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library(keras)
# load data
c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()
# reshape and rescale
x_train <- array_reshape(x_train, dim = c(nrow(x_train), 784)) / 255
x_test <- array_reshape(x_test, dim = c(nrow(x_test), 784)) / 255
# one-hot encode response
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)
# define and compile model
model <- keras_model_sequential()
model %>%
layer_dense(units = 32, activation = 'relu', input_shape = c(784),
name = "image") %>%
layer_dense(units = 16, activation = 'relu') %>%
layer_dense(units = 10, activation = 'softmax',
name = "prediction") %>%
compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_rmsprop(),
metrics = c('accuracy')
)
# train model
history <- model %>% fit(
x_train, y_train,
epochs = 30, batch_size = 128,
validation_split = 0.2
)
# save model
export_savedmodel(model, "keras-mnist", as_text = TRUE)
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