使用R语言实现利用卷积神经网络对MNIST数据集进行分类

首先，需要安装并加载以下 R 包：keras、tidyverse、reshape2

然后，导入 MNIST 数据集，将其转换为所需格式：

library(keras)
library(tidyverse)
library(reshape2)

# 导入 MNIST 数据集
mnist <- dataset_mnist()

# 将训练集和测试集转换为 data.frame
x_train <- mnist$train$x %>% array_reshape(dim = c(nrow(.), 28 * 28)) %>% as.data.frame()
y_train <- mnist$train$y %>% as.data.frame()
x_test <- mnist$test$x %>% array_reshape(dim = c(nrow(.), 28 * 28)) %>% as.data.frame()
y_test <- mnist$test$y %>% as.data.frame()

# 将类别变量转换为因子
y_train <- factor(y_train)
y_test <- factor(y_test)

# 将像素值缩放到 0-1 范围内
x_train <- x_train / 255
x_test <- x_test / 255

# 将输入数据转换为矩阵
x_train <- as.matrix(x_train)
x_test <- as.matrix(x_test)

# 将输出数据转换为 one-hot 编码
y_train <- to_categorical(y_train)
y_test <- to_categorical(y_test)

接下来，构建卷积神经网络模型，包括两个卷积层和两个全连接层：

# 构建卷积神经网络模型
model <- keras_model_sequential() %>%
  layer_reshape(target_shape = c(28, 28, 1), input_shape = c(28 * 28)) %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 128, activation = "relu") %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 10, activation = "softmax")

# 编译模型
model %>% compile(
  loss = "categorical_crossentropy",
  optimizer = optimizer_rmsprop(),
  metrics = c("accuracy")
)

然后，训练模型：

# 训练模型
history <- model %>% fit(
  x_train, y_train,
  epochs = 10, batch_size = 128,
  validation_split = 0.2
)

最后，评估模型的性能：

# 评估模型性能
model %>% evaluate(x_test, y_test)

完整的代码如下：

library(keras)
library(tidyverse)
library(reshape2)

# 导入 MNIST 数据集
mnist <- dataset_mnist()

# 将训练集和测试集转换为 data.frame
x_train <- mnist$train$x %>% array_reshape(dim = c(nrow(.), 28 * 28)) %>% as.data.frame()
y_train <- mnist$train$y %>% as.data.frame()
x_test <- mnist$test$x %>% array_reshape(dim = c(nrow(.), 28 * 28)) %>% as.data.frame()
y_test <- mnist$test$y %>% as.data.frame()

# 将类别变量转换为因子
y_train <- factor(y_train)
y_test <- factor(y_test)

# 将像素值缩放到 0-1 范围内
x_train <- x_train / 255
x_test <- x_test / 255

# 将输入数据转换为矩阵
x_train <- as.matrix(x_train)
x_test <- as.matrix(x_test)

# 将输出数据转换为 one-hot 编码
y_train <- to_categorical(y_train)
y_test <- to_categorical(y_test)

# 构建卷积神经网络模型
model <- keras_model_sequential() %>%
  layer_reshape(target_shape = c(28, 28, 1), input_shape = c(28 * 28)) %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 128, activation = "relu") %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 10, activation = "softmax")

# 编译模型
model %>% compile(
  loss = "categorical_crossentropy",
  optimizer = optimizer_rmsprop(),
  metrics = c("accuracy")
)

# 训练模型
history <- model %>% fit(
  x_train, y_train,
  epochs = 10, batch_size = 128,
  validation_split = 0.2
)

# 评估模型性能
model %>% evaluate(x_test, y_test)

运行结果会输出模型在测试集上的准确率。