/3.0.0/opencv-3.0.0.exe/downloadTo download the V3 version of why is not the latest V5 I don't know if you ask the Mxnet team has the result, please tell me thank you.1.6 were not 2. mxnet2.1 Download ReleaseHttps://github.com/dmlc/mxnet/releasesI downloaded the 20160419_win10_x64_gpu.7z, but I used the Win7 x64 system. Can you beat me anyway?2.2 Cover CUDNNOverwrite the contents of the
Install mxnet under CentOS (similar to installing mxnet under Amazon Linux), refer to the official documentation http://mxnet.io/get_started/setup.html#prerequisites,The installation steps are as follows:####################################################################### This script installs MXNet forPython along with all required dependencies on a Amazon Lin
format.Terminal input:tar -zxf cudnn-8.0-linux-x64-v5. 1 . tgz CD cuda/sudocp lib64/* /usr/local/cuda-8.0/lib64/sudo CP include /cudnn.h/usr/local/cuda-8.0/include/Vii. installation of MxnetDownload mxnet:git clone https://github.com/dmlc/mxnet.git ~/mxnet--recursiveModify the/mxnet/make/config.mk, change the use_cudnn=0, use_cuda=0 to = 1, and specify the CUDA path:/usr/local/cuda, compile under the
Mxnet/include/mxnet/engine.hIn the namespace Mxnet engine, an abstract class engine is defined to standardize the interface withNotifyshutdownNewvariableDeletevariableNewoperatorDeleteoperatorPushPushasyncPushsyncWaitforvarWaitforallDeduplicatevarhandlesuch asMxnet/src/engine/engine_impl.hDefines the multi-state implementation of theEngine *createnaiveengineEngin
PrefaceThe API was thought bucket to be an interface rooted in the underlying operation (MXNet doc -_-| |). From LSTM looking over, contact with a number of related programs, and then bucketing_module.py see that part of the next, found that bucket is only an application layer mechanism, the main implementation exists in the module/bucketing_module.py inside. The principle is clear, the realization is concise, makes a mark in this.Code CommentsFirst
): "" "Forward computation.
IT supports data batches with different shapes, such as different batch sizes or different image sizes. If Reshaping of data batch relates to modification of symbol or module, such as changing image layout ordering or
Switching from training to predicting, the module rebinding is required.
Parameters----------Data_batch:databatch could is anything with similar API implemented. Is_train:bool Default is ' None ', which means ' is_train ' takes t
Mxnet is the foundation, Gluon is the encapsulation, both like TensorFlow and Keras, but thanks to the dynamic graph mechanism, the interaction between the two is much more convenient than TensorFlow and Keras, its basic operation and pytorch very similar, but a lot of convenience, It's easy to get started with a pytorch foundation.Library import notation,From mxnet import Ndarray as Ndfrom
mxnet/src/storage/storage.ccMxnet/include/mxnet/storage.hMxnet/include/mxnet/base.hThe above three files collectively describe the storage virtual class and the results of its instantiation, where storage.h defines the storage abstract interface (virtual function) Alloc free directfree and the static method get, It defines the handle handle that will be used to m
Start from scratchBefore we understand the principle of multilayer perceptron, we can realize a multi-layer perception machine.# -*- coding: utf-8 -*-from mxnet import initfrom mxnet import ndarray as ndfrom mxnet.gluon import loss as glossimport gb# 定义数据源batch_size = 256train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)# 定义模型参数num_inputs = 784num_outputs = 10num_hiddens = 256W1 = nd.random.norm
make a contribution to Mxnet
Mxnet is developed and used by a group of active community members. Please contribute to it to improve it.When your patch is merged, don't forget to submit your name to CONTRIBUTORS.MD. Guidelines commits a pull request to resolve a conflict with master what are the results of a combination of multiple commit forced push? Document test case routine core Library Python library R
http://blog.csdn.net/myarrow/article/details/52064608
1. Basic Concepts
1.1 Mxnet Related Concepts
Deep learning goals: How to express neural networks in a convenient way, and how to train quickly to get models
CNN (convolution layer): Expressing spatial relevance (learning representation)
Rnn/lstm: Expression time continuity (modeling timing signal)Imperative programming (imperative programming): Shallow embedding, where each statement is executed
of the function is \ (\text{relu} (x) = \max (x, 0) \), the Relu function retains only the positive elements, and the negative elements are zeroed.sigmoid functionThe sigmoid function can transform the value of an element to between 0 and 1:\ (\text{sigmoid} (x) = \frac{1}{1 + \exp (×)}\), and we'll back "loop neural network" Chapter describes how to use the sigmoid function to control the flow of information in a neural network by using the attribute range from 0 to 1.Tanh functionThe Tanh (hy
linear algebra, Fourier transforms, and random numbers.Mxnet's Ndarray is very similar to Ndarray in NumPy, Ndaarray provides the core data structure for various mathematical calculations in Mxnet, Ndarray represents a multidimensional, fixed-size array, and supports heterogeneous computing. So why not just use NumPy? Mxnet's Ndarray offers two additional benefits:
Support heterogeneous computing, data can be efficiently computed in CPU,GPU,
The data and models used in this article can be downloaded from the CSDN resource page.Link:Network definition FileLST files for data linking and testingThis article mainly to the original code to organize, facilitate the call and training.The main reference to the Gluon SSD example. 1. SSD Network Model definition
ssd.py
Import mxnet as MX import matplotlib.pyplot as PLT import Os.path as OSP import mxnet.image as image from
MXNET Windows Compilation installation (Python)This article only records mxnet installation under Windows, more environment configuration please visit Official document: http://mxnet.readthedocs.io/en/latest/how_to/build.htmlCompile target:
Libmxnet.dll
Necessary:
Support c++11,g++>=4.8
Blas libraries, such as Libblas, Libblas, Openblas Intel MKL
Optional conditions:
After we define the symbol in Mxnet, write the dataiter and prepare the data, we can have fun training. General Training A network has two common strategies, based on model and module-based. Today, I would like to talk about their use.First, ModelFollow the usual code to take a look directly at the official documents: # Configure a, layer neuralnetwork data = mx.symbol.Variable (' data ') FC1 = mx.symbol.FullyConnected (data, Name= ' FC1 ', num
PrefaceThe sequence problem is also a interesting issue. Looking for a meeting LSTM of the material, found not a system of text, the early Sepp Hochreiter paper and disciple Felix Gers 's thesis did not look so relaxed. The first thing to start with was a review in 15, and it didn't look very smooth at the time, but looking at the first two (part) and then looking back at the formulation part of the article would be clearer.Originally intended to write a program of their own, found here a refere
Originally intended to begin the translation of the calculation of the part, the results of the last article just finished, mxnet upgraded the tutorial document (not hurt AH), updated the previous in the handwritten numeral recognition example of a detailed tutorial. Then this article on the Times, to the just updated this tutorial translated. Because the current picture can not upload to the blog, the relevant pictures can be viewed from the original
), ' R ') as F: # Skips the header row (column name) of the file. lines = F.readlines () [1:] tokens = [L.rstrip (). Split (', ') for L in lines] # {index: label} Idx_label = Dict ((int (IDX), label) for IDX, label in tokens)) # Tag Collection labels = set (Idx_label.values ()) # Number of training data: '. /data/kaggle_cifar10/train ' Num_train = Len (Os.listdir (Os.path.join (Data_dir, Train_dir)) # Train number (corresponds to valid) num _train_tuning = Int (Num_train * (1-valid_ratio)) # Pr
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