【python】matplotlib动态显示详解

脚本专栏 2024/11/26 佚名

3 2 1

1.matplotlib动态绘图

python在绘图的时候，需要开启 interactive mode。核心代码如下：

plt.ion(); #开启interactive mode 成功的关键函数
  fig = plt.figure(1);
  
  for i in range(100):
    filepath="E:/Model/weights-improvement-" + str(i + 1) + ".hdf5";
    model.load_weights(filepath);
    #测试数据
    x_new = np.linspace(low, up, 1000);
    y_new = getfit(model,x_new);
    # 显示数据
    plt.clf();
    plt.plot(x,y); 
    plt.scatter(x_sample, y_sample);
    plt.plot(x_new,y_new);
    
    ffpath = "E:/imgs/" + str(i) + ".jpg";
    plt.savefig(ffpath);
 
    plt.pause(0.01)       # 暂停0.01秒
    
  ani = animation.FuncAnimation(plt.figure(2), update,range(100),init_func=init, interval=500);
  ani.save("E:/test.gif",writer='pillow');
  
  plt.ioff()         # 关闭交互模式

2.实例

已知下面采样自Sin函数的数据：

　 x y 1 0.093 -0.81 2 0.58 -0.45 3 1.04 -0.007 4 1.55 0.48 5 2.15 0.89 6 2.62 0.997 7 2.71 0.995 8 2.73 0.993 9 3.03 0.916 10 3.14 0.86 11 3.58 0.57 12 3.66 0.504 13 3.81 0.369 14 3.83 0.35 15 4.39 -0.199 16 4.44 -0.248 17 4.6 -0.399 18 5.39 -0.932 19 5.54 -0.975 20 5.76 -0.999

通过一个简单的三层神经网络训练一个Sin函数的拟合器，并可视化模型训练过程的拟合曲线。

2.1 网络训练实现

主要做的事情是定义一个三层的神经网络，输入层节点数为1，隐藏层节点数为10，输出层节点数为1。

import math;
import random;
from matplotlib import pyplot as plt
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import Adam
import numpy as np
from keras.callbacks import ModelCheckpoint
import os
 
 
#采样函数
def sample(low, up, num):
  data = [];
  for i in range(num):
    #采样
    tmp = random.uniform(low, up);
    data.append(tmp);
  data.sort();
  return data;
 
#sin函数
def func(x):
  y = [];
  for i in range(len(x)):
    tmp = math.sin(x[i] - math.pi/3);
    y.append(tmp);
  return y;
 
#获取模型拟合结果
def getfit(model,x):  
  y = [];
  for i in range(len(x)):
    tmp = model.predict([x[i]], 10);
    y.append(tmp[0][0]);
  return y;
 
#删除同一目录下的所有文件
def del_file(path):
  ls = os.listdir(path)
  for i in ls:
    c_path = os.path.join(path, i)
    if os.path.isdir(c_path):
      del_file(c_path)
    else:
      os.remove(c_path)
 
if __name__ == '__main__':  
  path = "E:/Model/";
  del_file(path);
  
  low = 0;
  up = 2 * math.pi;
  x = np.linspace(low, up, 1000);
  y = func(x);
  
  # 数据采样
#   x_sample = sample(low,up,20);
  x_sample = [0.09326442022999694, 0.5812590520508311, 1.040490143783586, 1.5504427746047338, 2.1589557183817036, 2.6235357787018407, 2.712578091093361, 2.7379109336528167, 3.0339662651841186, 3.147676812083248, 3.58596337171837, 3.6621496731124314, 3.81130899864203, 3.833092859928872, 4.396611340802901, 4.4481080339256875, 4.609657879057151, 5.399731063412583, 5.54299720786794, 5.764084730699906];
  y_sample = func(x_sample);
  
  # callback
  filepath="E:/Model/weights-improvement-{epoch:00d}.hdf5";
  checkpoint= ModelCheckpoint(filepath, verbose=1, save_best_only=False, mode='max');
  callbacks_list= [checkpoint];
  
  # 建立顺序神经网络层次模型
  model = Sequential(); 
  model.add(Dense(10, input_dim=1, init='uniform', activation='relu'));
  model.add(Dense(1, init='uniform', activation='tanh'));
  adam = Adam(lr = 0.05);
  model.compile(loss='mean_squared_error', optimizer=adam, metrics=['accuracy']);
  model.fit(x_sample, y_sample, nb_epoch=1000, batch_size=20,callbacks=callbacks_list);
  
  #测试数据
  x_new = np.linspace(low, up, 1000);
  y_new = getfit(model,x_new);
  
  # 数据可视化
  plt.plot(x,y); 
  plt.scatter(x_sample, y_sample);
  plt.plot(x_new,y_new);
  
  plt.show();

2.2 模型保存

在神经网络训练的过程中，有一个非常重要的操作，就是将训练过程中模型的参数保存到本地，这是后面拟合过程可视化的基础。训练过程中保存的模型文件，如下图所示。

模型保存的关键在于fit函数中callback函数的设置，注意到，下面的代码，每次迭代，算法都会执行callbacks函数指定的函数列表中的方法。这里，我们的回调函数设置为ModelCheckpoint，其参数如下表所示：

参数含义 filename 字符串，保存模型的路径 verbose

信息展示模式，0或1

(Epoch 00001: saving model to ...)

mode ‘auto'，‘min'，‘max' monitor 需要监视的值 save_best_only 当设置为True时，监测值有改进时才会保存当前的模型。在save_best_only=True时决定性能最佳模型的评判准则，例如，当监测值为val_acc时，模式应为max，当监测值为val_loss时，模式应为min。在auto模式下，评价准则由被监测值的名字自动推断 save_weights_only 若设置为True，则只保存模型权重，否则将保存整个模型（包括模型结构，配置信息等） period CheckPoint之间的间隔的epoch数

 # callback
  filepath="E:/Model/weights-improvement-{epoch:00d}.hdf5";
  checkpoint= ModelCheckpoint(filepath, verbose=1, save_best_only=False, mode='max');
  callbacks_list= [checkpoint];
  
  # 建立顺序神经网络层次模型
  model = Sequential(); 
  model.add(Dense(10, input_dim=1, init='uniform', activation='relu'));
  model.add(Dense(1, init='uniform', activation='tanh'));
  adam = Adam(lr = 0.05);
  model.compile(loss='mean_squared_error', optimizer=adam, metrics=['accuracy']);
  model.fit(x_sample, y_sample, nb_epoch=1000, batch_size=20,callbacks=callbacks_list);

2.3 拟合过程可视化实现

利用上述保存的模型，我们就可以通过matplotlib实时地显示拟合过程。

import math;
import random;
from matplotlib import pyplot as plt
from keras.models import Sequential
from keras.layers.core import Dense
import numpy as np
import matplotlib.animation as animation
from PIL import Image
 
#定义kdd99数据预处理函数
def sample(low, up, num):
  data = [];
  for i in range(num):
    #采样
    tmp = random.uniform(low, up);
    data.append(tmp);
  data.sort();
  return data;
 
def func(x):
  y = [];
  for i in range(len(x)):
    tmp = math.sin(x[i] - math.pi/3);
    y.append(tmp);
  return y;
 
def getfit(model,x):  
  y = [];
  for i in range(len(x)):
    tmp = model.predict([x[i]], 10);
    y.append(tmp[0][0]);
  return y;
 
def init():
  fpath = "E:/imgs/0.jpg";
  img = Image.open(fpath);
  plt.axis('off') # 关掉坐标轴为 off
  return plt.imshow(img);
 
def update(i): 
  fpath = "E:/imgs/" + str(i) + ".jpg";
  img = Image.open(fpath);
  plt.axis('off') # 关掉坐标轴为 off
  return plt.imshow(img);
 
if __name__ == '__main__':  
  low = 0;
  up = 2 * math.pi;
  x = np.linspace(low, up, 1000);
  y = func(x);
  
  # 数据采样
#   x_sample = sample(low,up,20);
  x_sample = [0.09326442022999694, 0.5812590520508311, 1.040490143783586, 1.5504427746047338, 2.1589557183817036, 2.6235357787018407, 2.712578091093361, 2.7379109336528167, 3.0339662651841186, 3.147676812083248, 3.58596337171837, 3.6621496731124314, 3.81130899864203, 3.833092859928872, 4.396611340802901, 4.4481080339256875, 4.609657879057151, 5.399731063412583, 5.54299720786794, 5.764084730699906];
  y_sample = func(x_sample);
  
  # 建立顺序神经网络层次模型
  model = Sequential(); 
  model.add(Dense(10, input_dim=1, init='uniform', activation='relu'));
  model.add(Dense(1, init='uniform', activation='tanh'));
    
  plt.ion(); #开启interactive mode 成功的关键函数
  fig = plt.figure(1);
  
  for i in range(100):
    filepath="E:/Model/weights-improvement-" + str(i + 1) + ".hdf5";
    model.load_weights(filepath);
    #测试数据
    x_new = np.linspace(low, up, 1000);
    y_new = getfit(model,x_new);
    # 显示数据
    plt.clf();
    plt.plot(x,y); 
    plt.scatter(x_sample, y_sample);
    plt.plot(x_new,y_new);
    
    ffpath = "E:/imgs/" + str(i) + ".jpg";
    plt.savefig(ffpath);
 
    plt.pause(0.01)       # 暂停0.01秒
    
  ani = animation.FuncAnimation(plt.figure(2), update,range(100),init_func=init, interval=500);
  ani.save("E:/test.gif",writer='pillow');
  
  plt.ioff()