ium_434695/train.py

72 lines
2.6 KiB
Python
Raw Normal View History

2021-05-15 19:14:37 +02:00
import sys
2021-05-15 11:50:27 +02:00
import pandas as pd
import numpy as np
2021-05-15 19:14:37 +02:00
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 tensorflow.keras.models import Sequential
2021-05-16 19:48:11 +02:00
from sacred import Experiment
from datetime import datetime
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver
import pymongo
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
ex = Experiment("434695-mongo", interactive=False, save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
ex.observers.append(FileStorageObserver('my_runs'))
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
@ex.config
def my_config():
batch_param = int(sys.argv[1])
epoch_param = int(sys.argv[2])
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
def regression_model(epoch_param, batch_param, _run):
_run.info["prepare_model_ts"] = str(datetime.now())
# odczytanie danych z plików
vgsales_train = pd.read_csv('train.csv')
vgsales_test = pd.read_csv('test.csv')
vgsales_dev = pd.read_csv('dev.csv')
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
vgsales_train['Nintendo'] = vgsales_train['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
# podzial na X i y
X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_train = vgsales_train[['Nintendo']]
X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_test = vgsales_test[['Nintendo']]
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
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'))
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
2021-05-15 19:14:37 +02:00
2021-05-16 19:48:11 +02:00
# kompilacja
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# model fit
epochs = int(sys.argv[1])
batch_size = int(sys.argv[2])
# trenowanie modelu
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
# zapisanie modelu
model.save('vgsales_model.h5')
@ex.main
def my_main(epoch_param, batch_param):
print(regression_model())
r = ex.run()
ex.add_artifact("vgsales_model.h5")