从零开始机器学习-10（附录） 项目相关代码

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第十课：TensorFlow的基本使用方法

https://www.jianshu.com/p/195cba894f8d

第十课的完整代码如下：

import math

from IPython import display
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset

tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format

california_housing_dataframe = pd.read_csv(
    "https://raw.githubusercontent.com/sqy941013/learnmachinelearning/master/california_housing_train.csv",
    sep=",")

california_housing_dataframe = california_housing_dataframe.reindex(
    np.random.permutation(california_housing_dataframe.index))
california_housing_dataframe["median_house_value"] /= 1000.0
california_housing_dataframe

def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    """训练一个单特征的线性回归模型

    参数:
      features: 特征的Pandas DataFrame对象
      targets: 目标的Pandas DataFrame对象
      batch_size: 传给模型的批次大小
      shuffle: 是否对数据进行随机处理
      num_epochs: 指定周期数，若设置为None则无限循环
    返回:
      下一批数据的特征和标签组成的元组
    """

    # 将Pandas数据转换为Numpy字典
    features = {key: np.array(value) for key, value in dict(features).items()}

    # 创建数据集、配置批次和重复
    ds = Dataset.from_tensor_slices((features, targets))  # warning: 2GB limit
    ds = ds.batch(batch_size).repeat(num_epochs)

    # Shuffle the data, if specified
    if shuffle:
        ds = ds.shuffle(buffer_size=10000)

    # 返回下一批数据
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels


def train_model(learning_rate, steps, batch_size, input_feature="total_rooms"):
    """训练一个单特征的线性回归模型

    参数:
      learning_rate: 浮点型，定义学习的速率
      steps: 非零整型，训练的总步数
      batch_size:非零整型，批次大小
      input_feature: 字符串，california_housing_dataframe中作为输入特征的的一列
    """

    periods = 10
    steps_per_period = steps / periods

    my_feature = input_feature
    my_feature_data = california_housing_dataframe[[my_feature]]
    my_label = "median_house_value"
    targets = california_housing_dataframe[my_label]

    # 创建特征列
    feature_columns = [tf.feature_column.numeric_column(my_feature)]

    # 创建输入函数
    training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)
    prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)

    # 创建线性回归对象
    my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
    my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
    linear_regressor = tf.estimator.LinearRegressor(
        feature_columns=feature_columns,
        optimizer=my_optimizer
    )

    # 设置模型的绘图参数
    plt.figure(figsize=(15, 6))
    plt.subplot(1, 2, 1)
    plt.title("Learned Line by Period")
    plt.ylabel(my_label)
    plt.xlabel(my_feature)
    sample = california_housing_dataframe.sample(n=300)
    plt.scatter(sample[my_feature], sample[my_label])
    colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]

    # 训练模型
    print("Training model...")

    print("RMSE (on training data):")

    root_mean_squared_errors = []
    for period in range(0, periods):
        linear_regressor.train(
            input_fn=training_input_fn,
            steps=steps_per_period
        )
        # 计算预测
        predictions = linear_regressor.predict(input_fn=prediction_input_fn)
        predictions = np.array([item['predictions'][0] for item in predictions])

        # 计算损失
        root_mean_squared_error = math.sqrt(
            metrics.mean_squared_error(predictions, targets))
        # 输出损失值
        print("  period %02d : %0.2f" % (period, root_mean_squared_error))

        root_mean_squared_errors.append(root_mean_squared_error)
        # 跟踪权重和偏差
        y_extents = np.array([0, sample[my_label].max()])

        weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]
        bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')

        x_extents = (y_extents - bias) / weight
        x_extents = np.maximum(np.minimum(x_extents,
                                          sample[my_feature].max()),
                               sample[my_feature].min())
        y_extents = weight * x_extents + bias
        plt.plot(x_extents, y_extents, color=colors[period])
    print("Model training finished.")


    # 绘制损失图
    plt.subplot(1, 2, 2)
    plt.ylabel('RMSE')
    plt.xlabel('Periods')
    plt.title("Root Mean Squared Error vs. Periods")
    plt.tight_layout()
    plt.plot(root_mean_squared_errors)

    # 输出矫正数据表
    calibration_data = pd.DataFrame()
    calibration_data["predictions"] = pd.Series(predictions)
    calibration_data["targets"] = pd.Series(targets)
    display.display(calibration_data.describe())

    plt.show()

    print("Final RMSE (on training data): %0.2f" % root_mean_squared_error)



train_model(
    learning_rate=0.00002,
    steps=500,
    batch_size=5
)

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