def train(output_units=OUTPUT_UNITS, num_units=NUM_UNITS, loss=LOSS, learning_rate=LEARNING_RATE):
# Generate the training sequences
inputs, targets = generate_training_sequences(SEQUENCE_LENGTH)
# Build the network
model = build_model(output_units, num_units, loss, learning_rate)
# Train the model
model.fit(inputs, targets, epochs=EPOCHS, batch_size=BATCH_SIZE)
# Save the model
model.save(SAVE_MODEL_PATH)
def train(output_units=OUTPUT_UNITS, num_units=NUM_UNITS, loss=LOSS, learning_rate=LEARNING_RATE):
"""Train and save TF model.
:param output_units (int): Num output units
:param num_units (list of int): Num of units in hidden layers
:param loss (str): Type of loss function to use
:param learning_rate (float): Learning rate to apply
"""
# generate the training sequences
inputs, targets = generate_training_sequences(SEQUENCE_LENGTH)
# build the network
model = build_model(output_units, num_units, loss, learning_rate)
# train the model
model.fit(inputs, targets, epochs=EPOCHS, batch_size=BATCH_SIZE)
# save the model
model.save(SAVE_MODEL_PATH)
def train(output_units=OUTPUT_UNITS,
num_units=NUM_UNITS,
loss=LOSS,
learning_rate=LEARNING_RATE):
"""Entrenar y guardar modelo TF.
:param output_units (int): Número de unidades de salida
:param num_units (list of int): Número de unidades en capas ocultas
:param loss (str): Tipo de función de pérdida a utilizar
:param learning_rate (float): Tasa de aprendizaje para aplicar
"""
# generar las secuencias de entrenamiento
inputs, targets = generate_training_sequences(SEQUENCE_LENGTH)
# construir la red
model = build_model(output_units, num_units, loss, learning_rate)
# entrenar al modelo
model.fit(inputs, targets, epochs=EPOCHS, batch_size=BATCH_SIZE)
# guardar el modelo
model.save(SAVE_MODEL_PATH)