Found a workaround by adding keras.applications.mobilenet_v2.preprocess_input() as a layer in between an input layer and the premade model, like so:

input = keras.layers.Input([224, 224, 3])

pre = keras.applications.mobilenet_v2.preprocess_input(input)

net = keras.applications.MobileNetV2(
    input_tensor = pre,
    alpha=1.0,
    include_top=True,
    weights=None,
    classes=3,
    classifier_activation="softmax",
    name=None,
)
model = keras.Model(inputs=input, outputs=net.output)

Technically a different function than in the original question, but ultimately they both end up calling keras\src\applications\imagenet_utils.py and produce the same error when they do, so this should hopefully work for all preprocess_input functions that end up calling this.

“No attribute ‘ndim’” does sound like it’s expecting an array.

What TensorFlow version are you using? Are you using tf.keras, or stand-alone keras?

Make sure to import keras from tensorflow and use version 2.2.4-tf. Got the same error and this worked for me.

from tensorflow import keras
keras.__version__

2.2.4-tf

Method you are looking for is tf.compat.v1.data.make_initializable_iterator(dataset)

As long as you use tf.compat.v1, it should be enough to replace

iter_train_infer=train_data_infer.make_initializable_iterator()

with

iter_train_infer = tf.data.make_initializable_iterator(train_data_infer)

If you get make_initializable_iterator is not supported when eager execution is enabled, then add tf.compat.v1.disable_eager_execution() at startup of your application.

The Dataset.prefetch() method was introduced in TensorFlow 1.4, so to run that exact code fragment you will need to upgrade. Many performance-related optimizations were added in TensorFlow 1.4 and later.

As an alternative, you can use the (now-removed) output_buffer_size argument to map(), which acts as a prefetch buffer:

def getTestData(filename, dataSize, batch_size):
    dataset = tf.contrib.data.TFRecordDataset(filename)

    # NOTE: This signature for `Dataset.map()` has been deprecated, and will not
    # work in TensorFlow 1.7 or later.
    dataset = dataset.map(decodeTest, output_buffer_size=batch_size)

    dataset = dataset.shuffle(dataSize)
    dataset = dataset.batch(batch_size)
    return dataset

The NotFoundError is a bug that was fixed in TensorFlow 1.3. To work around it, you must define all datasets before creating a tf.Session in your program.

You can convert it to a list with list(ds) and then recompile it as a normal Dataset with tf.data.Dataset.from_tensor_slices(list(ds)). From there your nightmare begins again but at least it's a nightmare that other people have had before.

Note that for more complex datasets (e.g. nested dictionaries) you will need more preprocessing after calling list(ds), but this should work for the example you asked about.

This is far from a satisfying answer but unfortunately the class is entirely undocumented and none of the standard Dataset tricks work.

I have used an array and give it to the labels as class names. This solved my problem.