ium_434760/train.py

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

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(),
)

batch_size = int(sys.argv[1])
epochs = int(sys.argv[2])
mlflow.log_param("batch_size", batch_size)
mlflow.log_param("epochs", epochs)

history = model.fit(
    X,
    Y,
    batch_size=batch_size,
    epochs=epochs,
)
model.save(model_name)
signature = mlflow.models.signature.infer_signature(X, model.predict(X))
input_example = {
    "Age": 0.38,
    "Nationality": 0.175,
    "Position": 1,
    "Club" :0.91846154
}
mlflow.keras.save_model(model, "fifa_overall", signature=signature, input_example = input_example)