GRU in TensorFlow.

gru(name, gru_cell, all_input_var, step_input_var, step_hidden_var, output_nonlinearity_layer, hidden_state_init=tf.zeros_initializer(), hidden_state_init_trainable=False)

Gated Recurrent Unit (GRU).

  • name (str) – Name of the variable scope.

  • gru_cell (tf.keras.layers.Layer) – GRU cell used to generate outputs.

  • all_input_var (tf.Tensor) – Place holder for entire time-series inputs, with shape \((N, T, S^*)\).

  • step_input_var (tf.Tensor) – Place holder for step inputs, with shape \((N, S^*)\).

  • step_hidden_var (tf.Tensor) – Place holder for step hidden state, with shape \((N, H)\).

  • output_nonlinearity_layer (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.

  • hidden_state_init (callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.

  • hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.


Entire time-series outputs, with shape \((N, T, S^*)\). tf.Tensor: Step output, with shape \((N, S^*)\). tf.Tensor: Step hidden state, with shape \((N, H)\) tf.Tensor: Initial hidden state, with shape \((H, )\)

Return type