ium_434760/train.py

import pandas as pd
from os import path
from tensorflow import keras
from tensorflow.keras import layers
import sys

model_name = "model.h5"
train_data=pd.read_csv('train.csv')

input_columns=["Age","Nationality","Position","Club"]
X=train_data[input_columns].to_numpy()
Y=train_data[["Overall"]].to_numpy()
model = None

if path.exists(model_name):
    model = keras.models.load_model(model_name)
else:
    model = keras.Sequential(name="fifa_overall")
    model.add(keras.Input(shape=(len(input_columns),), name="player_info"))
    model.add(layers.Dense(4, activation="relu", name="layer1"))
    model.add(layers.Dense(8, activation="relu", name="layer2"))
    model.add(layers.Dense(8, activation="relu", name="layer3"))
    model.add(layers.Dense(5, activation="relu", name="layer4"))
    model.add(layers.Dense(1, activation="relu", name="output"))

    model.compile(
        optimizer=keras.optimizers.RMSprop(),
        loss=keras.losses.MeanSquaredError(),
    )

    history = model.fit(
        X,
        Y,
        batch_size=int(sys.argv[1]),
        epochs=int(sys.argv[2]),
    )

    model.save(model_name)

test_data=pd.read_csv('test.csv')
X_test=test_data[input_columns].to_numpy()
Y_test=test_data[["Overall"]].to_numpy()

results_train = model.evaluate(X, Y, batch_size=128)
results_test = model.evaluate(X_test, Y_test, batch_size=128)
y_pred = model(X_test)

lines = ["Name;Overall;Predicted overall\n"]
for i in range(len(X_test)):
    name = test_data["Name"][i]
    lines.append(f"{name};{int(Y_test[i])};{int(y_pred[i])}\n")

with open('results.csv', 'w+', encoding="UTF-8") as f:
    f.writelines(lines)

with open('evaluation_result.txt', 'w+', encoding="UTF-8") as f:
    f.write(f"Train: {str(results_train)}\nTest: {str(results_test)}")