贝叶斯优化cnn神经网络的代码,并详细解释
时间: 2023-08-11 20:19:48 浏览: 83
下面是一个使用贝叶斯优化调整CNN超参数的Python代码示例:
```python
import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern
from bayes_opt import BayesianOptimization
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten
from tensorflow.keras.utils import to_categorical
# 加载MNIST数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 对图像数据进行归一化和重新调整形状
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
X_train = np.expand_dims(X_train, axis=-1)
X_test = np.expand_dims(X_test, axis=-1)
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
# 定义CNN模型
def create_model(num_filters, kernel_size, learning_rate):
model = Sequential()
model.add(Conv2D(num_filters, kernel_size=kernel_size, activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(num_filters, kernel_size=kernel_size, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
optimizer = tf.keras.optimizers.Adam(lr=learning_rate)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
return model
# 定义代价函数
def cost_function(num_filters, kernel_size, learning_rate):
model = create_model(int(num_filters), int(kernel_size), learning_rate)
model.fit(X_train, y_train, epochs=1, validation_data=(X_test, y_test), verbose=0)
_, accuracy = model.evaluate(X_test, y_test, verbose=0)
return -accuracy
# 定义贝叶斯优化对象
bo = BayesianOptimization(cost_function, {'num_filters': (16, 64), 'kernel_size': (3, 5), 'learning_rate': (0.001, 0.01)}, random_state=42)
# 设置代理模型为高斯过程
kernel = Matern(length_scale=1, nu=2.5)
gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-4, normalize_y=True, n_restarts_optimizer=10, random_state=42)
bo.set_gp(gp)
# 运行贝叶斯优化
bo.maximize(init_points=10, n_iter=10)
# 输出最优超参数
print(bo.max)
```
该代码使用了Python中的BayesianOptimization库来实现贝叶斯优化。首先,它加载MNIST数据集并定义了CNN模型。然后,它定义了一个cost_function函数,该函数使用指定的超参数(num_filters,kernel_size和learning_rate)来创建CNN模型并计算其在MNIST测试集上的准确性。由于BayesianOptimization库要最大化代价函数,因此我们将准确性的负值作为返回值。
接下来,代码创建了一个BayesianOptimization对象,并使用高斯过程作为代理模型。然后,它运行了贝叶斯优化算法,最大化了代价函数。在每次迭代中,代理模型会根据先前的超参数和性能指标来选择下一个超参数组合。最后,代码输出了具有最高性能指标的超参数组合。
总之,该Python代码示例演示了如何使用贝叶斯优化调整CNN超参数,以获得最佳性能。