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.
from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(2, input_dim=1)) model.add(Dense(1))
In the example above, layers are added piecewise via the
The Sequential model API is great for developing deep learning models in most situations, but it also has some limitations.
For example, it is not straightforward to define models that may have:
- multiple different input sources,
- produce multiple output destinations, or
- models that re-use layers.
Functional Models (⭐️)
from keras.models import Model from keras.layers import Input from keras.layers import Dense # Define the input visible = Input(shape=(2,)) # Connecting layers hidden = Dense(2)(visible) # Create the model model = Model(inputs=visible, outputs=hidden)
The Keras functional API provides a more flexible way for defining models.
Specifically, it allows you to define multiple input or output models as well as models that share layers. More than that, it allows you to define ad hoc acyclic network graphs.
Models are defined by creating instances of layers and connecting them directly to each other in pairs, and then defining a
Model that specifies the layers to act as the input and output to the model, via the parameters
Take a look at how Sequential and Functional models are being used in the examples featured in the post "Embeddings in Keras: Train vs. Pretrained".
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