Nothing
library(keras)
mnist <- dataset_mnist()
train_images <- mnist$train$x
train_labels <- mnist$train$y
test_images <- mnist$test$x
test_labels <- mnist$test$y
network <- keras_model_sequential() %>%
layer_dense(units = 512, activation = "relu", input_shape = c(28*28)) %>%
layer_dense(units = 10, activation = "softmax")
network %>% compile(
optimizer = "rmsprop",
loss = "categorical_crossentropy",
metrics = c("accuracy")
)
train_images <- array_reshape(train_images, c(60000, 28*28))
train_images <- train_images / 255
test_images <- array_reshape(test_images, c(10000, 28*28))
test_images <- test_images / 255
train_labels <- to_categorical(train_labels)
test_labels <- to_categorical(test_labels)
network %>% fit(train_images, train_labels, epochs = 5, batch_size = 128)
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