ium_434788/Zadanie_5_Docker.py
2021-04-17 18:33:36 +02:00

48 lines
1.3 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
url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
wget.download(url, out='winequality-red.csv', bar=None)
wine=pd.read_csv('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=0.2,train_size=0.8, 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)
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))
print(classification_report(y_test,y_pred))