| import json |
|
|
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
|
|
| res = json.load(open("ouro_targets_analysis.json")) |
|
|
| fig, axes = plt.subplots(1, 2, figsize=(11, 4.2)) |
|
|
| ax = axes[0] |
| for t, c in res["norm_vs_dist"].items(): |
| ax.plot(range(1, len(c) + 1), c, label=f"target v{t}", lw=1.5) |
| ax.set_xlabel("distance to target (virtual layers)") |
| ax.set_ylabel("||J|| / sqrt(d)") |
| ax.set_title("Mean-Jacobian norm vs distance, per loop-end target") |
| ax.legend() |
|
|
| ax = axes[1] |
| sims = res["analogous_sim_per_offset"] |
| ax.plot(range(len(sims)), sims, lw=1.5, label="analogous position, across loops") |
| ax.axhline(res["mean_mismatched_sim"], color="grey", ls="--", lw=1, |
| label=f"mismatched-offset baseline ({res['mean_mismatched_sim']:.2f})") |
| ax.set_xlabel("physical layer offset l within loop") |
| ax.set_ylabel("mean cos sim of J_{s*48+l -> loop end}") |
| ax.set_ylim(0, 1) |
| ax.set_title("Weight-tying: same transport repeats each loop") |
| ax.legend() |
|
|
| fig.tight_layout() |
| fig.savefig("ouro_targets_curves.png", dpi=150) |
| print("DONE_TARGET_PLOTS") |
|
|
