Fractal patterns in weight updates, huh? The noise is usually just the optimizer’s jitter, but who knows—wavelet transforms might reveal hidden rhythms if there’s a real pattern. Give it a shot and see if the “code” holds up under a closer look.

Here’s a quick Python snippet that grabs a few weight‑update vectors, runs a Fast Fourier Transform on each, and logs the magnitude spectrum. It also gives you an optional wavelet view if you want to dig deeper into the “secret code.” ```python import numpy as np import matplotlib.pyplot as plt from scipy.signal import welch from pywt import wavedec # -------------------------------------------------- # 1. Load or simulate weight updates for a few epochs # -------------------------------------------------- # For demonstration we’ll create synthetic updates. # Replace this with your actual weight history: epochs = 5 updates_per_epoch = 100 weight_history = np.random.randn(epochs, updates_per_epoch, 512) # 512‑dim weights # -------------------------------------------------- # 2. FFT analysis per epoch # -------------------------------------------------- fft_spectra = [] for epoch in range(epochs): # Flatten all weight updates in this epoch flat_updates = weight_history[epoch].flatten() # Compute FFT fft_vals = np.fft.rfft(flat_updates) freqs = np.fft.rfftfreq(len(flat_updates)) mag = np.abs(fft_vals) fft_spectra.append((freqs, mag)) # Log (save) the spectrum np.save(f"fft_epoch_{epoch}.npy", mag) print(f"Epoch {epoch} FFT spectrum saved.") # -------------------------------------------------- # 3. Plot the spectra # -------------------------------------------------- plt.figure(figsize=(10, 6)) for epoch, (freqs, mag) in enumerate(fft_spectra): plt.plot(freqs, 20 * np.log10(mag + 1e-12), label=f"Epoch {epoch}") plt.xlabel("Frequency (Hz)") plt.ylabel("Magnitude (dB)") plt.title("FFT Spectrum of Weight Updates") plt.legend() plt.tight_layout() plt.show() # -------------------------------------------------- # 4. Optional: Wavelet decomposition for each epoch # -------------------------------------------------- wavelet_spectra = [] for epoch in range(epochs): flat_updates = weight_history[epoch].flatten() coeffs = wavedec(flat_updates, 'db4', level=5) # Keep detail coefficients (ignore the approximation at the end) detail_coeffs = coeffs[1:] # Concatenate for a single spectrum view wavelet_spectra.append(np.concatenate(detail_coeffs)) np.save(f"wavelet_epoch_{epoch}.npy", np.concatenate(detail_coeffs)) print(f"Epoch {epoch} wavelet detail coefficients saved.") # -------------------------------------------------- # 5. Quick wavelet plot # -------------------------------------------------- plt.figure(figsize=(10, 6)) for epoch, wavelet in enumerate(wavelet_spectra): plt.plot(wavelet, label=f"Epoch {epoch}") plt.xlabel("Coefficient Index") plt.ylabel("Amplitude") plt.title("Wavelet Detail Coefficients") plt.legend() plt.tight_layout() plt.show() ``` **What it does** 1. Simulates (or loads) a 3‑D array of weight updates: `epochs × updates_per_epoch × weight_dim`. 2. For each epoch, it flattens all updates, runs a real FFT, logs the magnitude spectrum to a `.npy` file, and prints a confirmation. 3. Plots all epochs on one graph so you can spot repeating peaks. 4. If you prefer a time‑frequency view, the script also performs a Daubechies‑4 wavelet decomposition, saves the detail coefficients, and plots them. Replace the synthetic `weight_history` with your real weight‑update tensors and you’re ready to hunt for those “hidden messages.” Good luck, and happy dancing with your optimizer!

I’m running the script now, flagging any peaks that persist across all epochs and checking their phase alignment. I’ll ping you once the spectra start looking like a coherent tune rather than random noise. Keep those sanity checks handy—those “whispers” might just be optimizer chatter if we’re not careful.