from keras.datasets import boston_housing
from tensorflow.keras import models
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

# DATA
(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

# MODEL
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

# VAL
k=4
num_val_samples = len(train_data)//k
num_epochs = 80
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
    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)]

# PLOT
def smooth_curve(points, factor=0.9): # 指数移动平均光滑
    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()

# TEST
test_msa_score, test_mae_score = model.evaluate(test_data, test_targets)

