用matplotlib绘制卷积神经网络(CNN)图

"""
Copyright (c) 2016, Gavin Weiguang Ding
All rights reserved.
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    modification, are permitted provided that the following conditions are met:
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    list of conditions and the following disclaimer.
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    this list of conditions and the following disclaimer in the documentation
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3. Neither the name of the copyright holder nor the names of its contributors
    may be used to endorse or promote products derived from this software
    without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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"""


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.collections import PatchCollection
%matplotlib inline

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


def add_layer(patches, colors, size=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])
    for ind in range(num):
        patches.append(Rectangle(loc_start + ind * loc_diff, size, size))
        if ind % 2:
            colors.append(Medium)
        else:
            colors.append(Light)


def add_mapping(patches, colors, start_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],
                    -start_ratio[1] * size_list[ind_bgn]])

    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([(start_ratio[0] + .5 * patch_size / size_list[ind_bgn]) *
                    size_list[ind_bgn + 1],
                    -(start_ratio[1] - .5 * patch_size / size_list[ind_bgn]) *
                    size_list[ind_bgn + 1]])

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


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

    patches = []
    colors = []

    fig, ax = plt.subplots()


    ############################
    # conv layers
    size_list = [32, 18, 10, 6, 4]
    num_list = [3, 32, 32, 48, 48]
    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)):
        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], size_list[ind]))


    ############################
    # in between layers
    start_ratio_list = [[0.4, 0.5], [0.4, 0.8], [0.4, 0.5], [0.4, 0.8]]
    patch_size_list = [5, 2, 5, 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],
                    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], patch_size_list[ind]), xy_off=[26, -65])


    ############################
    # fully connected layers
    size_list = [fc_unit_size, fc_unit_size, fc_unit_size]
    num_list = [768, 500, 2]
    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)):
        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\n', 'Fully\nconnected', 'Fully\nconnected']

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

    ############################
    colors += [0, 1]
    collection = PatchCollection(patches, cmap=plt.cm.gray)
    collection.set_array(np.array(colors))
    ax.add_collection(collection)
    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)

原文链接：https://github.com/gwding/draw_convnet