知识要点

• fetch_california_housing：加利福尼亚的房价数据，总计20640个样本，每个样本8个属性表示，以及房价作为target

• 超参数搜索的方式: 网格搜索, 随机搜索, 遗传算法搜索, 启发式搜索

• 超参数训练后用： gv.estimator调取最佳模型

• 函数式添加神经网络:

  • model.add(keras.layers.Dense(layer_size, activation = 'relu'))

  • model.compile(loss = 'mse', optimizer = optimizer)    # optimizer = keras.optimizers.SGD (learning_rate)

  • sklearn_model = KerasRegressor(build_fn = build_model)

from tensorflow.keras.wrappers.scikit_learn import KerasRegressor   # 回归神经网络
# 搜索最佳学习率
def build_model(hidden_layers = 1, layer_size = 30, learning_rate = 3e-3):model = keras.models.Sequential()model.add(keras.layers.Dense(layer_size, activation = 'relu', input_shape = x_train.shape[1:]))for _ in range(hidden_layers - 1):model.add(keras.layers.Dense(layer_size, activation = 'relu'))model.add(keras.layers.Dense(1))optimizer = keras.optimizers.SGD(learning_rate)model.compile(loss = 'mse', optimizer = optimizer)# model.summary()return model
sklearn_model = KerasRegressor(build_fn = build_model)

• callbacks = [keras.callbacks.EarlyStopping(patience = 5, min_delta = 1e-3)]  # 回调函数设置

• gv = GridSearchCV(sklearn_model, param_grid = params, n_jobs = 1, cv= 5,verbose = 1)  # 找最佳参数

• gv.fit(x_train_scaled, y_train)

1 导包

from tensorflow import keras
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
cpu=tf.config.list_physical_devices("CPU")
tf.config.set_visible_devices(cpu)
print(tf.config.list_logical_devices())

2 导入数据

from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_california_housinghousing = fetch_california_housing()
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)

3 标准化处理数据

from sklearn.preprocessing import StandardScaler, MinMaxScalerscaler =StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

4 函数式定义模型

from tensorflow.keras.wrappers.scikit_learn import KerasRegressor   # 回归神经网络
# 搜索最佳学习率
def build_model(hidden_layers = 1, layer_size = 30, learning_rate = 3e-3):model = keras.models.Sequential()model.add(keras.layers.Dense(layer_size, activation = 'relu', input_shape = x_train.shape[1:]))for _ in range(hidden_layers - 1):model.add(keras.layers.Dense(layer_size, activation = 'relu'))model.add(keras.layers.Dense(1))optimizer = keras.optimizers.SGD(learning_rate)model.compile(loss = 'mse', optimizer = optimizer)# model.summary()return model
sklearn_model = KerasRegressor(build_fn = build_model)

5 模型训练

callbacks = [keras.callbacks.EarlyStopping(patience = 5, min_delta = 1e-3)]
history = sklearn_model.fit(x_train_scaled, y_train, epochs = 10,validation_data = (x_valid_scaled, y_valid), callbacks = callbacks)

 6 超参数搜索

超参数搜索的方式:

• 网格搜索

  • 定义n维方格

  • 每个方格对应一组超参数

  • 一组一组参数尝试

• 随机搜索

• 遗传算法搜索

  • 对自然界的模拟

  • A: 初始化候选参数集合 --> 训练---> 得到模型指标作为生存概率

  • B: 选择 --> 交叉--> 变异 --> 产生下一代集合

  • C: 重新到A, 循环.

• 启发式搜索

  • 研究热点-- AutoML的一部分

  • 使用循环神经网络来生成参数

  • 使用强化学习来进行反馈, 使用模型来训练生成参数.

# 使用sklearn 的网格搜索, 或者随机搜索
from sklearn.model_selection import GridSearchCV, RandomizedSearchCVparams = {'learning_rate' : [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2],'hidden_layers': [2, 3, 4, 5], 'layer_size': [20, 60, 100]}gv = GridSearchCV(sklearn_model, param_grid = params, n_jobs = 1, cv= 5,verbose = 1)
gv.fit(x_train_scaled, y_train)

• 输出最佳参数

# 最佳得分
print(gv.best_score_)    # -0.47164334654808043
# 最佳参数
print(gv.best_params_)  # {'hidden_layers': 5,'layer_size': 100,'learning_rate':0.01}
# 最佳模型
print(gv.estimator)
''''''
gv.score

7 最佳参数建模

model = keras.models.Sequential()
model.add(keras.layers.Dense(100, activation = 'relu', input_shape = x_train.shape[1:]))
for _ in range(4):model.add(keras.layers.Dense(100, activation = 'relu'))
model.add(keras.layers.Dense(1))
optimizer = keras.optimizers.SGD(0.01)
model.compile(loss = 'mse', optimizer = optimizer)
model.summary()

callbacks = [keras.callbacks.EarlyStopping(patience = 5, min_delta = 1e-3)]
history = model.fit(x_train_scaled, y_train, epochs = 10,validation_data = (x_valid_scaled, y_valid), callbacks = callbacks)

 8 手动实现超参数搜索

• 根据参数进行多次模型的训练, 然后记录 loss

# 搜索最佳学习率
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 = 'mse', optimizer = optimizer, metrics = ['mse'])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)

# 画图
import pandas as pd
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(lr)plot_learning_curves(history)