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- function W = randInitializeWeights(L_in, L_out)
- %RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
- %incoming connections and L_out outgoing connections
- % W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights
- % of a layer with L_in incoming connections and L_out outgoing
- % connections.
- %
- % Note that W should be set to a matrix of size(L_out, 1 + L_in) as
- % the column row of W handles the "bias" terms
- %
-
- % You need to return the following variables correctly
- W = zeros(L_out, 1 + L_in);
- % ====================== YOUR CODE HERE ======================
- % Instructions: Initialize W randomly so that we break the symmetry while
- % training the neural network.
- %
- % Note: The first row of W corresponds to the parameters for the bias units
- %
-
- epsilon_init = sqrt(6) / sqrt(L_in + L_out);
- W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init;
-
-
-
-
- % =========================================================================
-
- end
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