keras房屋价格预测

本文主要介绍使用keras来做房屋价格预测。

示例代码：

from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from keras import backend as K
import matplotlib.pyplot as plt

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
'''
我们有404个训练样本和102个测试样本。 该数据包括13个功能。 输入数据中的13个功能如下
＃1。人均犯罪率。
＃2。占地面积超过25,000平方英尺的住宅用地比例。
＃3。每个城镇非零售业务占比的比例。
＃4。Charles River虚拟变量（如果管道限制河流则= 1;否则为0）。
＃5。一氧化氮浓度（每千万份）。
＃6。每个住宅的平均房间数。
＃7。1940年以前建造的自住单位比例。
＃8。到波士顿五个就业中心的加权距离。
＃9。径向高速公路的可达性指数。
＃10。每10,000美元的全额物业税率。
＃11。城镇的学生与教师比例。
＃12. 1000 *（Bk - 0.63）** 2其中Bk是城镇黑人的比例。
＃13.人口状况较低。
＃
＃目标是自住房屋的中位数，以千美元计算：
'''
print(train_data.shape)
print(train_targets)

'''
将神经网络输入所有采用不同范围的值都是有问题的。 网络可能能够
自动适应这种异构数据，但肯定会使学习变得更加困难。 处理此类数据
的一种广泛的最佳实践是进行特征标准化：对于输入数据中的每个特征（
输入数据矩阵中的一列），我们将减去特征的均值并除以标准差，因此
该特征将以0为中心，并具有单位标准偏差。
'''
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std

test_data -= mean
test_data /= std


'''
由于可用的样本很少，我们将使用一个非常小的网络，其中有两个隐藏层，
每个层有64个单元。 通常，拥有的训练数据越少，过度拟合就越差，
使用小型网络是缓解过度拟合的一种方法。
'''
# 搭建网络
def build_model():
    model = Sequential()
    model.add(Dense(64, activation='relu',
                    input_shape=(train_data.shape[1],)))
    model.add(Dense(64, activation='relu'))
    model.add(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 = []
for i in range(k):
    print('processing fold #', i)
    # 验证数据预处理：通过k来划分
    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
    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
    )

    # 创建已编译好的keras模型
    model = build_model()
    # 训练模型
    model.fit(partial_train_data, partial_train_targets,
              epochs=num_epochs, batch_size=1, verbose=0)
    # 通过验证集验证模型
    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
    all_scores.append(val_mae)

print(all_scores)

K.clear_session()

num_epochs = 500
all_mae_histories = []
for i in range(k):
    print('processing fold #', i)
    # Prepare the validation data: data from partition # k
    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]

    # Prepare the training data: data from all other partitions
    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)

    # Build the Keras model (already compiled)
    model = build_model()
    # Train the model (in silent mode, verbose=0)
    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)]


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


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()

运行结果：

(404, 13)
[15.2 42.3 50.  21.1 17.7 18.5 11.3 15.6 15.6 14.4 12.1 17.9 23.1 19.9
 15.7  8.8 50.  22.5 24.1 27.5 10.9 30.8 32.9 24.  18.5 13.3 22.9 34.7
 16.6 17.5 22.3 16.1 14.9 23.1 34.9 25.  13.9 13.1 20.4 20.  15.2 24.7
 22.2 16.7 12.7 15.6 18.4 21.  30.1 15.1 18.7  9.6 31.5 24.8 19.1 22.
 14.5 11.  32.  29.4 20.3 24.4 14.6 19.5 14.1 14.3 15.6 10.5  6.3 19.3
 19.3 13.4 36.4 17.8 13.5 16.5  8.3 14.3 16.  13.4 28.6 43.5 20.2 22.
 23.  20.7 12.5 48.5 14.6 13.4 23.7 50.  21.7 39.8 38.7 22.2 34.9 22.5
 31.1 28.7 46.  41.7 21.  26.6 15.  24.4 13.3 21.2 11.7 21.7 19.4 50.
 22.8 19.7 24.7 36.2 14.2 18.9 18.3 20.6 24.6 18.2  8.7 44.  10.4 13.2
 21.2 37.  30.7 22.9 20.  19.3 31.7 32.  23.1 18.8 10.9 50.  19.6  5.
 14.4 19.8 13.8 19.6 23.9 24.5 25.  19.9 17.2 24.6 13.5 26.6 21.4 11.9
 22.6 19.6  8.5 23.7 23.1 22.4 20.5 23.6 18.4 35.2 23.1 27.9 20.6 23.7
 28.  13.6 27.1 23.6 20.6 18.2 21.7 17.1  8.4 25.3 13.8 22.2 18.4 20.7
 31.6 30.5 20.3  8.8 19.2 19.4 23.1 23.  14.8 48.8 22.6 33.4 21.1 13.6
 32.2 13.1 23.4 18.9 23.9 11.8 23.3 22.8 19.6 16.7 13.4 22.2 20.4 21.8
 26.4 14.9 24.1 23.8 12.3 29.1 21.  19.5 23.3 23.8 17.8 11.5 21.7 19.9
 25.  33.4 28.5 21.4 24.3 27.5 33.1 16.2 23.3 48.3 22.9 22.8 13.1 12.7
 22.6 15.  15.3 10.5 24.  18.5 21.7 19.5 33.2 23.2  5.  19.1 12.7 22.3
 10.2 13.9 16.3 17.  20.1 29.9 17.2 37.3 45.4 17.8 23.2 29.  22.  18.
 17.4 34.6 20.1 25.  15.6 24.8 28.2 21.2 21.4 23.8 31.  26.2 17.4 37.9
 17.5 20.   8.3 23.9  8.4 13.8  7.2 11.7 17.1 21.6 50.  16.1 20.4 20.6
 21.4 20.6 36.5  8.5 24.8 10.8 21.9 17.3 18.9 36.2 14.9 18.2 33.3 21.8
 19.7 31.6 24.8 19.4 22.8  7.5 44.8 16.8 18.7 50.  50.  19.5 20.1 50.
 17.2 20.8 19.3 41.3 20.4 20.5 13.8 16.5 23.9 20.6 31.5 23.3 16.8 14.
 33.8 36.1 12.8 18.3 18.7 19.1 29.  30.1 50.  50.  22.  11.9 37.6 50.
 22.7 20.8 23.5 27.9 50.  19.3 23.9 22.6 15.2 21.7 19.2 43.8 20.3 33.2
 19.9 22.5 32.7 22.  17.1 19.  15.  16.1 25.1 23.7 28.7 37.2 22.6 16.4
 25.  29.8 22.1 17.4 18.1 30.3 17.5 24.7 12.6 26.5 28.7 13.3 10.4 24.4
 23.  20.  17.8  7.  11.8 24.4 13.8 19.4 25.2 19.4 19.4 29.1]
processing fold # 0
WARNING:tensorflow:From D:\Users\Seavan_CC\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:1349: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
2018-11-04 19:09:25.661535: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
processing fold # 1
processing fold # 2
processing fold # 3
[2.0597879414511198, 2.2206014052476033, 2.9226412867555522, 2.3122610349466304]
processing fold # 0
processing fold # 1
processing fold # 2
processing fold # 3