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 "---PYTHON---" && python3 --version && python3 -c "import numpy; print('numpy', numpy.__version__)"total 89 drwxr-xr-x 4 user user 4096 Jun 30 22:34 . drwxr-xr-x 21 root root 4096 Jun 30 22:34 .. -rw-r--r-- 1 user user 12853 Jun 30 22:34 PROMPT.txt -rw-r--r-- 1 user user 5 Jun 30 22:34 agent.pid -rw-r--r-- 1 user user 0 Jun 30 22:34 agent.stderr -rw-r--r-- 1 user user 4663 Jun 30 22:34 agent_trajectory.jsonl drwxr-xr-x 2 user user 60 Jun 30 22:34 data -rw-r--r-- 1 user user 0 Jun 30 22:34 run.log -rwxr-xr-x 1 user user 47612 Jun 30 11:01 run_checks.py drwxr-xr-x 3 user user 128 Jun 30 22:34 submission -rwxr-xr-x 1 user user 1376 Jun 30 11:01 test.sh -rwxr-xr-x 1 user user 629 Jun 30 11:01 timer.sh ---SUBMISSION--- total 100 drwxr-xr-x 3 user user 128 Jun 30 22:34 . drwxr-xr-x 4 user user 4096 Jun 30 22:34 .. drwxr-xr-x 2 user user 4096 Jun 30 22:34 __pycache__ -rw-r--r-- 1 user user 27741 Jun 30 11:01 autograd.py -rw-r--r-- 1 user user 2625 Jun 30 11:01 data.py -rw-r--r-- 1 user user 21670 Jun 30 11:01 nn.py -rw-r--r-- 1 user user 6253 Jun 30 11:01 optim.py -rw-r--r-- 1 user user 20833 Jun 30 11:01 ssl.py -rw-r--r-- 1 user user 3551 Jun 30 11:01 train.py ---DATA--- total 1804 drwxr-xr-x 2 user user 60 Jun 30 22:34 . drwxr-xr-x 4 user user 4096 Jun 30 22:34 .. -rw-r--r-- 1 user user 59874 Jun 30 11:01 labeled.npz -rw-r--r-- 1 user user 1778166 Jun 30 11:01 unlabeled.npz ---PYTHON--- Python 3.11.6 numpy 2.2.6
/app/submission/autograd.py
1 """Reverse-mode automatic differentiation over numpy arrays. 2 3 YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy 4 ONLY. No torch/jax/tensorflow/autograd/etc. The grader's conformance suite differentiates each 5 op below with central finite differences and compares against YOUR analytic backward, so every 6 backward must be correct (a single wrong reduction/Jacobian fails the gradient checks). 7 8 A ``Tensor`` wraps one ``numpy.ndarray`` (stored as ``float64`` in ``self.data``) and records 9 the op that produced it so ``.backward()`` can accumulate ``.grad`` into every leaf. 10 11 CONTRACT (the grader builds its own reference against this exact API): 12 * ``Tensor(data, requires_grad=False)`` -- ``self.data`` is ``float64``; ``self.grad`` is None 13 until backward, then a same-shape ``float64`` array. requires_grad leaves accumulate grad. 14 * Every op returns a NEW Tensor whose ``requires_grad`` is the OR of its differentiable inputs, 15 and whose ``_backward`` closure does ``input._accum(local_grad)`` (so multiple paths add up). 16 * ``t.backward(grad=None)`` -- seeds ``grad`` (all-ones for a scalar) and walks the graph in 17 reverse topological order, calling each node's ``_backward``. Repeated backward accumulates. 18 * BROADCASTING: binary ops broadcast like numpy; the backward MUST reduce (sum) the upstream 19 gradient back to each input's ORIGINAL shape (use the provided ``_unbroadcast`` helper). 20 * Numerically …[truncated 29481 chars]
/app/submission/nn.py
1 """Neural-network layers + several from-scratch models, built on YOUR autograd engine.
2
3 Implement every ``# TODO`` forward (the parameters + the ``named_params`` naming are already
4 wired for you; you compose the autograd ops). The grader checks each layer's forward against its
5 OWN reference AND finite-difference-checks the gradients that flow through your autograd, so the
6 composition must be exactly right.
7
8 LAYER / MODEL CHECKLIST (forward + grad checked):
9 Linear Embedding LayerNorm BatchNorm1d Dropout Conv2d MaxPool2d AvgPool2d
10 MultiHeadSelfAttention MLP TransformerBlock
11 RNNCell LSTMCell GRUCell
12 HiddenBlock + MLPClassifier (THE METRIC MODEL) | CNNClassifier (images)
13 | SeqClassifier (rnn/lstm/gru sequence)
14
15 CHECKPOINT NAMING CONTRACT for the METRIC model (model.npz; the grader loads YOUR ``.npz`` into
16 its own reference MLPClassifier by these exact keys/shapes -- do not rename):
17 MLPClassifier (model.npz):
18 layers.{i}.fc.weight (d_in_i, d_hidden) | layers.{i}.fc.bias (d_hidden,)
19 layers.{i}.ln.gamma/.beta (d_hidden,)
20 head.weight (d_hidden, num_classes) | head.bias (num_classes,)
21 (layer 0 has d_in_0 = in_dim; layers 1.. have d_in_i = d_hidden.)
22 CNNClassifier:
23 conv1.weight (C,1,3,3) | conv1.bias (C,) | conv2.weight (C,C,3,3) | conv2.bias (C,)
24 fc.weight (C*(img//4)*(img//4), n_classes) | fc.bias (n_classes,)
25 SeqClassifier, cell in {rnn (G=1), lstm (G=4), gru (G=3)}:
26 embed
…[truncated 22234 chars]/app/submission/optim.py
1 """Optimizers + LR schedulers over a flat list of autograd ``Tensor`` parameters. 2 3 Implement the ``# TODO`` update math + schedulers. Every optimizer updates ``param.data`` IN 4 PLACE using ``param.grad``. The grader runs one (and several) step(s) and compares your updated 5 params against its reference within a tight tolerance, so the math must be exact. ``t`` is the 6 1-based step count (track it yourself where bias correction is needed). 7 8 SGD (momentum ``mu``, coupled weight decay ``wd``, optional Nesterov): 9 g = grad + wd * param 10 v = mu * v + g 11 update = (g + mu * v) if nesterov else v 12 param -= lr * update 13 14 Adagrad: g = grad + wd*param ; s += g^2 ; param -= lr * g / (sqrt(s) + eps) 15 RMSprop: g = grad + wd*param ; s = alpha*s + (1-alpha)*g^2 ; param -= lr*g/(sqrt(s)+eps) 16 17 Adam (COUPLED / L2 weight decay folded into the gradient): 18 g = grad + wd*param 19 m = b1*m + (1-b1)*g ; v = b2*v + (1-b2)*g^2 20 mhat = m/(1-b1^t) ; vhat = v/(1-b2^t) 21 param -= lr * mhat / (sqrt(vhat) + eps) 22 23 AdamW (DECOUPLED weight decay -- decay the PARAM, not the moments): 24 m = b1*m + (1-b1)*g ; v = b2*v + (1-b2)*g^2 25 mhat = m/(1-b1^t) ; vhat = v/(1-b2^t) 26 param -= lr * ( mhat/(sqrt(vhat)+eps) + wd*param ) 27 28 Lion (sign of an interpolated momentum; decoupled weight decay): 29 update = sign( b1*m + (1-b1)*g ) 30 param -= lr * ( update + wd*param ) 31 m = b2*m + (1-b2)*g …[truncated 5297 chars]
/app/submission/data.py
1 """Data loading + minibatch iteration for the FixMatch task, plus tiny synthetic dataset builders
2 the component suite uses to exercise the CNN / sequence models.
3
4 Implement every ``# TODO`` body. ``/app/data/labeled.npz`` ships the FEW labeled examples (arrays
5 ``X`` (N_lab, dim) standardized features + ``y`` (N_lab,) integer labels) and
6 ``/app/data/unlabeled.npz`` ships the large UNLABELED pool (array ``X`` only).
7
8 Contract:
9 load_npz(path) -> (X float64 (N, dim), y int64 (N,) or None if the file has no ``y``)
10 n_classes(y) -> int (max label + 1)
11 epoch_batches(n, batch_size, rng) -> yields arrays of shuffled indices (last batch may be short)
12 get_batch(X, y, idx) -> (Xb float64 (B, dim), yb int64 (B,) or None, idx int64 (B,))
13 make_image_dataset(n, seed, img, n_classes) -> (X (n,1,img,img), y (n,)) for the CNN model
14 make_seq_dataset(n, seed, T, vocab, n_classes) -> (X (n,T) int, y (n,)) for the seq model
15 """
16 from __future__ import annotations
17
18 from typing import Optional, Tuple
19
20 import numpy as np
21
22
23 def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
24 # TODO: np.load the file; return (X as float64, y as int64 reshaped to 1-D or None if absent).
25 raise NotImplementedError("load_npz")
26
27
28 def n_classes(y: np.ndarray) -> int:
29 # TODO: return int(max(y)) + 1
30 raise NotImplementedError("n_classes")
31
32
33 def epoch_batches(n: int, batch_size: int, rng: np.random.G
…[truncated 1281 chars]/app/submission/ssl.py
1 """FixMatch (+ related MixMatch / UDA) semi-supervised-learning machinery, on top of YOUR numpy 2 autograd engine. THIS IS THE DOMAIN DELIVERABLE -- implement every ``# TODO`` body. 3 4 Each differentiable loss below is an autograd OP: it must build a scalar Tensor whose ``_backward`` 5 adds the correct analytic gradient into the input logits (the grader finite-difference gradient- 6 checks each one). The augmentation / gating / alignment / EMA pieces are pure numpy recursions the 7 grader checks for exactness. A wrong gradient, gate, alignment, sharpen, mixup, interleave, or EMA 8 update fails the domain milestone -> reward 0. 9 10 CHECKLIST (each is a separate graded check): 11 AUGMENTATION (numpy, label-preserving, no grad): 12 weak_augment strong_augment cutout mixup interleave / de_interleave 13 PSEUDO-LABELING / DISTRIBUTION CONTROL (numpy, no grad): 14 pseudo_label confidence_mask sharpen DistributionAligner EMAScalar 15 DIFFERENTIABLE LOSSES (autograd ops, hand-written analytic backward): 16 soft_cross_entropy consistency_loss mse_consistency kl_divergence 17 entropy_loss class_balance_loss fixmatch_loss 18 EMA TEACHER (numpy recursion): 19 EMATeacher 20 21 NUMERICAL CONVENTIONS (MUST match): 22 * soft_cross_entropy / consistency: per-row loss ``-sum_c target[c]*logp[c]`` weighted by 23 ``weight[n]`` and AVERAGED OVER THE FULL BATCH N (divide by N, not by the number of confident 24 rows). Backward wrt logits: `` …[truncated 20837 chars]
/app/submission/train.py
1 """FixMatch semi-supervised training loop using YOUR from-scratch numpy framework + YOUR SSL 2 machinery in ssl.py. 3 4 Implement the ``# TODO`` training loop. Per step: 5 * supervised: cross-entropy on a WEAKLY-augmented labeled batch; 6 * unlabeled: forward a WEAKLY-augmented view -> softmax -> (distribution-align) -> confidence-gated 7 pseudo-labels; then minimize the masked consistency loss between the STRONGLY-augmented view and 8 those (stop-gradient) pseudo-labels, weighted by lambda_u; 9 * backward -> grad-clip -> optimizer step (an LR warmup + cosine schedule is a good choice); 10 * EMA-update a teacher copy of the weights. 11 12 Save an ``.npz`` checkpoint of the EMA-averaged classifier state_dict so the grader can load your 13 weights into ITS reference classifier and score top-1 on the SEALED held-out TEST split. 14 15 PIN the architecture to match the grader (it rebuilds exactly this graph and loads your weights): 16 in_dim = X.shape[1], num_classes = max(y)+1, d_hidden = 256, n_layer = 3. 17 18 NOTE: a supervised-only run on the few labels overfits them and generalizes near the supervised 19 floor; a naive / collapsed FixMatch run (no distribution alignment / no supervised warmup / 20 mis-tuned gate) collapses to a constant predictor near chance. You need the full FixMatch recipe, 21 with correct component gradients, to clear the threshold. 22 """ 23 from __future__ import annotations 24 25 import argparse 26 import json 27 import math 2 …[truncated 2303 chars]
/app/run_checks.py
1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy DL framework + FixMatch SSL 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 fixmatch # run one milestone 9 python3 /app/run_checks.py --quiet # summary line only 10 11 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py / ssl.py), 12 exactly as the grader does, and runs a PUBLIC portion of the component conformance suite, printing 13 a clear per-check report so you can implement -> run -> read failures -> fix -> repeat. 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, for every 20 op and every layer/cell/model. (A wrong backward fails here.) 21 * nn_forward : mathematical PROPERTIES your forward must satisfy (softmax rows sum to 1, 22 log_softmax == log(softmax), layernorm/batchnorm row stats, the attention mask 23 is causal, sigmoid/tanh/relu/gelu definitions, shape inva …[truncated 50050 chars]
Inspect data shapes
python3 -c "
import numpy as np
d=np.load('data/labeled.npz'); print('labeled keys', list(d.keys()))
print('X', d['X'].shape, d['X'].dtype, 'y', d['y'].shape, d['y'].dtype)
print('classes', np.unique(d['y']), 'counts', np.bincount(d['y']))
u=np.load('data/unlabeled.npz'); print('unlab keys', list(u.keys()), 'X', u['X'].shape)
print('Xmean', d['X'].mean(), 'std', d['X'].std())
"labeled keys ['X', 'y'] X (200, 80) float32 y (200,) int64 classes [0 1 2 3 4 5 6 7 8 9] counts [20 20 20 20 20 20 20 20 20 20] unlab keys ['X'] X (6000, 80) Xmean -0.013586603 std 0.9973769
"""Reverse-mode automatic differentiation over numpy arrays."""
from __future__ import annotations
from typing import Callable, Iterable, List, Optional
import numpy as np
def _as_f64(x) -> np.ndarray:
return np.asarray(x, dtype=np.float64)
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for ax, sz in enumerate(shape):
if sz == 1 and grad.shape[ax] != 1:
grad = grad.sum(axis=ax, keepdims=True)
return grad.reshape(shape)
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):
if grad is None:
grad = np.ones_like(self.data)
topo: List[Tensor] = []
visited = set()
d
…[truncated 25994 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Fill simple nn forwards
cd /app/submission && python3 - <<'PY'
import re
src=open('nn.py').read()
repls = [
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: matmul x@weight then add bias (if present). Use ag.matmul / ag.add.
raise NotImplementedError("Linear.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
out = ag.matmul(x, self.weight)
if self.bias is not None:
out = ag.add(out, self.bias)
return out'''),
(''' def forward(self, idx) -> Tensor:
# TODO: return ag.embedding(self.weight, idx)
raise NotImplementedError("Embedding.forward")''',
''' def forward(self, idx) -> Tensor:
return ag.embedding(self.weight, idx)'''),
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: return ag.layernorm(x, self.gamma, self.beta, self.eps)
raise NotImplementedError("LayerNorm.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return ag.layernorm(x, self.gamma, self.beta, self.eps)'''),
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: ms = mean(x*x, -1, keepdims); inv = (ms+eps)**-0.5; return x*inv*gamma (autograd ops)
raise NotImplementedError("RMSNorm.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
ms = ag.mean(ag.mul(x, x), axis=-1, keepdims=True)
inv = ag.power(ag.add(ms, self.eps), -0.5)
return ag.mul(ag.mul(x, inv), self.gamma)'''),
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: g = ag.silu
…[truncated 1939 chars]done batch1
Fill complex nn forwards
python3 - <<'PY'
src=open('nn.py').read()
repls=[
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: implement causal multi-head self-attention as described in the docstring.
raise NotImplementedError("MultiHeadSelfAttention.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
B, Tt, _ = x.data.shape
H, dh = self.n_head, self.d_head
qkv = self.qkv(x) # (B, T, 3*d_model)
qkv = ag.reshape(qkv, (B, Tt, 3, H, dh))
qkv = ag.transpose(qkv, (2, 0, 3, 1, 4)) # (3, B, H, T, dh)
q = qkv[0]; k = qkv[1]; v = qkv[2] # each (B, H, T, dh)
kt = ag.transpose(k, (0, 1, 3, 2)) # (B, H, dh, T)
scores = ag.mul(ag.matmul(q, kt), 1.0 / np.sqrt(dh))
mask = np.triu(np.ones((Tt, Tt)), k=1) * (-1e9)
scores = ag.add(scores, Tensor(mask.reshape(1, 1, Tt, Tt)))
att = ag.softmax(scores, axis=-1)
out = ag.matmul(att, v) # (B, H, T, dh)
out = ag.transpose(out, (0, 2, 1, 3)) # (B, T, H, dh)
out = ag.reshape(out, (B, Tt, self.d_model))
return self.proj(out)'''),
(''' def forward(self, x: Tensor) -> Tensor:
# TODO: fc2(gelu(fc1(x)))
raise NotImplementedError("MLP.forward")''',
''' def forward(self, x: Tensor) -> Tensor:
return self.fc2(ag.gelu(self.fc1(x)))'''),
(''' def forward(self, x: Tensor) -> Tensor:
# T
…[truncated 4217 chars]done batch2
Fill optim implementations
python3 - <<'PY'
src=open('optim.py').read()
repls=[
(''' def step(self):
# TODO: SGD(+momentum, +coupled weight decay, +optional Nesterov) update.
raise NotImplementedError("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
upd = (g + self.momentum * self._v[i]) if self.nesterov else self._v[i]
p.data -= self.lr * upd'''),
(''' def step(self):
# TODO: Adagrad update (accumulate squared grads).
raise NotImplementedError("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] = self._s[i] + g * g
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)'''),
(''' def step(self):
# TODO: RMSprop update (EMA of squared grads).
raise NotImplementedError("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):
# TODO: Ad
…[truncated 3097 chars]optim done
Fill data.py
python3 - <<'PY'
src=open('data.py').read()
repls=[
('''def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
# TODO: np.load the file; return (X as float64, y as int64 reshaped to 1-D or None if absent).
raise NotImplementedError("load_npz")''',
'''def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
d = np.load(path)
X = np.asarray(d["X"], dtype=np.float64)
y = np.asarray(d["y"], dtype=np.int64).reshape(-1) if "y" in d else None
return X, y'''),
('''def n_classes(y: np.ndarray) -> int:
# TODO: return int(max(y)) + 1
raise NotImplementedError("n_classes")''',
'''def n_classes(y: np.ndarray) -> int:
return int(np.max(y)) + 1'''),
('''def epoch_batches(n: int, batch_size: int, rng: np.random.Generator):
# TODO: yield successive slices of a single rng.permutation(n) of size batch_size.
raise NotImplementedError("epoch_batches")''',
'''def epoch_batches(n: int, batch_size: int, rng: np.random.Generator):
perm = rng.permutation(n)
for start in range(0, n, batch_size):
yield perm[start:start + batch_size]'''),
('''def get_batch(X: np.ndarray, y: Optional[np.ndarray], idx: np.ndarray):
# TODO: gather X[idx] (float64) and y[idx] (int64, or None if y is None); return (Xb, yb, idx).
raise NotImplementedError("get_batch")''',
'''def get_batch(X: np.ndarray, y: Optional[np.ndarray], idx: np.ndarray):
idx = np.asarray(idx).astype(np.int64)
Xb = np.asarray(X[idx], dtype=np.float64)
…[truncated 1012 chars]data done
Fill ssl augmentation/gate functions
python3 - <<'PY'
src=open('ssl.py').read()
R=[]
def add(o,n): R.append((o,n))
add(''' # TODO: return X + rng.normal(0, sigma, X.shape) (X as float64)
raise NotImplementedError("weak_augment")''',
''' X = np.asarray(X, dtype=np.float64)
return X + rng.normal(0.0, sigma, X.shape)''')
add(''' # TODO: add jitter, then for each row zero round(mask_frac*dim) randomly-chosen coordinates.
raise NotImplementedError("strong_augment")''',
''' X = np.asarray(X, dtype=np.float64).copy()
X = X + rng.normal(0.0, sigma, X.shape)
n, dim = X.shape
k = int(round(mask_frac * dim))
if k > 0:
for i in range(n):
cols = rng.choice(dim, size=k, replace=False)
X[i, cols] = 0.0
return X''')
add(''' # TODO: copy X; for each row pick a random start and zero [start:start+length].
raise NotImplementedError("cutout")''',
''' X = np.asarray(X, dtype=np.float64).copy()
n, dim = X.shape
for i in range(n):
start = int(rng.integers(0, dim))
X[i, start:min(start + length, dim)] = 0.0
return X''')
add(''' # TODO: implement the convex combination described above; return (X_mix, y_mix, lam, perm).
raise NotImplementedError("mixup")''',
''' X = np.asarray(X, dtype=np.float64)
onehot = np.asarray(onehot, dtype=np.float64)
lam = float(rng.beta(alpha, alpha))
lam = max(lam, 1.0 - lam)
perm = rng.permutation(X.shape[0])
X_mix = lam * X + (1.0 - lam) * X[perm]
y_mix = lam
…[truncated 5427 chars]ssl batch1 done 17
Fill ssl loss ops
python3 - <<'PY'
src=open('ssl.py').read()
R=[]
def add(o,n): R.append((o,n))
add(''' # TODO: forward value via _stable_log_softmax_np; build out via _mk(val, (logits,)); set out._backward.
raise NotImplementedError("soft_cross_entropy")''',
''' logp = _stable_log_softmax_np(x)
rowloss = -(tgt * logp).sum(axis=1)
val = float((w * rowloss).sum() / n)
out = _mk(val, (logits,))
def _bw():
if logits.requires_grad:
sm = _stable_softmax_np(x)
tsum = tgt.sum(axis=1, keepdims=True)
grad = (w.reshape(-1, 1) / n) * (sm * tsum - tgt)
logits._accum(grad * out.grad)
out._backward = _bw
return out''')
add(''' # TODO: forward (sum of squared diffs, weighted, /N); backward through the softmax (see docstring).
raise NotImplementedError("mse_consistency")''',
''' s = _stable_softmax_np(x)
diff = s - tgt
val = float((w * (diff * diff).sum(axis=1)).sum() / n)
out = _mk(val, (student_logits,))
def _bw():
if student_logits.requires_grad:
v = (2.0 * w.reshape(-1, 1) / n) * diff
dot = (v * s).sum(axis=1, keepdims=True)
grad = s * (v - dot)
student_logits._accum(grad * out.grad)
out._backward = _bw
return out''')
add(''' # TODO: forward (include the teacher-entropy term so KL is reported); backward as above.
raise NotImplementedError("kl_divergence")''',
''' logp = _stable_log_softmax_np(x)
logt = np.lo
…[truncated 6121 chars]ssl batch2 done 14
Run all public checks quiet
cd /app && python3 run_checks.py --quiet 2>&1 | tail -20
---------------------------------------------------------------------- autograd_grads 77/77 | nn_forward 20/20 | optim 16/16 | fixmatch 31/31 | integration 2/2 | data 4/4 TOTAL 150/150 public checks passing All public checks pass. Now make sure model.npz clears the held-out test threshold, and remember the grader re-checks with DIFFERENT seeds/shapes.
/app/submission/train.py
def train(lab_path, unlab_path, out_path, steps=5000, seed=0):
dl = np.load(lab_path)
Xl = np.asarray(dl["X"], dtype=np.float64)
yl = np.asarray(dl["y"], dtype=np.int64).reshape(-1)
Xu = np.asarray(np.load(unlab_path)["X"], dtype=np.float64)
in_dim = Xl.shape[1]
C = int(yl.max()) + 1
# TODO: build the model + optimizer + EMA teacher (+ distribution aligner), then run the
# FixMatch training loop (supervised CE + masked consistency with confidence-gated, optionally
# distribution-aligned pseudo-labels), and save_checkpoint(out_path, ema.state_dict(), cfg).
raise NotImplementedError("train")def _predict(model, X, bs=512):
preds = []
for s in range(0, X.shape[0], bs):
logits = model(X[s:s + bs]).data
preds.append(logits.argmax(axis=1))
return np.concatenate(preds)
def train(lab_path, unlab_path, out_path, steps=4000, seed=0,
Xl=None, yl=None, Xu=None, val_X=None, val_y=None, verbose=True):
if Xl is None:
dl = np.load(lab_path)
Xl = np.asarray(dl["X"], dtype=np.float64)
yl = np.asarray(dl["y"], dtype=np.int64).reshape(-1)
Xu = np.asarray(np.load(unlab_path)["X"], dtype=np.float64)
in_dim = Xl.shape[1]
C = int(yl.max()) + 1
Nl, Nu = Xl.shape[0], Xu.shape[0]
# Hyperparameters (FixMatch recipe).
base_lr = 3e-3
wd = 5e-4
bs_l = 64
bs_u = 320
tau = 0.95
lambda_max = 1.0
warmup_unsup = 400 # supervised-only warmup steps
ramp_len = 800 # linear ramp of lambda_u
lr_warmup = 200
ema_decay = 0.999
weak_sigma = 0.1
strong_sigma = 0.5
strong_mask = 0.5
model = MLPClassifier(in_dim, C, d_hidden=D_HIDDEN, n_layer=N_LAYER, seed=seed)
opt = O.AdamW(model.params(), lr=base_lr, weight_decay=wd)
ema = S.EMATeacher(model, decay=ema_decay)
aligner = S.DistributionAligner(C, momentum=0.999)
rng = np.random.default_rng(seed + 13)
aug = np.random.default_rng(seed + 1234)
for step in range(steps):
lr = lr_at(step, steps, base_lr, lr_warmup)
opt.lr = lr
# supervised: wea
…[truncated 1894 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Tune: supervised vs fixmatch validation
cd /app/submission && cat > /tmp/tune.py <<'PY'
import sys, time
sys.path.insert(0,'/app/submission')
import numpy as np
import autograd as ag, optim as O, ssl as S
from nn import MLPClassifier
import train as TR
dl=np.load('/app/data/labeled.npz')
Xl=np.asarray(dl['X'],np.float64); yl=np.asarray(dl['y'],np.int64).reshape(-1)
Xu=np.asarray(np.load('/app/data/unlabeled.npz')['X'],np.float64)
C=int(yl.max())+1
# stratified split: hold out 6/class for val
rng=np.random.default_rng(0)
tr_idx=[]; va_idx=[]
for c in range(C):
idx=np.where(yl==c)[0]; rng.shuffle(idx)
va_idx+=list(idx[:6]); tr_idx+=list(idx[6:])
tr_idx=np.array(tr_idx); va_idx=np.array(va_idx)
Xtr,ytr=Xl[tr_idx],yl[tr_idx]; Xva,yva=Xl[va_idx],yl[va_idx]
print("train",Xtr.shape,"val",Xva.shape,"unlab",Xu.shape)
# supervised-only baseline
def sup_only(steps=800,seed=0):
m=MLPClassifier(Xtr.shape[1],C,d_hidden=256,n_layer=3,seed=seed)
opt=O.AdamW(m.params(),lr=3e-3,weight_decay=5e-4)
rng=np.random.default_rng(seed+5)
for st in range(steps):
bi=rng.integers(0,Xtr.shape[0],64)
loss=ag.cross_entropy(m(Xtr[bi]),ytr[bi])
opt.zero_grad(); loss.backward(); O.clip_grad_norm(m.params(),5.0); opt.step()
pred=m(Xva).data.argmax(1)
return float((pred==yva).mean())
t0=time.time()
sup_acc=np.mean([sup_only(seed=s) for s in range(3)])
print(f"SUPERVISED-ONLY val acc = {sup_acc:.3f} ({time.time()-t0:.1f}s)")
t0=time.time()
acc=TR.train(None,None,'/tmp/model_tune.npz',steps=3
…[truncated 195 chars]train (140, 80) val (60, 80) unlab (6000, 80) SUPERVISED-ONLY val acc = 0.478 (11.5s) step 0 lr=0.0000 loss=2.4954 mask_rate=0.000 val_ema_acc=0.100
Run tuning in background
nohup python3 -u /tmp/tune.py > /tmp/tune.log 2>&1 & echo "started pid $!"
started pid 1503
Check tuning log after 60s
sleep 60; cat /tmp/tune.log
<tool_use_error>Blocked: sleep 60 followed by: cat /tmp/tune.log. To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>
Check tuning log
cat /tmp/tune.log
train (140, 80) val (60, 80) unlab (6000, 80)
Wait for tuning to finish
until grep -q "FIXMATCH val acc" /tmp/tune.log; do sleep 5; done; echo "=== DONE ==="; cat /tmp/tune.log
Command running in background with ID: bgrncdyjg. Output is being written to: /tmp/claude-1000/-app/8c94ab8d-0283-411e-aa02-7c4322861da2/tasks/bgrncdyjg.output. You will be notified when it completes. To check interim output, use Read on that file path.
Read current tune log
sleep 1; cat /tmp/tune.log
train (140, 80) val (60, 80) unlab (6000, 80) SUPERVISED-ONLY val acc = 0.478 (13.1s) step 0 lr=0.0000 loss=2.4954 mask_rate=0.000 val_ema_acc=0.100
Check progress
sleep 1; tail -5 /tmp/tune.log
train (140, 80) val (60, 80) unlab (6000, 80) SUPERVISED-ONLY val acc = 0.478 (13.1s) step 0 lr=0.0000 loss=2.4954 mask_rate=0.000 val_ema_acc=0.100
Kill running tune
kill 1503 2>/dev/null; pkill -f tune.py 2>/dev/null; echo killed
Exit code 144
/app/submission/ssl.py
66 def strong_augment(X, rng, sigma: float = 0.5, mask_frac: float = 0.5) -> np.ndarray: 67 """Strong augmentation: LARGE jitter (std ``sigma``) + then zero a random fraction ``mask_frac`` 68 of the coordinates of EACH sample (a different random subset per row). Returns a NEW array.""" 69 X = np.asarray(X, dtype=np.float64).copy() 70 X = X + rng.normal(0.0, sigma, X.shape) 71 n, dim = X.shape 72 k = int(round(mask_frac * dim)) 73 if k > 0: 74 for i in range(n): 75 cols = rng.choice(dim, size=k, replace=False) 76 X[i, cols] = 0.0 77 return X 78 79
/app/submission/ssl.py
n, dim = X.shape
k = int(round(mask_frac * dim))
if k > 0:
for i in range(n):
cols = rng.choice(dim, size=k, replace=False)
X[i, cols] = 0.0
return X n, dim = X.shape
k = int(round(mask_frac * dim))
if k > 0:
# vectorized per-row selection of k distinct coordinates to zero
order = np.argsort(rng.random((n, dim)), axis=1)[:, :k]
rows = np.repeat(np.arange(n), k)
X[rows, order.reshape(-1)] = 0.0
return XThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
Verify integration and restart tuning
cd /app && python3 run_checks.py --only integration --quiet 2>&1 | tail -3 nohup python3 -u /tmp/tune.py > /tmp/tune.log 2>&1 & echo "started $!"
integration 2/2 TOTAL 2/2 public checks passing All public checks pass. Now make sure model.npz clears the held-out test threshold, and remember the grader re-checks with DIFFERENT seeds/shapes. started 1821
Wait for tuning result
until grep -q "FIXMATCH val acc" /tmp/tune.log; do sleep 5; done; echo "=== DONE ==="; cat /tmp/tune.log
No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 197/197 cases passed (all milestones pass: True) FINAL METRIC: held-out worst-class top-1 = 0.8062 >= threshold 0.72 REWARD = 1 (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_744809e58b2049c9. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_744809e58b2049c9 · verifier authoritative; classifier explanatory.