In [1]:

    

from keras.datasets import boston_housing


    

In [2]:

    

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


    

    




Downloading data from https://s3.amazonaws.com/keras-datasets/boston_housing.npz
57344/57026 [==============================] - 2s 37us/step

In [3]:

    

train_data.shape


    

    
Out[3]:





(404, 13)

In [4]:

    

test_data.shape


    

    
Out[4]:





(102, 13)

In [5]:

    

train_targets


    

    
Out[5]:





array([ 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])

In [6]:

    

mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std

test_data -= mean
test_data /= std


    

In [7]:

    

from keras import models
from keras import layers

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


    

In [15]:

    

# k-fold validation
import numpy as np

k = 4
num_val_samples = len(train_data) // k
num_epochs = 10
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,
             epochs=num_epochs, batch_size=1, verbose=0)
    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
    all_scores.append(val_mae)


    

    




processing fold # 0
processing fold # 1
processing fold # 2
processing fold # 3

In [16]:

    

all_scores


    

    
Out[16]:





[2.2411861372466135,
 2.4880141244076266,
 3.0590836730333839,
 2.7673001997541675]

In [17]:

    

np.mean(all_scores)


    

    
Out[17]:





2.6388960336104477

In [22]:

    

# k-fold validation
import numpy as np

k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
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)


    

    




processing fold # 0
processing fold # 1
processing fold # 2
processing fold # 3

In [23]:

    

average_mae_history = [ np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]


    

In [24]:

    

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()


    

In [ ]: