from tensorflow.keras.models import load_model
import joblib
import numpy as np

# Cargar
model = load_model('model')  # si es Keras SavedModel
scaler_X = joblib.load('scaler_X.joblib')  # si existe
scaler_y = joblib.load('scaler_y.joblib')  # si existe

# Predecir
# X_raw = ...  # tu array de features sin escalar
X_scaled = scaler_X.transform(X_raw)
y_pred_scaled = model.predict(X_scaled)
y_pred = scaler_y.inverse_transform(y_pred_scaled)

Notes

Asegúrate de que las versiones de TensorFlow / scikit-learn sean compatibles.
Completa este card con detalles del dataset, preprocessing y licencias si corresponde.

bermejo4/Tiresias_MLP_Longitude-w2-p1-v3

Model card for MLP_102030_W_2_PAST_1_Longitude version 3

Overview

Metrics

Model inputs / shape

Scalers

Usage example

Notes

bermejo4/
Tiresias_MLP_Longitude-w2-p1-v3

Model card for `MLP_102030_W_2_PAST_1_Longitude` version `3`