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# tf.contrib.rnn.IndyGRUCell

## Class IndyGRUCell

Independently Gated Recurrent Unit cell.

Inherits From: LayerRNNCell

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.

## __init__

View source

__init__(
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
name=None,
dtype=None
)


## Properties

### graph

DEPRECATED FUNCTION

## 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.