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Small improvements and fixed MLPPMLP.
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@ -109,7 +109,7 @@ void MLPPMLP::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
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// Calculating the weight/bias for layer 1
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Ref<MLPPMatrix> D1_1 = error->outer_product(_weights2);
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Ref<MLPPMatrix> D1_2 = D1_1->transposen()->hadamard_productn(avn.sigmoid_derivm(_z2));
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Ref<MLPPMatrix> D1_2 = D1_1->hadamard_productn(avn.sigmoid_derivm(_z2));
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Ref<MLPPMatrix> D1_3 = _input_set->transposen()->multn(D1_2);
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// weight an bias updation for layer 1
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@ -328,7 +328,7 @@ void MLPPMLP::initialize() {
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MLPPUtilities util;
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_weights1->resize(Size2i(_k, _n_hidden));
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_weights1->resize(Size2i(_n_hidden, _k));
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_weights2->resize(_n_hidden);
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_bias1->resize(_n_hidden);
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@ -397,38 +397,20 @@ MLPPMLP::MLPPMLP(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_ou
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_output_set = p_output_set;
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_y_hat.instance();
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_weights1.instance();
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_weights2.instance();
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_z2.instance();
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_a2.instance();
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_bias1.instance();
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_n_hidden = p_n_hidden;
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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MLPPActivation avn;
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_y_hat->resize(_n);
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_initialized = false;
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MLPPUtilities util;
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_weights1.instance();
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_weights1->resize(Size2i(_k, _n_hidden));
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_weights2.instance();
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_weights2->resize(_n_hidden);
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_bias1.instance();
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_bias1->resize(_n_hidden);
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util.weight_initializationm(_weights1);
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util.weight_initializationv(_weights2);
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util.bias_initializationv(_bias1);
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_bias2 = util.bias_initializationr();
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_z2.instance();
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_a2.instance();
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_initialized = true;
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initialize();
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}
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MLPPMLP::MLPPMLP() {
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