机器学习学习记录【持续更新】——TensorfFlow线性回归
TensorfFlow线性回归
利用TensorFlow构造一个线性模型的步骤
导入必要的库
from __future__ import print_function
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
加载数据集并且检查数据
california_housing_dataframe = pd.read_csv("https://download.mlcc.google.cn/mledu-datasets/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
california_housing_dataframe.describe()
构建模型
为了训练模型，我们将使用 TensorFlow Estimator API 提供的 LinearRegressor 接口。此 API 负责处理大量低级别模型搭建工作，并会提供执行模型训练、评估和推理的便利方法。
第 1 步：定义特征并配置特征列
在 TensorFlow 中，我们使用一种称为“特征列”的结构来表示特征的数据类型。特征列仅存储对特征数据的描述；不包含特征数据本身。
一开始，我们只使用一个数值输入特征 total_rooms。以下代码会从 california_housing_dataframe 中提取 total_rooms 数据，并使用 numeric_column 定义特征列，这样会将其数据指定为数值：
# Define the input feature: total_rooms.
my_feature = california_housing_dataframe[["total_rooms"]]
# Configure a numeric feature column for total_rooms.
feature_columns = [tf.feature_column.numeric_column("total_rooms")]
第 2 步：定义目标
接下来，我们将定义目标，也就是 median_house_value。同样，我们可以从 california_housing_dataframe 中提取它：
# Define the label.
targets = california_housing_dataframe["median_house_value"]
第 3 步：配置 LinearRegressor
接下来，我们将使用 LinearRegressor 配置线性回归模型，并使用 GradientDescentOptimizer（它会实现小批量随机梯度下降法 (SGD)）训练该模型。learning_rate 参数可控制梯度步长的大小。
注意：为了安全起见，我们还会通过 clip_gradients_by_norm 将梯度裁剪应用到我们的优化器。梯度裁剪可确保梯度大小在训练期间不会变得过大，梯度过大会导致梯度下降法失败。
# Use gradient descent as the optimizer for training the model.
my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)
my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
# Configure the linear regression model with our feature columns and optimizer.
# Set a learning rate of 0.0000001 for Gradient Descent.
linear_regressor = tf.estimator.LinearRegressor(
feature_columns=feature_columns,
optimizer=my_optimizer
)
第 4 步：定义输入函数
要将加利福尼亚州住房数据导入 LinearRegressor，我们需要定义一个输入函数，让它告诉 TensorFlow 如何对数据进行预处理，以及在模型训练期间如何批处理、随机处理和重复数据。
首先，我们将 Pandas 特征数据转换成 NumPy 数组字典。然后，我们可以使用 TensorFlow Dataset API 根据我们的数据构建 Dataset 对象，并将数据拆分成大小为 batch_size 的多批数据，以按照指定周期数 (num_epochs) 进行重复。
注意：如果将默认值 num_epochs=None 传递到 repeat()，输入数据会无限期重复。
然后，如果 shuffle 设置为 True，则我们会对数据进行随机处理，以便数据在训练期间以随机方式传递到模型。buffer_size 参数会指定 shuffle 将从中随机抽样的数据集的大小。
最后，输入函数会为该数据集构建一个迭代器，并向 LinearRegressor 返回下一批数据。
def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
"""Trains a linear regression model of one feature.
Args:
features: pandas DataFrame of features
targets: pandas DataFrame of targets
batch_size: Size of batches to be passed to the model
shuffle: True or False. Whether to shuffle the data.
num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely
Returns:
Tuple of (features, labels) for next data batch
"""
# Convert pandas data into a dict of np arrays.
features = {key:np.array(value) for key,value in dict(features).items()}
# Construct a dataset, and configure batching/repeating.
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)
# Return the next batch of data.
features, labels = ds.make_one_shot_iterator().get_next()
return features, labels
第 5 步：训练模型
现在，我们可以在 linear_regressor 上调用 train() 来训练模型。我们会将 my_input_fn 封装在 lambda 中，以便可以将 my_feature 和 target 作为参数传入（有关详情，请参阅此 TensorFlow 输入函数教程），首先，我们会训练 100 步。
_ = linear_regressor.train(
input_fn = lambda:my_input_fn(my_feature, targets),
steps=100
)
第 6 步：评估模型
我们基于该训练数据做一次预测，看看我们的模型在训练期间与这些数据的拟合情况。
注意：训练误差可以衡量您的模型与训练数据的拟合情况，但并不能衡量模型泛化到新数据的效果。在后面的练习中，您将探索如何拆分数据以评估模型的泛化能力。
sample = california_housing_dataframe.sample(n=300)
# Get the min and max total_rooms values.
x_0 = sample["total_rooms"].min()
x_1 = sample["total_rooms"].max()
# Retrieve the final weight and bias generated during training.
weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]
bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')
# Get the predicted median_house_values for the min and max total_rooms values.
y_0 = weight * x_0 + bias
y_1 = weight * x_1 + bias
# Plot our regression line from (x_0, y_0) to (x_1, y_1).
plt.plot([x_0, x_1], [y_0, y_1], c='r')
# Label the graph axes.
plt.ylabel("median_house_value")
plt.xlabel("total_rooms")
# Plot a scatter plot from our data sample.
plt.scatter(sample["total_rooms"], sample["median_house_value"])
# Display graph.
plt.show()
调整模型超参数
我们会在 10 个等分的时间段内使用此函数，以便观察模型在每个时间段的改善情况。
对于每个时间段，我们都会计算训练损失并绘制相应图表。这可以帮助您判断模型收敛的时间，或者模型是否需要更多迭代。
此外，我们还会绘制模型随着时间的推移学习的特征权重和偏差项值的曲线图。您还可以通过这种方式查看模型的收敛效果。
def train_model(learning_rate, steps, batch_size, input_feature="total_rooms"):
"""Trains a linear regression model of one feature.
Args:
learning_rate: A `float`, the learning rate.
steps: A non-zero `int`, the total number of training steps. A training step
consists of a forward and backward pass using a single batch.
batch_size: A non-zero `int`, the batch size.
input_feature: A `string` specifying a column from `california_housing_dataframe`
to use as input feature.
"""
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]
# Create feature columns.
feature_columns = [tf.feature_column.numeric_column(my_feature)]
# Create input functions.
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)
# Create a linear regressor object.
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
)
# Set up to plot the state of our model's line each period.
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)]
# Train the model, but do so inside a loop so that we can periodically assess
# loss metrics.
print("Training model...")
print("RMSE (on training data):")
root_mean_squared_errors = []
for period in range (0, periods):
# Train the model, starting from the prior state.
linear_regressor.train(
input_fn=training_input_fn,
steps=steps_per_period
)
# Take a break and compute predictions.
predictions = linear_regressor.predict(input_fn=prediction_input_fn)
predictions = np.array([item['predictions'][0] for item in predictions])
# Compute loss.
root_mean_squared_error = math.sqrt(
metrics.mean_squared_error(predictions, targets))
# Occasionally print the current loss.
print(" period %02d : %0.2f" % (period, root_mean_squared_error))
# Add the loss metrics from this period to our list.
root_mean_squared_errors.append(root_mean_squared_error)
# Finally, track the weights and biases over time.
# Apply some math to ensure that the data and line are plotted neatly.
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.")
# Output a graph of loss metrics over periods.
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)
# Output a table with calibration data.
calibration_data = pd.DataFrame()
calibration_data["predictions"] = pd.Series(predictions)
calibration_data["targets"] = pd.Series(targets)
display.display(calibration_data.describe())
print("Final RMSE (on training data): %0.2f" % root_mean_squared_error)
