Mnist convnet.R

library(keras)

model <- keras_model_sequential() %>%
    layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
                  input_shape = c(28, 28, 1)) %>%
    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_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu")

model <- model %>%
    layer_flatten() %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 10, activation = "softmax")

mnist <- dataset_mnist()
c(c(train_images, train_labels), c(test_images, test_labels)) %<-% mnist
train_images <- array_reshape(train_images, c(60000, 28, 28, 1))
train_images <- train_images / 255
test_images <- array_reshape(test_images, c(10000, 28, 28, 1))
test_images <- test_images / 255
train_labels <- to_categorical(train_labels)
test_labels <- to_categorical(test_labels)

model %>% compile(
    optimizer = "rmsprop",
    loss = "categorical_crossentropy",
    metrics = c("accuracy")
)

model %>% fit(
    train_images, train_labels,
    epochs = 5, batch_size=64,
    validation_split = 0.2
)

results <- model %>% evaluate(test_images, test_labels)

results