tensorflow常用函数记录
来源:互联网 发布:263网络通信 编辑:程序博客网 时间:2024/06/06 02:03
1.tf.range():产生一等差数列
示例代码如下:
[4, 5,6] [7, 8,9]]
[3, 3, 4,4]]
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12,15]
# 'limit' is 5
tf.range(limit) ==> [0, 1, 2, 3, 4]
2. tf.reshape()
示例代码如下:
# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3]
# tensor 't' is [[[1, 1], [2, 2]]
# [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2]
# tensor 't' is [[[1, 1, 1],
# [2, 2, 2]],
# [[3, 3, 3],
# [4, 4, 4]],
# [[5, 5, 5],
# [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,5, 5, 5, 6, 6, 6]
# -1 can also be used with higher dimensional shapes
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7
3. tf.concat()
示例代码如下:
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8,9], [10, 11, 12]]
tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6,10, 11, 12]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
4.
1 0
- tensorflow常用函数记录
- Tensorflow常用函数
- tensorflow 常用函数整理
- tensorflow常用函数介绍
- tensorflow常用函数笔记
- tensorflow常用优化函数
- tensorflow常用函数总结
- Tensorflow常用函数笔记
- TensorFlow常用函数
- TensorFlow常用函数
- tensorflow常用函数
- tensorflow笔记:常用函数
- TensorFlow常用函数
- tensorflow常用函数简述
- Tensorflow-常用函数
- Tensorflow常用函数说明
- tensorflow常用函数
- tensorflow常用函数
- 343. Integer Break
- 关于子函数中用new的问题
- 重写、覆盖、重载、多态的区别的分析
- jquery param 数组 带有 %5B%5D [] 问题
- java中的matches()方法
- tensorflow常用函数记录
- Tesorflow学习笔记(1)
- Tensorflow学习笔记(2)
- 几种开发者常见的开源软件协议的分析与介绍
- 五步搞定Android开发环境部署——非常详细的Android开发环境搭建教程
- QR分解-正交矩阵生成
- Tensorflow学习笔记(3)
- Java多线程中提到的原子性和可见性、有序性
- 版本更新2