tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/ 2>/dev/null
total 101 drwxr-xr-x 4 user user 4096 Jun 30 22:29 . drwxr-xr-x 21 root root 4096 Jun 30 22:28 .. -rw-r--r-- 1 user user 14238 Jun 30 22:29 PROMPT.txt -rw-r--r-- 1 user user 5 Jun 30 22:29 agent.pid -rw-r--r-- 1 user user 0 Jun 30 22:29 agent.stderr -rw-r--r-- 1 user user 4476 Jun 30 22:29 agent_trajectory.jsonl drwxr-xr-x 2 user user 60 Jun 30 22:29 data -rw-r--r-- 1 user user 0 Jun 30 22:29 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 22:29 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 22:29 . drwxr-xr-x 4 user user 4096 Jun 30 22:29 .. -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 22:29 . drwxr-xr-x 4 user user 4096 Jun 30 22:29 .. -rw-r--r-- 1 user user 2967336 Jun 30 11:01 train.npz
wc -l submission/*.py run_checks.py test.sh 2>/dev/null
98 submission/advtrain.py
162 submission/attack.py
473 submission/autograd.py
51 submission/data.py
378 submission/nn.py
219 submission/optim.py
79 submission/train.py
1187 run_checks.py
19 test.sh
2666 total/app/submission/autograd.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]
/app/submission/nn.py
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]
/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]python3 -c "
import numpy as np
d = np.load('data/train.npz')
print(list(d.keys()))
X, y = d['X'], d['y']
print('X', X.shape, X.dtype, X.min(), X.max())
print('y', y.shape, y.dtype, np.unique(y), np.bincount(y))
" && echo "---TEST.SH---" && cat test.sh['X', 'y']
X (3600, 1, 16, 16) float32 0.0 1.0
y (3600,) int64 [0 1 2 3 4 5] [593 592 629 596 599 591]
---TEST.SH---
#!/usr/bin/env bash
# Smoke test (NOT the grader): confirms the environment is wired -- numpy imports, the training
# split loads, and the submission stubs are present. It does NOT check correctness (the stubs raise
# NotImplementedError until you implement them).
set -uo pipefail
python3 - <<'PY'
import numpy as np, os
p = "/app/data/train.npz"
assert os.path.exists(p), "missing train.npz"
d = np.load(p)
print(f"train.npz: X{d['X'].shape} {d['X'].dtype} y{d['y'].shape} classes={len(set(d['y'].tolist()))}")
for f in ("autograd.py", "nn.py", "optim.py", "data.py", "attack.py", "advtrain.py", "train.py"):
assert os.path.exists(f"/app/submission/{f}"), f"missing /app/submission/{f}"
assert os.path.exists("/app/run_checks.py"), "missing /app/run_checks.py (public component runner)"
print("submission stubs present:", sorted(os.listdir("/app/submission")))
print("numpy", np.__version__)
PY
echo "app_test smoke OK (run 'python3 /app/run_checks.py' for the per-check progress report)"python3 -c "import numpy; print(numpy.__version__)"
2.2.6
"""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
def _restore_dims(g, shape, axis, keepdims) -> np.ndarray:
"""Broadcast a reduced gradient/array g back to full `shape`."""
g = np.asarray(g, dtype=np.float64)
if axis is None:
return np.broadcast_to(g, shape).astype(np.float64)
if not keepdims:
ax = axis if isinstance(axis, tuple) else (axis,)
ax = tuple(a % len(shape) for a in ax)
g = np.expand_dims(g, ax)
return np.broadcast_to(g, shape).astype(np.float64)
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")
def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()): # noqa: D401
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._
…[truncated 2534 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
cat >> /app/submission/autograd.py <<'PYEOF'
# ===================================================================== element-wise binary
def add(a, b) -> Tensor:
a = _ensure(a); b = _ensure(b)
rg = a.requires_grad or b.requires_grad
out = Tensor(a.data + b.data, requires_grad=rg, _parents=(a, b))
def _backward():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g, b.data.shape))
out._backward = _backward
return out
def sub(a, b) -> Tensor:
a = _ensure(a); b = _ensure(b)
rg = a.requires_grad or b.requires_grad
out = Tensor(a.data - b.data, requires_grad=rg, _parents=(a, b))
def _backward():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-g, b.data.shape))
out._backward = _backward
return out
def mul(a, b) -> Tensor:
a = _ensure(a); b = _ensure(b)
rg = a.requires_grad or b.requires_grad
out = Tensor(a.data * b.data, requires_grad=rg, _parents=(a, b))
def _backward():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g * b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * a.data, b.data.shape))
out._backward = _backward
return out
def div(a, b) -> Tensor:
a = _ensure(a); b = _ensure(b)
rg = a.re
…[truncated 3240 chars]appended binary/unary
cat >> /app/submission/autograd.py <<'PYEOF'
# ===================================================================== reductions / reshape
def sum(a, axis=None, keepdims=False) -> Tensor:
a = _ensure(a)
out = Tensor(a.data.sum(axis=axis, keepdims=keepdims),
requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
a._accum(_restore_dims(out.grad, a.data.shape, axis, keepdims))
out._backward = _backward
return out
def mean(a, axis=None, keepdims=False) -> Tensor:
a = _ensure(a)
out = Tensor(a.data.mean(axis=axis, keepdims=keepdims),
requires_grad=a.requires_grad, _parents=(a,))
n = a.data.size / max(1, out.data.size)
def _backward():
if a.requires_grad:
a._accum(_restore_dims(out.grad, a.data.shape, axis, keepdims) / n)
out._backward = _backward
return out
def _reduce_extreme(a, axis, keepdims, is_max):
a = _ensure(a)
val = a.data.max(axis=axis, keepdims=keepdims) if is_max else a.data.min(axis=axis, keepdims=keepdims)
out = Tensor(val, requires_grad=a.requires_grad, _parents=(a,))
def _backward():
if a.requires_grad:
full = _restore_dims(out.data, a.data.shape, axis, keepdims)
g = _restore_dims(out.grad, a.data.shape, axis, keepdims)
mask = (a.data == full).astype(np.float64)
denom = mask.sum(axis=axis, keepdims=True)
a._accum(g * mask / denom)
…[truncated 5684 chars]appended reductions
cat >> /app/submission/autograd.py <<'PYEOF'
# ===================================================================== nonlinearities
def relu(a) -> Tensor:
a = _ensure(a)
return _unary(a, np.maximum(a.data, 0.0), lambda: (a.data > 0).astype(np.float64))
def leaky_relu(a, slope: float = 0.01) -> Tensor:
a = _ensure(a)
return _unary(a, np.where(a.data > 0, a.data, slope * a.data),
lambda: np.where(a.data > 0, 1.0, slope))
def sigmoid(a) -> Tensor:
a = _ensure(a); s = 1.0 / (1.0 + np.exp(-a.data))
return _unary(a, s, lambda: s * (1.0 - s))
def tanh(a) -> Tensor:
a = _ensure(a); t = np.tanh(a.data)
return _unary(a, t, lambda: 1.0 - t * t)
def gelu(a) -> Tensor:
a = _ensure(a); x = a.data
cdf = 0.5 * (1.0 + _erf(x / np.sqrt(2.0)))
val = x * cdf
pdf = np.exp(-0.5 * x * x) / np.sqrt(2.0 * np.pi)
return _unary(a, val, lambda: cdf + x * pdf)
def softplus(a, beta: float = 1.0) -> Tensor:
a = _ensure(a); x = a.data
val = np.logaddexp(0.0, beta * x) / beta
return _unary(a, val, lambda: 1.0 / (1.0 + np.exp(-beta * x)))
def silu(a) -> Tensor:
a = _ensure(a); x = a.data; s = 1.0 / (1.0 + np.exp(-x))
return _unary(a, x * s, lambda: s + x * s * (1.0 - s))
def mish(a) -> Tensor:
a = _ensure(a); x = a.data
sp = np.logaddexp(0.0, x)
tsp = np.tanh(sp)
s = 1.0 / (1.0 + np.exp(-x))
return _unary(a, x * tsp, lambda: tsp + x * s * (1.0 - tsp * tsp))
def elu(a, alpha: float =
…[truncated 4477 chars]appended nonlin/losses
cat >> /app/submission/autograd.py <<'PYEOF'
# ===================================================================== normalization
def layernorm(a, gamma, beta, eps: float = 1e-5) -> Tensor:
a = _ensure(a); gamma = _ensure(gamma); beta = _ensure(beta)
x = a.data
mu = x.mean(axis=-1, keepdims=True)
var = x.var(axis=-1, keepdims=True)
istd = 1.0 / np.sqrt(var + eps)
xhat = (x - mu) * istd
out = Tensor(gamma.data * xhat + beta.data,
requires_grad=(a.requires_grad or gamma.requires_grad or beta.requires_grad),
_parents=(a, gamma, beta))
D = x.shape[-1]
def _backward():
g = out.grad
if gamma.requires_grad:
gamma._accum(_unbroadcast(g * xhat, gamma.data.shape))
if beta.requires_grad:
beta._accum(_unbroadcast(g, beta.data.shape))
if a.requires_grad:
dxhat = g * gamma.data
dx = istd * (dxhat - dxhat.mean(axis=-1, keepdims=True)
- xhat * (dxhat * xhat).mean(axis=-1, keepdims=True))
a._accum(dx)
out._backward = _backward
return out
def batchnorm(a, gamma, beta, eps: float = 1e-5) -> Tensor:
a = _ensure(a); gamma = _ensure(gamma); beta = _ensure(beta)
x = a.data
mu = x.mean(axis=0, keepdims=True)
var = x.var(axis=0, keepdims=True)
istd = 1.0 / np.sqrt(var + eps)
xhat = (x - mu) * istd
out = Tensor(gamma.data * xhat + beta.data,
requires_grad=(a.r
…[truncated 7491 chars]appended norm/conv/pool
cd /app/submission && python3 -c "
import numpy as np, autograd as ag
T=ag.Tensor
# conv input grad fd check
rng=np.random.default_rng(0)
x=rng.standard_normal((2,2,6,6)); W=rng.standard_normal((3,2,3,3)); b=rng.standard_normal(3)
xt=T(x.copy(),requires_grad=True)
ag.sum(ag.conv2d(xt,T(W),T(b),pad=1)).backward()
def fd(xv):
e=1e-5; g=np.zeros_like(xv); f=xv.reshape(-1); gf=g.reshape(-1)
for i in range(f.size):
o=f[i]; f[i]=o+e; fp=ag.conv2d(T(xv),T(W),T(b),pad=1).data.sum(); f[i]=o-e; fm=ag.conv2d(T(xv),T(W),T(b),pad=1).data.sum(); f[i]=o; gf[i]=(fp-fm)/(2*e)
return g
gn=fd(x.copy())
print('conv x err', np.abs(xt.grad-gn).max())
# layernorm
x=rng.standard_normal((4,6)); ga=T(rng.standard_normal(6)); be=T(rng.standard_normal(6))
lt=T(x.copy(),requires_grad=True); ag.sum(ag.layernorm(lt,ga,be)).backward()
def fdl(xv):
e=1e-5; g=np.zeros_like(xv); f=xv.reshape(-1); gf=g.reshape(-1)
for i in range(f.size):
o=f[i]; f[i]=o+e; fp=ag.layernorm(T(xv),ga,be).data.sum(); f[i]=o-e; fm=ag.layernorm(T(xv),ga,be).data.sum(); f[i]=o; gf[i]=(fp-fm)/(2*e)
return g
print('ln x err', np.abs(lt.grad-fdl(x.copy())).max())
print('OK')
"conv x err 2.547817068432323e-09 ln x err 1.445130681787532e-10 OK
python3 - <<'PY'
import re
f='nn.py'
s=open(f).read()
repl = [
(""" 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 1738 chars]done part1
python3 - <<'PY'
f='nn.py'
s=open(f).read()
repl=[
(""" 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 = gi[:, 0:H]; i_z = gi[:, H:2 * H]; i_n = gi[:, 2 * H:3 * H]
h_r = gh[:, 0:H]; h_z = gh[:, H:2 * H]; h_n = 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 = Tensor(np.ones_like(z.data))
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').\"\"\"
…[truncated 1736 chars]done part2
"""Data loading + minibatching for the shape-classification set."""
from __future__ import annotations
from typing import Iterator, Tuple
import numpy as np
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)
n_val = int(round(val_frac * n))
val_idx = perm[:n_val]
tr_idx = perm[n_val:]
return (X[tr_idx], y[tr_idx]), (X[val_idx], y[val_idx])
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]:
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:
y = np.asarray(y, dtype=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:
retu
…[truncated 24 chars]The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
python3 - <<'PY'
f='optim.py'
s=open(f).read()
repl=[
(''' 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
step = (g + self.momentum * self._v[i]) if self.nesterov else self._v[i]
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 is None:
c
…[truncated 4953 chars]done optim
cd /app && python3 run_checks.py --only autograd_grads 2>&1 | tail -20
grad/nn.Linear_x OK grad/nn.LayerNorm_x OK grad/nn.BatchNorm1d_x OK grad/cnn_grad_fc2.bias OK (max_abs=1.71e-11) grad/cnn_grad_conv1.weight OK (max_abs=2.36e-11) grad/cnn_grad_conv2.weight OK (max_abs=2.75e-11) grad/cnn_grad_fc1.weight OK (max_abs=3.44e-11) grad/cnn_input_grad OK (max_abs=1.86e-11) grad/nn.GroupNorm_x OK grad/nn.RMSNorm_x OK grad/rnncell OK (max_abs=1.03e-10) grad/grucell OK (max_abs=1.84e-10) grad/lstmcell OK (max_abs=4.13e-11) grad/rescnn_param OK (max_abs=2.00e-11) grad/rescnn_input OK (max_abs=2.21e-11) ------------------------------------------------------------------------ autograd_grads 77/82 TOTAL 77/82 public checks passing Fix the FAIL lines above, then re-run. Each names a backward/forward/optimizer/attack/adversarial-training bug.
python3 run_checks.py --only autograd_grads 2>&1 | grep FAIL
grad/mean_all FAIL (raised AxisError: axis 1 is out of bounds for array of dimension 0) grad/mean_axis FAIL (raised AxisError: axis 4 is out of bounds for array of dimension 0) grad/var_axis FAIL (raised AxisError: axis 4 is out of bounds for array of dimension 0) grad/var_keep FAIL (raised AxisError: axis 4 is out of bounds for array of dimension 0) grad/std_axis FAIL (raised AxisError: axis 4 is out of bounds for array of dimension 0) Fix the FAIL lines above, then re-run. Each names a backward/forward/optimizer/attack/adversarial-training bug.
cd /app/submission && python3 -c "
import numpy as np, autograd as ag, traceback
T=ag.Tensor
try:
t=T(np.random.randn(4,5),requires_grad=True)
ag.sum(ag.mean(t)).backward()
print('mean_all grad ok', t.grad.shape)
except Exception as e:
traceback.print_exc()
"Traceback (most recent call last):
File "<string>", line 6, in <module>
File "/app/submission/autograd.py", line 329, in mean
n = a.data.size / max(1, out.data.size)
^^^^^^^^^^^^^^^^^^^^^
File "/app/submission/autograd.py", line 355, in max
return _reduce_extreme(a, axis, keepdims, True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/submission/autograd.py", line 340, in _reduce_extreme
val = a.data.max(axis=axis, keepdims=keepdims) if is_max else a.data.min(axis=axis, keepdims=keepdims)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/numpy/_core/_methods.py", line 44, in _amax
return umr_maximum(a, axis, None, out, keepdims, initial, where)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 0python3 - <<'PY'
f='autograd.py'
s=open(f).read()
s=s.replace(" n = a.data.size / max(1, out.data.size)",
" n = a.data.size / out.data.size")
s=s.replace(" n = a.data.size / max(1, (a.data.sum(axis=axis, keepdims=True)).size)\n den = (n - 1) if unbiased else n\n val = (diff ** 2).sum(axis=axis, keepdims=keepdims) / den",
" n = a.data.size / (a.data.sum(axis=axis, keepdims=True)).size\n den = (n - 1) if unbiased else n\n val = (diff ** 2).sum(axis=axis, keepdims=keepdims) / den")
s=s.replace(" n = a.data.size / max(1, (a.data.sum(axis=axis, keepdims=True)).size)\n den = (n - 1) if unbiased else n\n v = (diff ** 2).sum(axis=axis, keepdims=True) / den + eps",
" n = a.data.size / (a.data.sum(axis=axis, keepdims=True)).size\n den = (n - 1) if unbiased else n\n v = (diff ** 2).sum(axis=axis, keepdims=True) / den + eps")
open(f,'w').write(s)
print("fixed")
PY
cd /app && python3 run_checks.py --only autograd_grads 2>&1 | tail -3fixed autograd_grads 82/82 TOTAL 82/82 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.
python3 run_checks.py --only nn_forward 2>&1 | tail -3 && python3 run_checks.py --only optim 2>&1 | tail -3 && python3 run_checks.py --only data 2>&1 | tail -3
nn_forward 22/22 TOTAL 22/22 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. optim 20/20 TOTAL 20/20 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. data 3/3 TOTAL 3/3 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.
python3 - <<'PY'
f='attack.py'
s=open(f).read()
repl=[
(''' raise NotImplementedError("implement loss_input_grad")''',
''' 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)'''),
(''' raise NotImplementedError("implement input_grad")''',
''' return loss_input_grad(model, x, y, lambda lg, t: ag.cross_entropy(lg, t))'''),
(''' raise NotImplementedError("implement cw_margin_loss")''',
''' logits = logits if isinstance(logits, Tensor) else Tensor(logits)
y = np.asarray(y)
N, C = logits.data.shape
onehot = np.zeros((N, C)); onehot[np.arange(N), y] = 1.0
z_y = ag.sum(ag.mul(logits, Tensor(onehot)), axis=1)
other = ag.add(logits, Tensor(onehot * (-1e9)))
z_other = ag.max(other, axis=1)
margin = ag.sub(z_y, z_other)
return ag.mean(ag.maximum(ag.mul(margin, -1.0), Tensor(np.full(N, -kappa))))'''),
(''' raise NotImplementedError("implement dlr_loss")''',
''' logits = logits if isinstance(logits, Tensor) else Tensor(logits)
y = np.asarray(y)
N, C = logits.data.shape
onehot = np.zeros((N, C)); onehot[np.arange(N), y] = 1.0
z_y = ag.sum(ag.mul(logits, Tensor(onehot)), axis=1)
other = ag.add(logits, Tensor(onehot * (-1e9)))
z_other = ag.max(other, axis=1)
z_max = ag.max(logits, axis=1)
z_mean = ag.mean(logits, axis=1)
num = ag.sub(z_y, z_other)
den = ag.add(ag.s
…[truncated 889 chars]Exit code 1 Traceback (most recent call last): File "<stdin>", line 2, in <module> FileNotFoundError: [Errno 2] No such file or directory: 'attack.py'
cd /app/submission && python3 - <<'PY'
f='attack.py'
s=open(f).read()
repl=[
(''' raise NotImplementedError("implement loss_input_grad")''',
''' 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)'''),
(''' raise NotImplementedError("implement input_grad")''',
''' return loss_input_grad(model, x, y, lambda lg, t: ag.cross_entropy(lg, t))'''),
(''' raise NotImplementedError("implement cw_margin_loss")''',
''' logits = logits if isinstance(logits, Tensor) else Tensor(logits)
y = np.asarray(y)
N, C = logits.data.shape
onehot = np.zeros((N, C)); onehot[np.arange(N), y] = 1.0
z_y = ag.sum(ag.mul(logits, Tensor(onehot)), axis=1)
other = ag.add(logits, Tensor(onehot * (-1e9)))
z_other = ag.max(other, axis=1)
margin = ag.sub(z_y, z_other)
return ag.mean(ag.maximum(ag.mul(margin, -1.0), Tensor(np.full(N, -kappa))))'''),
(''' raise NotImplementedError("implement dlr_loss")''',
''' logits = logits if isinstance(logits, Tensor) else Tensor(logits)
y = np.asarray(y)
N, C = logits.data.shape
onehot = np.zeros((N, C)); onehot[np.arange(N), y] = 1.0
z_y = ag.sum(ag.mul(logits, Tensor(onehot)), axis=1)
other = ag.add(logits, Tensor(onehot * (-1e9)))
z_other = ag.max(other, axis=1)
z_max = ag.max(logits, axis=1)
z_mean = ag.mean(logits, axis=1)
num = ag.sub(z_y, z_other)
…[truncated 911 chars]done attack part1
python3 - <<'PY'
f='attack.py'
s=open(f).read()
repl=[
(''' raise NotImplementedError("implement pgd_attack")''',
''' lo, hi = clip
x = np.asarray(x, dtype=np.float64)
if x0 is not None:
x_adv = np.asarray(x0, dtype=np.float64).copy()
elif rng is not None:
x_adv = x + rng.uniform(-eps, eps, size=x.shape)
else:
x_adv = x + np.random.default_rng().uniform(-eps, eps, size=x.shape)
x_adv = project_linf(x_adv, x, eps, lo, hi)
for _ in range(steps):
g = input_grad(model, x_adv, y)
x_adv = x_adv + alpha * np.sign(g)
x_adv = project_linf(x_adv, x, eps, lo, hi)
return x_adv'''),
(''' raise NotImplementedError("implement mi_fgsm")''',
''' lo, hi = clip
x = np.asarray(x, dtype=np.float64)
if rng is not None:
x_adv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
else:
x_adv = x.copy()
g_mom = np.zeros_like(x)
for _ in range(steps):
g = input_grad(model, x_adv, y)
l1 = np.sum(np.abs(g).reshape(x.shape[0], -1), axis=1).reshape(-1, *([1] * (x.ndim - 1)))
g_mom = mu * g_mom + g / (l1 + 1e-12)
x_adv = x_adv + alpha * np.sign(g_mom)
x_adv = project_linf(x_adv, x, eps, lo, hi)
return x_adv'''),
(''' raise NotImplementedError("implement targeted_pgd")''',
''' lo, hi = clip
x = np.asarray(x, dtype=np.float64)
if rng is not None:
x_adv = project_linf(x + rng.uniform(-eps, eps, size=x.sh
…[truncated 4183 chars]done attack part2 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.
cd /app/submission && python3 - <<'PY'
f='advtrain.py'
s=open(f).read()
repl=[
(''' raise NotImplementedError("implement pgd_at_loss")''',
''' return ag.cross_entropy(model(Tensor(x_adv)), y)'''),
(''' raise NotImplementedError("implement trades_loss")''',
''' cl = model(Tensor(x_clean)); ad = model(Tensor(x_adv))
ce = ag.cross_entropy(cl, y)
tgt = Tensor(cl.data.copy())
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)}'''),
(''' raise NotImplementedError("implement mart_loss")''',
''' y = np.asarray(y)
ad = model(Tensor(x_adv)); cl = model(Tensor(x_clean))
N, C = ad.data.shape
onehot = np.zeros((N, C)); onehot[np.arange(N), y] = 1.0
ce_adv = ag.cross_entropy(ad, y)
padv = ag.softmax(ad)
p_other = ag.max(ag.add(padv, Tensor(onehot * (-1e9))), axis=1)
one = Tensor(np.ones(N))
term2 = ag.mul(ag.mean(ag.log(ag.sub(one, p_other))), -1.0)
boosted = ag.add(ce_adv, term2)
pclean = ag.softmax(cl)
logpclean_det = Tensor(np.log(pclean.data + 1e-12))
logpadv = ag.log_softmax(ad)
kl_per = ag.sum(ag.mul(padv, ag.sub(logpadv, logpclean_det)), axis=1)
weight = Tensor(1.0 - pclean.data[np.arange(N), y])
kl_term = ag.mean(ag.mul(weight, kl_per))
loss = ag.add(boosted, ag.mul(kl_term, float(beta)))
return loss, {"ce": float(ce_adv.data), "kl": float(kl_term.data), "loss": float(loss.data)}'
…[truncated 2744 chars]done advtrain python3: can't open file '/app/submission/run_checks.py': [Errno 2] No such file or directory
cd /app && python3 run_checks.py --only advtrain 2>&1 | tail -20
[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=4.29e-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=2.35e-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.
python3 run_checks.py 2>&1 | tail -8
data/get_batch_shape OK ((16, 1, 8, 8),(16,)) data/train_val_split OK (30/10) data/to_onehot OK ------------------------------------------------------------------------ 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.
/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, restarts, rng, limit=400):
n = min(limit, X.shape[0])
Xs, ys = X[:n], y[:n]
clean_pred = model(Xs).data.argmax(1)
correct = clean_pred == ys
xadv = A.pgd_restarts(model, Xs, ys, eps, steps, alpha, restarts=restarts, rng=rng)
adv_pred = model(xadv).data.argmax(1)
robust = correct & (adv_pred == ys)
return float(robust.mean()), float(correct.mean())
def train(train_path, out_path, epochs=20, 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=10, 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."""
import math as _m
X, y = D.load_npz(train_path)
n_classes = int(y.max()) + 1
c_in = X.shape[1]
img = 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)
params = model.params()
opt = O.AdamW(params, lr=lr, betas=(0.9, 0.999), weight_decay=weight_decay, no_decay_ndim=1)
steps_per_epoch = int(_m.ceil(len(Xtr) / batch_size))
total_steps = epochs * steps_per_epoch
warmup = steps_per_epoch # one epoch warmup
step_count = 0
best_robust = -1.0
best_state = None
for epoch in range(epoc
…[truncated 1292 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
cd /app/submission && time python3 -c "
import train
train.train('/app/data/train.npz','/tmp/test_model.npz', epochs=1, seed=0)
" 2>&1 | tail -5real 0m1.582s user 0m0.876s sys 0m0.691s
python3 -c "
import train
train.train('/app/data/train.npz','/tmp/test_model.npz', epochs=2, seed=0)
" 2>&1 | tail -10(Bash completed with no output)
python3 -u -c "
import sys
import train
print('imported', flush=True)
s=train.train('/app/data/train.npz','/tmp/test_model.npz', epochs=2, seed=0)
print('returned', flush=True)
"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 = 0.5825 < 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_0c842e5bef2e47ed. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_0c842e5bef2e47ed · verifier authoritative; classifier explanatory.