本文介紹了OpenCV python sklearn隨機超參數搜索的實現，分享給大家，具體如下：
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"""房價預測數據集 使用sklearn執行超參數搜索"""import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport tensorflow as tffrom tensorflow_core.python.keras.api._v2 import keras # 不能使用 pythonfrom sklearn.preprocessing import StandardScalerfrom sklearn.datasets import fetch_california_housingfrom sklearn.model_selection import train_test_split, RandomizedSearchCVfrom scipy.stats import reciprocalos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'assert tf.__version__.startswith('2.')# 0.打印導入模塊的版本print(tf.__version__)print(sys.version_info)for module in mpl, np, sklearn, pd, tf, keras: print("%s version:%s" % (module.__name__, module.__version__))# 顯示學習曲線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.加載數據集 california 房價housing = fetch_california_housing()print(housing.DESCR)print(housing.data.shape)print(housing.target.shape)# 2.拆分數據集 訓練集 驗證集 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.數據集歸一化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)# 創建keras模型def build_model(hidden_layers=1, # 中間層的參數 layer_size=30, learning_rate=3e-3): # 創建網絡層 model = keras.models.Sequential() model.add(keras.layers.Dense(layer_size, activation="relu", input_shape=x_train.shape[1:])) # 隱藏層設置 for _ in range(hidden_layers - 1): model.add(keras.layers.Dense(layer_size, activation="relu")) model.add(keras.layers.Dense(1)) # 優化器學習率 optimizer = keras.optimizers.SGD(lr=learning_rate) model.compile(loss="mse", optimizer=optimizer) return modeldef main(): # RandomizedSearchCV # 1.轉化為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.執行超搜索參數 # cross_validation:訓練集分成n份, n-1訓練, 最后一份驗證. 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 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.打印模型訓練過程 plot_learning_curves(history)if __name__ == '__main__': main() |
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原文鏈接：https://blog.csdn.net/weixin_45875105/article/details/104008975
