Fast GRU implementation backed by <a href='https://developer.nvidia.com/cudnn'>CuDNN</a>.

    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
    )

    Arguments

    object

    Model or layer object

    units

    Positive integer, dimensionality of the output space.

    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.

    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.

    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.

    input_shape

    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.

    batch_input_shape

    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.

    batch_size

    Fixed batch size for layer

    dtype

    The data type expected by the input, as a string (float32, float64, int32...)

    name

    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.

    trainable

    Whether the layer weights will be updated during training.

    weights

    Initial weights for layer.

    References

    See also

    Other recurrent layers: layer_cudnn_lstm(), layer_gru(), layer_lstm(), layer_simple_rnn()