tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.List app structure
ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/ && echo "---CHECKS---" && wc -l run_checks.py test.sh 2>/dev/null
total 101 drwxr-xr-x 4 user user 4096 Jun 30 22:27 . drwxr-xr-x 21 root root 4096 Jun 30 22:27 .. -rw-r--r-- 1 user user 14238 Jun 30 22:27 PROMPT.txt -rw-r--r-- 1 user user 5 Jun 30 22:27 agent.pid -rw-r--r-- 1 user user 0 Jun 30 22:27 agent.stderr -rw-r--r-- 1 user user 4629 Jun 30 22:27 agent_trajectory.jsonl drwxr-xr-x 2 user user 60 Jun 30 22:27 data -rw-r--r-- 1 user user 0 Jun 30 22:27 run.log -rwxr-xr-x 1 user user 57740 Jun 30 11:01 run_checks.py drwxr-xr-x 2 user user 128 Jun 30 22:27 submission -rwxr-xr-x 1 user user 998 Jun 30 11:01 test.sh -rwxr-xr-x 1 user user 629 Jun 30 11:01 timer.sh ---SUBMISSION--- total 80 drwxr-xr-x 2 user user 128 Jun 30 22:27 . drwxr-xr-x 4 user user 4096 Jun 30 22:27 .. -rw-r--r-- 1 user user 5490 Jun 30 11:01 advtrain.py -rw-r--r-- 1 user user 8776 Jun 30 11:01 attack.py -rw-r--r-- 1 user user 18341 Jun 30 11:01 autograd.py -rw-r--r-- 1 user user 2167 Jun 30 11:01 data.py -rw-r--r-- 1 user user 15988 Jun 30 11:01 nn.py -rw-r--r-- 1 user user 8853 Jun 30 11:01 optim.py -rw-r--r-- 1 user user 3742 Jun 30 11:01 train.py ---DATA--- total 2904 drwxr-xr-x 2 user user 60 Jun 30 22:27 . drwxr-xr-x 4 user user 4096 Jun 30 22:27 .. -rw-r--r-- 1 user user 2967336 Jun 30 11:01 train.npz ---CHECKS--- 1187 run_checks.py 19 test.sh 1206 total
/app/submission/autograd.py
/app/submission/nn.py
1 """Reverse-mode automatic differentiation over numpy arrays (IMPLEMENT THIS). 2 3 A ``Tensor`` wraps a single ``numpy.ndarray`` (``float64``) and records the operation that 4 produced it so that ``.backward()`` can walk the graph in reverse-topological order and 5 accumulate ``.grad`` (the gradient of the scalar on which ``.backward()`` was called w.r.t. 6 this tensor) into every leaf. 7 8 This task is ADVERSARIAL ROBUSTNESS: the model is a small convolutional classifier and the 9 attacks (PGD/FGSM/MI-FGSM/PGD-L2/CW) ascend on the gradient of an attack loss w.r.t. the INPUT 10 image, so the autograd engine must (a) flow gradients back to PARAMETERS (for training) AND 11 (b) flow gradients back to the INPUT tensor (for the attack). BOTH directions are exercised by the 12 verifier's finite-difference gradient checks, so the convolution's input-gradient (the ``_col2im`` 13 adjoint) is load-bearing. 14 15 Contract (the verifier's conformance suite differentiates your analytic backward against finite 16 differences and checks your forward against its own reference, so match this exactly): 17 18 * ``Tensor(data, requires_grad=False)`` -- ``data`` is array-like, stored as ``float64`` in 19 ``self.data``; ``self.grad`` starts as ``None`` and becomes a same-shape ``float64`` array after 20 backward. ``requires_grad`` leaves accumulate grad; non-requiring leaves do not. 21 * Every op returns a NEW ``Tensor`` whose ``requires_grad`` is the OR of its differentiable inputs. 22 …[truncated 18629 chars]
1 """Neural-network layers + a small convolutional classifier, built on the autograd engine 2 (IMPLEMENT THE FORWARDS). 3 4 Every module exposes ``params()`` -> list[Tensor] (the leaf tensors with ``requires_grad=True``) 5 and ``named_params()`` -> dict[str, Tensor] with STABLE names that match the checkpoint format the 6 verifier loads. ``SmallCNN.state_dict()`` / ``load_state_dict()`` round-trip a plain 7 ``dict[str, np.ndarray]`` (saved as an ``.npz``); the verifier loads YOUR checkpoint into its OWN 8 reference SmallCNN via these names, so the names + shapes below are part of the contract. 9 10 Naming contract for SmallCNN (do not change -- the verifier indexes by these exact keys): 11 conv1.weight (C1, C_in, 3, 3) conv1.bias (C1,) 12 conv2.weight (C2, C1, 3, 3) conv2.bias (C2,) 13 fc1.weight (C2*(H/4)*(W/4), F) fc1.bias (F,) 14 fc2.weight (F, n_classes) fc2.bias (n_classes,) 15 16 Architecture (pinned): for an (N, C_in, H, W) input image batch, 17 conv1 (C_in->C1, 3x3, pad 1) -> relu 18 maxpool 2x2 (H,W -> H/2,W/2) 19 conv2 (C1->C2, 3x3, pad 1) -> relu 20 maxpool 2x2 (H/2,W/2 -> H/4,W/4) 21 flatten -> fc1 (-> F) -> relu -> fc2 (-> n_classes) logits. 22 ``Linear`` computes ``x @ weight + bias`` (weight is (in, out)). No BatchNorm inside SmallCNN (so 23 the attack runs a single deterministic eval-mode forward with no running-stat dependence). 24 25 The other …[truncated 15896 chars]
/app/submission/optim.py
1 """Optimizers + LR schedulers over a flat list of autograd ``Tensor`` parameters (IMPLEMENT THESE). 2 3 Every optimizer operates IN PLACE on ``param.data`` using ``param.grad`` (the gradient accumulated 4 by ``Tensor.backward``). ``zero_grad()`` resets every grad to None. The EXACT update formula each 5 optimizer must match (the verifier checks one and several steps against its own reference) is in 6 its docstring. 7 8 The adversarial-training loop uses AdamW (decoupled decay, with ``no_decay_ndim`` excluding 1-D 9 params/biases) + global grad-norm clipping; the rest of the optimizer zoo + the four LR schedulers 10 are part of the from-scratch framework the conformance suite audits. 11 """ 12 from __future__ import annotations 13 14 import math 15 from typing import List 16 17 import numpy as np 18 19 from autograd import Tensor 20 21 22 class Optimizer: 23 def __init__(self, params: List[Tensor]): 24 self.params = list(params) 25 26 def zero_grad(self): 27 for p in self.params: 28 p.grad = None 29 30 31 class SGD(Optimizer): 32 """SGD + momentum + optional Nesterov + (coupled/L2) weight decay: 33 g = grad + wd*param; v = mu*v + g; step = (g + mu*v) if nesterov else v; param -= lr*step.""" 34 35 def __init__(self, params, lr=0.1, momentum=0.0, weight_decay=0.0, nesterov=False): 36 super().__init__(params) 37 self.lr = lr 38 self.momentum = momentum 39 self.weight_decay = weight …[truncated 8125 chars]
/app/submission/data.py
1 """Data loading + minibatching for the shape-classification set (IMPLEMENT THESE).
2
3 The visible train split ships as ``/app/data/train.npz`` with arrays ``X`` (N, C, H, W) float
4 pixels in [0,1] and ``y`` (N,) int labels. The SEALED held-out split is NOT in the image -- the
5 verifier regenerates it from a secret feature seed and runs its own attack on it.
6
7 Contract:
8 load_npz(path) -> (X float64 (N,C,H,W), y int64 (N,))
9 train_val_split(X, y, val_frac, rng) -> ((Xtr,ytr),(Xval,yval)) random disjoint split (val slice
10 is a held-BACK slice of the VISIBLE train data you may use for best-checkpoint selection --
11 it is NOT the verifier's sealed held-out set)
12 iter_batches(X, y, batch_size, rng) -> yields (xb, yb) random-permutation minibatches
13 get_batch(X, y, batch_size, rng) -> a single random minibatch
14 to_onehot(y, n_classes) -> (N, n_classes) one-hot float matrix
15 clip01(X) -> X clamped into the [0,1] pixel domain
16 """
17 from __future__ import annotations
18
19 from typing import Iterator, Tuple
20
21 import numpy as np
22
23
24 def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
25 raise NotImplementedError("implement load_npz")
26
27
28 def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
29 rng: np.random.Generator):
30 raise NotImplementedError("implement train_val_split")
31
32
33 def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
34
…[truncated 814 chars]/app/submission/attack.py
1 """White-box evasion attacks built on the from-scratch autograd engine (IMPLEMENT THESE). 2 3 An adversary perturbs each input within a norm budget (L-inf eps in the [0,1] pixel domain by 4 default) and tries to make the classifier WRONG. Every attack ascends on the gradient of an attack 5 loss with respect to the INPUT image, which is exactly the gradient the autograd engine produces 6 when the input is wrapped in a ``requires_grad=True`` Tensor and the loss is backpropagated -- so a 7 correct ``conv2d`` input-gradient (the ``_col2im`` adjoint) is load-bearing. 8 9 THE PRIMITIVE 10 input_grad(model, x, y) d/dx of mean cross-entropy of model(x) vs y (ndarray). 11 loss_input_grad(model, x, y, loss_fn) d/dx of an ARBITRARY scalar attack loss loss_fn(logits,y). 12 13 L-INF ATTACKS 14 fgsm(model, x, y, eps) clip(x + eps*sign(input_grad)). 15 pgd_attack(model, x, y, eps, steps, alpha) iterated FGSM with random start + project to the 16 L-inf eps-ball around x AND the [0,1] box each step. 17 mi_fgsm(model, x, y, eps, steps, alpha, mu) momentum-iterative FGSM: accumulate a decaying 18 momentum of the L1-NORMALIZED gradient, step on its sign, 19 project each step. 20 targeted_pgd(model, x, y_target, eps, steps, alpha) DESCEND CE toward y_target (step on the 21 …[truncated 7820 chars]
/app/submission/advtrain.py
1 """Adversarial-training objectives built on the from-scratch autograd engine (IMPLEMENT THESE).
2
3 Several standard recipes turn a fragile classifier into a robust one. Each crafts adversarial
4 inputs per minibatch with the model's own attack, then takes a gradient step on a robustness-aware
5 loss; the recipes differ in the loss. All run a single forward+backward; the caller does grad-clip
6 + the optimizer step. ``x_clean`` / ``x_adv`` are ndarrays (N, C, H, W); ``y`` is an int (N,).
7
8 pgd_at_loss(model, x_adv, y) Madry PGD-AT: CE on the ADVERSARIAL batch.
9 trades_loss(model, x_clean, x_adv, y, beta) TRADES: CE(clean) + beta*KL(stopgrad(clean)||adv).
10 The clean branch INSIDE the KL is a STOP-GRADIENT
11 target (detached): the KL backprops only through
12 the adversarial branch.
13 mart_loss(model, x_clean, x_adv, y, beta) MART: boosted-CE on the adversarial batch
14 ( CE(adv,y) - mean log(1 - max_{j!=y} p_adv_j) ) +
15 beta * mean_i[ (1 - p_clean_i[y_i]) *
16 KL_i(softmax(adv)||stopgrad softmax(clean)) ].
17 alp_loss(model, x_clean, x_adv, y, lam) Adversarial Logit Pairing:
18 0.5*(CE(clean)+CE(ad
…[truncated 4278 chars]/app/submission/train.py
1 """Adversarial-training loop for the small CNN, using the from-scratch numpy framework 2 (IMPLEMENT THIS). 3 4 Standard (clean) training reaches ~99% CLEAN accuracy but ~chance ROBUST accuracy -- it is broken 5 by the verifier's STRONG multi-restart L-inf PGD attack. WEAK robust training is not enough either: 6 single-step (FGSM) adversarial training suffers "catastrophic overfitting" and is shattered by the 7 multi-step / multi-restart eval attack, and adversarial training at a SMALLER perturbation budget 8 than the eval budget leaves the model fragile exactly where it is attacked. The effective recipe is 9 full multi-step PGD ADVERSARIAL TRAINING (Madry et al. 2018) with the perturbation budget MATCHED 10 to the (larger) eval budget: 11 12 * on every minibatch, craft an inner k-step L-inf PGD adversarial batch against the CURRENT model 13 (attack.pgd_attack), at the eps the eval uses, then take the AdamW step on advtrain.pgd_at_loss 14 (cross-entropy on the adversarial batch); 15 * train long enough for robustness to converge (a few epochs is not enough at this eps); 16 * stability details that materially raise the achieved robust accuracy and make it reproducible: 17 (a) exclude biases (1-D params) from weight decay (AdamW no_decay_ndim=1); (b) clip the global 18 gradient norm (the adversarial loss surface is sharp); (c) keep the BEST-by-robust-accuracy 19 checkpoint, evaluated each epoch with the model's own multi-step PGD on a held-BACK slic …[truncated 2473 chars]
Inspect training data
python3 -c "
import numpy as np
d=np.load('data/train.npz')
for k in d.files: print(k, d[k].shape, d[k].dtype)
print('y unique', np.unique(d['y']))
print('X range', d['X'].min(), d['X'].max())
print('N', d['X'].shape)
"X (3600, 1, 16, 16) float32 y (3600,) int64 y unique [0 1 2 3 4 5] X range 0.0 1.0 N (3600, 1, 16, 16)
/app/run_checks.py
<system-reminder>[Truncated: PARTIAL view , showing lines 1-868 of 1188 total (29062 tokens, cap 25000). Call Read with offset=869 limit=868 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be further in the file.]</system-reminder> 1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy framework + the adversarial machinery. 3 4 Run this AS OFTEN AS YOU LIKE while you implement to get a per-check progress signal: 5 6 python3 /app/run_checks.py # check the framework in /app/submission 7 python3 /app/run_checks.py --submission /some/dir 8 python3 /app/run_checks.py --only attack # focus a single milestone 9 10 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py / attack.py 11 / advtrain.py), exactly as the grader does, and runs a PUBLIC portion of the component conformance 12 suite, printing a clear per-check report. Each FAIL line names the exact op / layer / optimizer / 13 attack / adversarial-training property that is wrong. 14 15 HOW THIS RELATES TO YOUR GRADE 16 ------------------------------ 17 This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against self-consistent 18 references it computes on its OWN: 19 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward (every op, 20 incl. the convolution input-gradient, the pools, an …[truncated 44212 chars]
/app/run_checks.py
869 def dlr_loss_grad():
870 rng = np.random.default_rng(sb + 60)
871 x0 = rng.standard_normal((5, 4)); y = rng.integers(0, 4, size=5)
872 lt = ag.Tensor(x0.copy(), requires_grad=True)
873 atk.dlr_loss(lt, y).backward()
874 gnum = _num_grad_input(lambda xv: np.array(float(atk.dlr_loss(ag.Tensor(xv), y).data)), x0.copy())
875 return _max_abs(lt.grad, gnum) < TOL_GRAD, f"max_abs={_max_abs(lt.grad, gnum):.2e}"
876
877 def dlr_input_grad_fd():
878 m = _model(61)
879 x0 = np.random.default_rng(sb + 62).standard_normal((3, 1, 8, 8)); y = np.array([0, 1, 2])
880 lf = lambda lg, t: atk.dlr_loss(lg, t)
881 gan = np.asarray(atk.loss_input_grad(m, x0, y, lf), np.float64)
882 gnum = _num_grad_input(lambda xv: np.array(float(lf(m(ag.Tensor(xv)), y).data)), x0.copy())
883 return _max_abs(gan, gnum) < TOL_GRAD, f"max_abs={_max_abs(gan, gnum):.2e}"
884
885 def bim_stronger():
886 m = _model(67)
887 x = np.clip(np.random.default_rng(sb + 68).random((8, 1, 8, 8)), 0, 1); y = np.array([0, 1, 2, 3, 0, 1, 2, 3]); eps = 0.12
888 xf = atk.fgsm(m, x, y, eps); xb = atk.bim(m, x, y, eps, 15, 0.025)
889 lf = float(ag.cross_entropy(m(xf), y).data); lb = float(ag.cross_entropy(m(xb), y).data)
890 return lb >= lf - 1e-6, f"fgsm={lf:.3f} bim={lb:.3f}"
891
892 def restarts_worst_case():
893 m = _model(69)
894 x = np.clip(np.random.default_
…[truncated 15653 chars]"""Reverse-mode automatic differentiation over numpy arrays."""
from __future__ import annotations
import math
from typing import Callable, Iterable, List, Optional
import numpy as np
_erf = np.vectorize(math.erf)
def _as_f64(x) -> np.ndarray:
return np.asarray(x, dtype=np.float64)
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
grad = np.asarray(grad, dtype=np.float64)
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for i, s in enumerate(shape):
if s == 1 and grad.shape[i] != 1:
grad = grad.sum(axis=i, keepdims=True)
return grad
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")
def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()):
self.data: np.ndarray = _as_f64(data)
self.grad: Optional[np.ndarray] = None
self.requires_grad: bool = bool(requires_grad)
self._backward: Callable[[], None] = lambda: None
self._parents: tuple = tuple(_parents)
@property
def shape(self):
return self.data.shape
@property
def ndim(self):
return self.data.ndim
def zero_grad(self):
self.grad = None
def _accum(self, g: np.ndarray):
if self.grad is None:
self.grad = np.zeros_like(self.data)
self.grad += g
def backward(self, grad: Optional[np.ndarray] = None):
topo: List["Tensor"] = []
visited = set()
…[truncated 7719 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Append reductions and nonlinearities
cat >> /app/submission/autograd.py << 'PYEOF'
# ===================================================================== reductions / reshape
def sum(a, axis=None, keepdims=False) -> Tensor:
a = _ensure(a)
out = Tensor(np.sum(a.data, axis=axis, keepdims=keepdims),
requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
a._accum(_expand(out.grad, axis, keepdims, a.shape))
out._backward = _backward
return out
def mean(a, axis=None, keepdims=False) -> Tensor:
a = _ensure(a)
out = Tensor(np.mean(a.data, axis=axis, keepdims=keepdims),
requires_grad=a.requires_grad, _parents=(a,))
axes = _reduce_axes(axis, a.data.ndim)
n = 1
for ax in axes:
n *= a.data.shape[ax]
def _backward():
if a.requires_grad:
a._accum(_expand(out.grad, axis, keepdims, a.shape) / n)
out._backward = _backward
return out
def max(a, axis=None, keepdims=False) -> Tensor:
a = _ensure(a)
val = np.max(a.data, axis=axis, keepdims=keepdims)
out = Tensor(val, requires_grad=a.requires_grad, _parents=(a,))
valk = np.max(a.data, axis=axis, keepdims=True)
def _backward():
if a.requires_grad:
mask = (a.data == valk).astype(np.float64)
counts = mask.sum(axis=axis, keepdims=True)
a._accum(_expand(out.grad, axis, keepdims, a.shape) * mask / counts)
out._backward = _backward
return out
def mi
…[truncated 5762 chars]done
Append losses, norm, embedding, dropout
cat >> /app/submission/autograd.py << 'PYEOF'
# ===================================================================== losses
def mse_loss(pred, target) -> Tensor:
pred = _ensure(pred)
t = target.data if isinstance(target, Tensor) else _as_f64(target)
diff = pred.data - t
n = pred.data.size
out = Tensor(np.mean(diff * diff), requires_grad=pred.requires_grad, _parents=(pred,))
def _backward():
if pred.requires_grad:
pred._accum(out.grad * 2.0 * diff / n)
out._backward = _backward
return out
def _softmax_np(x, axis=-1):
z = x - np.max(x, axis=axis, keepdims=True)
e = np.exp(z)
return e / np.sum(e, axis=axis, keepdims=True)
def cross_entropy(logits, targets) -> Tensor:
logits = _ensure(logits)
t = np.asarray(targets).astype(np.int64)
x = logits.data
n = x.shape[0]
z = x - np.max(x, axis=-1, keepdims=True)
lse = np.log(np.sum(np.exp(z), axis=-1, keepdims=True))
logp = z - lse
loss = -np.mean(logp[np.arange(n), t])
out = Tensor(loss, requires_grad=logits.requires_grad, _parents=(logits,))
sm = np.exp(logp)
def _backward():
if logits.requires_grad:
grad = sm.copy()
grad[np.arange(n), t] -= 1.0
logits._accum(out.grad * grad / n)
out._backward = _backward
return out
def nll_per_sample(logits, targets) -> Tensor:
logits = _ensure(logits)
t = np.asarray(targets).astype(np.int64)
x = logits.data
n = x.sha
…[truncated 4746 chars]done
Append conv, pool, reductions, pad2d
cat >> /app/submission/autograd.py << 'PYEOF'
# ===================================================================== conv2d (im2col)
def _im2col(x, kh, kw):
N, C, H, W = x.shape
OH = H - kh + 1
OW = W - kw + 1
cols = np.zeros((N, C, kh, kw, OH, OW), dtype=np.float64)
for i in range(kh):
for j in range(kw):
cols[:, :, i, j, :, :] = x[:, :, i:i + OH, j:j + OW]
cols = cols.reshape(N, C * kh * kw, OH * OW)
return cols, OH, OW
def _col2im(cols, xshape, kh, kw, pad):
N, C, H, W = xshape
Hp = H + 2 * pad
Wp = W + 2 * pad
OH = Hp - kh + 1
OW = Wp - kw + 1
cols = cols.reshape(N, C, kh, kw, OH, OW)
xpad = np.zeros((N, C, Hp, Wp), dtype=np.float64)
for i in range(kh):
for j in range(kw):
xpad[:, :, i:i + OH, j:j + OW] += cols[:, :, i, j, :, :]
if pad > 0:
return xpad[:, :, pad:pad + H, pad:pad + W]
return xpad
def conv2d(x, weight, bias, pad: int = 1) -> Tensor:
x = _ensure(x); weight = _ensure(weight); bias = _ensure(bias)
rg = x.requires_grad or weight.requires_grad or bias.requires_grad
N, C, H, W = x.data.shape
Cout, Cin, kh, kw = weight.data.shape
if pad > 0:
xp = np.pad(x.data, ((0, 0), (0, 0), (pad, pad), (pad, pad)))
else:
xp = x.data
cols, OH, OW = _im2col(xp, kh, kw)
Wcol = weight.data.reshape(Cout, -1)
o = np.einsum('oc,ncp->nop', Wcol, cols)
o = o + bias.data[None, :, None]
out = Tensor(o.reshap
…[truncated 5119 chars]done
Append extra nonlinearities and norms
cat >> /app/submission/autograd.py << 'PYEOF'
# ===================================================================== additional nonlinearities
def softplus(a, beta: float = 1.0) -> Tensor:
a = _ensure(a)
x = a.data
o = np.logaddexp(0.0, beta * x) / beta
out = Tensor(o, requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
sig = 1.0 / (1.0 + np.exp(-beta * x))
a._accum(out.grad * sig)
out._backward = _backward
return out
def silu(a) -> Tensor:
a = _ensure(a)
x = a.data
sig = 1.0 / (1.0 + np.exp(-x))
out = Tensor(x * sig, requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
a._accum(out.grad * (sig + x * sig * (1.0 - sig)))
out._backward = _backward
return out
def mish(a) -> Tensor:
a = _ensure(a)
x = a.data
sp = np.logaddexp(0.0, x)
tsp = np.tanh(sp)
out = Tensor(x * tsp, requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
sig = 1.0 / (1.0 + np.exp(-x))
a._accum(out.grad * (tsp + x * sig * (1.0 - tsp * tsp)))
out._backward = _backward
return out
def elu(a, alpha: float = 1.0) -> Tensor:
a = _ensure(a)
x = a.data
o = np.where(x > 0, x, alpha * (np.exp(x) - 1.0))
out = Tensor(o, requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
a._accum(
…[truncated 3207 chars]lines: 1102
Implement simple nn forwards
cd /app/submission && python3 - << 'PYEOF'
import re
f='nn.py'
s=open(f).read()
repls = [
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.conv2d(x, weight, bias, pad)."""
raise NotImplementedError("implement Conv2d.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.conv2d(x, self.weight, self.bias, self.pad)'''),
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.maxpool2d(x, k)."""
raise NotImplementedError("implement MaxPool2d.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.maxpool2d(x, self.k)'''),
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.avgpool2d(x, k)."""
raise NotImplementedError("implement AvgPool2d.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.avgpool2d(x, self.k)'''),
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.matmul(x, weight) + bias."""
raise NotImplementedError("implement Linear.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.add(ag.matmul(x, self.weight), self.bias)'''),
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.layernorm(x, weight, bias, eps)."""
raise NotImplementedError("implement LayerNorm.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.layernorm(x, self.weight, self.bias, self.eps)'''),
(''' def forward(self, x: Tensor) -> Tensor:
"""ag.batchnorm(x, weight, bias, eps)."""
…[truncated 1679 chars]ok part1
Implement recurrent cells and CNNs
python3 - << 'PYEOF'
f='nn.py'
s=open(f).read()
repls = [
(''' def forward(self, x: Tensor, h: Tensor) -> Tensor:
"""h' = tanh(x @ weight_ih^T + bias_ih + h @ weight_hh^T + bias_hh)."""
raise NotImplementedError("implement RNNCell.forward")''',
''' def forward(self, x: Tensor, h: Tensor) -> Tensor:
pre = ag.add(ag.add(ag.matmul(x, ag.transpose(self.weight_ih)), self.bias_ih),
ag.add(ag.matmul(h, ag.transpose(self.weight_hh)), self.bias_hh))
return ag.tanh(pre)'''),
(''' def forward(self, x: Tensor, h: Tensor) -> Tensor:
"""the GRU update above (slice the 3 gates out of the (N,3H) pre-activations)."""
raise NotImplementedError("implement GRUCell.forward")''',
''' def forward(self, x: Tensor, h: Tensor) -> Tensor:
H = self.hidden_size
gi = ag.add(ag.matmul(x, ag.transpose(self.weight_ih)), self.bias_ih)
gh = ag.add(ag.matmul(h, ag.transpose(self.weight_hh)), self.bias_hh)
i_r, i_z, i_n = gi[:, 0:H], gi[:, H:2 * H], gi[:, 2 * H:3 * H]
h_r, h_z, h_n = gh[:, 0:H], gh[:, H:2 * H], gh[:, 2 * H:3 * H]
r = ag.sigmoid(ag.add(i_r, h_r))
z = ag.sigmoid(ag.add(i_z, h_z))
n = ag.tanh(ag.add(i_n, ag.mul(r, h_n)))
one = ag.Tensor(np.ones((1, H)))
return ag.add(ag.mul(ag.sub(one, z), n), ag.mul(z, h))'''),
(''' def forward(self, x: Tensor, h: Tensor, c: Tensor):
"""the LSTM update above; return (h', c')."""
raise No
…[truncated 1653 chars]ok part2
/app/submission/data.py
def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
raise NotImplementedError("implement load_npz")
def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
rng: np.random.Generator):
raise NotImplementedError("implement train_val_split")
def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Iterator[Tuple[np.ndarray, np.ndarray]]:
raise NotImplementedError("implement iter_batches")
def get_batch(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Tuple[np.ndarray, np.ndarray]:
"""A single random minibatch (without replacement if possible). Shape-preserving."""
raise NotImplementedError("implement get_batch")
def to_onehot(y: np.ndarray, n_classes: int) -> np.ndarray:
"""(N, n_classes) one-hot float matrix from integer labels y."""
raise NotImplementedError("implement to_onehot")
def clip01(X: np.ndarray) -> np.ndarray:
"""Clamp X into the [0,1] pixel domain (the valid attack range)."""
raise NotImplementedError("implement clip01")def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
d = np.load(path)
X = np.asarray(d["X"], dtype=np.float64)
y = np.asarray(d["y"], dtype=np.int64)
return X, y
def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
rng: np.random.Generator):
n = X.shape[0]
perm = rng.permutation(n)
nval = int(round(n * val_frac))
vidx = perm[:nval]
tidx = perm[nval:]
return (X[tidx], y[tidx]), (X[vidx], y[vidx])
def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Iterator[Tuple[np.ndarray, np.ndarray]]:
n = X.shape[0]
perm = rng.permutation(n)
for s in range(0, n, batch_size):
idx = perm[s:s + batch_size]
yield X[idx], y[idx]
def get_batch(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Tuple[np.ndarray, np.ndarray]:
"""A single random minibatch (without replacement if possible). Shape-preserving."""
n = X.shape[0]
replace = batch_size > n
idx = rng.choice(n, size=batch_size, replace=replace)
return X[idx], y[idx]
def to_onehot(y: np.ndarray, n_classes: int) -> np.ndarray:
"""(N, n_classes) one-hot float matrix from integer labels y."""
y = np.asarray(y).astype(np.int64)
oh = np.zeros((y.shape[0], n_classes), dtype=np.float64)
oh[np.arange(y.shape[0]), y] = 1.0
return oh
def clip01(X: np.ndarray) -> np.ndarray:
"""Clamp X into the [0,1
…[truncated 75 chars]The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run autograd grad checks
cd /app && python3 run_checks.py --only autograd_grads 2>&1 | tail -40
grad/prod_axis OK (max_abs=5.65e-10) grad/cumsum_ax1 OK (max_abs=3.79e-11) grad/logsumexp_ax1 OK (max_abs=4.93e-11) grad/pad2d OK (max_abs=5.10e-11) grad/groupnorm_x OK grad/rmsnorm_x OK grad/layernorm_x OK grad/batchnorm_x OK grad/embedding OK grad/mse_loss OK grad/cross_entropy OK grad/kl_div_q OK grad/kl_div_p OK grad/conv2d_input OK (max_abs=1.09e-09) grad/conv2d_weight OK (max_abs=3.38e-09) grad/conv2d_bias OK grad/conv2d_pad0_input OK (max_abs=7.79e-10) grad/maxpool2d OK grad/avgpool2d OK grad/nn.Conv2d_x OK grad/nn.Linear_x OK grad/nn.LayerNorm_x OK grad/nn.BatchNorm1d_x OK grad/cnn_grad_fc2.bias OK (max_abs=1.21e-11) grad/cnn_grad_conv1.weight OK (max_abs=2.55e-11) grad/cnn_grad_conv2.weight OK (max_abs=2.61e-11) grad/cnn_grad_fc1.weight OK (ma …[truncated 815 chars]
Implement and check optimizers
python3 - << 'PYEOF'
f='/app/submission/optim.py'
s=open(f).read()
R=[
(''' def step(self):
raise NotImplementedError("implement SGD.step")''',
''' def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._v[i] = self.momentum * self._v[i] + g
v = self._v[i]
step = (g + self.momentum * v) if self.nesterov else v
p.data -= self.lr * step'''),
(''' def step(self):
raise NotImplementedError("implement Adagrad.step")''',
''' def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] += g * g
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)'''),
(''' def step(self):
raise NotImplementedError("implement RMSprop.step")''',
''' def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] = self.alpha * self._s[i] + (1 - self.alpha) * (g * g)
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)'''),
(''' def step(self):
raise NotImplementedError("implement Adam.step")''',
''' def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad
…[truncated 5034 chars]ok optim [optim] 20/20 optim/SGD_1step OK (max_abs=0.0e+00) optim/SGD_momentum_5step OK (max_abs=0.0e+00) optim/SGD_nesterov_5step OK (max_abs=0.0e+00) optim/Adagrad_5step OK (max_abs=0.0e+00) optim/RMSprop_5step OK (max_abs=0.0e+00) optim/Adam_10step_wd OK (max_abs=0.0e+00) optim/AdamW_10step_wd OK (max_abs=0.0e+00) optim/Adamax_8step OK (max_abs=0.0e+00) optim/NAdam_8step OK (max_abs=0.0e+00) optim/Lion_8step_wd OK (max_abs=0.0e+00) optim/Adadelta_8step OK (max_abs=0.0e+00) optim/clip_grad_norm_clips OK (max_abs=0.0e+00) optim/clip_grad_norm_noop OK (max_abs=0.0e+00) optim/clip_grad_value OK (max_abs=0.0e+00) optim/sched_cosine_lr OK (match) optim/sched_warmup_lr OK (match) optim/sched_step_lr OK (match) optim/sched_exponential_lr OK (match) optim/sched_polynomial_lr OK (match) optim/sched_linear_lr OK (match) ------------------------------------------------------------------------ optim 20/20 TOTAL 20/20 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST- …[truncated 90 chars]
"""White-box evasion attacks built on the from-scratch autograd engine."""
from __future__ import annotations
from typing import Callable, Optional, Tuple
import numpy as np
import autograd as ag
from autograd import Tensor
def loss_input_grad(model, x: np.ndarray, y: np.ndarray,
loss_fn: Callable[[Tensor, np.ndarray], Tensor]) -> np.ndarray:
xt = Tensor(np.asarray(x, dtype=np.float64), requires_grad=True)
logits = model(xt)
loss = loss_fn(logits, y)
loss.backward()
return np.asarray(xt.grad, dtype=np.float64)
def input_grad(model, x: np.ndarray, y: np.ndarray) -> np.ndarray:
return loss_input_grad(model, x, y, lambda lg, t: ag.cross_entropy(lg, t))
def _other_max(logits, y):
N, C = logits.shape
mask = np.zeros((N, C), dtype=np.float64)
mask[np.arange(N), np.asarray(y).astype(np.int64)] = -1e30
masked = ag.add(logits, Tensor(mask))
return ag.max(masked, axis=1)
def cw_margin_loss(logits, y, kappa: float = 0.0) -> Tensor:
logits = ag._ensure(logits)
N = logits.shape[0]
idx = (np.arange(N), np.asarray(y).astype(np.int64))
true = ag.getitem(logits, idx)
other = _other_max(logits, y)
margin = ag.sub(true, other)
neg_margin = ag.mul(margin, -1.0)
capped = ag.maximum(neg_margin, Tensor(np.full(N, -kappa)))
return ag.mean(capped)
def dlr_loss(logits, y, eps: float = 1e-12) -> Tensor:
logits = ag._ensure(logits)
N = logits.shape[0]
idx = (np.arange(N), np.asarr
…[truncated 7400 chars]The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run attack checks
python3 run_checks.py --only attack 2>&1 | tail -25
[attack] 18/18 attack/input_grad_fd OK (max_abs=2.73e-11) attack/cw_loss_input_grad_fd OK (max_abs=4.13e-12) attack/dlr_loss_grad OK (max_abs=1.04e-09) attack/dlr_loss_input_grad_fd OK (max_abs=3.51e-11) attack/fgsm_step OK attack/pgd_projection OK attack/mi_fgsm_projection OK attack/cw_pgd_projection OK attack/pgd_l2_projection OK attack/bim_projection OK attack/rfgsm_projection OK attack/ni_fgsm_projection OK attack/pgd_restarts_projection OK attack/dlr_pgd_projection OK attack/pgd_stronger_than_fgsm OK (fgsm=1.450 pgd=1.469) attack/bim_stronger_than_fgsm OK (fgsm=1.411 bim=1.423) attack/pgd_restarts_worst_case OK (single=1.521 restarts=1.525) attack/targeted_pgd_lowers_target_ce OK (1.388->1.333) ------------------------------------------------------------------------ attack 18/18 TOTAL 18/18 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD.
/app/submission/advtrain.py
def pgd_at_loss(model, x_adv: np.ndarray, y: np.ndarray) -> Tensor:
"""Madry PGD-AT loss: cross-entropy on the adversarial batch (trains parameters)."""
raise NotImplementedError("implement pgd_at_loss")
def trades_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 6.0) -> Tuple[Tensor, Dict[str, float]]:
"""TRADES loss = CE(clean, y) + beta * KL(stopgrad(softmax(clean)) || softmax(adv))."""
raise NotImplementedError("implement trades_loss")
def mart_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 5.0) -> Tuple[Tensor, Dict[str, float]]:
"""MART loss (boosted CE on adv + misclassification-aware weighted KL with the clean target
DETACHED); see the module docstring for the exact form."""
raise NotImplementedError("implement mart_loss")
def alp_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
lam: float = 0.5) -> Tuple[Tensor, Dict[str, float]]:
"""Adversarial Logit Pairing: 0.5*(CE(clean)+CE(adv)) + lam*mean||z_clean - z_adv||^2 (no detach)."""
raise NotImplementedError("implement alp_loss")
def label_smoothed_ce(logits, y, eps_ls: float = 0.1, n_classes: int = None) -> Tensor:
"""CE against a label-smoothed target ((1-eps_ls)*onehot + eps_ls/C*uniform), via log_softmax."""
raise NotImplementedError("implement label_smoothed_ce")
def robust_ce_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.nd
…[truncated 1782 chars]def _detach(t: Tensor) -> Tensor:
return Tensor(t.data.copy())
def pgd_at_loss(model, x_adv: np.ndarray, y: np.ndarray) -> Tensor:
"""Madry PGD-AT loss: cross-entropy on the adversarial batch (trains parameters)."""
return ag.cross_entropy(model(Tensor(np.asarray(x_adv, np.float64))), y)
def trades_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 6.0) -> Tuple[Tensor, Dict[str, float]]:
"""TRADES loss = CE(clean, y) + beta * KL(stopgrad(softmax(clean)) || softmax(adv))."""
cl = model(Tensor(np.asarray(x_clean, np.float64)))
ad = model(Tensor(np.asarray(x_adv, np.float64)))
ce = ag.cross_entropy(cl, y)
tgt = _detach(cl)
kl = ag.kl_div(tgt, ad)
loss = ag.add(ce, ag.mul(kl, float(beta)))
return loss, {"ce": float(ce.data), "kl": float(kl.data), "loss": float(loss.data)}
def mart_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 5.0) -> Tuple[Tensor, Dict[str, float]]:
"""MART loss (boosted CE on adv + misclassification-aware weighted KL with the clean target
DETACHED); see the module docstring for the exact form."""
yi = np.asarray(y).astype(np.int64)
cl = model(Tensor(np.asarray(x_clean, np.float64)))
ad = model(Tensor(np.asarray(x_adv, np.float64)))
N, C = ad.shape
# boosted CE on adversarial batch
ce_adv = ag.cross_entropy(ad, yi)
pa = ag.softmax(ad, axis=1)
mask = np.zeros((N, C), dtype=np.float
…[truncated 4469 chars]The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run advtrain, integration, data, nn checks
python3 run_checks.py --only advtrain 2>&1 | tail -20 && echo "===INTEG===" && python3 run_checks.py --only integration 2>&1 | tail -8 && echo "===DATA===" && python3 run_checks.py --only data 2>&1 | tail -6 && echo "===NN===" && python3 run_checks.py --only nn_forward 2>&1 | tail -5
[advtrain] 14/14 advtrain/at_param_grad OK (max_abs=2.56e-11) advtrain/trades_param_grad OK (max_abs=5.66e-04) advtrain/mart_param_grad OK (max_abs=1.14e-04) advtrain/alp_param_grad OK (max_abs=2.60e-11) advtrain/robust_ce_param_grad OK (max_abs=5.12e-11) advtrain/clp_param_grad OK (max_abs=2.84e-05) advtrain/kl_at_param_grad OK (max_abs=1.62e-03) advtrain/logit_squeeze_param_grad OK (max_abs=4.80e-11) advtrain/ls_at_param_grad OK (max_abs=2.76e-11) advtrain/label_smoothed_ce_grad OK advtrain/trades_stopgrad OK (max_abs=0.00e+00) advtrain/alp_pairing OK (max_abs=0.00e+00) advtrain/clp_stopgrad OK (max_abs=0.00e+00) advtrain/kl_at_stopgrad OK (max_abs=0.00e+00) ------------------------------------------------------------------------ advtrain 14/14 TOTAL 14/14 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD. ===INTEG=== integration/pgd_at_decreases OK (1.4247->1.3885) integration/trades_finite OK (1.393->1.337) integration/trades_decreases OK (1.3933->1.3367) --------------------------------------------------- …[truncated 878 chars]
Run full checks and smoke test
python3 run_checks.py 2>&1 | tail -5; echo "EXIT: $?"; echo "=== smoke ==="; bash test.sh 2>&1 | tail -15
------------------------------------------------------------------------ autograd_grads 82/82 | nn_forward 22/22 | optim 20/20 | attack 18/18 | advtrain 14/14 | integration 4/4 | data 3/3 TOTAL 163/163 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD. EXIT: 0 === smoke === train.npz: X(3600, 1, 16, 16) float32 y(3600,) classes=6 submission stubs present: ['__pycache__', 'advtrain.py', 'attack.py', 'autograd.py', 'data.py', 'nn.py', 'optim.py', 'train.py'] numpy 2.2.6 app_test smoke OK (run 'python3 /app/run_checks.py' for the per-check progress report)
/app/submission/train.py
def train(train_path, out_path, epochs=12, batch_size=128, lr=1.2e-3, eps=0.12,
at_steps=7, alpha=0.03, grad_clip=1.0, weight_decay=5e-4, val_frac=0.15,
sel_steps=7, seed=0, log_every=1):
"""Run multi-step PGD adversarial training and save the best-by-robust-val checkpoint to
``out_path``. See the module docstring for the recipe."""
raise NotImplementedError("implement the adversarial-training loop")def _robust_acc(model, X, y, eps, steps, alpha, seed, restarts=1, max_n=512):
n = min(max_n, X.shape[0])
Xs, ys = X[:n], y[:n]
correct = np.ones(n, dtype=bool)
# clean correctness
clean_pred = np.argmax(model(Xs).data, axis=1)
correct &= (clean_pred == ys)
for r in range(restarts):
rng = np.random.default_rng(seed + 7919 * (r + 1))
xadv = A.pgd_attack(model, Xs, ys, eps, steps, alpha, rng=rng)
pred = np.argmax(model(xadv).data, axis=1)
correct &= (pred == ys)
return float(np.mean(correct))
def train(train_path, out_path, epochs=30, batch_size=128, lr=1.2e-3, eps=0.12,
at_steps=10, alpha=0.03, grad_clip=1.0, weight_decay=5e-4, val_frac=0.12,
sel_steps=20, seed=0, log_every=1):
"""Run multi-step PGD adversarial training and save the best-by-robust-val checkpoint."""
X, y = D.load_npz(train_path)
n_classes = int(y.max()) + 1
c_in, img = X.shape[1], X.shape[2]
rng = np.random.default_rng(seed)
(Xtr, ytr), (Xval, yval) = D.train_val_split(X, y, val_frac, rng)
model = SmallCNN(n_classes=n_classes, c_in=c_in, img=img, c1=C1, c2=C2, ff=FF, seed=seed)
opt = O.AdamW(model.params(), lr=lr, betas=(0.9, 0.999),
weight_decay=weight_decay, no_decay_ndim=1)
best_sd = model.state_dict()
best_racc = -1.0
step = 0
total_steps = epochs * ((len(Xtr) + batch_size - 1) // batch_size)
for ep in range(epochs):
for xb, yb in D.iter_batche
…[truncated 1407 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Time 2-epoch training run
cd /app/submission && time python3 -c "
import time, numpy as np
import train as Tr
sd, racc = Tr.train('/app/data/train.npz', '/tmp/test_model.npz', epochs=2, at_steps=10, sel_steps=20)
" 2>&1 | tail -10real 0m1.295s user 0m1.584s sys 0m0.270s
Debug 2-epoch run
python3 -c "
import train as Tr
sd, racc = Tr.train('/app/data/train.npz', '/tmp/test_model.npz', epochs=2, at_steps=10, sel_steps=20)
print('done', racc)
" 2>&1 | tail -10(Bash completed with no output)
Run train.py main 2 epochs
python3 train.py --epochs 2 --at-steps 10 2>&1 | tail -20
(Bash completed with no output)
Run train capturing to file
python3 train.py --epochs 2 --at-steps 10 > /tmp/tr.log 2>&1; echo "exit=$?"; cat /tmp/tr.log
/bin/bash: line 1: 1543 Killed python3 train.py --epochs 2 --at-steps 10 > /tmp/tr.log 2>&1 exit=137
Check memory
free -m; echo "---"; ulimit -a | grep -i mem
total used free shared buff/cache available Mem: 481 255 176 1 63 226 Swap: 0 0 0 --- max locked memory (kbytes, -l) 8192 max memory size (kbytes, -m) unlimited virtual memory (kbytes, -v) unlimited
Profile single step memory
/usr/bin/time -v python3 -c "
import numpy as np, data as D, attack as A, advtrain as AT, optim as O
from nn import SmallCNN
X,y=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=6,c_in=1,img=16,c1=14,c2=28,ff=96,seed=0)
opt=O.AdamW(m.params(),lr=1e-3,no_decay_ndim=1)
xb,yb=X[:128],y[:128]
rng=np.random.default_rng(0)
for s in range(5):
xa=A.pgd_attack(m,xb,yb,0.12,10,0.03,rng=rng)
loss=AT.pgd_at_loss(m,xa,yb)
opt.zero_grad(); loss.backward(); O.clip_grad_norm(m.params(),1.0); opt.step()
print('step',s,float(loss.data))
" 2>&1 | grep -E "step|Maximum resident|Killed"(Bash completed with no output)
Test single PGD attack
python3 -c "
import numpy as np, data as D, attack as A, advtrain as AT, optim as O
from nn import SmallCNN
X,y=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=6,c_in=1,img=16,c1=14,c2=28,ff=96,seed=0)
xb,yb=X[:128],y[:128]
rng=np.random.default_rng(0)
xa=A.pgd_attack(m,xb,yb,0.12,10,0.03,rng=rng)
print('pgd ok', xa.shape)
" 2>&1 ; echo "exit=$?"/bin/bash: line 19: 1561 Killed python3 -c "
import numpy as np, data as D, attack as A, advtrain as AT, optim as O
from nn import SmallCNN
X,y=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=6,c_in=1,img=16,c1=14,c2=28,ff=96,seed=0)
xb,yb=X[:128],y[:128]
rng=np.random.default_rng(0)
xa=A.pgd_attack(m,xb,yb,0.12,10,0.03,rng=rng)
print('pgd ok', xa.shape)
" 2>&1
exit=137Test forward/backward at various batch sizes
python3 -c "
import numpy as np, data as D
from nn import SmallCNN
import autograd as ag
X,y=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=6,c_in=1,img=16,c1=14,c2=28,ff=96,seed=0)
for bs in [8,32,64,128]:
xb=X[:bs]
xt=ag.Tensor(xb,requires_grad=True)
l=ag.cross_entropy(m(xt),y[:bs])
l.backward()
print('bs',bs,'ok')
" 2>&1; echo "exit=$?"bs 8 ok bs 32 ok bs 64 ok bs 128 ok exit=0
Track RSS over manual PGD steps
python3 -c "
import numpy as np, data as D, resource
from nn import SmallCNN
import autograd as ag
X,y=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=6,c_in=1,img=16,c1=14,c2=28,ff=96,seed=0)
xb=X[:128].copy(); yb=y[:128]
for s in range(12):
xt=ag.Tensor(xb,requires_grad=True)
l=ag.cross_entropy(m(xt),yb)
l.backward()
xb=xb+0.01*np.sign(xt.grad)
rss=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
print('step',s,'maxrss_MB=%.1f'%rss)
" 2>&1; echo "exit=$?"No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 201/201 cases passed (all milestones pass: True) FINAL METRIC: PGD robust accuracy = 0.5775 < threshold 0.6 REWARD = 0 (1 iff every component milestone passes AND the sealed held-out metric clears its threshold) NOTE: the in-sandbox training in the 481MB e2b box was OOM-killed (0-byte checkpoint), so the raw in-sandbox reward was an infrastructure artifact. This reward is from a faithful LOCAL re-train of the agent's OWN train.py (24GB, no OOM) with its own default hyperparameters, then re-graded by the real verifier.
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_c1ae8806a94c4d6b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_c1ae8806a94c4d6b · verifier authoritative; classifier explanatory.