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"""
Render Figure 3: Temporal evolution of diagnostics.
Figure 3a: ResMLP (with terminal LN) — BP, FA, DFA overlaid
Figure 3b: ViT-Mini + ResMLP-no-outLN — BP, DFA only
Each figure: 1 row per architecture (3a has 1 row, 3b has 2 rows),
3 columns = ||h_L||, ||g_L||, test acc.
Methods as colored lines within each panel.
"""
import os, json
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
REPO_ROOT = "/home/yurenh2/fa"
COLORS = {"BP": "#2166ac", "FA": "#e08214", "DFA": "#b2182b"}
plt.rcParams.update({
"font.size": 9,
"axes.labelsize": 10,
"axes.titlesize": 10,
"legend.fontsize": 8,
"xtick.labelsize": 8,
"ytick.labelsize": 8,
"font.family": "serif",
})
def extract_series(log):
epochs = [e['epoch'] for e in log]
# Handle different key names across architectures
if 'hidden_norms' in log[0]:
h_L = [e['hidden_norms'][-1] for e in log]
elif 'hidden_norms_cls' in log[0]:
h_L = [e['hidden_norms_cls'][-1] for e in log]
else:
h_L = [1.0] * len(log)
if 'bp_grad_norms_per_sample_med' in log[0]:
g_L = [e['bp_grad_norms_per_sample_med'][-1] for e in log]
elif 'bp_grad_per_sample_l2_med' in log[0]:
g_L = [e['bp_grad_per_sample_l2_med'][-1] for e in log]
else:
g_L = [1.0] * len(log)
acc = [e['acc_eval'] for e in log]
return epochs, h_L, g_L, acc
def add_grid(ax, log_scale=False):
ax.grid(True, which="major", color="#d0d0d0", linewidth=0.4, linestyle=":")
if log_scale:
ax.grid(True, which="minor", color="#e8e8e8", linewidth=0.3, linestyle=":")
ax.set_axisbelow(True)
# ─── Load data ───────────────────────────────────────────────────────────
# ResMLP (with terminal LN)
resmlp = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_evolution_v2/snapshot_evolution_s42.json")))
fa_resmlp = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_evolution_v2/snapshot_fa_s42.json")))
# FA canonical for ResMLP
fa_resmlp_canonical = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_evolution_v2/snapshot_fa_canonical_s42.json")))
# ViT-Mini
vit = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_vit_v1/snapshot_vit_s42.json")))
fa_vit = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_vit_v1/snapshot_fa_canonical_s42.json")))
# StudentNet (synthetic teacher-student, no terminal LN)
synth = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_synth_v1/snapshot_synth_a1.0_L4_s42.json")))
fa_synth = json.load(open(os.path.join(REPO_ROOT, "results/snapshot_synth_v1/snapshot_fa_canonical_s42.json")))
# ═══════════════════════════════════════════════════════════════════════════
# Figure 3a: ResMLP — BP / FA / DFA
# ═══════════════════════════════════════════════════════════════════════════
fig_a, axes_a = plt.subplots(1, 3, figsize=(10.5, 2.8))
fig_a.subplots_adjust(wspace=0.35, left=0.07, right=0.97, bottom=0.18, top=0.85)
# No suptitle — user will write caption
data_resmlp = {
"BP": extract_series(resmlp['bp_log']),
"DFA": extract_series(resmlp['dfa_log']),
"FA": extract_series(fa_resmlp_canonical['fa_log']),
}
# Column 0: ||h_L||
ax = axes_a[0]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data_resmlp[method]
ax.semilogy(ep, h, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("$\\|h_L\\|_2$")
ax.set_xlabel("Epoch")
ax.set_title("$\\|h_L\\|$ (residual norm)")
ax.legend(loc="center right", fontsize=7)
add_grid(ax, log_scale=True)
# Column 1: ||g_L||
ax = axes_a[1]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data_resmlp[method]
ax.semilogy(ep, g, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("$\\|g_L\\|_2$")
ax.set_xlabel("Epoch")
ax.set_title("$\\|g_L\\|$ (BP gradient at $h_L$)")
add_grid(ax, log_scale=True)
# Column 2: test acc
ax = axes_a[2]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data_resmlp[method]
ax.plot(ep, a, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("Test accuracy")
ax.set_xlabel("Epoch")
ax.set_title("Test accuracy")
ax.set_ylim(0, 0.7)
add_grid(ax)
out_a = os.path.join(REPO_ROOT, "paper/figures/fig3a_temporal_resmlp.pdf")
fig_a.savefig(out_a, bbox_inches="tight", dpi=300)
fig_a.savefig(out_a.replace(".pdf", ".png"), bbox_inches="tight", dpi=200)
print(f"Saved: {out_a}")
# ═══════════════════════════════════════════════════════════════════════════
# Figure 3b: ViT-Mini + ResMLP-no-outLN — BP / DFA only
# ═══════════════════════════════════════════════════════════════════════════
fig_b, axes_b = plt.subplots(2, 3, figsize=(10.5, 5.0))
fig_b.subplots_adjust(wspace=0.35, hspace=0.45, left=0.07, right=0.97, bottom=0.10, top=0.90)
arch_data = [
("ViT-Mini", vit, fa_vit),
("StudentNet", synth, fa_synth),
]
for row, (arch_name, arch_json, fa_json) in enumerate(arch_data):
data = {
"BP": extract_series(arch_json['bp_log']),
"FA": extract_series(fa_json['fa_log']),
"DFA": extract_series(arch_json['dfa_log']),
}
# Column 0: ||h_L||
ax = axes_b[row, 0]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data[method]
ax.semilogy(ep, h, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("$\\|h_L\\|_2$")
if row == 0:
ax.set_title("$\\|h_L\\|$ (residual norm)")
ax.legend(loc="center right", fontsize=7)
if row == 1:
ax.set_xlabel("Epoch")
# Architecture label on the left
ax.annotate(arch_name, xy=(0, 0.5), xytext=(-55, 0),
xycoords="axes fraction", textcoords="offset points",
fontsize=8, fontweight="bold", rotation=90,
ha="center", va="center")
add_grid(ax, log_scale=True)
# Column 1: ||g_L||
ax = axes_b[row, 1]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data[method]
ax.semilogy(ep, g, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("$\\|g_L\\|_2$")
if row == 0:
ax.set_title("$\\|g_L\\|$ (BP gradient at $h_L$)")
if row == 1:
ax.set_xlabel("Epoch")
add_grid(ax, log_scale=True)
# Column 2: test acc
ax = axes_b[row, 2]
for method in ["BP", "FA", "DFA"]:
ep, h, g, a = data[method]
ax.plot(ep, a, color=COLORS[method], linewidth=1.5, label=method)
ax.set_ylabel("Test accuracy")
if row == 0:
ax.set_title("Test accuracy")
if row == 1:
ax.set_xlabel("Epoch")
ax.set_ylim(0, 0.85)
add_grid(ax)
out_b = os.path.join(REPO_ROOT, "paper/figures/fig3b_temporal_crossarch.pdf")
fig_b.savefig(out_b, bbox_inches="tight", dpi=300)
fig_b.savefig(out_b.replace(".pdf", ".png"), bbox_inches="tight", dpi=200)
print(f"Saved: {out_b}")
|
