void update_gradient(int iLayer, matrix_t& target) {
if (iLayer < _cnn_layers.size()) {
// Infer convolutional layer
const int i = iLayer;
_cnn_layers[i]->infer_hiddens();
if (iLayer + 1 < _cnn_layers.size()) {
_cnn_layers[i + 1]->visibles() = _cnn_layers[i]->hiddens();
update_gradient(iLayer + 1, target);
_cnn_layers[i + 1]->backprop_visibles();
_cnn_layers[i]->backprop_hidden_deltas(_cnn_layers[i + 1]->visibles());
_cnn_layers[i]->update_gradient();
} else {
// Transition from convolutional model to dense model
_nn_layers[0]->visibles() = reshape(_cnn_layers[i]->hiddens(), 1, _cnn_layers[i]->hiddens().count());
update_gradient(iLayer + 1, target);
// Transition back
_nn_layers[0]->backprop_visible_deltas();
_cnn_layers[i]->backprop_hidden_deltas(reshape(_nn_layers[0]->visible_deltas(), _model.cnn_layers()[i]->hiddens_size()));
_cnn_layers[i]->update_gradient();
}
} else if (iLayer - _cnn_layers.size() < _nn_layers.size()) {
// Infer dense layer
const int i = iLayer - _cnn_layers.size();
_nn_layers[i]->infer_hiddens();
if (i + 1 < _nn_layers.size()) {
_nn_layers[i + 1]->visibles() = _nn_layers[i]->hiddens();
update_gradient(iLayer + 1, target);
_nn_layers[i + 1]->backprop_visible_deltas();
_nn_layers[i]->backprop_hidden_deltas(_nn_layers[i + 1]->visible_deltas());
_nn_layers[i]->update_gradient();
} else {
_nn_layers[i]->calculate_deltas(target);
_nn_layers[i]->update_gradient();
}
} else {
assert(0); // should never happen
}
}
static void shapepulse(SRC_NODE *node)
{
COMMON_NODE *cmnode;
while (node != NULL) {
if (node->updateLater <= 0)
update_gradient(node);
if (node->ptime > 0.0 && node->power != 0.0 && node->visible) {
cmnode = (COMMON_NODE *) &node->node.common_node;
if (strlen(cmnode->pattern) == 1 && *cmnode->pattern == '?') {
pulse(node);
}
else {
if (node->device >= GRADDEV)
grad_shapepulse(node);
else
rf_shapepulse(node);
}
}
node = node->bnode;
}
}
// Does not require the hidden units to be inferred
virtual void update_gradient(matrix_t& target) {
update_gradient(0, target);
}
