项目背景：
波士顿房价预测，线性回归问题。基于Keras实现。使用 K 折验证可以可靠地评估模型。
此代码的交叉验证，没有调包。
from keras.datasets import boston_housing
from keras import models
from keras import layers
import numpy as np
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
# 标准化
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
# 测试数据标准化的均值和标准差都是在训练数据上计算得到的
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
k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
all_scores = []
all_mae_histories = []
for i in range(k):
print('processing fold #', i)
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]
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)
model = build_model()
history = model.fit(partial_train_data, partial_train_targets,
validation_data=(val_data, val_targets),
epochs=num_epochs, batch_size=1, verbose=0)
mae_history = history.history['val_mean_absolute_error']
all_mae_histories.append(mae_history)
average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]