challenging-america-word-ga.../cw8zad1.ipynb
2023-05-07 22:56:23 +02:00

160 KiB

Imports

import itertools
import lzma
import numpy as np
import regex as re
import torch
from torch import nn
from torch.utils.data import IterableDataset, DataLoader
from torchtext.vocab import build_vocab_from_iterator
from google.colab import drive

Definitions

Functions

def clean_line(line: str):
    # Preprocessing
    separated = line.split('\t')
    prefix = separated[6].replace(r'\n', ' ')
    suffix = separated[7].replace(r'\n', ' ')
    return prefix + ' ' + suffix
def get_words_from_line(line):
    line = clean_line(line)
    for word in line.split():
        yield word
def get_word_lines_from_file(file_name):
    with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:
        for line in fid:
            yield get_words_from_line(line)
def double_look_ahead_iterator(gen):
    prev_prev = None
    prev = None
    for item in gen:
        if prev_prev is not None:
            yield np.asarray((prev_prev, prev, item))
        prev_prev = prev
        prev = item
def prediction(words, model, top) -> str:
    words_tensor = [train_dataset.vocab.forward([word]) for word in words]
    ixs = torch.tensor(words_tensor).view(-1).to(device)
    out = model(ixs)
    top = torch.topk(out[0], top)
    top_indices = top.indices.tolist()
    top_probs = top.values.tolist()
    top_words = vocab.lookup_tokens(top_indices)
    zipped = list(zip(top_words, top_probs))
    for index, element in enumerate(zipped):
        unk = None
        if '<unk>' in element:
            unk = zipped.pop(index)
            zipped.append(('', unk[1]))
            break
    if unk is None:
        zipped[-1] = ('', zipped[-1][1])
    return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])
def create_outputs(folder_name, model, top):
    print(f'Creating outputs in {folder_name}')
    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:
        with open(f'{folder_name}/out-top={top}.tsv', 'w', encoding='utf-8', newline='\n') as f:
            for line in fid:
                separated = line.split('\t')
                prefix = separated[6].replace(r'\n', ' ').split()[-2:]
                output_line = prediction(prefix, model, top)
                f.write(output_line + '\n')
def train_model(lr):
    model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
    data = DataLoader(train_dataset, batch_size=batch_size)
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    criterion = torch.nn.NLLLoss()

    model.train()
    step = 0
    for batch in data:
        x = batch[:, :2]
        y = batch[:, 2]
        x = x.to(device)
        y = y.to(device)
        optimizer.zero_grad()
        ypredicted = model(x)
        loss = criterion(torch.log(ypredicted), y)
        if step % 100 == 0:
            print(step, loss)
        step += 1
        loss.backward()

        torch.nn.utils.clip_grad_norm_(model.parameters(), 10)


        optimizer.step()

    torch.save(model.state_dict(), path_to_model)
def with_hyperparams():
      train_model(lr=0.0001)
      model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
      model.load_state_dict(torch.load(path_to_model))
      model.eval()
      for top in [200, 400, 600]:
          create_outputs('dev-0', model, top)
          create_outputs('test-A', model, top)

Classes

class Trigrams(IterableDataset):
    def __init__(self, text_file, vocabulary_size):
        self.vocab = build_vocab_from_iterator(
            get_word_lines_from_file(text_file),
            max_tokens=vocabulary_size,
            specials=['<unk>'])
        self.vocab.set_default_index(self.vocab['<unk>'])
        self.vocabulary_size = vocabulary_size
        self.text_file = text_file

    def __iter__(self):
        return double_look_ahead_iterator(
            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
class SimpleTrigramNeuralLanguageModel(nn.Module):
    def __init__(self, vocabulary_size, embedding_size, hidden_size):
        super(SimpleTrigramNeuralLanguageModel, self).__init__()
        self.embedding_size = embedding_size
        self.embedding = nn.Embedding(vocabulary_size, embedding_size)
        self.lin1 = nn.Linear(2 * embedding_size, hidden_size)
        self.rel = nn.ReLU()
        self.lin2 = nn.Linear(hidden_size, vocabulary_size)
        self.sm = nn.Softmax()

    def forward(self, x):
        x = self.embedding(x).view((-1, 2 * self.embedding_size))
        x = self.lin1(x)
        x = self.rel(x)
        x = self.lin2(x)
        return self.sm(x)

Training

Params

vocab_size = 25000
embed_size = 300
hidden_size = 150
batch_size = 2000
device = 'cuda'
path_to_train = 'train/in.tsv.xz'
path_to_model = 'model1.bin'

Colab

drive.mount('/content/drive')
%cd /content/drive/MyDrive/
Mounted at /content/drive
/content/drive/MyDrive

Run

vocab = build_vocab_from_iterator(
    get_word_lines_from_file(path_to_train),
    max_tokens=vocab_size,
    specials=['<unk>']
)

vocab.set_default_index(vocab['<unk>'])
train_dataset = Trigrams(path_to_train, vocab_size)
with_hyperparams()
<ipython-input-12-cce599098537>:16: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  return self.sm(x)
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47800 tensor(5.2883, device='cuda:0', grad_fn=<NllLossBackward0>)
47900 tensor(5.6183, device='cuda:0', grad_fn=<NllLossBackward0>)
48000 tensor(5.4894, device='cuda:0', grad_fn=<NllLossBackward0>)
48100 tensor(5.5641, device='cuda:0', grad_fn=<NllLossBackward0>)
48200 tensor(5.5838, device='cuda:0', grad_fn=<NllLossBackward0>)
48300 tensor(5.3944, device='cuda:0', grad_fn=<NllLossBackward0>)
48400 tensor(5.5825, device='cuda:0', grad_fn=<NllLossBackward0>)
48500 tensor(5.2525, device='cuda:0', grad_fn=<NllLossBackward0>)
48600 tensor(5.5420, device='cuda:0', grad_fn=<NllLossBackward0>)
48700 tensor(5.4007, device='cuda:0', grad_fn=<NllLossBackward0>)
48800 tensor(5.5499, device='cuda:0', grad_fn=<NllLossBackward0>)
48900 tensor(5.3335, device='cuda:0', grad_fn=<NllLossBackward0>)
49000 tensor(5.3047, device='cuda:0', grad_fn=<NllLossBackward0>)
49100 tensor(5.3311, device='cuda:0', grad_fn=<NllLossBackward0>)
49200 tensor(5.4564, device='cuda:0', grad_fn=<NllLossBackward0>)
49300 tensor(5.4846, device='cuda:0', grad_fn=<NllLossBackward0>)
49400 tensor(5.7114, device='cuda:0', grad_fn=<NllLossBackward0>)
49500 tensor(5.8193, device='cuda:0', grad_fn=<NllLossBackward0>)
49600 tensor(5.4885, device='cuda:0', grad_fn=<NllLossBackward0>)
49700 tensor(5.5634, device='cuda:0', grad_fn=<NllLossBackward0>)
49800 tensor(5.3464, device='cuda:0', grad_fn=<NllLossBackward0>)
49900 tensor(5.1725, device='cuda:0', grad_fn=<NllLossBackward0>)
50000 tensor(5.3154, device='cuda:0', grad_fn=<NllLossBackward0>)
50100 tensor(5.2345, device='cuda:0', grad_fn=<NllLossBackward0>)
50200 tensor(5.3813, device='cuda:0', grad_fn=<NllLossBackward0>)
50300 tensor(5.0840, device='cuda:0', grad_fn=<NllLossBackward0>)
50400 tensor(5.4767, device='cuda:0', grad_fn=<NllLossBackward0>)
50500 tensor(5.3601, device='cuda:0', grad_fn=<NllLossBackward0>)
50600 tensor(5.5570, device='cuda:0', grad_fn=<NllLossBackward0>)
50700 tensor(5.6957, device='cuda:0', grad_fn=<NllLossBackward0>)
50800 tensor(5.4284, device='cuda:0', grad_fn=<NllLossBackward0>)
50900 tensor(5.4656, device='cuda:0', grad_fn=<NllLossBackward0>)
51000 tensor(5.1827, device='cuda:0', grad_fn=<NllLossBackward0>)
51100 tensor(5.5059, device='cuda:0', grad_fn=<NllLossBackward0>)
51200 tensor(5.6127, device='cuda:0', grad_fn=<NllLossBackward0>)
51300 tensor(5.3371, device='cuda:0', grad_fn=<NllLossBackward0>)
51400 tensor(5.1373, device='cuda:0', grad_fn=<NllLossBackward0>)
51500 tensor(5.3643, device='cuda:0', grad_fn=<NllLossBackward0>)
51600 tensor(5.2310, device='cuda:0', grad_fn=<NllLossBackward0>)
51700 tensor(5.4668, device='cuda:0', grad_fn=<NllLossBackward0>)
51800 tensor(5.2777, device='cuda:0', grad_fn=<NllLossBackward0>)
51900 tensor(5.7900, device='cuda:0', grad_fn=<NllLossBackward0>)
52000 tensor(5.5456, device='cuda:0', grad_fn=<NllLossBackward0>)
52100 tensor(5.4024, device='cuda:0', grad_fn=<NllLossBackward0>)
52200 tensor(5.3733, device='cuda:0', grad_fn=<NllLossBackward0>)
52300 tensor(4.8890, device='cuda:0', grad_fn=<NllLossBackward0>)
52400 tensor(5.1543, device='cuda:0', grad_fn=<NllLossBackward0>)
52500 tensor(5.3708, device='cuda:0', grad_fn=<NllLossBackward0>)
52600 tensor(5.1343, device='cuda:0', grad_fn=<NllLossBackward0>)
52700 tensor(5.4964, device='cuda:0', grad_fn=<NllLossBackward0>)
52800 tensor(5.4933, device='cuda:0', grad_fn=<NllLossBackward0>)
52900 tensor(5.1695, device='cuda:0', grad_fn=<NllLossBackward0>)
53000 tensor(5.5038, device='cuda:0', grad_fn=<NllLossBackward0>)
53100 tensor(5.6919, device='cuda:0', grad_fn=<NllLossBackward0>)
53200 tensor(5.6779, device='cuda:0', grad_fn=<NllLossBackward0>)
53300 tensor(5.3429, device='cuda:0', grad_fn=<NllLossBackward0>)
53400 tensor(5.4038, device='cuda:0', grad_fn=<NllLossBackward0>)
53500 tensor(5.2995, device='cuda:0', grad_fn=<NllLossBackward0>)
53600 tensor(5.4649, device='cuda:0', grad_fn=<NllLossBackward0>)
53700 tensor(5.2961, device='cuda:0', grad_fn=<NllLossBackward0>)
53800 tensor(5.3088, device='cuda:0', grad_fn=<NllLossBackward0>)
53900 tensor(5.4162, device='cuda:0', grad_fn=<NllLossBackward0>)
54000 tensor(5.9259, device='cuda:0', grad_fn=<NllLossBackward0>)
54100 tensor(5.2742, device='cuda:0', grad_fn=<NllLossBackward0>)
54200 tensor(5.5820, device='cuda:0', grad_fn=<NllLossBackward0>)
54300 tensor(5.0661, device='cuda:0', grad_fn=<NllLossBackward0>)
54400 tensor(5.1934, device='cuda:0', grad_fn=<NllLossBackward0>)
54500 tensor(5.2265, device='cuda:0', grad_fn=<NllLossBackward0>)
54600 tensor(5.5509, device='cuda:0', grad_fn=<NllLossBackward0>)
54700 tensor(5.5712, device='cuda:0', grad_fn=<NllLossBackward0>)
54800 tensor(5.3762, device='cuda:0', grad_fn=<NllLossBackward0>)
54900 tensor(5.2392, device='cuda:0', grad_fn=<NllLossBackward0>)
55000 tensor(5.4364, device='cuda:0', grad_fn=<NllLossBackward0>)
55100 tensor(5.5409, device='cuda:0', grad_fn=<NllLossBackward0>)
55200 tensor(5.5735, device='cuda:0', grad_fn=<NllLossBackward0>)
55300 tensor(5.4363, device='cuda:0', grad_fn=<NllLossBackward0>)
55400 tensor(5.1247, device='cuda:0', grad_fn=<NllLossBackward0>)
55500 tensor(5.2063, device='cuda:0', grad_fn=<NllLossBackward0>)
55600 tensor(5.4948, device='cuda:0', grad_fn=<NllLossBackward0>)
55700 tensor(5.5324, device='cuda:0', grad_fn=<NllLossBackward0>)
55800 tensor(5.0667, device='cuda:0', grad_fn=<NllLossBackward0>)
55900 tensor(5.3209, device='cuda:0', grad_fn=<NllLossBackward0>)
56000 tensor(5.3632, device='cuda:0', grad_fn=<NllLossBackward0>)
56100 tensor(5.4861, device='cuda:0', grad_fn=<NllLossBackward0>)
56200 tensor(5.3914, device='cuda:0', grad_fn=<NllLossBackward0>)
56300 tensor(4.9190, device='cuda:0', grad_fn=<NllLossBackward0>)
56400 tensor(5.4619, device='cuda:0', grad_fn=<NllLossBackward0>)
56500 tensor(5.1961, device='cuda:0', grad_fn=<NllLossBackward0>)
56600 tensor(5.2067, device='cuda:0', grad_fn=<NllLossBackward0>)
56700 tensor(5.7416, device='cuda:0', grad_fn=<NllLossBackward0>)
56800 tensor(5.4107, device='cuda:0', grad_fn=<NllLossBackward0>)
56900 tensor(5.4789, device='cuda:0', grad_fn=<NllLossBackward0>)
57000 tensor(5.5753, device='cuda:0', grad_fn=<NllLossBackward0>)
57100 tensor(5.3689, device='cuda:0', grad_fn=<NllLossBackward0>)
57200 tensor(5.6297, device='cuda:0', grad_fn=<NllLossBackward0>)
57300 tensor(5.6960, device='cuda:0', grad_fn=<NllLossBackward0>)
57400 tensor(5.3610, device='cuda:0', grad_fn=<NllLossBackward0>)
57500 tensor(5.4340, device='cuda:0', grad_fn=<NllLossBackward0>)
57600 tensor(5.8130, device='cuda:0', grad_fn=<NllLossBackward0>)
57700 tensor(5.5437, device='cuda:0', grad_fn=<NllLossBackward0>)
57800 tensor(5.4003, device='cuda:0', grad_fn=<NllLossBackward0>)
57900 tensor(5.4354, device='cuda:0', grad_fn=<NllLossBackward0>)
58000 tensor(5.3039, device='cuda:0', grad_fn=<NllLossBackward0>)
58100 tensor(5.5298, device='cuda:0', grad_fn=<NllLossBackward0>)
58200 tensor(5.4036, device='cuda:0', grad_fn=<NllLossBackward0>)
58300 tensor(5.5035, device='cuda:0', grad_fn=<NllLossBackward0>)
58400 tensor(5.4694, device='cuda:0', grad_fn=<NllLossBackward0>)
58500 tensor(5.4644, device='cuda:0', grad_fn=<NllLossBackward0>)
58600 tensor(5.3628, device='cuda:0', grad_fn=<NllLossBackward0>)
58700 tensor(5.5305, device='cuda:0', grad_fn=<NllLossBackward0>)
58800 tensor(5.5496, device='cuda:0', grad_fn=<NllLossBackward0>)
58900 tensor(5.1605, device='cuda:0', grad_fn=<NllLossBackward0>)
59000 tensor(5.4481, device='cuda:0', grad_fn=<NllLossBackward0>)
59100 tensor(5.5008, device='cuda:0', grad_fn=<NllLossBackward0>)
59200 tensor(5.5580, device='cuda:0', grad_fn=<NllLossBackward0>)
59300 tensor(5.4181, device='cuda:0', grad_fn=<NllLossBackward0>)
59400 tensor(5.1767, device='cuda:0', grad_fn=<NllLossBackward0>)
59500 tensor(5.5949, device='cuda:0', grad_fn=<NllLossBackward0>)
59600 tensor(5.1543, device='cuda:0', grad_fn=<NllLossBackward0>)
59700 tensor(5.4442, device='cuda:0', grad_fn=<NllLossBackward0>)
59800 tensor(5.2701, device='cuda:0', grad_fn=<NllLossBackward0>)
59900 tensor(5.4101, device='cuda:0', grad_fn=<NllLossBackward0>)
60000 tensor(5.3686, device='cuda:0', grad_fn=<NllLossBackward0>)
60100 tensor(5.2843, device='cuda:0', grad_fn=<NllLossBackward0>)
60200 tensor(5.5036, device='cuda:0', grad_fn=<NllLossBackward0>)
60300 tensor(5.3552, device='cuda:0', grad_fn=<NllLossBackward0>)
60400 tensor(5.5374, device='cuda:0', grad_fn=<NllLossBackward0>)
60500 tensor(5.1537, device='cuda:0', grad_fn=<NllLossBackward0>)
60600 tensor(5.4950, device='cuda:0', grad_fn=<NllLossBackward0>)
60700 tensor(5.2628, device='cuda:0', grad_fn=<NllLossBackward0>)
60800 tensor(5.5945, device='cuda:0', grad_fn=<NllLossBackward0>)
60900 tensor(5.5902, device='cuda:0', grad_fn=<NllLossBackward0>)
61000 tensor(5.4887, device='cuda:0', grad_fn=<NllLossBackward0>)
61100 tensor(5.2792, device='cuda:0', grad_fn=<NllLossBackward0>)
61200 tensor(5.5803, device='cuda:0', grad_fn=<NllLossBackward0>)
61300 tensor(5.4461, device='cuda:0', grad_fn=<NllLossBackward0>)
61400 tensor(5.0183, device='cuda:0', grad_fn=<NllLossBackward0>)
61500 tensor(5.3240, device='cuda:0', grad_fn=<NllLossBackward0>)
61600 tensor(5.4643, device='cuda:0', grad_fn=<NllLossBackward0>)
61700 tensor(5.3920, device='cuda:0', grad_fn=<NllLossBackward0>)
61800 tensor(5.5427, device='cuda:0', grad_fn=<NllLossBackward0>)
61900 tensor(5.8412, device='cuda:0', grad_fn=<NllLossBackward0>)
62000 tensor(5.4249, device='cuda:0', grad_fn=<NllLossBackward0>)
62100 tensor(5.5865, device='cuda:0', grad_fn=<NllLossBackward0>)
62200 tensor(5.3857, device='cuda:0', grad_fn=<NllLossBackward0>)
62300 tensor(5.0211, device='cuda:0', grad_fn=<NllLossBackward0>)
62400 tensor(5.2934, device='cuda:0', grad_fn=<NllLossBackward0>)
62500 tensor(5.2083, device='cuda:0', grad_fn=<NllLossBackward0>)
62600 tensor(5.2642, device='cuda:0', grad_fn=<NllLossBackward0>)
62700 tensor(4.9303, device='cuda:0', grad_fn=<NllLossBackward0>)
62800 tensor(5.1333, device='cuda:0', grad_fn=<NllLossBackward0>)
62900 tensor(5.5126, device='cuda:0', grad_fn=<NllLossBackward0>)
63000 tensor(4.8968, device='cuda:0', grad_fn=<NllLossBackward0>)
63100 tensor(5.3211, device='cuda:0', grad_fn=<NllLossBackward0>)
63200 tensor(5.4832, device='cuda:0', grad_fn=<NllLossBackward0>)
63300 tensor(5.4616, device='cuda:0', grad_fn=<NllLossBackward0>)
63400 tensor(5.3212, device='cuda:0', grad_fn=<NllLossBackward0>)
63500 tensor(5.2929, device='cuda:0', grad_fn=<NllLossBackward0>)
63600 tensor(5.4305, device='cuda:0', grad_fn=<NllLossBackward0>)
63700 tensor(5.2080, device='cuda:0', grad_fn=<NllLossBackward0>)
63800 tensor(5.4208, device='cuda:0', grad_fn=<NllLossBackward0>)
63900 tensor(5.4145, device='cuda:0', grad_fn=<NllLossBackward0>)
64000 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>)
64100 tensor(5.5111, device='cuda:0', grad_fn=<NllLossBackward0>)
64200 tensor(5.1437, device='cuda:0', grad_fn=<NllLossBackward0>)
64300 tensor(5.4269, device='cuda:0', grad_fn=<NllLossBackward0>)
64400 tensor(5.5086, device='cuda:0', grad_fn=<NllLossBackward0>)
64500 tensor(5.3559, device='cuda:0', grad_fn=<NllLossBackward0>)
64600 tensor(5.3799, device='cuda:0', grad_fn=<NllLossBackward0>)
64700 tensor(5.5940, device='cuda:0', grad_fn=<NllLossBackward0>)
64800 tensor(5.1958, device='cuda:0', grad_fn=<NllLossBackward0>)
64900 tensor(5.3498, device='cuda:0', grad_fn=<NllLossBackward0>)
65000 tensor(5.3998, device='cuda:0', grad_fn=<NllLossBackward0>)
65100 tensor(5.2237, device='cuda:0', grad_fn=<NllLossBackward0>)
65200 tensor(5.0362, device='cuda:0', grad_fn=<NllLossBackward0>)
65300 tensor(5.5109, device='cuda:0', grad_fn=<NllLossBackward0>)
65400 tensor(5.2673, device='cuda:0', grad_fn=<NllLossBackward0>)
65500 tensor(5.0693, device='cuda:0', grad_fn=<NllLossBackward0>)
65600 tensor(5.4907, device='cuda:0', grad_fn=<NllLossBackward0>)
65700 tensor(5.5288, device='cuda:0', grad_fn=<NllLossBackward0>)
65800 tensor(5.3971, device='cuda:0', grad_fn=<NllLossBackward0>)
65900 tensor(5.3500, device='cuda:0', grad_fn=<NllLossBackward0>)
66000 tensor(5.7787, device='cuda:0', grad_fn=<NllLossBackward0>)
66100 tensor(5.1555, device='cuda:0', grad_fn=<NllLossBackward0>)
66200 tensor(5.4229, device='cuda:0', grad_fn=<NllLossBackward0>)
66300 tensor(5.1499, device='cuda:0', grad_fn=<NllLossBackward0>)
66400 tensor(5.5168, device='cuda:0', grad_fn=<NllLossBackward0>)
66500 tensor(5.6282, device='cuda:0', grad_fn=<NllLossBackward0>)
66600 tensor(5.3283, device='cuda:0', grad_fn=<NllLossBackward0>)
66700 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>)
66800 tensor(5.3382, device='cuda:0', grad_fn=<NllLossBackward0>)
66900 tensor(5.2665, device='cuda:0', grad_fn=<NllLossBackward0>)
67000 tensor(5.3828, device='cuda:0', grad_fn=<NllLossBackward0>)
67100 tensor(5.2455, device='cuda:0', grad_fn=<NllLossBackward0>)
67200 tensor(5.7224, device='cuda:0', grad_fn=<NllLossBackward0>)
67300 tensor(5.5869, device='cuda:0', grad_fn=<NllLossBackward0>)
67400 tensor(5.4242, device='cuda:0', grad_fn=<NllLossBackward0>)
67500 tensor(5.4228, device='cuda:0', grad_fn=<NllLossBackward0>)
67600 tensor(5.3538, device='cuda:0', grad_fn=<NllLossBackward0>)
67700 tensor(5.1782, device='cuda:0', grad_fn=<NllLossBackward0>)
67800 tensor(5.3206, device='cuda:0', grad_fn=<NllLossBackward0>)
67900 tensor(5.2828, device='cuda:0', grad_fn=<NllLossBackward0>)
68000 tensor(5.3962, device='cuda:0', grad_fn=<NllLossBackward0>)
68100 tensor(5.3605, device='cuda:0', grad_fn=<NllLossBackward0>)
68200 tensor(5.1993, device='cuda:0', grad_fn=<NllLossBackward0>)
68300 tensor(5.3261, device='cuda:0', grad_fn=<NllLossBackward0>)
68400 tensor(5.8642, device='cuda:0', grad_fn=<NllLossBackward0>)
68500 tensor(5.1566, device='cuda:0', grad_fn=<NllLossBackward0>)
68600 tensor(5.3310, device='cuda:0', grad_fn=<NllLossBackward0>)
68700 tensor(5.3318, device='cuda:0', grad_fn=<NllLossBackward0>)
68800 tensor(5.5199, device='cuda:0', grad_fn=<NllLossBackward0>)
68900 tensor(5.3169, device='cuda:0', grad_fn=<NllLossBackward0>)
69000 tensor(5.2783, device='cuda:0', grad_fn=<NllLossBackward0>)
69100 tensor(5.4604, device='cuda:0', grad_fn=<NllLossBackward0>)
69200 tensor(5.3401, device='cuda:0', grad_fn=<NllLossBackward0>)
69300 tensor(5.0342, device='cuda:0', grad_fn=<NllLossBackward0>)
69400 tensor(5.3514, device='cuda:0', grad_fn=<NllLossBackward0>)
69500 tensor(5.1504, device='cuda:0', grad_fn=<NllLossBackward0>)
Creating outputs in dev-0
Creating outputs in test-A
0 tensor(10.3829, device='cuda:0', grad_fn=<NllLossBackward0>)
100 tensor(8.0792, device='cuda:0', grad_fn=<NllLossBackward0>)
200 tensor(7.3059, device='cuda:0', grad_fn=<NllLossBackward0>)
300 tensor(6.8478, device='cuda:0', grad_fn=<NllLossBackward0>)
400 tensor(6.6292, device='cuda:0', grad_fn=<NllLossBackward0>)
500 tensor(6.6597, device='cuda:0', grad_fn=<NllLossBackward0>)
600 tensor(6.7076, device='cuda:0', grad_fn=<NllLossBackward0>)
700 tensor(6.4022, device='cuda:0', grad_fn=<NllLossBackward0>)
800 tensor(6.1865, device='cuda:0', grad_fn=<NllLossBackward0>)
900 tensor(6.3715, device='cuda:0', grad_fn=<NllLossBackward0>)
1000 tensor(6.2953, device='cuda:0', grad_fn=<NllLossBackward0>)
1100 tensor(5.8570, device='cuda:0', grad_fn=<NllLossBackward0>)
1200 tensor(6.3739, device='cuda:0', grad_fn=<NllLossBackward0>)
1300 tensor(6.4504, device='cuda:0', grad_fn=<NllLossBackward0>)
1400 tensor(6.1518, device='cuda:0', grad_fn=<NllLossBackward0>)
1500 tensor(5.9614, device='cuda:0', grad_fn=<NllLossBackward0>)
1600 tensor(6.0159, device='cuda:0', grad_fn=<NllLossBackward0>)
1700 tensor(6.3196, device='cuda:0', grad_fn=<NllLossBackward0>)
1800 tensor(6.3034, device='cuda:0', grad_fn=<NllLossBackward0>)
1900 tensor(6.1724, device='cuda:0', grad_fn=<NllLossBackward0>)
2000 tensor(6.1985, device='cuda:0', grad_fn=<NllLossBackward0>)
2100 tensor(6.0150, device='cuda:0', grad_fn=<NllLossBackward0>)
2200 tensor(6.2215, device='cuda:0', grad_fn=<NllLossBackward0>)
2300 tensor(6.1963, device='cuda:0', grad_fn=<NllLossBackward0>)
2400 tensor(6.1551, device='cuda:0', grad_fn=<NllLossBackward0>)
2500 tensor(6.1821, device='cuda:0', grad_fn=<NllLossBackward0>)
2600 tensor(6.1207, device='cuda:0', grad_fn=<NllLossBackward0>)
2700 tensor(6.2244, device='cuda:0', grad_fn=<NllLossBackward0>)
2800 tensor(6.1407, device='cuda:0', grad_fn=<NllLossBackward0>)
2900 tensor(6.0838, device='cuda:0', grad_fn=<NllLossBackward0>)
3000 tensor(6.0838, device='cuda:0', grad_fn=<NllLossBackward0>)
3100 tensor(5.8551, device='cuda:0', grad_fn=<NllLossBackward0>)
3200 tensor(6.4406, device='cuda:0', grad_fn=<NllLossBackward0>)
3300 tensor(6.1330, device='cuda:0', grad_fn=<NllLossBackward0>)
3400 tensor(5.9802, device='cuda:0', grad_fn=<NllLossBackward0>)
3500 tensor(5.9609, device='cuda:0', grad_fn=<NllLossBackward0>)
3600 tensor(6.2390, device='cuda:0', grad_fn=<NllLossBackward0>)
3700 tensor(6.0141, device='cuda:0', grad_fn=<NllLossBackward0>)
3800 tensor(6.1221, device='cuda:0', grad_fn=<NllLossBackward0>)
3900 tensor(6.0129, device='cuda:0', grad_fn=<NllLossBackward0>)
4000 tensor(5.9146, device='cuda:0', grad_fn=<NllLossBackward0>)
4100 tensor(6.0411, device='cuda:0', grad_fn=<NllLossBackward0>)
4200 tensor(5.9824, device='cuda:0', grad_fn=<NllLossBackward0>)
4300 tensor(5.8674, device='cuda:0', grad_fn=<NllLossBackward0>)
4400 tensor(5.6331, device='cuda:0', grad_fn=<NllLossBackward0>)
4500 tensor(5.9987, device='cuda:0', grad_fn=<NllLossBackward0>)
4600 tensor(5.8823, device='cuda:0', grad_fn=<NllLossBackward0>)
4700 tensor(5.7188, device='cuda:0', grad_fn=<NllLossBackward0>)
4800 tensor(5.8505, device='cuda:0', grad_fn=<NllLossBackward0>)
4900 tensor(5.9353, device='cuda:0', grad_fn=<NllLossBackward0>)
5000 tensor(6.0726, device='cuda:0', grad_fn=<NllLossBackward0>)
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59400 tensor(5.1357, device='cuda:0', grad_fn=<NllLossBackward0>)
59500 tensor(5.5514, device='cuda:0', grad_fn=<NllLossBackward0>)
59600 tensor(5.1275, device='cuda:0', grad_fn=<NllLossBackward0>)
59700 tensor(5.4095, device='cuda:0', grad_fn=<NllLossBackward0>)
59800 tensor(5.2168, device='cuda:0', grad_fn=<NllLossBackward0>)
59900 tensor(5.3622, device='cuda:0', grad_fn=<NllLossBackward0>)
60000 tensor(5.3232, device='cuda:0', grad_fn=<NllLossBackward0>)
60100 tensor(5.2477, device='cuda:0', grad_fn=<NllLossBackward0>)
60200 tensor(5.4876, device='cuda:0', grad_fn=<NllLossBackward0>)
60300 tensor(5.3204, device='cuda:0', grad_fn=<NllLossBackward0>)
60400 tensor(5.5030, device='cuda:0', grad_fn=<NllLossBackward0>)
60500 tensor(5.1152, device='cuda:0', grad_fn=<NllLossBackward0>)
60600 tensor(5.4408, device='cuda:0', grad_fn=<NllLossBackward0>)
60700 tensor(5.2033, device='cuda:0', grad_fn=<NllLossBackward0>)
60800 tensor(5.5601, device='cuda:0', grad_fn=<NllLossBackward0>)
60900 tensor(5.5461, device='cuda:0', grad_fn=<NllLossBackward0>)
61000 tensor(5.4563, device='cuda:0', grad_fn=<NllLossBackward0>)
61100 tensor(5.2254, device='cuda:0', grad_fn=<NllLossBackward0>)
61200 tensor(5.5692, device='cuda:0', grad_fn=<NllLossBackward0>)
61300 tensor(5.4247, device='cuda:0', grad_fn=<NllLossBackward0>)
61400 tensor(4.9635, device='cuda:0', grad_fn=<NllLossBackward0>)
61500 tensor(5.2972, device='cuda:0', grad_fn=<NllLossBackward0>)
61600 tensor(5.4258, device='cuda:0', grad_fn=<NllLossBackward0>)
61700 tensor(5.3653, device='cuda:0', grad_fn=<NllLossBackward0>)
61800 tensor(5.5186, device='cuda:0', grad_fn=<NllLossBackward0>)
61900 tensor(5.8254, device='cuda:0', grad_fn=<NllLossBackward0>)
62000 tensor(5.3711, device='cuda:0', grad_fn=<NllLossBackward0>)
62100 tensor(5.5506, device='cuda:0', grad_fn=<NllLossBackward0>)
62200 tensor(5.3525, device='cuda:0', grad_fn=<NllLossBackward0>)
62300 tensor(4.9781, device='cuda:0', grad_fn=<NllLossBackward0>)
62400 tensor(5.2654, device='cuda:0', grad_fn=<NllLossBackward0>)
62500 tensor(5.1860, device='cuda:0', grad_fn=<NllLossBackward0>)
62600 tensor(5.2197, device='cuda:0', grad_fn=<NllLossBackward0>)
62700 tensor(4.8901, device='cuda:0', grad_fn=<NllLossBackward0>)
62800 tensor(5.0782, device='cuda:0', grad_fn=<NllLossBackward0>)
62900 tensor(5.4533, device='cuda:0', grad_fn=<NllLossBackward0>)
63000 tensor(4.8650, device='cuda:0', grad_fn=<NllLossBackward0>)
63100 tensor(5.2813, device='cuda:0', grad_fn=<NllLossBackward0>)
63200 tensor(5.4397, device='cuda:0', grad_fn=<NllLossBackward0>)
63300 tensor(5.4245, device='cuda:0', grad_fn=<NllLossBackward0>)
63400 tensor(5.2748, device='cuda:0', grad_fn=<NllLossBackward0>)
63500 tensor(5.2523, device='cuda:0', grad_fn=<NllLossBackward0>)
63600 tensor(5.3960, device='cuda:0', grad_fn=<NllLossBackward0>)
63700 tensor(5.1610, device='cuda:0', grad_fn=<NllLossBackward0>)
63800 tensor(5.3532, device='cuda:0', grad_fn=<NllLossBackward0>)
63900 tensor(5.3806, device='cuda:0', grad_fn=<NllLossBackward0>)
64000 tensor(5.3295, device='cuda:0', grad_fn=<NllLossBackward0>)
64100 tensor(5.4567, device='cuda:0', grad_fn=<NllLossBackward0>)
64200 tensor(5.1251, device='cuda:0', grad_fn=<NllLossBackward0>)
64300 tensor(5.3982, device='cuda:0', grad_fn=<NllLossBackward0>)
64400 tensor(5.4605, device='cuda:0', grad_fn=<NllLossBackward0>)
64500 tensor(5.3091, device='cuda:0', grad_fn=<NllLossBackward0>)
64600 tensor(5.3547, device='cuda:0', grad_fn=<NllLossBackward0>)
64700 tensor(5.5553, device='cuda:0', grad_fn=<NllLossBackward0>)
64800 tensor(5.1512, device='cuda:0', grad_fn=<NllLossBackward0>)
64900 tensor(5.3059, device='cuda:0', grad_fn=<NllLossBackward0>)
65000 tensor(5.3715, device='cuda:0', grad_fn=<NllLossBackward0>)
65100 tensor(5.1765, device='cuda:0', grad_fn=<NllLossBackward0>)
65200 tensor(4.9975, device='cuda:0', grad_fn=<NllLossBackward0>)
65300 tensor(5.4619, device='cuda:0', grad_fn=<NllLossBackward0>)
65400 tensor(5.2211, device='cuda:0', grad_fn=<NllLossBackward0>)
65500 tensor(5.0544, device='cuda:0', grad_fn=<NllLossBackward0>)
65600 tensor(5.4778, device='cuda:0', grad_fn=<NllLossBackward0>)
65700 tensor(5.4886, device='cuda:0', grad_fn=<NllLossBackward0>)
65800 tensor(5.3707, device='cuda:0', grad_fn=<NllLossBackward0>)
65900 tensor(5.3304, device='cuda:0', grad_fn=<NllLossBackward0>)
66000 tensor(5.7419, device='cuda:0', grad_fn=<NllLossBackward0>)
66100 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>)
66200 tensor(5.3704, device='cuda:0', grad_fn=<NllLossBackward0>)
66300 tensor(5.1073, device='cuda:0', grad_fn=<NllLossBackward0>)
66400 tensor(5.4869, device='cuda:0', grad_fn=<NllLossBackward0>)
66500 tensor(5.6025, device='cuda:0', grad_fn=<NllLossBackward0>)
66600 tensor(5.3030, device='cuda:0', grad_fn=<NllLossBackward0>)
66700 tensor(5.3760, device='cuda:0', grad_fn=<NllLossBackward0>)
66800 tensor(5.3238, device='cuda:0', grad_fn=<NllLossBackward0>)
66900 tensor(5.2442, device='cuda:0', grad_fn=<NllLossBackward0>)
67000 tensor(5.3488, device='cuda:0', grad_fn=<NllLossBackward0>)
67100 tensor(5.2200, device='cuda:0', grad_fn=<NllLossBackward0>)
67200 tensor(5.6754, device='cuda:0', grad_fn=<NllLossBackward0>)
67300 tensor(5.5589, device='cuda:0', grad_fn=<NllLossBackward0>)
67400 tensor(5.3765, device='cuda:0', grad_fn=<NllLossBackward0>)
67500 tensor(5.3911, device='cuda:0', grad_fn=<NllLossBackward0>)
67600 tensor(5.3410, device='cuda:0', grad_fn=<NllLossBackward0>)
67700 tensor(5.1323, device='cuda:0', grad_fn=<NllLossBackward0>)
67800 tensor(5.2726, device='cuda:0', grad_fn=<NllLossBackward0>)
67900 tensor(5.2314, device='cuda:0', grad_fn=<NllLossBackward0>)
68000 tensor(5.3615, device='cuda:0', grad_fn=<NllLossBackward0>)
68100 tensor(5.3275, device='cuda:0', grad_fn=<NllLossBackward0>)
68200 tensor(5.1481, device='cuda:0', grad_fn=<NllLossBackward0>)
68300 tensor(5.2834, device='cuda:0', grad_fn=<NllLossBackward0>)
68400 tensor(5.8378, device='cuda:0', grad_fn=<NllLossBackward0>)
68500 tensor(5.0982, device='cuda:0', grad_fn=<NllLossBackward0>)
68600 tensor(5.2805, device='cuda:0', grad_fn=<NllLossBackward0>)
68700 tensor(5.2916, device='cuda:0', grad_fn=<NllLossBackward0>)
68800 tensor(5.4921, device='cuda:0', grad_fn=<NllLossBackward0>)
68900 tensor(5.2871, device='cuda:0', grad_fn=<NllLossBackward0>)
69000 tensor(5.2191, device='cuda:0', grad_fn=<NllLossBackward0>)
69100 tensor(5.4146, device='cuda:0', grad_fn=<NllLossBackward0>)
69200 tensor(5.3098, device='cuda:0', grad_fn=<NllLossBackward0>)
69300 tensor(4.9947, device='cuda:0', grad_fn=<NllLossBackward0>)
69400 tensor(5.3038, device='cuda:0', grad_fn=<NllLossBackward0>)
69500 tensor(5.1063, device='cuda:0', grad_fn=<NllLossBackward0>)
Creating outputs in dev-0
Creating outputs in test-A
0 tensor(10.3276, device='cuda:0', grad_fn=<NllLossBackward0>)
100 tensor(7.9401, device='cuda:0', grad_fn=<NllLossBackward0>)
200 tensor(7.2381, device='cuda:0', grad_fn=<NllLossBackward0>)
300 tensor(6.8126, device='cuda:0', grad_fn=<NllLossBackward0>)
400 tensor(6.6045, device='cuda:0', grad_fn=<NllLossBackward0>)
500 tensor(6.6184, device='cuda:0', grad_fn=<NllLossBackward0>)
600 tensor(6.6869, device='cuda:0', grad_fn=<NllLossBackward0>)
700 tensor(6.3630, device='cuda:0', grad_fn=<NllLossBackward0>)
800 tensor(6.1966, device='cuda:0', grad_fn=<NllLossBackward0>)
900 tensor(6.3506, device='cuda:0', grad_fn=<NllLossBackward0>)
1000 tensor(6.2652, device='cuda:0', grad_fn=<NllLossBackward0>)
1100 tensor(5.8459, device='cuda:0', grad_fn=<NllLossBackward0>)
1200 tensor(6.3685, device='cuda:0', grad_fn=<NllLossBackward0>)
1300 tensor(6.4105, device='cuda:0', grad_fn=<NllLossBackward0>)
1400 tensor(6.1318, device='cuda:0', grad_fn=<NllLossBackward0>)
1500 tensor(5.9373, device='cuda:0', grad_fn=<NllLossBackward0>)
1600 tensor(5.9996, device='cuda:0', grad_fn=<NllLossBackward0>)
1700 tensor(6.2852, device='cuda:0', grad_fn=<NllLossBackward0>)
1800 tensor(6.2778, device='cuda:0', grad_fn=<NllLossBackward0>)
1900 tensor(6.1339, device='cuda:0', grad_fn=<NllLossBackward0>)
2000 tensor(6.1958, device='cuda:0', grad_fn=<NllLossBackward0>)
2100 tensor(5.9972, device='cuda:0', grad_fn=<NllLossBackward0>)
2200 tensor(6.2078, device='cuda:0', grad_fn=<NllLossBackward0>)
2300 tensor(6.1827, device='cuda:0', grad_fn=<NllLossBackward0>)
2400 tensor(6.1275, device='cuda:0', grad_fn=<NllLossBackward0>)
2500 tensor(6.1562, device='cuda:0', grad_fn=<NllLossBackward0>)
2600 tensor(6.0775, device='cuda:0', grad_fn=<NllLossBackward0>)
2700 tensor(6.2004, device='cuda:0', grad_fn=<NllLossBackward0>)
2800 tensor(6.1155, device='cuda:0', grad_fn=<NllLossBackward0>)
2900 tensor(6.0537, device='cuda:0', grad_fn=<NllLossBackward0>)
3000 tensor(6.0540, device='cuda:0', grad_fn=<NllLossBackward0>)
3100 tensor(5.8310, device='cuda:0', grad_fn=<NllLossBackward0>)
3200 tensor(6.3952, device='cuda:0', grad_fn=<NllLossBackward0>)
3300 tensor(6.1059, device='cuda:0', grad_fn=<NllLossBackward0>)
3400 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>)
3500 tensor(5.9202, device='cuda:0', grad_fn=<NllLossBackward0>)
3600 tensor(6.2096, device='cuda:0', grad_fn=<NllLossBackward0>)
3700 tensor(5.9983, device='cuda:0', grad_fn=<NllLossBackward0>)
3800 tensor(6.0919, device='cuda:0', grad_fn=<NllLossBackward0>)
3900 tensor(6.0015, device='cuda:0', grad_fn=<NllLossBackward0>)
4000 tensor(5.8796, device='cuda:0', grad_fn=<NllLossBackward0>)
4100 tensor(6.0101, device='cuda:0', grad_fn=<NllLossBackward0>)
4200 tensor(5.9665, device='cuda:0', grad_fn=<NllLossBackward0>)
4300 tensor(5.8365, device='cuda:0', grad_fn=<NllLossBackward0>)
4400 tensor(5.6078, device='cuda:0', grad_fn=<NllLossBackward0>)
4500 tensor(5.9602, device='cuda:0', grad_fn=<NllLossBackward0>)
4600 tensor(5.8495, device='cuda:0', grad_fn=<NllLossBackward0>)
4700 tensor(5.6834, device='cuda:0', grad_fn=<NllLossBackward0>)
4800 tensor(5.8261, device='cuda:0', grad_fn=<NllLossBackward0>)
4900 tensor(5.9137, device='cuda:0', grad_fn=<NllLossBackward0>)
5000 tensor(6.0360, device='cuda:0', grad_fn=<NllLossBackward0>)
5100 tensor(5.8791, device='cuda:0', grad_fn=<NllLossBackward0>)
5200 tensor(6.1084, device='cuda:0', grad_fn=<NllLossBackward0>)
5300 tensor(6.0378, device='cuda:0', grad_fn=<NllLossBackward0>)
5400 tensor(5.9057, device='cuda:0', grad_fn=<NllLossBackward0>)
5500 tensor(5.9146, device='cuda:0', grad_fn=<NllLossBackward0>)
5600 tensor(5.9022, device='cuda:0', grad_fn=<NllLossBackward0>)
5700 tensor(5.9767, device='cuda:0', grad_fn=<NllLossBackward0>)
5800 tensor(5.9410, device='cuda:0', grad_fn=<NllLossBackward0>)
5900 tensor(5.8609, device='cuda:0', grad_fn=<NllLossBackward0>)
6000 tensor(5.8036, device='cuda:0', grad_fn=<NllLossBackward0>)
6100 tensor(5.8270, device='cuda:0', grad_fn=<NllLossBackward0>)
6200 tensor(5.9282, device='cuda:0', grad_fn=<NllLossBackward0>)
6300 tensor(5.7968, device='cuda:0', grad_fn=<NllLossBackward0>)
6400 tensor(6.1270, device='cuda:0', grad_fn=<NllLossBackward0>)
6500 tensor(5.7318, device='cuda:0', grad_fn=<NllLossBackward0>)
6600 tensor(6.0448, device='cuda:0', grad_fn=<NllLossBackward0>)
6700 tensor(5.9031, device='cuda:0', grad_fn=<NllLossBackward0>)
6800 tensor(5.7908, device='cuda:0', grad_fn=<NllLossBackward0>)
6900 tensor(5.7183, device='cuda:0', grad_fn=<NllLossBackward0>)
7000 tensor(5.8839, device='cuda:0', grad_fn=<NllLossBackward0>)
7100 tensor(5.7365, device='cuda:0', grad_fn=<NllLossBackward0>)
7200 tensor(5.8651, device='cuda:0', grad_fn=<NllLossBackward0>)
7300 tensor(6.0091, device='cuda:0', grad_fn=<NllLossBackward0>)
7400 tensor(5.7031, device='cuda:0', grad_fn=<NllLossBackward0>)
7500 tensor(5.8671, device='cuda:0', grad_fn=<NllLossBackward0>)
7600 tensor(5.8997, device='cuda:0', grad_fn=<NllLossBackward0>)
7700 tensor(5.7679, device='cuda:0', grad_fn=<NllLossBackward0>)
7800 tensor(5.7867, device='cuda:0', grad_fn=<NllLossBackward0>)
7900 tensor(5.9113, device='cuda:0', grad_fn=<NllLossBackward0>)
8000 tensor(5.6723, device='cuda:0', grad_fn=<NllLossBackward0>)
8100 tensor(5.9791, device='cuda:0', grad_fn=<NllLossBackward0>)
8200 tensor(5.8998, device='cuda:0', grad_fn=<NllLossBackward0>)
8300 tensor(5.8103, device='cuda:0', grad_fn=<NllLossBackward0>)
8400 tensor(5.9573, device='cuda:0', grad_fn=<NllLossBackward0>)
8500 tensor(5.2251, device='cuda:0', grad_fn=<NllLossBackward0>)
8600 tensor(5.7472, device='cuda:0', grad_fn=<NllLossBackward0>)
8700 tensor(5.3789, device='cuda:0', grad_fn=<NllLossBackward0>)
8800 tensor(5.8526, device='cuda:0', grad_fn=<NllLossBackward0>)
8900 tensor(5.7923, device='cuda:0', grad_fn=<NllLossBackward0>)
9000 tensor(5.7036, device='cuda:0', grad_fn=<NllLossBackward0>)
9100 tensor(5.7377, device='cuda:0', grad_fn=<NllLossBackward0>)
9200 tensor(5.7688, device='cuda:0', grad_fn=<NllLossBackward0>)
9300 tensor(5.7391, device='cuda:0', grad_fn=<NllLossBackward0>)
9400 tensor(5.9497, device='cuda:0', grad_fn=<NllLossBackward0>)
9500 tensor(5.5777, device='cuda:0', grad_fn=<NllLossBackward0>)
9600 tensor(5.8298, device='cuda:0', grad_fn=<NllLossBackward0>)
9700 tensor(5.7534, device='cuda:0', grad_fn=<NllLossBackward0>)
9800 tensor(5.9139, device='cuda:0', grad_fn=<NllLossBackward0>)
9900 tensor(5.7988, device='cuda:0', grad_fn=<NllLossBackward0>)
10000 tensor(5.8364, device='cuda:0', grad_fn=<NllLossBackward0>)
10100 tensor(5.7934, device='cuda:0', grad_fn=<NllLossBackward0>)
10200 tensor(5.5965, device='cuda:0', grad_fn=<NllLossBackward0>)
10300 tensor(5.8358, device='cuda:0', grad_fn=<NllLossBackward0>)
10400 tensor(5.8457, device='cuda:0', grad_fn=<NllLossBackward0>)
10500 tensor(5.7757, device='cuda:0', grad_fn=<NllLossBackward0>)
10600 tensor(5.5855, device='cuda:0', grad_fn=<NllLossBackward0>)
10700 tensor(5.6421, device='cuda:0', grad_fn=<NllLossBackward0>)
10800 tensor(5.7135, device='cuda:0', grad_fn=<NllLossBackward0>)
10900 tensor(5.6907, device='cuda:0', grad_fn=<NllLossBackward0>)
11000 tensor(5.7571, device='cuda:0', grad_fn=<NllLossBackward0>)
11100 tensor(5.8093, device='cuda:0', grad_fn=<NllLossBackward0>)
11200 tensor(5.5920, device='cuda:0', grad_fn=<NllLossBackward0>)
11300 tensor(5.8946, device='cuda:0', grad_fn=<NllLossBackward0>)
11400 tensor(5.7888, device='cuda:0', grad_fn=<NllLossBackward0>)
11500 tensor(5.8484, device='cuda:0', grad_fn=<NllLossBackward0>)
11600 tensor(5.9122, device='cuda:0', grad_fn=<NllLossBackward0>)
11700 tensor(5.7712, device='cuda:0', grad_fn=<NllLossBackward0>)
11800 tensor(5.4625, device='cuda:0', grad_fn=<NllLossBackward0>)
11900 tensor(5.9522, device='cuda:0', grad_fn=<NllLossBackward0>)
12000 tensor(5.7293, device='cuda:0', grad_fn=<NllLossBackward0>)
12100 tensor(5.6809, device='cuda:0', grad_fn=<NllLossBackward0>)
12200 tensor(5.6963, device='cuda:0', grad_fn=<NllLossBackward0>)
12300 tensor(5.5903, device='cuda:0', grad_fn=<NllLossBackward0>)
12400 tensor(5.6758, device='cuda:0', grad_fn=<NllLossBackward0>)
12500 tensor(5.8388, device='cuda:0', grad_fn=<NllLossBackward0>)
12600 tensor(5.6493, device='cuda:0', grad_fn=<NllLossBackward0>)
12700 tensor(5.7067, device='cuda:0', grad_fn=<NllLossBackward0>)
12800 tensor(5.8122, device='cuda:0', grad_fn=<NllLossBackward0>)
12900 tensor(5.5808, device='cuda:0', grad_fn=<NllLossBackward0>)
13000 tensor(5.7339, device='cuda:0', grad_fn=<NllLossBackward0>)
13100 tensor(5.5628, device='cuda:0', grad_fn=<NllLossBackward0>)
13200 tensor(5.6367, device='cuda:0', grad_fn=<NllLossBackward0>)
13300 tensor(5.8845, device='cuda:0', grad_fn=<NllLossBackward0>)
13400 tensor(5.5808, device='cuda:0', grad_fn=<NllLossBackward0>)
13500 tensor(5.6065, device='cuda:0', grad_fn=<NllLossBackward0>)
13600 tensor(5.6312, device='cuda:0', grad_fn=<NllLossBackward0>)
13700 tensor(5.5297, device='cuda:0', grad_fn=<NllLossBackward0>)
13800 tensor(5.6371, device='cuda:0', grad_fn=<NllLossBackward0>)
13900 tensor(5.4678, device='cuda:0', grad_fn=<NllLossBackward0>)
14000 tensor(5.5841, device='cuda:0', grad_fn=<NllLossBackward0>)
14100 tensor(5.6667, device='cuda:0', grad_fn=<NllLossBackward0>)
14200 tensor(5.6490, device='cuda:0', grad_fn=<NllLossBackward0>)
14300 tensor(5.6490, device='cuda:0', grad_fn=<NllLossBackward0>)
14400 tensor(5.8014, device='cuda:0', grad_fn=<NllLossBackward0>)
14500 tensor(5.7761, device='cuda:0', grad_fn=<NllLossBackward0>)
14600 tensor(5.6229, device='cuda:0', grad_fn=<NllLossBackward0>)
14700 tensor(5.5781, device='cuda:0', grad_fn=<NllLossBackward0>)
14800 tensor(5.5083, device='cuda:0', grad_fn=<NllLossBackward0>)
14900 tensor(5.8224, device='cuda:0', grad_fn=<NllLossBackward0>)
15000 tensor(5.6680, device='cuda:0', grad_fn=<NllLossBackward0>)
15100 tensor(5.3498, device='cuda:0', grad_fn=<NllLossBackward0>)
15200 tensor(5.3971, device='cuda:0', grad_fn=<NllLossBackward0>)
15300 tensor(5.6708, device='cuda:0', grad_fn=<NllLossBackward0>)
15400 tensor(5.6057, device='cuda:0', grad_fn=<NllLossBackward0>)
15500 tensor(5.7612, device='cuda:0', grad_fn=<NllLossBackward0>)
15600 tensor(5.3966, device='cuda:0', grad_fn=<NllLossBackward0>)
15700 tensor(5.4845, device='cuda:0', grad_fn=<NllLossBackward0>)
15800 tensor(5.6853, device='cuda:0', grad_fn=<NllLossBackward0>)
15900 tensor(5.3362, device='cuda:0', grad_fn=<NllLossBackward0>)
16000 tensor(5.6539, device='cuda:0', grad_fn=<NllLossBackward0>)
16100 tensor(5.5410, device='cuda:0', grad_fn=<NllLossBackward0>)
16200 tensor(5.4011, device='cuda:0', grad_fn=<NllLossBackward0>)
16300 tensor(5.5504, device='cuda:0', grad_fn=<NllLossBackward0>)
16400 tensor(5.6887, device='cuda:0', grad_fn=<NllLossBackward0>)
16500 tensor(5.7357, device='cuda:0', grad_fn=<NllLossBackward0>)
16600 tensor(5.5474, device='cuda:0', grad_fn=<NllLossBackward0>)
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