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【python】matplotlib動態(tài)顯示詳解

更新時間：2019年04月11日 11:36:31 作者：CallMeJacky

這篇文章主要介紹了matplotlib動態(tài)顯示，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

1.matplotlib動態(tài)繪圖

python在繪圖的時候，需要開啟 interactive mode。核心代碼如下：

plt.ion(); #開啟interactive mode 成功的關(guān)鍵函數(shù)
  fig = plt.figure(1);
  
  for i in range(100):
    filepath="E:/Model/weights-improvement-" + str(i + 1) + ".hdf5";
    model.load_weights(filepath);
    #測試數(shù)據(jù)
    x_new = np.linspace(low, up, 1000);
    y_new = getfit(model,x_new);
    # 顯示數(shù)據(jù)
    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)       # 暫停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()         # 關(guān)閉交互模式

2.實(shí)例

已知下面采樣自Sin函數(shù)的數(shù)據(jù)：

	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

通過一個簡單的三層神經(jīng)網(wǎng)絡(luò)訓(xùn)練一個Sin函數(shù)的擬合器，并可視化模型訓(xùn)練過程的擬合曲線。

2.1 網(wǎng)絡(luò)訓(xùn)練實(shí)現(xiàn)

主要做的事情是定義一個三層的神經(jīng)網(wǎng)絡(luò)，輸入層節(jié)點(diǎn)數(shù)為1，隱藏層節(jié)點(diǎn)數(shù)為10，輸出層節(jié)點(diǎn)數(shù)為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
 
 
#采樣函數(shù)
def sample(low, up, num):
  data = [];
  for i in range(num):
    #采樣
    tmp = random.uniform(low, up);
    data.append(tmp);
  data.sort();
  return data;
 
#sin函數(shù)
def func(x):
  y = [];
  for i in range(len(x)):
    tmp = math.sin(x[i] - math.pi/3);
    y.append(tmp);
  return y;
 
#獲取模型擬合結(jié)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;
 
#刪除同一目錄下的所有文件
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);
  
  # 數(shù)據(jù)采樣
#   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];
  
  # 建立順序神經(jīng)網(wǎng)絡(luò)層次模型
  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);
  
  #測試數(shù)據(jù)
  x_new = np.linspace(low, up, 1000);
  y_new = getfit(model,x_new);
  
  # 數(shù)據(jù)可視化
  plt.plot(x,y); 
  plt.scatter(x_sample, y_sample);
  plt.plot(x_new,y_new);
  
  plt.show();

2.2 模型保存

在神經(jīng)網(wǎng)絡(luò)訓(xùn)練的過程中，有一個非常重要的操作，就是將訓(xùn)練過程中模型的參數(shù)保存到本地，這是后面擬合過程可視化的基礎(chǔ)。訓(xùn)練過程中保存的模型文件，如下圖所示。

模型保存的關(guān)鍵在于fit函數(shù)中callback函數(shù)的設(shè)置，注意到，下面的代碼，每次迭代，算法都會執(zhí)行callbacks函數(shù)指定的函數(shù)列表中的方法。這里，我們的回調(diào)函數(shù)設(shè)置為ModelCheckpoint，其參數(shù)如下表所示：

參數(shù)	含義
filename	字符串，保存模型的路徑
verbose	信息展示模式，0或1 (Epoch 00001: saving model to ...)
mode	‘a(chǎn)uto'，‘min'，‘max'
monitor	需要監(jiān)視的值
save_best_only	當(dāng)設(shè)置為True時，監(jiān)測值有改進(jìn)時才會保存當(dāng)前的模型。在save_best_only=True時決定性能最佳模型的評判準(zhǔn)則，例如，當(dāng)監(jiān)測值為val_acc時，模式應(yīng)為max，當(dāng)監(jiān)測值為val_loss時，模式應(yīng)為min。在auto模式下，評價準(zhǔn)則由被監(jiān)測值的名字自動推斷
save_weights_only	若設(shè)置為True，則只保存模型權(quán)重，否則將保存整個模型（包括模型結(jié)構(gòu)，配置信息等）
period	CheckPoint之間的間隔的epoch數(shù)

 # callback
  filepath="E:/Model/weights-improvement-{epoch:00d}.hdf5";
  checkpoint= ModelCheckpoint(filepath, verbose=1, save_best_only=False, mode='max');
  callbacks_list= [checkpoint];
  
  # 建立順序神經(jīng)網(wǎng)絡(luò)層次模型
  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 擬合過程可視化實(shí)現(xiàn)

利用上述保存的模型，我們就可以通過matplotlib實(shí)時地顯示擬合過程。

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
 
#定義kdd99數(shù)據(jù)預(yù)處理函數(shù)
def sample(low, up, num):
  data = [];
  for i in range(num):
    #采樣
    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') # 關(guān)掉坐標(biāo)軸為 off
  return plt.imshow(img);
 
def update(i): 
  fpath = "E:/imgs/" + str(i) + ".jpg";
  img = Image.open(fpath);
  plt.axis('off') # 關(guān)掉坐標(biāo)軸為 off
  return plt.imshow(img);
 
if __name__ == '__main__':  
  low = 0;
  up = 2 * math.pi;
  x = np.linspace(low, up, 1000);
  y = func(x);
  
  # 數(shù)據(jù)采樣
#   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);
  
  # 建立順序神經(jīng)網(wǎng)絡(luò)層次模型
  model = Sequential(); 
  model.add(Dense(10, input_dim=1, init='uniform', activation='relu'));
  model.add(Dense(1, init='uniform', activation='tanh'));
    
  plt.ion(); #開啟interactive mode 成功的關(guān)鍵函數(shù)
  fig = plt.figure(1);
  
  for i in range(100):
    filepath="E:/Model/weights-improvement-" + str(i + 1) + ".hdf5";
    model.load_weights(filepath);
    #測試數(shù)據(jù)
    x_new = np.linspace(low, up, 1000);
    y_new = getfit(model,x_new);
    # 顯示數(shù)據(jù)
    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)       # 暫停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()