fixed sacred
This commit is contained in:
patrycjalazna
parent
9dab0233db
commit
6596e3373e
BIN
avocado_model.h5
BIN
avocado_model.h5
Binary file not shown.
2001
avocado_test.csv
2001
avocado_test.csv
File diff suppressed because it is too large
Load Diff
6001
avocado_train.csv
6001
avocado_train.csv
File diff suppressed because it is too large
Load Diff
2001
avocado_validate.csv
2001
avocado_validate.csv
File diff suppressed because it is too large
Load Diff
@ -1,5 +0,0 @@
|
|||||||
{
|
|
||||||
"batch_size": 16,
|
|
||||||
"epochs": 10,
|
|
||||||
"seed": 752365448
|
|
||||||
}
|
|
||||||
@ -1 +0,0 @@
|
|||||||
{}
|
|
||||||
@ -1,85 +0,0 @@
|
|||||||
{
|
|
||||||
"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": false,
|
|
||||||
"url": "https://git.wmi.amu.edu.pl/s434742/ium_434742.git"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"sources": [
|
|
||||||
[
|
|
||||||
"sacred-training.py",
|
|
||||||
"_sources/sacred-training_58bada47539aa2ee4591c9929ac8c60f.py"
|
|
||||||
]
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"fail_trace": [
|
|
||||||
"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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-training.py\", line 68, in my_main\n print(prepare_model())\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",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 462, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 1867, in __init__\n self._open_handles(src, kwds)\n",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/pandas/io/parsers.py\", line 1362, in _open_handles\n self.handles = get_handle(\n",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/pandas/io/common.py\", line 642, in get_handle\n handle = open(\n",
|
|
||||||
"FileNotFoundError: [Errno 2] No such file or directory: 'avocado_train.csv'\n"
|
|
||||||
],
|
|
||||||
"heartbeat": "2021-05-14T18:58:43.968999",
|
|
||||||
"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": "2021-05-14T18:58:43.955201",
|
|
||||||
"status": "FAILED",
|
|
||||||
"stop_time": "2021-05-14T18:58:43.972665"
|
|
||||||
}
|
|
||||||
@ -1,5 +0,0 @@
|
|||||||
{
|
|
||||||
"batch_size": 16,
|
|
||||||
"epochs": 10,
|
|
||||||
"seed": 808581577
|
|
||||||
}
|
|
||||||
@ -1 +0,0 @@
|
|||||||
{}
|
|
||||||
@ -1,78 +0,0 @@
|
|||||||
{
|
|
||||||
"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_58bada47539aa2ee4591c9929ac8c60f.py"
|
|
||||||
]
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"fail_trace": [
|
|
||||||
"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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-training.py\", line 68, in my_main\n print(prepare_model())\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",
|
|
||||||
" 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",
|
|
||||||
"NameError: name 'epochs' is not defined\n"
|
|
||||||
],
|
|
||||||
"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"
|
|
||||||
}
|
|
||||||
@ -1,5 +0,0 @@
|
|||||||
{
|
|
||||||
"batch_size": 16,
|
|
||||||
"epochs": 10,
|
|
||||||
"seed": 981585385
|
|
||||||
}
|
|
||||||
@ -1,8 +0,0 @@
|
|||||||
INFO - file_observer - Running command 'my_main'
|
|
||||||
INFO - file_observer - Started run with ID "3"
|
|
||||||
9
|
|
||||||
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
|
|
||||||
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
|
||||||
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)
|
|
||||||
Epoch 1/10
|
|
||||||
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
|
|
||||||
@ -1 +0,0 @@
|
|||||||
{}
|
|
||||||
@ -1,87 +0,0 @@
|
|||||||
{
|
|
||||||
"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": [
|
|
||||||
"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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-training.py\", line 68, in my_main\n print(prepare_model())\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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-training.py\", line 60, in prepare_model\n model.save('avocado_model.h5')\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/keras/saving/saving_utils.py\", line 152, in model_metadata\n raise e\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/keras/utils/generic_utils.py\", line 508, in serialize_keras_object\n raise e\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
"NotImplementedError: Layer ModuleWrapper has arguments in `__init__` and therefore must override `get_config`.\n"
|
|
||||||
],
|
|
||||||
"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"
|
|
||||||
}
|
|
||||||
@ -1,5 +0,0 @@
|
|||||||
{
|
|
||||||
"batch_size": 16,
|
|
||||||
"epochs": 10,
|
|
||||||
"seed": 347156927
|
|
||||||
}
|
|
||||||
@ -1,18 +0,0 @@
|
|||||||
INFO - 434742-file - Running command 'my_main'
|
|
||||||
INFO - 434742-file - Started run with ID "4"
|
|
||||||
9
|
|
||||||
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
|
|
||||||
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
|
||||||
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)
|
|
||||||
Epoch 1/10
|
|
||||||
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
|
|
||||||
Epoch 2/10
|
|
||||||
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
|
|
||||||
Epoch 3/10
|
|
||||||
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
|
|
||||||
Epoch 4/10
|
|
||||||
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
|
|
||||||
Epoch 5/10
|
|
||||||
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
|
|
||||||
Epoch 6/10
|
|
||||||
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
|
|
||||||
@ -1 +0,0 @@
|
|||||||
{}
|
|
||||||
@ -1,85 +0,0 @@
|
|||||||
{
|
|
||||||
"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-fileobserver.py",
|
|
||||||
"name": "434742-file",
|
|
||||||
"repositories": [],
|
|
||||||
"sources": [
|
|
||||||
[
|
|
||||||
"sacred-fileobserver.py",
|
|
||||||
"_sources/sacred-fileobserver_3e3cbf02c631699c29e47a557c9a608d.py"
|
|
||||||
]
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"fail_trace": [
|
|
||||||
"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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-fileobserver.py\", line 68, in my_main\n print(prepare_model())\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",
|
|
||||||
" File \"/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/sacred-fileobserver.py\", line 60, in prepare_model\n model.save('avocado_model.h5')\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/keras/saving/saving_utils.py\", line 152, in model_metadata\n raise e\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
" File \"/usr/local/lib/python3.9/site-packages/keras/utils/generic_utils.py\", line 508, in serialize_keras_object\n raise e\n",
|
|
||||||
" 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",
|
|
||||||
" 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",
|
|
||||||
"NotImplementedError: Layer ModuleWrapper has arguments in `__init__` and therefore must override `get_config`.\n"
|
|
||||||
],
|
|
||||||
"heartbeat": "2021-05-15T10:56:44.017631",
|
|
||||||
"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,
|
|
||||||
"COMMAND": null,
|
|
||||||
"UPDATE": [],
|
|
||||||
"help": false,
|
|
||||||
"with": false
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"resources": [],
|
|
||||||
"result": null,
|
|
||||||
"start_time": "2021-05-15T10:56:10.769284",
|
|
||||||
"status": "FAILED",
|
|
||||||
"stop_time": "2021-05-15T10:56:44.020146"
|
|
||||||
}
|
|
||||||
@ -1,71 +0,0 @@
|
|||||||
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("434742-file", interactive=False, save_git_info=False)
|
|
||||||
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.automain
|
|
||||||
def my_main(epochs, batch_size):
|
|
||||||
print(prepare_model())
|
|
||||||
|
|
||||||
ex.run()
|
|
||||||
ex.add_artifact('avocado_model.h5')
|
|
||||||
@ -1,71 +0,0 @@
|
|||||||
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(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')
|
|
||||||
@ -1,71 +0,0 @@
|
|||||||
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')
|
|
||||||
Loading…
Reference in New Issue
Block a user