tf.contrib.rnn.IndyGRUCell

Independently Gated Recurrent Unit cell.

Inherits From: LayerRNNCell

tf.contrib.rnn.IndyGRUCell(
    num_units, activation=None, reuse=None, kernel_initializer=None,
    bias_initializer=None, name=None, dtype=None
)

Based on IndRNNs (https://arxiv.org/abs/1803.04831) and similar to GRUCell, yet with the $U_r$, $U_z$, and $U$ matrices in equations 5, 6, and 8 of http://arxiv.org/abs/1406.1078 respectively replaced by diagonal matrices, i.e. a Hadamard product with a single vector:

$$r_j = \sigma\left([\mathbf W_r\mathbf x]_j + [\mathbf u_r\circ \mathbf h_{(t-1)}]_j\right)$$

$$z_j = \sigma\left([\mathbf W_z\mathbf x]_j + [\mathbf u_z\circ \mathbf h_{(t-1)}]_j\right)$$

$$\tilde{h}^{(t)}_j = \phi\left([\mathbf W \mathbf x]_j + [\mathbf u \circ \mathbf r \circ \mathbf h_{(t-1)}]_j\right)$$

where $\circ$ denotes the Hadamard operator. This means that each IndyGRU node sees only its own state, as opposed to seeing all states in the same layer.

Args
`num_units`	int, The number of units in the GRU cell.
`activation`	Nonlinearity to use. Default: `tanh`.
`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.
`kernel_initializer`	(optional) The initializer to use for the weight matrices applied to the input.
`bias_initializer`	(optional) The initializer to use for the bias.
`name`	String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
`dtype`	Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`.

Attributes
`graph`	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph.
`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

`get_initial_state`

View source

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

`zero_state`

View source

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`.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/IndyGRUCell