tf.contrib.rnn.LayerRNNCell

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Class LayerRNNCell

Subclass of RNNCells that act like proper tf.Layer objects.

Inherits From: RNNCell

For backwards compatibility purposes, most RNNCell instances allow their call methods to instantiate variables via tf.compat.v1.get_variable. The underlying variable scope thus keeps track of any variables, and returning cached versions. This is atypical of tf.layer objects, which separate this part of layer building into a build method that is only called once.

Here we provide a subclass for RNNCell objects that act exactly as Layer objects do. They must provide a build method and their call methods do not access Variables tf.compat.v1.get_variable.

__init__

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__init__(
    trainable=True,
    name=None,
    dtype=None,
    **kwargs
)

Properties

graph

DEPRECATED FUNCTION

output_size

Integer or TensorShape: size of outputs produced by this cell.

scope_name

state_size

size(s) of state(s) used by this cell.

It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

tf.contrib.rnn.LayerRNNCell.get_initial_state

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get_initial_state(
    inputs=None,
    batch_size=None,
    dtype=None
)

tf.contrib.rnn.LayerRNNCell.zero_state

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zero_state(
    batch_size,
    dtype
)

Return zero-filled state tensor(s).

Args:

• batch_size: int, float, or unit Tensor representing the batch size.
• dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.