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DL框架之MXNet ：深度学习框架之MXNet 的简介、安装、使用方法、应用案例之详细攻略

發布時間：2025/3/21 pytorch 24 豆豆

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DL框架之MXNet ：深度學習框架之MXNet 的簡介、安裝、使用方法、應用案例之詳細攻略

MXNet 的簡介

1、優缺點

2、相關文章

3、相關鏈接

MXNet 的安裝

MXNet 的使用方法

1、個人使用總結

2、經典模型集合—MXNet Model Zoo

3、模型分類

MXNet 的應用案例

MXNet 的簡介

? ? ? ? A flexible and efficient library for deep learning.
　　MXNet 是亞馬遜(Amazon)選擇的深度學習庫，并且也許是最優秀的庫之一。它擁有類似于 Theano 和 TensorFlow 的數據流圖，為多 GPU 配置提供了良好的配置，有著類似于 Lasagne 和 Blocks 更高級別的模型構建塊，并且可以在你可以想象的任何硬件上運行（包括手機）。對 Python 的支持只是其冰山一角—MXNet 同樣提供了對 R、Julia、C++、Scala、Matlab，和 Javascript 的接口。
? ? ? ? MXNet 是一個旨在提高效率和靈活性的深度學習框架。像MXNet這樣的加速庫提供了強大的工具來幫助開發人員利用GPU和云計算的全部功能。雖然這些工具通常適用于任何數學計算，但MXNet特別強調加速大規模深度神經網絡的開發和部署。特別是，我們提供以下功能：

設備放置：使用MXNet，可以輕松指定每個數據結構的生存位置。
多GPU培訓：MXNet可以通過可用GPU的數量輕松擴展計算。
自動區分：MXNet自動執行曾經陷入神經網絡研究的衍生計算。
優化的預定義圖層：雖然您可以在MXNet中編寫自己的圖層，但預定義的圖層會針對速度進行優化，優于競爭庫。

? ? ? ? MXNet 官方自我評價：MXNet結合了高性能，干凈的代碼，高級API訪問和低級控制，是深度學習框架中獨一無二的選擇。

1、優缺點

優點：

速度的標桿

靈活的編程模型：非常靈活。支持命令式和符號式編程模型以最大化效率和性能。

從云端到客戶端可移植：可運行于多CPU、多GPU、集群、服務器、工作站甚至移動智能手機。

多語言支持：支持七種主流編程語言，包括C++、Python、R、Scala、Julia、Matlab和JavaScript。事實上，它是唯一支持所有 R 函數的構架。

本地分布式訓練：支持在多CPU/GPU設備上的分布式訓練，使其可充分利用云計算的規模優勢。

性能優化：使用一個優化的C++后端引擎并行I/O和計算，無論使用哪種語言都能達到最佳性能。

云端友好，可直接與S3，HDFS和Azure兼容

缺點：

最小的社區

比 Theano 學習更困難一點

2、相關文章

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

? ? ? ? MXnet是一個多語言機器學習（ML）庫，用于簡化ML算法的開發，特別是對于深度神經網絡。它嵌入在宿主語言中，將聲明性符號表達式與命令式張量計算混合在一起。它提供自動微分來推導梯度。MXnet具有計算和內存效率高的特點，可以在各種異構系統上運行，從移動設備到分布式GPU集群。本文介紹了MXnet的API設計和系統實現，并解釋了如何統一處理符號表達式和張量操作的嵌入。我們的初步實驗表明，在使用多個GPU機器的大規模深度神經網絡應用中，有著很好的結果。

3、相關鏈接

官網地址：http://mxnet.incubator.apache.org/
GitHub地址01：https://github.com/dmlc/mxnet
GitHub地址02：https://github.com/apache/incubator-mxnet/tree/master/example
MXNet - Python API：http://mxnet.incubator.apache.org/api/python/index.html#python-api-reference
PyPi地址：https://pypi.org/project/mxnet/
?

MXNet 的安裝

1、第一次安裝

pip install mxnet

Collecting mxnetDownloading https://files.pythonhosted.org/packages/d1/b6/38d9ab1b16c456224823e737f1bb95fe3ff056f3834fba01cd157d59b574/mxnet-1.4.0.post0-py2.py3-none-win_amd64.whl (21.9MB)100% |████████████████████████████████| 21.9MB 34kB/s Requirement already satisfied: requests<2.19.0,>=2.18.4 in f:\program files\python\python36\lib\site-packages (from mxnet) (2.18.4) Collecting graphviz<0.9.0,>=0.8.1 (from mxnet)Downloading https://files.pythonhosted.org/packages/53/39/4ab213673844e0c004bed8a0781a0721a3f6bb23eb8854ee75c236428892/graphviz-0.8.4-py2.py3-none-any.whl Collecting numpy<1.15.0,>=1.8.2 (from mxnet)Downloading https://files.pythonhosted.org/packages/dc/99/f824a73251589d9fcef2384f9dd21bd1601597fda92ced5882940586ec37/numpy-1.14.6-cp36-none-win_amd64.whl (13.4MB)100% |████████████████████████████████| 13.4MB 30kB/s Requirement already satisfied: certifi>=2017.4.17 in f:\program files\python\python36\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (2018.1.18) Requirement already satisfied: chardet<3.1.0,>=3.0.2 in f:\program files\python\python36\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (3.0.4) Requirement already satisfied: urllib3<1.23,>=1.21.1 in f:\program files\python\python36\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (1.22) Requirement already satisfied: idna<2.7,>=2.5 in f:\program files\python\python36\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (2.6)tensorflow-gpu 1.4.0 requires enum34>=1.1.6, which is not installed. tensorflow 1.10.0 has requirement numpy<=1.14.5,>=1.13.3, but you'll have numpy 1.14.6 which is incompatible. moviepy 0.2.3.2 has requirement decorator==4.0.11, but you'll have decorator 4.3.0 which is incompatible. moviepy 0.2.3.2 has requirement tqdm==4.11.2, but you'll have tqdm 4.25.0 which is incompatible. Installing collected packages: graphviz, numpy, mxnetFound existing installation: numpy 1.15.0rc1+mklUninstalling numpy-1.15.0rc1+mkl: Could not install packages due to an EnvironmentError: [WinError 5] 拒絕訪問。: 'f:\\program files\\python\\python36\\lib\\site-packages\\numpy\\core\\_multiarray_tests.cp36-win_amd64.pyd' Consider using the `--user` option or check the permissions.

遇到問題：成功解決Could not install packages due to an EnvironmentError: [WinError 5] 拒絕訪問。: 'f:\\program files\\p

2、第二次安裝

tensorflow-gpu 1.4.0 requires enum34>=1.1.6, which is not installed. tensorflow 1.10.0 has requirement numpy<=1.14.5,>=1.13.3, but you'll have numpy 1.14.6 which is incompatible. moviepy 0.2.3.2 has requirement decorator==4.0.11, but you'll have decorator 4.3.0 which is incompatible. moviepy 0.2.3.2 has requirement tqdm==4.11.2, but you'll have tqdm 4.25.0 which is incompatible.

MXNet 的使用方法

后期更新……

1、個人使用總結

DL框架之MXNet ：深度學習框架之MXNet 常見使用方法(個人使用)總結之詳細攻略

相關鏈接：http://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html

2、經典模型集合—MXNet Model Zoo

AliasNetwork# ParametersTop-1 AccuracyTop-5 AccuracyOrigin

alexnet	AlexNet	61,100,840	0.5492	0.7803	Converted from pytorch vision
densenet121	DenseNet-121	8,062,504	0.7497	0.9225	Converted from pytorch vision
densenet161	DenseNet-161	28,900,936	0.7770	0.9380	Converted from pytorch vision
densenet169	DenseNet-169	14,307,880	0.7617	0.9317	Converted from pytorch vision
densenet201	DenseNet-201	20,242,984	0.7732	0.9362	Converted from pytorch vision
inceptionv3	Inception V3 299x299	23,869,000	0.7755	0.9364	Converted from pytorch vision
mobilenet0.25	MobileNet 0.25	475,544	0.5185	0.7608	Trained with?script
mobilenet0.5	MobileNet 0.5	1,342,536	0.6307	0.8475	Trained with?script
mobilenet0.75	MobileNet 0.75	2,601,976	0.6738	0.8782	Trained with?script
mobilenet1.0	MobileNet 1.0	4,253,864	0.7105	0.9006	Trained with?script
mobilenetv2_1.0	MobileNetV2 1.0	3,539,136	0.7192	0.9056	Trained with?script
mobilenetv2_0.75	MobileNetV2 0.75	2,653,864	0.6961	0.8895	Trained with?script
mobilenetv2_0.5	MobileNetV2 0.5	1,983,104	0.6449	0.8547	Trained with?script
mobilenetv2_0.25	MobileNetV2 0.25	1,526,856	0.5074	0.7456	Trained with?script
resnet18_v1	ResNet-18 V1	11,699,112	0.7093	0.8992	Trained with?script
resnet34_v1	ResNet-34 V1	21,814,696	0.7437	0.9187	Trained with?script
resnet50_v1	ResNet-50 V1	25,629,032	0.7647	0.9313	Trained with?script
resnet101_v1	ResNet-101 V1	44,695,144	0.7834	0.9401	Trained with?script
resnet152_v1	ResNet-152 V1	60,404,072	0.7900	0.9438	Trained with?script
resnet18_v2	ResNet-18 V2	11,695,796	0.7100	0.8992	Trained with?script
resnet34_v2	ResNet-34 V2	21,811,380	0.7440	0.9208	Trained with?script
resnet50_v2	ResNet-50 V2	25,595,060	0.7711	0.9343	Trained with?script
resnet101_v2	ResNet-101 V2	44,639,412	0.7853	0.9417	Trained with?script
resnet152_v2	ResNet-152 V2	60,329,140	0.7921	0.9431	Trained with?script
squeezenet1.0	SqueezeNet 1.0	1,248,424	0.5611	0.7909	Converted from pytorch vision
squeezenet1.1	SqueezeNet 1.1	1,235,496	0.5496	0.7817	Converted from pytorch vision
vgg11	VGG-11	132,863,336	0.6662	0.8734	Converted from pytorch vision
vgg13	VGG-13	133,047,848	0.6774	0.8811	Converted from pytorch vision
vgg16	VGG-16	138,357,544	0.7323	0.9132	Trained with?script
vgg19	VGG-19	143,667,240	0.7411	0.9135	Trained with?script
vgg11_bn	VGG-11 with batch normalization	132,874,344	0.6859	0.8872	Converted from pytorch vision
vgg13_bn	VGG-13 with batch normalization	133,059,624	0.6884	0.8882	Converted from pytorch vision
vgg16_bn	VGG-16 with batch normalization	138,374,440	0.7310	0.9176	Trained with?script
vgg19_bn	VGG-19 with batch normalization	143,689,256	0.7433	0.9185	Trained with?script

get_model

Returns a pre-defined model by name

3、模型分類

ResNet

resnet18_v1	ResNet-18 V1 model from?“Deep Residual Learning for Image Recognition”?paper.
resnet34_v1	ResNet-34 V1 model from?“Deep Residual Learning for Image Recognition”?paper.
resnet50_v1	ResNet-50 V1 model from?“Deep Residual Learning for Image Recognition”?paper.
resnet101_v1	ResNet-101 V1 model from?“Deep Residual Learning for Image Recognition”?paper.
resnet152_v1	ResNet-152 V1 model from?“Deep Residual Learning for Image Recognition”?paper.
resnet18_v2	ResNet-18 V2 model from?“Identity Mappings in Deep Residual Networks”?paper.
resnet34_v2	ResNet-34 V2 model from?“Identity Mappings in Deep Residual Networks”?paper.
resnet50_v2	ResNet-50 V2 model from?“Identity Mappings in Deep Residual Networks”?paper.
resnet101_v2	ResNet-101 V2 model from?“Identity Mappings in Deep Residual Networks”?paper.
resnet152_v2	ResNet-152 V2 model from?“Identity Mappings in Deep Residual Networks”?paper.

ResNetV1	ResNet V1 model from?“Deep Residual Learning for Image Recognition”?paper.
ResNetV2	ResNet V2 model from?“Identity Mappings in Deep Residual Networks”?paper.
BasicBlockV1	BasicBlock V1 from?“Deep Residual Learning for Image Recognition”?paper.
BasicBlockV2	BasicBlock V2 from?“Identity Mappings in Deep Residual Networks”?paper.
BottleneckV1	Bottleneck V1 from?“Deep Residual Learning for Image Recognition”?paper.
BottleneckV2	Bottleneck V2 from?“Identity Mappings in Deep Residual Networks”?paper.
get_resnet	ResNet V1 model from?“Deep Residual Learning for Image Recognition”?paper.

VGG

vgg11	VGG-11 model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg13	VGG-13 model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg16	VGG-16 model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg19	VGG-19 model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg11_bn	VGG-11 model with batch normalization from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg13_bn	VGG-13 model with batch normalization from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg16_bn	VGG-16 model with batch normalization from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
vgg19_bn	VGG-19 model with batch normalization from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.

VGG	VGG model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.
get_vgg	VGG model from the?“Very Deep Convolutional Networks for Large-Scale Image Recognition”?paper.

Alexnet

alexnet

AlexNet model from the?“One weird trick...”?paper.

AlexNet

AlexNet model from the?“One weird trick...”?paper.

DenseNet

densenet121	Densenet-BC 121-layer model from the?“Densely Connected Convolutional Networks”?paper.
densenet161	Densenet-BC 161-layer model from the?“Densely Connected Convolutional Networks”?paper.
densenet169	Densenet-BC 169-layer model from the?“Densely Connected Convolutional Networks”?paper.
densenet201	Densenet-BC 201-layer model from the?“Densely Connected Convolutional Networks”?paper.

DenseNet

Densenet-BC model from the?“Densely Connected Convolutional Networks”?paper.

SqueezeNet

squeezenet1_0	SqueezeNet 1.0 model from the?“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”?paper.
squeezenet1_1	SqueezeNet 1.1 model from the?official SqueezeNet repo.

SqueezeNet

SqueezeNet model from the?“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”?paper.

Inception

inception_v3

Inception v3 model from?“Rethinking the Inception Architecture for Computer Vision”?paper.

Inception3

Inception v3 model from?“Rethinking the Inception Architecture for Computer Vision”?paper.

MobileNet

mobilenet1_0	MobileNet model from the?“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”?paper, with width multiplier 1.0.
mobilenet0_75	MobileNet model from the?“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”?paper, with width multiplier 0.75.
mobilenet0_5	MobileNet model from the?“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”?paper, with width multiplier 0.5.
mobilenet0_25	MobileNet model from the?“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”?paper, with width multiplier 0.25.
mobilenet_v2_1_0	MobileNetV2 model from the?“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”?paper.
mobilenet_v2_0_75	MobileNetV2 model from the?“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”?paper.
mobilenet_v2_0_5	MobileNetV2 model from the?“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”?paper.
mobilenet_v2_0_25	MobileNetV2 model from the?“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”?paper.

MobileNet	MobileNet model from the?“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”?paper.
MobileNetV2	MobileNetV2 model from the?“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”?paper.

MXNet 的應用案例

后期繼續更新……

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