如何使用OpenCV、Python和sklearn实现随机超参数搜索？
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本文共计587个文字，预计阅读时间需要3分钟。
本文介绍了使用OpenCV、Python和sklearn实现随机超参数搜索的实践。以下是一个简单的例子：
房价预测数据集，使用sklearn执行超参数搜索
pythonimport matplotlib as mplimport matplotlib.pyplot as pltimport numpy as np
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()
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持易盾网络。
本文共计587个文字，预计阅读时间需要3分钟。
本文介绍了使用OpenCV、Python和sklearn实现随机超参数搜索的实践。以下是一个简单的例子：
房价预测数据集，使用sklearn执行超参数搜索
pythonimport matplotlib as mplimport matplotlib.pyplot as pltimport numpy as np
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()
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持易盾网络。