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Cell class for the GRU layer.
Inherits From: GRUCell
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, implementation=2, 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.
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. |
implementation | Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 (default) will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. Default: 2. |
reset_after | GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before", True = "after" (default and CuDNN compatible). |
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.get_dropout_mask_for_cell
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
get_initial_state( inputs=None, batch_size=None, dtype=None )
get_recurrent_dropout_mask_for_cell
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
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
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/layers/GRUCell