以下内容是tensorflow目标检测官方demo,用于测试自己训练好的模型
Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the installation instructions befor
因为用于训练的.record格式文件时用的是csv格式文件，可以通过xml_to_csv.py程序处理，其中xml_df = xml_to_csv(image_path) 其中image_path为xml目录路径。
安装pandas后，运行xml_to_csv.bat文件，会生成label.csv文件。
generate_tfrecord.py用于生成自己的tfrecord格式文件，
# 工具库
import pandas as pd
# 泰坦尼克数据集
df_train = pd.read_csv('titanic.csv')
# 了解一下数据集
df_train.head()
df_train.shape # 查看维度df_train.info() # 数据集信息import pandas as pd
from sklearn import preprocessing
from sklearn import tree
from sklearn.datasets import load_boston # 波士顿房价
boston_house = load_boston()boston_feature_name = boston_house.feature_names
boston_features = boston_house.data
boston_target = boston_house.targetboston_featuimport pandas as pd
from sklearn import preprocessing
from sklearn import tree
from sklearn.datasets import load_iris # 花瓣种类
# 加载数据
iris = load_iris()
dir(iris) # iris包含的内容有哪些 如果不懂某些方法 可用 help(iris)
['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']
iris_feature_names = ir# 用于数据处理和分析的工具包
import pandas as pd
# 数据处理/特征工程的包
from sklearn import preprocessing
# 决策树建模包
from sklearn import tree
adult_data = pd.read_csv('./DecisionTree.csv')
# 读取前5行
adult_data.head(5)
adult_data.info() # 读入数据的信息<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 e#conding=utf-8
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from sklearn.preprocessing import PolynomialFeatures
# pandas 基础设置
pd.set_option('display.notebook_repr_html',False)
pd.set_option('disp#conding=utf-8
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from mpl_toolkits.mplot3d import axes3d
# pandas 基础设置
pd.set_option('display.notebook_repr_html',False)
pd.set_option('display.max_columns',Nonimport numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flatten # Flatten扁平化
from keras.optimizers import Adam# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_dat我们有时候训练集的数据不够时，可以尝试把一张图片做做变换，生成多张图片。
from keras.preprocessing.image import ImageDataGenerator, array_to_img ,img_to_array, load_img
import numpy as np