mxnet tutorial

"Introduction" recently trained its own datasets using the RFCN model of variable convolution, MSRA the official mxnet frameworkEnvironment Construction and configuration: http://www.cnblogs.com/andre-ma/p/8867031.htmlOne parameter modification:Two parameters modified in 1.1 ~/deformable-convnets/experiments/rfcn/cfgs/resnet_v1_101_voc0712_rfcn_dcn_end2end_ohem.yaml file (Yaml file contains all configuration information and hyper-parameters correspond

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-

/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

accepts a Symbol as the input: data = mx.symbol.Variable (' data ') fc1 = mx.symbol.FullyConnected (data, name= ' FC1 ', num_hidden=128) Act1 = Mx.symbol.Activation (FC1, name= ' relu1 ', act_type= "Relu") fc2 = mx.symbol.FullyConnected (Act1, name= ' FC2 ', num_hidden=10) Out = Mx.symbol.SoftmaxOutput (FC2, name = ' Softmax ') mod = Mx.mod.Module (out) # Create A module by given a Symbol Assume there is a valid mxnet data iterator data. We can

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/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

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

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

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

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

images in (filename, label) pairs. """Instance:Train_imgs = Gluon.data.vision.ImageFolderDataset ( data_dir+ '/hotdog/train ', transform=lambda X, y: Transform (X, Y, train_augs)) Test_imgs = Gluon.data.vision.ImageFolderDataset ( data_dir+ '/hotdog/test ', Transform=lambda x, Y:transform (x, Y, test_augs)) print (TRAIN_IMGS) print (train_imgs.synsets) data = Gluon.data.DataLoader (Train_imgs, Shuffle=true) Batch iterator: Gluon.data.DataLoaderWith a special method, t

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