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Save RMSE as metrics
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This commit is contained in:
Wojciech Jarmosz 2021-05-15 16:56:58 +02:00
parent 276a9ea711
commit 340888294d
11 changed files with 3 additions and 245 deletions
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7
sacred_exp.py
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@ -22,7 +22,7 @@ def my_config():
epochs = 100
@exp.capture
def training(verbose, epochs, _log):
def training(verbose, epochs, _log, _run):
pd.set_option("display.max_columns", None)
@ -68,11 +68,10 @@ def training(verbose, epochs, _log):
scores = model.evaluate(x=test_X,
y=test_Y)
evaluation_info = f"RMSE: {scores[1]}"
_log.info(evaluation_info)
_run.log_scalar("training.RMSE", scores[1])
@exp.automain
def run(verbose, epochs, _run):
def run(verbose, epochs):
training()
runner = exp.run()

5
sacred_file/1/config.json
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@ -1,5 +0,0 @@
{
"epochs": 100,
"seed": 80188794,
"verbose": 0
}

11
sacred_file/1/cout.txt
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@ -1,11 +0,0 @@
INFO - s434704 - Running command 'run'
INFO - s434704 - Started run with ID "1"
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
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
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)
WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
WARNING - tensorflow - Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
INFO - training - Verbose: 0, Epochs: 100
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
INFO - training - RMSE: 0.1313309669494629
INFO - s434704 - Completed after 0:00:08

1
sacred_file/1/metrics.json
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@ -1 +0,0 @@
{}

66
sacred_file/1/run.json
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@ -1,66 +0,0 @@
{
"artifacts": [],
"command": "run",
"experiment": {
"base_dir": "/Volumes/seagate/ium_434704",
"dependencies": [
"numpy==1.19.5",
"pandas==1.2.4",
"sacred==0.8.2",
"tensorflow==2.5.0rc1"
],
"mainfile": "sacred_exp.py",
"name": "s434704",
"repositories": [],
"sources": [
[
"sacred_exp.py",
"_sources/sacred_exp_8150ed54d93299dfccf6867ea7220971.py"
]
]
},
"heartbeat": "2021-05-15T14:30:25.850078",
"host": {
"ENV": {},
"cpu": "Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz",
"hostname": "Wojciechs-MacBook-Pro.local",
"os": [
"Darwin",
"macOS-11.2.1-x86_64-i386-64bit"
],
"python_version": "3.9.1"
},
"meta": {
"command": "run",
"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": "2021-05-15T14:30:17.351901",
"status": "COMPLETED",
"stop_time": "2021-05-15T14:30:25.848159"
}

5
sacred_file/2/config.json
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@ -1,5 +0,0 @@
{
"epochs": 100,
"seed": 426629893,
"verbose": 0
}

8
sacred_file/2/cout.txt
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@ -1,8 +0,0 @@
INFO - s434704 - Running command 'run'
INFO - s434704 - Started run with ID "2"
WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
WARNING - tensorflow - Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.
INFO - training - Verbose: 0, Epochs: 100
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
INFO - training - RMSE: 0.12944550812244415
INFO - s434704 - Completed after 0:00:05

BIN
sacred_file/2/linear_regression.h5
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Binary file not shown.

1
sacred_file/2/metrics.json
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@ -1 +0,0 @@
{}

64
sacred_file/2/run.json
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@ -1,64 +0,0 @@
{
"artifacts": [
"linear_regression.h5"
],
"command": "run",
"experiment": {
"base_dir": "/Volumes/seagate/ium_434704",
"dependencies": [
"numpy==1.19.5",
"pandas==1.2.4",
"sacred==0.8.2",
"tensorflow==2.5.0rc1"
],
"mainfile": "sacred_exp.py",
"name": "s434704",
"repositories": [],
"sources": [
[
"sacred_exp.py",
"_sources/sacred_exp_8150ed54d93299dfccf6867ea7220971.py"
]
]
},
"heartbeat": "2021-05-15T14:30:31.335228",
"host": {
"ENV": {},
"cpu": "Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz",
"hostname": "Wojciechs-MacBook-Pro.local",
"os": [
"Darwin",
"macOS-11.2.1-x86_64-i386-64bit"
],
"python_version": "3.9.1"
},
"meta": {
"command": "run",
"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": "2021-05-15T14:30:25.893032",
"status": "COMPLETED",
"stop_time": "2021-05-15T14:30:31.333523"
}

80
sacred_file/_sources/sacred_exp_8150ed54d93299dfccf6867ea7220971.py
View File

@ -1,80 +0,0 @@
import sys
import pandas as pd
import numpy as np
import tensorflow as tf
import os.path
from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
exp = Experiment("s434704", interactive=False, save_git_info=False)
exp.observers.append(FileStorageObserver("sacred_file"))
# exp.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name="sacred"))
@exp.config
def my_config():
verbose = 0
epochs = 100
@exp.capture
def training(verbose, epochs, _log):
pd.set_option("display.max_columns", None)
# Wczytanie danych
train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
# Stworzenie modelu
columns_to_use = ['Year', 'Runtime', 'Netflix']
train_X = tf.convert_to_tensor(train_data[columns_to_use])
train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
normalizer = preprocessing.Normalization(input_shape=[3,])
normalizer.adapt(train_X)
model = keras.Sequential([
keras.Input(shape=(len(columns_to_use),)),
normalizer,
layers.Dense(30, activation='relu'),
layers.Dense(10, activation='relu'),
layers.Dense(25, activation='relu'),
layers.Dense(1)
])
model.compile(loss='mean_absolute_error',
optimizer=tf.keras.optimizers.Adam(0.001),
metrics=[tf.keras.metrics.RootMeanSquaredError()])
params = f"Verbose: {verbose}, Epochs: {epochs}"
_log.info(params)
model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
model.save('linear_regression.h5')
# Evaluation
test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
columns_to_use = ['Year', 'Runtime', 'Netflix']
test_X = tf.convert_to_tensor(test_data[columns_to_use])
test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
scores = model.evaluate(x=test_X,
y=test_Y)
evaluation_info = f"RMSE: {scores[1]}"
_log.info(evaluation_info)
@exp.automain
def run(verbose, epochs, _run):
training()
runner = exp.run()
exp.add_source_file("./training.py")
exp.add_artifact("linear_regression.h5")