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Codestyle for MLPPANN.
This commit is contained in:
parent
3feed886c4
commit
e25892aacf
605
mlpp/ann/ann.cpp
605
mlpp/ann/ann.cpp
@ -16,35 +16,35 @@
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#include <random>
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std::vector<real_t> MLPPANN::model_set_test(std::vector<std::vector<real_t>> X) {
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if (!network.empty()) {
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network[0].input = X;
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network[0].forwardPass();
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if (!_network.empty()) {
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_network[0].input = X;
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_network[0].forwardPass();
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for (uint32_t i = 1; i < network.size(); i++) {
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].input = _network[i - 1].a;
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_network[i].forwardPass();
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}
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outputLayer->input = network[network.size() - 1].a;
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_output_layer->input = _network[_network.size() - 1].a;
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} else {
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outputLayer->input = X;
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_output_layer->input = X;
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}
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outputLayer->forwardPass();
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_output_layer->forwardPass();
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return outputLayer->a;
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return _output_layer->a;
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}
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real_t MLPPANN::model_test(std::vector<real_t> x) {
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if (!network.empty()) {
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network[0].Test(x);
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for (uint32_t i = 1; i < network.size(); i++) {
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network[i].Test(network[i - 1].a_test);
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if (!_network.empty()) {
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_network[0].Test(x);
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].Test(_network[i - 1].a_test);
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}
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outputLayer->Test(network[network.size() - 1].a_test);
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_output_layer->Test(_network[_network.size() - 1].a_test);
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} else {
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outputLayer->Test(x);
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_output_layer->Test(x);
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}
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return outputLayer->a_test;
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return _output_layer->a_test;
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}
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void MLPPANN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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@ -57,26 +57,26 @@ void MLPPANN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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real_t initial_learning_rate = learning_rate;
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alg.printMatrix(network[network.size() - 1].weights);
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alg.printMatrix(_network[_network.size() - 1].weights);
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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cost_prev = cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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auto grads = compute_gradients(y_hat, outputSet);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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auto grads = compute_gradients(_y_hat, _output_set);
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auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
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auto output_w_grad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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update_parameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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cumulative_hidden_layer_w_grad = alg.scalarMultiply(learning_rate / _n, cumulative_hidden_layer_w_grad);
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output_w_grad = alg.scalarMultiply(learning_rate / _n, output_w_grad);
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update_parameters(cumulative_hidden_layer_w_grad, output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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std::cout << learning_rate << std::endl;
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forward_pass();
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, outputSet);
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print_ui(epoch, cost_prev, _y_hat, _output_set);
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}
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epoch++;
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@ -95,28 +95,28 @@ void MLPPANN::sgd(real_t learning_rate, int max_epoch, bool ui) {
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real_t initial_learning_rate = learning_rate;
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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int outputIndex = distribution(generator);
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std::vector<real_t> y_hat = model_set_test({ inputSet[outputIndex] });
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cost_prev = cost({ y_hat }, { outputSet[outputIndex] });
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std::vector<real_t> y_hat = model_set_test({ _input_set[outputIndex] });
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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auto grads = compute_gradients(y_hat, { outputSet[outputIndex] });
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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auto grads = compute_gradients(y_hat, { _output_set[outputIndex] });
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auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
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auto output_w_grad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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cumulative_hidden_layer_w_grad = alg.scalarMultiply(learning_rate / _n, cumulative_hidden_layer_w_grad);
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output_w_grad = alg.scalarMultiply(learning_rate / _n, output_w_grad);
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update_parameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test({ inputSet[outputIndex] });
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update_parameters(cumulative_hidden_layer_w_grad, output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test({ _input_set[outputIndex] });
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, { outputSet[outputIndex] });
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print_ui(epoch, cost_prev, y_hat, { _output_set[outputIndex] });
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}
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epoch++;
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@ -137,33 +137,33 @@ void MLPPANN::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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auto grads = compute_gradients(y_hat, output_mini_batches[i]);
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auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
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auto output_w_grad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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cumulative_hidden_layer_w_grad = alg.scalarMultiply(learning_rate / _n, cumulative_hidden_layer_w_grad);
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output_w_grad = alg.scalarMultiply(learning_rate / _n, output_w_grad);
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update_parameters(cumulativeHiddenLayerWGrad, outputWGrad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(inputMiniBatches[i]);
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update_parameters(cumulative_hidden_layer_w_grad, output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(input_mini_batches[i]);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
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print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
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}
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}
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@ -186,50 +186,50 @@ void MLPPANN::momentum(real_t learning_rate, int max_epoch, int mini_batch_size,
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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std::vector<real_t> v_output;
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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auto grads = compute_gradients(y_hat, output_mini_batches[i]);
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auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
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auto output_w_grad = std::get<1>(grads);
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if (!network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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if (!_network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
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}
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if (v_output.empty()) {
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v_output.resize(outputWGrad.size());
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v_output.resize(output_w_grad.size());
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}
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if (nag) { // "Aposterori" calculation
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update_parameters(v_hidden, v_output, 0); // DON'T update bias.
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}
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v_hidden = alg.addition(alg.scalarMultiply(gamma, v_hidden), alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad));
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v_hidden = alg.addition(alg.scalarMultiply(gamma, v_hidden), alg.scalarMultiply(learning_rate / _n, cumulative_hidden_layer_w_grad));
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v_output = alg.addition(alg.scalarMultiply(gamma, v_output), alg.scalarMultiply(learning_rate / n, outputWGrad));
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v_output = alg.addition(alg.scalarMultiply(gamma, v_output), alg.scalarMultiply(learning_rate / _n, output_w_grad));
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update_parameters(v_hidden, v_output, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(inputMiniBatches[i]);
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y_hat = model_set_test(input_mini_batches[i]);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
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print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
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}
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}
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@ -252,49 +252,49 @@ void MLPPANN::adagrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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std::vector<real_t> v_output;
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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auto grads = compute_gradients(y_hat, output_mini_batches[i]);
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auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
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auto output_w_grad = std::get<1>(grads);
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if (!network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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if (!_network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
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}
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if (v_output.empty()) {
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v_output.resize(outputWGrad.size());
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v_output.resize(output_w_grad.size());
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}
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v_hidden = alg.addition(v_hidden, alg.exponentiate(cumulativeHiddenLayerWGrad, 2));
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v_hidden = alg.addition(v_hidden, alg.exponentiate(cumulative_hidden_layer_w_grad, 2));
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v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2));
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v_output = alg.addition(v_output, alg.exponentiate(output_w_grad, 2));
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std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
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std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output))));
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std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(cumulative_hidden_layer_w_grad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
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std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(output_w_grad, alg.scalarAdd(e, alg.sqrt(v_output))));
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update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(inputMiniBatches[i]);
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update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(input_mini_batches[i]);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
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print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
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}
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}
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epoch++;
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@ -315,55 +315,58 @@ void MLPPANN::adadelta(real_t learning_rate, int max_epoch, int mini_batch_size,
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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std::vector<real_t> v_output;
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
|
||||
auto grads = compute_gradients(y_hat, output_mini_batches[i]);
|
||||
auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
|
||||
auto output_w_grad = std::get<1>(grads);
|
||||
|
||||
if (!network.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
||||
if (!_network.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
|
||||
}
|
||||
|
||||
if (v_output.empty()) {
|
||||
v_output.resize(outputWGrad.size());
|
||||
v_output.resize(output_w_grad.size());
|
||||
}
|
||||
|
||||
v_hidden = alg.addition(alg.scalarMultiply(1 - b1, v_hidden), alg.scalarMultiply(b1, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(1 - b1, v_hidden), alg.scalarMultiply(b1, alg.exponentiate(cumulative_hidden_layer_w_grad, 2)));
|
||||
|
||||
v_output = alg.addition(v_output, alg.exponentiate(outputWGrad, 2));
|
||||
v_output = alg.addition(v_output, alg.exponentiate(output_w_grad, 2));
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(cumulativeHiddenLayerWGrad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
|
||||
std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(outputWGrad, alg.scalarAdd(e, alg.sqrt(v_output))));
|
||||
std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(cumulative_hidden_layer_w_grad, alg.scalarAdd(e, alg.sqrt(v_hidden))));
|
||||
std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(output_w_grad, alg.scalarAdd(e, alg.sqrt(v_output))));
|
||||
|
||||
update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(inputMiniBatches[i]);
|
||||
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(input_mini_batches[i]);
|
||||
|
||||
if (ui) {
|
||||
print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
|
||||
print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
@ -376,13 +379,13 @@ void MLPPANN::adam(real_t learning_rate, int max_epoch, int mini_batch_size, rea
|
||||
real_t initial_learning_rate = learning_rate;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
// always evaluate the result
|
||||
// always do forward pass only ONCE at end.
|
||||
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden;
|
||||
@ -391,30 +394,30 @@ void MLPPANN::adam(real_t learning_rate, int max_epoch, int mini_batch_size, rea
|
||||
std::vector<real_t> m_output;
|
||||
std::vector<real_t> v_output;
|
||||
while (true) {
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
|
||||
auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
|
||||
auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
|
||||
auto outputWGrad = std::get<1>(grads);
|
||||
auto grads = compute_gradients(y_hat, output_mini_batches[i]);
|
||||
auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
|
||||
auto output_w_grad = std::get<1>(grads);
|
||||
|
||||
if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
||||
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
||||
if (!_network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulative_hidden_layer_w_grad);
|
||||
v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
|
||||
}
|
||||
|
||||
if (m_output.empty() && v_output.empty()) {
|
||||
m_output.resize(outputWGrad.size());
|
||||
v_output.resize(outputWGrad.size());
|
||||
m_output.resize(output_w_grad.size());
|
||||
v_output.resize(output_w_grad.size());
|
||||
}
|
||||
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulative_hidden_layer_w_grad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulative_hidden_layer_w_grad, 2)));
|
||||
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, output_w_grad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(output_w_grad, 2)));
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_hidden);
|
||||
std::vector<std::vector<std::vector<real_t>>> v_hidden_hat = alg.scalarMultiply(1 / (1 - std::pow(b2, epoch)), v_hidden);
|
||||
@ -422,14 +425,14 @@ void MLPPANN::adam(real_t learning_rate, int max_epoch, int mini_batch_size, rea
|
||||
std::vector<real_t> m_output_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_output);
|
||||
std::vector<real_t> v_output_hat = alg.scalarMultiply(1 / (1 - std::pow(b2, epoch)), v_output);
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
|
||||
update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(inputMiniBatches[i]);
|
||||
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(input_mini_batches[i]);
|
||||
|
||||
if (ui) {
|
||||
print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
|
||||
print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -451,13 +454,13 @@ void MLPPANN::adamax(real_t learning_rate, int max_epoch, int mini_batch_size, r
|
||||
real_t initial_learning_rate = learning_rate;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
// always evaluate the result
|
||||
// always do forward pass only ONCE at end.
|
||||
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden;
|
||||
@ -466,43 +469,43 @@ void MLPPANN::adamax(real_t learning_rate, int max_epoch, int mini_batch_size, r
|
||||
std::vector<real_t> m_output;
|
||||
std::vector<real_t> u_output;
|
||||
while (true) {
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
|
||||
auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
|
||||
auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
|
||||
auto outputWGrad = std::get<1>(grads);
|
||||
auto grads = compute_gradients(y_hat, output_mini_batches[i]);
|
||||
auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
|
||||
auto output_w_grad = std::get<1>(grads);
|
||||
|
||||
if (!network.empty() && m_hidden.empty() && u_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
||||
u_hidden = alg.resize(u_hidden, cumulativeHiddenLayerWGrad);
|
||||
if (!_network.empty() && m_hidden.empty() && u_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulative_hidden_layer_w_grad);
|
||||
u_hidden = alg.resize(u_hidden, cumulative_hidden_layer_w_grad);
|
||||
}
|
||||
|
||||
if (m_output.empty() && u_output.empty()) {
|
||||
m_output.resize(outputWGrad.size());
|
||||
u_output.resize(outputWGrad.size());
|
||||
m_output.resize(output_w_grad.size());
|
||||
u_output.resize(output_w_grad.size());
|
||||
}
|
||||
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
||||
u_hidden = alg.max(alg.scalarMultiply(b2, u_hidden), alg.abs(cumulativeHiddenLayerWGrad));
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulative_hidden_layer_w_grad));
|
||||
u_hidden = alg.max(alg.scalarMultiply(b2, u_hidden), alg.abs(cumulative_hidden_layer_w_grad));
|
||||
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
||||
u_output = alg.max(alg.scalarMultiply(b2, u_output), alg.abs(outputWGrad));
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, output_w_grad));
|
||||
u_output = alg.max(alg.scalarMultiply(b2, u_output), alg.abs(output_w_grad));
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_hidden);
|
||||
|
||||
std::vector<real_t> m_output_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_output);
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, u_hidden)));
|
||||
std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, u_output)));
|
||||
std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_hidden_hat, alg.scalarAdd(e, u_hidden)));
|
||||
std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_output_hat, alg.scalarAdd(e, u_output)));
|
||||
|
||||
update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(inputMiniBatches[i]);
|
||||
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(input_mini_batches[i]);
|
||||
|
||||
if (ui) {
|
||||
print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
|
||||
print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -524,13 +527,13 @@ void MLPPANN::nadam(real_t learning_rate, int max_epoch, int mini_batch_size, re
|
||||
real_t initial_learning_rate = learning_rate;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
// always evaluate the result
|
||||
// always do forward pass only ONCE at end.
|
||||
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden;
|
||||
@ -539,47 +542,47 @@ void MLPPANN::nadam(real_t learning_rate, int max_epoch, int mini_batch_size, re
|
||||
std::vector<real_t> m_output;
|
||||
std::vector<real_t> v_output;
|
||||
while (true) {
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
|
||||
auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
|
||||
auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
|
||||
auto outputWGrad = std::get<1>(grads);
|
||||
auto grads = compute_gradients(y_hat, output_mini_batches[i]);
|
||||
auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
|
||||
auto output_w_grad = std::get<1>(grads);
|
||||
|
||||
if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
||||
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
||||
if (!_network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulative_hidden_layer_w_grad);
|
||||
v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
|
||||
}
|
||||
|
||||
if (m_output.empty() && v_output.empty()) {
|
||||
m_output.resize(outputWGrad.size());
|
||||
v_output.resize(outputWGrad.size());
|
||||
m_output.resize(output_w_grad.size());
|
||||
v_output.resize(output_w_grad.size());
|
||||
}
|
||||
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulative_hidden_layer_w_grad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulative_hidden_layer_w_grad, 2)));
|
||||
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, output_w_grad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(output_w_grad, 2)));
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_hidden);
|
||||
std::vector<std::vector<std::vector<real_t>>> v_hidden_hat = alg.scalarMultiply(1 / (1 - std::pow(b2, epoch)), v_hidden);
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden_final = alg.addition(alg.scalarMultiply(b1, m_hidden_hat), alg.scalarMultiply((1 - b1) / (1 - std::pow(b1, epoch)), cumulativeHiddenLayerWGrad));
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden_final = alg.addition(alg.scalarMultiply(b1, m_hidden_hat), alg.scalarMultiply((1 - b1) / (1 - std::pow(b1, epoch)), cumulative_hidden_layer_w_grad));
|
||||
|
||||
std::vector<real_t> m_output_hat = alg.scalarMultiply(1 / (1 - std::pow(b1, epoch)), m_output);
|
||||
std::vector<real_t> v_output_hat = alg.scalarMultiply(1 / (1 - std::pow(b2, epoch)), v_output);
|
||||
std::vector<real_t> m_output_final = alg.addition(alg.scalarMultiply(b1, m_output_hat), alg.scalarMultiply((1 - b1) / (1 - std::pow(b1, epoch)), outputWGrad));
|
||||
std::vector<real_t> m_output_final = alg.addition(alg.scalarMultiply(b1, m_output_hat), alg.scalarMultiply((1 - b1) / (1 - std::pow(b1, epoch)), output_w_grad));
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_hidden_final, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_output_final, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_hidden_final, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_output_final, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
|
||||
update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(inputMiniBatches[i]);
|
||||
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(input_mini_batches[i]);
|
||||
|
||||
if (ui) {
|
||||
print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
|
||||
print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -602,13 +605,13 @@ void MLPPANN::amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
real_t initial_learning_rate = learning_rate;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
// always evaluate the result
|
||||
// always do forward pass only ONCE at end.
|
||||
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto input_mini_batches = std::get<0>(batches);
|
||||
auto output_mini_batches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<std::vector<std::vector<real_t>>> m_hidden;
|
||||
@ -621,45 +624,45 @@ void MLPPANN::amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
|
||||
std::vector<real_t> v_output_hat;
|
||||
while (true) {
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, decayConstant, epoch, dropRate);
|
||||
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = model_set_test(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = model_set_test(input_mini_batches[i]);
|
||||
cost_prev = cost(y_hat, output_mini_batches[i]);
|
||||
|
||||
auto grads = compute_gradients(y_hat, outputMiniBatches[i]);
|
||||
auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
|
||||
auto outputWGrad = std::get<1>(grads);
|
||||
auto grads = compute_gradients(y_hat, output_mini_batches[i]);
|
||||
auto cumulative_hidden_layer_w_grad = std::get<0>(grads);
|
||||
auto output_w_grad = std::get<1>(grads);
|
||||
|
||||
if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
|
||||
v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
|
||||
v_hidden_hat = alg.resize(v_hidden_hat, cumulativeHiddenLayerWGrad);
|
||||
if (!_network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
|
||||
m_hidden = alg.resize(m_hidden, cumulative_hidden_layer_w_grad);
|
||||
v_hidden = alg.resize(v_hidden, cumulative_hidden_layer_w_grad);
|
||||
v_hidden_hat = alg.resize(v_hidden_hat, cumulative_hidden_layer_w_grad);
|
||||
}
|
||||
|
||||
if (m_output.empty() && v_output.empty()) {
|
||||
m_output.resize(outputWGrad.size());
|
||||
v_output.resize(outputWGrad.size());
|
||||
v_output_hat.resize(outputWGrad.size());
|
||||
m_output.resize(output_w_grad.size());
|
||||
v_output.resize(output_w_grad.size());
|
||||
v_output_hat.resize(output_w_grad.size());
|
||||
}
|
||||
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulativeHiddenLayerWGrad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulativeHiddenLayerWGrad, 2)));
|
||||
m_hidden = alg.addition(alg.scalarMultiply(b1, m_hidden), alg.scalarMultiply(1 - b1, cumulative_hidden_layer_w_grad));
|
||||
v_hidden = alg.addition(alg.scalarMultiply(b2, v_hidden), alg.scalarMultiply(1 - b2, alg.exponentiate(cumulative_hidden_layer_w_grad, 2)));
|
||||
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, outputWGrad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(outputWGrad, 2)));
|
||||
m_output = alg.addition(alg.scalarMultiply(b1, m_output), alg.scalarMultiply(1 - b1, output_w_grad));
|
||||
v_output = alg.addition(alg.scalarMultiply(b2, v_output), alg.scalarMultiply(1 - b2, alg.exponentiate(output_w_grad, 2)));
|
||||
|
||||
v_hidden_hat = alg.max(v_hidden_hat, v_hidden);
|
||||
|
||||
v_output_hat = alg.max(v_output_hat, v_output);
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_hidden, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> outputLayerUpdation = alg.scalarMultiply(learning_rate / n, alg.elementWiseDivision(m_output, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_hidden, alg.scalarAdd(e, alg.sqrt(v_hidden_hat))));
|
||||
std::vector<real_t> output_layer_updation = alg.scalarMultiply(learning_rate / _n, alg.elementWiseDivision(m_output, alg.scalarAdd(e, alg.sqrt(v_output_hat))));
|
||||
|
||||
update_parameters(hiddenLayerUpdations, outputLayerUpdation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(inputMiniBatches[i]);
|
||||
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
||||
y_hat = model_set_test(input_mini_batches[i]);
|
||||
|
||||
if (ui) {
|
||||
print_ui(epoch, cost_prev, y_hat, outputMiniBatches[i]);
|
||||
print_ui(epoch, cost_prev, y_hat, output_mini_batches[i]);
|
||||
}
|
||||
}
|
||||
|
||||
@ -676,82 +679,82 @@ void MLPPANN::amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
real_t MLPPANN::score() {
|
||||
MLPPUtilities util;
|
||||
forward_pass();
|
||||
return util.performance(y_hat, outputSet);
|
||||
return util.performance(_y_hat, _output_set);
|
||||
}
|
||||
|
||||
void MLPPANN::save(std::string fileName) {
|
||||
MLPPUtilities util;
|
||||
if (!network.empty()) {
|
||||
util.saveParameters(fileName, network[0].weights, network[0].bias, false, 1);
|
||||
for (uint32_t i = 1; i < network.size(); i++) {
|
||||
util.saveParameters(fileName, network[i].weights, network[i].bias, true, i + 1);
|
||||
if (!_network.empty()) {
|
||||
util.saveParameters(fileName, _network[0].weights, _network[0].bias, false, 1);
|
||||
for (uint32_t i = 1; i < _network.size(); i++) {
|
||||
util.saveParameters(fileName, _network[i].weights, _network[i].bias, true, i + 1);
|
||||
}
|
||||
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, true, network.size() + 1);
|
||||
util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
|
||||
} else {
|
||||
util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, false, network.size() + 1);
|
||||
util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPANN::set_learning_rate_scheduler(std::string type, real_t decayConstant) {
|
||||
lrScheduler = type;
|
||||
MLPPANN::decayConstant = decayConstant;
|
||||
void MLPPANN::set_learning_rate_scheduler(std::string type, real_t decay_constant) {
|
||||
_lr_scheduler = type;
|
||||
_decay_constant = decay_constant;
|
||||
}
|
||||
|
||||
void MLPPANN::set_learning_rate_scheduler_drop(std::string type, real_t decayConstant, real_t dropRate) {
|
||||
lrScheduler = type;
|
||||
MLPPANN::decayConstant = decayConstant;
|
||||
MLPPANN::dropRate = dropRate;
|
||||
void MLPPANN::set_learning_rate_scheduler_drop(std::string type, real_t decay_constant, real_t drop_rate) {
|
||||
_lr_scheduler = type;
|
||||
_decay_constant = decay_constant;
|
||||
_drop_rate = drop_rate;
|
||||
}
|
||||
|
||||
// https://en.wikipedia.org/wiki/Learning_rate
|
||||
// Learning Rate Decay (C2W2L09) - Andrew Ng - Deep Learning Specialization
|
||||
real_t MLPPANN::apply_learning_rate_scheduler(real_t learningRate, real_t decayConstant, real_t epoch, real_t dropRate) {
|
||||
if (lrScheduler == "Time") {
|
||||
return learningRate / (1 + decayConstant * epoch);
|
||||
} else if (lrScheduler == "Epoch") {
|
||||
return learningRate * (decayConstant / std::sqrt(epoch));
|
||||
} else if (lrScheduler == "Step") {
|
||||
return learningRate * std::pow(decayConstant, int((1 + epoch) / dropRate)); // Utilizing an explicit int conversion implicitly takes the floor.
|
||||
} else if (lrScheduler == "Exponential") {
|
||||
return learningRate * std::exp(-decayConstant * epoch);
|
||||
real_t MLPPANN::apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate) {
|
||||
if (_lr_scheduler == "Time") {
|
||||
return learning_rate / (1 + decay_constant * epoch);
|
||||
} else if (_lr_scheduler == "Epoch") {
|
||||
return learning_rate * (decay_constant / std::sqrt(epoch));
|
||||
} else if (_lr_scheduler == "Step") {
|
||||
return learning_rate * std::pow(decay_constant, int((1 + epoch) / drop_rate)); // Utilizing an explicit int conversion implicitly takes the floor.
|
||||
} else if (_lr_scheduler == "Exponential") {
|
||||
return learning_rate * std::exp(-decay_constant * epoch);
|
||||
}
|
||||
return learningRate;
|
||||
return learning_rate;
|
||||
}
|
||||
|
||||
void MLPPANN::add_layer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
|
||||
if (network.empty()) {
|
||||
network.push_back(MLPPOldHiddenLayer(n_hidden, activation, inputSet, weightInit, reg, lambda, alpha));
|
||||
network[0].forwardPass();
|
||||
if (_network.empty()) {
|
||||
_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, _input_set, weightInit, reg, lambda, alpha));
|
||||
_network[0].forwardPass();
|
||||
} else {
|
||||
network.push_back(MLPPOldHiddenLayer(n_hidden, activation, network[network.size() - 1].a, weightInit, reg, lambda, alpha));
|
||||
network[network.size() - 1].forwardPass();
|
||||
_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, _network[_network.size() - 1].a, weightInit, reg, lambda, alpha));
|
||||
_network[_network.size() - 1].forwardPass();
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPANN::add_output_layer(std::string activation, std::string loss, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
|
||||
if (!network.empty()) {
|
||||
outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha);
|
||||
if (!_network.empty()) {
|
||||
_output_layer = new MLPPOldOutputLayer(_network[_network.size() - 1].n_hidden, activation, loss, _network[_network.size() - 1].a, weightInit, reg, lambda, alpha);
|
||||
} else {
|
||||
outputLayer = new MLPPOldOutputLayer(k, activation, loss, inputSet, weightInit, reg, lambda, alpha);
|
||||
_output_layer = new MLPPOldOutputLayer(_k, activation, loss, _input_set, weightInit, reg, lambda, alpha);
|
||||
}
|
||||
}
|
||||
|
||||
MLPPANN::MLPPANN(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet) {
|
||||
inputSet = p_inputSet;
|
||||
outputSet = p_outputSet;
|
||||
MLPPANN::MLPPANN(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set) {
|
||||
_input_set = p_input_set;
|
||||
_output_set = p_output_set;
|
||||
|
||||
n = inputSet.size();
|
||||
k = inputSet[0].size();
|
||||
lrScheduler = "None";
|
||||
decayConstant = 0;
|
||||
dropRate = 0;
|
||||
_n = _input_set.size();
|
||||
_k = _input_set[0].size();
|
||||
_lr_scheduler = "None";
|
||||
_decay_constant = 0;
|
||||
_drop_rate = 0;
|
||||
}
|
||||
|
||||
MLPPANN::MLPPANN() {
|
||||
}
|
||||
|
||||
MLPPANN::~MLPPANN() {
|
||||
delete outputLayer;
|
||||
delete _output_layer;
|
||||
}
|
||||
|
||||
real_t MLPPANN::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
@ -759,92 +762,92 @@ real_t MLPPANN::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
MLPPCost mlpp_cost;
|
||||
real_t totalRegTerm = 0;
|
||||
|
||||
auto cost_function = outputLayer->cost_map[outputLayer->cost];
|
||||
auto cost_function = _output_layer->cost_map[_output_layer->cost];
|
||||
|
||||
if (!network.empty()) {
|
||||
for (uint32_t i = 0; i < network.size() - 1; i++) {
|
||||
totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
|
||||
if (!_network.empty()) {
|
||||
for (uint32_t i = 0; i < _network.size() - 1; i++) {
|
||||
totalRegTerm += regularization.regTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg);
|
||||
}
|
||||
}
|
||||
|
||||
return (mlpp_cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg);
|
||||
return (mlpp_cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg);
|
||||
}
|
||||
|
||||
void MLPPANN::forward_pass() {
|
||||
if (!network.empty()) {
|
||||
network[0].input = inputSet;
|
||||
network[0].forwardPass();
|
||||
if (!_network.empty()) {
|
||||
_network[0].input = _input_set;
|
||||
_network[0].forwardPass();
|
||||
|
||||
for (uint32_t i = 1; i < network.size(); i++) {
|
||||
network[i].input = network[i - 1].a;
|
||||
network[i].forwardPass();
|
||||
for (uint32_t i = 1; i < _network.size(); i++) {
|
||||
_network[i].input = _network[i - 1].a;
|
||||
_network[i].forwardPass();
|
||||
}
|
||||
outputLayer->input = network[network.size() - 1].a;
|
||||
_output_layer->input = _network[_network.size() - 1].a;
|
||||
} else {
|
||||
outputLayer->input = inputSet;
|
||||
_output_layer->input = _input_set;
|
||||
}
|
||||
|
||||
outputLayer->forwardPass();
|
||||
y_hat = outputLayer->a;
|
||||
_output_layer->forwardPass();
|
||||
_y_hat = _output_layer->a;
|
||||
}
|
||||
|
||||
void MLPPANN::update_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate) {
|
||||
void MLPPANN::update_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, std::vector<real_t> output_layer_updation, real_t learning_rate) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
outputLayer->weights = alg.subtraction(outputLayer->weights, outputLayerUpdation);
|
||||
outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
|
||||
_output_layer->weights = alg.subtraction(_output_layer->weights, output_layer_updation);
|
||||
_output_layer->bias -= learning_rate * alg.sum_elements(_output_layer->delta) / _n;
|
||||
|
||||
if (!network.empty()) {
|
||||
network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, hiddenLayerUpdations[0]);
|
||||
network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta));
|
||||
if (!_network.empty()) {
|
||||
_network[_network.size() - 1].weights = alg.subtraction(_network[_network.size() - 1].weights, hidden_layer_updations[0]);
|
||||
_network[_network.size() - 1].bias = alg.subtractMatrixRows(_network[_network.size() - 1].bias, alg.scalarMultiply(learning_rate / _n, _network[_network.size() - 1].delta));
|
||||
|
||||
for (int i = network.size() - 2; i >= 0; i--) {
|
||||
network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
|
||||
network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
|
||||
for (int i = _network.size() - 2; i >= 0; i--) {
|
||||
_network[i].weights = alg.subtraction(_network[i].weights, hidden_layer_updations[(_network.size() - 2) - i + 1]);
|
||||
_network[i].bias = alg.subtractMatrixRows(_network[i].bias, alg.scalarMultiply(learning_rate / _n, _network[i].delta));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPANN::compute_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
|
||||
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> MLPPANN::compute_gradients(std::vector<real_t> y_hat, std::vector<real_t> _output_set) {
|
||||
// std::cout << "BEGIN" << std::endl;
|
||||
MLPPCost mlpp_cost;
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> cumulativeHiddenLayerWGrad; // Tensor containing ALL hidden grads.
|
||||
std::vector<std::vector<std::vector<real_t>>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
|
||||
auto costDeriv = outputLayer->costDeriv_map[outputLayer->cost];
|
||||
auto outputAvn = outputLayer->activation_map[outputLayer->activation];
|
||||
outputLayer->delta = alg.hadamard_product((mlpp_cost.*costDeriv)(y_hat, outputSet), (avn.*outputAvn)(outputLayer->z, 1));
|
||||
std::vector<real_t> outputWGrad = alg.mat_vec_mult(alg.transpose(outputLayer->input), outputLayer->delta);
|
||||
outputWGrad = alg.addition(outputWGrad, regularization.regDerivTerm(outputLayer->weights, outputLayer->lambda, outputLayer->alpha, outputLayer->reg));
|
||||
auto costDeriv = _output_layer->costDeriv_map[_output_layer->cost];
|
||||
auto outputAvn = _output_layer->activation_map[_output_layer->activation];
|
||||
_output_layer->delta = alg.hadamard_product((mlpp_cost.*costDeriv)(y_hat, _output_set), (avn.*outputAvn)(_output_layer->z, 1));
|
||||
std::vector<real_t> output_w_grad = alg.mat_vec_mult(alg.transpose(_output_layer->input), _output_layer->delta);
|
||||
output_w_grad = alg.addition(output_w_grad, regularization.regDerivTerm(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg));
|
||||
|
||||
if (!network.empty()) {
|
||||
auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
|
||||
network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
|
||||
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
|
||||
if (!_network.empty()) {
|
||||
auto hiddenLayerAvn = _network[_network.size() - 1].activation_map[_network[_network.size() - 1].activation];
|
||||
_network[_network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(_output_layer->delta, _output_layer->weights), (avn.*hiddenLayerAvn)(_network[_network.size() - 1].z, 1));
|
||||
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(_network[_network.size() - 1].input), _network[_network.size() - 1].delta);
|
||||
|
||||
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
cumulative_hidden_layer_w_grad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(_network[_network.size() - 1].weights, _network[_network.size() - 1].lambda, _network[_network.size() - 1].alpha, _network[_network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
|
||||
for (int i = network.size() - 2; i >= 0; i--) {
|
||||
hiddenLayerAvn = network[i].activation_map[network[i].activation];
|
||||
network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
|
||||
hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
|
||||
cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
for (int i = _network.size() - 2; i >= 0; i--) {
|
||||
hiddenLayerAvn = _network[i].activation_map[_network[i].activation];
|
||||
_network[i].delta = alg.hadamard_product(alg.matmult(_network[i + 1].delta, alg.transpose(_network[i + 1].weights)), (avn.*hiddenLayerAvn)(_network[i].z, 1));
|
||||
hiddenLayerWGrad = alg.matmult(alg.transpose(_network[i].input), _network[i].delta);
|
||||
cumulative_hidden_layer_w_grad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
|
||||
}
|
||||
}
|
||||
return { cumulativeHiddenLayerWGrad, outputWGrad };
|
||||
return { cumulative_hidden_layer_w_grad, output_w_grad };
|
||||
}
|
||||
|
||||
void MLPPANN::print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
|
||||
std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);
|
||||
if (!network.empty()) {
|
||||
for (int i = network.size() - 1; i >= 0; i--) {
|
||||
void MLPPANN::print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> p_output_set) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, p_output_set));
|
||||
std::cout << "Layer " << _network.size() + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(_output_layer->weights, _output_layer->bias);
|
||||
if (!_network.empty()) {
|
||||
for (int i = _network.size() - 1; i >= 0; i--) {
|
||||
std::cout << "Layer " << i + 1 << ": " << std::endl;
|
||||
MLPPUtilities::UI(network[i].weights, network[i].bias);
|
||||
MLPPUtilities::UI(_network[i].weights, _network[i].bias);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -48,37 +48,37 @@ public:
|
||||
void add_layer(int n_hidden, std::string activation, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(std::string activation, std::string loss, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
|
||||
MLPPANN(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet);
|
||||
MLPPANN(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set);
|
||||
|
||||
MLPPANN();
|
||||
~MLPPANN();
|
||||
|
||||
protected:
|
||||
real_t apply_learning_rate_scheduler(real_t learningRate, real_t decayConstant, real_t epoch, real_t dropRate);
|
||||
real_t apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate);
|
||||
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
|
||||
void forward_pass();
|
||||
void update_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
|
||||
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> compute_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||
void update_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, std::vector<real_t> output_layer_updation, real_t learning_rate);
|
||||
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> compute_gradients(std::vector<real_t> y_hat, std::vector<real_t> _output_set);
|
||||
|
||||
void print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
|
||||
void print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> p_output_set);
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
|
||||
std::vector<MLPPOldHiddenLayer> network;
|
||||
MLPPOldOutputLayer *outputLayer;
|
||||
std::vector<MLPPOldHiddenLayer> _network;
|
||||
MLPPOldOutputLayer *_output_layer;
|
||||
|
||||
int n;
|
||||
int k;
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
std::string lrScheduler;
|
||||
real_t decayConstant;
|
||||
real_t dropRate;
|
||||
std::string _lr_scheduler;
|
||||
real_t _decay_constant;
|
||||
real_t _drop_rate;
|
||||
};
|
||||
|
||||
#endif /* ANN_hpp */
|
||||
Loading…
Reference in New Issue
Block a user