[Deep Learning | Machine Learning] Full of dry goods | Nearly 10,000 words summarize 12 amazing neural network visualization tools!

Neural network visualization refers to the technique of graphically displaying the structure, internal state and decision-making process of neural networks. Although neural networks excel at solving complex tasks, their inner workings are often treated as black boxes, difficult to explain and understand.

1. What are the difficulties in neural network visualization?

The difficulty of neural network visualization lies in the following aspects:

1. Complexity: The structure of a neural network is usually very complex, containing a large number of neurons and connections. For large-scale networks, it is a great challenge to accurately visualize the information of each neuron and connection.
2. Curse of Dimensionality: The input and hidden layers of a neural network are usually high-dimensional and difficult to display directly on a two-dimensional screen. Reducing dimensionality while preserving key information is a challenge.
3. Dynamicity: Neural networks are dynamic systems, constantly updating and adjusting weights and activations during training and inference. Capturing the dynamic behavior of the network and updating the visualization in real time is a complex task.
4. Interpretability: Neural networks are often viewed as black-box models with limited interpretability for their internal decisions and feature representations. Visualization requires explaining the features and decision-making process learned by the network to make it more understandable to humans.
5. Scalability: For large-scale neural networks, such as deep convolutional neural networks (CNN) and recurrent neural networks (RNN), visualization needs to be able to handle a large number of layers and parameters, and be scalable and efficient.
6. Choice of visualization method: There are many different visualization methods and techniques, and it is a challenge to choose an appropriate method to present the structure and internal information of the neural network.

2. Neural Network Visualization Method

Neural network visualization is a technique that graphically presents the structure and operation of a neural network. It can help us better understand the inner workings of neural networks and the feature extraction process. Here are a few common neural network visualization methods:

1. Visualize the network structure: You can use tools such as TensorBoard, Graphviz, etc. to display the structure of the neural network in a graphical manner. This visualization method can clearly show the hierarchical relationship, connection mode and number of parameters of the network.
2. Visualize neuron activation: By visualizing the activation of neurons, you can observe the process of neural network feature extraction for input data at different levels. A common approach is to use a pre-trained model, input an image, and visualize the activation maps of neurons in intermediate layers to understand the sensitivity of the network to different features.
3. Visualizing convolutional kernels: For Convolutional Neural Networks (CNN), visualizing convolutional kernels can help us understand how the network learns specific features and patterns. Convolution kernels can be visualized as images showing the specific features associated with each kernel.
4. Visualizing Gradients: Visualizing gradients in a neural network can help us understand how information flows through the network during backpropagation, and how each layer contributes to the gradient. By visualizing the gradient, it is possible to judge whether the network is experiencing gradient disappearance or gradient explosion.
5. Visualize network output: Visualizing the output of the network can help us understand the prediction or classification results of the network for different input data. For example, in image classification tasks, the classification probability of the network to the input image can be visualized as a histogram or heat map, so as to understand the confidence of the network for different categories, etc.

3. Tools for visual interpretation of neural networks

3.1 draw_convnet

draw_convnet is a Python library for drawing Convolutional Neural Network (CNN) structures. It can help us visualize the hierarchical structure, number of parameters and data flow of CNN, so as to better understand and analyze the network model.

The draw_convnet library provides a simple API that enables us to create CNN visualizations by specifying parameters such as layers, feature map size, and convolution kernel size. Here is a simple example using draw_convnet:

from draw_convnet import draw_convnet

# 定义CNN结构
convnet_layers = [
    ('conv2d', {
    
    'shape': [32, 32, 3], 'kernel': [5, 5], 'pad': 'same', 'activ': 'relu'}),
    ('pool', {
    
    'shape': [32, 32, 3], 'kernel': [2, 2], 'stride': 2}),
    ('conv2d', {
    
    'shape': [16, 16, 32], 'kernel': [5, 5], 'pad': 'valid', 'activ': 'relu'}),
    ('pool', {
    
    'shape': [16, 16, 32], 'kernel': [2, 2], 'stride': 2}),
    ('flatten', {
    
    }),
    ('dense', {
    
    'units': 256, 'activ': 'relu'}),
    ('dense', {
    
    'units': 10, 'activ': 'softmax'})
]

# 绘制CNN结构图
draw_convnet(convnet_layers, 'convnet.png')

In the above example, we first define a CNN hierarchical structure, which includes convolutional layers, pooling layers, fully connected layers, etc. We then use the draw_convnet function to create a visualization of the CNN and save it as an image file named convnet.png.

By using the draw_convnet library, we can intuitively understand the structure and parameter settings of CNN, as well as the way data flows in the network. This helps us better understand and debug CNN models, optimize and improve them.

https://github.com/gwding/draw_convnet

3.2 NNSVG

NNSVG (Neural Network SVG) is a Python library for generating scalable vector graphics (Scalable Vector Graphics) of neural network structures. It can visualize the structure of the neural network, including hierarchical structure, connection relationship and node information, so as to help us better understand and analyze the neural network model.

The main function of NNSVG is to generate corresponding SVG graphic files according to the given neural network parameters. This allows the resulting image to be scaled up or down losslessly without loss of image quality. This is very useful for demonstrating large and complex neural network structures, as we can freely resize the images as needed to see details or global structures.

Using NNSVG is very simple. Here is an example of using NNSVG to generate a neural network structure:

import nnsvg

# 定义神经网络结构
network = nnsvg.Network()
input_layer = nnsvg.Layer(784, name='Input')
hidden_layer1 = nnsvg.Layer(256, name='Hidden 1', activation='relu')
hidden_layer2 = nnsvg.Layer(128, name='Hidden 2', activation='relu')
output_layer = nnsvg.Layer(10, name='Output', activation='softmax')

# 建立网络层级关系
network.add_layer(input_layer)
network.add_layer(hidden_layer1)
network.add_layer(hidden_layer2)
network.add_layer(output_layer)
network.connect_layers()

# 生成SVG图像文件
network.save('network.svg')

In the above example, we first created a neural network structure, including input layer, hidden layer and output layer, and specified their number of nodes and activation function. Then, we use the add_layer method to add layers to the network, and use the connect_layers method to establish connections between layers. Finally, we save the resulting SVG image as a file named network.svg using the save method.

By using NNSVG, we can easily generate beautiful and scalable neural network structure diagrams for presentation, documentation or academic research purposes. This allows us to better communicate and understand the structure and architecture of neural network models.

http://alexlenail.me/NN-SVG/LeNet.html

3.3 PlotNeuralNet

PlotNeuralNet is a Python library for visualizing neural network structures. It provides easy-to-use functions, which can draw the connection relationship and node information of the neural network in a visual way, helping us better understand and display the neural network model.

Using PlotNeuralNet, we can generate high-quality neural network structure diagrams through concise code. The following is an example of using PlotNeuralNet to generate a neural network diagram:

from plotneuralnet import PlotNeuralNet

# 创建一个PlotNeuralNet实例
nn = PlotNeuralNet()

# 定义神经网络结构
nn.add_input_layer(784)
nn.add_hidden_layer(256, activation='relu')
nn.add_hidden_layer(128, activation='relu')
nn.add_output_layer(10, activation='softmax')

# 生成神经网络结构图
nn.build()

# 绘制神经网络结构图
nn.save_fig("neural_network.png")

In the above example, we first import the PlotNeuralNet library and create a PlotNeuralNet instance. Then, we use the add_input_layer, add_hidden_layer and add_output_layer methods to define the various layers of the neural network and specify the activation function. Next, we use the build method to generate a structure diagram of the neural network, and use the save_fig method to save the image as a file named neural_network.png.

Use PlotNeuralNet to easily create neural network structure diagrams, and the graphics are clear and beautiful. It provides some customization options, such as selection of activation function and modification of node style, in order to meet different needs. Whether demonstrating model structures in academic research or presenting neural network architectures in technical reports, PlotNeuralNet is a handy and useful tool.

https://github.com/HarisIqbal88/PlotNeuralNet

3.4 Tensorboard

TensorBoard is a tool for visualizing and monitoring the training process of deep learning models, powered by TensorFlow. It provides an intuitive web interface that can help us visualize the architecture of the model, the loss and indicator changes during training, the distribution of model parameters, and the visualization of training samples, etc.

The main functions of TensorBoard include:

Visual model diagram: TensorBoard can draw and display the calculation diagram of the neural network model, helping us understand the structure, hierarchical relationship and parameter connection of the model. This helps to debug and optimize the model's architecture.

Real-time loss and indicator curves: During the training process, TensorBoard can display the change curves of loss function, accuracy rate, verification indicators, etc. in real time. This allows us to easily monitor the training progress of the model, and adjust and optimize the training process.

Visualization of model parameter distribution: TensorBoard can visualize the distribution of model parameters, helping us understand the value range and distribution of model parameters. This can be very helpful for debugging models and spotting potential issues.

Feature visualization: TensorBoard provides some feature visualization tools, such as projection of embedding space, image generation, etc., which can help us understand the model's representation and learning process of input data.

Visualization of training samples: TensorBoard can display visualizations of training data, including images, audio, text, etc. This helps us to check and verify the effect of data preprocessing.

Using TensorBoard, we only need to save the log data during the training process to the specified directory, and run the TensorBoard command in the terminal, then we can access the web interface of TensorBoard in the browser to view and analyze the visualization results of the training process.

Overall, TensorBoard is a very powerful and practical tool that can help us better understand, debug and optimize deep learning models. Whether it is visualizing the model in research or monitoring the training process in engineering, TensorBoard is a very useful auxiliary tool.

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

3.5 Caffe

Caffe also provides the function of visualizing the neural network, allowing users to better understand and analyze the structure of the model.

Caffe's visual neural network is mainly realized through two tools: NetDraw and NetSurgery.

1. NetDraw: NetDraw is a visualization tool of Caffe that can draw and display the architecture diagram of the neural network model. Through NetDraw, users can visually view the hierarchical structure of the model, the connections between layers, and the attributes of layers. The network graphics generated by NetDraw can help users understand the organization of the model and the data flow path.
2. NetSurgery: NetSurgery is another tool of Caffe that can modify and adjust existing models before or after training. Through NetSurgery, users can directly edit and modify network model parameters, layer properties and connection relationships. This allows users to customize and optimize the model according to their needs.

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

3.6 Matlab

Matlab, a popular scientific computing and data analysis tool, also provides functions for visualizing neural networks. In Matlab, you can use Neural Network Toolbox to build, train and visualize neural network models.

Here are some common functions and tools for visualizing neural networks in Matlab:

1. neuralnetworkgui: neuralnetworkgui is an interactive graphical interface tool in Matlab that can be used to visualize and edit neural networks. Through neuralnetworkgui, users can intuitively build a neural network model, set the hierarchical structure, connection mode and parameters of the network, etc. The tool also provides visualized training progress and performance indicators, which is convenient for users to debug and optimize the model.
2. view: In Matlab, you can use the view function to visualize the constructed neural network model. Through the view function, users can view information such as the hierarchical structure, connection mode, and weight of the network. This function represents the network as a graph, which is convenient for users to understand and analyze the organization mode and data flow path of the model.
3. plotperf: The plotperf function can be used to draw performance indicator curves during training, such as loss function curves, accuracy curves, etc. By visualizing these curves, users can intuitively understand the performance changes of the model during the training process, so as to evaluate and adjust the model.
4. gensim: gensim is a function in Matlab, which is used to generate a simulation diagram of a neural network. This function can generate a simulation image of the network according to the given network structure and parameters, showing the connection and activation between neurons. This helps users better understand and analyze how neural networks work.

http://www.mathworks.com/help/nnet/ref/view.html

3.7 Keras.js

Keras.js is an open-source JavaScript library for running Keras models in a web browser. It provides an easy-to-use interface for loading and executing trained Keras models and making predictions in a web browser.

Keras.js also provides some functions for visualizing neural networks to help users better understand and analyze the structure of the model. The following are commonly used visualization functions in Keras.js:

Model Summary: Using Keras.js, it is easy to generate a summary of a neural network model. The summary provides information such as the model's hierarchical structure, layer names, output shape, and number of trainable parameters. This helps users quickly understand the composition and scale of the model.

Layer Visualization: Keras.js provides a way to visualize each layer of a neural network. Through this function, users can view the name, type and output shape of each layer. This helps users understand the role of different layers and the flow of information in a neural network model.

Weight Visualization: Keras.js allows users to visualize the weights of each layer in the neural network model. Weight visualization provides a heatmap or other visualization of the weight matrix, showing the connection strength and weight distribution between neurons. This helps users understand important features and connectivity patterns in the model.

Activation Visualization: Using Keras.js, users can visualize the output of activation functions in neural networks. This can be done by plotting a heatmap of the activation function or by visualizing the activation values ​​for each layer. Activation visualization helps users understand the information flow and feature extraction process of the model at different levels.

Through the above visualization functions, Keras.js enables users to intuitively understand and analyze the structure, weight distribution and activation of neural network models. This helps the user debug, optimize and interpret the model and provides a deeper understanding of how the model works.

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

3.8 DotNet

Visualizing neural networks in .NET is possible with the help of various tools and libraries. Here are a few commonly used methods:

TensorFlow.NET: TensorFlow.NET is a library that interfaces .NET to TensorFlow. It provides the ability to build and train neural networks in .NET and supports visualization of neural network structures. TensorFlow.NET can generate a visual diagram of the neural network structure by exporting the model's graph definition file (GraphDef), which can then be loaded and viewed using other tools (such as TensorBoard).

Accord.NET: Accord.NET is a powerful machine learning framework that provides various algorithms and tools for machine learning and image processing. It contains modules for building and training neural networks, and supports visualizing neural network structures as graphs. Accord.NET provides a class called NetworkGraph, which can convert the neural network into a graph definition in Graphviz DOT format, and then use the Graphviz tool to generate a visual chart.

Neural Network Designer: Neural Network Designer is a neural network visualization tool developed specifically for the .NET platform. It provides a visual interface that allows users to intuitively build and configure neural network structures and provides real-time visualization. Neural Network Designer supports many common neural network layers and activation functions, and can generate executable code that can be embedded in .NET applications.

https://github.com/martisak/dotnets

3.9 Graphviz

Graphviz is an open source graph visualization toolkit that can be used to visualize various graph structures, including neural networks. It provides a set of languages ​​and tools for describing graphics, and can convert the graphic structure definition into the form of images or vector graphics.

In terms of visualizing neural networks, Graphviz provides a variety of layout algorithms and drawing options, which can generate beautiful and easy-to-understand graphs according to user needs. Using Graphviz can help us intuitively understand the structure, hierarchical relationship and information flow of neural networks.

Here are the basic steps to visualize a neural network using Graphviz:

1. Installing Graphviz: First, you need to install the Graphviz package on your computer. You can download the version for your operating system from Graphviz's official website (https://graphviz.org/) and install it following the installation guide.
2. Define the neural network structure: Define the structure of the neural network in the code, including the connection relationship and activation function of each layer. This can be done using various deep learning frameworks like TensorFlow, PyTorch, Keras, etc.
3. Generate Graphviz graph definition: Convert the neural network structure into a Graphviz graph definition, which can be done using various libraries or custom code. For example, the pygraphviz library or the graphviz library in Python can be used to generate graph definitions in DOT format for Graphviz.
4. Drawing neural network graphs: Use the command-line tools or programming interfaces provided by Graphviz to convert graph definition files into actual images or vector graphics. Different layout algorithms and drawing options can be selected to obtain the most suitable graphics rendering effect.
5. Viewing and Saving Graphs: The resulting neural network graph can be viewed in a graphing tool (eg image viewer) or saved to disk as an image file (eg PNG, SVG).

http://www.graphviz.org/

3.10 ConX

ConX (Cognitive Architecture for Machine Learning) is a Python-based open source deep learning framework for building, training and evaluating neural network models. It is designed to simplify the process of deep learning and provide an intuitive way to define, visualize and debug neural networks.

Here are some features and functions of ConX:

1. Ease of use: ConX provides a concise API and an intuitive command line interface, enabling users to quickly start building neural network models. It uses a high-level interface similar to Keras, making the model definition and training process more intuitive and simple.
2. Multiple network types: ConX supports multiple types of neural networks, including fully connected networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and more. Users can choose the appropriate network type according to the application requirements.
3. Visualization tools: ConX provides built-in visualization tools that can help users intuitively understand the structure, parameters and training process of the model. It supports functions such as drawing network topology, drawing training curves, and visualizing weight and activation maps.
4. Model debugging and optimization: ConX has rich debugging and optimization functions, which can help users analyze and solve problems in the model. It provides visualized network output and intermediate layer output to help users understand the decision-making process of the model.
5. Integrated learning algorithm: ConX supports integrated learning algorithms, such as dropout and integrated average, which can improve the generalization ability and robustness of the model.
6. Model saving and loading: ConX provides a convenient model saving and loading function, users can save the trained model to disk, and reload it for prediction or continue training when needed.

https://conx.readthedocs.io/en/latest/index.html

3.11 ENNUI

ENNUI provides the following main functions and features:

Visualize the neural network structure: ENNUI can present the structure of the neural network in a graphical way, showing the relationship between various layers, neurons and connections. This helps users better understand the overall architecture of the network.

Distribution of neurons in a layer: ENNUI can display the distribution of neurons in each layer, including information such as activation values, weights, and biases. This allows users to observe differences and distributions between different neurons.

Feature visualization: ENNUI provides a feature visualization function that can display activation images of different features in the network. This is very helpful for understanding the features and patterns learned by the network.

Visualization of Neuron Response: Users can visualize the response of neurons to input data through ENNUI. This allows the user to observe the activation of different neurons for different inputs, thereby understanding how the network processes the input and extracts features.

Gradient Visualization: ENNUI can visualize the flow of gradients in neural networks. This is very helpful for understanding the propagation and backpropagation process of gradients in the network, and helps to debug and optimize the training process of the network.

https://math.mit.edu/ennui/

3.12 Neataptic

Neataptic is a JavaScript library for building and training neural networks. It is a lightweight, flexible, and easy-to-use library designed to help developers implement various types of neural network models.

Here are some of the features and capabilities of Neataptic:

Neural network type: Neataptic supports various types of neural networks, including Feedforward Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory Networks.

Network topology: You can use Neataptic to define the topology of the neural network, including the number of layers of neurons, the number of neurons in each layer, connection methods, etc.

Neuron model: Neataptic provides a series of commonly used neuron models, such as linear neuron, Sigmoid neuron, ReLU neuron, etc. You can choose a suitable activation function according to your needs.

Network Training: With Neataptic, you can train a neural network to fit a specific task. You can define a loss function, choose an optimizer, and update network parameters through the backpropagation algorithm.

Genetic Algorithms: Neataptic also provides support for genetic algorithms to aid in the evolution and optimization of neural networks. You can define fitness functions and use genetic algorithms to automatically search for optimal network structures and parameters.

Visualization tools: Neataptic provides several visualization tools that can help you understand and debug neural networks. You can visualize network topology, loss curves during training, and more.

https://wagenaartje.github.io/neataptic/