train.py 文件源码

def train_stats(m, trainloader, param_list = None):
    stats = {}
    params = filtered_params(m, param_list)    
    counts = 0,0
    for counts in enumerate(accumulate((reduce(lambda d1,d2: d1*d2, p[1].size()) for p in params)) ):
        pass
    stats['variables_optimized'] = counts[0] + 1
    stats['params_optimized'] = counts[1]

    before = time.time()
    losses = train(m, trainloader, param_list=param_list)
    stats['training_time'] = time.time() - before

    stats['training_loss'] = losses[-1] if len(losses) else float('nan')
    stats['training_losses'] = losses

    return stats