TensorFlow基本操作

import tensorflow as tf
# https://www.lfd.uci.edu/~gohlke/pythonlibs/#xgboost

tf.__version__

'1.10.0'

创建变量

a = 3
# 创建一个变量
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]]) 

y = tf.matmul(w, x)  


#全局变量初始化，创建变量时一定要初始化全局变量，创建常量时可以不进行初始化
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)#除了赋值操作，其他的操作都需要run才能生效。
    print (y.eval())#等价于print (sess.run(y))

[[2.]]

tensorflow很多操作跟numpy有些类似的

• tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]

• tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]

• tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]

• tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]

• tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]

• tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.] [-1. -1. -1.]]

• tf.linspace(10.0, 12.0, 3, name=“linspace”) => [ 10.0 11.0 12.0]

• tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]

创建常量

#生成的值服从具有指定平均值和标准偏差的正态分布
norm = tf.random_normal([2, 3], mean=-1, stddev=4)

# 洗牌
c = tf.constant([[1, 2], [3, 4], [5, 6]])#常量可以直接进行定义
shuff = tf.random_shuffle(c)

# 每一次执行结果都会不同
sess = tf.Session()
print (sess.run(norm))
print (sess.run(shuff))

[[-5.58110332  0.84881377  7.51961231]
 [ 3.27404118 -7.22483826  7.70631599]]
[[5 6]
 [1 2]
 [3 4]]

加减及赋值操作

state = tf.Variable(0)
new_value = tf.add(state, tf.constant(1))
update = tf.assign(state, new_value)#将new_value赋给state

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(state))    
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

0
1
2
3

np转化为tensor

import numpy as np
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
with tf.Session() as sess:
     print(sess.run(ta))

[[ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]

其他运算

a = tf.constant(5.0)
b = tf.constant(10.0)

x = tf.add(a, b, name="add")
y = tf.div(a, b, name="divide")

with tf.Session() as sess:
    print("a =", sess.run(a))
    print("b =", sess.run(b))
    print("a + b =", sess.run(x))
    print("a/b =", sess.run(y))

a = 5.0
b = 10.0
a + b = 15.0
a/b = 0.5

设置容器

input1 = tf.placeholder(tf.float32)#相当于提前放置一个确定了大小的容器，后面可以放入变量。
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1, input2)
with tf.Session() as sess:
    print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))

[array([ 14.], dtype=float32)]