{"about":"Lesson F3, gradients. Computed by tools/school/fill_gradients.mjs from system/school/gradients.mjs and the attention lab's model.json. Do not edit by hand.","modelJsonSha256":"67600098679443bf87c78f0f3ef1a1399ae51642b3e9608517105d7831219d5e","batch":16,"bowl":{"about":"f(v) = 1/2 (v - c)^T H (v - c), axes turned 30 degrees. Curvatures from a finite-difference Hessian at the minimum. predictedBound = 2 / largest curvature; measuredBound = largest step whose runs shrink, found by bisection on real runs. 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= (1 - x)^2 + 100 (y - x^2)^2 from (-1.2, 1), fixed step 0.001, 20000 steps; path thinned to 200 points. Curvatures from a finite-difference Hessian. The stable step at the minimum is 2 / largest curvature 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= (x^2 - 1)^2 + 1.5 (y - 0.4 x)^2 + 0.2 x. Step 0.05 until |grad| < 1e-9. basin[j][i] is -1 when descent from that grid point ends in the left valley, +1 for the right; grid spans [-2, 2]^2, rows are 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relative error |exact - numeric| / (|exact| + |numeric|) of each surface's gradient against central differences (h = 1e-5) at four points; the controls flip the gradient's sign or swap its two components and must fail.","points":[[0.3,-0.7],[-1.2,1],[1.5,0.2],[-0.4,-1.1]],"bowl":{"exact":5.4663e-12,"signFlippedControl":1,"swappedAxesControl":0.46468},"rosenbrock":{"exact":1.0552e-10,"signFlippedControl":1,"swappedAxesControl":0.96854},"two-valleys":{"exact":4.5656e-11,"signFlippedControl":1,"swappedAxesControl":0.99964}},"landscape":{"about":"Held-out loss (first 64 rows of the corpus's held-out set) on a 21 x 21 grid of theta + a d1 + b d2, a and b in [-1, 1], for the trained model and for the same model at its initialisation (seed 7). d1, d2 are seeded Gaussian directions with filter-wise normalisation (Li et al. 2018); values[j][i] has b = alphas[j], a = alphas[i]. slopeBackward is grad . d from the backward pass; slopeFiniteDiff is the same slope by central differences (h = 0.01).","note":"A 2D slice shows the loss on one flat sheet through a space with thousands of dimensions. It can show whether the trained point sits in a basin along these two directions, how wide that basin is relative to the weights' own size, and how the trained point differs from the starting point. It cannot show the loss along any other direction, so a slice that looks like a bowl does not prove the point is a minimum, and a smooth slice does not rule out sharp directions elsewhere. Two random directions in high dimension are nearly at right angles to each other and to the gradient, so the slice usually misses the steepest way 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