s487187/ium_487187
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

s

Browse Source
This commit is contained in:
Jakub Zaręba 2023-05-10 20:50:43 +02:00
parent 134226fa79
commit 44a4f174c0
1 changed files with 50 additions and 50 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
train.py
View File

@ -1,13 +1,13 @@
from sacred import Experiment from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver from sacred.observers import MongoObserver, FileStorageObserver
# ex = Experiment('s487187-training') ex = Experiment('s487187-training')
# ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
# ex.observers.append(FileStorageObserver('results')) ex.observers.append(FileStorageObserver('results'))
# ex.use_git = False ex.use_git = False
# @ex.config @ex.config
# def my_config(): def my_config():
data_file = 'data.csv' data_file = 'data.csv'
model_file = 'model.h5' model_file = 'model.h5'
epochs = 10 epochs = 10
@ -15,8 +15,8 @@ batch_size = 32
test_size = 0.2 test_size = 0.2
random_state = 42 random_state = 42
# @ex.capture @ex.capture
# def train_model(data_file, model_file, epochs, batch_size, test_size, random_state): def train_model(data_file, model_file, epochs, batch_size, test_size, random_state):
import pandas as pd import pandas as pd
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import MinMaxScaler
@ -60,10 +60,10 @@ print('Test accuracy:', accuracy)
model.save(model_file) model.save(model_file)
# return accuracy return accuracy
# @ex.main @ex.main
# def run_experiment(): def run_experiment():
# accuracy = train_model() accuracy = train_model()
# ex.log_scalar('accuracy', accuracy) ex.log_scalar('accuracy', accuracy)
# ex.add_artifact('model.h5') ex.add_artifact('model.h5')