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Small cleanups.

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Relintai 2023-04-26 10:07:40 +02:00
parent fffb3075e4
commit 1497a2c1b0
3 changed files with 29 additions and 21 deletions
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4
mlpp/ann/ann.cpp
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@ -877,7 +877,7 @@ MLPPANN::ComputeGradientsResult MLPPANN::compute_gradients(const Ref<MLPPVector>
Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
res.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
res.cumulative_hidden_layer_w_grad.push_back(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
for (int i = _network.size() - 2; i >= 0; i--) {
layer = _network[i];
@ -885,7 +885,7 @@ MLPPANN::ComputeGradientsResult MLPPANN::compute_gradients(const Ref<MLPPVector>
layer->set_delta(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z())));
hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
res.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
res.cumulative_hidden_layer_w_grad.push_back(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
}
}

10
mlpp/hidden_layer/hidden_layer.cpp
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@ -147,10 +147,11 @@ void MLPPHiddenLayer::forward_pass() {
initialize();
}
MLPPLinAlg alg;
MLPPActivation avn;
_z = alg.mat_vec_addnm(alg.matmultnm(_input, _weights), _bias);
_z->multb(_input, _weights);
_z->add_vec(_bias);
_a = avn.run_activation_norm_matrix(_activation, _z);
}
@ -159,10 +160,11 @@ void MLPPHiddenLayer::test(const Ref<MLPPVector> &x) {
initialize();
}
MLPPLinAlg alg;
MLPPActivation avn;
_z_test = alg.additionnm(alg.mat_vec_multnv(alg.transposenm(_weights), x), _bias);
_z_test = _weights->transposen()->mult_vec(x);
_z_test->add(_bias);
_a_test = avn.run_activation_norm_matrix(_activation, _z_test);
}

36
mlpp/lin_alg/mlpp_matrix.cpp
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@ -665,26 +665,28 @@ void MLPPMatrix::subb(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
}
void MLPPMatrix::mult(const Ref<MLPPMatrix> &B) {
ERR_FAIL_MSG("TODO");
ERR_FAIL_COND(!B.is_valid());
Size2i b_size = B->size();
ERR_FAIL_COND(_size != b_size);
ERR_FAIL_COND(_size.x != b_size.y || _size.y != b_size.x);
//TODO need to make a copy of this, resize, and use that to get results into this
const real_t *b_ptr = B->ptr();
real_t *c_ptr = ptrw();
for (int i = 0; i < _size.y; i++) {
for (int k = 0; k < b_size.y; k++) {
int ind_i_k = calculate_index(i, k);
for (int ay = 0; ay < _size.y; ay++) {
for (int by = 0; by < b_size.y; by++) {
int ind_ay_by = calculate_index(ay, by);
for (int j = 0; j < b_size.x; j++) {
int ind_i_j = calculate_index(i, j);
int ind_k_j = B->calculate_index(k, j);
for (int bx = 0; bx < b_size.x; bx++) {
int ind_ay_bx = calculate_index(ay, bx);
int ind_by_bx = B->calculate_index(by, bx);
c_ptr[ind_i_j] += c_ptr[ind_i_k] * b_ptr[ind_k_j];
//C->set_element(i, j, get_element(i, j) + get_element(i, k) * B->get_element(k, j
c_ptr[ind_ay_bx] += c_ptr[ind_ay_by] * b_ptr[ind_by_bx];
}
}
}
@ -694,11 +696,13 @@ Ref<MLPPMatrix> MLPPMatrix::multn(const Ref<MLPPMatrix> &B) const {
Size2i b_size = B->size();
ERR_FAIL_COND_V(_size != b_size, Ref<MLPPMatrix>());
ERR_FAIL_COND_V_MSG(_size.y != b_size.x || _size.x != b_size.y, Ref<MLPPMatrix>(), "_size.y != b_size.x || _size.x != b_size.y _size: " + _size.operator String() + " b_size: " + b_size.operator String());
Size2i rs = Size2i(b_size.x, _size.y);
Ref<MLPPMatrix> C;
C.instance();
C->resize(_size);
C->resize(rs);
const real_t *a_ptr = ptr();
const real_t *b_ptr = B->ptr();
@ -727,10 +731,12 @@ void MLPPMatrix::multb(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
Size2i a_size = A->size();
Size2i b_size = B->size();
ERR_FAIL_COND(a_size != b_size);
ERR_FAIL_COND_MSG(a_size.y != b_size.x || a_size.x != b_size.y, "a_size.y != b_size.x || a_size.x != b_size.y: a_size: " + a_size.operator String() + " b_size: " + b_size.operator String());
if (_size != a_size) {
resize(a_size);
Size2i rs = Size2i(b_size.x, a_size.y);
if (unlikely(_size != rs)) {
resize(rs);
}
const real_t *a_ptr = A->ptr();