keras
Introduction
1. Examples
2. Optimizers
3. Objectives
4. Models
5. Activations
6. Initializations
7. Regularizers
8. Constraints
9. Callbacks
10. Datasets
11. Layers, Core Layers
12. Layers, Convolutional Layers
13. Layers, Recurrent Layers
14. Layers, Advanced Activations Layers
15. Layers, Normalization Layers
16. Layers, Embedding Layers
17. Layers, Containers
18. Preprocessing, Sequence Preprocessing
19. Preprocessing, Text Preprocessing
20. Preprocessing, Image Preprocessing
21. Utils, Visualization Utilities

keras

Containers

Containers are ensembles of layers that can be interacted with through the same API as Layer objects.

Sequential

keras.layers.containers.Sequential(layers=[])

The Sequential container is a linear stack of layers. Apart from the add methods and the layers constructor argument, the API is identical to that of the Layer class.

This class is also the basis for the keras.models.Sequential architecture.

The layers constructor argument is a list of Layer instances.

Methods:

add(layer)

Add a new layer to the stack.