CN112270406A - Neural information visualization method of brain-like computer operating system - Google Patents
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Abstract
The invention discloses a neural information visualization method of a brain-like computer operating system, which comprises the following steps: obtaining a connection description file of the pulse neural network, and carrying out global coding on each layer of neuron nodes according to the connection description file, wherein each neuron node obtains a global unique identifier, each layer of neuron nodes are represented in a gathering way and are distinguished by different colors, and the connection relation of the neuron nodes between adjacent levels is represented by connecting edges and is distinguished by different colors; and calculating the importance of each neuron node according to the number of the connecting edges of the neuron nodes and the weight of the connecting edges, and selecting the neuron nodes for visualization according to the importance. The problems that a plurality of connection description files are needed, the number of neuron nodes is large, and connection lines among the neuron nodes are large and difficult to distinguish are solved. The neural information of the brain-like computer operating system is reasonably visualized, and the understanding of the working process of the impulse neural network is facilitated.
Description
Technical Field
The invention belongs to the technical field of novel computers, and particularly relates to a neural information visualization method of a brain-like computer operating system.
Background
Since 2006, deep learning has been highly concerned by scientific research institutions and the industry, and has been greatly developed in a plurality of fields such as images and voices, and comprehensively surpasses traditional algorithms in a plurality of fields. Spiking neural Networks (SNN-Spiking neural Networks) are often known as third generation artificial neural Networks. The first generation of neural networks were perceptrons, which were a simple neuron model and could only process binary data. The second generation neural networks include a wide variety of BP neural networks including more applications. And the impulse neural network more closely simulates the connection relation and the behavior of brain neurons.
At present, the tools for visualizing the convolutional neural network structure disclosed on the network are more, and mainly include: 1) netscope: the model definition file is used as input to obtain a visual structure diagram of the neural network, the visual structure diagram is a visual neural network topology tool based on a webpage, and only supports a caffe deep learning framework of Berkeley division of California university. 2) ConvNetDraw: the visualization of the output neural network model structure using visualization commands as input is also a web-based tool. The model of the structural block is used for displaying, the three-dimensional proportion adjustment can be carried out on the structural block, and the method is very visual and visual. 3) Netron: a visual structure diagram of a neural network is obtained by taking a model definition file and a model weight file (which can be default) as input, and the visual structure diagram is also based on a webpage and uses js and python and can support mainstream deep learning frameworks such as ONNX, Keras, CoreML, TensorFlow, caffe and MXNET.
The patent application of Cao Li Macro in China media university with publication number CN106372721A discloses a method for visualizing a large-scale neural network in a 3D form, and shows the structure of the neural network in a 3D form; qiufuyang et al, published under CN107392085A, of Shanghai precision metrology testing institute, discloses a method for visualizing a convolutional neural network, which can well demonstrate the superiority of the convolutional neural network for exploring the convolutional neural network; the patent application with publication number CN110782031A of Beijing computer technology and application research discloses a multi-frame convolutional neural network model structure visualization and network reconstruction method, which can visually modify different layers to realize network reconstruction and can change the attribute of the neural network and update and display the attribute in real time.
Due to the complexity of the impulse neural network structure of the brain-like computer operating system, when the impulse neural network structure of the brain-like computer operating system is visualized, the problems of more connection description files, more neuron nodes and more connecting lines among the neuron nodes are difficult to distinguish. Therefore, all the visualization methods described above are not suitable for visualization of the spiking neural network structure of the brain-like computer operating system.
Disclosure of Invention
The invention aims to provide a neural information visualization method of a brain-like computer operating system, which aims to solve the problems that a plurality of connection description files are provided, the number of neuron nodes is large, and a plurality of connecting lines among the neuron nodes are difficult to distinguish.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for visualizing neural information of a brain-like computer operating system, comprising the steps of:
obtaining a connection description file of the pulse neural network, and carrying out global coding on each layer of neuron nodes according to the connection description file, wherein each neuron node obtains a global unique identifier, each layer of neuron nodes are represented in a gathering way and are distinguished by different colors, and the connection relation of the neuron nodes between adjacent levels is represented by connecting edges and is distinguished by different colors;
and calculating the importance of each neuron node according to the number of the connecting edges of the neuron nodes and the weight of the connecting edges, and selecting the neuron nodes for visualization according to the importance.
Preferably, all the neuron nodes in the same layer of the spiking neural network are represented in an aggregation mode, and the neuron nodes in two adjacent layers are represented in the same color, and the aggregation representations of the neuron nodes in two adjacent layers are also located in adjacent positions, so that the connection relation between the neuron nodes in two adjacent layers can be represented conveniently.
Preferably, each layer of neuron nodes is aggregated into a geometric shape for representation. The geometric shape can be a smooth geometric shape without edges and corners, such as a circle, an ellipse and the like, and can also be a geometric shape with edges and corners, such as a matrix, a triangle and the like.
Preferably, all connection relationships between two layers of neuron nodes are represented by connecting edges in the same color. In order to improve the visualization definition, the color of the neuron node is different from the color of the corresponding connecting edge.
Preferably, when the neural information visualization is performed, the neuron information layout is stored in a data structure, and the data structure comprises a data unit, a connection relation unit and a layer unit, wherein the data unit is used for storing the global unique identifier, the visualization position coordinate and the level of the neuron node; the connection relation unit is used for storing two neuron nodes and the visual color of a connection edge between the two neuron nodes; the layer unit is used for storing the hierarchy name.
Because the number of the neuron nodes is large, whether a certain neuron is displayed or not is selected according to the number of the connecting edges of the neuron nodes and other neurons and the weight of the connecting edges. If too much sampling is carried out on the neuron nodes, the neuron nodes are too dense after visualization, and the connection relation among the neuron nodes cannot be seen; if the sampling of the neuron nodes is too little, the loss of main information can be caused, and the main structure of the impulse neural network cannot be observed. In order to perform reasonable sparse sampling on neurons, the importance of the neuron nodes needs to be calculated and ranked. Preferably, the importance of each neuron node is calculated using the following formula:
wherein ,
representing the ith neuron nodeiThe absolute importance of the preceding neuron, InNum represents the in-degree of the neuron node, w1Is the in degree coefficient of the neuron node, OutNum represents the out degree of the neuron node, w2Is the out degree coefficient of the neuron node, Weight represents the sum of absolute values of weights of the connected edges of the neuron node, w3Is the weight coefficient of the neuron node,
is the importance of the final neuron node,
represents the jth neuron nodejThe absolute significance of the previous neuron, n represents the total number of neuron nodes. The number of the connecting edges accessed to the current neuron node is used as the in degree of the current neuron node, and the number of the connecting edges accessed from the current neuron node is used as the out degree of the current neuron node.
Preferably, when the neuron nodes are selected according to the importance degree for visualization, an importance threshold value is set, and the neuron nodes with the importance greater than the importance threshold value are screened for visualization. Thus, the more edges the neuron node is connected to, the larger the absolute value of the weight of the edge is, the higher the weight is, the priority is kept.
Compared with the prior art, the invention has the beneficial effects that at least:
according to the neural information visualization method of the brain-like computer operating system, the problem of a large number of connection description files is solved by carrying out global coding on neurons, the neurons are screened and visualized according to the importance of the neuron nodes, the problem of difficulty in displaying a large number of neuron nodes is solved by adopting the sparseness processing method, meanwhile, the connection relation is expressed in a connection form and is distinguished by different colors, and the problem of difficulty in distinguishing a large number of connection lines among the neuron nodes is solved. The neural information of the brain-like computer operating system is reasonably visualized, and the understanding of the working process of the impulse neural network is facilitated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a method for visualizing neural information of a brain-like computer operating system according to an embodiment of the present invention;
FIG. 2 is an internal data structure of a connection description file of the spiking neural network provided by an embodiment of the present invention;
FIG. 3 is an internal data structure of an intermediate json file for visualization of neural information provided by an embodiment of the present invention;
FIG. 4 is a diagram of a quadruple within a data field of a json file according to an embodiment of the present invention;
FIG. 5 is a triple inside links fields of a json file provided by an embodiment of the present invention;
FIG. 6 is a neural information visualization diagram of an electroencephalogram simulated impulse neural network provided by an embodiment of the invention;
fig. 7 is a neural information visualization diagram of a memory model impulse neural network provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
In the process of carrying out neural information visualization on the impulse neural network of the brain-like computer operating system of Darwin No. 2 neural mimicry chip in Zhejiang university, the problems of more connection description files, more neuron nodes and more connecting lines among the neuron nodes are encountered, and the connection description files are not easy to distinguish. In order to solve the problem that neural information visualization is difficult due to the problems, the embodiment of the invention provides a neural information visualization method of a brain-like computer operating system, which is used for performing neural information visualization based on a pulse neural network model connection description file of the brain-like computer operating system of a Darwin No. 2 neural mimicry chip.
Fig. 1 is a flowchart of a method for visualizing neural information of a brain-like computer operating system according to an embodiment of the present invention. As shown in fig. 1, the neural information visualization method includes the steps of:
step 1, reading a connection description file of the impulse neural network according to the connection relation between layers of the impulse neural network and the sequence from an input layer to an output layer.
The connection description file of the impulse neural network describes the connection relationship between the neurons of the impulse neural network and the neurons. The connecting description file of the impulse neural network is multiple, and each file describes the connecting relation between two layers of neurons. Fig. 2 is an internal data structure of a connection description file of the impulse neural network. As shown in fig. 2, each row of the connection description file is a quadruple, the first field of the quadruple is the intra-layer number of the starting neuron, i.e. the number of the starting neuron in the starting layer, the second field is the intra-layer number of the target neuron, i.e. the number of the target neuron in the target layer, the third field is the weight, i.e. the weight of the processing input value of the neuron, which is the inherent property of the connecting edge of the neuron, and the fourth field is the pulse delay, i.e. the delay of the pulse signal from the starting neuron to the target neuron.
And 2, according to the read pulse neural network connection description file, globally numbering each layer of neuron nodes again, converting the intra-layer numbers into globally unique identifiers, and simultaneously determining the color of each layer of neurons to form the attributes of the neurons.
And 3, generating a triple (initial neuron number, target neuron number and edge color) for describing the node connection relation according to the connection description file and the self attribute of the neuron node, wherein the edge color is determined according to the layer where the edge connection neuron is located.
And 4, distributing the neuron nodes according to the connection relation between the neurons of the pulse neural network, wherein the neuron nodes of the same layer are gathered and placed in the same area, and the neurons of the adjacent layer are gathered and placed at the adjacent positions.
And 5, selecting whether a certain neuron is displayed or not according to the number of the connecting edges of the neuron and other neurons and the weight of the connecting edges.
Because the number of neuron nodes and connecting lines in the original spiking neural network is too many, if all the neuron nodes and connecting lines are displayed completely, all the node nodes and connecting lines are crowded together, and the key structure of the spiking neural network cannot be displayed, so that the spiking neural network needs to be displayed sparsely. If the sampling of the neurons is too much, the visualized neurons are too dense, and the connection relation among the neurons cannot be seen; if the sampling of the neuron is too little, the loss of main information can be caused, and the main structure of the impulse neural network cannot be observed. The method specifically comprises the steps of calculating the importance of the neurons according to the number of the connecting edges of the neurons and other neurons and the weight of the connecting edges, and sorting the neurons according to the importance, wherein the neurons with the greater importance are preferentially reserved.
The formula for the significance of neurons is as follows:
after calculating the importance of the neurons, setting an importance threshold value E (0,1)) to control which neurons are displayed, wherein the neurons with the importance greater than the threshold value are displayed, and the neurons smaller than the threshold value are hidden.
And 6, exporting a json file for describing the color of the neuron layout and the edge, and visualizing the exported json file by utilizing echarts.
In the embodiment, a json file is generated by adopting a python code, and the json file is visualized by adopting a graph type of echarts. FIG. 3 is a data structure of a json file, which contains three fields: and the data unit data is used for connecting the relationship units links and the layer units layers. The data field is an array, each internal element is a quadruple, and the quadruple contains the key value name, the position coordinate (x, y) and the belonging layer information of the neuron node, as shown in fig. 4. The links field is also an array, each element inside the array is a triplet, and the triplet includes the key value source of the starting neuron node, the key value target of the target neuron, and the color lineStyle of the link, as shown in fig. 5.
The neural information visualization results of the electroencephalogram simulation impulse neural network and the memory model impulse neural network by using the neural information visualization method of the brain-like computer operating system are shown in fig. 6 and 7, wherein the structure of the impulse neural network is clearly shown in the figures, namely, the connection relation between the neuron nodes of the impulse neural network and each neuron node is shown.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A neural information visualization method of a brain-like computer operating system, comprising the steps of:
obtaining a connection description file of the pulse neural network, and carrying out global coding on each layer of neuron nodes according to the connection description file, wherein each neuron node obtains a global unique identifier, each layer of neuron nodes are represented in a gathering way and are distinguished by different colors, and the connection relation of the neuron nodes between adjacent levels is represented by connecting edges and is distinguished by different colors;
and calculating the importance of each neuron node according to the number of the connecting edges of the neuron nodes and the weight of the connecting edges, and selecting the neuron nodes for visualization according to the importance.
2. The method for visualizing neural information of a brain-like computer operating system of claim 1, wherein all the neuron nodes in the same layer of the spiking neural network are represented in an aggregate manner and in the same color, and the aggregate representations of the neuron nodes in two adjacent layers are also in adjacent positions, so as to facilitate the representation of the connection relationship between the neuron nodes in two adjacent layers.
3. The method for visualizing neural information of a brain-like computer operating system of claim 1 or 2, wherein each layer of neuron nodes are clustered into geometric shapes for representation.
4. The method for visualizing neural information of a brain-like computer operating system of claim 1, wherein all connections between neuron nodes of two layers are represented by connecting edges in the same color.
5. The method of claim 1, wherein the color of the neuron node is different from the color of the corresponding connecting edge.
6. The neural information visualization method of the brain-like computer operating system according to claim 1, wherein in the neural information visualization, the neural information layout is stored in a data structure, the data structure comprises data units, connection relation units and layer units, wherein the data units are used for storing globally unique identifiers of the neural nodes, visualization position coordinates and the hierarchy; the connection relation unit is used for storing two neuron nodes and the visual color of a connection edge between the two neuron nodes; the layer unit is used for storing the hierarchy name.
7. The method of visualizing neural information of a brain-like computer operating system of claim 1, wherein the importance of each neuron node is calculated using the following formula:
wherein ,
representing the ith neuron nodeiThe absolute importance of the preceding neuron, InNum represents the in-degree of the neuron node, w1Is the in degree coefficient of the neuron node, OutNum represents the out degree of the neuron node, w2Is the out degree coefficient of the neuron node, Weight represents the sum of absolute values of weights of the connected edges of the neuron node, w3Is the weight coefficient of the neuron node,
is the importance of the final neuron node,
represents the jth neuron nodejThe absolute significance of the previous neuron, n represents the total number of neuron nodes.
8. The method as claimed in claim 1, wherein when the neuron nodes are selected for visualization according to their importance, an importance threshold is set, and neuron nodes with importance greater than the importance threshold are selected for visualization.
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| CN113791913A (en) * | 2021-11-16 | 2021-12-14 | 浙江大学 | Task migration method of brain-like computer operating system |
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