The 10th scheme of propeller RNA structure prediction competition
Team name: white crane with bright opposite wings and black bear flying Twin Oars
Members: Liu Jianjian, Shi Jingwei, Xiang Jianbiao, Yang Jingli
score: 3.722 rmsd_avg:0.269 rmsd_std:0.067
Introduction to competition questions
"RNA base unpaired probability" measures whether the RNA sequence can form a stable base pair at each point. It is an important attribute of RNA structure and can be used in mRNA vaccine sequence design, drug research and development and other fields. For example, the sequence of mRNA vaccine is usually unstable, and the point with high probability of RNA base unpaired is the position that is easy to be degraded; For another example, the point with high probability of RNA base unpaired is usually easier to interact with other RNA sequences to form RNA RNA binding. This feature is also widely used in disease diagnosis and RNA drug research and development.
This competition provides 5000 training data. Please develop a model to predict the probability of RNA base unpaired based on the training data and the propeller platform.
(Tips: in terms of machine learning framework, only the propeller deep learning framework is allowed)
Competition address: https://aistudio.baidu.com/aistudio/competition/detail/61
explain
It is the 10th place in the B list, which does not represent the final result of the game!
Project address
GitHub: https://github.com/jhcgt4869/Unpaired_Probability_Prediction_The_first_ten
AIStudio: https://aistudio.baidu.com/aistudio/projectdetail/1930064
Competition ideas
First understand the baseline and submit it with the baseline. It is found that the effect is good and seek further improvement.
It is found that the fitting process has been stopped in advance.
Then seek other optimization modes, and finally find a section point of achievement (a better place)
Through the fusion of several recent model s around this value, the final results are obtained and submitted
Competition data set
# Check the path where the dataset is located !tree /home/aistudio/data
/home/aistudio/data
├── data67691
│ └── test_log.txt
└── data82504
└── B_board_112_seqs.txt
2 directories, 2 files
Baseline system code structure
This baseline is based on paddlepaddle2 Version 0.
# Check source code file structure # !cd work; mkdir model !tree /home/aistudio/work -L 2
/home/aistudio/work ├── data │ ├── dev.txt │ ├── test_nolabel.txt │ └── train.txt ├── model │ ├── model_dev=0.0673 │ ├── model_dev=0.0674 │ ├── model_dev=0.0678 │ ├── model_dev=0.0749 │ ├── model_dev=0.0752 │ ├── model_dev=0.0756 │ ├── model_dev=0.0762 │ └── placeholder.txt ├── model-0 │ └── model_dev=0.0772 ├── README.txt ├── src │ ├── const.py │ ├── dataset.py │ ├── __init__.py │ ├── main.py │ ├── network.py │ ├── __pycache__ │ ├── utils.py │ └── vocabulary.py ├── test_log.txt └── train_log.txt 13 directories, 14 files
Training script
python src/main.py train --model-path-base [model_directory_name]
This code will train a model and save it to the specified location. The training log is saved to the file train by default_ log. txt
Note: due to the instability of initialization, multiple training may be required. The more reasonable verification set (dev) mean square error loss (MSE loss) is 0.05-0.08
Sample
python src/main.py train --model-path-base model
You will see a training log similar to the following
epoch 1 batch 40 processed 640 batch-loss 0.1984 epoch-elapsed 0h00m10s total-elapsed 0h00m11s epoch 1 batch 41 processed 656 batch-loss 0.2119 epoch-elapsed 0h00m10s total-elapsed 0h00m11s epoch 1 batch 42 processed 672 batch-loss 0.2205 epoch-elapsed 0h00m11s total-elapsed 0h00m11s epoch 1 batch 43 processed 688 batch-loss 0.2128 epoch-elapsed 0h00m11s total-elapsed 0h00m11s # Dev Average Loss: 0.212 (MSE) -> 0.461 (RMSD)
matters needing attention
Please use the configuration environment of GPU version to run this module
# To train: # python src/main.py train --model-path-base [model_directory_name] !cd work; python src/main.py train --model-path-base model
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
2021-05-12 13:40:41.025758
# python3 src/main.py train --model-path-base model
# Training set contains 4750 Sequences.
# Validation set contains 250 Sequences.
# Paddle: Using device: CUDAPlace(0)
# Initializing model...
initializing vacabularies... done.
Sequence(6): ['<START>', '<STOP>', 'A', 'C', 'G', 'U']
Brackets(5): ['<START>', '<STOP>', '(', ')', '.']
# Checking validation 10 times an epoch (every 475 batches)
W0512 13:40:42.600050 511 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0512 13:40:42.604969 511 device_context.cc:372] device: 0, cuDNN Version: 7.6.
# Epoch 1 starting.
epoch 1 batch 1 processed 1 batch-loss 0.2189 epoch-elapsed 0h00m00s total-elapsed 0h00m04s
epoch 1 batch 2 processed 2 batch-loss 0.1878 epoch-elapsed 0h00m00s total-elapsed 0h00m04s
epoch 1 batch 3 processed 3 batch-loss 0.2259 epoch-elapsed 0h00m00s total-elapsed 0h00m04s
epoch 1 batch 4 processed 4 batch-loss 0.2033 epoch-elapsed 0h00m00s total-elapsed 0h00m04s
epoch 1 batch 5 processed 5 batch-loss 0.2201 epoch-elapsed 0h00m01s total-elapsed 0h00m05s
epoch 1 batch 6 processed 6 batch-loss 0.2206 epoch-elapsed 0h00m01s total-elapsed 0h00m05s
epoch 1 batch 7 processed 7 batch-loss 0.2026 epoch-elapsed 0h00m01s total-elapsed 0h00m05s
epoch 1 batch 8 processed 8 batch-loss 0.2187 epoch-elapsed 0h00m01s total-elapsed 0h00m05s
epoch 1 batch 9 processed 9 batch-loss 0.2143 epoch-elapsed 0h00m01s total-elapsed 0h00m05s
epoch 1 batch 10 processed 10 batch-loss 0.2183 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 11 processed 11 batch-loss 0.2130 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 12 processed 12 batch-loss 0.2179 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 13 processed 13 batch-loss 0.2330 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 14 processed 14 batch-loss 0.2232 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 15 processed 15 batch-loss 0.1971 epoch-elapsed 0h00m02s total-elapsed 0h00m06s
epoch 1 batch 16 processed 16 batch-loss 0.1919 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 17 processed 17 batch-loss 0.1845 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 18 processed 18 batch-loss 0.1756 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 19 processed 19 batch-loss 0.2241 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 20 processed 20 batch-loss 0.2204 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 21 processed 21 batch-loss 0.2264 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 22 processed 22 batch-loss 0.1991 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 23 processed 23 batch-loss 0.2336 epoch-elapsed 0h00m03s total-elapsed 0h00m07s
epoch 1 batch 24 processed 24 batch-loss 0.2168 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 25 processed 25 batch-loss 0.2207 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 26 processed 26 batch-loss 0.2035 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 27 processed 27 batch-loss 0.2133 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 28 processed 28 batch-loss 0.2210 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 29 processed 29 batch-loss 0.2162 epoch-elapsed 0h00m04s total-elapsed 0h00m08s
epoch 1 batch 30 processed 30 batch-loss 0.2223 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 31 processed 31 batch-loss 0.2235 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 32 processed 32 batch-loss 0.2076 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 33 processed 33 batch-loss 0.2024 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 34 processed 34 batch-loss 0.2153 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 35 processed 35 batch-loss 0.2099 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 36 processed 36 batch-loss 0.2023 epoch-elapsed 0h00m05s total-elapsed 0h00m09s
epoch 1 batch 37 processed 37 batch-loss 0.2232 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 38 processed 38 batch-loss 0.2079 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 39 processed 39 batch-loss 0.2055 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 40 processed 40 batch-loss 0.2025 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 41 processed 41 batch-loss 0.2248 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 42 processed 42 batch-loss 0.2072 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 43 processed 43 batch-loss 0.2107 epoch-elapsed 0h00m06s total-elapsed 0h00m10s
epoch 1 batch 44 processed 44 batch-loss 0.2194 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 45 processed 45 batch-loss 0.1972 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 46 processed 46 batch-loss 0.1819 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 47 processed 47 batch-loss 0.1911 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 48 processed 48 batch-loss 0.2107 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 49 processed 49 batch-loss 0.1512 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 50 processed 50 batch-loss 0.1901 epoch-elapsed 0h00m07s total-elapsed 0h00m11s
epoch 1 batch 51 processed 51 batch-loss 0.1936 epoch-elapsed 0h00m08s total-elapsed 0h00m11s
epoch 1 batch 52 processed 52 batch-loss 0.1962 epoch-elapsed 0h00m08s total-elapsed 0h00m12s
epoch 1 batch 53 processed 53 batch-loss 0.1924 epoch-elapsed 0h00m08s total-elapsed 0h00m12s
epoch 1 batch 54 processed 54 batch-loss 0.2275 epoch-elapsed 0h00m08s total-elapsed 0h00m12s
epoch 1 batch 55 processed 55 batch-loss 0.1977 epoch-elapsed 0h00m08s total-elapsed 0h00m12s
epoch 1 batch 56 processed 56 batch-loss 0.2144 epoch-elapsed 0h00m08s total-elapsed 0h00m12s
epoch 1 batch 57 processed 57 batch-loss 0.1948 epoch-elapsed 0h00m09s total-elapsed 0h00m13s
epoch 1 batch 58 processed 58 batch-loss 0.1993 epoch-elapsed 0h00m09s total-elapsed 0h00m13s
epoch 1 batch 59 processed 59 batch-loss 0.2186 epoch-elapsed 0h00m09s total-elapsed 0h00m13s
epoch 1 batch 60 processed 60 batch-loss 0.1905 epoch-elapsed 0h00m09s total-elapsed 0h00m13s
epoch 1 batch 61 processed 61 batch-loss 0.2148 epoch-elapsed 0h00m09s total-elapsed 0h00m13s
epoch 1 batch 62 processed 62 batch-loss 0.1968 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 63 processed 63 batch-loss 0.1934 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 64 processed 64 batch-loss 0.1935 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 65 processed 65 batch-loss 0.2298 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 66 processed 66 batch-loss 0.1970 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 67 processed 67 batch-loss 0.1793 epoch-elapsed 0h00m10s total-elapsed 0h00m14s
epoch 1 batch 68 processed 68 batch-loss 0.2281 epoch-elapsed 0h00m11s total-elapsed 0h00m14s
epoch 1 batch 69 processed 69 batch-loss 0.1924 epoch-elapsed 0h00m11s total-elapsed 0h00m15s
epoch 1 batch 70 processed 70 batch-loss 0.1790 epoch-elapsed 0h00m11s total-elapsed 0h00m15s
^C
Traceback (most recent call last):
File "src/main.py", line 392, in <module>
main()
File "src/main.py", line 387, in main
args.callback(args)
File "src/main.py", line 165, in run_train
return_numpy=False)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py", line 1108, in run
return_merged=return_merged)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py", line 1238, in _run_impl
use_program_cache=use_program_cache)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py", line 1328, in _run_program
[fetch_var_name])
KeyboardInterrupt
Prediction script
python src/main.py test --model-path-base [saved_model_directory]
This code will predict a model, and the log and results will be saved to the file test by default_ log. txt
Sample
- Use unlabeled test sets to predict:
python src/main.py test --model-path-base model-0/model_dev\=0.0772/ - Use labeled test sets to predict and evaluate:
python src/main.py test_withlabel --model-path-base model-0/model_dev\=0.0772/
sample output# python3 src/main.py test_withlabel --model-path-base model-0/model_dev=0.0772 Loading data... Loading model... initializing vacabularies... done. Sequence(6): ['<START>', '<STOP>', 'A', 'C', 'G', 'U'] Brackets(5): ['<START>', '<STOP>', '(', ')', '.'] W0113 21:57:44.871776 221 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 11.0, Runtime API Version: 9.0 W0113 21:57:44.878015 221 device_context.cc:260] device: 0, cuDNN Version: 7.6. # Dev Average Loss: 0.0772 (MSE) -> 0.2778 (RMSD) # Test Average Loss: 0.0445 (MSE) -> 0.2111 (RMSD)
- Because the public data of the competition does not provide the label of the test set, the baseline model cannot run the preset test_withlabel, unless the user generates a labeled test set ~ / data / test txt.
matters needing attention
Please use the configuration environment of GPU version to run this module
Forecast description
It was found that the final result showed a fitting situation in the preliminary competition, and the effect was not very good. So I decided to stop early.
It is found that the effect is better at the position of 0.075.
Finally, the result of A ranking is No. 4, score: 4.66
In the semi-finals, the results of 0.0762 and 0.0749 were found to be poor, and the best was 0.0752 times, Jiawei 0.0756
Finally, the results are fused to get the final result.
!cd work; python src/main.py test --model-path-base model-0/model_dev\=0.0772
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): Loading data... Loading model... W0512 13:46:49.052799 817 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0512 13:46:49.058928 817 device_context.cc:372] device: 0, cuDNN Version: 7.6. 0.8769019 0.827059 0.124781355 0.07532252 0.07499607 0.1314325 0.08891902 0.13985236 0.09831676 0.08501828 0.07340165 0.07567302 0.1133676 0.09805709 0.06310087 0.056796186 0.057331596 0.0815781 0.27457792 0.9096467 0.93526053 0.9451846 0.9142914 0.109518185 0.076339886 0.048351273 0.049028248 0.05358911 0.060041353 0.074196644 0.13099869 0.88650066 0.8841263 0.108059675 0.078317374 0.05504336 0.056563973 0.081898704 0.243508 0.9161593 0.9409232 0.9298708 0.30104664 0.096637875 0.054647654 0.051664583 0.06739785 0.09219342 0.7264107 0.726659 0.73035246 0.7605198 0.89717275 0.90631706 0.8475187 0.9132216 0.91307867 0.7822155 0.079098985 0.06328807 0.09567865 0.09337111 0.056361835 0.05519329 0.07371606 0.20833306 0.8994241 0.9335145 0.15200244 0.16098142 0.13979791 0.83967584 0.9244599 0.93794787 0.93146956 0.8610643 0.14420095 0.104273684 0.08480854 0.0657236 0.07158427 0.104142815 0.69858843 0.8231929 0.7192306 0.7296086 0.8188443 0.70629466 0.73674756 0.86907995 0.8761509 0.7557834 0.27506483 0.27363357 0.2096101 0.12540138 0.09406437 0.12995595 0.14843474 0.88662666 0.9157279 0.92791003 0.921483 0.86955506 0.86588264 0.9174033 0.89678645 0.10066239 0.08461384 0.07529054 0.12906869 0.12231052 0.1496461 0.21498549 0.14801437 0.2764888 0.081257366 0.9033271 0.9342948 0.89194506 0.8807766 0.10806908 0.09785647 0.07428005 0.100757055 0.82896733 0.21201192 0.20115143 0.091858394 0.07151817 0.090858996 0.08316694 0.08005128 0.07875547 0.07702952 0.07359996 0.09018034 0.08683971 0.11228195 0.7524584 0.87401533 0.3552185 0.32328513 0.85190153 0.8004978 0.7834312 0.87545794 0.80943245 0.8141827 0.8870493 0.87456256 0.2247908 0.1776992 0.1528118 0.20111498 0.15980801 0.15832028 0.16394168 0.5951297 0.19600783 0.16838795 0.20453955 0.29506925 0.59307003 0.565762 0.30068 0.34491885 0.39538962 0.3886126 0.34946576 0.82175004 0.69261444 0.80722576 0.32902294 0.23999384 0.24694005 0.8471683 0.8440093 0.19210613 0.18218043 0.15031442 0.8006151 0.8973477 0.89688265 0.74138224 0.6770714 0.16547869 0.21786806 0.15676819 0.7439129 0.7421006 0.85367626 0.22139461 0.19860391 0.39737356 0.866............ 0.7619047
!cd work; python src/main.py test --model-path-base model/model_dev\=0.0756
count=0
list=[]
with open("work/test_log.txt","r")as f:
for i in f.readlines():
count+=1
list.append(i.split(" "))
with open(f"predict.files756/{count}.predict.txt","w")as ff:
for i in range(len(list[count-1])):
ff.write(list[count-1][i]+"\n")
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): Loading data... Loading model... W0512 13:47:54.666482 981 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0512 13:47:54.671232 981 device_context.cc:372] device: 0, cuDNN Version: 7.6. 0.92458844 0.8973756 0.083819814 0.048119456 0.04318866 0.06369634 0.061531093 0.08912887 0.07272421 0.06061156 0.047806975 0.04566294 0.05193742 0.0465386 0.038122084 0.035683643 0.035520226 0.044568405 0.089197926 0.9403354 0.95413107 0.9659275 0.947999 0.091526456 0.043470513 0.033027984 0.03248403 0.03333013 0.03653355 0.046932414 0.07692799 0.90853477 0.92064553 0.10450742 0.06477215 0.04051999 0.03910085 0.06434644 0.19825488 0.9514736 0.9613649 86 0.27746683 0.56432915 0.27165216 0.22117698 0.6343458 0.28548688 0.1490245 0.30219793 0.07924749 0.061275408 0.0712523 0.09512221 0.71393305 0.7728529 0.78344065 0.12242744 0.077154376 0.07725689 0.058522586 0.04672195 0.04593994 0.052065138 0.113416955 0.14666957 0.85096854 0.81819427 0.8156704 0.84576315 0.08336176 0.057449628 0.059872873 0.0978862 0.8592945 0.14375012 0.12232697 0.69887173 0.13994755 0.11359459 0.15944806 0.10881757 0.7315731 0.105488054 0.077173166 0.12365675 0.8588115 0.24234413 0.7861132 0.107930176 0.07159182 0.09324458 0.09120768 0.07186025 0.8105579 0.79349583 0.83615726 0.8965029 0.9502591 0.94215065 0.9564655 0.9442282 0.06240864 0.03893294 0.03351966 0.035541855 0.040954556 0.04281014 0.04482754 0.047412407 0.050068595 0.051022653 0.052231997 0.049969897 0.050768476 0.058887467.............................. 0.84340286
Code fusion
list1=[]
list2=[]
list3=[]
for i in range(1,113):
with open(f"/home/aistudio/predict.files752/{i}.predict.txt", "r") as f:
with open(f"/home/aistudio/predict.files756/{i}.predict.txt","r") as ff:
with open(f"/home/aistudio/predict.files/{i}.predict.txt","a") as fff:
for m in f.readlines():
list1.append((m.replace("\n","")))
for n in ff.readlines():
list2.append((n.replace("\n","")))
for j in range(0,len(list1)):
# print(list1[j])
if list1[j] and list2[j] !="\n":
a = (float(list1[j])+float(list2[j]))/2
list3.append(a)
# print(list3)
fff.write(str(list3[j])+"\n")
collect files
%cd /home/aistudio/predict.files !zip -r -o /home/aistudio/predict.files.zip ./ %cd /home/aistudio
/home/aistudio/predict.files adding: 52.predict.txt (deflated 69%) adding: 9.predict.txt (deflated 84%) adding: 71.predict.txt (deflated 68%) adding: 37.predict.txt (deflated 69%) adding: 38.predict.txt (deflated 69%) adding: 19.predict.txt (deflated 74%) adding: 86.predict.txt (deflated 67%) adding: 83.predict.txt (deflated 67%) adding: 17.predict.txt (deflated 74%) adding: 28.predict.txt (deflated 71%) adding: 101.predict.txt (deflated 67%) adding: 74.predict.txt (deflated 68%) adding: 80.predict.txt (deflated 68%) adding: 26.predict.txt (deflated 71%) adding: 33.predict.txt (deflated 70%) adding: 85.predict.txt (deflated 67%) adding: 90.predict.txt (deflated 67%) adding: 107.predict.txt (deflated 67%) adding: 24.predict.txt (deflated 72%) adding: 75.predict.txt (deflated 68%) adding: 44.predict.txt (deflated 69%) adding: 35.predict.txt (deflated 69%) adding: 82.predict.txt (deflated 68%) adding: 10.predict.txt (deflated 82%) adding: 64.predict.txt (deflated 68%) adding: 14.predict.txt (deflated 77%) adding: 48.predict.txt (deflated 69%) adding: 100.predict.txt (deflated 67%) adding: 68.predict.txt (deflated 68%) adding: 15.predict.txt (deflated 76%) adding: 66.predict.txt (deflated 68%) adding: 16.predict.txt (deflated 75%) adding: 70.predict.txt (deflated 68%) adding: 45.predict.txt (deflated 69%) adding: 98.predict.txt (deflated 67%) adding: 110.predict.txt (deflated 66%) adding: 36.predict.txt (deflated 69%) adding: 104.predict.txt (deflated 67%) adding: 27.predict.txt (deflated 71%) adding: 60.predict.txt (deflated 69%) adding: 8.predict.txt (deflated 85%) adding: 65.predict.txt (deflated 68%) adding: 77.predict.txt (deflated 68%) adding: 55.predict.txt (deflated 69%) adding: 92.predict.txt (deflated 67%) adding: 43.predict.txt (deflated 69%) adding: 81.predict.txt (deflated 68%) adding: 1.predict.txt (deflated 97%) adding: 29.predict.txt (deflated 70%) adding: 88.predict.txt (deflated 67%) adding: 25.predict.txt (deflated 71%) adding: 21.predict.txt (deflated 73%) adding: 46.predict.txt (deflated 69%) adding: 18.predict.txt (deflated 74%) adding: 13.predict.txt (deflated 78%) adding: 39.predict.txt (deflated 69%) adding: 84.predict.txt (deflated 67%) adding: 30.predict.txt (deflated 70%) adding: 22.predict.txt (deflated 72%) adding: 7.predict.txt (deflated 83%) adding: 91.predict.txt (deflated 67%) adding: 62.predict.txt (deflated 68%) adding: 20.predict.txt (deflated 73%) adding: 54.predict.txt (deflated 69%) adding: 97.predict.txt (deflated 67%) adding: 63.predict.txt (deflated 68%) adding: 99.predict.txt (deflated 67%) adding: 23.predict.txt (deflated 72%) adding: 76.predict.txt (deflated 68%) adding: 78.predict.txt (deflated 68%) adding: 51.predict.txt (deflated 69%) adding: 73.predict.txt (deflated 68%) adding: 5.predict.txt (deflated 83%) adding: 40.predict.txt (deflated 69%) adding: 103.predict.txt (deflated 67%) adding: 34.predict.txt (deflated 69%) adding: 67.predict.txt (deflated 68%) adding: 109.predict.txt (deflated 66%) adding: 79.predict.txt (deflated 68%) adding: 11.predict.txt (deflated 80%) adding: 6.predict.txt (deflated 80%) adding: 42.predict.txt (deflated 69%) adding: 2.predict.txt (deflated 79%) adding: 112.predict.txt (deflated 66%) adding: 50.predict.txt (deflated 69%) adding: 47.predict.txt (deflated 69%) adding: 72.predict.txt (deflated 68%) adding: 87.predict.txt (deflated 67%) adding: 105.predict.txt (deflated 67%) adding: 102.predict.txt (deflated 67%) adding: 32.predict.txt (deflated 70%) adding: 31.predict.txt (deflated 70%) adding: 69.predict.txt (deflated 68%) adding: 61.predict.txt (deflated 68%) adding: 94.predict.txt (deflated 67%) adding: 3.predict.txt (deflated 73%) adding: 53.predict.txt (deflated 69%) adding: 95.predict.txt (deflated 67%) adding: 41.predict.txt (deflated 69%) adding: 58.predict.txt (deflated 69%) adding: 96.predict.txt (deflated 67%) adding: 4.predict.txt (deflated 79%) adding: 56.predict.txt (deflated 69%) adding: 108.predict.txt (deflated 66%) adding: 59.predict.txt (deflated 69%) adding: 106.predict.txt (deflated 66%) adding: 49.predict.txt (deflated 69%) adding: 111.predict.txt (deflated 66%) adding: 93.predict.txt (deflated 67%) adding: 89.predict.txt (deflated 67%) adding: 57.predict.txt (deflated 69%) adding: 12.predict.txt (deflated 78%) /home/aistudio
That's all for this sharing. Thank you for your support!