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Diffstat (limited to 'experiments/cifar_frozen_vector_credit.py')
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diff --git a/experiments/cifar_frozen_vector_credit.py b/experiments/cifar_frozen_vector_credit.py new file mode 100644 index 0000000..acd26e6 --- /dev/null +++ b/experiments/cifar_frozen_vector_credit.py @@ -0,0 +1,648 @@ +""" +Phase 5B: Frozen CIFAR Vector Credit Transfer. + +Test whether direct vector credit field can recover better credit than scalar CB +on frozen BP-trained CIFAR representations. + +Methods compared: +- DFA (random) +- StateBridge_eT +- ScalarCB_eT +- ScalarCB_deltaL +- VectorField_eT_M{4,8,16} +- VectorField_deltaL_M{4,8,16} (if resources allow) +""" +import os +import sys +import json +import argparse +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torch.utils.data import DataLoader +import torchvision +import torchvision.transforms as transforms +import copy + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from models.residual_mlp import ResidualMLP +from models.value_net import ValueNet, SinusoidalTimeEmbed, create_ema_model, update_ema +from models.state_bridge import StateBridgeNet +from metrics.credit_metrics import ( + cosine_similarity_batch, perturbation_correlation, nudging_test +) + + +class VectorCreditNet(nn.Module): + """Direct vector credit field: a_phi(h_l, t_l, s) -> R^d.""" + def __init__(self, d_hidden, s_dim, time_embed_dim=32, hidden_dim=256, num_layers=3): + super().__init__() + self.ln = nn.LayerNorm(d_hidden) + self.time_embed = SinusoidalTimeEmbed(time_embed_dim) + input_dim = d_hidden + time_embed_dim + s_dim + layers = [] + for i in range(num_layers): + in_d = input_dim if i == 0 else hidden_dim + layers.append(nn.Linear(in_d, hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, d_hidden)) + self.net = nn.Sequential(*layers) + + def forward(self, h, t, s): + h_normed = self.ln(h) + t_emb = self.time_embed(t) + inp = torch.cat([h_normed, t_emb, s], dim=-1) + return self.net(inp) + + +def get_cifar10(batch_size=128): + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)), + ]) + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)), + ]) + trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) + testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) + train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True) + test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) + return train_loader, test_loader + + +def evaluate(model, test_loader, device): + model.eval() + correct, total = 0, 0 + with torch.no_grad(): + for x, y in test_loader: + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + logits = model(x) + correct += (logits.argmax(1) == y).sum().item() + total += x.size(0) + return correct / total + + +def train_bp_reference(model, train_loader, test_loader, device, epochs=100, lr=1e-3, wd=0.01): + optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=wd) + scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) + for epoch in range(1, epochs + 1): + model.train() + total_loss, correct, total = 0, 0, 0 + for x, y in train_loader: + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + logits = model(x) + loss = F.cross_entropy(logits, y) + optimizer.zero_grad() + loss.backward() + optimizer.step() + total_loss += loss.item() * x.size(0) + correct += (logits.argmax(1) == y).sum().item() + total += x.size(0) + scheduler.step() + if epoch % 20 == 0 or epoch == 1: + test_acc = evaluate(model, test_loader, device) + print(f" [BP ref] Ep {epoch}: loss={total_loss/total:.4f}, test={test_acc:.4f}") + test_acc = evaluate(model, test_loader, device) + print(f" [BP ref] Final: {test_acc:.4f}") + return test_acc + + +# ============================================================================= +# Estimator training functions (all on frozen model) +# ============================================================================= + +def train_state_bridge_frozen(model, train_loader, device, epochs, lr_fb): + d = model.d_hidden + L = model.num_blocks + state_pred = StateBridgeNet(d_hidden=d, s_dim=10, time_embed_dim=32, + hidden_dim=256, num_layers=3).to(device) + state_opt = optim.Adam(state_pred.parameters(), lr=lr_fb) + model.eval() + for epoch in range(1, epochs + 1): + state_pred.train() + total_loss, n = 0, 0 + for x, y in train_loader: + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + batch = x.size(0) + with torch.no_grad(): + logits, hiddens = model(x, return_hidden=True) + e_T = logits.softmax(dim=-1) + e_T[torch.arange(batch), y] -= 1 + s = e_T.detach() + hL_det = hiddens[-1].detach() + state_loss = 0.0 + for l in range(L): + h_l_det = hiddens[l].detach() + t_l = torch.full((batch,), l / L, device=device) + pred_hL = state_pred(h_l_det, t_l, s) + target_norm = hL_det.norm(dim=-1, keepdim=True).clamp(min=1.0) + state_loss += (((pred_hL - hL_det) / target_norm) ** 2).sum(dim=-1).mean() + state_loss /= L + state_opt.zero_grad() + state_loss.backward() + state_opt.step() + total_loss += state_loss.item() * batch + n += batch + if epoch % 20 == 0 or epoch == 1: + print(f" [SB] Ep {epoch}: loss={total_loss/n:.6f}") + return state_pred + + +def train_scalar_cb_frozen(model, train_loader, device, epochs, lr_fb, s_type='eT', + lam=0.1, K=4, sigma_bridge=0.05, ema_momentum=0.995, + term_grad_weight=1.0): + d = model.d_hidden + L = model.num_blocks + s_dim = 10 if s_type == 'eT' else d + value_net = ValueNet(d_hidden=d, s_dim=s_dim, time_embed_dim=32, + hidden_dim=256, num_layers=3).to(device) + value_net_ema = create_ema_model(value_net) + value_opt = optim.Adam(value_net.parameters(), lr=lr_fb) + model.eval() + for epoch in range(1, epochs + 1): + value_net.train() + total_vloss, n = 0, 0 + for x, y in train_loader: + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + batch = x.size(0) + with torch.no_grad(): + logits, hiddens = model(x, return_hidden=True) + e_T = logits.softmax(dim=-1) + e_T[torch.arange(batch), y] -= 1 + true_loss = F.cross_entropy(logits, y, reduction='none').detach() + hL_det = hiddens[-1].detach() + if s_type == 'eT': + s = e_T.detach() + else: + hL_req = hL_det.clone().requires_grad_(True) + logits_for_s = model.out_head(model.out_ln(hL_req)) + ce_for_s = F.cross_entropy(logits_for_s, y, reduction='sum') + s = torch.autograd.grad(ce_for_s, hL_req, create_graph=False)[0].detach() + + t_L = torch.ones(batch, device=device) + V_terminal = value_net(hL_det, t_L, s) + loss_term = ((V_terminal - true_loss) ** 2).mean() + + loss_tgrad = torch.tensor(0.0, device=device) + if term_grad_weight > 0: + hL_req = hL_det.clone().requires_grad_(True) + V_at_L = value_net(hL_req, t_L, s) + grad_V_L = torch.autograd.grad(V_at_L.sum(), hL_req, create_graph=True)[0] + hL_req2 = hL_det.clone().requires_grad_(True) + logits_tgt = model.out_head(model.out_ln(hL_req2)) + ce_loss = F.cross_entropy(logits_tgt, y, reduction='sum') + a_L_exact = torch.autograd.grad(ce_loss, hL_req2, create_graph=False)[0].detach() + loss_tgrad = ((grad_V_L - a_L_exact) ** 2).sum(dim=-1).mean() + + loss_bridge = 0.0 + for l in range(L): + h_l_det = hiddens[l].detach() + t_l = torch.full((batch,), l / L, device=device) + t_next = torch.full((batch,), (l + 1) / L, device=device) + V_l = value_net(h_l_det, t_l, s) + with torch.no_grad(): + h_next_det = hiddens[l + 1].detach() + log_terms = [] + for k in range(K): + noise = sigma_bridge * torch.randn_like(h_next_det) + V_next = value_net_ema(h_next_det + noise, t_next, s) + log_terms.append(-V_next / lam) + log_stack = torch.stack(log_terms, dim=-1) + V_target = -lam * (torch.logsumexp(log_stack, dim=-1) - np.log(K)) + loss_bridge += ((V_l - V_target.detach()) ** 2).mean() + loss_bridge /= L + + vloss = loss_term + loss_bridge + term_grad_weight * loss_tgrad + value_opt.zero_grad() + vloss.backward() + torch.nn.utils.clip_grad_norm_(value_net.parameters(), 1.0) + value_opt.step() + update_ema(value_net, value_net_ema, ema_momentum) + total_vloss += vloss.item() * batch + n += batch + if epoch % 20 == 0 or epoch == 1: + print(f" [CB_{s_type}] Ep {epoch}: vloss={total_vloss/n:.6f}") + return value_net + + +def train_vector_field_frozen(model, train_loader, device, epochs, lr_fb, + s_type='eT', M=4, eps=1e-3, beta=1.0, + term_weight=1.0): + """ + Train vector credit field on frozen CIFAR features. + Layer subsampling: each batch, randomly pick one layer for perturbation target. + Terminal matching always uses layer L. + """ + d = model.d_hidden + L = model.num_blocks + s_dim = 10 if s_type == 'eT' else d + + vector_net = VectorCreditNet(d_hidden=d, s_dim=s_dim, time_embed_dim=32, + hidden_dim=256, num_layers=3).to(device) + vec_opt = optim.Adam(vector_net.parameters(), lr=lr_fb) + model.eval() + + for epoch in range(1, epochs + 1): + vector_net.train() + total_vloss, n = 0, 0 + for x, y in train_loader: + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + batch = x.size(0) + + with torch.no_grad(): + logits, hiddens = model(x, return_hidden=True) + e_T = logits.softmax(dim=-1) + e_T[torch.arange(batch), y] -= 1 + + hL_det = hiddens[-1].detach() + + # Compute s + if s_type == 'eT': + s = e_T.detach() + else: + hL_req = hL_det.clone().requires_grad_(True) + logits_for_s = model.out_head(model.out_ln(hL_req)) + ce_for_s = F.cross_entropy(logits_for_s, y, reduction='sum') + s = torch.autograd.grad(ce_for_s, hL_req, create_graph=False)[0].detach() + + # Terminal matching + loss_term = torch.tensor(0.0, device=device) + if term_weight > 0: + t_L = torch.ones(batch, device=device) + a_terminal = vector_net(hL_det, t_L, s) + hL_req = hL_det.clone().requires_grad_(True) + logits_tgt = model.out_head(model.out_ln(hL_req)) + ce = F.cross_entropy(logits_tgt, y, reduction='sum') + delta_L = torch.autograd.grad(ce, hL_req, create_graph=False)[0].detach() + loss_term = ((a_terminal - delta_L) ** 2).sum(dim=-1).mean() + + # Perturbation target — subsample 1 random layer per batch + l = np.random.randint(0, L) + h_l_det = hiddens[l].detach() + t_l = torch.full((batch,), l / L, device=device) + a_l = vector_net(h_l_det, t_l, s) + + loss_proj = torch.tensor(0.0, device=device) + for _ in range(M): + v = torch.randn_like(h_l_det) + v = v / (v.norm(dim=-1, keepdim=True) + 1e-8) + + with torch.no_grad(): + # Use model.forward_from_layer for tail forward + logits_plus = model.forward_from_layer(h_l_det + eps * v, l) + loss_plus = F.cross_entropy(logits_plus, y, reduction='none') + logits_minus = model.forward_from_layer(h_l_det - eps * v, l) + loss_minus = F.cross_entropy(logits_minus, y, reduction='none') + g_j = (loss_plus - loss_minus) / (2 * eps) + + pred_j = (a_l * v).sum(dim=-1) + loss_proj = loss_proj + ((pred_j - g_j.detach()) ** 2).mean() + loss_proj = loss_proj / M + + vloss = term_weight * loss_term + beta * loss_proj + vec_opt.zero_grad() + vloss.backward() + torch.nn.utils.clip_grad_norm_(vector_net.parameters(), 1.0) + vec_opt.step() + total_vloss += vloss.item() * batch + n += batch + + if epoch % 20 == 0 or epoch == 1: + print(f" [vec_{s_type}_M{M}] Ep {epoch}: vloss={total_vloss/n:.6f}") + + return vector_net + + +# ============================================================================= +# Evaluation +# ============================================================================= +def evaluate_all(model, test_loader, device, estimators): + """Evaluate credit quality for all estimators on frozen features.""" + model.eval() + d = model.d_hidden + L = model.num_blocks + + # DFA baseline + dfa_Bs = [torch.randn(d, 10, device=device) / np.sqrt(10) for _ in range(L)] + + # Use multiple test batches for robust Gamma, single batch for rho/nudge (expensive) + results = {} + for name in list(estimators.keys()) + ['dfa']: + results[name] = { + 'bp_cosine': [[] for _ in range(L)], + 'perturbation_rho': [0.0] * L, + 'nudging_0.001': [0.0] * L, + 'nudging_0.003': [0.0] * L, + 'nudging_0.01': [0.0] * L, + } + + n_batches = min(10, len(test_loader)) + batch_idx = 0 + + for x, y in test_loader: + if batch_idx >= n_batches: + break + batch_idx += 1 + x = x.view(x.size(0), -1).to(device) + y = y.to(device) + batch = x.size(0) + + # BP gradients + for p in model.parameters(): + p.requires_grad_(True) + model.zero_grad() + logits_bp, hiddens_bp = model(x, return_hidden=True) + for l in range(L + 1): + hiddens_bp[l].retain_grad() + loss_bp = F.cross_entropy(logits_bp, y) + loss_bp.backward() + bp_grads = {l: hiddens_bp[l].grad.detach().clone() for l in range(L + 1)} + for p in model.parameters(): + p.requires_grad_(False) + + with torch.no_grad(): + logits, hiddens = model(x, return_hidden=True) + e_T = logits.softmax(dim=-1) + e_T[torch.arange(batch), y] -= 1 + s_eT = e_T.detach() + + hL_det = hiddens[-1].detach() + hL_req = hL_det.clone().requires_grad_(True) + logits_delta = model.out_head(model.out_ln(hL_req)) + ce_delta = F.cross_entropy(logits_delta, y, reduction='sum') + delta_L = torch.autograd.grad(ce_delta, hL_req, create_graph=False)[0].detach() + + for l in range(L): + h_l = hiddens[l].detach() + t_l = torch.full((batch,), l / L, device=device) + + def make_fwd_fn(start_l): + def fwd_fn(h): + with torch.no_grad(): + curr = h + for i in range(start_l, L): + curr = curr + model.blocks[i](curr) + out = model.out_head(model.out_ln(curr)) + return F.cross_entropy(out, y, reduction='none') + return fwd_fn + fwd_fn = make_fwd_fn(l) + + # DFA + a_dfa = (s_eT @ dfa_Bs[l].T).detach() + results['dfa']['bp_cosine'][l].append(cosine_similarity_batch(a_dfa, bp_grads[l])) + if batch_idx == 1: + results['dfa']['perturbation_rho'][l] = perturbation_correlation(h_l, a_dfa, fwd_fn, epsilon=1e-3, M=32) + for eta in [0.001, 0.003, 0.01]: + results['dfa'][f'nudging_{eta}'][l] = nudging_test(h_l, a_dfa, fwd_fn, eta=eta) + + # Other estimators + for name, est in estimators.items(): + if est['type'] == 'sb': + net = est['net'] + net.eval() + h_l_req = h_l.clone().requires_grad_(True) + pred_hL = net(h_l_req, t_l, s_eT) + pred_logits = model.out_head(model.out_ln(pred_hL)) + pred_loss = F.cross_entropy(pred_logits, y, reduction='sum') + a_l = torch.autograd.grad(pred_loss, h_l_req, create_graph=False)[0].detach() + elif est['type'] == 'cb': + net = est['net'] + net.eval() + s = s_eT if est['s_type'] == 'eT' else delta_L + h_l_req = h_l.clone().requires_grad_(True) + V_l = net(h_l_req, t_l, s) + a_l = torch.autograd.grad(V_l.sum(), h_l_req, create_graph=False)[0].detach() + elif est['type'] == 'vec': + net = est['net'] + net.eval() + s = s_eT if est['s_type'] == 'eT' else delta_L + a_l = net(h_l, t_l, s).detach() + + results[name]['bp_cosine'][l].append(cosine_similarity_batch(a_l, bp_grads[l])) + if batch_idx == 1: + results[name]['perturbation_rho'][l] = perturbation_correlation(h_l, a_l, fwd_fn, epsilon=1e-3, M=32) + for eta in [0.001, 0.003, 0.01]: + results[name][f'nudging_{eta}'][l] = nudging_test(h_l, a_l, fwd_fn, eta=eta) + + # Average bp_cosine + for name in results: + for l in range(L): + vals = results[name]['bp_cosine'][l] + results[name]['bp_cosine'][l] = float(np.mean(vals)) if vals else 0.0 + return results + + +# ============================================================================= +# Main +# ============================================================================= +def run_experiment(args): + device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu') + print(f"Using device: {device}") + os.makedirs(args.output_dir, exist_ok=True) + + torch.manual_seed(args.seed) + np.random.seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + + train_loader, test_loader = get_cifar10(batch_size=args.batch_size) + input_dim = 32 * 32 * 3 + + # Step 1: Load/train BP reference + bp_ckpt = os.path.join(args.output_dir, f'bp_ref_L{args.num_blocks}_d{args.d_hidden}_s{args.seed}.pt') + model = ResidualMLP(input_dim, args.d_hidden, 10, args.num_blocks).to(device) + + # Try loading from frozen_cifar directory first + alt_ckpt = f'results/frozen_cifar/bp_ref_L{args.num_blocks}_d{args.d_hidden}_s{args.seed}.pt' + if os.path.exists(alt_ckpt) and not args.retrain_bp: + print(f" Loading BP ref from {alt_ckpt}") + model.load_state_dict(torch.load(alt_ckpt, map_location=device)) + bp_acc = evaluate(model, test_loader, device) + elif os.path.exists(bp_ckpt) and not args.retrain_bp: + print(f" Loading BP ref from {bp_ckpt}") + model.load_state_dict(torch.load(bp_ckpt, map_location=device)) + bp_acc = evaluate(model, test_loader, device) + else: + bp_acc = train_bp_reference(model, train_loader, test_loader, device, + epochs=args.bp_epochs, lr=args.lr, wd=args.wd) + torch.save(model.state_dict(), bp_ckpt) + print(f" BP ref acc: {bp_acc:.4f}") + + model.eval() + for p in model.parameters(): + p.requires_grad_(False) + + L = args.num_blocks + d = args.d_hidden + + # Step 2: Train estimators + print(f"\n{'='*60}") + print(f"Training estimators (L={L}, d={d}, {args.estimator_epochs} epochs)") + print(f"{'='*60}") + + estimators = {} + + # StateBridge_eT + print("\n--- StateBridge_eT ---") + torch.manual_seed(args.seed + 1000) + sb = train_state_bridge_frozen(model, train_loader, device, args.estimator_epochs, args.lr_fb) + estimators['sb_eT'] = {'type': 'sb', 'net': sb, 's_type': 'eT'} + + # ScalarCB_eT + print("\n--- ScalarCB_eT ---") + torch.manual_seed(args.seed + 2000) + cb_eT = train_scalar_cb_frozen(model, train_loader, device, args.estimator_epochs, args.lr_fb, + s_type='eT', term_grad_weight=args.term_grad_weight) + estimators['cb_eT'] = {'type': 'cb', 'net': cb_eT, 's_type': 'eT'} + + # ScalarCB_deltaL + print("\n--- ScalarCB_deltaL ---") + torch.manual_seed(args.seed + 3000) + cb_dL = train_scalar_cb_frozen(model, train_loader, device, args.estimator_epochs, args.lr_fb, + s_type='deltaL', term_grad_weight=args.term_grad_weight) + estimators['cb_deltaL'] = {'type': 'cb', 'net': cb_dL, 's_type': 'deltaL'} + + # Vector fields + for M in args.M_values: + for s_type in args.vec_s_types: + tag = f'vec_{s_type}_M{M}' + print(f"\n--- {tag} ---") + torch.manual_seed(args.seed + 4000 + M * 100 + (0 if s_type == 'eT' else 1)) + vnet = train_vector_field_frozen(model, train_loader, device, + args.estimator_epochs, args.lr_fb, + s_type=s_type, M=M, eps=args.pert_eps, + beta=args.pert_beta, term_weight=args.term_weight_vec) + estimators[tag] = {'type': 'vec', 'net': vnet, 's_type': s_type} + + # Step 3: Evaluate + print(f"\n{'='*60}") + print("Evaluating credit quality") + print(f"{'='*60}") + results = evaluate_all(model, test_loader, device, estimators) + + # Print summary + all_methods = ['dfa', 'sb_eT', 'cb_eT', 'cb_deltaL'] + \ + [f'vec_{st}_M{M}' for M in args.M_values for st in args.vec_s_types] + labels = { + 'dfa': 'DFA', 'sb_eT': 'StateBridge_eT', + 'cb_eT': 'ScalarCB_eT', 'cb_deltaL': 'ScalarCB_deltaL', + } + for M in args.M_values: + for st in args.vec_s_types: + labels[f'vec_{st}_M{M}'] = f'Vec_{st}_M{M}' + + print(f"\n{'Method':<25} {'Gamma':>8} {'rho':>8} {'nudge':>10}") + print("-" * 55) + + summary = {} + for m in all_methods: + if m not in results: + continue + r = results[m] + mg = np.mean(r['bp_cosine']) + mr = np.mean(r['perturbation_rho']) + mn = np.mean(r['nudging_0.003']) + summary[m] = {'mean_gamma': float(mg), 'mean_rho': float(mr), 'mean_nudge': float(mn)} + print(f"{labels.get(m, m):<25} {mg:>8.4f} {mr:>8.4f} {mn:>10.6f}") + + # Per-layer detail + print(f"\n--- Per-layer Gamma ---") + for l in range(L): + row = f" L{l}: " + for m in all_methods: + if m in results: + row += f" {results[m]['bp_cosine'][l]:>8.4f}" + print(row) + + print(f"\n--- Per-layer rho ---") + for l in range(L): + row = f" L{l}: " + for m in all_methods: + if m in results: + row += f" {results[m]['perturbation_rho'][l]:>8.4f}" + print(row) + + # Save + save_data = { + 'config': { + 'num_blocks': L, 'd_hidden': d, 'seed': args.seed, + 'bp_acc': float(bp_acc), 'estimator_epochs': args.estimator_epochs, + }, + 'summary': summary, + 'per_layer': {m: { + 'bp_cosine': results[m]['bp_cosine'], + 'perturbation_rho': results[m]['perturbation_rho'], + 'nudging_0.003': results[m]['nudging_0.003'], + } for m in all_methods if m in results}, + } + out_path = os.path.join(args.output_dir, + f'frozen_vec_L{L}_d{d}_s{args.seed}.json') + with open(out_path, 'w') as f: + json.dump(save_data, f, indent=2) + print(f"\nResults saved to {out_path}") + + # Judgment + cb_eT_gamma = summary.get('cb_eT', {}).get('mean_gamma', 0) + cb_eT_rho = summary.get('cb_eT', {}).get('mean_rho', 0) + best_vec_gamma = max(summary.get(m, {}).get('mean_gamma', 0) for m in summary if m.startswith('vec_')) + best_vec_rho = max(summary.get(m, {}).get('mean_rho', 0) for m in summary if m.startswith('vec_')) + best_vec_name = max((m for m in summary if m.startswith('vec_')), + key=lambda m: summary[m]['mean_gamma'] + summary[m]['mean_rho'], + default='none') + + print(f"\n{'='*60}") + print("JUDGMENT") + print(f"{'='*60}") + print(f"ScalarCB_eT: Gamma={cb_eT_gamma:.4f}, rho={cb_eT_rho:.4f}") + print(f"Best vector ({best_vec_name}): Gamma={best_vec_gamma:.4f}, rho={best_vec_rho:.4f}") + + dg = best_vec_gamma - cb_eT_gamma + dr = best_vec_rho - cb_eT_rho + print(f"Delta: Gamma={dg:+.4f}, rho={dr:+.4f}") + + if dg >= 0.05 and dr >= 0.05: + print("TRANSFER SUCCESS: Vector field significantly outperforms scalar CB on frozen CIFAR.") + elif dg > 0 and dr > 0: + print("MARGINAL: Vector field slightly better, but deltas below 0.05 threshold.") + else: + print("TRANSFER FAILED: Vector field does not outperform scalar CB on frozen CIFAR.") + + return save_data + + +def main(): + parser = argparse.ArgumentParser(description='Phase 5B: Frozen CIFAR Vector Transfer') + parser.add_argument('--num_blocks', type=int, default=4) + parser.add_argument('--d_hidden', type=int, default=256) + parser.add_argument('--batch_size', type=int, default=128) + parser.add_argument('--bp_epochs', type=int, default=100) + parser.add_argument('--estimator_epochs', type=int, default=100) + parser.add_argument('--lr', type=float, default=1e-3) + parser.add_argument('--lr_fb', type=float, default=1e-3) + parser.add_argument('--wd', type=float, default=0.01) + parser.add_argument('--term_grad_weight', type=float, default=1.0) + parser.add_argument('--term_weight_vec', type=float, default=1.0) + parser.add_argument('--pert_eps', type=float, default=1e-3) + parser.add_argument('--pert_beta', type=float, default=1.0) + parser.add_argument('--M_values', type=int, nargs='+', default=[4, 8, 16]) + parser.add_argument('--vec_s_types', type=str, nargs='+', default=['eT']) + parser.add_argument('--seed', type=int, default=42) + parser.add_argument('--gpu', type=int, default=2) + parser.add_argument('--output_dir', type=str, default='results/frozen_cifar_vec') + parser.add_argument('--retrain_bp', action='store_true') + args = parser.parse_args() + run_experiment(args) + + +if __name__ == '__main__': + main() |