def load_dataset(transforms, batch_size, root='./data/'): """ Import data set :param transforms: :param batch_size: :param root :return: """ # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root=root, download=True, transform=transforms, train=True), batch_size=batch_size ) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root=root, download=True, transform=transforms, train=False), batch_size=batch_size ) return pan18loader_train, pan18loader_valid
import torch.utils.data import dataset from echotorch.transforms import text # Experience parameter batch_size = 64 n_epoch = 1 window_size = 700 training_set_size = 10 test_set_size = 2 training_samples = training_set_size + test_set_size stride = 100 # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root='./data/', download=True, transform=text.Character(), train=True), batch_size=1 ) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root='./data/', download=True, transform=text.Character(), train=False), batch_size=1 ) # Get training data for i, data in enumerate(pan18loader_train): # Inputs and c inputs, label = data # TRAINING
# Experience parameter n_epoch = 1 window_size = 700 training_set_size = 10 test_set_size = 2 training_samples = training_set_size + test_set_size stride = 100 # Argument parser args = functions.argument_parser_training_model() # Get transforms transforms = functions.text_transformer(args.n_gram, settings.window_size) # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader(dataset.SCDSimpleDataset( root='./extended/', download=True, transform=transforms, train=True), batch_size=1) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader(dataset.SCDSimpleDataset( root='./extended/', download=True, transform=transforms, train=False), batch_size=1) # Loss function loss_function = nn.CrossEntropyLoss() # Bi-directional Embedding GRU model = models.BiEGRU(window_size=settings.window_size, vocab_size=settings.voc_sizes[args.n_gram], hidden_dim=settings.hidden_dim, n_classes=2)
# Imports import torch.utils.data import dataset from echotorch.transforms import text # Experience parameter batch_size = 64 n_epoch = 1 window_size = 700 training_set_size = 10 test_set_size = 2 training_samples = training_set_size + test_set_size stride = 100 # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader(dataset.SCDSimpleDataset( root='./data/', download=True, transform=text.Character(), train=True), batch_size=1) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader(dataset.SCDSimpleDataset( root='./data/', download=True, transform=text.Character(), train=False), batch_size=1) # For each epoch for epoch in range(n_epoch): # Training loss training_loss = 0.0 # Get training data for i, data in enumerate(pan18loader_train): # Inputs and c
# Argument parser args = functions.argument_parser_training_model() # Get transforms transforms = functions.text_transformer(args.n_gram, settings.window_size) # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader( dataset.SCDPartsDataset(root='./data/', download=True, transform=transforms, train=True), batch_size=1 ) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root='./data/', download=True, transform=transforms, train=False), batch_size=1 ) # Samples samples = list() n_samples = 0 # Get training data for i, data in enumerate(pan18loader_train): # Parts and c parts, _ = data # Add to samples samples.append(parts) n_samples += 1
ltransforms.MaxIndex(max_id=settings.voc_sizes[args.n_gram]) ]) else: transforms = ltransforms.Compose([ ltransforms.ToLower(), ltransforms.Character2Gram(), ltransforms.ToIndex(start_ix=1), ltransforms.ToLength(length=window_size), ltransforms.Reshape((-1)), ltransforms.MaxIndex(max_id=settings.voc_sizes[args.n_gram]) ]) # end if # Style change detection dataset, training set pan18loader_train = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root='./extended2/', download=True, transform=transforms, train=True), batch_size=args.batch_size ) # Style change detection dataset, validation set pan18loader_valid = torch.utils.data.DataLoader( dataset.SCDSimpleDataset(root='./extended2/', download=True, transform=transforms, train=False), batch_size=args.batch_size ) # Loss function loss_function = nn.CrossEntropyLoss() # CNN Distance learning model = models.CNNSCD( input_dim=window_size,