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## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(eval = FALSE)
## ------------------------------------------------------------------------
# install.packages(tfdeploy)
## ------------------------------------------------------------------------
# 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 = 256, activation = 'relu', input_shape = c(784),
# name = "image") %>%
# layer_dense(units = 128, 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 = 35, batch_size = 128,
# validation_split = 0.2
# )
## ------------------------------------------------------------------------
# preds <- predict(model, x_test[1:5,])
## ------------------------------------------------------------------------
# library(tfdeploy)
# export_savedmodel(model, "savedmodel")
## ------------------------------------------------------------------------
# view_savedmodel("savedmodel")
## ------------------------------------------------------------------------
# library(tfdeploy)
# serve_savedmodel('savedmodel', browse = TRUE)
## ------------------------------------------------------------------------
# library(cloudml)
# cloudml_deploy("savedmodel", name = "keras_mnist", version = "keras_mnist_1")
## ------------------------------------------------------------------------
# export_savedmodel(model, "savedmodel")
## ------------------------------------------------------------------------
# library(tfestimators)
#
# mtcars_input_fn <- function(data, num_epochs = 1) {
# input_fn(data,
# features = c("disp", "cyl"),
# response = "mpg",
# batch_size = 32,
# num_epochs = num_epochs)
# }
#
# cols <- feature_columns(column_numeric("disp"), column_numeric("cyl"))
#
# model <- linear_regressor(feature_columns = cols)
#
# indices <- sample(1:nrow(mtcars), size = 0.80 * nrow(mtcars))
# train <- mtcars[indices, ]
# test <- mtcars[-indices, ]
#
# model %>% train(mtcars_input_fn(train, num_epochs = 10))
#
# export_savedmodel(model, "savedmodel")
## ------------------------------------------------------------------------
# library(tensorflow)
#
# sess <- tf$Session()
# datasets <- tf$contrib$learn$datasets
# mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
#
# # Note that we define x as the input tensor
# # and y as the output tensor that will contain
# # the scores. These are referenced in export_savedmodel
# x <- tf$placeholder(tf$float32, shape(NULL, 784L))
# W <- tf$Variable(tf$zeros(shape(784L, 10L)))
# b <- tf$Variable(tf$zeros(shape(10L)))
# y <- tf$nn$softmax(tf$matmul(x, W) + b)
# y_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
# cross_entropy <- tf$reduce_mean(
# -tf$reduce_sum(y_ * tf$log(y), reduction_indices=1L)
# )
#
# optimizer <- tf$train$GradientDescentOptimizer(0.5)
# train_step <- optimizer$minimize(cross_entropy)
#
# init <- tf$global_variables_initializer()
# sess$run(init)
#
# for (i in 1:1000) {
# batches <- mnist$train$next_batch(100L)
# batch_xs <- batches[[1]]
# batch_ys <- batches[[2]]
# sess$run(train_step,
# feed_dict = dict(x = batch_xs, y_ = batch_ys))
# }
#
# export_savedmodel(
# sess,
# "savedmodel",
# inputs = list(image_input = x),
# outputs = list(scores = y))
## ------------------------------------------------------------------------
# library(cloudml)
# cloudml_deploy("savedmodel", name = "keras_mnist")
## ------------------------------------------------------------------------
# library(rsconnect)
# deployTFModel('savedmodel', account = <username>, server = <internal_connect_server>)
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