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Fix typo.

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Relintai 2023-02-14 00:00:52 +01:00
parent aa8043621e
commit 686d81a258
1 changed files with 4 additions and 8 deletions
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12
mlpp/tanh_reg/tanh_reg.cpp
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@ -88,8 +88,7 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set); Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.tanh_derivv(_z))))); _weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.tanh_derivv(_z)))));
//_reg _weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, MLPPReg::REGULARIZATION_TYPE_NONE);
// Calculating the bias gradients // Calculating the bias gradients
_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.tanh_derivv(_z))) / _n; _bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.tanh_derivv(_z))) / _n;
@ -149,8 +148,7 @@ void MLPPTanhReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
// Weight Updation // Weight Updation
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * error * (1 - y_hat * y_hat), input_set_row_tmp)); _weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * error * (1 - y_hat * y_hat), input_set_row_tmp));
//_reg _weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, MLPPReg::REGULARIZATION_TYPE_NONE);
// Bias updation // Bias updation
_bias -= learning_rate * error * (1 - y_hat * y_hat); _bias -= learning_rate * error * (1 - y_hat * y_hat);
@ -197,8 +195,7 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
// Calculating the weight gradients // Calculating the weight gradients
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(current_input_batch_entry), alg.hadamard_productnv(error, avn.tanh_derivv(z))))); _weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(current_input_batch_entry), alg.hadamard_productnv(error, avn.tanh_derivv(z)))));
//_reg _weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, MLPPReg::REGULARIZATION_TYPE_NONE);
// Calculating the bias gradients // Calculating the bias gradients
_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.tanh_derivv(_z))) / _n; _bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.tanh_derivv(_z))) / _n;
@ -282,8 +279,7 @@ real_t MLPPTanhReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y)
MLPPReg regularization; MLPPReg regularization;
MLPPCost mlpp_cost; MLPPCost mlpp_cost;
//_reg return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, MLPPReg::REGULARIZATION_TYPE_NONE);
} }
real_t MLPPTanhReg::evaluatev(const Ref<MLPPVector> &x) { real_t MLPPTanhReg::evaluatev(const Ref<MLPPVector> &x) {