画出神经网络结构图

latex 自带 Tikz 画图包 Example: Kalman Filter System Model.

基于 Matplotlib 的Viznet

在线生成卷积网络结构图：ConvNetDraw

示例:

···python
import numpy as np
from viznet import connecta2a, node_sequence, NodeBrush, EdgeBrush, DynamicShow

def draw_feed_forward(ax, num_node_list):
num_hidden_layer = len(num_node_list) - 2 # 隐藏层数
token_list = ['\sigma^z'] + ['y^{(%s)}' % (i + 1) for i in range(num_hidden_layer)] + ['\psi']
kind_list = ['nn.input'] + ['nn.hidden'] * num_hidden_layer + ['nn.output']
radius_list = [0.3] + [0.2] * num_hidden_layer + [0.3] # 半径大小
y_list = - 1.5 * np.arange(len(num_node_list)) # 每一层节点所在的位置的纵轴坐标，全取负值说明网络是自顶而下的

seq_list = [] for n, kind, radius, y in zip(num_node_list, kind_list, radius_list, y_list): b = NodeBrush(kind, ax) seq_list.append(node_sequence(b, n, center=(0, y))) eb = EdgeBrush('-->', ax) for st, et in zip(seq_list[:-1], seq_list[1:]): connecta2a(st, et, eb) #for i, layer_nodes in enumerate(seq_list): #[node.text('$z_%i^{(%i)}$'%(j, i), 'center', fontsize=16) for j, node in enumerate(layer_nodes)] return seq_list

def real_bp():
with DynamicShow((6, 6), '_feed_forward.png') as d: # 隐藏坐标轴
draw_feed_forward(d.ax, num_node_list=[5, 4, 1])

if name == 'main':
real_bp()
···