tf.keras.layers.GRUCell     View source on GitHub    Cell class for the GRU layer. Inherits From: GRUCell, Layer, Module 
tf.keras.layers.GRUCell(
    units, activation='tanh', recurrent_activation='sigmoid',
    use_bias=True, kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros', kernel_regularizer=None,
    recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None,
    recurrent_constraint=None, bias_constraint=None, dropout=0.0,
    recurrent_dropout=0.0, reset_after=True, **kwargs
)
 See the Keras RNN API guide for details about the usage of RNN API. This class processes one step within the whole time sequence input, whereas tf.keras.layer.GRU processes the whole sequence. For example: 
inputs = tf.random.normal([32, 10, 8])
rnn = tf.keras.layers.RNN(tf.keras.layers.GRUCell(4))
output = rnn(inputs)
print(output.shape)
(32, 4)
rnn = tf.keras.layers.RNN(
   tf.keras.layers.GRUCell(4),
   return_sequences=True,
   return_state=True)
whole_sequence_output, final_state = rnn(inputs)
print(whole_sequence_output.shape)
(32, 10, 4)
print(final_state.shape)
(32, 4)

 


 Arguments
  units   Positive integer, dimensionality of the output space.  
  activation   Activation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).  
  recurrent_activation   Activation function to use for the recurrent step. Default: sigmoid (sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).  
  use_bias   Boolean, (default True), whether the layer uses a bias vector.  
  kernel_initializer   Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: glorot_uniform.  
  recurrent_initializer   Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: orthogonal.  
  bias_initializer   Initializer for the bias vector. Default: zeros.  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix. Default: None.  
  recurrent_regularizer   Regularizer function applied to the recurrent_kernel weights matrix. Default: None.  
  bias_regularizer   Regularizer function applied to the bias vector. Default: None.  
  kernel_constraint   Constraint function applied to the kernel weights matrix. Default: None.  
  recurrent_constraint   Constraint function applied to the recurrent_kernel weights matrix. Default: None.  
  bias_constraint   Constraint function applied to the bias vector. Default: None.  
  dropout   Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.  
  recurrent_dropout   Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.  
  reset_after   GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before", True = "after" (default and CuDNN compatible).    Call arguments:  
inputs: A 2D tensor, with shape of [batch, feature]. 
states: A 2D tensor with shape of [batch, units], which is the state from the previous time step. For timestep 0, the initial state provided by user will be feed to cell. 
training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when dropout or recurrent_dropout is used.  Methods get_dropout_mask_for_cell View source 
get_dropout_mask_for_cell(
    inputs, training, count=1
)
 Get the dropout mask for RNN cell's input. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
 


 Args
  inputs   The input tensor whose shape will be used to generate dropout mask.  
  training   Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.  
  count   Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.   
 


 Returns   List of mask tensor, generated or cached mask based on context.  
 get_initial_state View source 
get_initial_state(
    inputs=None, batch_size=None, dtype=None
)
 get_recurrent_dropout_mask_for_cell View source 
get_recurrent_dropout_mask_for_cell(
    inputs, training, count=1
)
 Get the recurrent dropout mask for RNN cell. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
 


 Args
  inputs   The input tensor whose shape will be used to generate dropout mask.  
  training   Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.  
  count   Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.   
 


 Returns   List of mask tensor, generated or cached mask based on context.  
 reset_dropout_mask View source 
reset_dropout_mask()
 Reset the cached dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. reset_recurrent_dropout_mask View source 
reset_recurrent_dropout_mask()
 Reset the cached recurrent dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.