SimpleRNN layer
A Keras recurrent layer that processes sequences by iteratively updating recurrent state across timesteps.
Deep Learning with Python, Third Edition
A Keras recurrent layer that processes sequences by iterating a simple recurrent cell over timesteps.
- How it is used
- Processes vector sequences for recurrent modeling, especially when temporal patterns are not invariant under translation.
- How deep it goes
- Defines batch shapes, output modes, stacking, and vanishing gradients, but stops short of recommending SimpleRNN for most practical work.
- Examples
- Uses variable-length inputs, 120-step shape checks, final-versus-full outputs, and a three-layer recurrent stack.
- What you can do after
- Apply it via Keras by configuring input shapes, return_sequences, and recurrent-layer stacks.
Read how Deep Learning with Python, Third Edition explains SimpleRNN layer →
Deep Learning with R, Third Edition
A SimpleRNN layer is a Keras recurrent layer that computes sequence representations using basic recurrent state updates.
- How it is used
- Processes batched time series and supplies either sequence-wide or final-step representations to subsequent layers.
- How deep it goes
- Defines input and output shapes, variable-length handling, stacking, and long-term-dependency limitations without deriving recurrent internals.
- Examples
- Demonstrates variable-length inputs, final-step output, full-sequence output, and stacked SimpleRNN layers in R/Keras.
- What you can do after
- Operate/configure SimpleRNN layers for fixed- or variable-length sequences and choose appropriate output modes.
Read how Deep Learning with R, Third Edition explains SimpleRNN layer →
Related concepts
- recurrent neural networkkind of · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
- Kerasuses · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
- return_sequencesuses · return_sequences is a recurrent-layer setting that determines whether outputs are returned for every timestep or only the final timestep.
- tensoruses · A tensor is a multidimensional, array-like structure of values indexed along zero or more dimensions and used for numerical computation.
- arrayuses · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
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