Exemple #1
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    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])
Exemple #2
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    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])
Exemple #3
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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)
"""
Exemple #4
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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