Webbprune_var (var_name, pruned_dims, pruned_ratio, apply="impretive") ¶. 按指定的比例inplace地对原模型的单个卷积层及其相关卷积层进行剪裁。 参数: var_name(str) - 卷积层 weight 变量的名称。 可以通过 parameters API 获得所有卷积层 weight 变量的名称。; pruned_dims(list) - 待废弃选项。 卷积层 weight 变量中 Filter 数量所在 ... WebbPruning is a horticultural, arboricultural, and silvicultural practice involving the selective removal of certain parts of a plant, such as branches, buds, or roots . The practice entails …
Data Mining - Pruning (a decision tree, decision rules)
WebbThe layer-wisely pruned ratio does not keep the same as the whole model pruned ratio since empirically features in shallow layers are more redundant than the deep. For example, We keep layer ... Webb8 juli 2024 · Structured model pruning is a promising approach to alleviate these requirements. Using the VGG-16 model as an example, we measure the accuracy … ondc startups
L1NormFilterPruner — PaddleSlim 文档
Webbsensitivity¶ paddleslim.prune.sensitivity (program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) ¶ 源代码. 计算网络中每个卷积层的敏感度。每个卷积层的敏感度信息统计方法为:依次剪掉当前卷积层不同比例的输出通道数,在测试集上计算剪裁后的精度损失。 Webb28 juni 2024 · pruned_ratio = pruned/total print('Pre-processing Successful!') # simple test model after Pre-processing prune (simple set BN scales to zeros) def test(model): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} if args.dataset == 'cifar10': test_loader = torch.utils.data.DataLoader( Webbbetween neighboring channels to inference performance.Besides, the tradeoff between accuracy and pruned ratio is a noteworthy problem. In order to achieve a better balance between the pruned ratio and accuracy, the work in [15] proposed a efficient approach to channel pruning, based on the genetic algorithm and sparse learning, and another is a viola a string instrument