#!/usr/bin/python # -*- coding: UTF- -*- # @date: // : # @name: first_tf_1223 # @author:vickey-wu from __future__ import print_function import tensorflow as tf import os # disable error os.environ[' # constant node1 = tf.constant(3.0, dtype=tf.float3…

可以参考官网:https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#android-studio 官网讲的很简单: Android Studio may be used to build the demo in conjunction with Bazel. First, make sure that you can build with Bazel following the above…

Ans:pip install tensorflow (note:  version number of pip and python must be  consistent)…

首先是把tensorflow克隆到本地一份. git clone --recurse-submodules https://github.com/tensorflow/tensorflow.git 既然是谷歌官方要求的,最好把--recurse-submodules加上,文档说可以避免一些数据结构序列化时的编译问题. 这是android demo的github主页. 准备编译 1.安装bazel bazel是谷歌自己的构建工具.tensorflow只能部分支持cmake或者gradle,而baz…

TensorFlow Ops 1. Fun with TensorBoard In TensorFlow, you collectively call constants, variables, operators as ops. TensorFlow is not just a software library, but a suite of softwares that include TensorFlow, TensorBoard, and Tensor Serving. To make…

awesome-nlp  A curated list of resources dedicated to Natural Language Processing Maintainers - Keon Kim, Martin Park Please read the contribution guidelines before contributing. Please feel free to pull requests, or email Martin Park (sp3005@nyu.edu…

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…

只是简单demo, 可以看出tensorflow非常简洁,适合快速实验     import tensorflow as tf import numpy as np import melt_dataset import sys from sklearn.metrics import roc_auc_score     def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01))     def…

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…

之前的博客我们已经对RNN模型有了个粗略的了解.作为一个时序性模型,RNN的强大不需要我在这里重复了.今天,让我们来看看除了RNN外另一个特殊的,同时也是广为人知的强大的神经网络模型,即CNN模型.今天的讨论主要是基于Tensorflow的CIFAR10教程,不过作为对比,我们也会对Tensorflow的MINST教程作解析以及对比.很快大家就会发现,逻辑上考虑,其实内容都是大同小异的.由于所对应的目标不一样,在数据处理方面可能存在着些许差异,这里我们以CIFAR10的为基准,有兴趣的朋友欢迎去…