test.py

import tensorflow as tf
import pandas as pd

from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import CategoricalCrossentropy

import gc


from utils.helper import *
from utils.constants import *
from models.MLP import MultiLayerPerceptron



results=[]
histories=[]

try:
    for dataset in  UNIVARIATE_DATASET_NAMES_2018:
        with tf.device("/device:GPU:0"):
            dataset="ArrowHead"
            # Load data
            df = read_dataset(dataset)

            # apply one-hot encoder
            y_train=to_categorical(df[1])
            y_test=to_categorical(df[3])

            # Compile the model
            model = MultiLayerPerceptron(input_size=df[0].shape[1], num_labels=y_train.shape[1])
            model.compile(optimizer=Adam(), loss=CategoricalCrossentropy(), metrics=['accuracy'])

            # Train the model
            history = model.fit(df[0], y_train, batch_size=16, epochs=1000, validation_split=0.2)

            # Evaluate the model
            loss, accuracy = model.evaluate(df[2], y_test)

            results.append([dataset, loss, accuracy])
            histories.append(history.history)
        clear_gpu_memory()
        gc.collect()
            
    pd.DataFrame(results, columns=["Dataset","Test loss", "Test accuracy"]).to_csv("result.csv", index=False)

except Exception as e:
    print("An error occurred:", e)


for i in range(len(histories)):
    plot_loss(histories[i], UNIVARIATE_DATASET_NAMES_2018[i])