1. View All repository configuration information
Git config-l or git config -- lis
If no, add the GIT username email information.
Git config-global user. name "tylerdeng"Git config-global user. Email "[email protected]"
2. Generate a key
The email address you configured is required.
$ Ssh-keygen-t rsa-c "[email protected]" association? Mailbox number configured in the repository
$ Cat ~ /. Ssh/id_r
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
Git: Configure identity and query, create a code repository, view files in the repository, and share commands. git Repository
1. Configure identity and query
Git config -- glocal user. name "me" git config -- glocal user. email "A@me.c"
2. Create a code repository
Cd aa/bb/
Git init
3. view files in the
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
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
I often write essays. It is very convenient to participate in version control. Sometimes, due to various reasons (such as network failure), I cannot clone them to the local machine, and I want to write new essays, there will be a lot of submission history in the write process. When I merge it into the casual folder, I also want to submit the history.
Now we can simulate this scenario:
1. Create the essays folder and add essay1.md; 2. Create the TMP folder, add the essays2.md folder, add the
/libcudnn*4.4 Installing additional dependent 4.4.1 Configuration environment variables按照的教程,在terminal中输入以下命令:sudo gedit ~/.bash_profile #打开. bash_profileThen, at the end of the open text, add:Export ld_library_path="$LD _library_path:/usr/local/cuda/lib64:/usr/local/cuda/extras/cupti/lib64 "export cuda_home=/usr/local/cudaContinue to enter in terminal:SOURCE ~/.bash_profile #使更改的环境变量生效Of course, there are other tutorials written in the file ~/.BASHRC file, similar to the one above. This method
Through a few routines, we gradually established a perceptual knowledge of tensorflow. This article will further from the internal principle of deep understanding, and then for reading source to lay a good foundation.1. Graph (graph)The TensorFlow calculation is abstracted as a forward graph that includes several nodes. As shown in the example:The corresponding TensorFl
Introduction and use of Caffe-tensorflow conversion
Caffe-tensorflow can convert Caffe network definition file and pre-training parameters into TensorFlow form, including TensorFlow network structure source code and NPY format weight file.Download the source code from GitHub and enter the source directory to run conve
Install the TENSORFLOW-GPU environment: Python environment, TENSORFLOW-GPU package, CUDA,CUDNNFirst, install the PYTHON,PIP3 directly to the official website to download, download and install your favorite versionHttps://www. python. org/Tip: Remember to check the ADD environment variable when you install the last stepIn the cmd input PIP3 test PIP3 can use, can not use, manually open the path of the Python
TensorFlow and tensorflow
Overview
The newly uploaded mcnn contains complete data read/write examples. For details, refer.
The official website provides three methods for Tensorflow to read data:
Feeding: each step of TensorFlow execution allows Python code to supply data.
Read data from a file: at the beginning o
software environment used in the study. For the last 4 years, open source software Torch7, the machine learning Library, has been our primary research platform, combining the perfect flexibility and very fast runtime execution to ensure rapid modeling. Our team is proud to have contributed to the open source project, which has evolved from the occasional bug fix to being the core maintainer of several key modules. With Google ' s recent open source release oftensorflow, we INITiated a project t
TensorFlow can identify the image files that can be used via NumPy, using TF. Variable or tf.placeholder is loaded into the tensorflow, or it can be read by a function (Tf.read), and when there are too many image files, the pipeline is usually read using the method of the queue. Here are two ways to generate TensorFlow image formats, which provide input and outpu
TensorFlow is a deep learning package developed by Google and is currently only supported on Linux and OSX. But this fall may have a Windows-enabled version of it, so for developers who use Windows, there's no need to wait for the fall or go to Linux and OSX TensorFlow. There are two ways to run on Windows, one is to install the virtual machine and install the Ubuntu system, install
The TensorFlow model is used to store/load the tensorflow model.
TensorFlow model saving/loading
When we use an algorithm model online, we must first save the trained model. Tensorflow saves models in a different way than sklearn. sklearn is very direct. the dump and load methods of sklearn. externals. joblib can be sa
TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization
During the optimization of the neural network model, we will encounter many problems, such as how to set the learning rate. We can quickly approach the optimal solution in the early stage of training through exponential attenuation, after training, the system enters the optimal region stably. For the over-fitting probl
Readers may recall the Tf.nn module in this series (ii) and (vi), the most concerned of which is the conv2d function.First, the blog (ii) MNIST routine convolutional.py key source list: DEF model (data, Train=false): "" "the model definition. " " # 2D convolution, with ' same ' padding (i.e. the output feature map has # the same size as the input). Note that {strides} is a 4D array whose # shape matches the data layout: [image index, y, x, depth]. CONV = tf.nn.conv2d (data,
TensorFlow realize Classic Depth Learning Network (4): TensorFlow realize ResNet
ResNet (Residual neural network)-He Keming residual, a team of Microsoft Paper Networks, has successfully trained 152-layer neural networks using residual unit to shine on ILSVRC 2015 , get the first place achievement, obtain 3.57% top-5 error rate, the effect is very outstanding. The structure of ResNet can accelerate the tra
Tensorflow creates variables and searches for variables by name. tensorflow Variables
Environment: Ubuntu14.04, tensorflow = 1.4 (bazel source code installation), Anaconda python = 3.6
There are two main methods to declare variables:Tf. VariableAndTf. get_variable, The biggest difference between the two is:
(1) tf. Variable is a class with many attribute function
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