params in function added
This commit is contained in:
patrycjalazna
parent
4fc45ed8ef
commit
89ee49bca3
BIN
avocado_model.h5
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avocado_model.h5
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my_runs/1/config.json
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my_runs/1/config.json
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{
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"batch_size": 16,
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"epochs": 10,
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"seed": 752365448
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}
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0
my_runs/1/cout.txt
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0
my_runs/1/cout.txt
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1
my_runs/1/metrics.json
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my_runs/1/metrics.json
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{}
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my_runs/1/run.json
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my_runs/1/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-training.py",
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"name": "file_observer",
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"repositories": [
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{
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"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
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"dirty": false,
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"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
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}
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],
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"sources": [
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[
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"sacred-training.py",
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"_sources/sacred-training_58bada47539aa2ee4591c9929ac8c60f.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-training.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-training.py\", line 28, in prepare_model\n avocado_train = pd.read_csv('avocado_train.csv')\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 610, in read_csv\n return _read(filepath_or_buffer, kwds)\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 462, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 819, in __init__\n self._engine = self._make_engine(self.engine)\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 1050, in _make_engine\n return mapping[engine](self.f, **self.options) # type: ignore[call-arg]\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 1867, in __init__\n self._open_handles(src, kwds)\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 1362, in _open_handles\n self.handles = get_handle(\n",
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" File \"/usr/local/lib/python3.9/site-packages/pandas/io/common.py\", line 642, in get_handle\n handle = open(\n",
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"FileNotFoundError: [Errno 2] No such file or directory: 'avocado_train.csv'\n"
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],
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"heartbeat": "2021-05-14T18:58:43.968999",
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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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}
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},
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"resources": [],
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"result": null,
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"start_time": "2021-05-14T18:58:43.955201",
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"status": "FAILED",
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"stop_time": "2021-05-14T18:58:43.972665"
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}
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my_runs/2/config.json
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my_runs/2/config.json
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{
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"batch_size": 16,
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"epochs": 10,
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"seed": 808581577
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}
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0
my_runs/2/cout.txt
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0
my_runs/2/cout.txt
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1
my_runs/2/metrics.json
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1
my_runs/2/metrics.json
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@ -0,0 +1 @@
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{}
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78
my_runs/2/run.json
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78
my_runs/2/run.json
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@ -0,0 +1,78 @@
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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-training.py",
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"name": "file_observer",
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"repositories": [
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{
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"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
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"dirty": true,
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"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
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}
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],
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"sources": [
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[
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"sacred-training.py",
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"_sources/sacred-training_58bada47539aa2ee4591c9929ac8c60f.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-training.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-training.py\", line 51, in prepare_model\n model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))\n",
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"NameError: name 'epochs' is not defined\n"
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],
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"heartbeat": "2021-05-14T18:59:21.424846",
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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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||||||
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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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||||||
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"options": {
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||||||
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"--beat-interval": null,
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||||||
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"--capture": null,
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||||||
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"--comment": null,
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||||||
|
"--debug": false,
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||||||
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"--enforce_clean": false,
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||||||
|
"--file_storage": null,
|
||||||
|
"--force": false,
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||||||
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"--help": false,
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||||||
|
"--loglevel": null,
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||||||
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"--mongo_db": null,
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||||||
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"--name": null,
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||||||
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"--pdb": false,
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||||||
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"--print-config": false,
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||||||
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"--priority": null,
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||||||
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"--queue": false,
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||||||
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"--s3": null,
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||||||
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"--sql": null,
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||||||
|
"--tiny_db": null,
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||||||
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"--unobserved": false
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||||||
|
}
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||||||
|
},
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||||||
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"resources": [],
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||||||
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"result": null,
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||||||
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"start_time": "2021-05-14T18:59:21.272740",
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"status": "FAILED",
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||||||
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"stop_time": "2021-05-14T18:59:21.427473"
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||||||
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}
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||||||
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my_runs/3/config.json
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my_runs/3/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": 981585385
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}
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my_runs/3/cout.txt
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my_runs/3/cout.txt
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INFO - file_observer - Running command 'my_main'
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INFO - file_observer - Started run with ID "3"
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9
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2021-05-14 20:59:43.136009: 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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2021-05-14 20:59:43.542832: 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: 3:02:52 - loss: 0.6931 - accuracy: 0.4375
49/375 [==>...........................] - ETA: 0s - loss: 0.6929 - accuracy: 0.6856
96/375 [======>.......................] - ETA: 0s - loss: 0.6925 - accuracy: 0.7135
129/375 [=========>....................] - ETA: 0s - loss: 0.6922 - accuracy: 0.7033
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1
my_runs/3/metrics.json
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my_runs/3/metrics.json
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@ -0,0 +1 @@
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{}
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87
my_runs/3/run.json
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87
my_runs/3/run.json
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@ -0,0 +1,87 @@
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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-training.py",
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"name": "file_observer",
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"repositories": [
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{
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"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
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"dirty": true,
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"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
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}
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],
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"sources": [
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[
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"sacred-training.py",
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"_sources/sacred-training_36981fcdfc28636f97f6ecc881108ea8.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-training.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-training.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": "2021-05-14T19:00:19.975884",
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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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||||||
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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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}
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},
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"resources": [],
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"result": null,
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"start_time": "2021-05-14T18:59:43.051621",
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"status": "FAILED",
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"stop_time": "2021-05-14T19:00:19.978586"
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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
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from sklearn import preprocessing
|
||||||
|
from sklearn.linear_model import LinearRegression
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
import tensorflow as tf
|
||||||
|
from tensorflow import keras
|
||||||
|
from tensorflow.keras.layers import Input, Dense, Activation,Dropout
|
||||||
|
from tensorflow.keras.models import Model
|
||||||
|
from tensorflow.keras.callbacks import EarlyStopping
|
||||||
|
from keras.models import Sequential
|
||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver
|
||||||
|
|
||||||
|
ex = Experiment("file_observer")
|
||||||
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
epochs = 10
|
||||||
|
batch_size = 16
|
||||||
|
|
||||||
|
@ex.capture
|
||||||
|
def prepare_model(epochs, batch_size):
|
||||||
|
# odczytanie danych z plików
|
||||||
|
avocado_train = pd.read_csv('avocado_train.csv')
|
||||||
|
avocado_test = pd.read_csv('avocado_test.csv')
|
||||||
|
avocado_validate = pd.read_csv('avocado_validate.csv')
|
||||||
|
|
||||||
|
|
||||||
|
# podzial na X i y
|
||||||
|
X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
||||||
|
y_train = avocado_train[['type']]
|
||||||
|
X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
||||||
|
y_test = avocado_test[['type']]
|
||||||
|
|
||||||
|
print(X_train.shape[1])
|
||||||
|
# keras model
|
||||||
|
model = Sequential()
|
||||||
|
model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
|
||||||
|
model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
|
||||||
|
|
||||||
|
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
|
||||||
|
|
||||||
|
# kompilacja
|
||||||
|
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
|
||||||
|
|
||||||
|
# trenowanie modelu
|
||||||
|
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
|
||||||
|
|
||||||
|
# predykcja
|
||||||
|
prediction = model.predict(X_test)
|
||||||
|
|
||||||
|
# ewaluacja
|
||||||
|
rmse = mean_squared_error(y_test, prediction)
|
||||||
|
|
||||||
|
# zapisanie modelu
|
||||||
|
model.save('avocado_model.h5')
|
||||||
|
|
||||||
|
return rmse
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@ex.main
|
||||||
|
def my_main():
|
||||||
|
print(prepare_model())
|
||||||
|
|
||||||
|
ex.run()
|
||||||
|
ex.add_artifact('avocado_model.h5')
|
||||||
@ -0,0 +1,71 @@
|
|||||||
|
import sys
|
||||||
|
from keras.backend import mean
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from sklearn import preprocessing
|
||||||
|
from sklearn.linear_model import LinearRegression
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
import tensorflow as tf
|
||||||
|
from tensorflow import keras
|
||||||
|
from tensorflow.keras.layers import Input, Dense, Activation,Dropout
|
||||||
|
from tensorflow.keras.models import Model
|
||||||
|
from tensorflow.keras.callbacks import EarlyStopping
|
||||||
|
from keras.models import Sequential
|
||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver
|
||||||
|
|
||||||
|
ex = Experiment("file_observer")
|
||||||
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
epochs = 10
|
||||||
|
batch_size = 16
|
||||||
|
|
||||||
|
@ex.capture
|
||||||
|
def prepare_model():
|
||||||
|
# odczytanie danych z plików
|
||||||
|
avocado_train = pd.read_csv('avocado_train.csv')
|
||||||
|
avocado_test = pd.read_csv('avocado_test.csv')
|
||||||
|
avocado_validate = pd.read_csv('avocado_validate.csv')
|
||||||
|
|
||||||
|
|
||||||
|
# podzial na X i y
|
||||||
|
X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
||||||
|
y_train = avocado_train[['type']]
|
||||||
|
X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
||||||
|
y_test = avocado_test[['type']]
|
||||||
|
|
||||||
|
print(X_train.shape[1])
|
||||||
|
# keras model
|
||||||
|
model = Sequential()
|
||||||
|
model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
|
||||||
|
model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
|
||||||
|
|
||||||
|
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
|
||||||
|
|
||||||
|
# kompilacja
|
||||||
|
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
|
||||||
|
|
||||||
|
# trenowanie modelu
|
||||||
|
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
|
||||||
|
|
||||||
|
# predykcja
|
||||||
|
prediction = model.predict(X_test)
|
||||||
|
|
||||||
|
# ewaluacja
|
||||||
|
rmse = mean_squared_error(y_test, prediction)
|
||||||
|
|
||||||
|
# zapisanie modelu
|
||||||
|
model.save('avocado_model.h5')
|
||||||
|
|
||||||
|
return rmse
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@ex.main
|
||||||
|
def my_main():
|
||||||
|
print(prepare_model())
|
||||||
|
|
||||||
|
ex.run()
|
||||||
|
ex.add_artifact('avocado_model.h5')
|
||||||
@ -23,7 +23,7 @@ def my_config():
|
|||||||
batch_size = 16
|
batch_size = 16
|
||||||
|
|
||||||
@ex.capture
|
@ex.capture
|
||||||
def prepare_model():
|
def prepare_model(epochs, batch_size):
|
||||||
# odczytanie danych z plików
|
# odczytanie danych z plików
|
||||||
avocado_train = pd.read_csv('avocado_train.csv')
|
avocado_train = pd.read_csv('avocado_train.csv')
|
||||||
avocado_test = pd.read_csv('avocado_test.csv')
|
avocado_test = pd.read_csv('avocado_test.csv')
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user