Stephen Smith's Blog All things Sage 300… The Road to TensorFlow – Part 7: Finally Some Code leave a comment » Introduction Well after a long journey through Linux, Python, Python Libraries, the Stock Market, an Introduction to Neural Networks and tr…

      Deep Deterministic Policy Gradients in TensorFlow AUG 21, 2016 This blog from: http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html Introduction Deep Reinforcement Learning has recently gained a lot of traction in the machine learning commu…

1.0 TensorFlow graphs Tensorflow是基于graph based computation: 如: a=(b+c)∗(c+2) 可分解为 d=b+c e=c+2 a=d∗e 这样做的目的在于,d=b+c,e=c+2,这两个式子就可以并行地计算. 2.0 A Simple TensorFlow example import tensorflow as tf # first, create a TensorFlow constant const = tf.constant(…

[源码解析] 深度学习分布式训练框架 horovod (7) --- DistributedOptimizer 目录 [源码解析] 深度学习分布式训练框架 horovod (7) --- DistributedOptimizer 0x00 摘要 0x01 背景概念 1.1 深度学习框架 1.2 Tensorflow Optimizer 0x02 总体架构 2.1 总体思路 3.2 总体调用关系 0x04 TensorFlow 1.x 4.1 _DistributedOptimizer 4.2 c…

Variable Tensorflow使用Variable类表达.更新.存储模型参数. Variable是在可变更的,具有保持性的内存句柄,存储着Tensor 在整个session运行之前,图中的全部Variable必须被初始化 Variable的值在sess.run(init)之后就确定了 Tensor的值要在sess.run(x)之后才确定 创建的Variable被添加到默认的collection中 tf.GraphKeys中包含了所有默认集合的名称,可以通过查看__dict__发现具体集合…

Reference:Stack Overflow. The name parameter is optional (you can create variables and constants with or without it), and the variable you use in your program does not depend on it. Names can be helpful in a couple of places: When you want to save or…

initialize_all_variables已被弃用,将在2017-03-02之后删除. 说明更新:使用tf.global_variables_initializer代替. 就把tf.initialize_all_variables()改为global_variables_initializer()就可以了 原文:https://blog.csdn.net/wqqgo/article/details/75675323…

本文是在阅读官方文档后的一些个人理解. 官方文档地址:https://www.tensorflow.org/versions/r0.12/get_started/basic_usage.html#basic-usage 关于tensor和op的理解 Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors, performs some computation, and pr…

#写libsvm格式 数据 write libsvm     #!/usr/bin/env python #coding=gbk # ============================================================================== # \file gen-records.py # \author chenghuige # \date 2016-08-12 11:52:01.952044 # \Description # ========…

这里做了一些小的修改,感谢谷歌rd的帮助,使得能够统一处理dense的数据,或者类似文本分类这样sparse的输入数据.后续会做进一步学习优化,比如如何多线程处理. 具体如何处理sparse 主要是使用embedding_lookup_sparse,参考 https://github.com/tensorflow/tensorflow/issues/342 两个文件 melt.py binary_classification.py 代码和数据已经上传到 https://github.com/ch…