tensorflow c tutorial

such.tensorflow1.6 or 1.7 with CUDA9.1 is not good, should use 9.0, I was the pit. But fortunately there is a solution, thank you for this article:79433298So I wrote a detailed tutorial on using CUDA9.1 's TensorFlow:79871564Update: TensorFlow package is relatively large, installed more slowly than the ordinary small package, please ensure that the program is ru

Ai This concept seems to suddenly fire up, the beginning of the big score to win Li Shishi Alphago success attracted a lot of attention, but in fact, look at your phone's voice assistant, face recognition on the camera, today's headlines to help you automatically filter out the news, as well as the major music software song "Daily Recommended" ... All kinds of AI have already entered all aspects of our lives. Profoundly affected us, it can be said, this is an AI era.In fact, at the end of last y

). The course content is basically code-based programming, there will be a small amount of deep learning theoretical content. The course starts with some of the most basic knowledge from TensorFlow's most basic diagrams (graphs), sessions (session), tensor (tensor), variables (Variable), and gradually talks about the basics of TensorFlow, And the use of CNN and LSTM in TensorFlow. After the course, we will

powerful influence can lead to the development of a field, as was the case with previous Android systems and Map reduce technologies.Although TensorFlow's official version of the tutorial has been published, but the full English tutorial narrative inevitably make domestic researchers read a little laborious, and personal understanding of the different will cause the inconvenience of use, translated into Ch

environment variable configuration is not directly accessible to the bin and lib\x64 under the package, in the path to add these two paths.Once installed, there will not be more than four environmental variables, and two need to add them themselves. C:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0C:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0\binC:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0\lib\x64C:\Program Files\nvidia GPU Computing TOOLKIT\CUDA\V8.0\LIBNVVP

({x:mnist.test.images, y_: Mnist.test.labels}))The results are as follows:[[email protected] $] python digital_recognition.pyextracting. /train-images-idx3-ubyte.gzextracting. /train-labels-idx1-ubyte.gzextracting. /t10k-images-idx3-ubyte.gzextracting. /t10k-labels-idx1-ubyte.gz0.9039ExplainFlags. Define_string ('data_dir'mnist_data/ ' Directory for storing data')Indicates that we use Mnist_data's top level directory as a storage directory for training data, and if we do not have good training

variable, environment variable, left advanced system settings, properties---Edit text with path editPaste the directory of the Python folder up to the end and add a ";"That is, paste C:\Users\lobsterwww\AppData\Local\Programs\Python\Python36;Click the directory again to see the newly pasted directory is addedExit system settingsstep3 Installation NumPy if not installed, you cannot install TensorFlow directly under PIP. Go to https://pypi.python.org/p

This section corresponds to Google Open source TensorFlow object Detection API Object recognition System Quick start Step (i):Quick Start:jupyter notebook for off-the-shelf inferenceThe steps in this section are simple and do the following:1. After installing Jupyter in the first section, enter the Models folder directory at the Ternimal terminal to execute the command:Jupyter-notebook　　2. The Web page opens Jupyter access to the Object_detection fold

Session:with Tf.device ("/gpu:0"): Session.run (init) coord=tf.train.Coordinator () Threads= Tf.train.start_queue_runners (coord=coord) Max_iter=10000ITER=0ifOs.path.exists (Os.path.join ("Model",'model.ckpt')) isTrue:tf.train.Saver (Max_to_keep=none). Restore (Session, Os.path.join ("Model",'model.ckpt')) whileiterMax_iter:#Loss_np,_,label_np,image_np,inf_np=session.run ([Loss,opti,batch_image,batch_label,inf])B_batch_image,b_batch_label =Session.run ([Batch_image,batch_label]) l

Background: The latest version of Tensoflow has supported Python3.6First, download and install the Anaconda3 built-in Python3.6 version https://www.continuum.io/downloads do not modify its recommended options when installingThen download and install Cuda 8.0 https://developer.nvidia.com/cuda-downloadsThen download and install CUDNN 5.1 (the official recommended version, the latest version is not guaranteed to use) Link: Http://pan.baidu.com/s/1jHK0EFW Password: ai9f add cudnn extracted files to

TensorFlow Official Tutorial: The last layer of the retraining model to cope with the new classification This article mainly includes the following content: TensorFlow Official Tutorial re-training the final layer of the model to cope with the new classification flowers the inception model for the dataset re-training

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)

Basic knowledge 1. Use diagram (graphs) to represent the calculation 2. Execute diagram in session 3. Using tensor (tensors) to represent data 4. Maintain state through variable (variables) 5. Use of supply (feeds) and retrieval (fetches) for incoming or outgoing data (in the process of looking at the code, there are not clear functions, go to the Python API here to find the corresponding function to see https://www.tensorflow.org/api_docs/python/) My installation

TensorFlow is an open source software library that uses data flow diagrams for numerical calculations. In other words, that's the best way to build a deep learning model. This article collates some excellent tutorials and a list of projects on TensorFlow. First, the tutorial TensorFlow

networks, is a very simple task. I use the MNIST handwritten number recognition in the official tutorial as an example to show the code. The entire program is basically consistent with the official routine, however, some machine learning or convolutional neural networks should be able to quickly understand the meaning of the Code. # Encoding = UTF-8 import tensorflow as tf import numpy as np from

TensorFlow Learning Notes 4: Distributed TensorFlow Brief Introduction The TensorFlow API provides cluster, server, and supervisor to support distributed training of models. The distributed training introduction about TensorFlow can refer to distributed TensorFlow. A simpl

Preface: TensorFlow There are many basic concepts to understand, the best way is to go to the official website followed by the tutorial step by step, there are some translated version, compared to see to help understand: tensorflow1.0 document translation text: One, the necessary process of building and executing the calculation diagram 1,graph (Figure calculation): see TF. Graph classUsing

Mnist is an entry-level computer-vision dataset that contains 60,000 training data and 10,000 test data. Each sample is a variety of handwritten digital pictures below: It also contains the corresponding label for each picture, telling us this is a number. For example, the above four pictures are labeled 5,0,4,1. Mnist's official website: http://yann.lecun.com/exdb/mnist/ You can view the current maximum record for the project: http://rodrigob.github.io/are_we_there_yet/build/classification_dat

Development environment: Mac OS 10.12.5Python 2.7.10GCC 4.2.1Mac default is no pip, install PIP.sudo easy_install pip1. Installing virtualenvsudo pip install virtualenv--upgradeCreate a working directory:sudo virtualenv--system-site-packages ~/tensorflowMake the directory, activate the sandboxCD ~/tensorflowSOURCE Bin/activateInstall TensorFlow in 2.virtualenvAfter entering the sandbox, execute the following command to install

1. Download and install Anaconda1.1 downloadDownload the Linux version from Anaconda official website (https://www.continuum.io/downloads)https://repo.continuum.io/archive/(Recommended python3.5)1.2 InstallationCD ~/downloadssudo bash anaconda2-5.0.1-linux-x86_64.sh (download the corresponding version of Python2.7 here)Ask if you want to add the Anaconda bin to the user's environment variable and select yes!Installation is complete.2. Install tensorflow2.1 set up