tf.contrib.rnn.InputProjectionWrapper

class tf.contrib.rnn.InputProjectionWrapper

class tf.contrib.rnn.core_rnn_cell.InputProjectionWrapper

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Operator adding an input projection to the given cell.

Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the projection on this batch-concatenated sequence, then split it.

Properties

output_size

state_size

Methods

__init__(cell, num_proj, input_size=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.
• input_size: Deprecated and unused.

Raises:

• TypeError: if cell is not an RNNCell.

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 x 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 x s] for each s in state_size.

Defined in tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py.