params in function added
This commit is contained in:
patrycjalazna
parent
4fc45ed8ef
commit
89ee49bca3
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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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}
|
||||
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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1
my_runs/1/metrics.json
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@ -0,0 +1 @@
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{}
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85
my_runs/1/run.json
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85
my_runs/1/run.json
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@ -0,0 +1,85 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
|
||||
"base_dir": "/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka",
|
||||
"dependencies": [
|
||||
"keras-nightly==2.5.0.dev2021032900",
|
||||
"numpy==1.19.5",
|
||||
"pandas==1.2.3",
|
||||
"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",
|
||||
"name": "file_observer",
|
||||
"repositories": [
|
||||
{
|
||||
"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
|
||||
"dirty": false,
|
||||
"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
|
||||
}
|
||||
],
|
||||
"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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"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",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i5-5287U CPU @ 2.90GHz",
|
||||
"hostname": "patrycjalazna.local",
|
||||
"os": [
|
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"Darwin",
|
||||
"macOS-10.15.7-x86_64-i386-64bit"
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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": {
|
||||
"--beat-interval": null,
|
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"--capture": null,
|
||||
"--comment": null,
|
||||
"--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,
|
||||
"--loglevel": null,
|
||||
"--mongo_db": null,
|
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"--name": null,
|
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"--pdb": false,
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"--print-config": false,
|
||||
"--priority": null,
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"--queue": false,
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"--s3": null,
|
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"--sql": null,
|
||||
"--tiny_db": null,
|
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"--unobserved": false
|
||||
}
|
||||
},
|
||||
"resources": [],
|
||||
"result": null,
|
||||
"start_time": "2021-05-14T18:58:43.955201",
|
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"status": "FAILED",
|
||||
"stop_time": "2021-05-14T18:58:43.972665"
|
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}
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||||
5
my_runs/2/config.json
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5
my_runs/2/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": 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",
|
||||
"experiment": {
|
||||
"base_dir": "/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka",
|
||||
"dependencies": [
|
||||
"keras-nightly==2.5.0.dev2021032900",
|
||||
"numpy==1.19.5",
|
||||
"pandas==1.2.3",
|
||||
"sacred==0.8.2",
|
||||
"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",
|
||||
"repositories": [
|
||||
{
|
||||
"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
|
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"dirty": true,
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"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
|
||||
}
|
||||
],
|
||||
"sources": [
|
||||
[
|
||||
"sacred-training.py",
|
||||
"_sources/sacred-training_58bada47539aa2ee4591c9929ac8c60f.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"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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],
|
||||
"heartbeat": "2021-05-14T18:59:21.424846",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i5-5287U CPU @ 2.90GHz",
|
||||
"hostname": "patrycjalazna.local",
|
||||
"os": [
|
||||
"Darwin",
|
||||
"macOS-10.15.7-x86_64-i386-64bit"
|
||||
],
|
||||
"python_version": "3.9.4"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
"--capture": null,
|
||||
"--comment": null,
|
||||
"--debug": false,
|
||||
"--enforce_clean": false,
|
||||
"--file_storage": null,
|
||||
"--force": false,
|
||||
"--help": false,
|
||||
"--loglevel": null,
|
||||
"--mongo_db": null,
|
||||
"--name": null,
|
||||
"--pdb": false,
|
||||
"--print-config": false,
|
||||
"--priority": null,
|
||||
"--queue": false,
|
||||
"--s3": null,
|
||||
"--sql": null,
|
||||
"--tiny_db": null,
|
||||
"--unobserved": false
|
||||
}
|
||||
},
|
||||
"resources": [],
|
||||
"result": null,
|
||||
"start_time": "2021-05-14T18:59:21.272740",
|
||||
"status": "FAILED",
|
||||
"stop_time": "2021-05-14T18:59:21.427473"
|
||||
}
|
||||
5
my_runs/3/config.json
Normal file
5
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
|
||||
}
|
||||
8
my_runs/3/cout.txt
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8
my_runs/3/cout.txt
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@ -0,0 +1,8 @@
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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
Normal file
1
my_runs/3/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
87
my_runs/3/run.json
Normal file
87
my_runs/3/run.json
Normal file
@ -0,0 +1,87 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
|
||||
"base_dir": "/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka",
|
||||
"dependencies": [
|
||||
"keras-nightly==2.5.0.dev2021032900",
|
||||
"numpy==1.19.5",
|
||||
"pandas==1.2.3",
|
||||
"sacred==0.8.2",
|
||||
"scikit-learn==0.24.1",
|
||||
"tensorflow==2.5.0rc1"
|
||||
],
|
||||
"mainfile": "sacred-training.py",
|
||||
"name": "file_observer",
|
||||
"repositories": [
|
||||
{
|
||||
"commit": "4fc45ed8ef6201f91d818362878d3155dfe4295e",
|
||||
"dirty": true,
|
||||
"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
|
||||
}
|
||||
],
|
||||
"sources": [
|
||||
[
|
||||
"sacred-training.py",
|
||||
"_sources/sacred-training_36981fcdfc28636f97f6ecc881108ea8.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"fail_trace": [
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||||
"Traceback (most recent call last):\n",
|
||||
" 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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],
|
||||
"heartbeat": "2021-05-14T19:00:19.975884",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i5-5287U CPU @ 2.90GHz",
|
||||
"hostname": "patrycjalazna.local",
|
||||
"os": [
|
||||
"Darwin",
|
||||
"macOS-10.15.7-x86_64-i386-64bit"
|
||||
],
|
||||
"python_version": "3.9.4"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
"--capture": null,
|
||||
"--comment": null,
|
||||
"--debug": false,
|
||||
"--enforce_clean": false,
|
||||
"--file_storage": null,
|
||||
"--force": false,
|
||||
"--help": false,
|
||||
"--loglevel": null,
|
||||
"--mongo_db": null,
|
||||
"--name": null,
|
||||
"--pdb": false,
|
||||
"--print-config": false,
|
||||
"--priority": null,
|
||||
"--queue": false,
|
||||
"--s3": null,
|
||||
"--sql": null,
|
||||
"--tiny_db": null,
|
||||
"--unobserved": false
|
||||
}
|
||||
},
|
||||
"resources": [],
|
||||
"result": null,
|
||||
"start_time": "2021-05-14T18:59:43.051621",
|
||||
"status": "FAILED",
|
||||
"stop_time": "2021-05-14T19:00:19.978586"
|
||||
}
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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("file_observer")
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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
|
||||
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
|
||||
|
||||
@ex.capture
|
||||
def prepare_model():
|
||||
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')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user