tf.contrib.checkpoint.List

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An append-only sequence type which is trackable.

tf.contrib.checkpoint.List(
    *args, **kwargs
)

Maintains checkpoint dependencies on its contents (which must also be trackable), and forwards any Layer metadata such as updates and losses.

Note that List is purely a container. It lets a tf.keras.Model or other trackable object know about its contents, but does not call any Layer instances which are added to it. To indicate a sequence of Layer instances which should be called sequentially, use tf.keras.Sequential.

Example usage:

class HasList(tf.keras.Model):

  def __init__(self):
    super(HasList, self).__init__()
    self.layer_list = tf.contrib.checkpoint.List([layers.Dense(3)])
    self.layer_list.append(layers.Dense(4))

  def call(self, x):
    aggregation = 0.
    for l in self.layer_list:
      x = l(x)
      aggregation += tf.reduce_sum(x)
    return aggregation

This kind of wrapping is necessary because Trackable objects do not (yet) deeply inspect regular Python data structures, so for example assigning a regular list (self.layer_list = [layers.Dense(3)]) does not create a checkpoint dependency and does not add the Layer instance's weights to its parent Model.

Methods

append

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append(
    value
)

Add a new trackable value.

copy

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copy()

count

count(
    value
)

S.count(value) -> integer -- return number of occurrences of value

extend

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extend(
    values
)

Add a sequence of trackable values.

index

index(
    value, start=0, stop=None
)

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

__add__

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__add__(
    other
)

__contains__

__contains__(
    value
)

__eq__

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__eq__(
    other
)

Return self==value.

__getitem__

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__getitem__(
    key
)

__iter__

__iter__()

__len__

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__len__()

__mul__

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__mul__(
    n
)

__radd__

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__radd__(
    other
)

__rmul__

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__rmul__(
    n
)