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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