Fully-connected RNN where the output is to be fed back to input.

    Fully-connected RNN where the output is to be fed back to input.

    layer_simple_rnn(
      object,
      units,
      activation = "tanh",
      use_bias = TRUE,
      return_sequences = FALSE,
      return_state = FALSE,
      go_backwards = FALSE,
      stateful = FALSE,
      unroll = FALSE,
      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,
      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.

    activation

    Activation function to use. Default: hyperbolic tangent (tanh). If you pass NULL, no activation is applied (ie. "linear" activation: a(x) = x).

    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.

    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.

    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.

    Input shapes

    3D tensor with shape (batch_size, timesteps, input_dim), (Optional) 2D tensors with shape (batch_size, output_dim).

    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, units).

    • if return_sequences: 3D tensor with shape (batch_size, timesteps, units).

    • else, 2D tensor with shape (batch_size, units).

    Masking

    This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an embedding layer 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.

    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 = c(...) to the first layer in your model. For functional models with 1 or more Input layers, pass batch_shape = c(...) to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a vector of integers, e.g. c(32, 10, 100).

    • Specify shuffle = FALSE when calling fit().

    To reset the states of your model, call 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 keyword argument states. The value of states should be a numpy array or list of numpy arrays representing the initial state of the RNN layer.

    References

    See also

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