tensorflow 机器学习波士顿房价预测笔记

程序员文章站 2022-05-02 18:54:46

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机器学习Machine Learning
机器学习(Machine Learning, ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。
它是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域，它主要使用归纳、综合而不是演绎。

我学习的机器学习问题两种：
1.分类问题
2.线性回归问题
波士顿房价预测属于线性回归问题

学习了吴恩达老师的机器学习

波士顿房价预测
一、安装tensorflow

安装很简单，如果你的网络流程的话，最好可以*
官方安装简单
使用 Python 的 pip 软件包管理器安装 TensorFlow
没有安装pip的需要先安装pip

windows安装

pip install --upgrade tensorflow

验证安装效果：

python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"

有结果输出没报错，就是安装成功了

二、开始写波士顿房价预测例子代码

from __future__ import absolute_import, division, print_function

import tensorflow as tf
from tensorflow import keras

import numpy as np

# 数据路径  现在直接放在项目目录下，方便用
train_path = ["csv/train_data.csv", "csv/train_labels.csv"]
test_path = ["csv/test_data.csv", "csv/test_labels.csv"]

# 数据读取  直接用numpy提供的loadtxt函数进行读取csv格式的数据 
# loadtxt函数的参数(路径, 读取csv数据的分隔符, 读取的csv的列数, 读取的格式)
train_data = np.loadtxt(train_path[0],delimiter=',',usecols=np.arange(0,13),encoding='UTF-8-sig')
train_labels = np.loadtxt(train_path[1],delimiter=',',usecols=(0),encoding='UTF-8-sig') 
test_data = np.loadtxt(test_path[0],delimiter=',',usecols=np.arange(0,13),encoding='UTF-8-sig')
test_labels = np.loadtxt(test_path[1],delimiter=',',usecols=(0),encoding='UTF-8-sig') 

# boston_housing = keras.datasets.boston_housing

# (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()

# Shuffle the training set
order = np.argsort(np.random.random(train_labels.shape))
train_data = train_data[order]
train_labels = train_labels[order]
print(train_labels)

print('------')
print(train_data[0])
print(type(train_data))
print(len(train_data))
# print(train_labels)

# 没有id这个attribute
# print("Training set: {}".format(train_data.id))  # 404 examples, 13 features

print("Training set: {}".format(train_data.shape))  # 404 examples, 13 features
print("Testing set:  {}".format(test_data.shape))   # 102 examples, 13 features

print(train_data[0])  # Display sample features, notice the different scales

import pandas as pd

column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
                'TAX', 'PTRATIO', 'B', 'LSTAT']

df = pd.DataFrame(train_data, columns=column_names)
df.head()
print(df.head())

print(train_labels[0:10])  # Display first 10 entries

# Test data is *not* used when calculating the mean and std

mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

print(train_data[0])  # First training sample, normalized

def build_model():
  model = keras.Sequential([
    keras.layers.Dense(64, activation=tf.nn.relu,
                       input_shape=(train_data.shape[1],)),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(1)
  ])

  optimizer = tf.train.RMSPropOptimizer(0.001)

  model.compile(loss='mse',
                optimizer=optimizer,
                metrics=['mae'])
  return model

model = build_model()
model.summary()

# Display training progress by printing a single dot for each completed epoch
class PrintDot(keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs):
    if epoch % 100 == 0: print('')
    print('.', end='')

EPOCHS = 200

# Store training stats
history = model.fit(train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0,
                    callbacks=[PrintDot()])


import matplotlib.pyplot as plt

def plot_history(history):
  plt.figure()
  plt.xlabel('Epoch')
  plt.ylabel('Mean Abs Error [1000$]')
  plt.plot(history.epoch, np.array(history.history['mean_absolute_error']),
           label='Train Loss')
  plt.plot(history.epoch, np.array(history.history['val_mean_absolute_error']),
           label = 'Val loss')
  plt.legend()
  plt.ylim([0, 5])
  plt.show()


plot_history(history)

model = build_model()

# The patience parameter is the amount of epochs to check for improvement
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)

history = model.fit(train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0,
                    callbacks=[early_stop, PrintDot()])

plot_history(history)

[loss, mae] = model.evaluate(test_data, test_labels, verbose=0)

print("Testing set Mean Abs Error: ${:7.2f}".format(mae * 1000))

test_predictions = model.predict(test_data).flatten()

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [1000$]')
plt.ylabel('Predictions [1000$]')
plt.axis('equal')
plt.xlim(plt.xlim())
plt.ylim(plt.ylim())
_ = plt.plot([-100, 100], [-100, 100])
plt.show()

print("测试数据的真实标签y值")
print(test_labels)
print("测试数据的预测标签y值")
print(test_predictions)
test_predictions

np.savetxt('csv/test_predictions.csv', test_predictions, delimiter = ',')

error = test_predictions - test_labels
plt.hist(error, bins = 50)
plt.xlabel("Prediction Error [1000$]")
_ = plt.ylabel("Count")
plt.show()