IndyGRUCell - LostTech.TensorFlow Documentation

Type IndyGRUCell

Namespace tensorflow.contrib.rnn

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:

$$r_j = \sigma\left([\mathbf W_r\mathbf x]_j + [\mathbf u_r\circ \mathbf h_{(t-1)}]_j\right)$$ $$z_j = \sigma\left([\mathbf W_z\mathbf x]_j + [\mathbf u_z\circ \mathbf h_{(t-1)}]_j\right)$$ $$\tilde{h}^{(t)}_j = \phi\left([\mathbf W \mathbf x]_j + [\mathbf u \circ \mathbf r \circ \mathbf h_{(t-1)}]_j\right)$$

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.

Properties

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

object graph get;

object graph_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

InputSpec input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object output_size get;

Integer or TensorShape: size of outputs produced by this cell.

object output_size_dyn get;

Integer or TensorShape: size of outputs produced by this cell.

object PythonObject get;

object rnncell_scope get; set;

string scope_name get;

object scope_name_dyn get;

object state_size get;

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.

object state_size_dyn get;