(
)as sess:
for tensor in tensors:
y = sess.run(tensor)
print
(y)# 6
# [2 2 2]
# [3 3]
# [[6]]
# 6# 6

tf.multiply(

x,y,
name=none # a name for the operation (optional).
)

import tensorflow as tf

a = tf.constant([[
1,2]
,[3,
4]])
b = tf.constant([[
1,3]
,[2,
1]])
y = tf.multiply(a, b)
with tf.session(
)as sess:
res = sess.run(y)
print
(res)
# [[1 6]
# [6 4]]

import tensorflow as tf

x = tf.constant([[1.0, 2.0],
[3.0, 4.0]])
a = 0.5 * x
b = tf.multiply(0.5, x)
tensors = [a, b]
with tf.session() as sess:
for tensor in tensors:
y = sess.run(tensor)
print(y)
# [[0.5 1. ]
# [1.5 2. ]]
# [[0.5 1. ]
# [1.5 2. ]]

tf.matmul(

a,b,
transpose_a=false,
transpose_b=false,
adjoint_a=false,
adjoint_b=false,
a_is_sparse=false,
b_is_sparse=false,
name=none
)

import tensorflow as tf

a = tf.constant([[
1,2]
,[3,
4]])
b = tf.constant([[
1,3]
,[2,
1]])
y = tf.matmul(a, b)
with tf.session(
)as sess:
res = sess.run(y)
print
(res)
# [[ 5 5]
# [11 13]]

import tensorflow as tf

x = tf.constant([[1, 2],
[3, 4]])
a = 1 + x
b = tf.add(1, x)
tensors = [a, b]
with tf.session() as sess:
for tensor in tensors:
y = sess.run(tensor)
print(y)
# [[2 3]
# [4 5]]
# [[2 3]
# [4 5]]

import tensorflow as tf

x = tf.constant([[1.8, 2.2],
[3.0, 4.1]])
y = tf.cast(x, tf.int32)
with tf.session() as sess:
res = sess.run(y)
print(res)
# [[1 2]
# [3 4]]

import tensorflow as tf

x = tf.constant([[1.8, 2.2],
[3.0, 4.1]])
y = tf.cast(x, tf.int32)
with tf.session() as sess:
res = sess.run(y)
print(res)
# [[1 2]
# [3 4]]

import tensorflow as tf

x = [1, 5, 3]
y = [2, 4, 6]
z = tf.maximum(x, y)
with tf.session() as sess:
res = sess.run(z)
print(res)
# [2 5 6]

import tensorflow as tf

x = [1, 5, 3]
y = [2, 4, 6]
z = tf.minimum(x, y)
with tf.session() as sess:
res = sess.run(z)
print(res)
# [1 4 3] 每個維度返回乙個最小值

# import tensorflow as tf

a = tf.argmax([[1,3,4,5,6]], axis=1)
b = tf.argmax([[1,3,4], [2,4,1]], axis=1)
c = tf.argmin([[1,3,4,5,6]], axis=1)
d = tf.argmin([[1,3,4], [2,4,1]], axis=1)
tensors = [a ,b, c, d]
with tf.session() as sess:
for tensor in tensors:
y = sess.run(tensor)
print(y)
# [4]
# [2 1]
# [0]
# [0 2]

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