apache mxnet

, Figsize=( the, the)) forIinch Range(n): Figs[i].imshow (Images[i].asnumpy ()) Figs[i].axes.get_xaxis (). Set_visible (False) Figs[i].axes.get_yaxis (). Set_visible (False) plt.show () data, label=cifar_100_train[1:9]Print(data.shape, label) show_images (data)Downloading E:/Data/MXNet/cifar100\cifar-100-binary.tar.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/cifar100/cif

It's a bit of a mouthful to find out a name. - -#Everyone in the deep learning, should have met the memory is not enough to use, and then have to pain minus Batchszie, or cut their own network structure it? Finally ran out of the effect is not satisfactory, always feel that they have been targeted by the world. What happens when you encounter this situation? Please use Mxnet's Miracle Dafa to save your memory! Lu Xun once said: "You do not try, how can you know that your idea is really so bad?"T

mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the

installing Mxnet on Ubuntu The languages that mxnet now support include Python, R, Julia, and Scala. For Python and R users on the Ubuntu operating system, Mxnet provides a series of Git Bash scripts to install mxnet dependencies and mxnet libraries. If the operating system

I want to write a series of deep learning simple practical tutorials, using mxnet to do the implementation of the platform of the example code to explain the deep learning commonly used in some technical direction and practical examples. The main content of this series is inclined to explain practical examples, from the sample and code to learn to solve practical problems. I will default the reader has the basic knowledge of neural network and deep le

Introduction to mxnet Deep Learning LibraryAbstract: Mxnet is a deep learning library that supports languages such as C + +, Python, R, Scala, Julia, Matlab, and JavaScript; Support command and symbol programming; Can run on CPU,GPU, clusters, servers, desktops or mobile devices. Mxnet is the cxxnet of the next generation, Cxxnet learn from the idea of Caffe, but

Note: This article is original, Noah Zhang (http://www.cnblogs.com/noahzn/)My notebook configuration is relatively low, want to install a lightweight mxnet try, installed after the error, not a valid application, Can't find libinfo.py et cetera, the same problem on GitHub also have a lot of people ask, but the author said also do not know where the problem, their own toss for two days, finally installed can use, share the following:First Report my mac

Run Mxnet on the Docker Docker is a system that allows you to build a self-contained Linux operating system that can run in isolation on your computer. In a self-contained Linux system, you can run mxnet and other software packages that don't interact with the packages on your computer. Mxnet provides 2 docker mirrors that run

Mxnet DocumentationMxnet Official TutorialsFramework Introduction"MXNet" First play _ Infrastructure and API"MXNet" second bomb _gluon building model"MXNet" third bomb _gluon model parameters"MXNet" IV _gluon custom layerThe image processing of "

These two days have not been prepared for this week's content, in fact, I am not idle, but by another thing attracted attention. Today I saw a day mxnet document, a little notes, a little harvest, notes will be posted in another essay.This article I want to compare Theano and Mxnet,torch (Torch basically useless, so can only say some intuitive feeling). I care about them mainly from the following aspects:1.

Comparison between Caffe, TensorFlow, and MXnet open source libraries Recently, Google opened up its internal deep learning framework TensorFlow [1] and discussed the three open-source libraries in combination with the open-source MXNet [2] and Caffe [3, among them, only Caffe has carefully read the source code. The other two libraries only read the official documentation and some comments from researchers.

1 just started using pyinstaller-f ship_detect.py packing paperFile "site-packages\osgeo\__init__.py", line 17, in swig_import_helperImportError: No module named ‘_gdal‘The solution to this error is not to use-f direct Pyinstaller ship_detect.py and then find Osgeo._gdal in dist to rename it to _gdal, then this error solved2 But another error was reported. Modulenotfounderror:no module named ' Pandas._libs.tslibs.np_datetime 'Just started trying to modify the Hooks folder inside the Pyinstaller

Recently started a GTX 1070 notebook, preface want to Win10 on the GPU run model, so there is the next installation GPU version of the bumpy course of mxnet, after multiple experiments finally fixed python and R installation Mxnet, the main points are recorded as follows:I mainly refer to these 2 blog posts:https://my.oschina.net/qinhui99/blog/845249http://blog.csdn.net/u010414386/article/details/533041771.

Win10 + Python + MXNet + VS2015 configuration, win10mxnet The project needs to use MTCNN to detect, align, and cut faces. MXNet is used as the framework, but various frameworks in my Ubuntu are in disorder, if you don't want to worry about it, you have to configure one in windows. However, there was not much information on the Internet. After struggling for a few days, I decided to leave such a document. F

Mxnet framework is used to do image-related projects, There are two main ways of reading images: the first is read. rec format files, similar to the Caffe framework of Lmdb, the advantage is that. rec files are stable and can be reproduced on other computers, with the disadvantage that space (. rec file size is basically the size of the image storage), and the addition and deletion of data is not flexible. The second is. LST and the way images are com

Installation Environment: Win 10 Professional Edition 64-bit + Visual Studio Community.Record the process of installing configuration mxnet in a GPU-equipped environment. The process uses Mxnet release's pre-built package directly, without using CMake compilation itself. Online has a lot of their own compiled tutorials, the process is more cumbersome, the direct use of the release package for beginners more

Projects need to use MTCNN to detect, align, cut out the face, it is using mxnet as a framework, but my own Ubuntu in various frameworks mess, do not want to mess up the heart to be in Windows with a. Helpless online information is not much, struggled a few days later decided to leave such a document.The first thing we're using is not the set of mxnet that DMLC on GitHub, which is not updated in the Windows

Mxnet Community questions about using mxnet If you are unsure how to use mxnet when doing something, or if you have questions about applying it to a specific problem, post the issue in StackOverflow and tag-mxnet. Here you can check out the questions about Mxnet. Problem T

Building a data set# -*- coding: utf-8 -*-from mxnet import initfrom mxnet import ndarray as ndfrom mxnet.gluon import loss as glossimport gbn_train = 20n_test = 100num_inputs = 200true_w = nd.ones((num_inputs, 1)) * 0.01true_b = 0.05features = nd.random.normal(shape=(n_train+n_test, num_inputs))labels = nd.dot(features, true_w) + true_blabels += nd.random.normal(scale=0.01, shape=labels.shape)train_feature

Using the Mxnet training picture class classifier1, prepare the data:(1) Create a root directory and then, for each category of pictures to create a subfolder, each type of picture into the corresponding subfolder.--root:----Class1----Class2......----CLASSNA list of the training sets and test sets is generated first, and the commands are as follows: Python ~/mxnet/tools/im2rec.py--list True--recursive true-