def get_lr()

in train.py [0:0]

• 11 lines of code
• 6 McCabe index (conditional complexity)

def get_lr(it, max_lr, max_steps):
    min_lr = max_lr * 0.1
    warmup_steps = max_steps * 0.03
    # 1) linear warmup for warmup_iters steps
    if it < warmup_steps:
        return max_lr * (it+1) / warmup_steps
    # 2) if it > lr_decay_iters, return min learning rate
    if it > max_steps:
        return min_lr
    # 3) in between, use cosine decay down to min learning rate
    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff starts at 1 and goes to 0
    return min_lr + coeff * (max_lr - min_lr)