tensorflow

Ubuntu 17 comes with Python 3.6 directly with the command installation PIP3 install TensorFlow will be prompted to have a module suitable for python3.5, not suitable for python3.6.I solved this, I installed the lower version of the TensorFlow,Download the lower version of https://mirrors.tuna.tsinghua.edu.cn/help/tensorflow/from the Tsinghua University imageSelec

Install TensorFlow (CPU version)Familiar with the Anaconda command, you can refer to http://www.jianshu.com/p/d2e15200ee9bThe official recommendation is that you have the GPU immediately, but you can also install a CPU version, and the command to create the environment is:conda create -n tensorflowpython=3.6(Be sure to specify the Python version, I did not write python=3.6 at first, after various failures)Download the installation package first, the d

TensorFlow installed under Windows for study purposes, if you want to do the technology, see the relevant blog: CentOS7 installation TensorFlow1 , installation Pytho3.5First go to the Anaconda website to download the Windows version of the software, here Select the v3.6 version.Https://www.continuum.io2 , after the installation is complete, open Anaconda PromptThen we enter a command to see the installable version of

Reprint Please specify link: http://www.cnblogs.com/SSSR/p/5630534.htmlExamples in Tflearn training VGG16 project: https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py has not been tested successfully.The next project is to use a model that has been trained by others to make predictions, and the test works very well.Github:https://github.com/ry/tensorflow-vgg16 This project has been tested successfully, the effect is very good

1 Learning Goals: Learn the basic TensorFlow concept Using classes in TensorFlow LinearRegressor and predicting the median house value of each city block based on a single input feature Estimating the accuracy of model predictions using RMS error (RMSE) Improve model accuracy by adjusting the model's hyper-parameters Note: Data is based on California State 1990 census data.2 settin

TensorFlow InstallationSystem CentOS 7.0 64-bit:Python version: (note TensorFlow currently only supports python2.7 version)Install PIPYum update-y yum install-y python python-devel epel-release.noarch python-pipInstalling TensorFlow with PIPPip Install HTTPS://STORAGE.GOOGLEAPIS.COM/TENSORFLOW/LINUX/CPU/

TensorFlow is used to train a simple binary classification neural network model. Use TensorFlow to implement the 4.7 pattern classification exercise in neural networks and machine learning The specific problem is to classify the dual-Crescent dataset as shown in. Tools used: Python3.5 tensorflow1.2.1 numpy matplotlib 1. Generate a two-month Dataset Def produceData (r, w, d, num): r1 = r-w/2 r2 = r + w/2 #

Mac Python3 installation TensorFlow error:futures requires Python ' >=2.6, Correspondence Resolution Link: https://github.com/tensorflow/tensorflow/issues/16478Same problem here on Ubuntu 16.04 with Python 3.5.Clearly, Python 3 doesn ' t need futures. And with Python 2.7, using:pip2 install tensorflow-gpu==1.5.0It goes

CX10 TensorFlow Application Practice of the most fire frame based on Python play to AIThe beginning of the new year, learning to be early, drip records, learning is progress!Do not look everywhere, seize the promotion of their own.For learning difficulties do not know how to improve themselves can be added: 1225462853 get information.CX103 TensorFlow Application Practice of the most fire frame based on Pyth

Just bought the new machine and installed the latest Ubuntu system. The GPU support is not selected according to the process installed on the official website. Download the source code. Git is required, and if you don't have Git installed you need to install it first. git clone --recurse-submodules https://github.com/tensorflow/tensorflow Installing BazelInstall dependencies First

The Ndarrray in List/tuple,numpy in Python and the tensor in TensorFlow.In Python, List/tuple understands that a sequence of data is understood only from the memory point of view, not the number of mathematical bids, vectors, and tensor.From the Python memory point of view, is a numeric value, length 1, and is not a sequence;From the perspective of NumPy and TensorFlow mathematics, it is a scalar, shape is (), its axis is 0;[1,2,3,4,5,6]From the Pytho

Objective Some time ago because the subject needs to use a period of time tensorflow, feel this framework is very interesting, in addition to can build complex neural network, but also can optimize the other needs of the calculation model, so I always want to learn to write a similar diagram calculation framework. In the last few days, the group will finish the decision to implement a rough version diagram computing framework that mimics the

1. General machine learning predictive function coefficient (y=0.1x+0.3) #-*-CODING:GBK-*- import tensorflow as tf import numpy as NP #生成数据, y=0.1x+0.3 X_data=np.random.rand ( Astype (np.float32) y_data=x_data*0.1+0.3 # # #开始创建tensorflow结构 ### WEIGHT=TF. Variable (Tf.random_uniform ([1],-1.0,1.0)) BIASES=TF. Variable (Tf.zeros ([1])) y=weight*x_data+biases #误差 Loss=tf.reduce_mean (Tf.square (y-y_data)

In the deep learning network structure, the categories of each layer can be divided into these kinds: convolution layer, full connection layer, Relu layer, pool layer and reverse convolution layer. At present, in pixel-level estimation and end-to-end learning problems, full convolution network shows his advantage, there is a very important layer, the convolution of the feature map sampling (deconvolution) to the input image dimension space, is the deconvolution layer. So how does it come to be a

the cause of the problem Today, try to install the CPU version of TensorFlow (GPU is not supported) by PIP3 install TensorFlow installation. The installation went well and I ran the simplest Hello wolrd example. The results do run out and a warning pops up. 2017-11-28 09:07:17.849180:i tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instru

Today learned the next TensorFlow official website on the CIFAR10 section, found some API has not seen before, here to tidy up a bit.CIFAR10 Tutorial Address 1. The first is the initialization of some parameters FLAGS = Tf.app.flags.FLAGS # Basic model parameters. Tf.app.flags.DEFINE_integer (' batch_size ', +, "" "Number of images to process in a batch." ") Tf.app.flags.DEFINE_string (' Data_dir ', '/temp/cifar10_data ',

TensorFlow and Theano and Keras are deep learning frameworks, TensorFlow and Theano are more flexible and difficult to learn, they are actually a differentiator. Keras is actually TensorFlow and Keras interface (Keras as the front end, TensorFlow or Theano as the back end), it is also very flexible, and relatively eas

Law One: Cycle Print Templates for (x, y) in Zip (Tf.global_variables (), Sess.run (Tf.global_variables ())): print ' \ n ', X, y Example # Coding=utf-8 Import TensorFlow as tf def func (In_put, Layer_name, is_training=true): With Tf.variable_scope (layer _name, REUSE=TF. Auto_reuse): bn = Tf.contrib.layers.batch_norm (Inputs=in_put, decay=0.9, Is_training=is_training, Updates_coll Ections=none) return

TensorFlow model save/load When we use an algorithmic model on-line, we must first save the trained model. TensorFlow the way to save the model is not the same as Sklearn, Sklearn is straightforward, a sklearn.externals.joblib dump and load method can be saved and loaded using. and TensorFlow because of the graph, operation these concepts, save and load the mode

TensorFlow is not a Machine Learning specific library, instead, are a general purpose computation library that represent s computations with graphs. Its core are implemented in C + + and there are also bindings for different languages. The bindings for the "Go" programming language, differently from the Python ones, are a useful tool Rflow in Go but also for understanding how TensorFlow is implemented under