Independently Gated Recurrent Unit cell.
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:
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.
num_units: int, The number of units in the GRU cell.
activation: Nonlinearity to use. Default:
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
Nonemeans use the type of the first input). Required when
buildis called before
__init__( num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None, name=None, dtype=None )
get_initial_state( inputs=None, batch_size=None, dtype=None )
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
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.
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
[batch_size, s] for each s in