Python TfTrainEvalModelFactory示例

示例#1

文件： monde_ar.py 项目： pawelc/NeuralLikelihoods

from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/uci_large/hepmass')

    conf.num_workers = 2
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 20000, '1': 20000}

    exp.data_loader = registry.hepmass(x_slice=slice(0), y_slice=slice(None))

    exp.model_factory = TfTrainEvalModelFactory(Model(name="MONDE_AR_BLOCK"))

    exp.hyper_param_search = GridSearch([
        Categorical([8, 10], name='nl'),
        Categorical([60, 80], name='nb'),
        Categorical(['tanh'], name='tr'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-3], name='opt_lr'),
    ])

    exp.early_stopping = EarlyStop(monitor_every_epoch=1, patience=[30])

    exp.run()

示例#2

from data import registry
from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/sin_t')

    conf.num_workers = 4
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 10000, '1': 10000}

    exp.data_loader = registry.sin_t_noise()

    exp.model_factory = TfTrainEvalModelFactory(Model(name="RNADE_laplace"))

    exp.hyper_param_search = GridSearch([
        Categorical([1, 16, 32, 64, 128], name='km'),
        Categorical([1, 16, 32, 64, 128], name='sh'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-4, 1e-3, 1e-2], name='opt_lr'),
    ])

    exp.early_stopping = EarlyStop(monitor_every_epoch=1, patience=[30])

    exp.run()

示例#3

文件： rnade_deep_normal.py 项目： pawelc/NeuralLikelihoods

from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/mv_nonlinear')

    conf.num_workers = 4
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 10000, '1': 10000}

    exp.data_loader = registry.mv_nonlinear()

    exp.model_factory = TfTrainEvalModelFactory(
        Model(name="RNADE_deep_normal"))

    exp.hyper_param_search = GridSearch([
        Categorical([32, 64, 128], name='km'),
        Categorical([64, 128, 512], name='sh'),
        Categorical([1, 2, 3, 4, 5], name='nh'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-4, 1e-3, 1e-2], name='opt_lr'),
    ])

    exp.early_stopping = EarlyStop(monitor_every_epoch=1, patience=[30])

    exp.run()

示例#4

文件： maf.py 项目： pawelc/NeuralLikelihoods

from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/inv_sin_normal')

    conf.num_workers = 4
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 10000, '1': 10000}

    exp.data_loader = registry.inv_sin_normal()

    exp.model_factory = TfTrainEvalModelFactory(Model(name="MAF"))

    exp.hyper_param_search = GridSearch([
        Categorical([1, 2, 3, 4, 5], name='nb'),
        Categorical([32, 128, 256], name='sh'),
        Categorical([1, 2, 3], name='nh'),
        Categorical([16, 32, 64], name='shc'),
        Categorical([True, False], name='bn'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-4, 1e-3, 1e-2], name='opt_lr'),
    ])

    exp.early_stopping = EarlyStop(monitor_every_epoch=1, patience=[30])

示例#5

文件： monde_copula_const_cov.py 项目： pawelc/NeuralLikelihoods

from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/inv_sin_t')

    conf.num_workers = 4
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 10000, '1': 10000}

    exp.data_loader = registry.inv_sin_t()

    exp.model_factory = TfTrainEvalModelFactory(
        Model(name="MONDE_copula_const_cov"))

    exp.hyper_param_search = GridSearch([
        Categorical([32, 64, 128], name='hxy_sh'),
        Categorical([1, 2, 3], name='hxy_nh'),
        Categorical([32, 64, 128], name='x_sh'),
        Categorical([1, 2, 3], name='x_nh'),
        Categorical([16, 32], name='hxy_x'),
        Categorical([0.05, 0.01], name='clr'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-4, 1e-3, 1e-2], name='opt_lr'),
    ])

    exp.early_stopping = EarlyStop(monitor_every_epoch=1, patience=[30])

示例#6

文件： pumonde.py 项目： pawelc/NeuralLikelihoods

from experiment.early_stop import EarlyStop
from experiment.experiment import Experiment
from experiment.hyper_param_opt import GridSearch
from models.tensorflow.model import Model
from models.tensorflow.tf_train_eval import TfTrainEvalModelFactory

if __name__ == '__main__':
    exp = Experiment('density/synthetic/mv_nonlinear')

    conf.num_workers = 4
    conf.visible_device_list = [0, 1]
    conf.eval_batch_size = {'0': 10000, '1': 10000}

    exp.data_loader = registry.mv_nonlinear()

    exp.model_factory = TfTrainEvalModelFactory(Model(name="PumondePFor"))

    exp.hyper_param_search = GridSearch([
        Categorical([64, 128, 256], name='xs'),
        Categorical([1, 2, 3], name='xn'),
        Categorical([64, 128, 256], name='hxys'),
        Categorical([1, 2, 3], name='hxyn'),
        Categorical([0, 16], name='hxyxs'),
        Categorical([64, 128, 256], name='xycs'),
        Categorical([1, 2, 3], name='xycn'),
        Categorical([128], name='bs'),
        Categorical([1], name='rs'),
        Categorical(['AdamOptimizer'], name='opt'),
        Categorical([1e-4, 1e-3, 1e-2], name='opt_lr'),
    ])