boston.py

from keras.datasets import boston_housing

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

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

test_data -= mean
test_data /= std

from keras import models
from keras import layers


def build_model():
    input = layers.Input(shape=(train_data.shape[1],))
    output = layers.Dense(64, activation='relu')(input)
    output = layers.Dense(64, activation='relu')(output)
    output = layers.Dense(1)(output)
    model = models.Model(input=input, output=output)
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    return model


import numpy as np

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

num_epochs = 500
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)]

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()


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()

model = build_model()
model.fit(train_data, train_targets, epochs=80, batch_size=16, verbose=0)
test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)
print(test_mse_score)

import matplotlib.pyplot as plt

result = model.predict(test_data)
x = range(1, len(test_targets) + 1)
plt.scatter(x, test_targets, c='b')
plt.scatter(x, result, c='r')
plt.show()