Tensorflow 变量操作

import tensorflow as tf
a = 3
# Create a variable.
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]]) 
#矩阵乘法
y = tf.matmul(w, x)  

print(y)
#必须先初始化变量，然后才能运行矩阵乘法
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print (y.eval())


# 最好用float32数据格式
tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]

# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]

# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]

# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]

# Constant 2-D tensor populated with scalar value -1.
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]

# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]

有点类似与numpy的操作

#创建一个随机矩阵，mean均值，stddev方差
norm = tf.random_normal([2, 3], mean=-1, stddev=4)

# 对tenser格式进行洗牌操作
c = tf.constant([[1, 2], [3, 4], [5, 6]])
shuff = tf.random_shuffle(c)

# 每次运行这些操作时，都会生成不同的结果
sess = tf.Session()
print (sess.run(norm))
print (sess.run(shuff))

state = tf.Variable(0)
#state加上常量1
new_value = tf.add(state, tf.constant(1))
#对state进行更新
update = tf.assign(state, new_value)

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))

#对当前tenser进行保存
#tf.train.Saver
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]])
y = tf.matmul(w, x)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
# Do some work with the model.
# Save the variables to disk.
    save_path = saver.save(sess, "C://tensorflow//model//test")
    print ("Model saved in file: ", save_path)

#将numpy格式转化为tengsor
import numpy as np
a = np.zeros((3  ,3))
ta = tf.convert_to_tensor(a)
with tf.Session() as sess:
     print(sess.run(ta))

#tensor变量占位
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.mul(input1, input2)
with tf.Session() as sess:
    print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))

Tensorflow 变量操作

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