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使用Keras处理回归问题——以预测房价为例

1.波士顿房价数据集:

本次使用的波士顿房价数据集包含506个样本,其中404个训练样本和102个测试样本。每个样本包含13个特征,需要注意的是每个特征都有不同的取值范围,包括0-1、0-100、1-12等不同的取值范围。

2.分步骤实现:

2.1 加载波士顿房价数据集

2.2 数据预处理

2.3 构建网络

2.4 使用K折验证

2.5 绘制验证分数

3.全流程代码:

import numpy as np
from keras.datasets import boston_housing
from keras import models
from keras import layers
import matplotlib.pyplot as plt

# 加载数据集
(train_data,train_targets),(test_data,test_targets) = boston_housing.load_data()

# 数据预处理
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std

test_data -= mean
test_data /= std

# 构建网络
def build_model():
    model = models.Sequential()
    model.add(layers.Dense(64,activation='relu',input_shape=(train_data.shape[1],)))
    model.add(layers.Dense(64,activation='relu'))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop',loss='mse',metrics=['mae'])
    return model

# 使用K折验证
k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
all_mae_histories = []

for i in range(k):
    # print('processing fold #', i)

    # 准备验证数据:第k个分区的数据
	val_data = train_data[i * num_val_samples:(i + 1) * num_val_samples]
	val_targets = train_targets[i * num_val_samples:(i + 1) * num_val_samples]
	
	# 准备训练数据:其他所有分区的数据
	partial_train_data = np.concatenate([train_data[:i * num_val_samples], train_data[(i + 1) * num_val_samples:]],
											axis=0)
	partial_train_targets = np.concatenate([train_targets[:i * num_val_samples], train_targets[(i + 1) * num_val_samples:]],
										   axis=0)

	# 构建keras模型(已编译)
	model = build_model()
	# 训练模型,verbose=0表示静默模式
	history = model.fit(partial_train_data, partial_train_targets, validation_data=(val_data, val_targets),
						epochs=num_epochs, batch_size=1, verbose=0)
	# print(history.history.keys())
	mae_history = history.history['val_mae']
	all_mae_histories.append(mae_history)

# 计算所有轮次中的k折验证分数平均值
average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]

print(average_mae_history)

# 绘制验证分数
plt.plot(range(1,len(average_mae_history)+1),average_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.show()

Reference:
《Deep Learning with Python》

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