Sequential Model 이란?

model = Sequential()

python - What is meant by sequential model in Keras - Stack Overflow

What is meant by sequential model in Keras

There are two ways to build Keras models: sequential and functional.

The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs.

Alternatively, the functional API allows you to create models that have a lot more flexibility as you can easily define models where layers connect to more than just the previous and next layers. In fact, you can connect layers to (literally) any other layer. As a result, creating complex networks such as siamese networks and residual networks become possible.

for more details visit : https://machinelearningmastery.com/keras-functional-api-deep-learning/

How to Use the Keras Functional API for Deep Learning

 

 

The Sequential model (keras.io)

Keras documentation: The Sequential model

When to use a Sequential model

A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

 

A Sequential model is not appropriate when:

• Your model has multiple inputs or multiple outputs
• Any of your layers has multiple inputs or multiple outputs
• You need to do layer sharing
• You want non-linear topology (e.g. a residual connection, a multi-branch model)