写一个用于CNN模型的Random Search算法代码
时间: 2023-06-24 08:04:30 浏览: 45
以下是一个示例代码,用于使用随机搜索算法优化CNN模型的超参数:
```python
import random
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
X = np.load('X.npy')
y = np.load('y.npy')
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# 定义超参数搜索空间
param_space = {
'lr': [0.001, 0.01, 0.1],
'batch_size': [32, 64, 128],
'num_filters': [32, 64, 128],
'kernel_size': [3, 5, 7],
'dropout_rate': [0.25, 0.5, 0.75]
}
# 定义模型架构
def create_model(lr, num_filters, kernel_size, dropout_rate):
model = Sequential()
model.add(Conv2D(num_filters, kernel_size, activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout_rate))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
optimizer = Adam(lr=lr)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
# 随机搜索算法
num_iterations = 10
best_accuracy = 0
for i in range(num_iterations):
# 随机选择超参数
lr = random.choice(param_space['lr'])
batch_size = random.choice(param_space['batch_size'])
num_filters = random.choice(param_space['num_filters'])
kernel_size = random.choice(param_space['kernel_size'])
dropout_rate = random.choice(param_space['dropout_rate'])
# 构建模型
model = create_model(lr, num_filters, kernel_size, dropout_rate)
# 训练模型
model.fit(X_train, y_train, batch_size=batch_size, epochs=10, verbose=0)
# 在测试集上评估模型
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test.argmax(axis=1), y_pred.argmax(axis=1))
# 更新最优模型
if accuracy > best_accuracy:
best_accuracy = accuracy
best_lr = lr
best_batch_size = batch_size
best_num_filters = num_filters
best_kernel_size = kernel_size
best_dropout_rate = dropout_rate
# 输出最优超参数和准确率
print('Best hyperparameters:')
print('lr:', best_lr)
print('batch_size:', best_batch_size)
print('num_filters:', best_num_filters)
print('kernel_size:', best_kernel_size)
print('dropout_rate:', best_dropout_rate)
print('Accuracy:', best_accuracy)
```
在这个示例代码中,我们使用了一个包含学习率、批量大小、滤波器数量、卷积核大小和丢失率的超参数搜索空间。我们通过随机选择这些超参数的值来构建不同的模型,并在测试集上评估它们的准确率。最后,我们选择具有最高准确率的模型作为最优模型,并输出其超参数和准确率。
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