context("Save")
train_mnist_graph <- function(sess) {
IPython <- IPython <- reticulate::import("IPython")
py_capture_output <- IPython$utils$capture$capture_output
with(py_capture_output(), {
datasets <- tf$contrib$learn$datasets
mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
})
if (tf_version() >= "1.14")
placeholder <- tf$compat$v1$placeholder
else
placeholder <- tf$placeholder
x <- 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_ <- placeholder(tf$float32, shape(NULL, 10L))
cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * log(y), reduction_indices = 1L))
if (tf_version() >= "1.14")
optimizer <- tf$compat$v1$train$GradientDescentOptimizer(0.5)
else
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train_step <- optimizer$minimize(cross_entropy)
if (tf_version() >= "1.14")
init <- tf$compat$v1$global_variables_initializer()
else
init <- tf$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))
}
correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L))
accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
sess$run(accuracy, feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels))
list(input = x, output = y)
}
# TODO: consider testing in a new R session with tf$compat$v1$disable_eager_execution()
# skip("Don't have sessions to export when running eager.")
if (!tf$executing_eagerly())
test_that("export_savedmodel() works with MNIST", {
skip_if_no_tensorflow()
temp_path <- tempfile()
if (tf_version() >= "1.14")
sess <- tf$compat$v1$Session()
else
sess <- tf$Session()
tensors <- train_mnist_graph(sess)
export_savedmodel(
sess,
temp_path,
inputs = list(images = tensors$input),
outputs = list(scores = tensors$output)
)
expect_true(file.exists(file.path(temp_path, "saved_model.pb")))
})
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