深度学习模型调参-基于keras的python学习笔记（四）

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适用于少量数据的实验是非常有效的方法。

from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier


# 构建模型
def create_model(optimizer='adam', init='glorot_uniform'):
    # 构建模型
    model = Sequential()
    model.add(Dense(units=12, kernel_initializer=init, input_dim=8, activation='relu'))
    model.add(Dense(units=8, kernel_initializer=init, activation='relu'))
    model.add(Dense(units=1, kernel_initializer=init, activation='sigmoid'))

    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    return model

seed = 7
# 设定随机数种子
np.random.seed(seed)

# 导入数据
dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
# 分割输入x和输出Y
x = dataset[:, 0 : 8]
Y = dataset[:, 8]

#创建模型 for scikit-learn
model = KerasClassifier(build_fn=create_model, verbose=0)

# 构建需要调参的参数
param_grid = {}
param_grid['optimizer'] = ['rmsprop', 'adam']
param_grid['init'] = ['glorot_uniform', 'normal', 'uniform']
param_grid['epochs'] = [50, 100, 150, 200]
param_grid['batch_size'] = [5, 10, 20]

# 调参
grid = GridSearchCV(estimator=model, param_grid=param_grid)
results = grid.fit(x, Y)

# 输出结果
print('Best: %f using %s' % (results.best_score_, results.best_params_))
means = results.cv_results_['mean_test_score']
stds = results.cv_results_['std_test_score']
params = results.cv_results_['params']

for mean, std, param in zip(means, stds, params):
    print('%f (%f) with: %r' % (mean, std, param))

笔者用了20分钟！！！而且运行一次不一定能找到最优的参数组合，需要多次重复执行。