Python get_chainer_cifar100 예제들

예제 #1

파일: train_resnet32_cifar100_gpu.py 프로젝트: bupt-renpei/branchynet

from networks import resnet_cifar
branchyNet = resnet_cifar.get_network(n_class=100, n_layer=5)

# branchyNet.print_models()  # ren +
print '0. Define Network'

branchyNet.to_gpu()
branchyNet.training()

# Import Data

# from datasets import pcifar10  # original
# x_train, y_train, _, _ = pcifar10.get_data()  # original

from _tool import chainerDataset
x_train, y_train, _, _ = chainerDataset.get_chainer_cifar100()

print '1. Import Data'

# Settings

TRAIN_BATCHSIZE = 32  # 64
# TEST_BATCHSIZE = 64  # 1  #  ren -
TRAIN_NUM_EPOCHS = 100

branchyNet.verbose = False  # ren +
branchyNet.gpu = True  # ren +

# Train Main Network

print '2. Train Main Network'

예제 #2

# Load Network

import dill
with open("_models/train_resnet32_cifar100_gpu_(network).bn", "r") as f:
    branchyNet = dill.load(f)

# branchyNet.print_models()  # ren +
print '0. Load Network'

# Import Data

# from datasets import pcifar10  # original
# _, _, x_test, y_test = pcifar10.get_data()  # original

from _tool import chainerDataset
_, _, x_test, y_test = chainerDataset.get_chainer_cifar100()

print '1. Import Data'

# Settings

TEST_BATCHSIZE = 1

print '2. set network to inference mode'

branchyNet.to_gpu()
branchyNet.testing()
branchyNet.verbose = False
branchyNet.gpu = True  # ren +

# Test main