tf.data.experimental.get_single_element
Returns the single element in dataset as a nested structure of tensors.
tf.data.experimental.get_single_element(
dataset
)
This function enables you to use a tf.data.Dataset in a stateless "tensor-in tensor-out" expression, without creating a tf.compat.v1.data.Iterator. This can be useful when your preprocessing transformations are expressed as a Dataset, and you want to use the transformation at serving time. For example:
input_batch = tf.compat.v1.placeholder(tf.string, shape=[BATCH_SIZE])
def preprocessing_fn(input_str):
# ...
return image, label
dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
.batch(BATCH_SIZE))
image_batch, label_batch = tf.data.experimental.get_single_element(dataset)
| Returns |
|---|
A nested structure of tf.Tensor objects, corresponding to the single element of dataset. |
| Raises |
|---|
TypeError | if dataset is not a tf.data.Dataset object. InvalidArgumentError (at runtime): if dataset does not contain exactly one element. |