OpenCV python sklearn如何實(shí)現(xiàn)隨機(jī)超參數(shù)搜索

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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：

"""
房價(jià)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(jià)
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()

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