Python output_file_name Examples

Example #1

                    help='sample when decoding for generation')
parser.add_argument('--log_interval', type=int, default=1,
                    help='interval to log autoencoder training results')

# Other
parser.add_argument('--seed', type=int, default=1111,
                    help='random seed')
parser.add_argument('--cuda', dest='cuda', action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda', dest='cuda', action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

# create default output file name
args.outf = output_file_name(args.outf, "bleu")

# make output directory if it doesn't already exist
make_output_directory(args.outf)

os.environ['CUDA_VISIBLE_DEVICES'] = args.device_id

# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:

Example #2

File: train-gelu-mlp.py Project: tfaod/arae-project

parser.add_argument('--cuda',
                    dest='cuda',
                    action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda',
                    dest='cuda',
                    action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

args = parser.parse_args()

args.data_path,
# create default output file name
args.outf = output_file_name(args.data_path, args.outf, "gelu-mlp")

# make output directory if it doesn't already exist
make_output_directory(args.data_path, args.outf)

os.environ['CUDA_VISIBLE_DEVICES'] = args.device_id

# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:

Example #3

parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--cuda',
                    dest='cuda',
                    action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda',
                    dest='cuda',
                    action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

args = parser.parse_args()

# create default output file name if no output
args.outf = output_file_name(args.data_path, args.outf, "cnn")

# make output directory if it doesn't already exist
make_output_directory(args.outf)

os.environ['CUDA_VISIBLE_DEVICES'] = args.device_id

# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:

Example #4

File: train_glove.py Project: tfaod/arae-project

# Other
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--cuda',
                    dest='cuda',
                    action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda',
                    dest='cuda',
                    action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

# create default output file name
args.outf = output_file_name(args.outf, "glove")

# make output directory if it doesn't already exist
make_output_directory(args.outf)

os.environ['CUDA_VISIBLE_DEVICES'] = args.device_id

# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:

Example #5

File: train-gelu-cnn2.py Project: tfaod/arae-project

                    help='interval to log autoencoder training results')

# Other
parser.add_argument('--seed', type=int, default=1111,
                    help='random seed')
parser.add_argument('--cuda', dest='cuda', action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda', dest='cuda', action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

args = parser.parse_args()

# create default output file name if no output
args.outf = output_file_name(args.data_path, args.outf, "gelu_cnn1")

# make output directory if it doesn't already exist
make_output_directory(args.data_path, args.outf)


# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:
        torch.cuda.manual_seed(args.seed)

Example #6

File: train-gelu-cnn1-experimental.py Project: tfaod/arae-project

parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--cuda',
                    dest='cuda',
                    action='store_true',
                    help='use CUDA')
parser.add_argument('--no-cuda',
                    dest='cuda',
                    action='store_true',
                    help='not using CUDA')
parser.set_defaults(cuda=True)
parser.add_argument('--device_id', type=str, default='0')

args = parser.parse_args()

# create default output file name if no output
args.outf = output_file_name(args.data_path, args.outf,
                             "gelu_cnn_model_summary")

# make output directory if it doesn't already exist
make_output_directory(args.data_path, args.outf)

# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
    if not args.cuda:
        print("WARNING: You have a CUDA device, "
              "so you should probably run with --cuda")
    else:
        torch.cuda.manual_seed(args.seed)