TensorFlow2利用Auto-MPG数据集实现神经网络回归任务
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2022-07-08 09:42:26
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1. 导入所需的库
import tensorflow as tf
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
for i in [tf, pd, sns]:
print(i.__name__,": ",i.__version__,sep="")输出：
tensorflow: 2.2.0
pandas: 0.25.3
seaborn: 0.10.12. 下载并导入数据集
Auto MPG数据集主要收集了70年代末到80年代初不同品牌的汽车燃油效率，数据特征有每加仑量程数、气缸数、排量、马力、车重、加速效率、生产年份、生产地、汽车名字等。
官网地址：https://archive.ics.uci.edu/ml/datasets/auto+mpg
2.1 下载数据集
datasetPath = tf.keras.utils.get_file("auto-mpg.data","http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
datasetPath输出：
Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data
32768/30286 [================================] - 0s 7us/step
Out[3]:
'C:\\Users\\my-pc\\.keras\\datasets\\auto-mpg.data'2.2 使用Pandas导入数据集
columnNames = ["MPG","Cylinders","Displacement","Horsepower","Weight","Accleration","Model Year","Origin"]
rawDataset = pd.read_csv(datasetPath, names=columnNames, na_values="?",comment="\t",sep=" ",skipinitialspace=True)
dataset = rawDataset.copy()
dataset.tail()输出：
MPG Cylinders Displacement Horsepower Weight Accleration Model Year Origin
393 27.0 4 140.0 86.0 2790.0 15.6 82 1
394 44.0 4 97.0 52.0 2130.0 24.6 82 2
395 32.0 4 135.0 84.0 2295.0 11.6 82 1
396 28.0 4 120.0 79.0 2625.0 18.6 82 1
397 31.0 4 119.0 82.0 2720.0 19.4 82 13. 数据清洗
# 统计数据中的NA值
dataset.isna().sum()输出：
MPG 0
Cylinders 0
Displacement 0
Horsepower 6
Weight 0
Accleration 0
Model Year 0
Origin 0
dtype: int64可以看到Horsepower列中存在NA值，需要将这些值的样本删除。
dataset = dataset.dropna()
dataset.isna().sum()输出：
MPG 0
Cylinders 0
Displacement 0
Horsepower 0
Weight 0
Accleration 0
Model Year 0
Origin 0
dtype: int64dataset["Origin"]输出：
0 1
1 1
2 1
3 1
4 1
..
393 1
394 2
395 1
396 1
397 1
Name: Origin, Length: 392, dtype: int64Origin列表示汽车的产地，分别用1，2，3存储。但是产地没有大小之分，所以用独热编码更合适。
3.1 方法1
Origin = dataset.pop("Origin") # 从dataset中删除Origin列
dataset["USA"] = (Origin == 1)*1.0
dataset["Europe"] = (Origin == 2)*1.0
dataset["Japan"] = (Origin == 3)*1.0
dataset.head()输出：
3.2 方法2
dataset["Origin"] = dataset["Origin"].map({1:"USA",2:"Europe",3:"Japan"})
dataset.tail()输出：
dataset = pd.get_dummies(dataset, prefix="",prefix_sep="")
dataset.tail()输出：
3.3 拆分数据集为训练集和测试集
# 按训练集80%，验证集20%的比例拆分
trainDataset = dataset.sample(frac=0.8, random_state=0)
testDataset = dataset.drop(trainDataset.index)
print(trainDataset.shape, testDataset.shape)输出：
(314, 10) (78, 10)4. 数据可视化
4.1 绘图展示特征相关性
sns.pairplot(trainDataset[["MPG","Cylinders","Displacement","Weight"]],diag_kind="kde")输出：
4.2 输出统计指标
trainStats = trainDataset.describe()
trainStats.pop("MPG")
trainStats = trainStats.transpose()
trainStats输出：
5. 数据预处理
5.1 拆分训练特征和标签
trainLabels = trainDataset.pop("MPG")
testLabels = testDataset.pop("MPG")
print(trainLabels.shape, testLabels.shape)输出：
(314,) (78,)5.2 数据归一化
观察4.2中结果发现，各个特征平均值和标准差相差很大。如果直接拿数据进行建模，使得模型误认为值大的重要程度高，值小的重要程度低。为了避免这种错误的产生，需要对数据进行归一化操作。
常用的归一化有两种方法：
- 1. 标准归一化：(样本值-均值)/标准差，这种归一化的结果为均值为0，方差为1的分布
- 2. 最大最小归一化：(样本值-最小值)/(最大值-最小值)，这种归一化结果为[0,1]的分布
def Norm(x):
return (x-trainStats["mean"])/trainStats["std"]
normedTrainDataset = Norm(trainDataset)
normedTestDataset = Norm(testDataset)
stat = normedTrainDataset.describe()
stat.transpose()[["mean","std"]]输出：
6. 模型构建
6.1 构建模型结构
model = tf.keras.Sequential([
tf.keras.layers.Dense(64,activation="relu",input_shape=[len(trainDataset.keys())]),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(1)
])
model.summary()输出：
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_3 (Dense) (None, 64) 640
_________________________________________________________________
dense_4 (Dense) (None, 64) 4160
_________________________________________________________________
dense_5 (Dense) (None, 1) 65
=================================================================
Total params: 4,865
Trainable params: 4,865
Non-trainable params: 0
_________________________________________________________________6.2 编译模型
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss="mse",
optimizer=optimizer,
metrics=["mae","mse"])7. 训练模型
class PrintDot(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs):
if epoch %100 == 0:
print("")
print(".",end="")
epochs = 1000
history = model.fit(normedTrainDataset,
trainLabels,
epochs=epochs,
validation_split=0.2,
verbose=0,
callbacks=[PrintDot()])输出：
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....................................................................................................hist = pd.DataFrame(history.history)
hist["epoch"] = history.epoch
hist.tail()输出：
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)输出：
从上图中可以看出，大约训练100之后，训练集上的错误率一直在下降，而验证集上的错误率反而升高了，这表明模型已经过拟合了。此时，我们需要修改模型的训练方式，让模型在验证集错误率不再下降时停止训练，防止过拟合的发生。
model = tf.keras.Sequential([
tf.keras.layers.Dense(64,activation="relu",input_shape=[len(trainDataset.keys())]),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(1)
])
model.summary()
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss="mse",
optimizer=optimizer,
metrics=["mae","mse"])
class PrintDot(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs):
if epoch %100 == 0:
print("")
print(".",end="")
epochs = 1000
earlyStop = tf.keras.callbacks.EarlyStopping(monitor="val_loss",patience=10)#当验证集上错误率不再下降时停止训练
history = model.fit(normedTrainDataset,
trainLabels,
epochs=epochs,
validation_split=0.2,
verbose=0,
callbacks=[earlyStop,PrintDot()])
plot_history(history)输出：
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_6 (Dense) (None, 64) 640
_________________________________________________________________
dense_7 (Dense) (None, 64) 4160
_________________________________________________________________
dense_8 (Dense) (None, 1) 65
=================================================================
Total params: 4,865
Trainable params: 4,865
Non-trainable params: 0
_________________________________________________________________
.................................................................8. 模型评估
利用测试集对训练的模型进行评估
loss, mae, mse = model.evaluate(normedTestDataset, testLabels, verbose=2)
print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))输出：
3/3 - 0s - loss: 6.0542 - mae: 1.9105 - mse: 6.0542
Testing set Mean Abs Error: 1.91 MPG9. 利用模型进行测试
testPredictions = model.predict(normedTestDataset).flatten()
a = plt.axes(aspect="equal")
plt.scatter(testLabels, testPredictions)
plt.xlabel("True Values [MPG]")
plt.ylabel("Predictions [MPG]")
lims = [0,50]
plt.xlim(lims)
plt.ylim(lims)
_ = plt.plot(lims,lims)输出：
模型预测的结果较好，预测值与真实值几乎相差无几。
error = testPredictions - testLabels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")输出：
误差分布并非完全吻合的高斯分布，这可能是由于数据量太小的原因。