ium_434788/Zajęcia7/Zadanie_1_Sacred.py

76 lines
2.1 KiB
Python

from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import wget
import numpy as np
from sacred.observers import FileStorageObserver
from sacred import Experiment
from datetime import datetime
import os
ex = Experiment("file_observer", interactive=True)
ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
@ex.config
def my_config():
train_size_param = 0.8
test_size_param = 0.2
@ex.capture
def prepare_model(train_size_param, test_size_param, _run):
_run.info["prepare_model_ts"] = str(datetime.now())
url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
wget.download(url, out='Zajęcia7/winequality-red.csv', bar=None)
wine=pd.read_csv('Zajęcia7/winequality-red.csv')
wine
y = wine.quality
y.head()
x = wine.drop(['quality'], axis= 1)
x.head()
x=((x-x.min())/(x.max()-x.min())) #Normalizacja
x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=test_size_param, train_size=train_size_param, random_state=21)
def regression_model():
model = Sequential()
model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
model.add(Dense(64,activation = "relu"))
model.add(Dense(1,activation = "relu"))
model.compile(optimizer = "adam", loss = "mean_squared_error")
return model
model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)
model.save('Zajęcia7/saved_model')
y_pred = model.predict(x_test)
y_pred[:5]
y_pred = np.around(y_pred, decimals=0)
y_pred[:5]
print(accuracy_score(y_test, y_pred))
_run.info["Final Results: "] = classification_report(y_test,y_pred)
return(classification_report(y_test,y_pred))
@ex.main
def my_main(train_size_param, test_size_param):
print(prepare_model()) ## Nie musimy przekazywać wartości
r = ex.run()
ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")