对房价预测进行超参数搜索

• 超参数搜索

• • 为什么要超参数搜索？

• 神经网络有很多训练过程中不变的参数
• • 网络结构参数：几层，每层宽度，每层激活函数等
  • 训练参数：batch_size，学习率，学习率衰减算法
• 手工人力耗费时间

搜索策列：

• 网格搜索

• 随机搜索

• 遗传算法搜索

• 启发式搜索

对房价预测进行超参数搜索

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)

from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

from sklearn.model_selection import train_test_split

x_train_all, x_test, y_train_all, y_test = train_test_split(
    housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
    x_train_all, y_train_all, random_state = 11)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

超参数搜索，只搜索学习率，用一层for循环来搜索

# learning_rate: [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2]
# W = W + grad * learning_rate

learning_rates = [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2]
histories = []
for lr in learning_rates:
    model = keras.models.Sequential([
        keras.layers.Dense(30, activation='relu',
                           input_shape=x_train.shape[1:]),
        keras.layers.Dense(1),
    ])
    optimizer = keras.optimizers.SGD(lr)
    model.compile(loss="mean_squared_error", optimizer=optimizer)
    callbacks = [keras.callbacks.EarlyStopping(
        patience=5, min_delta=1e-2)]
    history = model.fit(x_train_scaled, y_train,
                        validation_data = (x_valid_scaled, y_valid),
                        epochs = 100,
                        callbacks = callbacks)
    histories.append(history)

def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    plt.gca().set_ylim(0, 1)
    plt.show()
for lr, history in zip(learning_rates, histories):
    print("Learning rate: ", lr)
    plot_learning_curves(history)