Layer base, network input, dense layer functions in lasagne.
This recipe explains what are the layer base function and network input and dense layer functions in lasagne layers.
Recipe Objective - What are the layer base function, network input, and dense layer functions in lasagne layers?
Layer base classes:-
1. Layer - "The Layer" class represents a single layer of a neural network.
2. MergeLayer - This class represents a layer that aggregates input from multiple layers.
Network input:-
1. InputLayer - This layer holds a symbolic variable that represents a network input.
Dense layers:-
1. DenseLayer - A fully connected layer.
2. NINLayer - This is the Network-in-network layer.
Links for the more related projects:-
/projects/data-science-projects/deep-learning-projects
/projects/data-science-projects/neural-network-projects
Example:-
from lasagne.layers import InputLayer, DenseLayer
ly_in = InputLayer((100, 20))
ly1 = DenseLayer(ly_in, num_units=50)
ly_in = InputLayer((None, 10, 20, 30))
print(DenseLayer(ly_in, num_units=50).output_shape)
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