tf.contrib.rnn.InputProjectionWrapper

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Class InputProjectionWrapper

Operator adding an input projection to the given cell.

Inherits From: RNNCell

__init__

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__init__(
    cell,
    num_proj,
    activation=None,
    input_size=None,
    reuse=None
)

Create a cell with input projection.

Args:

• cell: an RNNCell, a projection of inputs is added before it.
• num_proj: Python integer. The dimension to project to.
• activation: (optional) an optional activation function.
• input_size: Deprecated and unused.
• reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.

Raises:

• TypeError: if cell is not an RNNCell.

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.InputProjectionWrapper.get_initial_state

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

tf.contrib.rnn.InputProjectionWrapper.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.