tf.compat.v1.data.Iterator

tf.compat.v1.data.Iterator(
    iterator_resource, initializer, output_types, output_shapes, output_classes
)

@staticmethod
from_string_handle(
    string_handle, output_types, output_shapes=None, output_classes=None
)

train_iterator = tf.data.Dataset(...).make_one_shot_iterator()
train_iterator_handle = sess.run(train_iterator.string_handle())

test_iterator = tf.data.Dataset(...).make_one_shot_iterator()
test_iterator_handle = sess.run(test_iterator.string_handle())

handle = tf.compat.v1.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
    handle, train_iterator.output_types)

next_element = iterator.get_next()
loss = f(next_element)

train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})

@staticmethod
from_structure(
    output_types, output_shapes=None, shared_name=None, output_classes=None
)

iterator = Iterator.from_structure(tf.int64, tf.TensorShape([]))

dataset_range = Dataset.range(10)
range_initializer = iterator.make_initializer(dataset_range)

dataset_evens = dataset_range.filter(lambda x: x % 2 == 0)
evens_initializer = iterator.make_initializer(dataset_evens)

# Define a model based on the iterator; in this example, the model_fn
# is expected to take scalar tf.int64 Tensors as input (see
# the definition of 'iterator' above).
prediction, loss = model_fn(iterator.get_next())

# Train for `num_epochs`, where for each epoch, we first iterate over
# dataset_range, and then iterate over dataset_evens.
for _ in range(num_epochs):
  # Initialize the iterator to `dataset_range`
  sess.run(range_initializer)
  while True:
    try:
      pred, loss_val = sess.run([prediction, loss])
    except tf.errors.OutOfRangeError:
      break

  # Initialize the iterator to `dataset_evens`
  sess.run(evens_initializer)
  while True:
    try:
      pred, loss_val = sess.run([prediction, loss])
    except tf.errors.OutOfRangeError:
      break

get_next(
    name=None
)

dataset = ...  # A `tf.data.Dataset` object.
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()

# Build a TensorFlow graph that does something with each element.
loss = model_function(next_element)
optimizer = ...  # A `tf.compat.v1.train.Optimizer` object.
train_op = optimizer.minimize(loss)

with tf.compat.v1.Session() as sess:
  try:
    while True:
      sess.run(train_op)
  except tf.errors.OutOfRangeError:
    pass

get_next_as_optional()

make_initializer(
    dataset, name=None
)

string_handle(
    name=None
)