1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
|
# About Keras layers
All Keras layers have a number of methods in common:
- `layer.get_weights()`: returns the weights of the layer as a list of Numpy arrays.
- `layer.set_weights(weights)`: sets the weights of the layer from a list of Numpy arrays (with the same shapes as the output of `get_weights`).
- `layer.get_config()`: returns a dictionary containing the configuration of the layer. The layer can be reinstantiated from its config via:
```python
layer = Dense(32)
config = layer.get_config()
reconstructed_layer = Dense.from_config(config)
```
Or:
```python
from keras import layers
config = layer.get_config()
layer = layers.deserialize({'class_name': layer.__class__.__name__,
'config': config})
```
If a layer has a single node (i.e. if it isn't a shared layer), you can get its input tensor, output tensor, input shape and output shape via:
- `layer.input`
- `layer.output`
- `layer.input_shape`
- `layer.output_shape`
If the layer has multiple nodes (see: [the concept of layer node and shared layers](/getting-started/functional-api-guide/#the-concept-of-layer-node)), you can use the following methods:
- `layer.get_input_at(node_index)`
- `layer.get_output_at(node_index)`
- `layer.get_input_shape_at(node_index)`
- `layer.get_output_shape_at(node_index)`
|
