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：
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 *** 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：
信息展示模式，0或1
(Epoch 00001: saving model to ...)
# 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()
以上所述是小编给大家介绍的matplotlib动态显示详解整合，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对考高分网网站的支持！
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