fixed saving sacred runs
This commit is contained in:
patrycjalazna
parent
bad2a78013
commit
9dab0233db
@ -57,7 +57,7 @@ pipeline {
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steps{
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archiveArtifacts 'eval_results.txt'
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archiveArtifacts 'eval_plot.png'
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archiveArtifacts 'my_runs/*'
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archiveArtifacts 'sacred_runs/**'
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}
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}
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5
my_runs/4/config.json
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5
my_runs/4/config.json
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@ -0,0 +1,5 @@
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{
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"batch_size": 16,
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"epochs": 10,
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"seed": 347156927
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}
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18
my_runs/4/cout.txt
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18
my_runs/4/cout.txt
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INFO - 434742-file - Running command 'my_main'
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INFO - 434742-file - Started run with ID "4"
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9
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2021-05-15 12:56:10.913293: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
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To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
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2021-05-15 12:56:11.240918: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
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Epoch 1/10
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1/375 [..............................] - ETA: 2:39:04 - loss: 0.6933 - accuracy: 0.5000
47/375 [==>...........................] - ETA: 0s - loss: 0.6929 - accuracy: 0.4739
93/375 [======>.......................] - ETA: 0s - loss: 0.6925 - accuracy: 0.4976
139/375 [==========>...................] - ETA: 0s - loss: 0.6919 - accuracy: 0.5453
189/375 [==============>...............] - ETA: 0s - loss: 0.6910 - accuracy: 0.5839
239/375 [==================>...........] - ETA: 0s - loss: 0.6899 - accuracy: 0.6141
284/375 [=====================>........] - ETA: 0s - loss: 0.6886 - accuracy: 0.6368
335/375 [=========================>....] - ETA: 0s - loss: 0.6870 - accuracy: 0.6577
375/375 [==============================] - 27s 3ms/step - loss: 0.6855 - accuracy: 0.6718 - val_loss: 0.6227 - val_accuracy: 0.8720
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Epoch 2/10
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1/375 [..............................] - ETA: 0s - loss: 0.6240 - accuracy: 1.0000
23/375 [>.............................] - ETA: 0s - loss: 0.6206 - accuracy: 0.9091
62/375 [===>..........................] - ETA: 0s - loss: 0.6215 - accuracy: 0.8915
94/375 [======>.......................] - ETA: 0s - loss: 0.6186 - accuracy: 0.8912
129/375 [=========>....................] - ETA: 0s - loss: 0.6156 - accuracy: 0.8901
165/375 [============>.................] - ETA: 0s - loss: 0.6127 - accuracy: 0.8887
212/375 [===============>..............] - ETA: 0s - loss: 0.6089 - accuracy: 0.8872
257/375 [===================>..........] - ETA: 0s - loss: 0.6052 - accuracy: 0.8867
304/375 [=======================>......] - ETA: 0s - loss: 0.6012 - accuracy: 0.8866
352/375 [===========================>..] - ETA: 0s - loss: 0.5970 - accuracy: 0.8866
375/375 [==============================] - 1s 2ms/step - loss: 0.5949 - accuracy: 0.8866 - val_loss: 0.4903 - val_accuracy: 0.9040
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Epoch 3/10
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1/375 [..............................] - ETA: 0s - loss: 0.4609 - accuracy: 0.9375
31/375 [=>............................] - ETA: 0s - loss: 0.4950 - accuracy: 0.8832
79/375 [=====>........................] - ETA: 0s - loss: 0.4923 - accuracy: 0.8815
126/375 [=========>....................] - ETA: 0s - loss: 0.4901 - accuracy: 0.8802
171/375 [============>.................] - ETA: 0s - loss: 0.4869 - accuracy: 0.8797
217/375 [================>.............] - ETA: 0s - loss: 0.4840 - accuracy: 0.8798
260/375 [===================>..........] - ETA: 0s - loss: 0.4811 - accuracy: 0.8807
308/375 [=======================>......] - ETA: 0s - loss: 0.4779 - accuracy: 0.8818
355/375 [===========================>..] - ETA: 0s - loss: 0.4746 - accuracy: 0.8830
375/375 [==============================] - 1s 1ms/step - loss: 0.4732 - accuracy: 0.8834 - val_loss: 0.3904 - val_accuracy: 0.9075
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Epoch 4/10
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1/375 [..............................] - ETA: 0s - loss: 0.4200 - accuracy: 0.9375
40/375 [==>...........................] - ETA: 0s - loss: 0.4032 - accuracy: 0.9083
92/375 [======>.......................] - ETA: 0s - loss: 0.3957 - accuracy: 0.9041
145/375 [==========>...................] - ETA: 0s - loss: 0.3931 - accuracy: 0.9020
195/375 [==============>...............] - ETA: 0s - loss: 0.3905 - accuracy: 0.9013
248/375 [==================>...........] - ETA: 0s - loss: 0.3886 - accuracy: 0.8999
295/375 [======================>.......] - ETA: 0s - loss: 0.3872 - accuracy: 0.8988
345/375 [==========================>...] - ETA: 0s - loss: 0.3856 - accuracy: 0.8981
375/375 [==============================] - 0s 1ms/step - loss: 0.3844 - accuracy: 0.8977 - val_loss: 0.3302 - val_accuracy: 0.9100
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Epoch 5/10
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1/375 [..............................] - ETA: 0s - loss: 0.3368 - accuracy: 0.8750
39/375 [==>...........................] - ETA: 0s - loss: 0.3672 - accuracy: 0.8816
88/375 [======>.......................] - ETA: 0s - loss: 0.3620 - accuracy: 0.8832
140/375 [==========>...................] - ETA: 0s - loss: 0.3537 - accuracy: 0.8875
191/375 [==============>...............] - ETA: 0s - loss: 0.3490 - accuracy: 0.8900
240/375 [==================>...........] - ETA: 0s - loss: 0.3459 - accuracy: 0.8911
289/375 [======================>.......] - ETA: 0s - loss: 0.3436 - accuracy: 0.8918
340/375 [==========================>...] - ETA: 0s - loss: 0.3417 - accuracy: 0.8920
375/375 [==============================] - 0s 1ms/step - loss: 0.3404 - accuracy: 0.8921 - val_loss: 0.2945 - val_accuracy: 0.9100
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Epoch 6/10
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1/375 [..............................] - ETA: 1s - loss: 0.2755 - accuracy: 0.9375
24/375 [>.............................] - ETA: 0s - loss: 0.3345 - accuracy: 0.8822
55/375 [===>..........................] - ETA: 0s - loss: 0.3241 - accuracy: 0.8859
84/375 [=====>........................] - ETA: 0s - loss: 0.3210 - accuracy: 0.8850
94/375 [======>.......................] - ETA: 0s - loss: 0.3203 - accuracy: 0.8852
105/375 [=======>......................] - ETA: 0s - loss: 0.3193 - accuracy: 0.8857
124/375 [========>.....................] - ETA: 0s - loss: 0.3183 - accuracy: 0.8859
150/375 [===========>..................] - ETA: 0s - loss: 0.3178 - accuracy: 0.8855
179/375 [=============>................] - ETA: 0s - loss: 0.3175 - accuracy: 0.8853
211/375 [===============>..............] - ETA: 0s - loss: 0.3166 - accuracy: 0.8857
239/375 [==================>...........] - ETA: 0s - loss: 0.3155 - accuracy: 0.8861
253/375 [===================>..........] - ETA: 0s - loss: 0.3150 - accuracy: 0.8863
275/375 [=====================>........] - ETA: 0s - loss: 0.3141 - accuracy: 0.8867
301/375 [=======================>......] - ETA: 0s - loss: 0.3131 - accuracy: 0.8871
329/375 [=========================>....] - ETA: 0s - loss: 0.3121 - accuracy: 0.8876
349/375 [==========================>...] - ETA: 0s - loss: 0.3114 - accuracy: 0.8879
372/375 [============================>.] - ETA: 0s - loss: 0.3107 - accuracy: 0.8882
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1
my_runs/4/metrics.json
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1
my_runs/4/metrics.json
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{}
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85
my_runs/4/run.json
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85
my_runs/4/run.json
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{
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"artifacts": [],
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"command": "my_main",
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"experiment": {
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"base_dir": "/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka",
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"dependencies": [
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"keras-nightly==2.5.0.dev2021032900",
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"numpy==1.19.5",
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"pandas==1.2.3",
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"sacred==0.8.2",
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"scikit-learn==0.24.1",
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"tensorflow==2.5.0rc1"
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],
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"mainfile": "sacred-fileobserver.py",
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"name": "434742-file",
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"repositories": [],
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"sources": [
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[
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"sacred-fileobserver.py",
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"_sources/sacred-fileobserver_3e3cbf02c631699c29e47a557c9a608d.py"
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]
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]
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},
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"fail_trace": [
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"Traceback (most recent call last):\n",
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" File \"/usr/local/lib/python3.9/site-packages/sacred/config/captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n",
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" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-fileobserver.py\", line 68, in my_main\n print(prepare_model())\n",
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" File \"/usr/local/lib/python3.9/site-packages/sacred/config/captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n",
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" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-fileobserver.py\", line 60, in prepare_model\n model.save('avocado_model.h5')\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/engine/training.py\", line 2086, in save\n save.save_model(self, filepath, overwrite, include_optimizer, save_format,\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/saving/save.py\", line 146, in save_model\n hdf5_format.save_model_to_hdf5(\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/saving/hdf5_format.py\", line 110, in save_model_to_hdf5\n model_metadata = saving_utils.model_metadata(model, include_optimizer)\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/saving/saving_utils.py\", line 152, in model_metadata\n raise e\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/saving/saving_utils.py\", line 149, in model_metadata\n model_config['config'] = model.get_config()\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/engine/sequential.py\", line 471, in get_config\n layer_configs.append(generic_utils.serialize_keras_object(layer))\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/utils/generic_utils.py\", line 508, in serialize_keras_object\n raise e\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/utils/generic_utils.py\", line 503, in serialize_keras_object\n config = instance.get_config()\n",
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" File \"/usr/local/lib/python3.9/site-packages/keras/engine/base_layer.py\", line 695, in get_config\n raise NotImplementedError('Layer %s has arguments in `__init__` and '\n",
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"NotImplementedError: Layer ModuleWrapper has arguments in `__init__` and therefore must override `get_config`.\n"
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],
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"heartbeat": "2021-05-15T10:56:44.017631",
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"host": {
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"ENV": {},
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"cpu": "Intel(R) Core(TM) i5-5287U CPU @ 2.90GHz",
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"hostname": "patrycjalazna.local",
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"os": [
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"Darwin",
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"macOS-10.15.7-x86_64-i386-64bit"
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],
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"python_version": "3.9.4"
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},
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"meta": {
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"command": "my_main",
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"options": {
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"--beat-interval": null,
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"--capture": null,
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"--comment": null,
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"--debug": false,
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"--enforce_clean": false,
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"--file_storage": null,
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"--force": false,
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"--help": false,
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"--loglevel": null,
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"--mongo_db": null,
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"--name": null,
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"--pdb": false,
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"--print-config": false,
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"--priority": null,
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"--queue": false,
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"--s3": null,
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"--sql": null,
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"--tiny_db": null,
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"--unobserved": false,
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"COMMAND": null,
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"UPDATE": [],
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"help": false,
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"with": false
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}
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},
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"resources": [],
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"result": null,
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"start_time": "2021-05-15T10:56:10.769284",
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"status": "FAILED",
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"stop_time": "2021-05-15T10:56:44.020146"
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}
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@ -0,0 +1,71 @@
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import sys
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from keras.backend import mean
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from keras.models import Sequential
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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ex = Experiment("434742-file", interactive=False, save_git_info=False)
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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epochs = 10
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batch_size = 16
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@ex.capture
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def prepare_model(epochs, batch_size):
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# odczytanie danych z plików
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avocado_train = pd.read_csv('avocado_train.csv')
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avocado_test = pd.read_csv('avocado_test.csv')
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avocado_validate = pd.read_csv('avocado_validate.csv')
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# podzial na X i y
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X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_train = avocado_train[['type']]
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X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_test = avocado_test[['type']]
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print(X_train.shape[1])
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# keras model
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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# kompilacja
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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# trenowanie modelu
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
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# predykcja
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prediction = model.predict(X_test)
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# ewaluacja
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rmse = mean_squared_error(y_test, prediction)
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# zapisanie modelu
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model.save('avocado_model.h5')
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return rmse
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@ex.automain
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def my_main(epochs, batch_size):
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print(prepare_model())
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ex.run()
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ex.add_artifact('avocado_model.h5')
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@ -15,7 +15,7 @@ from sacred import Experiment
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from sacred.observers import FileStorageObserver
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ex = Experiment("434742-file", interactive=False, save_git_info=False)
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ex.observers.append(FileStorageObserver('my_runs'))
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ex.observers.append(FileStorageObserver('sacred_runs/my_runs'))
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@ex.config
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def my_config():
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