Converts a tf.data.Dataset
to an iterable of NumPy arrays.
tfds.as_numpy(
dataset: Tree[TensorflowElem]
) -> Tree[NumpyElem]
Used in the notebooks
Used in the tutorials |
---|
as_numpy
converts a possibly nested structure of tf.data.Dataset
s
and tf.Tensor
s to iterables of NumPy arrays and NumPy arrays, respectively.
Note that because TensorFlow has support for ragged tensors and NumPy has
no equivalent representation,
tf.RaggedTensor
s
are left as-is for the user to deal with them (e.g. using to_list()
).
In TF 1 (i.e. graph mode), tf.RaggedTensor
s are returned as
tf.ragged.RaggedTensorValue
s.
Example:
ds = tfds.load(name="mnist", split="train")
ds_numpy = tfds.as_numpy(ds) # Convert `tf.data.Dataset` to Python generator
for ex in ds_numpy:
# `{'image': np.array(shape=(28, 28, 1)), 'labels': np.array(shape=())}`
print(ex)
Args | |
---|---|
dataset
|
a possibly nested structure of tf.data.Dataset s and/or
tf.Tensor s.
|
Returns | |
---|---|
A structure matching dataset where tf.data.Dataset s are converted to
generators of NumPy arrays and tf.Tensor s are converted to NumPy arrays.
|