代码拉取完成,页面将自动刷新
网络源码路径:https://github.com/gaussic/text-classification-cnn-rnn
论文地址:https://arxiv.org/abs/1408.5882
使用卷积神经网络进行中文文本分类
名称:全量数据集THUCTC
路径:训练子集 10.136.165.4服务器:/turingDataset/CarPeting_Textcnn/cnews
requirements.txt随网络模型归档至gitlab仓。
tensorflow==1.15.0
numpy
scikit-learn
scipy
(是否需要手工迁移)
执行:
全量精度:bash run_npu_1p_acc.sh
CI性能: bash run_npu_1p_perf.sh
ckpt
文件和graph
文件存放路径: 10.136.165.4服务器:/turingDataset/results/CarPeting_TF_Textcnn/ckpt_npu路径
精度性能示例:(部分打屏结果如下)
Iter: 100, Train Loss: 0.87, Train Acc: 75.00%, Val Loss: 1.1, Val Acc: 68.00%, Time: 0:00:36 *
Iter: 200, Train Loss: 0.33, Train Acc: 87.50%, Val Loss: 0.7, Val Acc: 77.28%, Time: 0:00:38 *
Iter: 300, Train Loss: 0.12, Train Acc: 96.88%, Val Loss: 0.48, Val Acc: 85.34%, Time: 0:00:41 *
Iter: 400, Train Loss: 0.34, Train Acc: 90.62%, Val Loss: 0.41, Val Acc: 88.04%, Time: 0:00:43 *
Iter: 500, Train Loss: 0.2, Train Acc: 92.19%, Val Loss: 0.35, Val Acc: 90.38%, Time: 0:00:45 *
Iter: 600, Train Loss: 0.35, Train Acc: 95.31%, Val Loss: 0.31, Val Acc: 90.90%, Time: 0:00:47 *
Iter: 700, Train Loss: 0.29, Train Acc: 92.19%, Val Loss: 0.32, Val Acc: 90.64%, Time: 0:00:49
2021-03-18 19:51:57.214150: W tf_adapter/util/infershape_util.cc:313] The InferenceContext of node _SOURCE is null.
2021-03-18 19:51:57.214226: W tf_adapter/util/infershape_util.cc:313] The InferenceContext of node _SINK is null.
ckpt
文件,loss+perf_npu.txt存放路径: 10.136.165.4服务器:/turingDataset/results/CarPeting_TF_Textcnn/ckpt_gpu路径
精度性能示例:(部分打屏结果如下)
Iter: 0, Train Loss: 2.3, Train Acc: 10.94%, Val Loss: 2.3, Val Acc: 8.92%, Time: 0:00:01 *
Iter: 100, Train Loss: 0.88, Train Acc: 73.44%, Val Loss: 1.2, Val Acc: 68.46%, Time: 0:00:04 *
Iter: 200, Train Loss: 0.38, Train Acc: 92.19%, Val Loss: 0.75, Val Acc: 77.32%, Time: 0:00:07 *
Iter: 300, Train Loss: 0.22, Train Acc: 92.19%, Val Loss: 0.46, Val Acc: 87.08%, Time: 0:00:09 *
Iter: 400, Train Loss: 0.24, Train Acc: 90.62%, Val Loss: 0.4, Val Acc: 88.62%, Time: 0:00:12 *
Iter: 500, Train Loss: 0.16, Train Acc: 96.88%, Val Loss: 0.36, Val Acc: 90.38%, Time: 0:00:15 *
Iter: 600, Train Loss: 0.084, Train Acc: 96.88%, Val Loss: 0.35, Val Acc: 91.36%, Time: 0:00:17 *
Iter: 700, Train Loss: 0.21, Train Acc: 93.75%, Val Loss: 0.26, Val Acc: 92.58%, Time: 0:00:20 *
step | GPU loss | NPU loss |
---|---|---|
NPU/GPU=2
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