layer_gru
Gated Recurrent Unit - Cho et al.
Description
There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed.
Usage
layer_gru(
object,
units, activation = "tanh",
recurrent_activation = "sigmoid",
use_bias = TRUE,
return_sequences = FALSE,
return_state = FALSE,
go_backwards = FALSE,
stateful = FALSE,
unroll = FALSE,
time_major = FALSE,
reset_after = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
kernel_regularizer = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
dropout = 0,
recurrent_dropout = 0,
... )
Arguments
Arguments | Description |
---|---|
object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input() ). The return value depends on object . If object is: - missing or NULL , the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
units | Positive integer, dimensionality of the output space. |
activation | Activation function to use. Default: hyperbolic tangent (tanh ). If you pass NULL , no activation is applied (ie. “linear” activation: a(x) = x ). |
recurrent_activation | Activation function to use for the recurrent step. |
use_bias | Boolean, whether the layer uses a bias vector. |
return_sequences | Boolean. Whether to return the last output in the output sequence, or the full sequence. |
return_state | Boolean (default FALSE). Whether to return the last state in addition to the output. |
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. |
time_major | If True, the inputs and outputs will be in shape [timesteps, batch, feature] , whereas in the False case, it will be [batch, timesteps, feature] . Using time_major = TRUE is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. |
reset_after | GRU convention (whether to apply reset gate after or before matrix multiplication). FALSE = “before” (default), TRUE = “after” (CuDNN compatible). |
kernel_initializer | Initializer for the kernel weights matrix, used for the linear transformation of the inputs. |
recurrent_initializer | Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. |
bias_initializer | Initializer for the bias vector. |
kernel_regularizer | Regularizer function applied to the kernel weights matrix. |
recurrent_regularizer | Regularizer function applied to the recurrent_kernel weights matrix. |
bias_regularizer | Regularizer function applied to the bias vector. |
activity_regularizer | Regularizer function applied to the output of the layer (its “activation”).. |
kernel_constraint | Constraint function applied to the kernel weights matrix. |
recurrent_constraint | Constraint function applied to the recurrent_kernel weights matrix. |
bias_constraint | Constraint function applied to the bias vector. |
dropout | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. |
recurrent_dropout | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. |
… | Standard Layer args. |
Details
The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel
and recurrent_kernel
. Use reset_after = TRUE
and recurrent_activation = "sigmoid"
.
Section
Input shapes
N-D tensor with shape (batch_size, timesteps, ...)
, or (timesteps, batch_size, ...)
when time_major = TRUE
.
Output shape
if
return_state
: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape(batch_size, state_size)
, wherestate_size
could be a high dimension tensor shape.if
return_sequences
: N-D tensor with shape[batch_size, timesteps, output_size]
, whereoutput_size
could be a high dimension tensor shape, or[timesteps, batch_size, output_size]
whentime_major
isTRUE
else, N-D tensor with shape
[batch_size, output_size]
, whereoutput_size
could be a high dimension tensor shape.
Masking
This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use layer_embedding()
with the mask_zero
parameter set to TRUE
.
Statefulness in RNNs
You can set RNN layers to be ‘stateful’, which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches. For intuition behind statefulness, there is a helpful blog post here: https://philipperemy.github.io/keras-stateful-lstm/ To enable statefulness:
Specify
stateful = TRUE
in the layer constructor.Specify a fixed batch size for your model. For sequential models, pass
batch_input_shape = list(...)
to the first layer in your model. For functional models with 1 or more Input layers, passbatch_shape = list(...)
to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a list of integers, e.g.list(32, 10, 100)
. For dimensions which can vary (are not known ahead of time), useNULL
in place of an integer, e.g.list(32, NULL, NULL)
.Specify
shuffle = FALSE
when callingfit()
.To reset the states of your model, call
layer$reset_states()
on either a specific layer, or on your entire model.
Initial State of RNNs
You can specify the initial state of RNN layers symbolically by calling them with the keyword argument initial_state.
The value of initial_state should be a tensor or list of tensors representing the initial state of the RNN layer. You can specify the initial state of RNN layers numerically by calling reset_states
with the named argument states.
The value of states
should be an array or list of arrays representing the initial state of the RNN layer.
Passing external constants to RNNs
You can pass “external” constants to the cell using the constants
named argument of RNN$__call__
(as well as RNN$call
) method. This requires that the cell$call
method accepts the same keyword argument constants
. Such constants can be used to condition the cell transformation on additional static inputs (not changing over time), a.k.a. an attention mechanism.
References
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
See Also
https://www.tensorflow.org/guide/keras/rnn
Other recurrent layers:
layer_cudnn_gru()
,layer_cudnn_lstm()
,layer_lstm()
,layer_rnn()
,layer_simple_rnn()