Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = FALSE
)
## -----------------------------------------------------------------------------
# library(tfhub)
# m <- hub_load("path/to/a/module_dir")
## -----------------------------------------------------------------------------
# Sys.setenv(TFHUB_CACHE_DIR = "/my_module_cache")
## -----------------------------------------------------------------------------
# m <- hub_load("https://tfhub.dev/google/progan-128/1")
## -----------------------------------------------------------------------------
# y <- m(x)
## -----------------------------------------------------------------------------
# outputs <- m(list(apples=x1, oranges=x2), signature="fruit_to_pet", as_dict=TRUE)
# y1 = outputs$cats
# y2 = outputs$dogs
## -----------------------------------------------------------------------------
# library(keras)
#
# mnist <- dataset_mnist()
#
# input <- layer_input(shape(28,28), dtype = "int32")
#
# output <- input %>%
# layer_flatten() %>%
# layer_lambda(tensorflow::tf_function(function(x) tf$cast(x, tf$float32)/255)) %>%
# layer_dense(units = 10, activation = "softmax")
#
# model <- keras_model(input, output)
#
# model %>%
# compile(
# loss = "sparse_categorical_crossentropy",
# optimizer = "adam",
# metrics = "acc"
# )
#
# model %>%
# fit(x = mnist$train$x, y = mnist$train$y, validation_split = 0.2, epochs =1 )
#
# save_model_tf(model, "my_module/", include_optimizer = FALSE)
## -----------------------------------------------------------------------------
# module <- hub_load("my_module/")
#
# predictions <- module(mnist$test$x) %>%
# tf$argmax(axis = 1L)
#
# mean(as.integer(predictions) == mnist$test$y)
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