but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with the Matrix library. If not, see <http://www.gnu.org/licenses/>.
@author: Steve Deng
"""
import train
from config import Config
from lib import load
import numpy as np
import os
if __name__ == '__main__':
data_root = '/home/hfl/dataset/timeseries/UCR_TS_Archive_2015'
fname = 'ArrowHead'
out_fname = "uncond_vanilla"
data = load.read_data(data_root, fname, norm=False)
trX = np.reshape(data.X_train, data.X_train.shape + (1, 1))
teX = np.reshape(data.X_test, data.X_test.shape + (1, 1))
trX = np.vstack([trX, teX])
height = trX.shape[1]
width = trX.shape[2]
c_dim = trX.shape[3]
y_dim = data.n_class
conf = Config(trX, fname, out_fname, img_h=height, img_w=width,c_dim=c_dim, state='train')
train.train(conf)
The Matrix library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with the Matrix library. If not, see <http://www.gnu.org/licenses/>.
@author: Steve Deng
"""
import train
from config import Config
from lib import load
import numpy as np
if __name__ == '__main__':
data_root = '/home/hfl/dataset/timeseries/UCR_TS_Archive_2015'
fname = 'ArrowHead'
out_fname = "uncond_dcgan_base_mmd"
data = load.read_data(data_root, fname)
trX = np.reshape(data.X_train, data.X_train.shape + (1, 1))
teX = np.reshape(data.X_test, data.X_test.shape + (1, 1))
trX = np.vstack([trX, teX])
height = trX.shape[1]
width = trX.shape[2]
c_dim = trX.shape[3]
conf = Config(trX, fname, out_fname, img_h=height, img_w=width,c_dim=c_dim, state='train')
train.train(conf)
along with the Matrix library. If not, see <http://www.gnu.org/licenses/>.
@author: Steve Deng
"""
import train
from config import Config
from lib import load
import numpy as np
import os
import tensorflow as tf
if __name__ == '__main__':
data_root = '/home/hfl/dataset/timeseries/UCR_TS_Archive_2015'
out_fname = "uncond_vanilla"
fname_list = [f for f in os.listdir(data_root)
if os.path.isfile(f) is False]
for fname in fname_list:
tf.reset_default_graph()
data = load.read_data(data_root, fname, norm=False)
trX = np.reshape(data.X_train, data.X_train.shape + (1, 1))
teX = np.reshape(data.X_test, data.X_test.shape + (1, 1))
trX = np.vstack([trX, teX])
height = trX.shape[1]
width = trX.shape[2]
c_dim = trX.shape[3]
y_dim = data.n_class
conf = Config(trX, fname, out_fname, img_h=height, img_w=width, c_dim=c_dim,state='train')
train.train(conf)
