raise SystemExit("params folder exists! select a new params path please")
class MyInit(mx.init.Initializer):
xavier = mx.init.Xavier()
uniform = mx.init.Uniform()
def _init_weight(self, name, data):
if len(data.shape) < 2:
self.uniform._init_weight(name, data)
print('Init', name, data.shape, 'with Uniform')
else:
self.xavier._init_weight(name, data)
print('Init', name, data.shape, 'with Xavier')
if __name__ == "__main__":
# read all data from graph singal matrix file
all_data = read_and_generate_dataset(graph_signal_matrix_filename, num_of_vertices, num_of_features, num_of_weeks, num_of_days, num_of_hours, points_per_hour, num_for_predict)
# test set ground truth
true_value = all_data['test']['target'].transpose((0, 2, 1)).reshape(all_data['test']['target'].shape[0], -1)
# training set data loader
train_loader = gluon.data.DataLoader(
gluon.data.ArrayDataset(
nd.array(all_data['train']['week'], ctx = ctx),
nd.array(all_data['train']['day'], ctx = ctx),
nd.array(all_data['train']['recent'], ctx = ctx),
nd.array(all_data['train']['target'], ctx = ctx)
),
batch_size = batch_size,
shuffle = True
)
# check parameters file
if os.path.exists(params_path) and not FLAGS.force:
raise SystemExit("Params folder exists! Select a new params path please!")
else:
if os.path.exists(params_path):
shutil.rmtree(params_path)
os.makedirs(params_path)
print('Create params directory %s' % (params_path))
if __name__ == "__main__":
# read all data from graph signal matrix file
print("Reading data...")
#Input: train / valid / test : length x 3 x NUM_POINT x 12
all_data = read_and_generate_dataset(graph_signal_matrix_filename,
num_of_weeks, num_of_days,
num_of_hours, num_for_predict,
points_per_hour, merge)
# test set ground truth
true_value = all_data['test']['target']
print(true_value.shape)
# training set data loader
train_loader = DataLoader(TensorDataset(
torch.Tensor(all_data['train']['week']),
torch.Tensor(all_data['train']['day']),
torch.Tensor(all_data['train']['recent']),
torch.Tensor(all_data['train']['target'])),
batch_size=batch_size,
shuffle=True)
