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- Next, I will teach you how to use the networkx library in Python to draw a beautiful and standard neural network.
- Neural networks with different structures will be drawn according to the specified number of layers and nodes.
The networkx library can be used to create and manipulate graph-type data structures, including undirected graphs, directed graphs, weighted graphs, and more.
Neural networks can be viewed as a graph data structure, so they can be created using the networkx library and operated visually.
Simple example: Draw a 2-layer neural network
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First, you need to install the networkx library in advance, and then import networkx and matplotlib in the code. Then use DiGraph to create a directed graph G.
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The network we want to draw includes 5 nodes. The nodes in the first layer are numbered 1, 2, and the nodes in the second layer are 3, 4, 5. We use add_edge from 1 to 3, 4, 5, and from 2 to 3, 4, 5, connect an edge.
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In order to make the drawn image look like a neural network, we need to set the coordinates for these 5 nodes. Create a dictionary pos. The key of the dictionary is the name of the node, and the value of the dictionary is the location of the node.
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Finally, use the nx.draw function to draw.
where G is the graph to be drawn,
pos is the coordinate of the node in the graph,
with_labels = True, represents the name of the drawing node
node_color and edgecolor are the colors of nodes and edges
linewidths and width are the thickness of nodes and edges
node_size is the size of the node
# Import networkx and matplotlib in the code
import networkx as nx
import matplotlib.pyplot as plt
G = nx.DiGraph() # Use DiGraph to create a directed graph G
# The network includes 5 nodes
# The node numbers on the first layer are 1 and 2, and the node numbers on the second layer are 3, 4 and 5.
G.add_edge(1, 3) # from 1 to 3
G.add_edge(1, 4) # from 1 to 4
G.add_edge(1, 5) # from 1 to 5
G.add_edge(2, 3) # from 2 to 3
G.add_edge(2, 4) # from 2 to 4
G.add_edge(2, 5) # from 2 to 5
# Create dictionary pos, the key of the dictionary is the name of the node
#The value of the dictionary is the location of the node
# Nodes 1 and 2 are in one column
# 3, 4, 5 in one column
# So set the x coordinates of 1 and 2 to 0; 3, 4, and 5 to 1
#Nodes in the same group can be evenly distributed in the same column
# So we set the y coordinates of 1 and 2 to 0.25 and 0.75
# The y coordinates of 3, 4, and 5 are 0.2, 0.5, and 0.8
# {Node name: (node x coordinate, node y coordinate)}
pos = {<!-- -->
1: (0, 0.25), # Coordinates of node 1 (0,0.25)
2: (0, 0.75), # Coordinates of node 2 (0,0.75)
3: (1, 0.2), # Coordinates of node 3 (1, 0.2)
4: (1, 0.5), # Coordinates of node 4 (1, 0.5)
5: (1, 0.8), # The coordinates of node 5 (1, 0.8)
}
# Use nx.draw function to draw
nx.draw(G, #The picture to be drawn
pos, #Coordinates of nodes in the graph
with_labels=True, # Draw the name of the node
node_color='white', #The color of the node
edgecolors='black', # edge colors
linewidths=3, #The thickness of the node
width=2, # The thickness of the edge
node_size=1000 #The size of the node
)
plt.show() # Use the show method to display graphics
Customized function, freely draw neural network according to parameters
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# Import networkx and matplotlib in the code
import networkx as nx
import matplotlib.pyplot as plt
G = nx.DiGraph() # Use DiGraph to create a directed graph G
# The network includes 5 nodes
# The node numbers on the first layer are 1 and 2, and the node numbers on the second layer are 3, 4 and 5.
G.add_edge(1, 3) # from 1 to 3
G.add_edge(1, 4) # from 1 to 4
G.add_edge(1, 5) # from 1 to 5
G.add_edge(2, 3) # from 2 to 3
G.add_edge(2, 4) # from 2 to 4
G.add_edge(2, 5) # from 2 to 5
# Create dictionary pos, the key of the dictionary is the name of the node
#The value of the dictionary is the location of the node
# Nodes 1 and 2 are in one column
# 3, 4, 5 in one column
# So set the x coordinates of 1 and 2 to 0; 3, 4, and 5 to 1
#Nodes in the same group can be evenly distributed in the same column
# So we set the y coordinates of 1 and 2 to 0.25 and 0.75
# The y coordinates of 3, 4, and 5 are 0.2, 0.5, and 0.8
# {Node name: (node x coordinate, node y coordinate)}
pos = {<!-- -->
1: (0, 0.25), # Coordinates of node 1 (0,0.25)
2: (0, 0.75), # Coordinates of node 2 (0,0.75)
3: (1, 0.2), # Coordinates of node 3 (1, 0.2)
4: (1, 0.5), # Coordinates of node 4 (1, 0.5)
5: (1, 0.8), # The coordinates of node 5 (1, 0.8)
}
# Use nx.draw function to draw
nx.draw(G, #The picture to be drawn
pos, #Coordinates of nodes in the graph
with_labels=True, # Draw the name of the node
node_color='white', #The color of the node
edgecolors='black', # edge colors
linewidths=3, #The thickness of the node
width=2, # The thickness of the edge
node_size=1000 #The size of the node
)
plt.show()
# Draw the corresponding neural network based on the number of neurons in the input layer, hidden layer, and output layer.
def draw_network_digraph(input_num, hidden_num, output_num):
G = nx.DiGraph() # Create a graph G
# Connect the edge between the input layer and the hidden layer
for i in range(input_num):
for j in range(hidden_num):
G.add_edge(i, input_num + j)
# Connect the edge between the hidden layer and the output layer
for i in range(hidden_num):
for j in range(output_num):
G.add_edge(input_num + i, input_num + hidden_num + j)
pos = dict() # Calculate the coordinate pos of each node
# The coordinates of the node, (x, y) are set to:
# (0,i-input_num/2)
# (1,i-hidden_num)/2)
# (2,i-output_num/2)
# According to the number of nodes in each layer, distribute the nodes from the middle to both sides
for i in range(0, input_num):
pos[i] = (0, i - input_num / 2)
for i in range(0, hidden_num):
hidden = i + input_num
pos[hidden] = (1, i - hidden_num / 2)
for i in range(0, output_num):
output = i + input_num + hidden_num
pos[output] = (2, i - output_num / 2)
# Call nx.draw to draw the neural network
nx.draw(G, #The picture to be drawn
pos, #Coordinates of nodes in the graph
with_labels=False, # Draw the name of the node
node_color='white', #The color of the node
edgecolors='black', # edge colors
linewidths=3, #The thickness of the node
width=2, # The thickness of the edge
node_size=1000 #The size of the node
)
if __name__ == '__main__':
# Try multiple sets of parameters to draw neural networks with different structures
draw_network_digraph(3, 5, 2)
plt.show()
draw_network_digraph(5, 2, 6)
plt.show()
draw_network_digraph(1, 10, 1)
plt.show()