Consider loading the following dataset from tensorflow datasets
(ds_train, ds_test), ds_info= tfds.load('mnist', split=['train', 'test'],
shuffle_files=True,
as_supervised=True,with_info=True)
However, the website said
#https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator
#Warning: SOME ARGUMENTS ARE DEPRECATED: (output_shapes, output_types). They will be removed in a future version.
#Instructions for updating: Use output_signature instead
but none of the
ds_train.output_shapes
ds_train.output_types
ds_train.output_signature
were working
A similar issue was mentioned here #https://github.com/tensorflow/datasets/issues/102 , so right now only the temporary fix
shape_of_data=tf.compat.v1.data.get_output_shapes(ds_train)
was working, which returned
(TensorShape([None, 28, 28, 1]), TensorShape([None]))
Another updated function was working, but one could not get the TensorShape out of the argument
tf.data.DatasetSpec(ds_train)
returned
DatasetSpec(<_OptionsDataset shapes: ((28, 28, 1), ()), types: (tf.uint8, tf.int64)>, TensorShape([]))
which could not be assigned.
What's the updated function or attributes to get the shape of the generator/iterator?
One can use dataset.element_spec
:
import tensorflow_datasets as tfds
(ds_train, ds_test), ds_info = tfds.load(
"mnist",
split=["train", "test"],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
ds_train.element_spec
# (TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None),
# TensorSpec(shape=(), dtype=tf.int64, name=None))
ds_train.element_spec[0].shape
# TensorShape([28, 28, 1])