as_tensor.tf_dataset: Get the single element of the dataset. Documentation

Get the single element of the dataset.


The function enables you to use a TF Dataset in a stateless "tensor-in tensor-out" expression, without creating an iterator. This facilitates the ease of data transformation on tensors using the optimized TF Dataset abstraction on top of them.


## S3 method for class ''
as_tensor(x, ..., name = NULL)

## S3 method for class ''
as.array(x, ...)



A TF Dataset


passed on to tensorflow::as_tensor()


(Optional.) A name for the TensorFlow operation.


For example, consider a preprocess_batch() which would take as an input a batch of raw features and returns the processed feature.

preprocess_one_case <- function(x) x + 100

preprocess_batch   <- function(raw_features) {
  batch_size <- dim(raw_features)[1]
  ds <- raw_features %>%
    tensor_slices_dataset() %>%
    dataset_map(preprocess_one_case, num_parallel_calls = batch_size) %>%

raw_features <- array(seq(prod(4, 5)), c(4, 5))

In the above example, the batch of raw_features was converted to a TF Dataset. Next, each of the raw_feature cases in the batch was mapped using the preprocess_one_case and the processed features were grouped into a single batch. The final dataset contains only one element which is a batch of all the processed features.

Note: The dataset should contain only one element. Now, instead of creating an iterator for the dataset and retrieving the batch of features, the as_tensor() function is used to skip the iterator creation process and directly output the batch of features.

This can be particularly useful when your tensor transformations are expressed as TF Dataset operations, and you want to use those transformations while serving your model.

See Also

tfdatasets documentation built on June 30, 2022, 1:04 a.m.