Class RNN

Base class for recurrent layers. 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.

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, by passing if sequential model: batch_input_shape = (...) to the first layer in your model. else for functional model with 1 or more Input layers: batch_shape = (...) to all the first layers in your model.This is the expected shape of your inputs including the batch size.It should be a tuple of integers, e.g. (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.

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.

You can pass "external" constants to the cell using the constants keyword 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.

Inheritance

System.Object

RNN

Implements

System.IDisposable

Inherited Members

BaseLayer.Set(BaseLayer[])

Base.Parameters

Base.None

Base.Init()

Base.ToPython()

Base.InvokeStaticMethod(Object, String, Dictionary<String, Object>)

Base.InvokeMethod(String, Dictionary<String, Object>)

Base.Item[String]

Keras.Instance

Keras.keras

Keras.keras2onnx

Keras.tfjs

Keras.Dispose()

Keras.ToTuple(Array)

Keras.ToList(Array)

System.Object.Equals(System.Object)

System.Object.Equals(System.Object, System.Object)

System.Object.GetHashCode()

System.Object.GetType()

System.Object.MemberwiseClone()

System.Object.ReferenceEquals(System.Object, System.Object)

System.Object.ToString()

Namespace: Keras.Layers

Assembly: Keras.dll

Syntax

public class RNN : BaseLayer, IDisposable

Constructors

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RNN()

Initializes a new instance of the RNN class.

Declaration

public RNN()

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RNN(RNN, Boolean, Boolean, Boolean, Boolean, Boolean, Nullable<Int32>, Nullable<Int32>, Shape)

Initializes a new instance of the RNN class.

Declaration

public RNN(RNN cell, bool return_sequences = false, bool return_state = false, bool go_backwards = false, bool stateful = false, bool unroll = false, int? input_dim = default(int? ), int? input_length = default(int? ), Shape input_shape = null)

Parameters

Type	Name	Description
RNN	cell	A RNN cell instance.
System.Boolean	return_sequences	Boolean. Whether to return the last output in the output sequence, or the full sequence.
System.Boolean	return_state	Boolean. Whether to return the last state in addition to the output.
System.Boolean	go_backwards	Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
System.Boolean	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.
System.Boolean	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.
System.Nullable<System.Int32>	input_dim	dimensionality of the input (integer). This argument (or alternatively, the keyword argument input_shape) is required when using this layer as the first layer in a model..
System.Nullable<System.Int32>	input_length	Length of input sequences, to be specified when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g. via the input_shape argument)
Shape	input_shape	3D tensor with shape (batch_size, timesteps, input_dim).

Implements

System.IDisposable