def __init__(self, network_params=None):
logger.info("Testeo de NeuralNetwork con datos de Combined Cycle Power Plant")
# Datos
logger.info("Cargando datos...")
data = load_ccpp()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 30, 1], activation='ReLU',
classification=False, seed=123)
self.model = NeuralNetwork(network_params)
# Seteo a mano
self.model.set_l1(5e-7)
self.model.set_l2(3e-4)
self.model.set_dropout_ratios([0.0, 0.0])
def __init__(self, network_params=None):
logger.info(
"Testeo de NeuralNetwork con datos de Combined Cycle Power Plant")
# Datos
logger.info("Cargando datos...")
data = load_ccpp()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 30, 1],
activation='ReLU',
classification=False,
seed=123)
self.model = NeuralNetwork(network_params)
# Seteo a mano
self.model.set_l1(5e-7)
self.model.set_l2(3e-4)
self.model.set_dropout_ratios([0.0, 0.0])
from learninspy.core.model import NeuralNetwork, NetworkParameters
from learninspy.core.optimization import OptimizerParameters
from learninspy.core.stops import criterion
from learninspy.utils.data import LocalLabeledDataSet, load_ccpp
from learninspy.utils.evaluation import RegressionMetrics
from learninspy.utils.plots import plot_fitting
from learninspy.utils.fileio import get_logger
import os
logger = get_logger(name='learninspy-demo_ccpp')
# -- 1.a) Carga de datos
logger.info("Cargando datos de Combined Cycle Power Plant ...")
dataset = load_ccpp()
dataset = LocalLabeledDataSet(dataset)
rows, cols = dataset.shape
logger.info("Dimension de datos: %i x %i", rows, cols)
train, valid, test = dataset.split_data([0.5, 0.3,
0.2]) # Particiono en conjuntos
# -- 1.b) Normalización
"""
std = StandardScaler()
std.fit(train)
train = std.transform(train)
valid = std.transform(valid)
test = std.transform(test)
"""
from learninspy.core.model import NeuralNetwork, NetworkParameters
from learninspy.core.optimization import OptimizerParameters
from learninspy.core.stops import criterion
from learninspy.utils.data import LocalLabeledDataSet, load_ccpp
from learninspy.utils.evaluation import RegressionMetrics
from learninspy.utils.plots import plot_fitting
from learninspy.utils.fileio import get_logger
import os
logger = get_logger(name='learninspy-demo_ccpp')
# -- 1.a) Carga de datos
logger.info("Cargando datos de Combined Cycle Power Plant ...")
dataset = load_ccpp()
dataset = LocalLabeledDataSet(dataset)
rows, cols = dataset.shape
logger.info("Dimension de datos: %i x %i", rows, cols)
train, valid, test = dataset.split_data([0.5, 0.3, 0.2]) # Particiono en conjuntos
# -- 1.b) Normalización
"""
std = StandardScaler()
std.fit(train)
train = std.transform(train)
valid = std.transform(valid)
test = std.transform(test)
"""
# -- 2) Selección de parámetros