2、tensorflow 变量的初始化

https://blog.csdn.net/mzpmzk/article/details/78636137

关于张量tensor的介绍

import tensorflow as tf
import numpy as np

x = tf.convert_to_tensor([[0,1,2],[3,4,5],[6,7,8]])
y = tf.constant([1,2,3,4,5,6], shape = [2,3])
z = tf.constant(-1, shape=[3,3])
a1 = tf.fill([2,3], 9) #可忽略
a2 = tf.tile([[1,2],[3,4],[5,6]] , [2,3])
# tf.range(2,10,2)
# xx = tf.cast(x,tf.int32)   #强制数据类型转换
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print("x:",sess.run(x))
    print("y:",sess.run(y))
    print("z:",sess.run(z))
    print("a1:",sess.run(a1))
    print("a2:",sess.run(a2))

('x:', '/n', array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]], dtype=int32))
('y:', array([[1, 2, 3],
       [4, 5, 6]], dtype=int32))
('z:', array([[-1, -1, -1],
       [-1, -1, -1],
       [-1, -1, -1]], dtype=int32))
('a1:', array([[9, 9, 9],
       [9, 9, 9]], dtype=int32))
('a2:', array([[1, 2, 1, 2, 1, 2],
       [3, 4, 3, 4, 3, 4],
       [5, 6, 5, 6, 5, 6],
       [1, 2, 1, 2, 1, 2],
       [3, 4, 3, 4, 3, 4],
       [5, 6, 5, 6, 5, 6]], dtype=int32))

其他：
1. tf.pad

tf.pad(tensor, paddings, mode='CONSTANT', name=None)
paddings： is an integer tensor with shape [n, 2]，n是 tensor 的维度
For example:
# 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1,], [2, 2]].
# paddings[0, 0/1]: 沿着第 0 维(x轴)在 tensor 上方/下方补 1 圈零
# paddings[1, 0/1]: 沿着第 1 维(y轴)在 tensor 左方/右方补 2 圈零
tf.pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
                                     [0, 0, 1, 2, 3, 0, 0],
                                     [0, 0, 4, 5, 6, 0, 0],
                                     [0, 0, 0, 0, 0, 0, 0]]