tf.contrib.rnn.FusedRNNCellAdaptor

This is an adaptor for RNNCell classes to be used with FusedRNNCell.

Inherits From: FusedRNNCell

tf.contrib.rnn.FusedRNNCellAdaptor(
    cell, use_dynamic_rnn=False
)

Args
`cell`	an instance of a subclass of a `rnn_cell.RNNCell`.
`use_dynamic_rnn`	whether to use dynamic (or static) RNN.

Methods

__call__(
    inputs, initial_state=None, dtype=None, sequence_length=None, scope=None
)

Run this fused RNN on inputs, starting from the given state.

Args
`inputs`	`3-D` tensor with shape `[time_len x batch_size x input_size]` or a list of `time_len` tensors of shape `[batch_size x input_size]`.
`initial_state`	either a tensor with shape `[batch_size x state_size]` or a tuple with shapes `[batch_size x s] for s in state_size`, if the cell takes tuples. If this is not provided, the cell is expected to create a zero initial state of type `dtype`.
`dtype`	The data type for the initial state and expected output. Required if `initial_state` is not provided or RNN state has a heterogeneous dtype.
`sequence_length`	Specifies the length of each sequence in inputs. An `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0, time_len)`. Defaults to `time_len` for each element.
`scope`	`VariableScope` or `string` for the created subgraph; defaults to class name.

Returns

Returns
A pair containing: Output: A `3-D` tensor of shape `[time_len x batch_size x output_size]` or a list of `time_len` tensors of shape `[batch_size x output_size]`, to match the type of the `inputs`. Final state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `initial_state`.

A pair containing:

Output: A 3-D tensor of shape [time_len x batch_size x output_size] or a list of time_len tensors of shape [batch_size x output_size], to match the type of the inputs.
Final state: Either a single 2-D tensor, or a tuple of tensors matching the arity and shapes of initial_state.