用R语言编写不同超参数的CNN回归,找到最优的超参数组合?
时间: 2024-05-15 17:12:26 浏览: 13
要找到最优的超参数组合,可以使用网格搜索法或随机搜索法。下面是使用网格搜索法的示例代码:
首先,加载必要的库:
``` r
library(keras)
library(tensorflow)
library(caret)
```
然后,准备数据。假设我们要预测房价,有一个包含房价和其他特征的数据集。我们需要将数据集划分为训练集和测试集。
``` r
# 加载数据
data <- read.csv("housing.csv")
# 划分数据为训练集和测试集
set.seed(123)
train_index <- createDataPartition(data$medv, p = 0.8, list = FALSE)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]
# 标准化数据
preproc <- preProcess(train_data[, -14], method = c("center", "scale"))
train_data[, -14] <- predict(preproc, train_data[, -14])
test_data[, -14] <- predict(preproc, test_data[, -14])
# 创建输入和输出
x_train <- as.matrix(train_data[, -14])
y_train <- train_data$medv
x_test <- as.matrix(test_data[, -14])
y_test <- test_data$medv
```
接下来,定义CNN回归模型。在这个例子中,我们使用一个简单的2层CNN,每层都有16个卷积核。我们还添加了一个Flatten层和一个Dense层。
``` r
# 定义CNN回归模型
model <- keras_model_sequential() %>%
layer_conv_2d(filters = 16, kernel_size = c(3, 3), input_shape = c(13, 1, 1), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_conv_2d(filters = 16, kernel_size = c(3, 3), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_flatten() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1)
# 编译模型
model %>% compile(loss = "mse", optimizer = "adam")
```
接下来,我们准备定义要搜索的超参数。在这个例子中,我们将搜索以下超参数:
- 卷积核的数量
- 第一层卷积核的大小
- 第二层卷积核的大小
- dropout率
- batch_size
``` r
# 定义超参数空间
param_grid <- expand.grid(
filters = c(8, 16),
kernel_size_1 = c(2, 3),
kernel_size_2 = c(2, 3),
dropout = c(0, 0.2),
batch_size = c(32, 64)
)
```
接下来,我们定义一个函数,用于训练模型并评估超参数的性能。
``` r
# 定义函数
train_keras <- function(filters, kernel_size_1, kernel_size_2, dropout, batch_size) {
# 定义模型
model <- keras_model_sequential() %>%
layer_conv_2d(filters = filters, kernel_size = c(kernel_size_1, kernel_size_1), input_shape = c(13, 1, 1), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_conv_2d(filters = filters, kernel_size = c(kernel_size_2, kernel_size_2), activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_flatten() %>%
layer_dropout(rate = dropout) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1)
# 编译模型
model %>% compile(loss = "mse", optimizer = "adam")
# 训练模型
history <- model %>% fit(
x_train, y_train,
epochs = 10,
batch_size = batch_size,
validation_split = 0.2,
verbose = 0
)
# 计算测试集上的性能
y_pred <- model %>% predict(x_test)
mse <- mean((y_test - y_pred)^2)
# 返回结果
return(list(params = list(filters = filters, kernel_size_1 = kernel_size_1, kernel_size_2 = kernel_size_2, dropout = dropout, batch_size = batch_size), mse = mse))
}
```
最后,我们使用caret包中的train函数来执行网格搜索。
``` r
# 执行网格搜索
result <- caret::train(
x = NULL, y = NULL,
method = train_keras,
trControl = trainControl(method = "cv", number = 3),
tuneGrid = param_grid
)
# 打印最优的超参数组合和测试集上的MSE
result$bestTune
result$results[which.min(result$results$RMSE), ]
```
这样就可以找到最优的超参数组合了。