Python Collection Exemples

Exemple #1

def run(dtrain, dtest, epochs=10, verbose=False):
    args = AttrDict(**dict(
        save_dir="_models",
        iters=epochs,
        epochs=epochs,
        bootstrap_epochs=1,
        ncams=1,
        verbose=verbose
    ))

    mnist = Collection('multiinput_edge_dropout_mpcc_{}'.format(args.ncams), args.save_dir, nepochs=args.epochs, verbose=args.verbose)
    ncams = args.ncams
    mnist.set_model_family(MultiInputEdgeDropoutFamily, ninputs=ncams, resume=False,
                            merge_function="max_pool_concat", drop_comm_train=dtrain,
                            drop_comm_test=dtest, input_dims=1, output_dims=10)
    train, test = get_mnist()
    mnist.add_trainset(train)
    mnist.add_testset(test)
    mnist.set_searchspace(
        nfilters_embeded=[3],
        nlayers_embeded=[2],
        nfilters_cloud=[3],
        nlayers_cloud=[2],
        lr=[1e-3],
        branchweight=[.1],
        ent_T=[100]
    )

    # currently optimize based on the validation accuracy of the main model
    traces = mnist.train(niters=args.iters, bootstrap_nepochs=args.bootstrap_epochs)
    return traces[-1]['y']

Exemple #2

def train_model(args, model_type, nfilters):
    train, test = chainer.datasets.get_cifar10(ndim=3)
    # train, test = chainer.datasets.get_mnist(ndim=3)

    trainer = Collection(model_type,
                         args.save_dir,
                         nepochs=args.epochs,
                         verbose=args.verbose)
    trainer.set_model_family(model_dict[model_type],
                             ninputs=1,
                             resume=False,
                             input_dims=train._datasets[0].shape[1],
                             merge_function="max_pool_concat",
                             drop_comm_train=0.5,
                             drop_comm_test=0.5)
    trainer.add_trainset(train)
    trainer.add_testset(test)
    trainer.set_searchspace(nfilters_embeded=[nfilters],
                            nlayers_embeded=[2],
                            nfilters_cloud=[32],
                            nlayers_cloud=[2],
                            lr=[1e-3],
                            branchweight=[.1],
                            ent_T=[100])
    res = trainer.train(niters=args.iters,
                        bootstrap_nepochs=args.bootstrap_epochs)
    return max_acc(res)[0]

Exemple #3

Fichier : comparison.py Projet : yyuzhongpv/ddnn

def train_model(args, model_type, nfilters):
    trainer = Collection(model_type,
                         args.save_dir,
                         nepochs=args.epochs,
                         verbose=args.verbose)
    trainer.set_model_family(model_dict[model_type])
    train, test = chainer.datasets.get_mnist(ndim=3)
    data_shape = train._datasets[0].shape[1:]
    trainer.add_trainset(train)
    trainer.add_testset(test)
    trainer.set_searchspace(nfilters_embeded=[nfilters],
                            nlayers_embeded=[2],
                            lr=[1e-3])
    res = trainer.train(niters=args.iters,
                        bootstrap_nepochs=args.bootstrap_epochs)
    return max_acc(res)[0]

Exemple #4

Fichier : test_binary_fixed.py Projet : yyuzhongpv/ddnn

            acc = max(acc, t['y'])
            best_idx = i
    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=80)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=2)
parser.add_argument('-v', '--verbose', action='store_true')
args = parser.parse_args()

mnist = Collection('binary_base_fixed_cifar10',
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)
mnist.set_model_family(BinaryBaseFixedFamily)

#train, test = chainer.datasets.get_mnist(ndim=3)
train, test = chainer.datasets.get_cifar10(ndim=3)

#from chainer.datasets.sub_dataset import SubDataset
#train = SubDataset(train, 0, 500)
#test = SubDataset(train, 0, 500)

mnist.add_trainset(train)
mnist.add_testset(test)

mnist.set_searchspace(pretrain_nepochs=[20],

Exemple #5

Fichier : exp_increasing.py Projet : yyuzhongpv/ddnn

    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=100)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=100)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=str, default="0")
args = parser.parse_args()

args.ncams = [int(i) for i in args.ncams.split(",")]
mnist = Collection('binary_multiinput_increasing_mpcc_{}'.format(
    reduce(lambda x, y: str(x) + str(y), args.ncams)),
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,
                       ninputs=len(ncams),
                       batchsize=700,
                       merge_function="max_pool_concat")

train, test = get_mvmc_flatten_eval(ncams)
#print("train",len(train))
#print("test",len(test))
#import sys
#sys.exit(0)
#train, test = chainer.datasets.get_cifar10(ndim=3)

Exemple #6

            acc = max(acc, t['y'])
            best_idx = i
    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=40)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=2)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=int, default=6)
args = parser.parse_args()

mnist = Collection('multiinput_{}'.format(args.ncams),
                   args.save_dir,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputFamily, ninputs=ncams)

train, test = get_mvmc_flatten(range(ncams))
#train, test = chainer.datasets.get_cifar10(ndim=3)
#print(train[1])
#print(len(train[0]))
#from chainer.datasets.sub_dataset import SubDataset
#train = SubDataset(train, 0, 500)
#test = SubDataset(train, 0, 500)

mnist.add_trainset(train)
mnist.add_testset(test)

Exemple #7

    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=100)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=100)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=str, default="0,1,2,3,4,5")
args = parser.parse_args()

args.ncams = [int(i) for i in args.ncams.split(",")]
mnist = Collection('binary_multiinput_weights_mpcc_{}'.format(args.ncams[-1]),
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,
                       ninputs=len(ncams),
                       batchsize=700,
                       merge_function="max_pool_concat")

train, test = get_mvmc_flatten_eval(ncams)
#print("train",len(train))
#print("test",len(test))
#import sys
#sys.exit(0)
#train, test = chainer.datasets.get_cifar10(ndim=3)

Exemple #8

Fichier : binary_hybrid_analysis.py Projet : yyuzhongpv/ddnn

            acc = max(acc, t['y'])
            best_idx = i
    return acc, best_idx

parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int,  default=20)
parser.add_argument('-b', '--bootstrap_epochs', type=int,  default=2)
parser.add_argument('-v', '--verbose', action='store_true')
args = parser.parse_args()

train, test = chainer.datasets.get_mnist(ndim=3)


hybrid = Collection('simple_hybrid', args.save_dir, nepochs=args.epochs, verbose=args.verbose)
hybrid.set_model_family(SimpleHybridFamily)
hybrid.add_trainset(train)
hybrid.add_testset(test)
hybrid.set_searchspace(
    nfilters_embeded=[64],
    nlayers_embeded=[2],
    nfilters_cloud=[64],
    nlayers_cloud=[2],
    lr=[1e-3],
    branchweight=[.5],
    ent_T=[0., 0.005, 0.001, 0.1, 100]
)
hybrid.set_chooser(deepopt.chooser.GridChooser())

Exemple #9

Fichier : exp_mem.py Projet : yyuzhongpv/ddnn

    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=100)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=100)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=str, default="0,1,2,3,4,5")
args = parser.parse_args()

args.ncams = [int(i) for i in args.ncams.split(",")]
mnist = Collection('exp_mem_percent_{}'.format(
    reduce(lambda x, y: str(x) + str(y), args.ncams)),
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,
                       ninputs=len(ncams),
                       batchsize=700,
                       merge_function="max_pool_concat")

train, test = get_mvmc_flatten_eval(ncams)
#print("train",len(train))
#print("test",len(test))
#import sys
#sys.exit(0)
#train, test = chainer.datasets.get_cifar10(ndim=3)

Exemple #10

            best_idx = i
    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int,  default=100)
parser.add_argument('-b', '--bootstrap_epochs', type=int,  default=100)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=str,  default="0")
args = parser.parse_args()

args.ncams = [int(i) for i in args.ncams.split(",")]
suffix = "_".join([str(i) for i in args.ncams])
mnist = Collection('binary_multiinput_individual_mpcc_1_nolocal{}'.format(suffix), args.save_dir, input_dims=3, nepochs=args.epochs, verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,ninputs=len(ncams),batchsize=700,merge_function="max_pool_concat")

train, test = get_mvmc_flatten_eval(ncams)
#print("train",len(train))
#print("test",len(test))
#import sys
#sys.exit(0)
#train, test = chainer.datasets.get_cifar10(ndim=3)
#print(train[1])
#print(len(train[0]))
#from chainer.datasets.sub_dataset import SubDataset
#train = SubDataset(train, 0, 500)
#test = SubDataset(train, 0, 500)

Exemple #11

Fichier : exp_combining_max_pool.py Projet : yyuzhongpv/ddnn

            best_idx = i
    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=40)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=2)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=int, default=6)
args = parser.parse_args()

mnist = Collection('multiinput_edge_mpmp_{}'.format(args.ncams),
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,
                       ninputs=ncams,
                       batchsize=400,
                       merge_function="max_pool")

train, test = get_mvmc_flatten(range(ncams))
#train, test = chainer.datasets.get_cifar10(ndim=3)
#print(train[1])
#print(len(train[0]))
#from chainer.datasets.sub_dataset import SubDataset
#train = SubDataset(train, 0, 500)

Exemple #12

Fichier : test_binary.py Projet : yyuzhongpv/ddnn

from elaas.elaas import Collection
from elaas.family.simple import SimpleHybridFamily
from elaas.family.binary import BinaryFamily
from visualize import visualize
import deepopt.chooser

parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int,  default=20)
parser.add_argument('-b', '--bootstrap_epochs', type=int,  default=2)
parser.add_argument('-v', '--verbose', action='store_true')
args = parser.parse_args()

mnist = Collection('binary', args.save_dir, nepochs=args.epochs, verbose=args.verbose)
mnist.set_model_family(BinaryFamily)

train, test = chainer.datasets.get_mnist(ndim=3)
mnist.add_trainset(train)
mnist.add_testset(test)

# mnist.set_searchspace(
#     nfilters_embeded=[5, 10],
#     nlayers_embeded=[1, 2],
#     nfilters_cloud=[5],
#     nlayers_cloud=[1],
#     lr=[1e-3],
#     branchweight=[.1],
#     ent_T=[0.1, 0.2, 0.4]
# )

Exemple #13

Fichier : test_float_float.py Projet : yyuzhongpv/ddnn

            acc = max(acc, t['y'])
            best_idx = i
    return acc, best_idx


parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=40)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=2)
parser.add_argument('-v', '--verbose', action='store_true')
args = parser.parse_args()

mnist = Collection('float_float_cifar10',
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)
mnist.set_model_family(FloatFloatFamily)

#train, test = chainer.datasets.get_mnist(ndim=3)
train, test = chainer.datasets.get_cifar10(ndim=3)

#from chainer.datasets.sub_dataset import SubDataset
#train = SubDataset(train, 0, 500)
#test = SubDataset(train, 0, 500)

mnist.add_trainset(train)
mnist.add_testset(test)

mnist.set_searchspace(nfilters_embeded=[64],

Exemple #14

Fichier : exp_combining.py Projet : vezed/ddnn

parser = argparse.ArgumentParser(description='Hybrid Example')
parser.add_argument('-s', '--save_dir', default='_models')
parser.add_argument('--iters', type=int, default=100)
parser.add_argument('-e', '--epochs', type=int, default=100)
parser.add_argument('-b', '--bootstrap_epochs', type=int, default=100)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-n', '--ncams', type=str, default="0,1,2,3,4,5")
parser.add_argument('-m', '--merge', type=str, default="max_pool_concat")

args = parser.parse_args()
merge = args.merge

args.ncams = [int(i) for i in args.ncams.split(",")]
mnist = Collection('binary_combining_{}_{}'.format(
    merge, reduce(lambda x, y: str(x) + str(y), args.ncams)),
                   args.save_dir,
                   input_dims=3,
                   nepochs=args.epochs,
                   verbose=args.verbose)

ncams = args.ncams
mnist.set_model_family(MultiInputEdgeFamily,
                       ninputs=len(ncams),
                       batchsize=700,
                       merge_function=merge)

train, test = get_mvmc_flatten_eval(ncams)
#print("train",len(train))
#print("test",len(test))
#import sys
#sys.exit(0)
#train, test = chainer.datasets.get_cifar10(ndim=3)