jensandClaude Sonnet 5
82d019ba48
2-layer DeepStack (9x9->24, 24->4), has saved weights and training data. Passes: reconstruction error 0.0007 vs. mean-baseline 0.14 -- exercises upDownPass through more than one layer for the first time in the suite. 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>
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#include <string>
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#include <vector>
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#include <cmath>
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#include <armadillo>
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#include "Rbm.hpp"
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#include "AStack.hpp"
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#include "DeepStack.hpp"
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#include "StackCreator.hpp"
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// Minimal, dependency-free smoke test suite: for each named project under
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// prj/<name>/, load it, load its weights and training batch, run a full
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// up-pass/down-pass reconstruction, and sanity-check the result. This
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// replaces the old ad-hoc, since-bitrotted manual test program.
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namespace
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{
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struct TestResult
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{
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bool passed;
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std::string message;
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};
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// A model that hasn't learned anything shouldn't beat the trivial baseline
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// of reconstructing every row as just the training batch's per-feature mean.
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double meanBaselineError(const arma::mat &batch)
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{
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arma::mat meanRow = arma::mean(batch, 0);
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arma::mat baseline = arma::repmat(meanRow, batch.n_rows, 1);
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return Rbm::rms_error_accu(batch - baseline);
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}
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TestResult testProject(const std::string &name)
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{
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AStack *pStack = StackCreator::fromFile(".", name);
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if (!pStack)
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{
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return {false, "failed to parse project file"};
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}
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bool weightsOk = pStack->loadWeights(".");
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size_t numTraining = pStack->loadTrainingBatch(".");
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if (!weightsOk)
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{
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delete pStack;
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return {false, "failed to load weights"};
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}
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if (numTraining == 0)
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{
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delete pStack;
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return {false, "failed to load training batch"};
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}
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if (pStack->type() != AStack::StackType::Deep)
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{
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delete pStack;
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return {true, "skipped (reconstruction check only supports Deep stacks so far)"};
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}
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DeepStack *pDeep = static_cast<DeepStack*>(pStack);
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arma::mat batch = pStack->trainingBatch();
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arma::mat reconstruction = pDeep->upDownPass(pStack->getFirstLayer(), batch);
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delete pStack;
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if (reconstruction.n_rows != batch.n_rows || reconstruction.n_cols != batch.n_cols)
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{
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return {false, "reconstruction shape mismatch"};
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}
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double err = Rbm::rms_error_accu(batch - reconstruction);
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if (!std::isfinite(err))
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{
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return {false, "reconstruction error is not finite (NaN/Inf)"};
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}
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double baseline = meanBaselineError(batch);
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if (err > baseline * 1.5)
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{
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return {false, "reconstruction error " + std::to_string(err) +
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" is worse than 1.5x the trivial mean-baseline " + std::to_string(baseline)};
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}
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return {true, "reconstruction error = " + std::to_string(err) +
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" (mean-baseline = " + std::to_string(baseline) + ")"};
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}
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} // namespace
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int main()
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{
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std::vector<std::string> projects = {"1-hot", "prims", "norb_small_16h_v2", "norb_small_16h", "mnist_2", "prims_deep"};
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int numPassed = 0;
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for (const std::string &name : projects)
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{
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std::cout << "=== " << name << " ===" << std::endl;
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TestResult result = testProject(name);
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std::cout << (result.passed ? "PASS" : "FAIL") << ": " << result.message << std::endl << std::endl;
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if (result.passed)
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{
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numPassed++;
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}
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}
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std::cout << numPassed << "/" << projects.size() << " projects passed" << std::endl;
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return numPassed == (int)projects.size() ? 0 : 1;
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}
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