Can only be run on GPU, with the TensorFlow backend.
layer_cudnn_gru(
object,
units,
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,
return_sequences = FALSE,
return_state = FALSE,
stateful = FALSE,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
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.
Positive integer, dimensionality of the output space.
Initializer for the kernel
weights matrix, used
for the linear transformation of the inputs.
Initializer for the recurrent_kernel
weights
matrix, used for the linear transformation of the recurrent state.
Initializer for the bias vector.
Regularizer function applied to the kernel
weights matrix.
Regularizer function applied to the
recurrent_kernel
weights matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel
weights
matrix.
Constraint function applied to the
recurrent_kernel
weights matrix.
Constraint function applied to the bias vector.
Boolean. Whether to return the last output in the output sequence, or the full sequence.
Boolean (default FALSE). Whether to return the last state in addition to the output.
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.
Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
Shapes, including the batch size. For instance,
batch_input_shape=c(10, 32)
indicates that the expected input will be
batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32)
indicates batches of an arbitrary number of 32-dimensional vectors.
Fixed batch size for layer
The data type expected by the input, as a string (float32
,
float64
, int32
...)
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
Whether the layer weights will be updated during training.
Initial weights for layer.
Other recurrent layers:
layer_cudnn_lstm()
,
layer_gru()
,
layer_lstm()
,
layer_rnn()
,
layer_simple_rnn()