s434695
8172699654
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72 lines
2.6 KiB
Python
Executable File
72 lines
2.6 KiB
Python
Executable File
import sys
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.models import Sequential
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from sacred import Experiment
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from datetime import datetime
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from sacred.observers import FileStorageObserver
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from sacred.observers import MongoObserver
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import pymongo
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ex = Experiment("434695-mongo", interactive=False, save_git_info=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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batch_param = int(sys.argv[1])
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epoch_param = int(sys.argv[2])
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def regression_model(epoch_param, batch_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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# odczytanie danych z plików
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vgsales_train = pd.read_csv('train.csv')
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vgsales_test = pd.read_csv('test.csv')
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vgsales_dev = pd.read_csv('dev.csv')
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vgsales_train['Nintendo'] = vgsales_train['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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# podzial na X i y
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X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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y_train = vgsales_train[['Nintendo']]
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X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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y_test = vgsales_test[['Nintendo']]
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print(X_train.shape[1])
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# keras model
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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# kompilacja
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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# model fit
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epochs = int(sys.argv[1])
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batch_size = int(sys.argv[2])
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# trenowanie modelu
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
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# zapisanie modelu
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model.save('vgsales_model.h5')
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@ex.main
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def my_main(epoch_param, batch_param):
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print(regression_model())
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r = ex.run()
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ex.add_artifact("vgsales_model.h5") |