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引包

seaborn 是绘制矩阵图的包

import pathlib

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers

print(tf.__version__)

数据准备

# 下载数据
dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")

column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
                'Acceleration', 'Model Year', 'Origin']
raw_dataset = pd.read_csv(dataset_path, names=column_names,
                      na_values = "?", comment='\t',
                      sep=" ", skipinitialspace=True)

dataset = raw_dataset.copy()
# 清洗数据
dataset = dataset.dropna()
origin = dataset.pop('Origin')
dataset['USA'] = (origin == 1)*1.0
dataset['Europe'] = (origin == 2)*1.0
dataset['Japan'] = (origin == 3)*1.0

# 拆分数据集
train_dataset = dataset.sample(frac=0.8,random_state=0)
test_dataset = dataset.drop(train_dataset.index)

# 分离标签
train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')

# 数据规范
def norm(x):
  return (x - train_stats['mean']) / train_stats['std']
normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)

模型准备

def build_model():
    model = keras.Sequential([
        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
        layers.Dense(64, activation='relu'),
        layers.Dense(1)
    ])

    optimizer = tf.keras.optimizers.RMSprop(0.001)

    model.compile(loss='mse',
                  optimizer=optimizer,
                  metrics=['mae', 'mse'])
    return model
model = build_model()
# 打印模型
model.summary()

# 检查下是否可以工作
example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)
print(example_result)

跑起来

# 通过为每个完成的时期打印一个点来显示训练进度
class PrintDot(keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs):
    if epoch % 100 == 0: print('')
    print('.', end='')

EPOCHS = 1000

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

# 用history把训练信息提取出来
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
print(hist.tail())

# 用图的形式看下效果
def plot_history(history):
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Abs Error [MPG]')
    plt.plot(hist['epoch'], hist['mae'],
             label='Train Error')
    plt.plot(hist['epoch'], hist['val_mae'],
             label='Val Error')
    plt.ylim([0, 5])
    plt.legend()

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Square Error [$MPG^2$]')
    plt.plot(hist['epoch'], hist['mse'],
             label='Train Error')
    plt.plot(hist['epoch'], hist['val_mse'],
             label='Val Error')
    plt.ylim([0, 20])
    plt.legend()
    plt.show()


plot_history(history)
# 结果有些过拟合，调整下
# patience 值用来检查改进 epochs 的数量
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

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

plot_history(history)

# 最后在测试集上试试
loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)

print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))

做预测

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

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [MPG]')
plt.ylabel('Predictions [MPG]')
plt.axis('equal')
plt.axis('square')
plt.xlim([0,plt.xlim()[1]])
plt.ylim([0,plt.ylim()[1]])
_ = plt.plot([-100, 100], [-100, 100])

error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")

整体代码

import pathlib

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers

print(tf.__version__)

dataset_path = keras.utils.get_file("auto-mpg.data",
                                    "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
print(dataset_path)

column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']
raw_dataset = pd.read_csv(dataset_path, names=column_names,
                          na_values="?", comment='\t',
                          sep=" ", skipinitialspace=True)

dataset = raw_dataset.copy()
print(dataset.tail())

dataset.isna().sum()
dataset = dataset.dropna()
origin = dataset.pop('Origin')
dataset['USA'] = (origin == 1) * 1.0
dataset['Europe'] = (origin == 2) * 1.0
dataset['Japan'] = (origin == 3) * 1.0
print(dataset.tail())

train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

sns.pairplot(train_dataset[["MPG", "Cylinders", "Displacement", "Weight"]], diag_kind="kde")
plt.show()
train_stats = train_dataset.describe()
train_stats.pop("MPG")
train_stats = train_stats.transpose()
print(train_stats)

train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')


def norm(x):
    return (x - train_stats['mean']) / train_stats['std']


normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)


def build_model():
    model = keras.Sequential([
        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
        layers.Dense(64, activation='relu'),
        layers.Dense(1)
    ])

    optimizer = tf.keras.optimizers.RMSprop(0.001)

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


def build_model():
    model = keras.Sequential([
        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
        layers.Dense(64, activation='relu'),
        layers.Dense(1)
    ])

    optimizer = tf.keras.optimizers.RMSprop(0.001)

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


model = build_model()
# 打印模型
model.summary()

# 检查下是否可以工作
example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)
print(example_result)


# 通过为每个完成的时期打印一个点来显示训练进度
class PrintDot(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs):
        if epoch % 100 == 0: print('')
        print('.', end='')


EPOCHS = 1000

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

# 用history把训练信息提取出来
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
print(hist.tail())


def plot_history(history):
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Abs Error [MPG]')
    plt.plot(hist['epoch'], hist['mae'],
             label='Train Error')
    plt.plot(hist['epoch'], hist['val_mae'],
             label='Val Error')
    plt.ylim([0, 5])
    plt.legend()

    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('Mean Square Error [$MPG^2$]')
    plt.plot(hist['epoch'], hist['mse'],
             label='Train Error')
    plt.plot(hist['epoch'], hist['val_mse'],
             label='Val Error')
    plt.ylim([0, 20])
    plt.legend()
    plt.show()


plot_history(history)

model = build_model()

# patience 值用来检查改进 epochs 的数量
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

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

plot_history(history)

loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)

print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))
test_predictions = model.predict(normed_test_data).flatten()

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [MPG]')
plt.ylabel('Predictions [MPG]')
plt.axis('equal')
plt.axis('square')
plt.xlim([0,plt.xlim()[1]])
plt.ylim([0,plt.ylim()[1]])
_ = plt.plot([-100, 100], [-100, 100])

error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")