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)