`GRU`

class```
keras.layers.GRU(
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,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
seed=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
reset_after=True,
use_cudnn="auto",
**kwargs
)
```

Gated Recurrent Unit - Cho et al. 2014.

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend.

The requirements to use the cuDNN implementation are:

`activation`

==`tanh`

`recurrent_activation`

==`sigmoid`

`dropout`

== 0 and`recurrent_dropout`

== 0`unroll`

is`False`

`use_bias`

is`True`

`reset_after`

is`True`

- Inputs, if use masking, are strictly right-padded.
- Eager execution is enabled in the outermost context.

There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original and has the order reversed.

The second variant is compatible with CuDNNGRU (GPU-only) and allows
inference on CPU. Thus it has separate biases for `kernel`

and
`recurrent_kernel`

. To use this variant, set `reset_after=True`

and
`recurrent_activation='sigmoid'`

.

For example:

```
>>> inputs = np.random.random((32, 10, 8))
>>> gru = keras.layers.GRU(4)
>>> output = gru(inputs)
>>> output.shape
(32, 4)
>>> gru = keras.layers.GRU(4, return_sequences=True, return_state=True)
>>> whole_sequence_output, final_state = gru(inputs)
>>> whole_sequence_output.shape
(32, 10, 4)
>>> 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 should use 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`

.**activity_regularizer**: Regularizer function applied to the output of the layer (its "activation"). 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.**seed**: Random seed for dropout.**return_sequences**: Boolean. Whether to return the last output in the output sequence, or the full sequence. Default:`False`

.**return_state**: Boolean. Whether to return the last state in addition to the output. Default:`False`

.**go_backwards**: Boolean (default`False`

). If`True`

, process the input sequence backwards and return the reversed sequence.**stateful**: Boolean (default:`False`

). If`True`

, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.**unroll**: Boolean (default:`False`

). If`True`

, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.**reset_after**: GRU convention (whether to apply reset gate after or before matrix multiplication).`False`

is`"before"`

,`True`

is`"after"`

(default and cuDNN compatible).**use_cudnn**: Whether to use a cuDNN-backed implementation.`"auto"`

will attempt to use cuDNN when feasible, and will fallback to the default implementation if not.

**Call arguments**

**inputs**: A 3D tensor, with shape`(batch, timesteps, feature)`

.**mask**: Binary tensor of shape`(samples, timesteps)`

indicating whether a given timestep should be masked (optional). An individual`True`

entry indicates that the corresponding timestep should be utilized, while a`False`

entry indicates that the corresponding timestep should be ignored. Defaults to`None`

.**training**: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if`dropout`

or`recurrent_dropout`

is used (optional). Defaults to`None`

.**initial_state**: List of initial state tensors to be passed to the first call of the cell (optional,`None`

causes creation of zero-filled initial state tensors). Defaults to`None`

.