在R Part2中利用MNIST数据利用深度网络读取手写数字

# 安装和加载Keras库
install.packages("keras")
library(keras)

# 加载MNIST数据集
mnist <- dataset_mnist()
x_train <- mnist$train$x
y_train <- mnist$train$y
x_test <- mnist$test$x
y_test <- mnist$test$y

# 数据预处理
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
x_train <- x_train / 255
x_test <- x_test / 255
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

# 构建深度神经网络模型
model <- keras_model_sequential()
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>% 
  layer_dropout(rate = 0.3) %>% 
  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 = 20, batch_size = 128,
  validation_split = 0.2
)

# 评估模型
score <- model %>% evaluate(x_test, y_test, verbose = 0)
cat('Test loss:', score[[1]], '\n')
cat('Test accuracy:', score[[2]], '\n')