def imshow(inp):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
plt.show()
# end imsho
# Argument parser
args = functions.argument_parser_training_model('image')
# Image augmentation and normalization
image_transforms = dict()
image_transforms['train'] = functions.image_transformer('train')
image_transforms['val'] = functions.image_transformer('val')
# Image data set
pan18loader_training, pan18loader_validation = functions.load_images_dataset(
image_transforms,
args.batch_size, args.val_batch_size
)
# Loss function
loss_function = nn.CrossEntropyLoss()
# You should have received a copy of the GNU General Public License
# along with Foobar. If not, see <http://www.gnu.org/licenses/>.
#
# Imports
import torch
from torchlanguage import models
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import copy
import os
from tools import functions, settings
# Parse argument
args = functions.argument_parser_training_model('tweet')
# Transformer
transformer = functions.tweet_transformer(args.lang, args.n_gram)
# Load data sets
pan17loader_training, pan17loader_validation, pan18loader_training, pan18loader_validation = \
functions.load_tweets_dataset(args.lang, transformer, args.batch_size, args.val_batch_size)
# Loss function
loss_function = nn.CrossEntropyLoss()
# Model
model = models.CNNCTweet(text_length=settings.min_length, vocab_size=settings.voc_sizes[args.n_gram][args.lang],
embedding_dim=args.dim)
if args.cuda:
