import numpy as np
from utils import CreateOutputDir, GetLogger
from hypernetwork import Hypernetwork
from cifar10_data_fetcher import Cifar10DataFetcher
from params import GeneralParameters, HypernetworkHyperParameters, Cifar10Params, ResNetCifar10HyperParameters
initialize_from_checkpoint = False
output_dir = CreateOutputDir('output/', __file__)
logger = GetLogger(log_file_mode='a' if initialize_from_checkpoint else 'w',
log_file_path=os.path.join(output_dir, 'log.txt'))
# create parameter objects
general_params = GeneralParameters()
hparams = HypernetworkHyperParameters()
target_hparams = ResNetCifar10HyperParameters()
image_params = Cifar10Params()
# override
hparams.initialization_std = 1e-3
hparams.learning_rate = 1e-6
hparams.learning_rate_rate = 0.9999
hparams.lamBda = 1e8
target_hparams.batch_type = 'BATCH_TYPE5'
# set random seeds
np.random.seed(general_params.seed)
tf.set_random_seed(general_params.seed)
def __init__(self,
x,
y,
mode: str = 'EVAL',
general_params=GeneralParameters(),
hnet_hparams=HypernetworkHyperParameters(),
target_hparams:
ResNetHyperParameters = ResNetCifar10HyperParameters(),
image_params: DataParams = Cifar10Params(),
devices=None,
graph=None):
"""
Args:
x: image batch
y: labels (one hot)
mode: either `'TRAIN'` or `'EVAL'`. determines whether to add optimization ops
general_params:
hnet_hparams:
target_hparams:
image_params:
devices: (optional) a dictionary, with keys `'cpu'` and `'gpus'`, where the former is the cpu device and the latter is a list of gpu devices.
graph:
"""
if mode.upper() in ['TRAIN', 'TRAINING']:
mode = 'TRAIN'
elif mode.upper() in [
'VALIDATE', 'VALIDATION', 'VALIDATING', 'EVALUATE', 'EVAL',
'EVALUATION', 'EVALUATING', 'TEST', 'TESTING'
]:
mode = 'EVAL'
else:
raise ValueError(
"mode needs to be one of 'TRAIN','VALIDATE','INFER'")
self.general_params = general_params
self.hnet_hparams = hnet_hparams
self.target_hparams = target_hparams
self.image_params = image_params
self.graph = graph
if devices is None:
devices = GetDevices()
self.devices = devices
self.cpu = devices['cpu']
# we will cycle over the GPU list, so that our code is agnostic to the number of GPUs
self.gpus = cycle(
devices['gpus']) if len(devices['gpus']) > 0 else cycle(
[devices['cpu']])
self.x = x
self.y = y
if graph is None:
graph = tf.get_default_graph()
self.graph = graph
with self.graph.as_default():
self.z = tf.placeholder(tf.float32,
[None, hnet_hparams.input_noise_size])
self.is_training = tf.Variable(False,
trainable=False,
name='is_training')
with tf.variable_scope('generator', reuse=tf.AUTO_REUSE):
self.__AddGeneratorOps()
self.__AddEvaluationOps()
if mode == 'TRAIN':
self.__AddOptimizationOps()
self.initializer = tf.variables_initializer(
self.graph.get_collection('variables'), name='initializer')
import numpy as np
from utils import CreateOutputDir, GetLogger
from hypernetwork import Hypernetwork
from cifar10_data_fetcher import Cifar10DataFetcher
from params import GeneralParameters, HypernetworkHyperParameters, Cifar10Params, ResNetCifar10HyperParameters
initialize_from_checkpoint = False
output_dir = CreateOutputDir('output/', __file__)
logger = GetLogger(log_file_mode='a' if initialize_from_checkpoint else 'w',
log_file_path=os.path.join(output_dir, 'log.txt'))
# create parameter objects
general_params = GeneralParameters()
hparams = HypernetworkHyperParameters()
target_hparams = ResNetCifar10HyperParameters()
image_params = Cifar10Params()
# override
hparams.initialization_std = 1e-3
hparams.learning_rate = 4e-7
hparams.learning_rate_rate = 0.99999
hparams.create_optimizer = lambda learning_rate, hnet_hparams: tf.train.AdamOptimizer(
learning_rate)
hparams.with_residual_connections = True
target_hparams.batch_type = 'BATCH_TYPE5'
# set random seeds
np.random.seed(general_params.seed)
import numpy as np
from utils import CreateOutputDir, GetLogger
from hypernetwork import Hypernetwork
from cifar10_data_fetcher import Cifar10DataFetcher
from params import GeneralParameters, HypernetworkHyperParameters
initialize_from_checkpoint = False
output_dir = CreateOutputDir('output', __file__)
logger = GetLogger(log_file_mode='a' if initialize_from_checkpoint else 'w',
log_file_path=os.path.join(output_dir, 'log.txt'))
# create parameter objects
general_params = GeneralParameters()
hparams = HypernetworkHyperParameters()
# set random seeds
np.random.seed(general_params.seed)
tf.set_random_seed(general_params.seed)
# get training data pipeline.
training_data = Cifar10DataFetcher('TRAIN', batch_size=hparams.batch_size)
validation_data = Cifar10DataFetcher('VALIDATION',
batch_size=hparams.batch_size)
# create hypernet
hnet = Hypernetwork(training_data.image,
training_data.label,
'TRAIN',
hnet_hparams=hparams)