MNIST 데이터 분석하기

이것을 하기 위해서는 

1. Anaconda를 설치
2. 관련 설정을 해야 한다.

그런 다음에 다음의 코드를 실행하면 된다.

# Keras with R
install.packages("devtools")
install.packages("yaml")
install.packages("tensorflow")
install.packages("keras")
install.packages("reticulate")
library(devtools)
library(yaml)
library(tensorflow)
tensorflow::install_tensorflow(version = "gpu")
library(keras)
library(reticulate)

# Import Data
mnist  <- keras::dataset_mnist()
x_train <- mnist$train$x
y_train <- mnist$train$y
x_test  <- mnist$test$x
y_test  <- mnist$test$y

# reshape
x_train <- reticulate::array_reshape(x_train, c(nrow(x_train), 784))
x_test  <- reticulate::array_reshape(x_test, c(nrow(x_test), 784))

x_train <- x_train / 255
x_test  <- x_test / 255

y_train <- keras::to_categorical(y_train, num_classes = 10)
y_test  <- keras::to_categorical(y_test, num_classes = 10)

# Defining the Model
model <- keras::keras_model_sequential() 

model %>% 
keras::layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
keras::layer_dropout(rate = 0.4) %>% 
keras::layer_dense(units = 128, activation = 'relu') %>%
keras::layer_dropout(rate = 0.3) %>%
keras::layer_dense(units = 10, activation = 'softmax')

summary(model)

# Compling the Model
model %>% 

     keras::compile(loss       = 'categorical_crossentropy',
                       optimizer = optimizer_rmsprop(),
                       metrics   = c('accuracy'))

# Training the Model
history <- model %>% 
     keras::fit(x_train,
                y_train,
                epochs         = 30,
                batch_size     = 128,
                validation_split = 0.2)

plot(history)

# Evaluating the Model
model %>% 
     keras::evaluate(x_test, y_test)

# Prediction
model %>% 
     keras::predict_classes(x_test)

[출처] https://keras.rstudio.com/