Surgery

In many applications, it’s important to be able to access only part of the network. NeuPy supports a few methods that allow to slice network. These methods can change structure of the network or provide access to specific layers.

Slice networks

In NeuPy, it’s possible to slice neural networks in order to get part of the network with different input or output layers.

>>> from neupy.layers import *
>>>
>>> network = join(
...     Input(10),
...     Relu(20, name='relu-2'),
...     Relu(30, name='relu-3'),
...     Relu(40, name='relu-4'),
...     Relu(50, name='relu-5'),
... )
>>> network
Input(10) > Relu(20) > Relu(30) > Relu(40) > Relu(50)

The end method can change network’s output layer. For example, we want to get output from the relu-4 layer instead of the relu-5.

>>> network.end('relu-4')
Input(10) > Relu(20) > Relu(30) > Relu(40)

The same can be done for the input layers with help of the start method.

>>> network.start('relu-2')
Relu(20) > Relu(30) > Relu(40) > Relu(50)

These methods can be combined in sequence

>>> network.start('relu-2').end('relu-4')
Relu(20) > Relu(30) > Relu(40)

Also, it’s possible to point into multiple input and output layers

>>> from neupy.layers import *
>>>
>>> network = Input(10) > Relu(20, name='relu-2')
>>>
>>> output_1 = Relu(30, name='relu-3') > Sigmoid(1)
>>> output_2 = Relu(40, name='relu-4') > Sigmoid(2)
>>>
>>> network = network > [output_1, output_2]
>>>
>>> network
10 -> [... 6 layers ...] -> [(1,), (2,)]
>>>
>>> network.end('relu-3', 'relu-4')
10 -> [... 4 layers ...] -> [(30,), (40,)]

Layer instance can be used as identifiers for the slicing method instead of the names.

>>> from neupy.layers import *
>>>
>>> input_layer = Input(10)
>>> relu_2 = Relu(20)
>>> relu_3 = Relu(30)
>>>
>>> network = input_layer > relu_2 > relu_3
>>> network
Input(10) > Relu(20) > Relu(30)
>>>
>>> network.end(relu_2)
Input(10) > Relu(20)

Find layers by name

Each name is a unique identifier for the layer inside of the network. Any layer can be accessed using the layer method.

>>> from neupy.layers import *
>>>
>>> network = join(
...     Input(10, name='input-1'),
...     Relu(8, name='relu-0'),
...     Relu(5, name='relu-1'),
... )
>>>
>>> network.layer('relu-0')
Relu(8)
>>>
>>> network.layer('relu-1')
Relu(5)

Exception will be triggered in case if layer with specified name wasn’t defined in the network.

>>> network.layer('test')
Traceback (most recent call last):
  ...
NameError: Cannot find layer with name 'test'