小編給大家分享一下OpenCV python sklearn如何實(shí)現(xiàn)隨機(jī)超參數(shù)搜索，希望大家閱讀完這篇文章之后都有所收獲，下面讓我們一起去探討吧！
本文介紹了OpenCV python sklearn隨機(jī)超參數(shù)搜索的實(shí)現(xiàn)，分享給大家，具體如下：
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"""
房價預(yù)測數(shù)據(jù)集 使用sklearn執(zhí)行超參數(shù)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.打印導(dǎo)入模塊的版本
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, sklearn, pd, tf, keras:
print("%s version:%s" % (module.__name__, module.__version__))
# 顯示學(xué)習(xí)曲線
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.加載數(shù)據(jù)集 california 房價
housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)
# 2.拆分?jǐn)?shù)據(jù)集 訓(xùn)練集 驗(yàn)證集 測試集
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.數(shù)據(jù)集歸一化
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)
# 創(chuàng)建keras模型
def build_model(hidden_layers=1, # 中間層的參數(shù)
layer_size=30,
learning_rate=3e-3):
# 創(chuàng)建網(wǎng)絡(luò)層
model = keras.models.Sequential()
model.add(keras.layers.Dense(layer_size, activation="relu",
input_shape=x_train.shape[1:]))
# 隱藏層設(shè)置
for _ in range(hidden_layers - 1):
model.add(keras.layers.Dense(layer_size,
activation="relu"))
model.add(keras.layers.Dense(1))
# 優(yōu)化器學(xué)習(xí)率
optimizer = keras.optimizers.SGD(lr=learning_rate)
model.compile(loss="mse", optimizer=optimizer)
return model
def main():
# RandomizedSearchCV
# 1.轉(zhuǎn)化為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.定義超參數(shù)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.執(zhí)行超搜索參數(shù)
# cross_validation:訓(xùn)練集分成n份, n-1訓(xùn)練, 最后一份驗(yàn)證.
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.顯示超參數(shù)
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.打印模型訓(xùn)練過程
plot_learning_curves(history)
if __name__ == '__main__':
main()看完了這篇文章，相信你對“OpenCV python sklearn如何實(shí)現(xiàn)隨機(jī)超參數(shù)搜索”有了一定的了解，如果想了解更多相關(guān)知識，歡迎關(guān)注創(chuàng)新互聯(lián)成都網(wǎng)站設(shè)計公司行業(yè)資訊頻道，感謝各位的閱讀！
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