ium_464903/Biblioteka_DL_predykcja.ipynb

Predykcja na modelu zapisanym w pliku

Import bibliotek

import keras
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
import numpy as np
from sklearn.model_selection import train_test_split

Pobranie modelu z pliku

model = keras.models.load_model('./model.keras')

Przygotowanie danych do testowania modelu

dataset = pd.read_csv('./lettuce-growth-days/lettuce_dataset_updated.csv', encoding='ISO-8859-1')

ph_level = dataset['pH Level'].values.tolist()
temp_F = dataset['Temperature (F)'].values.tolist()
humid = dataset['Humidity'].values.tolist()
days = dataset['Growth Days'].values.tolist()
plant_id = dataset['Plant_ID'].values.tolist()

X = []
Y = []

id = plant_id[0]
temp_sum = 0
humid_sum = 0
ph_level_sum = 0
day = 1

for i in range(0, len(plant_id)):
    if plant_id[i] == id:
        temp_sum += temp_F[i]
        humid_sum += humid[i]
        ph_level_sum += ph_level[i]
        day = days[i]
    else:
        temp = []
        temp.append(temp_sum/day)
        temp.append(humid_sum/day)
        temp.append(ph_level_sum/day)
        X.append(temp)
        Y.append(day)
        temp_sum = 0
        humid_sum = 0
        ph_level_sum = 0
        day = 1
        id = plant_id[i]

scaler = MinMaxScaler()
X = scaler.fit_transform(X)
X = np.array(X)
Y = np.array(Y)

encoder = OneHotEncoder(sparse=False)
y_onehot = encoder.fit_transform(Y.reshape(-1,1))

X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)

C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
  warnings.warn(

Testowanie modelu

test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Dokładność testowa: {test_accuracy:.2%}")

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 216ms/step - accuracy: 0.8571 - loss: 0.7300
Dokładność testowa: 85.71%

Predykcja na zbiorze testowym

predictions = model.predict(X_test)

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step

Zapisanie wyników predykcji do pliku tekstowego

with open("predictions.txt", "w") as txt_file:
    for line in predictions:
        txt_file.write(" ".join(str(line)) + "\n")