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ImpressionsToday, I tested the model of my own training, and YOLOv2 done a comparison, the detection is correct, YOLOv2 version of the accuracy is not high, but there are a lot of SSD did not detect, recall rate is not high. Note that the SSD environment is Python3, and running on the python2 will be problematic. TENSORFLOW-GPU, OPENCV installation reference my blog: SSD environment installation 1 Making data setsThe most troublesome is the producti
Update to TensorFlow 1.4 I. Read input data 1. If the database size can be fully read in memory, use the simplest numpy arrays format: 1). Convert the Npy file into a TF. Tensor2). Using Dataset.from_tensor_slices ()Example: # Load The training data into two numpy arrays, for example using ' np.load () '. With Np.load ("/var/data/training_data.npy") as data: features = data["Features"] labels = data["Labels"] # assume that each row of features corresp
using the specified GPU and GPU memory in TensorFlow This document is set up using the GPU 3 settings used by the GPU 2 Python code settings used in the 1 Terminal execution Program TensorFlow use of the memory size 3.1 quantitative settings memory 3.2 Set video memory on demand Reprint please specify the source: Http://www.cnblogs.com/darkknightzh/p/6591923.html Reference URL: Http://stackoverflow.com/que
Catalogue Graphics driver Installation Cuda Installation CUDNN Installation TENSORFLOW-GPU Installation this time using the host configuration:CPU:i7-8700k graphics :gtx-1080tiFirst, install the video driverOpen a Command Window (ctrl+alt+t)sudo apt-get purge nvidia*sudo add-apt-repository ppa:graphics-drivers/ppasudo apt-sudoinstall nvidia-384 nvidia-settingsif the error Add-apt-repository does not exist, run the following c
TensorFlow installation and jupyter notebook configuration, tensorflowjupyter Tensorflow uses anaconda for ubuntu installation and jupyter notebook running directory and remote access configuration Install Anaconda in Ubuntu bash ~/file_path/file_name.sh After the license is displayed, press Ctrl + C to skip it, and yes to agree. After the installation is complete, ask whether to add the path or modify the
TensorFlow saver specifies variable access, tensorflowsaver Today, I would like to share with you the point of using the saver of TensorFlow to access the trained model. 1. Use saver to access variables;2. Use saver to access specified variables. Use saver to access variables. Let's not talk much about it. first go to the code # Coding = utf-8import OS import tensorflow
In the summary of Bayesian individualized sequencing (BPR) algorithm, we discuss the principle of Bayesian personalized sequencing (Bayesian personalized Ranking, hereinafter referred to as BPR), and we will use BPR to make a simple recommendation from the practical point of view. Since the existing mainstream open source class library has no BPR, and it is relatively simple, so with TensorFlow to implement a simple BPR algorithm, let us begin.1. BPR
Software Version Window10 X64 Python 3.6.4 (64-bit) CUDA CUDA Toolkit 9.0 (Sept 2017) CuDNN CuDNN v7.0.5 (Dec 5), for CUDA 9.0 The above version of the test passed.Installation steps:1. to install python, remember to tick pip. 2. detects if CUDA is supported .For more information on the NVIDIA website, see: Https://developer.nvidia.com/cuda-gpus, you can see if you can use
1. Preparation Windows 10 system, 3.6GHZ CPU, 16G memory Visual Studio or 2015 Download and install Git Download and install CMake Download Install Swigwin If you do not need Python bindings, you can skip Clone TensorFlow Switch TensorFlow to the git tag you want to compile Modify Tensorflow/contrib/cmake/cmakelists.txtif(Tensorflow_optimize_for
First, you can install a anaconda. You can then use the Python pip to install a specific version of the TensorFlow, such as Pip Install tensorflow-gpu==1.1.0 Upgrade to the latest: GPU Version: Pip Install--upgrade Tensorflow-gpu CPU Version: Pip Install--upgrade TensorFlow ============== How to view the curr
If TensorFlow is so great, why open source it rather than keep it proprietary? The answer is simpler than you might think:we believe, which machine learning are a key ingredient to the innovative product S and technologies of the future. Growing fast, but lacks standard tools. By sharing "What we believe to be one of the best machine learning toolboxes in the world, we hope to create an open Standa Rd for exchanging the ideas and putting machine learn
Some time ago, made a compilation of the example of CC, finally finally fix ... But to compile in the IDE is not successful, continue to explore.Now share, explore the process, welcome nagging, Exchange.http://home.cnblogs.com/u/mydebug/Prepare: inception_dec_2015 files to the Data folderConcrete Look Https://github.com/tensorflow/tensorflow/tree/master/tensorflow
you can play with no GPU. Van Gogh painting: Ubuntu TensorFlow CPU Edition July Online Development/marketing team Xiao Zhe, Li Wei, JulyDate: September 27, 2016First, prefaceSeptember 22, our development/marketing team of two colleagues using DL study Van Gogh painting, Installation Cuda 8.0 times countless pits, many friends seek refuge from the pit. Therefore, 3 days later, September 25, the tutorial will teach you from start to finish using DL
1, from the Anaconda Official website (https://www.continuum.io/downloads) Download the Linux version of the installation files (recommended Python version 2.7), run SH to complete the installation.After installing the Anaconda, python3.5 and other related tools are installed.2, Installation Pymysql>>> pip Install Pymysql3. After the installation is complete, open the terminal and create the TensorFlow virtual environmentIn the prompt, enter:>>> Conda
Recently in the study of Huang Wenjian TensorFlow Books, hope to do a summary of learning.Softmax Regression Algorithm principle: When we predict a picture, we will calculate the probability of each number, such as 3 probability is the probability of 3%,5 is 6%,1 probability is 80%, then return 1.TensorFlow version: 0.8.0# import handwriting recognition data, TensorFlow
Support original, more content Welcome to the author blog:http://www.china10s.com/blog/?p=490 Machine learning This method of calculation has been known to the world in the last century, but it has not been developed because of the computer-limited computing power and network speed. With the Moore effect, the current computer performance has soared, even in the hands of the iphone, than the United States on the moon on the machine used to be stronger. Therefore, in this context, machine learning
LeNet5 convolution neural network forward propagation # TensorFlow actual combat Google Depth Learning Framework 06 image recognition and convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # filename:LeNet5_infernece.py # LeNet5 forward propagate import TensorFlow as TF # 1. Set the parameters of the neural network Input_node = 784 Output_node = Ten im
~/ Nvidia_cuda-7.5_samples/bin/x86_64/linux/release ./devicequery CD ~/nvidia_cuda-7.5_samples/1_utilities /bandwidthtest make ./bandwidthtest1 2 3 4 5 6 7 8 9 10 11 If two test results are pass, it means that Cuda is running normally. Reference links Cuda-7.5-toolkit 2. Install TensorFlow Essential Python-pip and Python-dev. in this window, enter the command: $ sudo apt-get install Python-pip python-dev Notice that there is already a $ symbol in t
Define Cell In a lot of RNN paper we see similar graphs: Each of these small rectangles represents a cell. Each cell is a slightly more complex structure, as shown in the following diagram: The context in the diagram is a cell structure, and you can see that it accepts input (T), context (t-1), and then outputs output (t), such as the Rnn cell, which we use to stack up in our task, That is, the current layer of the cell output also as the next layer of input, so can be introduced into each ce
Brief introduction This article describes the second method of data import for TensorFlow. This approach is somewhat cumbersome to maintain efficiency. There are several steps to be divided:-Write all samples to binary (execute only once)-Create tensor to read a sample from a binary file-Create tensor, randomly read a mini-batch from binary files-Mini-batchtensor the incoming network as an input node. binary files Use Tf.python_io. Tfrecordwriter crea
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