分享展示神經(jīng)網(wǎng)絡(luò)的N個(gè)利器。

1、PlotNeuralNet

使用Latex繪制神經(jīng)網(wǎng)絡(luò)。傳送門：https://github.com/HarisIqbal88/PlotNeuralNet

FCN-8模型

overleaf上Latex代碼：https://www.overleaf.com/read/kkqntfxnvbsk

FCN-32模型

overleaf上Latex代碼：https://www.overleaf.com/read/wsxpmkqvjnbs

Holistically-Nested Edge Detection

overleaf上Latex代碼：https://www.overleaf.com/read/jxhnkcnwhfxp

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2、Matlab

https://www.mathworks.com/help/deeplearning/ref/view.html;jsessionid=bd77484ba149c98d4d410abed983

[x,t]?=?iris_dataset; net?=?patternnet; net?=?configure(net,x,t); view(net)

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3、NN-SVG

一個(gè)在線工具，點(diǎn)點(diǎn)就闊以了：http://alexlenail.me/NN-SVG/LeNet.html

FCNN模型AlexNet模型LeNet模型

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4、graphcore

回到神經(jīng)網(wǎng)絡(luò)最初的地方，像生物細(xì)胞神經(jīng)元neurons一樣展示神經(jīng)網(wǎng)絡(luò)。https://www.graphcore.ai/posts/what-does-machine-learning-look-like

生物細(xì)胞神經(jīng)元模式圖AlexNet模型Resnet 50模型

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5、graphviz

http://www.graphviz.org/

之前介紹過(guò)一個(gè)類似繪制網(wǎng)絡(luò)關(guān)系的工具????盤一盤社交網(wǎng)絡(luò)分析常用networks

4層網(wǎng)絡(luò)

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6、Keras

深度學(xué)習(xí)框架Keras下的一個(gè)小模塊，

https://keras.io/api/utils/model_plotting_utils/

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7、neataptic

https://github.com/wagenaartje/neataptic

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8、Quiver

https://github.com/keplr-io/quiver

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9、Keras.js

在線工具

https://transcranial.github.io/keras-js/#/inception-v3

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10、Netscope CNN Analyzer

http://dgschwend.github.io/netscope/quickstart.html

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11、keras-sequential-ascii

https://github.com/stared/keras-sequential-ascii/

VGG 16 Architecture

???????????OPERATION???????????DATA?DIMENSIONS???WEIGHTS(N)???WEIGHTS(%)Input???#####??????3??224??224InputLayer?????|???-------------------?????????0?????0.0%#####??????3??224??224Convolution2D????\|/??-------------------??????1792?????0.0%relu???#####?????64??224??224Convolution2D????\|/??-------------------?????36928?????0.0%relu???#####?????64??224??224MaxPooling2D???Y?max?-------------------?????????0?????0.0%#####?????64??112??112Convolution2D????\|/??-------------------?????73856?????0.1%relu???#####????128??112??112Convolution2D????\|/??-------------------????147584?????0.1%relu???#####????128??112??112MaxPooling2D???Y?max?-------------------?????????0?????0.0%#####????128???56???56Convolution2D????\|/??-------------------????295168?????0.2%relu???#####????256???56???56Convolution2D????\|/??-------------------????590080?????0.4%relu???#####????256???56???56Convolution2D????\|/??-------------------????590080?????0.4%relu???#####????256???56???56MaxPooling2D???Y?max?-------------------?????????0?????0.0%#####????256???28???28Convolution2D????\|/??-------------------???1180160?????0.9%relu???#####????512???28???28Convolution2D????\|/??-------------------???2359808?????1.7%relu???#####????512???28???28Convolution2D????\|/??-------------------???2359808?????1.7%relu???#####????512???28???28MaxPooling2D???Y?max?-------------------?????????0?????0.0%#####????512???14???14Convolution2D????\|/??-------------------???2359808?????1.7%relu???#####????512???14???14Convolution2D????\|/??-------------------???2359808?????1.7%relu???#####????512???14???14Convolution2D????\|/??-------------------???2359808?????1.7%relu???#####????512???14???14MaxPooling2D???Y?max?-------------------?????????0?????0.0%#####????512????7????7Flatten???|||||?-------------------?????????0?????0.0%#####???????25088Dense???XXXXX?-------------------?102764544????74.3%relu???#####????????4096Dense???XXXXX?-------------------??16781312????12.1%relu???#####????????4096Dense???XXXXX?-------------------???4097000?????3.0%softmax???#####????????1000

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12、TensorBoard

一個(gè)評(píng)估深度學(xué)習(xí)框架TensorFlow模型的強(qiáng)力工具。

https://www.tensorflow.org/tensorboard/graphs

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13、Caffe

同樣是深度學(xué)習(xí)框架Caffe下的一個(gè)小工具，

https://github.com/BVLC/caffe/blob/master/python/caffe/draw.py

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14、TensorSpace

3D模式展示神經(jīng)網(wǎng)絡(luò)，

https://tensorspace.org/

ACGAN模型Vgg16模型LeNet模型

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