Python SvmOnMnist Exemples

Exemple #1

logging.basicConfig(level=logging.INFO)

from robo.fmin import entropy_search

from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist, SvmOnVehicle, SvmOnCovertype
from hpolib.benchmarks.ml.residual_networks import ResidualNeuralNetworkOnCIFAR10
from hpolib.benchmarks.ml.conv_net import ConvolutionalNeuralNetworkOnCIFAR10, ConvolutionalNeuralNetworkOnSVHN

run_id = int(sys.argv[1])
dataset = sys.argv[2]
seed = int(sys.argv[3])

rng = np.random.RandomState(seed)

if dataset == "mnist":
    f = SvmOnMnist(rng=rng)
    num_iterations = 15
    output_path = "./experiments/fabolas/results/svm_%s/entropy_search_%d" % (
        dataset, run_id)
elif dataset == "vehicle":
    f = SvmOnVehicle(rng=rng)
    num_iterations = 15
    output_path = "./experiments/fabolas/results/svm_%s/entropy_search_%d" % (
        dataset, run_id)
elif dataset == "covertype":
    f = SvmOnCovertype(rng=rng)
    num_iterations = 15
    output_path = "./experiments/fabolas/results/svm_%s/entropy_search_%d" % (
        dataset, run_id)
elif dataset == "cifar10":
    f = ConvolutionalNeuralNetworkOnCIFAR10(rng=rng)

Exemple #2

from robo.solver.hyperband_datasets_size import HyperBand_DataSubsets

from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist, SvmOnVehicle, SvmOnCovertype
#from hpolib.benchmarks.ml.residual_networks import ResidualNeuralNetworkOnCIFAR10
from hpolib.benchmarks.ml.conv_net import ConvolutionalNeuralNetworkOnCIFAR10
#, ConvolutionalNeuralNetworkOnSVHN


run_id = int(sys.argv[1])
dataset = sys.argv[2]
seed = int(sys.argv[3])

rng = np.random.RandomState(seed)
if dataset == "mnist":
    f = SvmOnMnist(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
    s_max = f.train.shape[0]
    s_min = 100
elif dataset == "vehicle":
    f = SvmOnVehicle(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
    s_max = f.train.shape[0]
    s_min = 100
elif dataset == "mnist_random":
    f = SvmOnMnist(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
    s_max = f.train.shape[0]
    s_min = s_max 
elif dataset == "vehicle_random":
    f = SvmOnVehicle(rng=rng)

Exemple #3

logging.basicConfig(level=logging.INFO)

from robo.solver.hyperband_datasets_size import HyperBand_DataSubsets

from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist, SvmOnVehicle, SvmOnCovertype
from hpolib.benchmarks.ml.residual_networks import ResidualNeuralNetworkOnCIFAR10
from hpolib.benchmarks.ml.conv_net import ConvolutionalNeuralNetworkOnCIFAR10, ConvolutionalNeuralNetworkOnSVHN

run_id = int(sys.argv[1])
dataset = sys.argv[2]
seed = int(sys.argv[3])

rng = np.random.RandomState(seed)

if dataset == "mnist":
    f = SvmOnMnist(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (
        dataset, run_id)
elif dataset == "vehicle":
    f = SvmOnVehicle(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (
        dataset, run_id)
elif dataset == "covertype":
    f = SvmOnCovertype(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (
        dataset, run_id)
elif dataset == "cifar10":
    f = ConvolutionalNeuralNetworkOnCIFAR10(rng=rng)
    output_path = "./experiments/fabolas/results/cnn_%s/hyperband_%d" % (
        dataset, run_id)
elif dataset == "svhn":

Exemple #4

Fichier : run_mtbo.py Projet : numairmansur/RoBO

from robo.fmin import mtbo

from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist, SvmOnVehicle, SvmOnCovertype, SvmOnAdult, SvmOnHiggs, SvmOnLetter
from hpolib.benchmarks.ml.residual_networks import ResidualNeuralNetworkOnCIFAR10
from hpolib.benchmarks.ml.conv_net import ConvolutionalNeuralNetworkOnCIFAR10, ConvolutionalNeuralNetworkOnSVHN


run_id = int(sys.argv[1])
dataset = sys.argv[2]
seed = int(sys.argv[3])

rng = np.random.RandomState(seed)

if dataset == "mnist":
    f = SvmOnMnist(rng=rng)
    num_iterations = 30
    output_path = "./experiments/fabolas/results/svm_%s/mtbo_%d" % (dataset, run_id)
elif dataset == "vehicle":
    f = SvmOnVehicle(rng=rng)
    num_iterations = 30
    output_path = "./experiments/fabolas/results/svm_%s/mtbo_%d" % (dataset, run_id)
elif dataset == "covertype":
    f = SvmOnCovertype(rng=rng)
    num_iterations = 30
    output_path = "./experiments/fabolas/results/svm_%s/mtbo_%d" % (dataset, run_id)
elif dataset == "adult":
    f = SvmOnAdult(rng=rng)
    num_iterations = 30
    output_path = "./experiments/fabolas/results/svm_%s/mtbo_%d" % (dataset, run_id)
elif dataset == "letter":

Exemple #5

Fichier : run_hyperband.py Projet : numairmansur/RoBO

from robo.solver.hyperband_datasets_size_original_incumbent import HyperBand_DataSubsetsOriginalIncumbent

from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist, SvmOnVehicle, SvmOnCovertype, SvmOnAdult, SvmOnHiggs, SvmOnLetter
from hpolib.benchmarks.ml.residual_networks import ResidualNeuralNetworkOnCIFAR10
from hpolib.benchmarks.ml.conv_net import ConvolutionalNeuralNetworkOnCIFAR10, ConvolutionalNeuralNetworkOnSVHN


run_id = int(sys.argv[1])
dataset = sys.argv[2]
seed = int(sys.argv[3])

rng = np.random.RandomState(seed)

if dataset == "mnist":
    f = SvmOnMnist(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
elif dataset == "vehicle":
    f = SvmOnVehicle(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
elif dataset == "covertype":
    f = SvmOnCovertype(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
elif dataset == "higgs":
    f = SvmOnHiggs(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
elif dataset == "adult":
    f = SvmOnAdult(rng=rng)
    output_path = "./experiments/fabolas/results/svm_%s/hyperband_%d" % (dataset, run_id)
elif dataset == "letter":
    f = SvmOnLetter(rng=rng)