def drowPicture(History):
pyplot.plot(History.history['loss'])
pyplot.plot(History.history['val_loss'])
pyplot.plot(History.history['acc'])
pyplot.plot(History.history['val_acc'])
pyplot.title('model train vs validation loss')
pyplot.ylabel('loss')
pyplot.xlabel('epoch')
pyplot.legend(['train', 'validation'], loc='upper right')
pyplot.show()
return (train_metrics, test_metrics)
# 不带约束的
# model = Model(FEATURE_NAMES, hidden_units=10)
# model.build_train_op(0.01, unconstrained=True)
# results = training_helper(model, train_df, test_df, 100, num_iterations_per_loop=44, num_loops=500)
# print("Train Error", results[0]["last_error_rate"])
# print("Train Violation", results[0]["last_max_constraint_violation"])
model = Model(FEATURE_NAMES, hidden_units=10)
model.build_train_op(0.01, unconstrained=False)
results = training_helper(model, train_df, test_df, 100, num_iterations_per_loop=44, num_loops=500)
print("Train Error", results[0]["last_error_rate"])
print("Train Violation", results[0]["last_max_constraint_violation"])
"""画图"""
plt.title("Error Rate vs Epoch")
plt.plot(range(100, len(results[0]["all_errors"])), results[0]["all_errors"][100:], color="green")
plt.xlabel("Epoch")
plt.show()
plt.title("Violation vs Epoch")
plt.plot(range(100, len(results[0]["all_violations"])), results[0]["all_violations"][100:], color="blue")
plt.xlabel("Epoch")
plt.show()
from pandas import read_csv
from Example_matplotlib import pyplot
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# specify columns to plot
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
# plot each column
pyplot.figure()
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc='right')
i += 1
pyplot.show()
return X, y
# define model
model = Sequential()
model.add(LSTM(10, input_shape=(1, 1)))
# activation是激活函数的选择，linear是线性函数
model.add(Dense(1, activation='linear'))
# compile model， loss是损失函数的取值方式，mse是mean_squared_error，代表均方误差，
# optimizer是优化控制器的选择，AdamOptimizer通过使用动量（参数的移动平均数）来改善传统梯度下降
model.compile(loss='mse', optimizer='adam')
# fit model
X, y = get_train()
valX, valY = get_val()
# validation_data是要验证的测试集，shuffle代表是否混淆打乱数据
# 不合格的原因是epochs=100,训练周期不足
history = model.fit(X,
y,
epochs=100,
validation_data=(valX, valY),
shuffle=False)
# plot train and validation loss
# loss是训练集的损失函数值，val_loss是验证数据集的损失值
pyplot.plot(history.history['loss'])
pyplot.plot(history.history['val_loss'])
pyplot.title('model train vs validation loss')
pyplot.ylabel('loss')
pyplot.xlabel('epoch')
pyplot.legend(['train', 'validation'], loc='upper right')
pyplot.show()
# -*-coding:utf-8-*-
# @Time : 2019/6/18 0018 10:58
# @Author :zhuxinquan
# @File : matplotlib_01.py
import Example_matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
plt.figure(1)
# map=Basemap()
map = Basemap(llcrnrlon=70, llcrnrlat=3, urcrnrlon=139,
urcrnrlat=54) # 画中国 经135度2分30秒-东经73度40分，北纬3度52分-北纬53度33分
map.drawcoastlines()
map.drawcountries() # 加海岸线
# map.drawrivers(color='blue', linewidth=0.3) # 加河流
CHN = r"C:\Users\Administrator\Downloads"
map.readshapefile(CHN + r'\gadm36_CHN_shp\gadm36_CHN_1',
'states',
drawbounds=True) # 加省界
# map.readshapefile(CHN+r'\gadm36_TWN_shp\gadm36_TWN_1', 'taiwan', drawbounds=True) # 加台湾
plt.title(r'$World\ Map$', fontsize=24)
plt.show()
def pic_map():
import numpy as np
import pandas as pd
import Example_matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from Example_matplotlib.patches import Polygon
from Example_matplotlib.colors import rgb2hex
from Example_matplotlib.collections import PatchCollection
from Example_matplotlib import pylab
plt.rcParams['font.sans-serif'] = ['KaiTi'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(20, 10)) # 长和宽
# m= Basemap(llcrnrlon=73, llcrnrlat=18, urcrnrlon=135, urcrnrlat=55) #指定中国的经纬度
m = Basemap(llcrnrlon=77,
llcrnrlat=14,
urcrnrlon=140,
urcrnrlat=51,
projection='lcc',
lat_1=33,
lat_2=45,
lon_0=100) # ‘lcc'将投影方式设置为兰伯特投影
# projection='ortho' # 投影方式设置为正投影——类似地球仪
CHN = r'C:\Users\Administrator\Desktop\MyProject\Test_module\Test_matplotlib\data_base'
m.readshapefile(CHN + r'\gadm36_CHN_shp\gadm36_CHN_1',
'states',
drawbounds=True) # 加省界
# 读取数据
df = pd.read_csv('./data_base/data/chnpop.csv')
df['省名'] = df.省名.str[:2]
df.set_index('省名', inplace=True)
# 把每个省的数据映射到colormap上
statenames = []
colors = {}
patches = []
cmap = plt.cm.YlOrRd # 国旗色红黄色调
vmax = 10**8
vmin = 3 * 10**6
# 处理地图包里的省名
for shapedict in m.states_info:
statename = shapedict['NL_NAME_1']
p = statename.split('|')
if len(p) > 1:
s = p[1]
else:
s = p[0]
s = s[:2]
if s == '黑龍':
s = '黑龙'
statenames.append(s)
pop = df['人口数'][s]
colors[s] = cmap(np.sqrt(
(pop - vmin) / (vmax - vmin)))[:3] # 根据归一化后的人口数映射颜色
# exit()
ax = plt.gca()
for nshape, seg in enumerate(m.states):
color = rgb2hex(colors[statenames[nshape]])
poly = Polygon(seg, facecolor=color, edgecolor=color)
patches.append(poly)
ax.add_patch(poly)
# 图片绘制加上台湾（台湾不可或缺）
m.readshapefile(CHN + '\gadm36_TWN_shp\gadm36_TWN_1',
'taiwan',
drawbounds=True)
for nshape, seg in enumerate(m.taiwan):
poly = Polygon(seg, facecolor='w')
patches.append(poly)
ax.add_patch(poly)
# 添加colorbar 渐变色legend
colors1 = [i[1] for i in colors.values()]
colorVotes = plt.cm.YlOrRd
p = PatchCollection(patches, cmap=colorVotes)
p.set_array(np.array(colors1))
pylab.colorbar(p)
#4266831.094478747, 1662846.3046657
lon = 4097273.638675578
lat = 4008859.232616643
x, y = m(lon, lat)
plt.text(x,
y,
'国都',
fontsize=120,
fontweight='bold',
ha='left',
va='bottom',
color='k')
m.scatter(x, y, s=200, marker='*', facecolors='r', edgecolors='B') # 绘制首都
plt.title(u'祝鑫泉画的World Map ', fontsize=24)
plt.savefig("./data_base/result/Chinese_Map1.png", dpi=100) # 指定分辨率
plt.show()
ax = plt.gca()
for nshape, seg in enumerate(m.states):
poly = Polygon(seg, facecolor='r')
ax.add_patch(poly)
# 画上台湾省
m.readshapefile(CHN + '\gadm36_TWN_shp\gadm36_TWN_1',
'taiwan',
drawbounds=True)
for nshape, seg in enumerate(m.taiwan):
poly = Polygon(seg, facecolor='r')
ax.add_patch(poly)
for shapedict in m.states_info:
statename = shapedict['NL_NAME_1']
p = statename.split('|')
if len(p) > 1:
s = p[1]
else:
s = p[0]
print(s)
# for shapedict in m.taiwan_info:
#
# s = shapedict['NAME_CHINE']
# print(s)
plt.title(r'$祝鑫泉画的World\ Map$', fontsize=24)
plt.show()