波士顿房价问题

波士顿房价 Tensorflow Keras 代码

#! /usr/bin/python
import tensorflow as tf
from tensorflow import keras

from keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

print(train_data.shape)
print(test_data.shape)
print( train_targets )

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():
    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

import numpy as np

k=4
num_val_samples = len(train_data) // k
num_epochs =500
all_mae_histories =[]
all_scores=[]

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