71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import sys
|
|
from keras.backend import mean
|
|
import pandas as pd
|
|
import numpy as np
|
|
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 keras.models import Sequential
|
|
from sacred import Experiment
|
|
from sacred.observers import MongoObserver
|
|
|
|
ex = Experiment("434742-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.config
|
|
def my_config():
|
|
epochs = 10
|
|
batch_size = 16
|
|
|
|
@ex.capture
|
|
def prepare_model(epochs, batch_size):
|
|
# odczytanie danych z plików
|
|
avocado_train = pd.read_csv('avocado_train.csv')
|
|
avocado_test = pd.read_csv('avocado_test.csv')
|
|
avocado_validate = pd.read_csv('avocado_validate.csv')
|
|
|
|
|
|
# podzial na X i y
|
|
X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
|
y_train = avocado_train[['type']]
|
|
X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
|
|
y_test = avocado_test[['type']]
|
|
|
|
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'))
|
|
|
|
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
|
|
|
|
# kompilacja
|
|
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
|
|
|
|
# trenowanie modelu
|
|
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
|
|
|
|
# predykcja
|
|
prediction = model.predict(X_test)
|
|
|
|
# ewaluacja
|
|
rmse = mean_squared_error(y_test, prediction)
|
|
|
|
# zapisanie modelu
|
|
model.save('avocado_model.h5')
|
|
|
|
return rmse
|
|
|
|
|
|
|
|
@ex.automain
|
|
def my_main(epochs, batch_size):
|
|
print(prepare_model())
|
|
|
|
ex.run()
|
|
ex.add_artifact('avocado_model.h5') |