Tensorflow_R_MNIST 예제 (Keras)

Tensorflow를 R에서 테스트 진행하여 가장 기본 예제인 MNIST를 사용합니다.

R - 3.5.3

RStudio - 1.1.463

OS - Windows10

Mem - 16G

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참고 - https://tensorflow.rstudio.com/tutorials/beginners/

TensorFlow for R

 

Tensorflow_R.R

jonghee

2020-04-13

#install.packages("tensorflow")
#install.packages("keras")

# Tensorflow 라이브러리 로드
library(tensorflow)
#install_tensorflow()

# dplyr / keras 라이브러리 로드
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(keras)

# MNIST 데이터 셋 로드
mnist <- dataset_mnist()
mnist$train$x <- mnist$train$x/255
mnist$test$x <- mnist$test$x/255

# 모델 생성
# Activation - RELU
# DROPOUT 0.2
# ACtivation - SOFTMAX
model <- keras_model_sequential() %>% 
  layer_flatten(input_shape = c(28, 28)) %>% 
  layer_dense(units = 128, activation = "relu") %>% 
  layer_dropout(0.2) %>% 
  layer_dense(10, activation = "softmax")

summary(model)

## Model: "sequential"
## ________________________________________________________________________________
## Layer (type)                        Output Shape                    Param #     
## ================================================================================
## flatten (Flatten)                   (None, 784)                     0           
## ________________________________________________________________________________
## dense (Dense)                       (None, 128)                     100480      
## ________________________________________________________________________________
## dropout (Dropout)                   (None, 128)                     0           
## ________________________________________________________________________________
## dense_1 (Dense)                     (None, 10)                      1290        
## ================================================================================
## Total params: 101,770
## Trainable params: 101,770
## Non-trainable params: 0
## ________________________________________________________________________________

# LOSS FUNCTION - Sparse Categoricla Crossentropy
# Optimizer - ADAM
# Metrics - ACCURACY
model %>% 
  compile(
    loss = "sparse_categorical_crossentropy",
    optimizer = "adam",
    metrics = "accuracy"
  )

# epochs - 5
model %>% 
  fit(
    x = mnist$train$x, y = mnist$train$y,
    epochs = 5,
    validation_split = 0.3,
    verbose = 2
  )

# prediction
predictions <- predict(model, mnist$test$x)
head(predictions, 2)

##              [,1]         [,2]         [,3]         [,4]         [,5]
## [1,] 1.580725e-07 2.300006e-08 2.606068e-05 4.458812e-04 9.343597e-11
## [2,] 7.055627e-08 2.735742e-04 9.996517e-01 6.442662e-05 1.110566e-13
##              [,6]         [,7]         [,8]         [,9]        [,10]
## [1,] 8.591602e-07 5.672325e-11 9.995247e-01 3.809913e-07 1.920494e-06
## [2,] 2.437132e-06 9.999303e-08 5.518335e-13 7.729958e-06 5.565607e-13

# Evaluate
model %>% 
  evaluate(mnist$test$x, mnist$test$y, verbose = 0)

## $loss
## [1] 0.08904964
## 
## $accuracy
## [1] 0.9734

# 생성된 모델 저장
save_model_tf(object = model, filepath = "model")

# 저장된 모델 로드
reloaded_model <- load_model_tf("model")
all.equal(predict(model, mnist$test$x), predict(reloaded_model, mnist$test$x))

## [1] TRUE