DeepLncLoc安装中遇到的问题
pip install -i https://pypi.douban.com/simple gensim
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple gensim
pip install -i https://pypi.douban.com/simple -U gensim
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版权声明：本文为CSDN博主「咕噜oo」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_44081582/article/details/112645029
gensim函数库中Word2Vec函数size，iter参数错误解决（ __init__() got an unexpected keyword argument ‘size‘）
说明经过包的更新，官方已经将size换成了更专业的vector_size
重点来了：已经将以前的iter迭代次数，换成了epochs，所以大家用的时候要将二者进行替换；
https://blog.csdn.net/lcy6239/article/details/115786432?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-1.no_search_link&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-1.no_search_link
AssertionError: Torch not compiled with CUDA enabled
Linux下PyTorch、CUDA Toolkit 及显卡驱动版本对应关系（附详细安装步骤）
https://blog.csdn.net/weixin_42069606/article/details/105198845?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param&depth_1utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param
CPU版结果：
(base) C:\Users\Thinkpad\DeepLncLoc-master>nvidia-smi
Wed Oct 13 15:50:16 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 471.41 Driver Version: 471.41 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Quadro P620 WDDM | 00000000:01:00.0 Off | N/A |
| N/A 0C P8 N/A / N/A | 75MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
(base) C:\Users\Thinkpad\DeepLncLoc-master>python train.py --k 3 --d 64 --s 64 --f 128 --metrics MaF --device "cpu"
Loading the raw data...
857it [00:00, 26404.07it/s]
Getting the mapping variables for label and label id......
100%|████████████████████████████████████████████████████████████████████████████████████████| 857/857 [00:00<?, ?it/s]
Getting the mapping variables for kmers and kmers id......
100%|██████████████████████████████████████████████████████████████████████████████| 857/857 [00:00<00:00, 1566.64it/s]
Start train_valid_test split......
train sample size: 685
valid sample size: 172
test sample size: 0
Loaded cache from cache/char2vec_k3_d64.pkl.
CV_1:
===========DataClass Describe===========
CLASS TRAIN VALID TEST
Cytoplasm 0.382 0.384 -1.000
Nucleus 0.380 0.378 -1.000
Exosome 0.032 0.035 -1.000
Ribosome 0.102 0.105 -1.000
Cytosol 0.104 0.099 -1.000
========================================
========== Epoch: 1 ==========
[Total Train] ACC= 0.533; MaF= 0.242; MiAUC= 0.795; MaAUC= 0.603;
[Total Valid] ACC= 0.523; MaF= 0.237; MiAUC= 0.776; MaAUC= 0.527;
=================================
Bingo!!! Get a better Model with val MaF: 0.237!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 2 ==========
[Total Train] ACC= 0.543; MaF= 0.247; MiAUC= 0.828; MaAUC= 0.724;
[Total Valid] ACC= 0.517; MaF= 0.236; MiAUC= 0.802; MaAUC= 0.634;
=================================
========== Epoch: 3 ==========
[Total Train] ACC= 0.568; MaF= 0.297; MiAUC= 0.847; MaAUC= 0.809;
[Total Valid] ACC= 0.541; MaF= 0.265; MiAUC= 0.819; MaAUC= 0.709;
=================================
Bingo!!! Get a better Model with val MaF: 0.265!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 4 ==========
[Total Train] ACC= 0.581; MaF= 0.314; MiAUC= 0.856; MaAUC= 0.828;
[Total Valid] ACC= 0.529; MaF= 0.261; MiAUC= 0.819; MaAUC= 0.727;
=================================
========== Epoch: 5 ==========
[Total Train] ACC= 0.581; MaF= 0.352; MiAUC= 0.860; MaAUC= 0.843;
[Total Valid] ACC= 0.483; MaF= 0.238; MiAUC= 0.813; MaAUC= 0.726;
=================================
========== Epoch: 6 ==========
[Total Train] ACC= 0.566; MaF= 0.329; MiAUC= 0.861; MaAUC= 0.860;
[Total Valid] ACC= 0.442; MaF= 0.211; MiAUC= 0.809; MaAUC= 0.731;
=================================
========== Epoch: 7 ==========
[Total Train] ACC= 0.623; MaF= 0.381; MiAUC= 0.876; MaAUC= 0.861;
[Total Valid] ACC= 0.535; MaF= 0.264; MiAUC= 0.822; MaAUC= 0.723;
=================================
========== Epoch: 8 ==========
[Total Train] ACC= 0.647; MaF= 0.496; MiAUC= 0.894; MaAUC= 0.878;
[Total Valid] ACC= 0.500; MaF= 0.286; MiAUC= 0.821; MaAUC= 0.737;
=================================
Bingo!!! Get a better Model with val MaF: 0.286!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 9 ==========
[Total Train] ACC= 0.685; MaF= 0.565; MiAUC= 0.907; MaAUC= 0.892;
[Total Valid] ACC= 0.523; MaF= 0.304; MiAUC= 0.827; MaAUC= 0.752;
=================================
Bingo!!! Get a better Model with val MaF: 0.304!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 10 ==========
[Total Train] ACC= 0.679; MaF= 0.496; MiAUC= 0.903; MaAUC= 0.902;
[Total Valid] ACC= 0.488; MaF= 0.286; MiAUC= 0.812; MaAUC= 0.717;
=================================
========== Epoch: 11 ==========
[Total Train] ACC= 0.631; MaF= 0.471; MiAUC= 0.897; MaAUC= 0.913;
[Total Valid] ACC= 0.453; MaF= 0.228; MiAUC= 0.807; MaAUC= 0.724;
=================================
========== Epoch: 12 ==========
[Total Train] ACC= 0.743; MaF= 0.630; MiAUC= 0.925; MaAUC= 0.927;
[Total Valid] ACC= 0.547; MaF= 0.302; MiAUC= 0.826; MaAUC= 0.736;
=================================
========== Epoch: 13 ==========
[Total Train] ACC= 0.752; MaF= 0.660; MiAUC= 0.933; MaAUC= 0.933;
[Total Valid] ACC= 0.541; MaF= 0.298; MiAUC= 0.824; MaAUC= 0.731;
=================================
========== Epoch: 14 ==========
[Total Train] ACC= 0.766; MaF= 0.673; MiAUC= 0.939; MaAUC= 0.942;
[Total Valid] ACC= 0.512; MaF= 0.299; MiAUC= 0.822; MaAUC= 0.736;
=================================
========== Epoch: 15 ==========
[Total Train] ACC= 0.801; MaF= 0.767; MiAUC= 0.951; MaAUC= 0.939;
[Total Valid] ACC= 0.558; MaF= 0.341; MiAUC= 0.821; MaAUC= 0.734;
=================================
Bingo!!! Get a better Model with val MaF: 0.341!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 16 ==========
[Total Train] ACC= 0.784; MaF= 0.704; MiAUC= 0.946; MaAUC= 0.947;
[Total Valid] ACC= 0.535; MaF= 0.297; MiAUC= 0.823; MaAUC= 0.727;
=================================
========== Epoch: 17 ==========
[Total Train] ACC= 0.780; MaF= 0.754; MiAUC= 0.950; MaAUC= 0.954;
[Total Valid] ACC= 0.483; MaF= 0.302; MiAUC= 0.811; MaAUC= 0.727;
=================================
========== Epoch: 18 ==========
[Total Train] ACC= 0.800; MaF= 0.770; MiAUC= 0.961; MaAUC= 0.956;
[Total Valid] ACC= 0.541; MaF= 0.334; MiAUC= 0.821; MaAUC= 0.732;
=================================
========== Epoch: 19 ==========
[Total Train] ACC= 0.816; MaF= 0.770; MiAUC= 0.960; MaAUC= 0.963;
[Total Valid] ACC= 0.523; MaF= 0.309; MiAUC= 0.820; MaAUC= 0.714;
=================================
========== Epoch: 20 ==========
[Total Train] ACC= 0.847; MaF= 0.809; MiAUC= 0.967; MaAUC= 0.971;
[Total Valid] ACC= 0.541; MaF= 0.337; MiAUC= 0.820; MaAUC= 0.722;
=================================
========== Epoch: 21 ==========
[Total Train] ACC= 0.853; MaF= 0.814; MiAUC= 0.971; MaAUC= 0.972;
[Total Valid] ACC= 0.558; MaF= 0.346; MiAUC= 0.821; MaAUC= 0.721;
=================================
Bingo!!! Get a better Model with val MaF: 0.346!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 22 ==========
[Total Train] ACC= 0.847; MaF= 0.802; MiAUC= 0.971; MaAUC= 0.974;
[Total Valid] ACC= 0.547; MaF= 0.322; MiAUC= 0.824; MaAUC= 0.722;
=================================
========== Epoch: 23 ==========
[Total Train] ACC= 0.858; MaF= 0.824; MiAUC= 0.975; MaAUC= 0.978;
[Total Valid] ACC= 0.523; MaF= 0.309; MiAUC= 0.822; MaAUC= 0.723;
=================================
========== Epoch: 24 ==========
[Total Train] ACC= 0.831; MaF= 0.810; MiAUC= 0.972; MaAUC= 0.981;
[Total Valid] ACC= 0.523; MaF= 0.329; MiAUC= 0.817; MaAUC= 0.722;
=================================
========== Epoch: 25 ==========
[Total Train] ACC= 0.877; MaF= 0.843; MiAUC= 0.980; MaAUC= 0.984;
[Total Valid] ACC= 0.570; MaF= 0.355; MiAUC= 0.822; MaAUC= 0.720;
=================================
Bingo!!! Get a better Model with val MaF: 0.355!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv1.pkl".
========== Epoch: 26 ==========
[Total Train] ACC= 0.873; MaF= 0.838; MiAUC= 0.981; MaAUC= 0.985;
[Total Valid] ACC= 0.535; MaF= 0.317; MiAUC= 0.822; MaAUC= 0.723;
=================================
========== Epoch: 27 ==========
[Total Train] ACC= 0.874; MaF= 0.848; MiAUC= 0.982; MaAUC= 0.988;
[Total Valid] ACC= 0.535; MaF= 0.318; MiAUC= 0.820; MaAUC= 0.716;
=================================
========== Epoch: 28 ==========
[Total Train] ACC= 0.877; MaF= 0.847; MiAUC= 0.984; MaAUC= 0.988;
[Total Valid] ACC= 0.558; MaF= 0.350; MiAUC= 0.815; MaAUC= 0.711;
=================================
========== Epoch: 29 ==========
[Total Train] ACC= 0.885; MaF= 0.841; MiAUC= 0.982; MaAUC= 0.989;
[Total Valid] ACC= 0.558; MaF= 0.335; MiAUC= 0.818; MaAUC= 0.695;
=================================
========== Epoch: 30 ==========
[Total Train] ACC= 0.904; MaF= 0.870; MiAUC= 0.988; MaAUC= 0.990;
[Total Valid] ACC= 0.564; MaF= 0.347; MiAUC= 0.817; MaAUC= 0.708;
=================================
========== Epoch: 31 ==========
[Total Train] ACC= 0.901; MaF= 0.874; MiAUC= 0.987; MaAUC= 0.990;
[Total Valid] ACC= 0.547; MaF= 0.337; MiAUC= 0.816; MaAUC= 0.717;
=================================
========== Epoch: 32 ==========
[Total Train] ACC= 0.904; MaF= 0.875; MiAUC= 0.987; MaAUC= 0.991;
[Total Valid] ACC= 0.541; MaF= 0.350; MiAUC= 0.809; MaAUC= 0.705;
=================================
========== Epoch: 33 ==========
[Total Train] ACC= 0.896; MaF= 0.878; MiAUC= 0.987; MaAUC= 0.991;
[Total Valid] ACC= 0.529; MaF= 0.319; MiAUC= 0.815; MaAUC= 0.701;
=================================
========== Epoch: 34 ==========
[Total Train] ACC= 0.905; MaF= 0.882; MiAUC= 0.988; MaAUC= 0.992;
[Total Valid] ACC= 0.535; MaF= 0.319; MiAUC= 0.820; MaAUC= 0.710;
=================================
========== Epoch: 35 ==========
[Total Train] ACC= 0.915; MaF= 0.897; MiAUC= 0.990; MaAUC= 0.993;
[Total Valid] ACC= 0.576; MaF= 0.351; MiAUC= 0.817; MaAUC= 0.698;
=================================
The val MaF has not improved for more than 10 steps in epoch 35, stop training.
25 epochs and 0.355 val Score 's model load finished.
============ Result ============
[Total Train] ACC= 0.877; MaF= 0.843; MiAUC= 0.980; MaAUC= 0.984;
[Total Valid] ACC= 0.570; MaF= 0.355; MiAUC= 0.822; MaAUC= 0.720;
----------------------------MACRO INDICTOR----------------------------
rate MCCi Pi Ri Fi
Cytoplasm 0.38 0.487 0.676 0.697 0.687
Cytosol 0.10 0.332 0.389 0.412 0.400
Exosome 0.03 nan 0.000 0.000 0.000
Nucleus 0.38 0.322 0.543 0.677 0.603
Ribosome 0.10 0.054 0.200 0.056 0.087
----------------------------------------------------------------------
================================
CV_2:
===========DataClass Describe===========
CLASS TRAIN VALID TEST
Cytoplasm 0.382 0.384 -1.000
Nucleus 0.380 0.378 -1.000
Exosome 0.034 0.029 -1.000
Ribosome 0.102 0.105 -1.000
Cytosol 0.102 0.105 -1.000
========================================
========== Epoch: 1 ==========
[Total Train] ACC= 0.429; MaF= 0.173; MiAUC= 0.780; MaAUC= 0.645;
[Total Valid] ACC= 0.390; MaF= 0.143; MiAUC= 0.760; MaAUC= 0.507;
=================================
Bingo!!! Get a better Model with val MaF: 0.143!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 2 ==========
[Total Train] ACC= 0.528; MaF= 0.290; MiAUC= 0.827; MaAUC= 0.754;
[Total Valid] ACC= 0.506; MaF= 0.232; MiAUC= 0.800; MaAUC= 0.605;
=================================
Bingo!!! Get a better Model with val MaF: 0.232!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 3 ==========
[Total Train] ACC= 0.536; MaF= 0.307; MiAUC= 0.837; MaAUC= 0.799;
[Total Valid] ACC= 0.465; MaF= 0.213; MiAUC= 0.800; MaAUC= 0.646;
=================================
========== Epoch: 4 ==========
[Total Train] ACC= 0.561; MaF= 0.294; MiAUC= 0.852; MaAUC= 0.814;
[Total Valid] ACC= 0.529; MaF= 0.241; MiAUC= 0.819; MaAUC= 0.716;
=================================
Bingo!!! Get a better Model with val MaF: 0.241!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 5 ==========
[Total Train] ACC= 0.607; MaF= 0.357; MiAUC= 0.860; MaAUC= 0.834;
[Total Valid] ACC= 0.517; MaF= 0.240; MiAUC= 0.819; MaAUC= 0.710;
=================================
========== Epoch: 6 ==========
[Total Train] ACC= 0.584; MaF= 0.338; MiAUC= 0.861; MaAUC= 0.850;
[Total Valid] ACC= 0.459; MaF= 0.206; MiAUC= 0.812; MaAUC= 0.703;
=================================
========== Epoch: 7 ==========
[Total Train] ACC= 0.623; MaF= 0.403; MiAUC= 0.876; MaAUC= 0.856;
[Total Valid] ACC= 0.529; MaF= 0.259; MiAUC= 0.824; MaAUC= 0.703;
=================================
Bingo!!! Get a better Model with val MaF: 0.259!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 8 ==========
[Total Train] ACC= 0.635; MaF= 0.456; MiAUC= 0.891; MaAUC= 0.873;
[Total Valid] ACC= 0.488; MaF= 0.239; MiAUC= 0.828; MaAUC= 0.764;
=================================
========== Epoch: 9 ==========
[Total Train] ACC= 0.682; MaF= 0.461; MiAUC= 0.899; MaAUC= 0.890;
[Total Valid] ACC= 0.552; MaF= 0.272; MiAUC= 0.832; MaAUC= 0.745;
=================================
Bingo!!! Get a better Model with val MaF: 0.272!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 10 ==========
[Total Train] ACC= 0.676; MaF= 0.488; MiAUC= 0.902; MaAUC= 0.900;
[Total Valid] ACC= 0.500; MaF= 0.229; MiAUC= 0.829; MaAUC= 0.765;
=================================
========== Epoch: 11 ==========
[Total Train] ACC= 0.638; MaF= 0.466; MiAUC= 0.899; MaAUC= 0.901;
[Total Valid] ACC= 0.541; MaF= 0.259; MiAUC= 0.830; MaAUC= 0.761;
=================================
========== Epoch: 12 ==========
[Total Train] ACC= 0.736; MaF= 0.639; MiAUC= 0.924; MaAUC= 0.912;
[Total Valid] ACC= 0.541; MaF= 0.315; MiAUC= 0.838; MaAUC= 0.784;
=================================
Bingo!!! Get a better Model with val MaF: 0.315!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 13 ==========
[Total Train] ACC= 0.726; MaF= 0.623; MiAUC= 0.929; MaAUC= 0.918;
[Total Valid] ACC= 0.558; MaF= 0.335; MiAUC= 0.838; MaAUC= 0.773;
=================================
Bingo!!! Get a better Model with val MaF: 0.335!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 14 ==========
[Total Train] ACC= 0.730; MaF= 0.577; MiAUC= 0.925; MaAUC= 0.935;
[Total Valid] ACC= 0.506; MaF= 0.251; MiAUC= 0.827; MaAUC= 0.750;
=================================
========== Epoch: 15 ==========
[Total Train] ACC= 0.739; MaF= 0.578; MiAUC= 0.932; MaAUC= 0.935;
[Total Valid] ACC= 0.547; MaF= 0.316; MiAUC= 0.828; MaAUC= 0.731;
=================================
========== Epoch: 16 ==========
[Total Train] ACC= 0.746; MaF= 0.698; MiAUC= 0.942; MaAUC= 0.946;
[Total Valid] ACC= 0.465; MaF= 0.275; MiAUC= 0.827; MaAUC= 0.765;
=================================
========== Epoch: 17 ==========
[Total Train] ACC= 0.810; MaF= 0.737; MiAUC= 0.954; MaAUC= 0.952;
[Total Valid] ACC= 0.587; MaF= 0.360; MiAUC= 0.841; MaAUC= 0.758;
=================================
Bingo!!! Get a better Model with val MaF: 0.360!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 18 ==========
[Total Train] ACC= 0.815; MaF= 0.759; MiAUC= 0.956; MaAUC= 0.960;
[Total Valid] ACC= 0.512; MaF= 0.288; MiAUC= 0.835; MaAUC= 0.757;
=================================
========== Epoch: 19 ==========
[Total Train] ACC= 0.832; MaF= 0.805; MiAUC= 0.962; MaAUC= 0.959;
[Total Valid] ACC= 0.599; MaF= 0.388; MiAUC= 0.841; MaAUC= 0.777;
=================================
Bingo!!! Get a better Model with val MaF: 0.388!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv2.pkl".
========== Epoch: 20 ==========
[Total Train] ACC= 0.839; MaF= 0.798; MiAUC= 0.966; MaAUC= 0.967;
[Total Valid] ACC= 0.541; MaF= 0.303; MiAUC= 0.837; MaAUC= 0.760;
=================================
========== Epoch: 21 ==========
[Total Train] ACC= 0.842; MaF= 0.778; MiAUC= 0.967; MaAUC= 0.969;
[Total Valid] ACC= 0.570; MaF= 0.338; MiAUC= 0.835; MaAUC= 0.724;
=================================
========== Epoch: 22 ==========
[Total Train] ACC= 0.851; MaF= 0.813; MiAUC= 0.972; MaAUC= 0.973;
[Total Valid] ACC= 0.576; MaF= 0.360; MiAUC= 0.837; MaAUC= 0.746;
=================================
========== Epoch: 23 ==========
[Total Train] ACC= 0.787; MaF= 0.777; MiAUC= 0.964; MaAUC= 0.974;
[Total Valid] ACC= 0.465; MaF= 0.295; MiAUC= 0.820; MaAUC= 0.741;
=================================
========== Epoch: 24 ==========
[Total Train] ACC= 0.839; MaF= 0.818; MiAUC= 0.974; MaAUC= 0.974;
[Total Valid] ACC= 0.581; MaF= 0.354; MiAUC= 0.834; MaAUC= 0.754;
=================================
========== Epoch: 25 ==========
[Total Train] ACC= 0.876; MaF= 0.835; MiAUC= 0.978; MaAUC= 0.982;
[Total Valid] ACC= 0.547; MaF= 0.330; MiAUC= 0.838; MaAUC= 0.750;
=================================
========== Epoch: 26 ==========
[Total Train] ACC= 0.863; MaF= 0.809; MiAUC= 0.974; MaAUC= 0.984;
[Total Valid] ACC= 0.500; MaF= 0.282; MiAUC= 0.829; MaAUC= 0.722;
=================================
========== Epoch: 27 ==========
[Total Train] ACC= 0.888; MaF= 0.862; MiAUC= 0.981; MaAUC= 0.986;
[Total Valid] ACC= 0.541; MaF= 0.331; MiAUC= 0.835; MaAUC= 0.743;
=================================
========== Epoch: 28 ==========
[Total Train] ACC= 0.879; MaF= 0.858; MiAUC= 0.983; MaAUC= 0.986;
[Total Valid] ACC= 0.547; MaF= 0.375; MiAUC= 0.835; MaAUC= 0.743;
=================================
========== Epoch: 29 ==========
[Total Train] ACC= 0.891; MaF= 0.882; MiAUC= 0.985; MaAUC= 0.987;
[Total Valid] ACC= 0.535; MaF= 0.348; MiAUC= 0.834; MaAUC= 0.766;
=================================
The val MaF has not improved for more than 10 steps in epoch 29, stop training.
19 epochs and 0.388 val Score 's model load finished.
============ Result ============
[Total Train] ACC= 0.832; MaF= 0.805; MiAUC= 0.962; MaAUC= 0.959;
[Total Valid] ACC= 0.599; MaF= 0.388; MiAUC= 0.841; MaAUC= 0.777;
----------------------------MACRO INDICTOR----------------------------
rate MCCi Pi Ri Fi
Cytoplasm 0.38 0.518 0.611 0.879 0.720
Cytosol 0.10 0.405 0.538 0.389 0.452
Exosome 0.03 -0.013 0.000 0.000 0.000
Nucleus 0.38 0.335 0.600 0.554 0.576
Ribosome 0.10 0.245 0.667 0.111 0.190
----------------------------------------------------------------------
================================
CV_3:
===========DataClass Describe===========
CLASS TRAIN VALID TEST
Cytoplasm 0.382 0.384 -1.000
Nucleus 0.380 0.378 -1.000
Exosome 0.034 0.029 -1.000
Ribosome 0.104 0.099 -1.000
Cytosol 0.102 0.105 -1.000
========================================
========== Epoch: 1 ==========
[Total Train] ACC= 0.494; MaF= 0.229; MiAUC= 0.793; MaAUC= 0.655;
[Total Valid] ACC= 0.491; MaF= 0.212; MiAUC= 0.785; MaAUC= 0.601;
=================================
Bingo!!! Get a better Model with val MaF: 0.212!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv3.pkl".
========== Epoch: 2 ==========
[Total Train] ACC= 0.561; MaF= 0.275; MiAUC= 0.830; MaAUC= 0.753;
[Total Valid] ACC= 0.520; MaF= 0.270; MiAUC= 0.800; MaAUC= 0.637;
=================================
Bingo!!! Get a better Model with val MaF: 0.270!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv3.pkl".
========== Epoch: 3 ==========
[Total Train] ACC= 0.545; MaF= 0.284; MiAUC= 0.841; MaAUC= 0.794;
[Total Valid] ACC= 0.468; MaF= 0.240; MiAUC= 0.800; MaAUC= 0.664;
=================================
========== Epoch: 4 ==========
[Total Train] ACC= 0.566; MaF= 0.309; MiAUC= 0.854; MaAUC= 0.827;
[Total Valid] ACC= 0.497; MaF= 0.258; MiAUC= 0.803; MaAUC= 0.694;
=================================
========== Epoch: 5 ==========
[Total Train] ACC= 0.625; MaF= 0.384; MiAUC= 0.871; MaAUC= 0.841;
[Total Valid] ACC= 0.480; MaF= 0.252; MiAUC= 0.801; MaAUC= 0.693;
=================================
========== Epoch: 6 ==========
[Total Train] ACC= 0.636; MaF= 0.391; MiAUC= 0.878; MaAUC= 0.861;
[Total Valid] ACC= 0.497; MaF= 0.257; MiAUC= 0.809; MaAUC= 0.707;
=================================
========== Epoch: 7 ==========
[Total Train] ACC= 0.641; MaF= 0.463; MiAUC= 0.892; MaAUC= 0.875;
[Total Valid] ACC= 0.509; MaF= 0.280; MiAUC= 0.807; MaAUC= 0.718;
=================================
Bingo!!! Get a better Model with val MaF: 0.280!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv3.pkl".
========== Epoch: 8 ==========
[Total Train] ACC= 0.603; MaF= 0.404; MiAUC= 0.883; MaAUC= 0.883;
[Total Valid] ACC= 0.450; MaF= 0.230; MiAUC= 0.796; MaAUC= 0.696;
=================================
========== Epoch: 9 ==========
[Total Train] ACC= 0.713; MaF= 0.578; MiAUC= 0.912; MaAUC= 0.896;
[Total Valid] ACC= 0.503; MaF= 0.303; MiAUC= 0.803; MaAUC= 0.719;
=================================
Bingo!!! Get a better Model with val MaF: 0.303!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv3.pkl".
========== Epoch: 10 ==========
[Total Train] ACC= 0.676; MaF= 0.549; MiAUC= 0.911; MaAUC= 0.904;
[Total Valid] ACC= 0.532; MaF= 0.308; MiAUC= 0.801; MaAUC= 0.723;
=================================
Bingo!!! Get a better Model with val MaF: 0.308!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv3.pkl".
========== Epoch: 11 ==========
[Total Train] ACC= 0.694; MaF= 0.549; MiAUC= 0.917; MaAUC= 0.916;
[Total Valid] ACC= 0.485; MaF= 0.271; MiAUC= 0.806; MaAUC= 0.709;
=================================
========== Epoch: 12 ==========
[Total Train] ACC= 0.711; MaF= 0.560; MiAUC= 0.921; MaAUC= 0.926;
[Total Valid] ACC= 0.474; MaF= 0.246; MiAUC= 0.807; MaAUC= 0.709;
=================================
========== Epoch: 13 ==========
[Total Train] ACC= 0.720; MaF= 0.531; MiAUC= 0.922; MaAUC= 0.932;
[Total Valid] ACC= 0.444; MaF= 0.250; MiAUC= 0.800; MaAUC= 0.688;
=================================
========== Epoch: 14 ==========
[Total Train] ACC= 0.739; MaF= 0.654; MiAUC= 0.936; MaAUC= 0.939;
[Total Valid] ACC= 0.509; MaF= 0.261; MiAUC= 0.809; MaAUC= 0.712;
=================================
========== Epoch: 15 ==========
[Total Train] ACC= 0.746; MaF= 0.722; MiAUC= 0.942; MaAUC= 0.946;
[Total Valid] ACC= 0.450; MaF= 0.263; MiAUC= 0.791; MaAUC= 0.700;
=================================
========== Epoch: 16 ==========
[Total Train] ACC= 0.787; MaF= 0.686; MiAUC= 0.946; MaAUC= 0.952;
[Total Valid] ACC= 0.503; MaF= 0.299; MiAUC= 0.800; MaAUC= 0.712;
=================================
========== Epoch: 17 ==========
[Total Train] ACC= 0.813; MaF= 0.770; MiAUC= 0.958; MaAUC= 0.958;
[Total Valid] ACC= 0.485; MaF= 0.279; MiAUC= 0.798; MaAUC= 0.698;
=================================
========== Epoch: 18 ==========
[Total Train] ACC= 0.843; MaF= 0.821; MiAUC= 0.966; MaAUC= 0.961;
[Total Valid] ACC= 0.474; MaF= 0.292; MiAUC= 0.798; MaAUC= 0.707;
=================================
========== Epoch: 19 ==========
[Total Train] ACC= 0.834; MaF= 0.809; MiAUC= 0.966; MaAUC= 0.962;
[Total Valid] ACC= 0.491; MaF= 0.305; MiAUC= 0.787; MaAUC= 0.699;
=================================
========== Epoch: 20 ==========
[Total Train] ACC= 0.819; MaF= 0.772; MiAUC= 0.962; MaAUC= 0.968;
[Total Valid] ACC= 0.503; MaF= 0.292; MiAUC= 0.799; MaAUC= 0.705;
=================================
The val MaF has not improved for more than 10 steps in epoch 20, stop training.
10 epochs and 0.308 val Score 's model load finished.
============ Result ============
[Total Train] ACC= 0.676; MaF= 0.549; MiAUC= 0.911; MaAUC= 0.904;
[Total Valid] ACC= 0.532; MaF= 0.308; MiAUC= 0.801; MaAUC= 0.723;
----------------------------MACRO INDICTOR----------------------------
rate MCCi Pi Ri Fi
Cytoplasm 0.39 0.413 0.570 0.803 0.667
Cytosol 0.11 0.267 0.318 0.389 0.350
Exosome 0.03 -0.019 0.000 0.000 0.000
Nucleus 0.38 0.271 0.574 0.477 0.521
Ribosome 0.10 nan 0.000 0.000 0.000
----------------------------------------------------------------------
================================
CV_4:
===========DataClass Describe===========
CLASS TRAIN VALID TEST
Cytoplasm 0.384 0.378 -1.000
Nucleus 0.380 0.378 -1.000
Exosome 0.032 0.035 -1.000
Ribosome 0.104 0.099 -1.000
Cytosol 0.102 0.105 -1.000
========================================
========== Epoch: 1 ==========
[Total Train] ACC= 0.456; MaF= 0.198; MiAUC= 0.779; MaAUC= 0.589;
[Total Valid] ACC= 0.433; MaF= 0.190; MiAUC= 0.767; MaAUC= 0.537;
=================================
Bingo!!! Get a better Model with val MaF: 0.190!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv4.pkl".
========== Epoch: 2 ==========
[Total Train] ACC= 0.475; MaF= 0.218; MiAUC= 0.811; MaAUC= 0.747;
[Total Valid] ACC= 0.444; MaF= 0.195; MiAUC= 0.781; MaAUC= 0.618;
=================================
Bingo!!! Get a better Model with val MaF: 0.195!!!
Model saved in "out/CNN_s64_f128_k3_d64_cv4.pkl".
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