Method BP_Multi_Thread_Core

LycorisNet/sources/individual.cpp:305–378

One of the functor of BP_Multi_Thread().

    // One of the functor of BP_Multi_Thread().
    void Individual::BP_Multi_Thread_Core(uint32_t start, uint32_t end) {
        auto ci = this->clone();

        float output[outputNum];
        std::map<uint32_t, float> gradient; // Store the gradient of all nodes.
        for (uint32_t z = start; z < end; ++z) {
            uint32_t data_p = 0;
            ci->forward(ci->args->inputArray[z], output);

            gradient.clear();
            if (ci->args->mode == "predict") { // Predict.
                for (uint32_t j = 0; j < ci->nodeSlice->size(); ++j) {
                    auto index = (*ci->nodeSlice)[ci->nodeSlice->size() - 1 - j];
                    auto n = (*ci->nodeMap)[index];

                    // The output nodes.
                    if (index >= inputNum && index < (inputNum + outputNum)) {
                        gradient[index] = n->value - ci->args->desireArray[z][index - inputNum];
                    }

                    auto grad = gradient[index] * (n->value > 0 ? 1.0f : 0.2f);

                    for (auto &iter : *n->genomeMap) {
                        auto p = gradient.find(iter.first.in);
                        if (p != gradient.end()) {
                            gradient[iter.first.in] += grad * iter.second.weight;
                        } else {
                            gradient[iter.first.in] = grad * iter.second.weight;
                        }

                        args->batchData[z][data_p++] = 0 - ci->args->lr * grad * (*ci->nodeMap)[iter.first.in]->value;
                    }

                    args->batchData[z][data_p++] = 0 - ci->args->lr * grad;
                }
            } else { // Classify.
                LycorisUtils::softmax(output, outputNum);

                for (uint32_t j = 0; j < ci->nodeSlice->size(); ++j) {
                    auto index = (*ci->nodeSlice)[ci->nodeSlice->size() - 1 - j];
                    auto n = (*ci->nodeMap)[index];

                    // The output nodes.
                    if (index >= inputNum && index < (inputNum + outputNum)) {
                        gradient[index] = output[index - inputNum] - ci->args->desireArray[z][index - inputNum];
                    }

                    auto grad = gradient[index] * (n->value > 0 ? 1.0f : 0.2f);

                    for (auto &iter : *n->genomeMap) {
                        auto p = gradient.find(iter.first.in);
                        if (p != gradient.end()) {
                            gradient[iter.first.in] += grad * iter.second.weight;
                        } else {
                            gradient[iter.first.in] = grad * iter.second.weight;
                        }

                        args->batchData[z][data_p++] = 0 - ci->args->lr * grad * (*ci->nodeMap)[iter.first.in]->value;