R/test.R

In samuelmacedo83/tf.distributed:

library(keras)
library(reticulate)

tf <- import("tensorflow")
strategy <- tf$distribute$MirroredStrategy()

######### data manipulation ##################
# mnist dataset
mnist <- 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 <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
# rescale
x_train <- x_train / 255
x_test <- x_test / 255

y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)
#################################################


model <- keras_model_sequential()
with(strategy$scope(), {
  model %>%
    layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>%
    layer_dropout(rate = 0.4) %>%
    layer_dense(units = 128, activation = 'relu') %>%
    layer_dropout(rate = 0.3) %>%
    layer_dense(units = 10, activation = 'softmax')
})


model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

history <- model %>% fit(
  x_train, y_train,
  epochs = 10, batch_size = 128,
  validation_split = 0.2
)