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tf.contrib.rnn.IndyGRUCell

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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__

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

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

Methods

get_initial_state

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

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.