【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