void FFN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Gradient(const arma::mat& /* unused */,
const size_t /* unused */,
arma::mat& gradient)
{
NetworkGradients(gradient, network);
Backward<>(error, network);
UpdateGradients<>(network);
}
void CNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Gradient(const arma::mat& /* unused */,
const size_t i,
arma::mat& gradient)
{
Evaluate(parameter, i, false);
NetworkGradients(gradient, network);
Backward<>(error, network);
UpdateGradients<>(network);
}
void RNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Gradient(const arma::mat& /* unused */,
const size_t i,
arma::mat& gradient)
{
if (gradient.is_empty())
{
gradient = arma::zeros<arma::mat>(parameter.n_rows, parameter.n_cols);
}
else
{
gradient.zeros();
}
Evaluate(parameter, i, false);
arma::mat currentGradient = arma::mat(gradient.n_rows, gradient.n_cols);
NetworkGradients(currentGradient, network);
const arma::mat input = arma::mat(predictors.colptr(i), predictors.n_rows,
1, false, true);
// Iterate through the input sequence and perform the feed backward pass.
for (seqNum = seqLen - 1; seqNum >= 0; seqNum--)
{
// Load the network activation for the upcoming backward pass.
LoadActivations(input.rows(seqNum * inputSize, (seqNum + 1) *
inputSize - 1), network);
// Perform the backward pass.
if (seqOutput)
{
arma::mat seqError = error.unsafe_col(seqNum);
Backward(seqError, network);
}
else
{
Backward(error, network);
}
// Link the parameters and update the gradients.
LinkParameter(network);
UpdateGradients<>(network);
// Update the overall gradient.
gradient += currentGradient;
if (seqNum == 0) break;
}
}
