jensandClaude Sonnet 5
1acc05bc24
Single-layer Deep stack (4x2->16), has saved weights and training data. Passes: reconstruction error ~0.000000 vs. mean-baseline 0.16. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016K8Gu7Qejd11JbdiHZqYAs
110 lines
2.9 KiB
C++
110 lines
2.9 KiB
C++
#include <iostream>
|
|
#include <string>
|
|
#include <vector>
|
|
#include <cmath>
|
|
#include <armadillo>
|
|
#include "Rbm.hpp"
|
|
#include "AStack.hpp"
|
|
#include "DeepStack.hpp"
|
|
#include "StackCreator.hpp"
|
|
|
|
// Minimal, dependency-free smoke test suite: for each named project under
|
|
// prj/<name>/, load it, load its weights and training batch, run a full
|
|
// up-pass/down-pass reconstruction, and sanity-check the result. This
|
|
// replaces the old ad-hoc, since-bitrotted manual test program.
|
|
|
|
namespace
|
|
{
|
|
|
|
struct TestResult
|
|
{
|
|
bool passed;
|
|
std::string message;
|
|
};
|
|
|
|
// A model that hasn't learned anything shouldn't beat the trivial baseline
|
|
// of reconstructing every row as just the training batch's per-feature mean.
|
|
double meanBaselineError(const arma::mat &batch)
|
|
{
|
|
arma::mat meanRow = arma::mean(batch, 0);
|
|
arma::mat baseline = arma::repmat(meanRow, batch.n_rows, 1);
|
|
return Rbm::rms_error_accu(batch - baseline);
|
|
}
|
|
|
|
TestResult testProject(const std::string &name)
|
|
{
|
|
AStack *pStack = StackCreator::fromFile(".", name);
|
|
if (!pStack)
|
|
{
|
|
return {false, "failed to parse project file"};
|
|
}
|
|
|
|
bool weightsOk = pStack->loadWeights(".");
|
|
size_t numTraining = pStack->loadTrainingBatch(".");
|
|
|
|
if (!weightsOk)
|
|
{
|
|
delete pStack;
|
|
return {false, "failed to load weights"};
|
|
}
|
|
if (numTraining == 0)
|
|
{
|
|
delete pStack;
|
|
return {false, "failed to load training batch"};
|
|
}
|
|
|
|
if (pStack->type() != AStack::StackType::Deep)
|
|
{
|
|
delete pStack;
|
|
return {true, "skipped (reconstruction check only supports Deep stacks so far)"};
|
|
}
|
|
|
|
DeepStack *pDeep = static_cast<DeepStack*>(pStack);
|
|
arma::mat batch = pStack->trainingBatch();
|
|
arma::mat reconstruction = pDeep->upDownPass(pStack->getFirstLayer(), batch);
|
|
delete pStack;
|
|
|
|
if (reconstruction.n_rows != batch.n_rows || reconstruction.n_cols != batch.n_cols)
|
|
{
|
|
return {false, "reconstruction shape mismatch"};
|
|
}
|
|
|
|
double err = Rbm::rms_error_accu(batch - reconstruction);
|
|
if (!std::isfinite(err))
|
|
{
|
|
return {false, "reconstruction error is not finite (NaN/Inf)"};
|
|
}
|
|
|
|
double baseline = meanBaselineError(batch);
|
|
if (err > baseline * 1.5)
|
|
{
|
|
return {false, "reconstruction error " + std::to_string(err) +
|
|
" is worse than 1.5x the trivial mean-baseline " + std::to_string(baseline)};
|
|
}
|
|
|
|
return {true, "reconstruction error = " + std::to_string(err) +
|
|
" (mean-baseline = " + std::to_string(baseline) + ")"};
|
|
}
|
|
|
|
} // namespace
|
|
|
|
int main()
|
|
{
|
|
std::vector<std::string> projects = {"1-hot", "prims", "norb_small_16h_v2", "norb_small_16h", "mnist_2", "prims_deep", "count"};
|
|
|
|
int numPassed = 0;
|
|
for (const std::string &name : projects)
|
|
{
|
|
std::cout << "=== " << name << " ===" << std::endl;
|
|
TestResult result = testProject(name);
|
|
std::cout << (result.passed ? "PASS" : "FAIL") << ": " << result.message << std::endl << std::endl;
|
|
if (result.passed)
|
|
{
|
|
numPassed++;
|
|
}
|
|
}
|
|
|
|
std::cout << numPassed << "/" << projects.size() << " projects passed" << std::endl;
|
|
return numPassed == (int)projects.size() ? 0 : 1;
|
|
}
|