Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
eval = (identical(Sys.getenv("EVAL_VIGNETTE", "false"), "true") || identical(Sys.getenv("CI"), "true")) && (tensorflow::tf_version() >= "2.0")
)
## -----------------------------------------------------------------------------
# library(tfdatasets)
# hearts_dataset <- tensor_slices_dataset(hearts)
# spec <- feature_spec(hearts_dataset, target ~ .)
## -----------------------------------------------------------------------------
# spec %>%
# step_numeric_column(age)
## ---- eval=FALSE--------------------------------------------------------------
# # Represent a 10-element vector in which each cell contains a tf$float32.
# spec %>%
# step_numeric_column(bowling, shape = 10)
#
# # Represent a 10x5 matrix in which each cell contains a tf$float32.
# spec %>%
# step_numeric_column(my_matrix, shape = c(10, 5))
## ---- eval=FALSE--------------------------------------------------------------
# # use a function that defines tensorflow ops.
# spec %>%
# step_numeric_column(age, normalizer_fn = function(x) (x-10)/5)
#
# # use a scaler
# spec %>%
# step_numeric_column(age, normalizer_fn = scaler_standard())
## -----------------------------------------------------------------------------
# # First, convert the raw input to a numeric column.
# spec <- spec %>%
# step_numeric_column(age)
#
# # Then, bucketize the numeric column.
# spec <- spec %>%
# step_bucketized_column(age, boundaries = c(30, 50, 70))
## ---- eval=FALSE--------------------------------------------------------------
# # Create categorical output for an integer feature named "my_feature_b",
# # The values of my_feature_b must be >= 0 and < num_buckets
# spec <- spec %>%
# step_categorical_column_with_identity(my_feature_b, num_buckets = 4)
## -----------------------------------------------------------------------------
# spec <- spec %>%
# step_categorical_column_with_vocabulary_list(
# thal,
# vocabulary_list = c("fixed", "normal", "reversible")
# )
## ---- eval=FALSE--------------------------------------------------------------
# spec <- spec %>%
# step_categorical_column_with_vocabulary_file(thal, vocabulary_file = "thal.txt")
## -----------------------------------------------------------------------------
# spec <- spec %>%
# step_categorical_column_with_hash_bucket(thal, hash_bucket_size = 100)
## ---- eval=FALSE--------------------------------------------------------------
# spec <- feature_spec(dataset, target ~ latitute + longitude) %>%
# step_numeric_column(latitude, longitude) %>%
# step_bucketized_column(latitude, boundaries = c(latitude_edges)) %>%
# step_bucketized_column(longitude, boundaries = c(longitude_edges)) %>%
# step_crossed_column(latitude_longitude = c(latitude, longitude), hash_bucket_size = 100)
## ---- eval=FALSE--------------------------------------------------------------
# spec <- feature_spec(dataset, target ~ .) %>%
# step_categorical_column_with_vocabulary_list(product_class) %>%
# step_indicator_column(product_class)
## ---- eval=FALSE--------------------------------------------------------------
# spec <- feature_spec(dataset, target ~ .) %>%
# step_categorical_column_with_vocabulary_list(product_class) %>%
# step_embedding_column(product_class, dimension = 3)
## -----------------------------------------------------------------------------
# library(keras)
# library(dplyr)
#
# spec <- feature_spec(hearts, target ~ .) %>%
# step_numeric_column(
# all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
# normalizer_fn = scaler_standard()
# ) %>%
# step_categorical_column_with_vocabulary_list(thal) %>%
# step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65)) %>%
# step_indicator_column(thal) %>%
# step_embedding_column(thal, dimension = 2) %>%
# step_crossed_column(c(thal, bucketized_age), hash_bucket_size = 10) %>%
# step_indicator_column(crossed_thal_bucketized_age)
#
# spec <- fit(spec)
#
# input <- layer_input_from_dataset(hearts %>% select(-target))
# output <- input %>%
# layer_dense_features(feature_columns = dense_features(spec)) %>%
# layer_dense(units = 1, activation = "sigmoid")
#
# model <- keras_model(input, output)
#
# model %>%
# compile(
# loss = "binary_crossentropy",
# optimizer = "adam",
# metrics = "accuracy"
# )
#
# model %>%
# fit(
# x = hearts %>% select(-target), y = hearts$target,
# validation_split = 0.2
# )
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