GRUCell( input_layer, n_units, previous_state = None, activation = tf.tanh, gate_activation = tf.sigmoid, w_init = tf.initializers.glorot_uniform(), u_init = tf.initializers.orthogonal(), bias_init = tf.initializers.zeros(), u_dropconnect = None, w_dropconnect = None, x_dropout = None, r_dropout = None, y_dropout = None, dropout_locked = True, regularized = False, share_state_with = None, name = 'gru_cell' )
Gated Recurrent Unit Cell.
Performs a single step with a gated recurrent unit where. These units have two gates: The first defines how much do we use the values from the recurrent connection to predict the current state The second
.compute( input_layer, *previous_state )
.reuse_with( input_layer, *previous_state, regularized = None, name = None )