python 绘画神经网络结构图_生成神经网络结构图的代码

利用python画出神经网络结构图
可以设定网络层数以及每层的大小等，具体使用方法仔细阅读代码即可。
效果如下：

import os
import numpy as np
import matplotlib.pyplot as plt
plt.rcdefaults()
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
from matplotlib.patches import Circle

NumDots = 4
NumConvMax = 8
NumFcMax = 20
White = 1.
Light = 0.7
Medium = 0.5
Dark = 0.3
Darker = 0.15
Black = 0.


def add_layer(patches, colors, size=(24, 24), num=5,
              top_left=[0, 0],
              loc_diff=[3, -3],
              ):
    # add a rectangle
    top_left = np.array(top_left)
    loc_diff = np.array(loc_diff)
    loc_start = top_left - np.array([0, size[0]])
    for ind in range(num):
        patches.append(Rectangle(loc_start + ind * loc_diff, size[1], size[0]))
        if ind % 2:
            colors.append(Medium)
        else:
            colors.append(Light)


def add_layer_with_omission(patches, colors, size=(24, 24),
                            num=5, num_max=8,
                            num_dots=4,
                            top_left=[0, 0],
                            loc_diff=[3, -3],
                            ):
    # add a rectangle
    top_left = np.array(top_left)
    loc_diff = np.array(loc_diff)
    loc_start = top_left - np.array([0, size[0]])
    this_num = min(num, num_max)
    start_omit = (this_num - num_dots) // 2
    end_omit = this_num - start_omit
    start_omit -= 1
    for ind in range(this_num):
        if (num > num_max) and (start_omit < ind < end_omit):
            omit = True
        else:
            omit = False

        if omit:
            patches.append(
                Circle(loc_start + ind * loc_diff + np.array(size) / 2, 0.5))
        else:
            patches.append(Rectangle(loc_start + ind * loc_diff,
                                     size[1], size[0]))

        if omit:
            colors.append(Black)
        elif ind % 2:
            colors.append(Medium)
        else:
            colors.append(Light)


def add_mapping(patches, colors, start_ratio, end_ratio, patch_size, ind_bgn,
                top_left_list, loc_diff_list, num_show_list, size_list):

    start_loc = top_left_list[ind_bgn] \
        + (num_show_list[ind_bgn] - 1) * np.array(loc_diff_list[ind_bgn]) \
        + np.array([start_ratio[0] * (size_list[ind_bgn][1] - patch_size[1]),
                    - start_ratio[1] * (size_list[ind_bgn][0] - patch_size[0])]
                   )




    end_loc = top_left_list[ind_bgn + 1] \
        + (num_show_list[ind_bgn + 1] - 1) * np.array(
            loc_diff_list[ind_bgn + 1]) \
        + np.array([end_ratio[0] * size_list[ind_bgn + 1][1],
                    - end_ratio[1] * size_list[ind_bgn + 1][0]])


    patches.append(Rectangle(start_loc, patch_size[1], -patch_size[0]))
    colors.append(Dark)
    patches.append(Line2D([start_loc[0], end_loc[0]],
                          [start_loc[1], end_loc[1]]))
    colors.append(Darker)
    patches.append(Line2D([start_loc[0] + patch_size[1], end_loc[0]],
                          [start_loc[1], end_loc[1]]))
    colors.append(Darker)
    patches.append(Line2D([start_loc[0], end_loc[0]],
                          [start_loc[1] - patch_size[0], end_loc[1]]))
    colors.append(Darker)
    patches.append(Line2D([start_loc[0] + patch_size[1], end_loc[0]],
                          [start_loc[1] - patch_size[0], end_loc[1]]))
    colors.append(Darker)



def label(xy, text, xy_off=[0, 4]):
    plt.text(xy[0] + xy_off[0], xy[1] + xy_off[1], text,
             family='sans-serif', size=8)


if __name__ == '__main__':

    fc_unit_size = 2
    layer_width = 40
    flag_omit = True

    patches = []
    colors = []

    fig, ax = plt.subplots()


    ############################
    # conv layers
    size_list = [(28, 28), (28, 28), (14, 14), (14, 14), (7, 7)]
    num_list = [3, 64, 64, 64, 64]
    x_diff_list = [0, layer_width, layer_width, layer_width, layer_width]
    text_list = ['Inputs'] + ['Feature\nmaps'] * (len(size_list) - 1)
    loc_diff_list = [[3, -3]] * len(size_list)

    num_show_list = list(map(min, num_list, [NumConvMax] * len(num_list)))
    top_left_list = np.c_[np.cumsum(x_diff_list), np.zeros(len(x_diff_list))]

    for ind in range(len(size_list)-1,-1,-1):
        if flag_omit:
            add_layer_with_omission(patches, colors, size=size_list[ind],
                                    num=num_list[ind],
                                    num_max=NumConvMax,
                                    num_dots=NumDots,
                                    top_left=top_left_list[ind],
                                    loc_diff=loc_diff_list[ind])
        else:
            add_layer(patches, colors, size=size_list[ind],
                      num=num_show_list[ind],
                      top_left=top_left_list[ind], loc_diff=loc_diff_list[ind])
        label(top_left_list[ind], text_list[ind] + '\n{}@{}x{}'.format(
            num_list[ind], size_list[ind][0], size_list[ind][1]))

    ############################
    # in between layers
    start_ratio_list = [[0.4, 0.5], [0.4, 0.8], [0.4, 0.5], [0.4, 0.8]]
    end_ratio_list = [[0.4, 0.5], [0.4, 0.8], [0.4, 0.5], [0.4, 0.8]]
    patch_size_list = [(5, 5), (2, 2), (5, 5), (2, 2)]
    ind_bgn_list = range(len(patch_size_list))
    text_list = ['Convolution', 'Max-pooling', 'Convolution', 'Max-pooling']

    for ind in range(len(patch_size_list)):
        add_mapping(
            patches, colors, start_ratio_list[ind], end_ratio_list[ind],
            patch_size_list[ind], ind,
            top_left_list, loc_diff_list, num_show_list, size_list)
        label(top_left_list[ind], text_list[ind] + '\n{}x{} kernel'.format(
            patch_size_list[ind][0], patch_size_list[ind][1]), xy_off=[26, -65]
        )


    ############################
    # fully connected layers
    size_list = [(fc_unit_size, fc_unit_size)] * 3
    num_list = [384, 192, 10]
    num_show_list = list(map(min, num_list, [NumFcMax] * len(num_list)))
    x_diff_list = [sum(x_diff_list) + layer_width, layer_width, layer_width]
    top_left_list = np.c_[np.cumsum(x_diff_list), np.zeros(len(x_diff_list))]
    loc_diff_list = [[fc_unit_size, -fc_unit_size]] * len(top_left_list)
    text_list = ['Hidden\nunits'] * (len(size_list) - 1) + ['Outputs']

    for ind in range(len(size_list)):
        if flag_omit:
            add_layer_with_omission(patches, colors, size=size_list[ind],
                                    num=num_list[ind],
                                    num_max=NumFcMax,
                                    num_dots=NumDots,
                                    top_left=top_left_list[ind],
                                    loc_diff=loc_diff_list[ind])
        else:
            add_layer(patches, colors, size=size_list[ind],
                      num=num_show_list[ind],
                      top_left=top_left_list[ind],
                      loc_diff=loc_diff_list[ind])
        label(top_left_list[ind], text_list[ind] + '\n{}'.format(
            num_list[ind]))

    text_list = ['Flatten\nand Fully\nconnected', 'Fully\nconnected', 'Fully\nconnected']

    for ind in range(len(size_list)):
        label(top_left_list[ind], text_list[ind], xy_off=[-10, -65])

    ############################
    for patch, color in zip(patches, colors):
        patch.set_color(color * np.ones(3))
        if isinstance(patch, Line2D):
            ax.add_line(patch)
        else:
            patch.set_edgecolor(Black * np.ones(3))
            ax.add_patch(patch)

    plt.tight_layout()
    plt.axis('equal')
    plt.axis('off')
    plt.show()
    fig.set_size_inches(8, 2.5)

    fig_dir = './'
    fig_ext = '.png'
    fig.savefig(os.path.join(fig_dir, 'convnet_fig' + fig_ext),
                bbox_inches='tight', pad_inches=0)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219