Version 2 con funciones incluidas

keras_model_functions.py

@@ -0,0 +1,59 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping
+
+def huber_loss(y_true, y_pred, delta=1.0):
+    """
+    Función de pérdida Huber personalizada.
+    """
+    error = y_true - y_pred
+    is_small_error = tf.abs(error) <= delta
+    small_error_loss = 0.5 * tf.square(error)
+    big_error_loss = delta * (tf.abs(error) - 0.5 * delta)
+    return tf.where(is_small_error, small_error_loss, big_error_loss)
+
+def create_sequences(data, time_steps):
+    """
+    Función para crear secuencias de datos de entrada y salida.
+    """
+    X, y = [], []
+    for i in range(len(data) - time_steps):
+        X.append(data[i:(i + time_steps), :])
+        y.append(data[i + time_steps, 0])
+    return np.array(X), np.array(y)
+
+def build_lstm_model(time_steps, input_size):
+    """
+    Función para construir el modelo LSTM mejorado.
+    """
+    model = Sequential([
+        LSTM(100, return_sequences=True, input_shape=(time_steps, input_size)),
+        Dropout(0.2),
+        LSTM(100),
+        Dropout(0.2),
+        Dense(50),
+        Dense(1)
+    ])
+
+    optimizer = Adam(learning_rate=0.001)
+    model.compile(optimizer=optimizer, loss=huber_loss)
+    return model
+
+def train_model(model, X_train, y_train, X_val, y_val, epochs=200, batch_size=32):
+    """
+    Función para entrenar el modelo LSTM mejorado.
+    """
+    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
+
+    history = model.fit(
+        X_train, y_train,
+        batch_size=batch_size,
+        epochs=epochs,
+        validation_data=(X_val, y_val),
+        callbacks=[early_stopping],
+        verbose=1
+    )
+
+    return history