【画图】基于Python的神经网络可视化工具

项目链接：

https://github.com/Prodicode/ann-visualizer?utm_source=mybridge&utm_medium=blog&utm_campaign=read_more

A python library for visualizing Artificial Neural Networks (ANN)

如何安装

From Github

1. Download the ann_visualizer folder from the github repository.
2. Place the ann_visualizer folder in the same directory as your main python script.

From pip

Use the following command:

pip3 install ann_visualizer
 

Make sure you have graphviz installed. Install it using:

sudo apt-get install graphviz && pip3 install graphviz
 

人工神经网络绘图示例

  

   

    

     

    
    

     

      import keras;
     
    
   

    

     

    
    

     

      from keras.models import Sequential;
     
    
   

    

     

    
    

     

      from keras.layers import Dense;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      network = Sequential();
     
    
   

    

     

    
    

     
 #Hidden Layer#1
     
    
   

    

     

    
    

     

      network.add(Dense(units=6,
     
    
   

    

     

    
    

     

       activation='relu',
     
    
   

    

     

    
    

     

       kernel_initializer='uniform',
     
    
   

    

     

    
    

     

       input_dim=11));
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #Hidden Layer#2
     
    
   

    

     

    
    

     

      network.add(Dense(units=6,
     
    
   

    

     

    
    

     

       activation='relu',
     
    
   

    

     

    
    

     

       kernel_initializer='uniform'));
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #Exit Layer
     
    
   

    

     

    
    

     

      network.add(Dense(units=1,
     
    
   

    

     

    
    

     

       activation='sigmoid',
     
    
   

    

     

    
    

     

       kernel_initializer='uniform'));
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      from ann_visualizer.visualize import ann_viz;
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ann_viz(network, title="");
     
    
  
 

卷积神经网络绘图示例

  

   

    

     

    
    

     

      import keras;
     
    
   

    

     

    
    

     

      from keras.models import Sequential;
     
    
   

    

     

    
    

     

      from keras.layers import Dense;
     
    
   

    

     

    
    

     

      from ann_visualizer.visualize import ann_viz
     
    
   

    

     

    
    

     

      model = build_cnn_model()
     
    
   

    

     

    
    

     

      ann_viz(model, title="")
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      def build_cnn_model():
     
    
   

    

     

    
    

     

        model = keras.models.Sequential()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(
     
    
   

    

     

    
    

     

       Conv2D(
     
    
   

    

     

    
    

     
 32, (3, 3),
     
    
   

    

     

    
    

     

       padding="same",
     
    
   

    

     

    
    

     

       input_shape=(32, 32, 3),
     
    
   

    

     

    
    

     

       activation="relu"))
     
    
   

    

     

    
    

     

        model.add(Dropout(0.2))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(
     
    
   

    

     

    
    

     

       Conv2D(
     
    
   

    

     

    
    

     
 32, (3, 3),
     
    
   

    

     

    
    

     

       padding="same",
     
    
   

    

     

    
    

     

       input_shape=(32, 32, 3),
     
    
   

    

     

    
    

     

       activation="relu"))
     
    
   

    

     

    
    

     

        model.add(MaxPooling2D(pool_size=(2, 2)))
     
    
   

    

     

    
    

     

        model.add(Dropout(0.2))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(
     
    
   

    

     

    
    

     

       Conv2D(
     
    
   

    

     

    
    

     
 64, (3, 3),
     
    
   

    

     

    
    

     

       padding="same",
     
    
   

    

     

    
    

     

       input_shape=(32, 32, 3),
     
    
   

    

     

    
    

     

       activation="relu"))
     
    
   

    

     

    
    

     

        model.add(Dropout(0.2))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(
     
    
   

    

     

    
    

     

       Conv2D(
     
    
   

    

     

    
    

     
 64, (3, 3),
     
    
   

    

     

    
    

     

       padding="same",
     
    
   

    

     

    
    

     

       input_shape=(32, 32, 3),
     
    
   

    

     

    
    

     

       activation="relu"))
     
    
   

    

     

    
    

     

        model.add(MaxPooling2D(pool_size=(2, 2)))
     
    
   

    

     

    
    

     

        model.add(Dropout(0.2))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(Flatten())
     
    
   

    

     

    
    

     

        model.add(Dense(512, activation="relu"))
     
    
   

    

     

    
    

     

        model.add(Dropout(0.2))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

        model.add(Dense(10, activation="softmax"))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
  return model
     
    
  
 

文章来源: kings.blog.csdn.net，作者：人工智能博士，版权归原作者所有，如需转载，请联系作者。

原文链接：kings.blog.csdn.net/article/details/89891005