【python】matplotlib动态显示详解

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

import math;import random;from matplotlib import pyplot as pltfrom keras.models import Sequentialfrom keras.layers.core import Densefrom keras.optimizers import Adamimport numpy as npfrom keras.callbacks import ModelCheckpointimport 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();

 # 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);

import math;import random;from matplotlib import pyplot as pltfrom keras.models import Sequentialfrom keras.layers.core import Denseimport numpy as npimport matplotlib.animation as animationfrom 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()