[人工智能]赞赞赞????N个神经网络可视化工具??????

分享展示神经网络的N个利器。

1、PlotNeuralNet

使用Latex绘制神经网络。传送门：https://github.com/HarisIqbal88/PlotNeuralNet

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

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

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

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)

3、NN-SVG

一个在线工具，点点就阔以了：http://alexlenail.me/NN-SVG/LeNet.html

4、graphcore

回到神经网络最初的地方，像生物细胞神经元neurons一样展示神经网络。https://www.graphcore.ai/posts/what-does-machine-learning-look-like

5、graphviz

http://www.graphviz.org/

之前介绍过一个类似绘制网络关系的工具👉盘一盘社交网络分析常用networks

6、Keras

深度学习框架Keras下的一个小模块，

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

7、neataptic

https://github.com/wagenaartje/neataptic

8、Quiver

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

9、Keras.js

在线工具

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

10、Netscope CNN Analyzer

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

11、keras-sequential-ascii

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

VGG 16 Architecture

           OPERATION           DATA DIMENSIONS   WEIGHTS(N)   WEIGHTS(%)

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

12、TensorBoard

一个评估深度学习框架TensorFlow模型的强力工具。

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

13、Caffe

同样是深度学习框架Caffe下的一个小工具，

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

14、TensorSpace

3D模式展示神经网络，

https://tensorspace.org/

-END-