TensorFlow实现模型评估


Posted in Python onSeptember 07, 2018

我们需要评估模型预测值来评估训练的好坏。

模型评估是非常重要的,随后的每个模型都有模型评估方式。使用TensorFlow时,需要把模型评估加入到计算图中,然后在模型训练完后调用模型评估。

在训练模型过程中,模型评估能洞察模型算法,给出提示信息来调试、提高或者改变整个模型。但是在模型训练中并不是总需要模型评估,我们将展示如何在回归算法和分类算法中使用它。

训练模型之后,需要定量评估模型的性能如何。在理想情况下,评估模型需要一个训练数据集和测试数据集,有时甚至需要一个验证数据集。

想评估一个模型时就得使用大批量数据点。如果完成批量训练,我们可以重用模型来预测批量数据点。但是如果要完成随机训练,就不得不创建单独的评估器来处理批量数据点。

分类算法模型基于数值型输入预测分类值,实际目标是1和0的序列。我们需要度量预测值与真实值之间的距离。分类算法模型的损失函数一般不容易解释模型好坏,所以通常情况是看下准确预测分类的结果的百分比。

不管算法模型预测的如何,我们都需要测试算法模型,这点相当重要。在训练数据和测试数据上都进行模型评估,以搞清楚模型是否过拟合。

# TensorFlowm模型评估
#
# This code will implement two models. The first
# is a simple regression model, we will show how to
# call the loss function, MSE during training, and
# output it after for test and training sets.
#
# The second model will be a simple classification
# model. We will also show how to print percent
# classified correctly during training and after
# for both the test and training sets.

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()

# 创建计算图
sess = tf.Session()

# 回归例子:
# We will create sample data as follows:
# x-data: 100 random samples from a normal ~ N(1, 0.1)
# target: 100 values of the value 10.
# We will fit the model:
# x-data * A = target
# 理论上, A = 10.

# 声明批量大小
batch_size = 25

# 创建数据集
x_vals = np.random.normal(1, 0.1, 100)
y_vals = np.repeat(10., 100)
x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# 八二分训练/测试数据 train/test = 80%/20%
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

# 创建变量 (one model parameter = A)
A = tf.Variable(tf.random_normal(shape=[1,1]))

# 增加操作到计算图
my_output = tf.matmul(x_data, A)

# 增加L2损失函数到计算图
loss = tf.reduce_mean(tf.square(my_output - y_target))

# 创建优化器
my_opt = tf.train.GradientDescentOptimizer(0.02)
train_step = my_opt.minimize(loss)

# 初始化变量
init = tf.global_variables_initializer()
sess.run(init)

# 迭代运行
# 如果在损失函数中使用的模型输出结果经过转换操作,例如,sigmoid_cross_entropy_with_logits()函数,
# 为了精确计算预测结果,别忘了在模型评估中也要进行转换操作。
for i in range(100):
  rand_index = np.random.choice(len(x_vals_train), size=batch_size)
  rand_x = np.transpose([x_vals_train[rand_index]])
  rand_y = np.transpose([y_vals_train[rand_index]])
  sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
  if (i+1)%25==0:
    print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
    print('Loss = ' + str(sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})))

# 评估准确率(loss)
mse_test = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_test]), y_target: np.transpose([y_vals_test])})
mse_train = sess.run(loss, feed_dict={x_data: np.transpose([x_vals_train]), y_target: np.transpose([y_vals_train])})
print('MSE on test:' + str(np.round(mse_test, 2)))
print('MSE on train:' + str(np.round(mse_train, 2)))

# 分类算法案例
# We will create sample data as follows:
# x-data: sample 50 random values from a normal = N(-1, 1)
#     + sample 50 random values from a normal = N(1, 1)
# target: 50 values of 0 + 50 values of 1.
#     These are essentially 100 values of the corresponding output index
# We will fit the binary classification model:
# If sigmoid(x+A) < 0.5 -> 0 else 1
# Theoretically, A should be -(mean1 + mean2)/2

# 重置计算图
ops.reset_default_graph()

# 加载计算图
sess = tf.Session()

# 声明批量大小
batch_size = 25

# 创建数据集
x_vals = np.concatenate((np.random.normal(-1, 1, 50), np.random.normal(2, 1, 50)))
y_vals = np.concatenate((np.repeat(0., 50), np.repeat(1., 50)))
x_data = tf.placeholder(shape=[1, None], dtype=tf.float32)
y_target = tf.placeholder(shape=[1, None], dtype=tf.float32)

# 分割数据集 train/test = 80%/20%
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

# 创建变量 (one model parameter = A)
A = tf.Variable(tf.random_normal(mean=10, shape=[1]))

# Add operation to graph
# Want to create the operstion sigmoid(x + A)
# Note, the sigmoid() part is in the loss function
my_output = tf.add(x_data, A)

# 增加分类损失函数 (cross entropy)
xentropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target))

# Create Optimizer
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(xentropy)

# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)

# 运行迭代
for i in range(1800):
  rand_index = np.random.choice(len(x_vals_train), size=batch_size)
  rand_x = [x_vals_train[rand_index]]
  rand_y = [y_vals_train[rand_index]]
  sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
  if (i+1)%200==0:
    print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))
    print('Loss = ' + str(sess.run(xentropy, feed_dict={x_data: rand_x, y_target: rand_y})))

# 评估预测
# 用squeeze()函数封装预测操作,使得预测值和目标值有相同的维度。
y_prediction = tf.squeeze(tf.round(tf.nn.sigmoid(tf.add(x_data, A))))
# 用equal()函数检测是否相等,
# 把得到的true或false的boolean型张量转化成float32型,
# 再对其取平均值,得到一个准确度值。
correct_prediction = tf.equal(y_prediction, y_target)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc_value_test = sess.run(accuracy, feed_dict={x_data: [x_vals_test], y_target: [y_vals_test]})
acc_value_train = sess.run(accuracy, feed_dict={x_data: [x_vals_train], y_target: [y_vals_train]})
print('Accuracy on train set: ' + str(acc_value_train))
print('Accuracy on test set: ' + str(acc_value_test))

# 绘制分类结果
A_result = -sess.run(A)
bins = np.linspace(-5, 5, 50)
plt.hist(x_vals[0:50], bins, alpha=0.5, label='N(-1,1)', color='white')
plt.hist(x_vals[50:100], bins[0:50], alpha=0.5, label='N(2,1)', color='red')
plt.plot((A_result, A_result), (0, 8), 'k--', linewidth=3, label='A = '+ str(np.round(A_result, 2)))
plt.legend(loc='upper right')
plt.title('Binary Classifier, Accuracy=' + str(np.round(acc_value_test, 2)))
plt.show()

输出:

Step #25 A = [[ 5.79096079]]
Loss = 16.8725
Step #50 A = [[ 8.36085415]]
Loss = 3.60671
Step #75 A = [[ 9.26366138]]
Loss = 1.05438
Step #100 A = [[ 9.58914948]]
Loss = 1.39841
MSE on test:1.04
MSE on train:1.13
Step #200 A = [ 5.83126402]
Loss = 1.9799
Step #400 A = [ 1.64923656]
Loss = 0.678205
Step #600 A = [ 0.12520729]
Loss = 0.218827
Step #800 A = [-0.21780498]
Loss = 0.223919
Step #1000 A = [-0.31613481]
Loss = 0.234474
Step #1200 A = [-0.33259964]
Loss = 0.237227
Step #1400 A = [-0.28847221]
Loss = 0.345202
Step #1600 A = [-0.30949864]
Loss = 0.312794
Step #1800 A = [-0.33211425]
Loss = 0.277342
Accuracy on train set: 0.9625
Accuracy on test set: 1.0

TensorFlow实现模型评估

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持三水点靠木。

Python 相关文章推荐
python端口扫描系统实现方法
Nov 19 Python
python利用dir函数查看类中所有成员函数示例代码
Sep 08 Python
python单例模式实例解析
Aug 28 Python
python2与python3中关于对NaN类型数据的判断和转换方法
Oct 30 Python
浅谈Python 多进程默认不能共享全局变量的问题
Jan 11 Python
python psutil监控进程实例
Dec 17 Python
基于Tensorflow高阶读写教程
Feb 10 Python
PyCharm 在Windows的有用快捷键详解
Apr 07 Python
Keras中的两种模型:Sequential和Model用法
Jun 27 Python
详解使用python爬取抖音app视频(appium可以操控手机)
Jan 26 Python
用python修改excel表某一列内容的操作方法
Jun 11 Python
python基础之//、/与%的区别详解
Jun 10 Python
使用tensorflow实现线性svm
Sep 07 #Python
Python多进程池 multiprocessing Pool用法示例
Sep 07 #Python
详解python while 函数及while和for的区别
Sep 07 #Python
使用TensorFlow实现SVM
Sep 06 #Python
使用Python制作自动推送微信消息提醒的备忘录功能
Sep 06 #Python
python实现机器学习之多元线性回归
Sep 06 #Python
python实现机器学习之元线性回归
Sep 06 #Python
You might like
php curl 模拟登录并获取数据实例详解
2016/12/22 PHP
PHP编程计算日期间隔天数的方法
2017/04/26 PHP
PHP实现防盗链的方法分析
2017/07/25 PHP
php变量与JS变量实现不通过跳转直接交互的方法
2017/08/25 PHP
javascript随机之洗牌算法深入分析
2014/06/07 Javascript
客户端验证用户名和密码的方法详解
2016/06/16 Javascript
js在ie下打开对话窗口的方法小结
2016/10/24 Javascript
jQuery实现按比例缩放图片的方法
2017/04/29 jQuery
Angular路由ui-router配置详解
2018/08/01 Javascript
Vue中的vue-resource示例详解
2018/11/02 Javascript
微信小程序实现选项卡效果
2018/11/06 Javascript
vue+webpack 更换主题N种方案优劣分析
2019/10/28 Javascript
js仿360开机效果
2019/12/26 Javascript
Vue.js中Line第三方登录api的实现代码
2020/06/29 Javascript
[01:03]DOTA2新的征程 你的脚印值得踏上
2014/08/13 DOTA
九步学会Python装饰器
2015/05/09 Python
python中numpy基础学习及进行数组和矢量计算
2017/02/12 Python
Django Admin 实现外键过滤的方法
2017/09/29 Python
python opencv实现切变换 不裁减图片
2018/07/26 Python
python通过移动端访问查看电脑界面
2020/01/06 Python
pyinstaller 3.6版本通过pip安装失败的解决办法(推荐)
2020/01/18 Python
python字符串常用方法及文件简单读写的操作方法
2020/03/04 Python
Python3爬虫ChromeDriver的安装实例
2021/02/06 Python
Clarria化妆品官方网站:购买天然和有机化妆品系列
2018/04/08 全球购物
巴西体育用品商店:Lojão dos Esportes
2018/07/21 全球购物
应届毕业生自我鉴定范文
2013/12/27 职场文书
高一生物教学反思
2014/01/17 职场文书
见习期自我鉴定
2014/01/31 职场文书
护士自我评价
2014/02/01 职场文书
研发工程师岗位职责
2014/04/28 职场文书
党员廉洁自律个人总结
2015/02/13 职场文书
安全教育主题班会总结
2015/08/14 职场文书
Nginx+SpringBoot实现负载均衡的示例
2021/03/31 Servers
Java GUI编程菜单组件实例详解
2022/04/07 Java/Android
Java无向树分析 实现最小高度树
2022/04/09 Javascript
Python+Pillow+Pytesseract实现验证码识别
2022/05/11 Python