keras fit函数利用验证集调整超参数的代码
时间: 2024-03-23 16:36:47 浏览: 57
好的,以下是使用Keras fit函数进行超参数调整的代码示例:
```python
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
# 构建模型
def create_model(optimizer='rmsprop', init='glorot_uniform'):
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer=init, activation='relu'))
model.add(Dense(1, kernel_initializer=init, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
# 导入数据
dataset = np.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# 划分数据集
X = dataset[:,0:8]
Y = dataset[:,8]
# 划分训练集和验证集
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2, random_state=42)
# 构建模型
model = KerasClassifier(build_fn=create_model, verbose=0)
# 定义超参数
param_grid = {
'batch_size': [10, 20, 40],
'epochs': [10, 50, 100],
'optimizer': ['rmsprop', 'adam']
}
# 调参
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(X_train, Y_train, validation_data=(X_val, Y_val))
# 输出结果
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
```
在这个示例中,我们使用了KerasClassifier将Keras模型转换为Scikit-Learn模型,并使用GridSearchCV函数进行超参数调整。我们定义了三个超参数:批次大小、迭代次数和优化器,并将它们作为参数传递给GridSearchCV函数。我们还使用train_test_split函数将数据集划分为训练集和验证集。最后,我们在GridSearchCV函数中使用validation_data参数将验证集传递给模型。最后,我们输出了最佳得分和最佳参数。
阅读全文