To create a fully connected layer from scratch in MATLAB, we need to define the weights and biases for the layer and implement the forward pass procedure. Here's an example implementation:
main.m665 chars27 lines
In this implementation, we define a custom class FullyConnectedLayer
that extends MATLAB's nnet.layer.Layer
class. The properties InputSize
, OutputSize
, Weights
, and Bias
are defined to store layer information.
The initialize
method initializes the layer's weights and biases with random normal values. The predict
method defines the forward pass computation, where we take the input X
and output the result Z
computed by the formula Z = Weights*X + Bias
.
To use this layer in a neural network model, we can create an object of this layer and add it to our model:
main.m85 chars4 lines
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