Intermediate Python for Data Science | Datacamp
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The intermediate Python course is crucial to your data science curriculum. Learn to visualize real data with Matplotlib's functions and get to know new data structures such as the dictionary and th E Pandas DataFrame. After covering key concepts such as Boolean logic, control flow and loops in Python, you ' re ready to blend t
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
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
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 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
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
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
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,
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
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
Use tensorflow to implement the elastic network regression algorithm and tensorflow Algorithm
This article provides examples of tensorflow's implementation of the elastic network Regression Algorithm for your reference. The specific content is as follows:
Python code:
# Using tensorflow to implement an elastic network algorithm (multi-variable) # using the iris d
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
Tensorflow simple verification code recognition application, tensorflow Verification Code
Simple Tensorflow verification code recognition application for your reference. The specific content is as follows:
1. Tensorflow Installation MethodI will not go into details here.
2. Training setAs well as testing and the follow
TensorFlow is used for simple linear regression and gradient descent examples. tensorflow gradient
Linear regression is supervised learning. Therefore, the method and supervised learning should be the same. First, a training set is given and a linear function is learned based on the training set, then, test whether the function is trained (that is, whether the function is sufficient to fit the training set
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 TensorFlow to t
TensorFlow requires Python 3.5/3.6 64bit version:Specific installation methods can be viewed: https://www.tensorflow.org/install/install_windows Enter Python at the command prompt to start and view the current version: To view the specific version information, enter:1 python-v Download the new 64bit version of Python for installation.Windows Python3.6.5 64bit:https://www.python.org/ftp/python/3.6.5/python-3.6.5-amd64.exeWindows
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
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