def __init__(self, extra):
database.DatasetBase.__init__(self, extra)
self.name = 'lfw'
self.target = 'lfw.vae'
self.data_dir = '../_datasets/lfw'
self.task = 'train'
self.output_dir = None
self.device = '0'
self.log = params.Log(print_invl=20,
save_summaries_invl=20,
save_model_invl=1000,
test_invl=1000,
val_invl=1000,
max_iter=999999)
""" net """
self.net = params.Net('kin_vae')
self.net.set_z_dim(100)
""" train """
self.train = params.Phase('train')
self.train.lr = [params.LearningRate(), params.LearningRate()]
self.train.lr[0].set_fixed(learning_rate=0.000005)
self.train.lr[1].set_fixed(learning_rate=0.00005)
self.train.optimizer = [params.Optimizer(), params.Optimizer()]
self.train.optimizer[0].set_rmsprop()
self.train.optimizer[1].set_rmsprop()
self.train.data = params.Data(batchsize=32,
entry_path='train_1.txt',
shuffle=True,
total_num=5400,
loader='load_image',
reader_thread=32)
self.train.data = self.set_data_attr(self.train.data)
""" test """
self.test = params.Phase('test')
self.test.data = params.Data(batchsize=100,
entry_path='test_1.txt',
shuffle=False,
total_num=600,
loader='load_image',
reader_thread=1)
self.test.data = self.set_data_attr(self.test.data)
""" val """
self.val = params.Phase('val')
self.val.data = params.Data(batchsize=100,
entry_path='train_1.txt',
shuffle=False,
total_num=5400,
loader='load_image',
reader_thread=1)
self.val.data = self.set_data_attr(self.val.data)
def __init__(self, extra):
database.DatasetBase.__init__(self, extra)
r = self._read_config_file
""" base """
self.name = 'mnist'
self.target = 'cnn.classification' # 'ml.active.sampler'
self.data_dir = '../_datasets/mnist'
self.task = 'train'
self.output_dir = None
self.device = '0'
""" log """
self.log = params.Log(
print_invl=20,
save_summaries_invl=10,
save_model_invl=500,
test_invl=500,
val_invl=500,
max_iter=999999)
""" net """
self.net = params.Net('cifarnet')
self.net.set_weight_decay(0.0001)
self.net.set_dropout_keep(0.5)
""" train """
self.train = params.Phase('train')
self.train.lr = [params.LearningRate()]
self.train.lr[0].set_fixed(learning_rate=0.001)
self.train.optimizer = [params.Optimizer()]
self.train.optimizer[0].set_momentum()
self.train.data = params.Data(
batchsize=32,
entry_path="train.txt",
shuffle=True,
total_num=55000,
loader='load_image')
self.train.data = self.set_data_attr(self.train.data)
""" test """
self.test = params.Phase('test')
self.test.data = params.Data(
batchsize=50,
entry_path="test.txt",
shuffle=False,
total_num=10000,
loader='load_image',
reader_thread=1)
self.test.data = self.set_data_attr(self.test.data)
def __init__(self, extra):
database.DatasetBase.__init__(self, extra)
self.name = 'mnist'
self.target = 'gan.acgan'
self.data_dir = '../_datasets/mnist'
self.task = 'train'
self.output_dir = None
self.device = '0'
self.log = params.Log(
print_invl=20,
save_summaries_invl=10,
save_model_invl=500,
test_invl=500,
val_invl=500,
max_iter=999999)
""" net """
self.net = params.Net('cvae')
self.net.set_z_dim(100)
""" train """
self.train = params.Phase('train')
self.train.lr = [params.LearningRate(), params.LearningRate()]
self.train.lr[0].set_fixed(learning_rate=0.001)
self.train.lr[1].set_fixed(learning_rate=0.001)
self.train.optimizer = [params.Optimizer(), params.Optimizer()]
self.train.optimizer[0].set_adam(beta1=0.5)
self.train.optimizer[1].set_adam(beta1=0.5)
self.train.data = params.Data(
batchsize=32,
entry_path="train.txt",
shuffle=True,
total_num=55000,
loader='load_image')
self.train.data = self.set_data_attr(self.train.data)
""" test """
self.test = params.Phase('test')
self.test.data = params.Data(batchsize=100)
self.test.data.set_label(num_classes=10)
def __init__(self, extra):
database.DatasetBase.__init__(self, extra)
r = self._read_config_file
""" base """
self.name = 'avec2014.audio'
self.target = 'avec.audio.cnn'
self.data_dir = '../_datasets/AVEC2014_Audio'
self.task = 'train'
self.output_dir = None
self.device = '0'
""" log """
self.log = params.Log(print_invl=1,
save_summaries_invl=10,
save_model_invl=500,
test_invl=500,
val_invl=500,
max_iter=999999)
""" net """
self.net = params.Net('audionet')
self.net.set_weight_decay(0.0001)
# self.net.set_dropout_keep(0.5)
""" train """
self.train = params.Phase('train')
self.train.lr = [params.LearningRate()]
self.train.lr[0].set_fixed(learning_rate=0.001)
self.train.optimizer = [params.Optimizer()]
self.train.optimizer[0].set_adam()
self.train.data = params.Data(batchsize=32,
entry_path="pp_trn_succ64.txt",
shuffle=True,
total_num=15292,
loader='load_audio',
reader_thread=32)
self.train.data = self.set_data_attr(self.train.data)
""" test """
self.test = params.Phase('test')
self.test.data = params.Data(batchsize=50,
entry_path="pp_tst_succ64.txt",
shuffle=False,
total_num=25465,
loader='load_audio',
reader_thread=1)
self.test.data = self.set_data_attr(self.test.data)
def __init__(self, extra):
database.DatasetBase.__init__(self, extra)
r = self._read_config_file
""" base """
self.name = r('kinface2.vae', 'name')
self.target = r('kinvae.bidirect11', 'target')
self.data_dir = r('../_datasets/kinface2', 'data_dir')
self.task = r('train', 'task')
self.output_dir = r(None, 'output_dir')
self.device = '0'
self.log = params.Log(
print_invl=20,
save_summaries_invl=20,
save_model_invl=500,
test_invl=500,
val_invl=500,
max_iter=15000)
""" Net """
self.net = params.Net('kin_vae')
self.net.set_z_dim(100)
""" train """
self.train = params.Phase('train')
self.train.lr = [params.LearningRate(), params.LearningRate()]
self.train.lr[0].set_fixed(learning_rate=r(0.000005, 'train.lr0'))
self.train.lr[1].set_fixed(learning_rate=r(0.00005, 'train.lr1'))
self.train.optimizer = [params.Optimizer(), params.Optimizer()]
self.train.optimizer[0].set_rmsprop()
self.train.optimizer[1].set_rmsprop()
self.train.data = params.Data(
batchsize=r(32, 'train.batchsize'),
entry_path=r('train_1.txt', 'train.entry_path'),
shuffle=True,
total_num=r(1600, 'train.total_num'),
loader='load_image',
reader_thread=32)
self.train.data = self.set_data_attr(self.train.data)
""" test """
self.test = params.Phase('test')
self.test.data = params.Data(
batchsize=r(100, 'test.batchsize'),
entry_path=r('test_1.txt', 'test.entry_path'),
shuffle=False,
total_num=r(400, 'test.total_num'),
loader='load_image',
reader_thread=1)
self.test.data = self.set_data_attr(self.test.data)
""" val """
self.val = params.Phase('val')
self.val.data = params.Data(
batchsize=r(100, 'val.batchsize'),
total_num=r(1600, 'val.total_num'),
shuffle=False,
entry_path=r('train_1.txt', 'val.entry_path'),
loader='load_image',
reader_thread=1)
self.val.data = self.set_data_attr(self.val.data)