What are the helper functions in lasagne layers?

This recipe explains what are the helper functions in lasagne layers.

Recipe Objective - What are the helper functions in lasagne layers?

Helper function computes the output of the network at one or more layers. We can define the input to propagate through

the network instead of using the input variable associated with the network’s input layer.

A Deep Dive into the Types of Neural Networks

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Helper Functions:-

1. get_output - Computes the output of the network at one or more given layers.

2. get_output_shape - Computes the output shape of the network at one or more given layers.

3. get_all_layers - This function collects all layers below one or more given Layer instance..

4. get_all_params - Returns a list of Theano shared variables or expressions that parameterize the layer.

5. count_params - This function counts all parameters of all layers below one or more given Layer instances.

6. get_all_param_values - This function returns the values of the parameters of all layers below one or more given Layer instances.

Example:-

from lasagne.layers import InputLayer, DenseLayer
import lasagne
ly_in = InputLayer((100, 20))
ly1 = DenseLayer(ly_in, num_units=50)
print(lasagne.layers.get_all_layers(ly1) == [ly_in, ly1])

ly2 = DenseLayer(ly_in, num_units=10)
print(lasagne.layers.get_all_layers([ly2, ly1]) == [ly_in, ly2, ly1])
print(lasagne.layers.get_all_layers([ly1, ly2]) == [ly_in, ly1, ly2])

ly3 = DenseLayer(ly2, num_units=20)
print(lasagne.layers.get_all_layers(ly3) == [ly_in, ly2, ly3])

print(lasagne.layers.get_all_layers(ly3, treat_as_input=[ly2]) == [ly2, ly3])

param_count = lasagne.layers.count_params(ly1)
param_count

all_param_values = lasagne.layers.get_all_param_values(ly1)
(all_param_values[0] == ly1.W.get_value()).all()

(all_param_values[1] == ly1.b.get_value()).all()

all_param_values_2 = lasagne.layers.get_all_param_values(ly1)
# all_param_values is now [l1.W.get_value(), l1.b.get_value()]
lasagne.layers.set_all_param_values(ly1, all_param_values_2)

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