Sequential model
A sequential model is a neural-network model comprising a linear, ordered stack of layers through which data flows from input to output.
Deep Learning with R, Third Edition
A sequential model is a Keras model that represents a simple linear stack of layers with one input and one output.
- How it is used
- Builds simple Keras models when one input can pass through a linear stack of layers to one output.
- How deep it goes
- Explains construction, deferred weight creation, input shapes, summaries, naming, and API limits, but stops short of advanced graph architectures.
- Examples
- Uses chained Dense layers, incremental layer addition, explicit input shapes, model summaries, parameter counts, and named layers.
- What you can do after
- Build it from scratch with keras_model_sequential(), inspect its structure, and diagnose shape or construction issues with build() and summaries.
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Deep Learning with Python, Third Edition
A Keras class for building models as a linear stack of layers.
- How it is used
- Builds simple one-input, one-output Keras models as linear stacks of layers.
- How deep it goes
- Explains direct and incremental construction, delayed weight creation, input-shape building, summaries, naming, and architecture limits.
- Examples
- Demonstrates Dense classifiers, incremental add calls, explicit input shapes, build(), weights, summary(), and named layers.
- What you can do after
- Build it from scratch with keras.Sequential, inspect its structure, and choose a more flexible API when the architecture outgrows a stack.
Read how Deep Learning with Python, Third Edition explains Sequential model →
Related concepts
- Functional APIalternative to · The Functional API is a Keras interface for constructing neural-network models as directed computation graphs by connecting layers and tensors.
- Keras modelkind of · A Keras model is a trainable object representing a neural-network computation and providing interfaces for training, evaluation, prediction, and saving.
- model subclassingalternative to · Model subclassing is a technique for defining a neural-network model by creating a class that extends a model base class and specifies its computation.
- listequivalent to · A list is an ordered collection of elements arranged in a sequence.
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