This article is void
My next installment is the TensorFlow and Keras truth.
Environment:
Anaconda4.2;python3.5;windows10,64,cuda
Previous hard cuda9.1 useless, we want to use the GPU must choose cuda8.0, I thought the official will be corresponding update, naive. First TensorFlow don't recognize, moreover cudnn own all do not recognize, only 8.0.
Keras and TensorFlow
The following small series for everyone to bring a TensorFlow installation and Jupyter notebook configuration method. Small series feel very good, now share to everyone, also for everyone to make a reference. Let's take a look at it with a little knitting.
TensorFlow using Anaconda in Ubuntu installation method and Jupyter notebook run directory and Remote access configuration
Install Anaconda under Ubuntu
TensorFlow Varibale usage, tensorflowvaribale
-------------------------------------------
Reprinted Please note: from blog
Xiuyuxuanchen
Address: http://www.cnblogs.com/greentomlee/
-------------------------------------------Varibale usage
Instance:
Example:
First:
#! /Usr/bin/env python
This statement specifies the python runtime environment. There are two ways to specify this method. One is to specify the python path ---#! /Usr/bin/python
1. New environment python3.5 in Anaconda, I am using the new environment in Anaconda-navigator, Python version selection 3.52. Activate the newly added environment, pay attention to win, no source, direct useActivate New_env_name3. Install TensorFlow, reference https://github.com/tensorflow/tensorflow/blob/r1.0/tensorflow
Recently learning TensorFlow, of course, the premise is to install a good framework, many online tutorials are virtual or conda, from my experience, Windows is currently only supported Python3.5 version of the installation, Python official online has instructions:I Python27 because of the commonly used is the change of a bit:Download anaconda2 and install to D:\anacondaDownload Anaconda3: Note The version, the integration must be Python3.5, the latest
The first blog, the main record of the installation of deep Learning Framework (TensorFlow), installed very simple (a word), started I thought it was troublesome, no n card, do not know how to start. Okay, here we are.I generally like the pydev of Eclipse (personal feeling is very useful), then Python is mainly Anconda (Tsinghua Mirror), loading TensorFlow1. Download Anconda3, Baidu ancond Tsinghua Mirror, download anconda3-cp35-cp35m-xxxx, download t
Official website: http://tensorflow.org/Installation steps:1, sudo apt-get install Python-pip python-dev python-virtualenv2, virtualenv--system-site-packages ~/tensorflow3. CD ~/tensorflow4. Source Bin/activate # If using bash5, (TensorFlow) $ pip Install TENSORFLOW-0.5.0-CP27-NONE-LINUX_X86_64.WHLTest:1. Open the terminal input CD TensorFlow2. SOURCE Bin/activate3. Python4. Enter the following example afte
In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them. This blog is divided into three parts, the first is to explain tensorflow related function
first, to enter the Anaconda website Download
https://www.anaconda.com/download/
Everyone according to their own computer configuration download the corresponding version, I download here is the version of python3.6version 64bit
After downloading and installing, open CMD, enter ' Conda--version ', if output the following information
4.3.21
Anaconda installation was successful.
2. Install TensorFlow
Because the foreign mirror download is s
In a previous tutorial series I went over some of the theory behind recurrent neural (Networks) and the Rnns N of a simple RNN from scratch. That's a useful exercise, but in practice we do libraries like tensorflow with high-level primitives for dealing S.
With this using an RNN should is as easy as calling a function, right? Unfortunately that ' s not quite the case. In this post I want the some of the best practices for working with Rnns in
The previous article has introduced the implementation of convolution, this article we learn the deconvolution principle, again, after understanding the deconvolution principle, in the back hand-written Python code to implement the deconvolution. 1 Inverse convolution principle
The deconvolution principle does not work well with text descriptions, where the deconvolution process is described directly in a simple example.
Suppose the input is as follows:
[[1,0,1],
[0,2,1],
[1,1,0]]
The deconvo
This book is published by only cloud technology Caicloud, the main content is familiar with the basic structure of TensorFlow framework and practical application in the field of depth learning.For specific code see:1. Official:Caicloud/tensorflow-tutorial:example tensorflow codes and Caicloud TensorFlow as a Service de
3 Ways of reading data
There are 3 ways to read data in a TensorFlow program:Supply data (feeding): At each step in the TensorFlow program, let the Python code supply the data.Reading data from a file: At the beginning of the TensorFlow graph, let an input pipeline read the data from the file.Preload data: Define constants or variables in the
TensorFlow Serving,gpu
TensorFlow serving is an open source tool that is designed to deploy a trained model for inference.TensorFlow serving GitHub AddressThis paper mainly introduces the installation of TensorFlow serving and supports the GPU model. Install dependent Bazel
TensorFlow serving requires 0.4.5 above Bazel
This article references from: The Python-tensorflow Tutorial series TensorFlow Getting Started: Using graphs to represent computational tasks. Executes the diagram in the context of what is referred to as a conversation (session). Use tensor (tensor) to represent the data. Maintains state through variable (Variable). Use feeds and fetches to assign values to or fetch data from any operation (arbitrary opera
Contact TensorFlow Small white, online tutorials a lot, image classification should belong to a more classic example, especially Google pushed slim, but the online tutorial omitted many details will lead to run, after debugging finally ran out
The result is OK, share
My environment, cuda8.0+cudnn5.1+python2.7.
About TENSORFLOW,CUDA+CUDNN Installation Recommended Tutorials:
http://blog.csdn.net/xierhacker/ar
Keras mixed with TensorFlow Keras and TensorFlow using tensorfow Fly Keras
Recently, TensorFlow has updated its new version to 1.4. Many updates have been made, and it is of course important to add Tf.keras. After all, Keras for the convenience of the model building everyone is obvious to all.
Likes the Keras style model constructs but does not like the
This article reproduced from: https://zhuanlan.zhihu.com/p/23361413, the original title: TensorFlow Serving Taste Fresh
In the 2016, machine learning became more popular in the post-war era of Alpha go and Li Shishi. Google also launched the TensorFlow serving this year and added a fire.TensorFlow serving is a high-performance open Source library for machine learning model serving. It can deploy the traine
If I can help you, I'll give you some praise.
Powered by Liu Yarong-standing on the shoulders of giants
All kinds of the tyranny Python tensorflow: xxxxxx ' Module ' object has no attribute ' xxxxx '
This example is: TensorFlow, ' module ' object has no attribute ' placeholder '
My environment:
Win10x64
Anaconda 1.5
Python3.6
tensorflow1.2.1
tensorflow-gpu1.1.
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