OpenCV python sklearn随机超参数搜索的实现_Python

OpenCV python sklearn随机超参数搜索的实现

2020-04-18 10:48廷益--飞鸟 Python

这篇文章主要介绍了OpenCV python sklearn随机超参数搜索的实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

本文介绍了OpenCV python sklearn随机超参数搜索的实现，分享给大家，具体如下：

				?

									"""

									房价预测数据集 使用sklearn执行超参数搜索

									"""

									import matplotlib as mpl

									import matplotlib.pyplot as plt

									import numpy as np

									import sklearn

									import pandas as pd

									import os

									import sys

									import tensorflow as tf

									from tensorflow_core.python.keras.api._v2 import keras # 不能使用 python

									from sklearn.preprocessing import StandardScaler

									from sklearn.datasets import fetch_california_housing

									from sklearn.model_selection import train_test_split, RandomizedSearchCV

									from scipy.stats import reciprocal

									os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

									assert tf.__version__.startswith('2.')

									# 0.打印导入模块的版本

									print(tf.__version__)

									print(sys.version_info)

									for module in mpl, np, sklearn, pd, tf, keras:

									  print("%s version:%s" % (module.__name__, module.__version__))

									# 显示学习曲线

									def plot_learning_curves(his):

									  pd.DataFrame(his.history).plot(figsize=(8, 5))

									  plt.grid(True)

									  plt.gca().set_ylim(0, 1)

									  plt.show()

									# 1.加载数据集 california 房价

									housing = fetch_california_housing()

									print(housing.DESCR)

									print(housing.data.shape)

									print(housing.target.shape)

									# 2.拆分数据集 训练集 验证集 测试集

									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)

									# 3.数据集归一化

									scaler = StandardScaler()

									x_train_scaled = scaler.fit_transform(x_train)

									x_valid_scaled = scaler.fit_transform(x_valid)

									x_test_scaled = scaler.fit_transform(x_test)

									# 创建keras模型

									def build_model(hidden_layers=1, # 中间层的参数

									        layer_size=30,

									        learning_rate=3e-3):

									  # 创建网络层

									  model = keras.models.Sequential()

									  model.add(keras.layers.Dense(layer_size, activation="relu",

									                 input_shape=x_train.shape[1:]))

									 # 隐藏层设置

									  for _ in range(hidden_layers - 1):

									    model.add(keras.layers.Dense(layer_size,

									                   activation="relu"))

									  model.add(keras.layers.Dense(1))

									  # 优化器学习率

									  optimizer = keras.optimizers.SGD(lr=learning_rate)

									  model.compile(loss="mse", optimizer=optimizer)

									  return model

									def main():

									  # RandomizedSearchCV

									  # 1.转化为sklearn的model

									  sk_learn_model = keras.wrappers.scikit_learn.KerasRegressor(build_model)

									  callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

									  history = sk_learn_model.fit(x_train_scaled, y_train, epochs=100,

									                 validation_data=(x_valid_scaled, y_valid),

									                 callbacks=callbacks)

									  # 2.定义超参数集合

									  # f(x) = 1/(x*log(b/a)) a <= x <= b

									  param_distribution = {

									    "hidden_layers": [1, 2, 3, 4],

									    "layer_size": np.arange(1, 100),

									    "learning_rate": reciprocal(1e-4, 1e-2),

									  }

									  # 3.执行超搜索参数

									  # cross_validation:训练集分成n份, n-1训练, 最后一份验证.

									  random_search_cv = RandomizedSearchCV(sk_learn_model, param_distribution,

									                     n_iter=10,

									                     cv=3,

									                     n_jobs=1)

									  random_search_cv.fit(x_train_scaled, y_train, epochs=100,

									             validation_data=(x_valid_scaled, y_valid),

									             callbacks=callbacks)

									  # 4.显示超参数

									  print(random_search_cv.best_params_)

									  print(random_search_cv.best_score_)

									  print(random_search_cv.best_estimator_)

									  model = random_search_cv.best_estimator_.model

									  print(model.evaluate(x_test_scaled, y_test))

									  # 5.打印模型训练过程

									  plot_learning_curves(history)

									if __name__ == '__main__':

									  main()