mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
af71fb882f
* Dump autogradpp into PyTorch * Fixed up CMake for autogradpp/C++ API * Made cereal a submodule * Change search location of autogradpps mnist directory * Add test_api to CI * Download MNIST from the internet instead of storing in repo * Fix warnings
100 lines
2.4 KiB
C++
100 lines
2.4 KiB
C++
#include "test.h"
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bool test_optimizer_xor(Optimizer optim, std::shared_ptr<ContainerList> model) {
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float running_loss = 1;
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int epoch = 0;
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while (running_loss > 0.1) {
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auto bs = 4U;
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auto inp = at::CPU(at::kFloat).tensor({bs, 2});
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auto lab = at::CPU(at::kFloat).tensor({bs});
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for (auto i = 0U; i < bs; i++) {
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auto a = std::rand() % 2;
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auto b = std::rand() % 2;
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auto c = a ^ b;
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inp[i][0] = a;
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inp[i][1] = b;
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lab[i] = c;
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}
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// forward
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auto x = Var(inp);
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auto y = Var(lab, false);
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for (auto layer : *model) x = layer->forward({x})[0].sigmoid_();
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Variable loss = at::binary_cross_entropy(x, y);
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optim->zero_grad();
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backward(loss);
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optim->step();
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running_loss = running_loss * 0.99 + loss.data().sum().toCFloat() * 0.01;
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if (epoch > 3000) {
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return false;
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}
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epoch++;
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}
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return true;
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}
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CASE("optim/sgd") {
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = SGD(model, 1e-1).momentum(0.9).nesterov().weight_decay(1e-6).make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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CASE("optim/adagrad") {
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = Adagrad(model, 1.0).weight_decay(1e-6).lr_decay(1e-3).make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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CASE("optim/rmsprop") {
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{
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = RMSprop(model, 1e-1).momentum(0.9).weight_decay(1e-6).make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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{
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = RMSprop(model, 1e-1).centered().make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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}
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CASE("optim/adam") {
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = Adam(model, 1.0).weight_decay(1e-6).make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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CASE("optim/amsgrad") {
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auto model = ContainerList()
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.append(Linear(2, 8).make())
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.append(Linear(8, 1).make())
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.make();
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auto optim = Adam(model, 0.1).weight_decay(1e-6).amsgrad().make();
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EXPECT(test_optimizer_xor(optim, model));
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}
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