# This program 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 General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # this program; if not, write to the Free Software Foundation, Inc., 51 # Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. # # Copyright Nils Schaetti <*****@*****.**> # Imports import torch.utils.data from echotorch import datasets from echotorch.transforms import text # Reuters C50 dataset reutersloader = torch.utils.data.DataLoader(datasets.SFGramDataset( tokenizer=text.Token(), root="../../data/sfgram/", download=True, transform=text.GloveVector()), batch_size=1, shuffle=True) # Get training data for this fold for i, data in enumerate(reutersloader): # Inputs and labels inputs, labels = data # end for
parser = argparse.ArgumentParser(description="Word embedding for AA") # Argument parser.add_argument("--output", type=str, help="Embedding output file", default='.') parser.add_argument("--dim", type=int, help="Embedding dimension", default=300) parser.add_argument("--n-features", type=int, help="Number of features", default=30) parser.add_argument("--no-cuda", action='store_true', default=False, help="Enables CUDA training") parser.add_argument("--epoch", type=int, help="Epoch", default=300) parser.add_argument("--steps", type=int, help="Steps to backwards", default=5) args = parser.parse_args() # Use CUDA? args.cuda = not args.no_cuda and torch.cuda.is_available() # Word embedding transform = text.Token() # Reuters C50 dataset reutersloader = torch.utils.data.DataLoader(datasets.ReutersC50Dataset(download=True, n_authors=15, transform=transform), batch_size=1, shuffle=False) # Token to ix token_to_ix = dict() ix_to_token = dict() # Loss function # loss_function = nn.NLLLoss() loss_function = nn.CrossEntropyLoss() # Set fold and training mode