Save RMSE as metrics
This commit is contained in:
Wojciech Jarmosz
parent
276a9ea711
commit
340888294d
@ -22,7 +22,7 @@ def my_config():
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epochs = 100
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epochs = 100
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@exp.capture
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@exp.capture
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def training(verbose, epochs, _log):
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def training(verbose, epochs, _log, _run):
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pd.set_option("display.max_columns", None)
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pd.set_option("display.max_columns", None)
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@ -68,11 +68,10 @@ def training(verbose, epochs, _log):
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scores = model.evaluate(x=test_X,
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scores = model.evaluate(x=test_X,
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y=test_Y)
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y=test_Y)
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evaluation_info = f"RMSE: {scores[1]}"
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_run.log_scalar("training.RMSE", scores[1])
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_log.info(evaluation_info)
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@exp.automain
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@exp.automain
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def run(verbose, epochs, _run):
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def run(verbose, epochs):
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training()
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training()
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runner = exp.run()
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runner = exp.run()
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@ -1,5 +0,0 @@
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{
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"epochs": 100,
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"seed": 80188794,
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"verbose": 0
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}
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@ -1,11 +0,0 @@
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INFO - s434704 - Running command 'run'
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INFO - s434704 - Started run with ID "1"
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2021-05-15 16:30:17.771747: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
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To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
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2021-05-15 16:30:18.525767: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
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WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
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WARNING - tensorflow - Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
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INFO - training - Verbose: 0, Epochs: 100
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1/11 [=>............................] - ETA: 1s - loss: 0.0957 - root_mean_squared_error: 0.1177
11/11 [==============================] - 0s 674us/step - loss: 0.1033 - root_mean_squared_error: 0.1313
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INFO - training - RMSE: 0.1313309669494629
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INFO - s434704 - Completed after 0:00:08
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@ -1 +0,0 @@
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{}
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@ -1,66 +0,0 @@
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{
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"artifacts": [],
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"command": "run",
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"experiment": {
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"base_dir": "/Volumes/seagate/ium_434704",
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"dependencies": [
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"numpy==1.19.5",
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"pandas==1.2.4",
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"sacred==0.8.2",
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"tensorflow==2.5.0rc1"
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],
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"mainfile": "sacred_exp.py",
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"name": "s434704",
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"repositories": [],
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"sources": [
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[
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"sacred_exp.py",
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"_sources/sacred_exp_8150ed54d93299dfccf6867ea7220971.py"
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]
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]
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},
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"heartbeat": "2021-05-15T14:30:25.850078",
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"host": {
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"ENV": {},
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"cpu": "Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz",
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"hostname": "Wojciechs-MacBook-Pro.local",
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"os": [
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"Darwin",
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"macOS-11.2.1-x86_64-i386-64bit"
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],
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"python_version": "3.9.1"
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},
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"meta": {
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"command": "run",
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"options": {
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"--beat-interval": null,
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"--capture": null,
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"--comment": null,
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"--debug": false,
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"--enforce_clean": false,
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"--file_storage": null,
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"--force": false,
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"--help": false,
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"--loglevel": null,
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"--mongo_db": null,
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"--name": null,
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"--pdb": false,
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"--print-config": false,
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"--priority": null,
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"--queue": false,
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"--s3": null,
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"--sql": null,
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"--tiny_db": null,
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"--unobserved": false,
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"COMMAND": null,
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"UPDATE": [],
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"help": false,
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"with": false
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}
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},
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"resources": [],
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"result": null,
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"start_time": "2021-05-15T14:30:17.351901",
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"status": "COMPLETED",
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"stop_time": "2021-05-15T14:30:25.848159"
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}
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@ -1,5 +0,0 @@
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{
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"epochs": 100,
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"seed": 426629893,
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"verbose": 0
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}
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@ -1,8 +0,0 @@
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INFO - s434704 - Running command 'run'
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INFO - s434704 - Started run with ID "2"
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WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
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WARNING - tensorflow - Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
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INFO - training - Verbose: 0, Epochs: 100
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1/11 [=>............................] - ETA: 0s - loss: 0.0914 - root_mean_squared_error: 0.1140
11/11 [==============================] - 0s 638us/step - loss: 0.1024 - root_mean_squared_error: 0.1294
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INFO - training - RMSE: 0.12944550812244415
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INFO - s434704 - Completed after 0:00:05
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Binary file not shown.
@ -1 +0,0 @@
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{}
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@ -1,64 +0,0 @@
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{
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"artifacts": [
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"linear_regression.h5"
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],
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"command": "run",
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"experiment": {
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"base_dir": "/Volumes/seagate/ium_434704",
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"dependencies": [
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"numpy==1.19.5",
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"pandas==1.2.4",
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"sacred==0.8.2",
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"tensorflow==2.5.0rc1"
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],
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"mainfile": "sacred_exp.py",
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"name": "s434704",
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"repositories": [],
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"sources": [
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[
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"sacred_exp.py",
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"_sources/sacred_exp_8150ed54d93299dfccf6867ea7220971.py"
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]
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]
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},
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"heartbeat": "2021-05-15T14:30:31.335228",
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"host": {
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"ENV": {},
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"cpu": "Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz",
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"hostname": "Wojciechs-MacBook-Pro.local",
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"os": [
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"Darwin",
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"macOS-11.2.1-x86_64-i386-64bit"
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],
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"python_version": "3.9.1"
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},
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"meta": {
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"command": "run",
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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": "2021-05-15T14:30:25.893032",
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"status": "COMPLETED",
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"stop_time": "2021-05-15T14:30:31.333523"
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}
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@ -1,80 +0,0 @@
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import sys
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import os.path
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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exp = Experiment("s434704", interactive=False, save_git_info=False)
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exp.observers.append(FileStorageObserver("sacred_file"))
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# exp.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name="sacred"))
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@exp.config
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def my_config():
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verbose = 0
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epochs = 100
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@exp.capture
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def training(verbose, epochs, _log):
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pd.set_option("display.max_columns", None)
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# Wczytanie danych
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train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
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# Stworzenie modelu
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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train_X = tf.convert_to_tensor(train_data[columns_to_use])
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train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
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normalizer = preprocessing.Normalization(input_shape=[3,])
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normalizer.adapt(train_X)
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model = keras.Sequential([
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keras.Input(shape=(len(columns_to_use),)),
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normalizer,
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layers.Dense(30, activation='relu'),
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layers.Dense(10, activation='relu'),
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layers.Dense(25, activation='relu'),
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layers.Dense(1)
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])
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model.compile(loss='mean_absolute_error',
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optimizer=tf.keras.optimizers.Adam(0.001),
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metrics=[tf.keras.metrics.RootMeanSquaredError()])
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params = f"Verbose: {verbose}, Epochs: {epochs}"
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_log.info(params)
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model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
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model.save('linear_regression.h5')
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# Evaluation
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test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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test_X = tf.convert_to_tensor(test_data[columns_to_use])
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test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
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scores = model.evaluate(x=test_X,
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y=test_Y)
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evaluation_info = f"RMSE: {scores[1]}"
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_log.info(evaluation_info)
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@exp.automain
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def run(verbose, epochs, _run):
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training()
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runner = exp.run()
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exp.add_source_file("./training.py")
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exp.add_artifact("linear_regression.h5")
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