Iterating over a Dataset TF 2.0 with for loop

Question

This problem is about how to iterate over a TF Dataset given that make_initializable_iterator() is deprecated.

I read a data set with the function below

Answer 1

It is not very clear what you want to get at your output. If you want to get the values of the dataset output you should execute eagerly. Example:

tf.compat.v1.enable_eager_execution()

def read_dataset_new(filename, target='delay'):
    ds = tf.data.TFRecordDataset(filename)
    ds = ds.map(lambda buf: parse(buf, target=target))
    ds = ds.batch(1)
    return ds
# This should return your key values for each example.
for features, labels in read_dataset_new(self.tf_rcrds_fl_nm):
    features_keys = features.keys()
# This should return your tensor values if they supposed to be numeric.
for features, labels in read_dataset_new(self.tf_rcrds_fl_nm):
    features_array = numpy.array(features)