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This 2-class linear classifier training loop has bugs preventing convergence.
Fix it so that after 50 epochs the loss is below 0.5 and validation accuracy is above 0.80.
Model: nn.Linear(10, 2), dataset: fixed 2-class (160 train, 40 val samples).
Print losses as: LOSSES:[val1, val2, ...]
Print validation accuracy as: VAL_ACC:X.XX
"""
BUGGY_CODE = """
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
torch.manual_seed(0)
# Generate fixed training and validation datasets with learnable pattern
# y = 1 if first feature > 0, else 0
X_train = torch.randn(160, 10)
y_train = (X_train[:, 0] > 0).long()
X_val = torch.randn(40, 10)
y_val = (X_val[:, 0] > 0).long()
train_dataset = TensorDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
model = nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters(), lr=10.0) # BUG 1: lr too high
criterion = nn.CrossEntropyLoss()
losses = []
for epoch in range(50):
for x, y in train_loader:
optimizer.zero_grad()
logits = model(x)
loss = criterion(logits, y)
optimizer.step() # BUG 2: step before backward
loss.backward() # BUG 3: backward after step
losses.append(loss.item())
# Validation
model.eval()
with torch.no_grad():
val_logits = model(X_val)
val_preds = val_logits.argmax(dim=1)
val_acc = (val_preds == y_val).float().mean().item()
print('##METRICS_START##')
print('LOSSES:' + str(losses))
print('VAL_ACC:' + str(round(val_acc, 4)))
print('##METRICS_END##')
"""
GROUND_TRUTH_BUGS = [
"optimizer.step() called before loss.backward()",
"learning rate 10.0 should be ~0.001",
]
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