波士顿房价总结

1、加载数据集；
2、准备数据；
3、模型定义；
4、K折验证；
5、绘制验证分数；
6、训练最终模型。

# 加载波士顿房价数据
from keras.datasets import boston_housing

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

# 准备数据
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std

# 模型定义
from keras import models
from keras import layers

def build_model():
    model = models.Sequential() # 因为需要将同一个模型多次实例化，所以用一个函数来构建模型
    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)) # 无激活函数，标量回归的典型设置
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    return  model

'''
# k 折验证
import numpy as np

k = 4 # 4折
num_val_sample = len(train_data) // 4
num_epochs = 100
all_scores = []

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_sample: (i + 1) * num_val_sample]
    val_targets = train_targets[i * num_val_sample: (i + 1) * num_val_sample]

    # 准备训练数据：其它所有分区的数据
    partial_train_data = np.concatenate([train_data[:i * num_val_sample],
                                         train_data[(i + 1) * num_val_sample:]],
                                        axis=0)
    partial_train_targets = np.concatenate([train_targets[:i * num_val_sample],
                                         train_targets[(i + 1) * num_val_sample:]],
                                        axis=0)

    model = build_model() # 构建Keras模型（已编译）
    model.fit(partial_train_data, partial_train_targets,
              epochs=num_epochs, batch_size=1, verbose=0) # 训练模型（静默模式，verbose=0）
    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0) # 在验证数据上评估模型
    all_scores.append(val_mae)

print(all_scores)
print(np.mean(all_scores))
'''


# 保存每折的验证结果
import numpy as np

k = 4 # 4折
num_val_samples = len(train_data) // 4
num_epochs = 500
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：其它所有分区的数据
    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)

    model = build_model() # 构建keras模型（已编译）
    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)

# 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)
]

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

# 绘制验证分数（删除前10个数据点）
def smooth_curve(points, factor=0.9):
    smoothed_points = []
    for point in points:
        if smoothed_points:
            previous = smoothed_points[-1]
            smoothed_points.append(previous * factor + point * (1 - factor))
        else:
            smoothed_points.append(point)
    return smoothed_points

smooth_mae_history = smooth_curve(average_mae_history[10:])

plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.show()

'''
# 训练最终模型
model = build_model() # 一个全新的编译好的模型
model.fit(train_data, train_targets,
          epochs=80, batch_size=16, verbose=0) # 在所有训练数据上训练模型
test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)
print(test_mae_score)
'''

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