└── transformer-autoencoder.ipynb /transformer-autoencoder.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "colab_type": "text", 7 | "id": "bMySaUidhvWx" 8 | }, 9 | "source": [ 10 | "# Autoencoders for text embeddings " 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "metadata": { 17 | "ExecuteTime": { 18 | "end_time": "2019-03-20T18:01:29.479713Z", 19 | "start_time": "2019-03-20T18:01:25.337208Z" 20 | }, 21 | "colab": { 22 | "base_uri": "https://localhost:8080/", 23 | "height": 34 24 | }, 25 | "colab_type": "code", 26 | "id": "fDVWZDdWhvWy", 27 | "outputId": "0f3a7609-bdd1-4d62-d8e1-7bb0ae90f5ef", 28 | "pycharm": { 29 | "is_executing": false 30 | } 31 | }, 32 | "outputs": [ 33 | { 34 | "name": "stderr", 35 | "output_type": "stream", 36 | "text": [ 37 | "The pre-trained model you are loading is an uncased model but you have set `do_lower_case` to False. We are setting `do_lower_case=True` for you but you may want to check this behavior.\n" 38 | ] 39 | }, 40 | { 41 | "name": "stdout", 42 | "output_type": "stream", 43 | "text": [ 44 | "use_cuda: True, n_gpu: 4, devices: ['Tesla K80', 'Tesla K80', 'Tesla K80', 'Tesla K80']\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "%reload_ext autoreload\n", 50 | "%autoreload 2\n", 51 | "%matplotlib inline\n", 52 | "%config InlineBackend.figure_format = 'retina'\n", 53 | "\n", 54 | "# ! pip install pytorch-pretrained-bert==0.6.2\n", 55 | "from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForTokenClassification\n", 56 | "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=False)\n", 57 | "\n", 58 | "from IPython.display import clear_output\n", 59 | "import re, math, time, itertools\n", 60 | "import copy\n", 61 | "from itertools import islice\n", 62 | "import numpy as np\n", 63 | "import pandas as pd\n", 64 | "import seaborn as sns\n", 65 | "import matplotlib.pyplot as plt\n", 66 | "from collections import Counter\n", 67 | "from tqdm import tnrange, tqdm_notebook\n", 68 | "\n", 69 | "from scipy.sparse import csr_matrix\n", 70 | "import sklearn\n", 71 | "from sklearn import metrics\n", 72 | "from sklearn.datasets import fetch_20newsgroups\n", 73 | "from sklearn.preprocessing import StandardScaler\n", 74 | "from sklearn.model_selection import train_test_split\n", 75 | "from sklearn.linear_model import SGDClassifier, LogisticRegression\n", 76 | "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", 77 | "\n", 78 | "import torch\n", 79 | "import torch.nn as nn\n", 80 | "import torch.nn.functional as F\n", 81 | "from torch.utils.data import DataLoader, TensorDataset\n", 82 | "from torch.autograd import Variable\n", 83 | "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n", 84 | "\n", 85 | "from fastai import *\n", 86 | "from fastai.text import *\n", 87 | "\n", 88 | "from torchtext import data, datasets\n", 89 | "\n", 90 | "# sns.set_context(\"paper\")\n", 91 | "use_cuda = torch.cuda.is_available()\n", 92 | "device = torch.device(\"cuda:0\" if use_cuda else \"cpu\")\n", 93 | "n_gpu = torch.cuda.device_count()\n", 94 | "print(f'use_cuda: {use_cuda}, n_gpu: {n_gpu}, devices: {[torch.cuda.get_device_name(i) for i in range(n_gpu)]}')" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": { 100 | "colab_type": "text", 101 | "id": "-L_QO55chvW2" 102 | }, 103 | "source": [ 104 | "## Data" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 43, 110 | "metadata": { 111 | "colab": {}, 112 | "colab_type": "code", 113 | "id": "kZTjw9MRm2DF", 114 | "pycharm": { 115 | "is_executing": false 116 | } 117 | }, 118 | "outputs": [], 119 | "source": [ 120 | "# !wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n", 121 | "# !gunzip aclImdb_v1.tar.gz" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 7, 127 | "metadata": { 128 | "colab": {}, 129 | "colab_type": "code", 130 | "id": "xZqnHZCFpHVP", 131 | "pycharm": { 132 | "is_executing": false 133 | } 134 | }, 135 | "outputs": [], 136 | "source": [ 137 | "# !tar -xvf aclImdb_v1.tar" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 6, 143 | "metadata": { 144 | "colab": {}, 145 | "colab_type": "code", 146 | "id": "sw1VMp9YBhez", 147 | "pycharm": { 148 | "is_executing": false 149 | } 150 | }, 151 | "outputs": [], 152 | "source": [ 153 | "def texts_from_folders(src, names):\n", 154 | " texts,labels = [],[]\n", 155 | " for idx,name in enumerate(names):\n", 156 | " path = os.path.join(src, name)\n", 157 | " for fname in sorted(os.listdir(path)):\n", 158 | " fpath = os.path.join(path, fname)\n", 159 | " texts.append(open(fpath).read())\n", 160 | " labels.append(idx)\n", 161 | " return texts,np.array(labels)\n", 162 | " \n", 163 | "PATH='aclImdb/'\n", 164 | "names = ['neg','pos']\n", 165 | "X_train, y_train = texts_from_folders(f'{PATH}train',names)\n", 166 | "X_val, y_val = texts_from_folders(f'{PATH}test',names)" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 5, 172 | "metadata": { 173 | "ExecuteTime": { 174 | "end_time": "2019-03-20T18:01:33.859808Z", 175 | "start_time": "2019-03-20T18:01:32.455428Z" 176 | }, 177 | "colab": {}, 178 | "colab_type": "code", 179 | "id": "mKnwrmbFhvW5" 180 | }, 181 | "outputs": [], 182 | "source": [ 183 | "# news_tr = fetch_20newsgroups(subset='train', shuffle=True, random_state=42, remove=('headers', 'footers', 'quotes'))\n", 184 | "# news_test = fetch_20newsgroups(subset='test', shuffle=True, random_state=42, remove=('headers', 'footers', 'quotes'))\n", 185 | "# target_names = news_tr.target_names\n", 186 | "\n", 187 | "# expr = r'[^a-zA-Z0-9!.,?]'\n", 188 | "# parser = re.compile(expr)\n", 189 | "\n", 190 | "# def preprocess(sent): return parser.sub(r' ', sent).lower()\n", 191 | "\n", 192 | "# data_tr = list(map(preprocess, news_tr.data)); \n", 193 | "# data_test = list(map(preprocess, news_test.data)); data_tr[0]\n", 194 | "\n", 195 | "# X_train, y_train = data_tr, news_tr.target\n", 196 | "# X_val, X_test, y_val, y_test = train_test_split(data_test, news_test.target, test_size=0.5, random_state=42, stratify=news_test.target)\n", 197 | "# len(X_train), len(X_val), len(X_test)" 198 | ] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": { 203 | "colab_type": "text", 204 | "id": "sqLteEeHhvW-" 205 | }, 206 | "source": [ 207 | "y_train мы никогда не будем использовать, X_train нужен в качестве тренировки автокодировщиков - self-supervised" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 2, 213 | "metadata": { 214 | "ExecuteTime": { 215 | "end_time": "2019-03-16T17:34:42.993836Z", 216 | "start_time": "2019-03-16T17:34:42.856475Z" 217 | }, 218 | "colab": {}, 219 | "colab_type": "code", 220 | "id": "iPp0R61vhvX-" 221 | }, 222 | "outputs": [], 223 | "source": [ 224 | "SAMPLE_SIZES = [100, 200, 500, 1000, 2000, 5000, 10000, 24998]\n", 225 | "\n", 226 | "def sample_data(x, y, size=100):\n", 227 | " size = size / len(x)\n", 228 | " _, x, _, y = train_test_split(x, y, test_size=size, random_state=42, stratify=y)\n", 229 | " return x, y \n", 230 | "\n", 231 | "def get_acc_on_samples(x_tr, y_train, x_v, y_val):\n", 232 | " accuracy_hist = []\n", 233 | " if type(x_tr) == scipy.sparse.csr.csr_matrix: \n", 234 | " sparse = True\n", 235 | " else:\n", 236 | " sparse = False\n", 237 | " if sparse: x_tr = x_tr.todense()\n", 238 | " for size in SAMPLE_SIZES:\n", 239 | " x, y = sample_data(x_tr, y_train, size)\n", 240 | " if sparse: x = csr_matrix(x)\n", 241 | " svm = SGDClassifier(loss=\"log\", max_iter=1000, tol=1e-3)\n", 242 | " svm.fit(x, y)\n", 243 | " pred = svm.predict(x_v)\n", 244 | " acc = sklearn.metrics.accuracy_score(y_val, pred)\n", 245 | " accuracy_hist.append(acc)\n", 246 | " return accuracy_hist\n", 247 | " \n", 248 | "def plot_new_hist(hist3, label):\n", 249 | " hist1, hist2 = ([0.65752, 0.74968, 0.78416, 0.80984, 0.83036, 0.858, 0.86852, 0.87552],\n", 250 | " [0.66448, 0.73196, 0.766, 0.79224, 0.82652, 0.85756, 0.8718, 0.88248])\n", 251 | " ax = plt.axes()\n", 252 | " plt.ylabel(\"Accuracy\")\n", 253 | " plt.xlabel(\"# of training examples\")\n", 254 | " plt.plot(np.array(SAMPLE_SIZES)[:, None],np.array(hist1)[:, None], label='tf-idf')\n", 255 | " plt.plot(np.array(SAMPLE_SIZES)[:, None],np.array(hist2)[:, None], label='bag')\n", 256 | " plt.plot(np.array(SAMPLE_SIZES)[:, None],np.array(hist3)[:, None], label=label)\n", 257 | " plt.legend()\n", 258 | " plt.grid()\n", 259 | " plt.xscale(\"log\")\n", 260 | " ax.set_xlim([0,25000])\n", 261 | " ax.set_ylim([0.5,1])\n", 262 | " x = np.array(SAMPLE_SIZES)\n", 263 | " plt.xticks(x, SAMPLE_SIZES[:-1]+[SAMPLE_SIZES[-1]+2]) #24998 fix" 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "metadata": { 269 | "colab_type": "text", 270 | "id": "arbbOZshhvXB" 271 | }, 272 | "source": [ 273 | "## Bert tokenization" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 6, 279 | "metadata": { 280 | "colab": {}, 281 | "colab_type": "code", 282 | "id": "E1d88yVcBK0Z" 283 | }, 284 | "outputs": [], 285 | "source": [ 286 | "# sent = 'i was wondering if anyone out there could enlighten me on this car i saw the other day. it was a 2 door sports car, looked to be from the late 60s early 70s. it was called a bricklin. the doors were really small. in addition, the front bumper was separate from the rest of the body. this is all i know. if anyone can tellme a model name, engine specs, years of production, where this car is made, history, or whatever info you have on this funky looking car, please e mail.'\n", 287 | "# tokenized_texts = [tokenizer.tokenize(sent)]\n", 288 | "# print(tokenized_texts[0])" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 5, 294 | "metadata": { 295 | "colab": { 296 | "base_uri": "https://localhost:8080/", 297 | "height": 282 298 | }, 299 | "colab_type": "code", 300 | "id": "oRXS67QZhvXJ", 301 | "outputId": "1feff401-bab4-4567-f701-4fdbf8c355fa", 302 | "pycharm": { 303 | "is_executing": false 304 | } 305 | }, 306 | "outputs": [ 307 | { 308 | "data": { 309 | "image/png": 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\n", 310 | "text/plain": [ 311 | "
" 312 | ] 313 | }, 314 | "metadata": { 315 | "image/png": { 316 | "height": 250, 317 | "width": 383 318 | }, 319 | "needs_background": "light" 320 | }, 321 | "output_type": "display_data" 322 | } 323 | ], 324 | "source": [ 325 | "def tokenize_text(text):\n", 326 | " res = [tokenizer.tokenize(d) for d in text] \n", 327 | " res = [['[CLS]'] + td[0:510] + ['[SEP]'] for td in res]\n", 328 | " return res\n", 329 | "X_train_b = tokenize_text(X_train)\n", 330 | "X_val_b = tokenize_text(X_val)\n", 331 | "plt.hist([len(d) for d in X_train_b], bins=100, range=(0, 1000));" 332 | ] 333 | }, 334 | { 335 | "cell_type": "markdown", 336 | "metadata": { 337 | "colab_type": "text", 338 | "heading_collapsed": true, 339 | "id": "baZXgN_LhvXX" 340 | }, 341 | "source": [ 342 | "## Bert Contextual embeddings" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": 181, 348 | "metadata": { 349 | "ExecuteTime": { 350 | "end_time": "2019-03-13T17:28:32.439641Z", 351 | "start_time": "2019-03-13T17:28:25.250006Z" 352 | }, 353 | "colab": { 354 | "base_uri": "https://localhost:8080/", 355 | "height": 34 356 | }, 357 | "colab_type": "code", 358 | "hidden": true, 359 | "id": "A8hE7pdJhvXX", 360 | "outputId": "99d0074c-1d20-4260-ba27-aa59087fb73a", 361 | "pycharm": { 362 | "is_executing": false 363 | } 364 | }, 365 | "outputs": [ 366 | { 367 | "data": { 368 | "text/plain": [ 369 | "DataParallel(\n", 370 | " (module): BertModel(\n", 371 | " (embeddings): BertEmbeddings(\n", 372 | " (word_embeddings): Embedding(30522, 768)\n", 373 | " (position_embeddings): Embedding(512, 768)\n", 374 | " (token_type_embeddings): Embedding(2, 768)\n", 375 | " (LayerNorm): BertLayerNorm()\n", 376 | " (dropout): Dropout(p=0.1)\n", 377 | " )\n", 378 | " (encoder): BertEncoder(\n", 379 | " (layer): ModuleList(\n", 380 | " (0): BertLayer(\n", 381 | " (attention): BertAttention(\n", 382 | " (self): BertSelfAttention(\n", 383 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 384 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 385 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 386 | " (dropout): Dropout(p=0.1)\n", 387 | " )\n", 388 | " (output): BertSelfOutput(\n", 389 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 390 | " (LayerNorm): BertLayerNorm()\n", 391 | " (dropout): Dropout(p=0.1)\n", 392 | " )\n", 393 | " )\n", 394 | " (intermediate): BertIntermediate(\n", 395 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 396 | " )\n", 397 | " (output): BertOutput(\n", 398 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 399 | " (LayerNorm): BertLayerNorm()\n", 400 | " (dropout): Dropout(p=0.1)\n", 401 | " )\n", 402 | " )\n", 403 | " (1): BertLayer(\n", 404 | " (attention): BertAttention(\n", 405 | " (self): BertSelfAttention(\n", 406 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 407 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 408 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 409 | " (dropout): Dropout(p=0.1)\n", 410 | " )\n", 411 | " (output): BertSelfOutput(\n", 412 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 413 | " (LayerNorm): BertLayerNorm()\n", 414 | " (dropout): Dropout(p=0.1)\n", 415 | " )\n", 416 | " )\n", 417 | " (intermediate): BertIntermediate(\n", 418 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 419 | " )\n", 420 | " (output): BertOutput(\n", 421 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 422 | " (LayerNorm): BertLayerNorm()\n", 423 | " (dropout): Dropout(p=0.1)\n", 424 | " )\n", 425 | " )\n", 426 | " (2): BertLayer(\n", 427 | " (attention): BertAttention(\n", 428 | " (self): BertSelfAttention(\n", 429 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 430 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 431 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 432 | " (dropout): Dropout(p=0.1)\n", 433 | " )\n", 434 | " (output): BertSelfOutput(\n", 435 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 436 | " (LayerNorm): BertLayerNorm()\n", 437 | " (dropout): Dropout(p=0.1)\n", 438 | " )\n", 439 | " )\n", 440 | " (intermediate): BertIntermediate(\n", 441 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 442 | " )\n", 443 | " (output): BertOutput(\n", 444 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 445 | " (LayerNorm): BertLayerNorm()\n", 446 | " (dropout): Dropout(p=0.1)\n", 447 | " )\n", 448 | " )\n", 449 | " (3): BertLayer(\n", 450 | " (attention): BertAttention(\n", 451 | " (self): BertSelfAttention(\n", 452 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 453 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 454 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 455 | " (dropout): Dropout(p=0.1)\n", 456 | " )\n", 457 | " (output): BertSelfOutput(\n", 458 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 459 | " (LayerNorm): BertLayerNorm()\n", 460 | " (dropout): Dropout(p=0.1)\n", 461 | " )\n", 462 | " )\n", 463 | " (intermediate): BertIntermediate(\n", 464 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 465 | " )\n", 466 | " (output): BertOutput(\n", 467 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 468 | " (LayerNorm): BertLayerNorm()\n", 469 | " (dropout): Dropout(p=0.1)\n", 470 | " )\n", 471 | " )\n", 472 | " (4): BertLayer(\n", 473 | " (attention): BertAttention(\n", 474 | " (self): BertSelfAttention(\n", 475 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 476 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 477 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 478 | " (dropout): Dropout(p=0.1)\n", 479 | " )\n", 480 | " (output): BertSelfOutput(\n", 481 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 482 | " (LayerNorm): BertLayerNorm()\n", 483 | " (dropout): Dropout(p=0.1)\n", 484 | " )\n", 485 | " )\n", 486 | " (intermediate): BertIntermediate(\n", 487 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 488 | " )\n", 489 | " (output): BertOutput(\n", 490 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 491 | " (LayerNorm): BertLayerNorm()\n", 492 | " (dropout): Dropout(p=0.1)\n", 493 | " )\n", 494 | " )\n", 495 | " (5): BertLayer(\n", 496 | " (attention): BertAttention(\n", 497 | " (self): BertSelfAttention(\n", 498 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 499 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 500 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 501 | " (dropout): Dropout(p=0.1)\n", 502 | " )\n", 503 | " (output): BertSelfOutput(\n", 504 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 505 | " (LayerNorm): BertLayerNorm()\n", 506 | " (dropout): Dropout(p=0.1)\n", 507 | " )\n", 508 | " )\n", 509 | " (intermediate): BertIntermediate(\n", 510 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 511 | " )\n", 512 | " (output): BertOutput(\n", 513 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 514 | " (LayerNorm): BertLayerNorm()\n", 515 | " (dropout): Dropout(p=0.1)\n", 516 | " )\n", 517 | " )\n", 518 | " (6): BertLayer(\n", 519 | " (attention): BertAttention(\n", 520 | " (self): BertSelfAttention(\n", 521 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 522 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 523 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 524 | " (dropout): Dropout(p=0.1)\n", 525 | " )\n", 526 | " (output): BertSelfOutput(\n", 527 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 528 | " (LayerNorm): BertLayerNorm()\n", 529 | " (dropout): Dropout(p=0.1)\n", 530 | " )\n", 531 | " )\n", 532 | " (intermediate): BertIntermediate(\n", 533 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 534 | " )\n", 535 | " (output): BertOutput(\n", 536 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 537 | " (LayerNorm): BertLayerNorm()\n", 538 | " (dropout): Dropout(p=0.1)\n", 539 | " )\n", 540 | " )\n", 541 | " (7): BertLayer(\n", 542 | " (attention): BertAttention(\n", 543 | " (self): BertSelfAttention(\n", 544 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 545 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 546 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 547 | " (dropout): Dropout(p=0.1)\n", 548 | " )\n", 549 | " (output): BertSelfOutput(\n", 550 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 551 | " (LayerNorm): BertLayerNorm()\n", 552 | " (dropout): Dropout(p=0.1)\n", 553 | " )\n", 554 | " )\n", 555 | " (intermediate): BertIntermediate(\n", 556 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 557 | " )\n", 558 | " (output): BertOutput(\n", 559 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 560 | " (LayerNorm): BertLayerNorm()\n", 561 | " (dropout): Dropout(p=0.1)\n", 562 | " )\n", 563 | " )\n", 564 | " (8): BertLayer(\n", 565 | " (attention): BertAttention(\n", 566 | " (self): BertSelfAttention(\n", 567 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 568 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 569 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 570 | " (dropout): Dropout(p=0.1)\n", 571 | " )\n", 572 | " (output): BertSelfOutput(\n", 573 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 574 | " (LayerNorm): BertLayerNorm()\n", 575 | " (dropout): Dropout(p=0.1)\n", 576 | " )\n", 577 | " )\n", 578 | " (intermediate): BertIntermediate(\n", 579 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 580 | " )\n", 581 | " (output): BertOutput(\n", 582 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 583 | " (LayerNorm): BertLayerNorm()\n", 584 | " (dropout): Dropout(p=0.1)\n", 585 | " )\n", 586 | " )\n", 587 | " (9): BertLayer(\n", 588 | " (attention): BertAttention(\n", 589 | " (self): BertSelfAttention(\n", 590 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 591 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 592 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 593 | " (dropout): Dropout(p=0.1)\n", 594 | " )\n", 595 | " (output): BertSelfOutput(\n", 596 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 597 | " (LayerNorm): BertLayerNorm()\n", 598 | " (dropout): Dropout(p=0.1)\n", 599 | " )\n", 600 | " )\n", 601 | " (intermediate): BertIntermediate(\n", 602 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 603 | " )\n", 604 | " (output): BertOutput(\n", 605 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 606 | " (LayerNorm): BertLayerNorm()\n", 607 | " (dropout): Dropout(p=0.1)\n", 608 | " )\n", 609 | " )\n", 610 | " (10): BertLayer(\n", 611 | " (attention): BertAttention(\n", 612 | " (self): BertSelfAttention(\n", 613 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 614 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 615 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 616 | " (dropout): Dropout(p=0.1)\n", 617 | " )\n", 618 | " (output): BertSelfOutput(\n", 619 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 620 | " (LayerNorm): BertLayerNorm()\n", 621 | " (dropout): Dropout(p=0.1)\n", 622 | " )\n", 623 | " )\n", 624 | " (intermediate): BertIntermediate(\n", 625 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 626 | " )\n", 627 | " (output): BertOutput(\n", 628 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 629 | " (LayerNorm): BertLayerNorm()\n", 630 | " (dropout): Dropout(p=0.1)\n", 631 | " )\n", 632 | " )\n", 633 | " (11): BertLayer(\n", 634 | " (attention): BertAttention(\n", 635 | " (self): BertSelfAttention(\n", 636 | " (query): Linear(in_features=768, out_features=768, bias=True)\n", 637 | " (key): Linear(in_features=768, out_features=768, bias=True)\n", 638 | " (value): Linear(in_features=768, out_features=768, bias=True)\n", 639 | " (dropout): Dropout(p=0.1)\n", 640 | " )\n", 641 | " (output): BertSelfOutput(\n", 642 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 643 | " (LayerNorm): BertLayerNorm()\n", 644 | " (dropout): Dropout(p=0.1)\n", 645 | " )\n", 646 | " )\n", 647 | " (intermediate): BertIntermediate(\n", 648 | " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", 649 | " )\n", 650 | " (output): BertOutput(\n", 651 | " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", 652 | " (LayerNorm): BertLayerNorm()\n", 653 | " (dropout): Dropout(p=0.1)\n", 654 | " )\n", 655 | " )\n", 656 | " )\n", 657 | " )\n", 658 | " (pooler): BertPooler(\n", 659 | " (dense): Linear(in_features=768, out_features=768, bias=True)\n", 660 | " (activation): Tanh()\n", 661 | " )\n", 662 | " )\n", 663 | ")" 664 | ] 665 | }, 666 | "execution_count": 181, 667 | "metadata": {}, 668 | "output_type": "execute_result" 669 | } 670 | ], 671 | "source": [ 672 | "model = BertModel.from_pretrained('bert-base-uncased')\n", 673 | "model.eval()\n", 674 | "if n_gpu > 1: model = torch.nn.DataParallel(model)\n", 675 | "model.to(device)" 676 | ] 677 | }, 678 | { 679 | "cell_type": "code", 680 | "execution_count": 10, 681 | "metadata": { 682 | "ExecuteTime": { 683 | "end_time": "2019-03-13T23:29:09.405217Z", 684 | "start_time": "2019-03-13T23:29:09.284640Z" 685 | }, 686 | "colab": {}, 687 | "colab_type": "code", 688 | "hidden": true, 689 | "id": "d9Wqib8yhvXZ", 690 | "pycharm": { 691 | "is_executing": false 692 | } 693 | }, 694 | "outputs": [], 695 | "source": [ 696 | "def get_bert_word_emb(text_array):\n", 697 | " indexed_tokens_data = [tokenizer.convert_tokens_to_ids(td) for td in text_array]\n", 698 | " segments_ids_data = [[0 for _ in it] for it in indexed_tokens_data]\n", 699 | " tokens_tensor_data = [torch.tensor([it], device=device) for it in indexed_tokens_data]\n", 700 | " segments_tensors_data = [torch.tensor([si], device=device) for si in segments_ids_data]\n", 701 | " embs = torch.zeros([len(text_array), 512, 768], device=device)\n", 702 | " with torch.no_grad():\n", 703 | " for i in range(len(tokens_tensor_data)):\n", 704 | " encoded_layers, _ = model(tokens_tensor_data[i], segments_tensors_data[i])\n", 705 | " num_words = encoded_layers[0].shape[1]\n", 706 | " embs[i, :num_words, :] = encoded_layers[11][0]\n", 707 | " if i % 1000 == 0: \n", 708 | " print(i)\n", 709 | " print(strftime(\"%Y-%m-%d %H:%M:%S\", localtime()))\n", 710 | " return embs" 711 | ] 712 | }, 713 | { 714 | "cell_type": "code", 715 | "execution_count": 11, 716 | "metadata": { 717 | "ExecuteTime": { 718 | "end_time": "2019-03-12T16:45:09.203599Z", 719 | "start_time": "2019-03-12T14:50:08.628827Z" 720 | }, 721 | "colab": {}, 722 | "colab_type": "code", 723 | "hidden": true, 724 | "id": "lty_3Nd3hvXc", 725 | "pycharm": { 726 | "is_executing": false 727 | } 728 | }, 729 | "outputs": [], 730 | "source": [ 731 | "def get_bert_doc_emb(text_array):\n", 732 | " indexed_tokens_data = [tokenizer.convert_tokens_to_ids(td) for td in text_array]\n", 733 | " segments_ids_data = [[0 for _ in it] for it in indexed_tokens_data]\n", 734 | " tokens_tensor_data = [torch.tensor([it]).to(device) for it in indexed_tokens_data]\n", 735 | " segments_tensors_data = [torch.tensor([si]).to(device) for si in segments_ids_data]\n", 736 | " doc_embeddings = []\n", 737 | " with torch.no_grad():\n", 738 | " for i in tnrange(len(tokens_tensor_data)):\n", 739 | " encoded_layers, _ = model(tokens_tensor_data[i], segments_tensors_data[i])\n", 740 | " doc_embeddings.append(encoded_layers[11][0, 0,:]) # get last embeding of [CLS] token\n", 741 | " return doc_embeddings" 742 | ] 743 | }, 744 | { 745 | "cell_type": "code", 746 | "execution_count": 167, 747 | "metadata": {}, 748 | "outputs": [], 749 | "source": [] 750 | }, 751 | { 752 | "cell_type": "code", 753 | "execution_count": 55, 754 | "metadata": {}, 755 | "outputs": [], 756 | "source": [ 757 | "# doc_embeddings = []\n", 758 | "# with torch.no_grad():\n", 759 | "# for tokens in tqdm_notebook(X_train_b):\n", 760 | "# t = torch.tensor(tokenizer.convert_tokens_to_ids(tokens), dtype=torch.long).to(device)\n", 761 | "# encoded_layers, _ = model(input_ids=t.unsqueeze_(0))\n", 762 | "# doc_embeddings.append(encoded_layers[11][0, 0,:])" 763 | ] 764 | }, 765 | { 766 | "cell_type": "code", 767 | "execution_count": 7, 768 | "metadata": {}, 769 | "outputs": [], 770 | "source": [ 771 | "# tr_tensors = [torch.tensor([tokenizer.convert_tokens_to_ids(i)]).to(device) for i in X_train_b]\n", 772 | "# val_tensors = [torch.tensor([tokenizer.convert_tokens_to_ids(i)]).to(device) for i in X_val_b]" 773 | ] 774 | }, 775 | { 776 | "cell_type": "code", 777 | "execution_count": 40, 778 | "metadata": {}, 779 | "outputs": [], 780 | "source": [ 781 | "# def my_collate(batch):\n", 782 | "# data = [item[0] for item in batch]\n", 783 | "# target = [item[1] for item in batch]\n", 784 | "# target = torch.LongTensor(target)\n", 785 | "# return [data, target]" 786 | ] 787 | }, 788 | { 789 | "cell_type": "code", 790 | "execution_count": 168, 791 | "metadata": {}, 792 | "outputs": [], 793 | "source": [ 794 | "class MyDataset(Dataset):\n", 795 | " def __init__(self, x, y):\n", 796 | " texts = torch.zeros(25000, 512, dtype=torch.long)\n", 797 | " for i, t in enumerate(x):\n", 798 | " t = torch.tensor(tokenizer.convert_tokens_to_ids(t), dtype=torch.long)\n", 799 | " texts[i, :t.size(0)] = t\n", 800 | " self.data = texts\n", 801 | " self.y = y\n", 802 | "\n", 803 | " def __getitem__(self, index):\n", 804 | " return self.data[index], self.y[index]\n", 805 | "\n", 806 | " def __len__(self):\n", 807 | " return len(self.data)\n", 808 | "\n", 809 | "batch_size = 4\n", 810 | "loader = DataLoader(dataset=MyDataset(X_val_b, y_val),\n", 811 | " batch_size=batch_size)\n", 812 | "# val_loader = DataLoader(dataset=MyDataset(X_val_b, y_val),\n", 813 | "# batch_size=4,\n", 814 | "# collate_fn=my_collate)" 815 | ] 816 | }, 817 | { 818 | "cell_type": "code", 819 | "execution_count": 182, 820 | "metadata": {}, 821 | "outputs": [], 822 | "source": [ 823 | "def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True):\n", 824 | " input_ids = input_ids[0][input_ids[0].nonzero()].transpose(0,1)\n", 825 | " if attention_mask is None:\n", 826 | " attention_mask = torch.ones_like(input_ids)\n", 827 | " if token_type_ids is None:\n", 828 | " token_type_ids = torch.zeros_like(input_ids)\n", 829 | "\n", 830 | " # We create a 3D attention mask from a 2D tensor mask.\n", 831 | " # Sizes are [batch_size, 1, 1, to_seq_length]\n", 832 | " # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]\n", 833 | " # this attention mask is more simple than the triangular masking of causal attention\n", 834 | " # used in OpenAI GPT, we just need to prepare the broadcast dimension here.\n", 835 | " extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n", 836 | "\n", 837 | " # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n", 838 | " # masked positions, this operation will create a tensor which is 0.0 for\n", 839 | " # positions we want to attend and -10000.0 for masked positions.\n", 840 | " # Since we are adding it to the raw scores before the softmax, this is\n", 841 | " # effectively the same as removing these entirely.\n", 842 | " extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility\n", 843 | " extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n", 844 | "\n", 845 | " embedding_output = self.embeddings(input_ids, token_type_ids)\n", 846 | " encoded_layers = self.encoder(embedding_output,\n", 847 | " extended_attention_mask,\n", 848 | " output_all_encoded_layers=output_all_encoded_layers)\n", 849 | " sequence_output = encoded_layers[-1]\n", 850 | " pooled_output = self.pooler(sequence_output)\n", 851 | " if not output_all_encoded_layers:\n", 852 | " encoded_layers = encoded_layers[-1]\n", 853 | " return encoded_layers[11][0, 0,:].unsqueeze_(0)\n", 854 | "\n", 855 | "BertModel.forward = forward" 856 | ] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "execution_count": 183, 861 | "metadata": {}, 862 | "outputs": [ 863 | { 864 | "data": { 865 | "application/vnd.jupyter.widget-view+json": { 866 | "model_id": "b47436f13ef94122a5d7f4c71fc2be9a", 867 | "version_major": 2, 868 | "version_minor": 0 869 | }, 870 | "text/plain": [ 871 | "HBox(children=(IntProgress(value=0, max=6250), HTML(value='')))" 872 | ] 873 | }, 874 | "metadata": {}, 875 | "output_type": "display_data" 876 | } 877 | ], 878 | "source": [ 879 | "doc_embeddings = []\n", 880 | "with torch.no_grad():\n", 881 | " for data in tqdm_notebook(loader):\n", 882 | " inp = data[0].to(device)\n", 883 | " code = model(inp)\n", 884 | " doc_embeddings.append(code)" 885 | ] 886 | }, 887 | { 888 | "cell_type": "code", 889 | "execution_count": 184, 890 | "metadata": {}, 891 | "outputs": [], 892 | "source": [ 893 | "vl_doc = torch.stack(doc_embeddings)" 894 | ] 895 | }, 896 | { 897 | "cell_type": "code", 898 | "execution_count": 185, 899 | "metadata": {}, 900 | "outputs": [], 901 | "source": [ 902 | "vl_doc = vl_doc.reshape(25000, 768)\n", 903 | "vl_doc = vl_doc.cpu().numpy()" 904 | ] 905 | }, 906 | { 907 | "cell_type": "code", 908 | "execution_count": 20, 909 | "metadata": { 910 | "colab": { 911 | "base_uri": "https://localhost:8080/", 912 | "height": 49, 913 | "resources": { 914 | "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": { 915 | "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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 */", 916 | "headers": [ 917 | [ 918 | "content-type", 919 | "text/css" 920 | ] 921 | ], 922 | "ok": true, 923 | "status": 200, 924 | "status_text": "" 925 | } 926 | } 927 | }, 928 | "colab_type": "code", 929 | "id": "u46fh8TdhvXf", 930 | "outputId": "dc622a31-e57c-4cbf-8a56-570fd3bf1bfd", 931 | "pycharm": { 932 | "is_executing": true 933 | } 934 | }, 935 | "outputs": [], 936 | "source": [ 937 | "# tr_doc = get_bert_doc_emb(X_train_b)\n", 938 | "# val_doc = get_bert_doc_emb(X_val_b)" 939 | ] 940 | }, 941 | { 942 | "cell_type": "code", 943 | "execution_count": 0, 944 | "metadata": { 945 | "ExecuteTime": { 946 | "end_time": "2019-03-12T16:56:19.846663Z", 947 | "start_time": "2019-03-12T16:56:19.785715Z" 948 | }, 949 | "colab": {}, 950 | "colab_type": "code", 951 | "hidden": true, 952 | "id": "_9ERuZHuhvXi" 953 | }, 954 | "outputs": [], 955 | "source": [ 956 | "# tr_doc = torch.stack(tr_doc).cpu().numpy()\n", 957 | "# val_doc = torch.stack(val_doc).cpu().numpy()\n", 958 | "# test_doc = torch.stack(test_doc).cpu().numpy()" 959 | ] 960 | }, 961 | { 962 | "cell_type": "code", 963 | "execution_count": 186, 964 | "metadata": { 965 | "ExecuteTime": { 966 | "end_time": "2019-03-12T17:07:41.338429Z", 967 | "start_time": "2019-03-12T17:07:40.971696Z" 968 | }, 969 | "colab": {}, 970 | "colab_type": "code", 971 | "hidden": true, 972 | "id": "QHI6gqj2hvXl" 973 | }, 974 | "outputs": [], 975 | "source": [ 976 | "# np.save('data/docs_emb_bert/tr_doc.npy', tr_doc)\n", 977 | "# np.save('data/docs_emb_bert/vl_doc.npy', vl_doc)\n", 978 | "# np.save('data/docs_emb_bert/test_doc.npy', test_doc)" 979 | ] 980 | }, 981 | { 982 | "cell_type": "code", 983 | "execution_count": 0, 984 | "metadata": { 985 | "ExecuteTime": { 986 | "end_time": "2019-03-12T17:08:48.441897Z", 987 | "start_time": "2019-03-12T17:08:48.359611Z" 988 | }, 989 | "colab": {}, 990 | "colab_type": "code", 991 | "hidden": true, 992 | "id": "7NReqvLEhvXn" 993 | }, 994 | "outputs": [], 995 | "source": [ 996 | "# tr_doc = np.load('data/docs_emb_bert/tr_doc.npy')\n", 997 | "# val_doc = np.load('data/docs_emb_bert/val_doc.npy')\n", 998 | "# test_doc = np.load('data/docs_emb_bert/test_doc.npy')" 999 | ] 1000 | }, 1001 | { 1002 | "cell_type": "code", 1003 | "execution_count": 188, 1004 | "metadata": { 1005 | "ExecuteTime": { 1006 | "end_time": "2019-03-12T16:56:46.076062Z", 1007 | "start_time": "2019-03-12T16:56:44.891214Z" 1008 | }, 1009 | "colab": {}, 1010 | "colab_type": "code", 1011 | "hidden": true, 1012 | "id": "n9t6EH7_hvXq", 1013 | "outputId": "fba58ff3-ca1b-4b87-ddcc-d58c86a86f78" 1014 | }, 1015 | "outputs": [ 1016 | { 1017 | "name": "stdout", 1018 | "output_type": "stream", 1019 | "text": [ 1020 | "0.86212\n" 1021 | ] 1022 | } 1023 | ], 1024 | "source": [ 1025 | "svm = SGDClassifier(max_iter=1000, tol=1e-3)\n", 1026 | "svm.fit(tr_doc, y_train)\n", 1027 | "pred = svm.predict(vl_doc)\n", 1028 | "\n", 1029 | "print(sklearn.metrics.accuracy_score(y_val, pred))" 1030 | ] 1031 | }, 1032 | { 1033 | "cell_type": "code", 1034 | "execution_count": 207, 1035 | "metadata": {}, 1036 | "outputs": [ 1037 | { 1038 | "name": "stderr", 1039 | "output_type": "stream", 1040 | "text": [ 1041 | "/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:13: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n", 1042 | "Invalid limit will be ignored.\n" 1043 | ] 1044 | }, 1045 | { 1046 | "data": { 1047 | "image/png": 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\n", 1048 | "text/plain": [ 1049 | "
" 1050 | ] 1051 | }, 1052 | "metadata": { 1053 | "image/png": { 1054 | "height": 265, 1055 | "width": 401 1056 | }, 1057 | "needs_background": "light" 1058 | }, 1059 | "output_type": "display_data" 1060 | } 1061 | ], 1062 | "source": [ 1063 | "hist = get_acc_on_samples(tr_doc, vl_doc)\n", 1064 | "plot_new_hist(hist, 'BERT-emb')" 1065 | ] 1066 | }, 1067 | { 1068 | "cell_type": "markdown", 1069 | "metadata": { 1070 | "colab_type": "text", 1071 | "id": "BX_WRmSDhvXt" 1072 | }, 1073 | "source": [ 1074 | "# CountVectorizer and TfidfVectorizer" 1075 | ] 1076 | }, 1077 | { 1078 | "cell_type": "markdown", 1079 | "metadata": { 1080 | "colab_type": "text", 1081 | "id": "vb56-p2bhvXt" 1082 | }, 1083 | "source": [ 1084 | "Преобразуем датасет с помощью CountVectorizer и TfidfVectorizer." 1085 | ] 1086 | }, 1087 | { 1088 | "cell_type": "code", 1089 | "execution_count": 7, 1090 | "metadata": { 1091 | "colab": {}, 1092 | "colab_type": "code", 1093 | "id": "U5IgqOqVhvXv" 1094 | }, 1095 | "outputs": [], 1096 | "source": [ 1097 | "count_vect = CountVectorizer(tokenizer=tokenizer.tokenize, vocabulary=tokenizer.vocab)\n", 1098 | "X_train_counts = count_vect.fit_transform(X_train)\n", 1099 | "X_val_counts = count_vect.transform(X_val)" 1100 | ] 1101 | }, 1102 | { 1103 | "cell_type": "code", 1104 | "execution_count": 61, 1105 | "metadata": { 1106 | "ExecuteTime": { 1107 | "end_time": "2019-03-16T16:45:29.093215Z", 1108 | "start_time": "2019-03-16T16:45:16.147725Z" 1109 | }, 1110 | "colab": {}, 1111 | "colab_type": "code", 1112 | "id": "rQP4KDs_hvXz" 1113 | }, 1114 | "outputs": [], 1115 | "source": [ 1116 | "X_train_c = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in X_train_counts])\n", 1117 | "X_val_c = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in X_val_counts])\n", 1118 | "\n", 1119 | "X_train_c = csr_matrix(X_train_c)\n", 1120 | "X_val_c = csr_matrix(X_val_c)" 1121 | ] 1122 | }, 1123 | { 1124 | "cell_type": "code", 1125 | "execution_count": 62, 1126 | "metadata": { 1127 | "ExecuteTime": { 1128 | "end_time": "2019-03-16T16:47:10.713375Z", 1129 | "start_time": "2019-03-16T16:45:58.619603Z" 1130 | }, 1131 | "colab": {}, 1132 | "colab_type": "code", 1133 | "id": "5_CkBkmPhvX5" 1134 | }, 1135 | "outputs": [], 1136 | "source": [ 1137 | "idf_vectorizer = TfidfVectorizer(tokenizer=tokenizer.tokenize, vocabulary=tokenizer.vocab)\n", 1138 | "X_train_idf = idf_vectorizer.fit_transform(X_train)\n", 1139 | "X_val_idf = idf_vectorizer.transform(X_val)" 1140 | ] 1141 | }, 1142 | { 1143 | "cell_type": "code", 1144 | "execution_count": 214, 1145 | "metadata": { 1146 | "ExecuteTime": { 1147 | "end_time": "2019-03-16T16:45:43.963218Z", 1148 | "start_time": "2019-03-16T16:45:43.678877Z" 1149 | }, 1150 | "colab": { 1151 | "base_uri": "https://localhost:8080/", 1152 | "height": 34 1153 | }, 1154 | "colab_type": "code", 1155 | "id": "uCsHtnN8hvXx", 1156 | "outputId": "510afb39-63c9-423e-ee6b-8d244631e271" 1157 | }, 1158 | "outputs": [ 1159 | { 1160 | "name": "stdout", 1161 | "output_type": "stream", 1162 | "text": [ 1163 | "0.86536\n" 1164 | ] 1165 | } 1166 | ], 1167 | "source": [ 1168 | "svm = SGDClassifier(max_iter=1000, tol=1e-3)\n", 1169 | "svm.fit(X_train_counts, y_train)\n", 1170 | "pred = svm.predict(X_val_counts)\n", 1171 | "\n", 1172 | "print(sklearn.metrics.accuracy_score(y_val, pred))" 1173 | ] 1174 | }, 1175 | { 1176 | "cell_type": "code", 1177 | "execution_count": 215, 1178 | "metadata": { 1179 | "ExecuteTime": { 1180 | "end_time": "2019-03-16T17:02:18.683675Z", 1181 | "start_time": "2019-03-16T17:02:18.423987Z" 1182 | }, 1183 | "colab": { 1184 | "base_uri": "https://localhost:8080/", 1185 | "height": 34 1186 | }, 1187 | "colab_type": "code", 1188 | "id": "1if_u-UchvX1", 1189 | "outputId": "c950ca97-b091-4df7-d39b-088c2410e898" 1190 | }, 1191 | "outputs": [ 1192 | { 1193 | "name": "stdout", 1194 | "output_type": "stream", 1195 | "text": [ 1196 | "0.87036\n" 1197 | ] 1198 | } 1199 | ], 1200 | "source": [ 1201 | "svm = SGDClassifier(max_iter=1000, tol=1e-3)\n", 1202 | "svm.fit(X_train_c, y_train)\n", 1203 | "pred = svm.predict(X_val_c)\n", 1204 | "\n", 1205 | "print(sklearn.metrics.accuracy_score(y_val, pred))" 1206 | ] 1207 | }, 1208 | { 1209 | "cell_type": "code", 1210 | "execution_count": 216, 1211 | "metadata": { 1212 | "ExecuteTime": { 1213 | "end_time": "2019-03-16T16:47:12.235483Z", 1214 | "start_time": "2019-03-16T16:47:11.968150Z" 1215 | }, 1216 | "colab": { 1217 | "base_uri": "https://localhost:8080/", 1218 | "height": 34 1219 | }, 1220 | "colab_type": "code", 1221 | "id": "WylqzawVhvX7", 1222 | "outputId": "f675812b-16cd-49cd-b99a-f02e8463c05f" 1223 | }, 1224 | "outputs": [ 1225 | { 1226 | "name": "stdout", 1227 | "output_type": "stream", 1228 | "text": [ 1229 | "0.88368\n" 1230 | ] 1231 | } 1232 | ], 1233 | "source": [ 1234 | "svm = SGDClassifier(max_iter=1000, tol=1e-3)\n", 1235 | "svm.fit(X_train_idf, y_train)\n", 1236 | "pred = svm.predict(X_val_idf)\n", 1237 | "\n", 1238 | "print(sklearn.metrics.accuracy_score(y_val, pred))" 1239 | ] 1240 | }, 1241 | { 1242 | "cell_type": "code", 1243 | "execution_count": 217, 1244 | "metadata": { 1245 | "ExecuteTime": { 1246 | "end_time": "2019-03-16T17:02:42.028568Z", 1247 | "start_time": "2019-03-16T17:02:27.038221Z" 1248 | }, 1249 | "colab": { 1250 | "base_uri": "https://localhost:8080/", 1251 | "height": 51 1252 | }, 1253 | "colab_type": "code", 1254 | "id": "_wSvSqzihvYB", 1255 | "outputId": "e828070f-3cab-435c-e50c-288b22f6f415" 1256 | }, 1257 | "outputs": [ 1258 | { 1259 | "data": { 1260 | "text/plain": [ 1261 | "([0.66316, 0.74088, 0.78824, 0.79388, 0.83624, 0.85904, 0.8688, 0.87488],\n", 1262 | " [0.651, 0.71972, 0.75584, 0.7942, 0.82252, 0.84444, 0.86748, 0.87996])" 1263 | ] 1264 | }, 1265 | "execution_count": 217, 1266 | "metadata": {}, 1267 | "output_type": "execute_result" 1268 | } 1269 | ], 1270 | "source": [ 1271 | "hist1 = get_acc_on_samples(X_train_idf, X_val_idf)\n", 1272 | "hist2 = get_acc_on_samples(X_train_c, X_val_c)\n", 1273 | "hist1, hist2" 1274 | ] 1275 | }, 1276 | { 1277 | "cell_type": "code", 1278 | "execution_count": 218, 1279 | "metadata": { 1280 | "ExecuteTime": { 1281 | "end_time": "2019-03-16T17:03:12.092356Z", 1282 | "start_time": "2019-03-16T17:03:11.750085Z" 1283 | }, 1284 | "colab": { 1285 | "base_uri": "https://localhost:8080/", 1286 | "height": 301 1287 | }, 1288 | "colab_type": "code", 1289 | "id": "IFd3-l5PhvYD", 1290 | "outputId": "8dc01603-699b-4941-dd97-4bbae628db78" 1291 | }, 1292 | "outputs": [ 1293 | { 1294 | "name": "stderr", 1295 | "output_type": "stream", 1296 | "text": [ 1297 | "No handles with labels found to put in legend.\n", 1298 | "/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:8: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n", 1299 | "Invalid limit will be ignored.\n" 1300 | ] 1301 | }, 1302 | { 1303 | "data": { 1304 | "text/plain": [ 1305 | "([,\n", 1306 | " ,\n", 1307 | " ,\n", 1308 | " ,\n", 1309 | " ,\n", 1310 | " ,\n", 1311 | " ,\n", 1312 | " ],\n", 1313 | " )" 1314 | ] 1315 | }, 1316 | "execution_count": 218, 1317 | "metadata": {}, 1318 | "output_type": "execute_result" 1319 | }, 1320 | { 1321 | "data": { 1322 | "image/png": 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\n", 1323 | "text/plain": [ 1324 | "
" 1325 | ] 1326 | }, 1327 | "metadata": { 1328 | "image/png": { 1329 | "height": 252, 1330 | "width": 388 1331 | }, 1332 | "needs_background": "light" 1333 | }, 1334 | "output_type": "display_data" 1335 | } 1336 | ], 1337 | "source": [ 1338 | "ax = plt.axes()\n", 1339 | "x = np.array(SAMPLE_SIZES)[:, None]\n", 1340 | "plt.plot(x, np.array(hist1)[:, None], label='')\n", 1341 | "plt.plot(x, np.array(hist2)[:, None], label='')\n", 1342 | "plt.legend()\n", 1343 | "plt.grid()\n", 1344 | "plt.xscale(\"log\")\n", 1345 | "ax.set_xlim([0,25000])\n", 1346 | "ax.set_ylim([0.5,1])\n", 1347 | "plt.xticks(x, SAMPLE_SIZES[:-1]+[SAMPLE_SIZES[-1]+2]) #24998 fix" 1348 | ] 1349 | }, 1350 | { 1351 | "cell_type": "markdown", 1352 | "metadata": { 1353 | "colab_type": "text", 1354 | "id": "vHW-j9fZhvYH" 1355 | }, 1356 | "source": [ 1357 | "# Pytorch" 1358 | ] 1359 | }, 1360 | { 1361 | "cell_type": "code", 1362 | "execution_count": 10, 1363 | "metadata": { 1364 | "ExecuteTime": { 1365 | "end_time": "2019-03-17T18:53:46.788527Z", 1366 | "start_time": "2019-03-17T18:53:46.595697Z" 1367 | }, 1368 | "colab": {}, 1369 | "colab_type": "code", 1370 | "id": "tMLZLqWXhvYH" 1371 | }, 1372 | "outputs": [], 1373 | "source": [ 1374 | "def custom_loss(input, target):\n", 1375 | " return nn.functional.binary_cross_entropy(input, target) * 1000" 1376 | ] 1377 | }, 1378 | { 1379 | "cell_type": "code", 1380 | "execution_count": 4, 1381 | "metadata": { 1382 | "ExecuteTime": { 1383 | "end_time": "2019-03-16T14:45:02.051450Z", 1384 | "start_time": "2019-03-16T14:45:01.985376Z" 1385 | }, 1386 | "colab": {}, 1387 | "colab_type": "code", 1388 | "id": "gI2BgzudhvYJ" 1389 | }, 1390 | "outputs": [], 1391 | "source": [ 1392 | "def transform_data_with_autoencoder(model, data):\n", 1393 | " if type(data) == scipy.sparse.csr.csr_matrix: data = data.todense()\n", 1394 | " tensor_data = torch.Tensor(data).to(device)\n", 1395 | " codes = model.encoder(tensor_data)\n", 1396 | " return codes.detach().cpu().numpy()" 1397 | ] 1398 | }, 1399 | { 1400 | "cell_type": "code", 1401 | "execution_count": 5, 1402 | "metadata": { 1403 | "ExecuteTime": { 1404 | "end_time": "2019-03-18T00:07:58.469009Z", 1405 | "start_time": "2019-03-18T00:07:58.276052Z" 1406 | }, 1407 | "colab": {}, 1408 | "colab_type": "code", 1409 | "id": "EtpvyMNXhvYL" 1410 | }, 1411 | "outputs": [], 1412 | "source": [ 1413 | "class Autoencoder(nn.Module):\n", 1414 | " def __init__(self, input_size, hidden_size=256):\n", 1415 | " super(Autoencoder, self).__init__()\n", 1416 | " self.encoder = nn.Sequential(\n", 1417 | " nn.Linear(input_size, hidden_size),\n", 1418 | " nn.ReLU()\n", 1419 | " )\n", 1420 | " self.decoder = nn.Sequential(\n", 1421 | " nn.Linear(hidden_size, input_size),\n", 1422 | " nn.Sigmoid()\n", 1423 | " )\n", 1424 | "\n", 1425 | " def forward(self, x):\n", 1426 | " x = self.encoder(x)\n", 1427 | " x = self.decoder(x)\n", 1428 | " return x" 1429 | ] 1430 | }, 1431 | { 1432 | "cell_type": "code", 1433 | "execution_count": 0, 1434 | "metadata": { 1435 | "colab": {}, 1436 | "colab_type": "code", 1437 | "id": "Mbl8rLeYhvYN" 1438 | }, 1439 | "outputs": [], 1440 | "source": [ 1441 | "class FFNN(nn.Module):\n", 1442 | " def __init__(self, num_labels=20):\n", 1443 | " super(FFNN, self).__init__()\n", 1444 | " self.num_labels = num_labels\n", 1445 | " self.dropout = torch.nn.Dropout(0.1)\n", 1446 | " self.classifier = torch.nn.Linear(768, num_labels)\n", 1447 | "\n", 1448 | " def forward(self, x):\n", 1449 | " x = self.dropout(x)\n", 1450 | " x = self.classifier(x)\n", 1451 | " return x" 1452 | ] 1453 | }, 1454 | { 1455 | "cell_type": "markdown", 1456 | "metadata": { 1457 | "colab_type": "text", 1458 | "id": "Okb1VsdGhvYP" 1459 | }, 1460 | "source": [ 1461 | "## Wikitext" 1462 | ] 1463 | }, 1464 | { 1465 | "cell_type": "code", 1466 | "execution_count": 0, 1467 | "metadata": { 1468 | "colab": {}, 1469 | "colab_type": "code", 1470 | "id": "02qLqDnGhvYP" 1471 | }, 1472 | "outputs": [], 1473 | "source": [ 1474 | "PATH = Path('data/wikitext-2')" 1475 | ] 1476 | }, 1477 | { 1478 | "cell_type": "code", 1479 | "execution_count": 0, 1480 | "metadata": { 1481 | "ExecuteTime": { 1482 | "end_time": "2019-03-18T23:35:30.491426Z", 1483 | "start_time": "2019-03-18T23:35:30.298397Z" 1484 | }, 1485 | "colab": {}, 1486 | "colab_type": "code", 1487 | "id": "7aof1BsdhvYR" 1488 | }, 1489 | "outputs": [], 1490 | "source": [ 1491 | "def read_file(path, filename):\n", 1492 | " docs = []\n", 1493 | " with open(path/filename, encoding='utf8') as f:\n", 1494 | " doc = ''\n", 1495 | " flag=False\n", 1496 | " for line in f:\n", 1497 | " if line != ' \\n' and line[1] != '=':\n", 1498 | " doc += line\n", 1499 | " flag = True\n", 1500 | " elif flag:\n", 1501 | " docs.append(doc)\n", 1502 | " doc = ''\n", 1503 | " flag = False\n", 1504 | " \n", 1505 | " return np.array(docs)" 1506 | ] 1507 | }, 1508 | { 1509 | "cell_type": "code", 1510 | "execution_count": 0, 1511 | "metadata": { 1512 | "colab": {}, 1513 | "colab_type": "code", 1514 | "id": "tgKbjkmjhvYU" 1515 | }, 1516 | "outputs": [], 1517 | "source": [ 1518 | "train_wiki_raw = read_file(PATH, 'wiki.train.tokens')\n", 1519 | "val_wiki_raw = read_file(PATH, 'wiki.valid.tokens')\n", 1520 | "test_wiki_raw = read_file(PATH, 'wiki.test.tokens')\n", 1521 | "# train_wiki_raw[0]" 1522 | ] 1523 | }, 1524 | { 1525 | "cell_type": "code", 1526 | "execution_count": 0, 1527 | "metadata": { 1528 | "colab": {}, 1529 | "colab_type": "code", 1530 | "id": "6mdhQzyphvYX" 1531 | }, 1532 | "outputs": [], 1533 | "source": [ 1534 | "def preprocess_wiki(text):\n", 1535 | " text = list(map(lambda x: x.lower(), text)); \n", 1536 | " text = list(map(lambda x: x.replace(\"\", \"UNK\"), text));\n", 1537 | " text = list(map(lambda x: x.replace(\"@.@\", \".\"), text));\n", 1538 | " text = list(map(lambda x: x.replace(\"@-@\", \"-\"), text));\n", 1539 | " text = list(map(lambda x: x.replace(\"@,@\", \",\"), text));\n", 1540 | " text = list(map(lambda x: x.replace(\"@-@\", \"-\"), text));\n", 1541 | " return text" 1542 | ] 1543 | }, 1544 | { 1545 | "cell_type": "code", 1546 | "execution_count": 0, 1547 | "metadata": { 1548 | "colab": {}, 1549 | "colab_type": "code", 1550 | "id": "BGn-5CBNhvYY", 1551 | "outputId": "fa19c1ce-59ff-4eff-e46d-5bdd1b52e8cd" 1552 | }, 1553 | "outputs": [ 1554 | { 1555 | "data": { 1556 | "text/plain": [ 1557 | "(5515, 540, 620)" 1558 | ] 1559 | }, 1560 | "execution_count": 31, 1561 | "metadata": { 1562 | "tags": [] 1563 | }, 1564 | "output_type": "execute_result" 1565 | } 1566 | ], 1567 | "source": [ 1568 | "train_wiki = preprocess_wiki(train_wiki_raw)\n", 1569 | "val_wiki = preprocess_wiki(val_wiki_raw)\n", 1570 | "test_wiki = preprocess_wiki(test_wiki_raw)\n", 1571 | "len(train_wiki), len(val_wiki), len(test_wiki)" 1572 | ] 1573 | }, 1574 | { 1575 | "cell_type": "code", 1576 | "execution_count": 0, 1577 | "metadata": { 1578 | "colab": {}, 1579 | "colab_type": "code", 1580 | "id": "6mUAQF57hvYb" 1581 | }, 1582 | "outputs": [], 1583 | "source": [ 1584 | "# print(tokenizer.tokenize(train_wiki[0]))" 1585 | ] 1586 | }, 1587 | { 1588 | "cell_type": "code", 1589 | "execution_count": 0, 1590 | "metadata": { 1591 | "colab": {}, 1592 | "colab_type": "code", 1593 | "id": "rgIu1SWohvYf", 1594 | "outputId": "fa336cf2-029d-474b-a576-402022eff0e5" 1595 | }, 1596 | "outputs": [ 1597 | { 1598 | "name": "stdout", 1599 | "output_type": "stream", 1600 | "text": [ 1601 | "CPU times: user 32.4 s, sys: 11.8 ms, total: 32.4 s\n", 1602 | "Wall time: 32.4 s\n" 1603 | ] 1604 | } 1605 | ], 1606 | "source": [ 1607 | "%%time\n", 1608 | "w1 = count_vect.fit_transform(train_wiki)\n", 1609 | "w2 = count_vect.transform(val_wiki)\n", 1610 | "w3 = count_vect.transform(test_wiki)" 1611 | ] 1612 | }, 1613 | { 1614 | "cell_type": "code", 1615 | "execution_count": 0, 1616 | "metadata": { 1617 | "colab": {}, 1618 | "colab_type": "code", 1619 | "id": "THFbMqfohvYh", 1620 | "outputId": "4cb566b8-9d11-46db-f485-4b9bbcf863a6" 1621 | }, 1622 | "outputs": [ 1623 | { 1624 | "name": "stdout", 1625 | "output_type": "stream", 1626 | "text": [ 1627 | "CPU times: user 3.65 s, sys: 961 ms, total: 4.61 s\n", 1628 | "Wall time: 4.61 s\n" 1629 | ] 1630 | } 1631 | ], 1632 | "source": [ 1633 | "%%time\n", 1634 | "X_train_w = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in w1])\n", 1635 | "X_val_w = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in w2])\n", 1636 | "X_test_w = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in w3])" 1637 | ] 1638 | }, 1639 | { 1640 | "cell_type": "code", 1641 | "execution_count": 0, 1642 | "metadata": { 1643 | "colab": {}, 1644 | "colab_type": "code", 1645 | "id": "4VARgpF-hvYj", 1646 | "outputId": "113883f0-01e4-4a5b-cfae-c4ab6d7d7ae5" 1647 | }, 1648 | "outputs": [ 1649 | { 1650 | "data": { 1651 | "text/plain": [ 1652 | "((5515, 30522), (540, 30522), (620, 30522))" 1653 | ] 1654 | }, 1655 | "execution_count": 35, 1656 | "metadata": { 1657 | "tags": [] 1658 | }, 1659 | "output_type": "execute_result" 1660 | } 1661 | ], 1662 | "source": [ 1663 | "X_train_w.shape, X_val_w.shape, X_test_w.shape" 1664 | ] 1665 | }, 1666 | { 1667 | "cell_type": "markdown", 1668 | "metadata": { 1669 | "colab_type": "text", 1670 | "id": "vVKfdu5bhvYq" 1671 | }, 1672 | "source": [ 1673 | "## wiki" 1674 | ] 1675 | }, 1676 | { 1677 | "cell_type": "code", 1678 | "execution_count": 6, 1679 | "metadata": { 1680 | "colab": {}, 1681 | "colab_type": "code", 1682 | "id": "Y6jgr6ohhvYq" 1683 | }, 1684 | "outputs": [], 1685 | "source": [ 1686 | "# model = Autoencoder(len(tokenizer.vocab)).to(device)" 1687 | ] 1688 | }, 1689 | { 1690 | "cell_type": "code", 1691 | "execution_count": 7, 1692 | "metadata": { 1693 | "colab": {}, 1694 | "colab_type": "code", 1695 | "id": "IOKSk5n0hvYs" 1696 | }, 1697 | "outputs": [], 1698 | "source": [ 1699 | "# t = torch.Tensor(X_train_w)\n", 1700 | "# t2 = torch.Tensor(X_val_w)\n", 1701 | "# tr_d = TensorDataset(t,t)\n", 1702 | "# val_d = TensorDataset(t2,t2)\n", 1703 | "# tr_dl = DataLoader(tr_d, bs, shuffle=True)\n", 1704 | "# val_dl = DataLoader(val_d, bs, shuffle=True)\n", 1705 | "# data = DataBunch(tr_dl, val_dl)" 1706 | ] 1707 | }, 1708 | { 1709 | "cell_type": "code", 1710 | "execution_count": 8, 1711 | "metadata": { 1712 | "colab": {}, 1713 | "colab_type": "code", 1714 | "id": "ig4H58A-hvYt", 1715 | "outputId": "e7494367-63a1-40c7-f940-e73f9a1afae5" 1716 | }, 1717 | "outputs": [], 1718 | "source": [ 1719 | "# learn = Learner(data=data, model=model, loss_func=custom_loss)\n", 1720 | "# if n_gpu > 1: learn.model = torch.nn.DataParallel(learn.model)" 1721 | ] 1722 | }, 1723 | { 1724 | "cell_type": "code", 1725 | "execution_count": 9, 1726 | "metadata": { 1727 | "colab": {}, 1728 | "colab_type": "code", 1729 | "id": "QdAhtm6dhvYw", 1730 | "outputId": "ceb1183c-a418-46a0-8b24-87eb28c83894" 1731 | }, 1732 | "outputs": [], 1733 | "source": [ 1734 | "# learn.lr_find()\n", 1735 | "# learn.recorder.plot()" 1736 | ] 1737 | }, 1738 | { 1739 | "cell_type": "code", 1740 | "execution_count": 10, 1741 | "metadata": { 1742 | "colab": {}, 1743 | "colab_type": "code", 1744 | "id": "DKauv4OShvYy", 1745 | "outputId": "19c56fa2-0ecf-4797-d201-942236fdfb9b" 1746 | }, 1747 | "outputs": [], 1748 | "source": [ 1749 | "# learn.fit_one_cycle(10, slice(1e-03), wd=0.1)\n", 1750 | "# learn.save('model-pretrained')" 1751 | ] 1752 | }, 1753 | { 1754 | "cell_type": "markdown", 1755 | "metadata": { 1756 | "colab_type": "text", 1757 | "id": "riAviqbmhvY2" 1758 | }, 1759 | "source": [ 1760 | "## news20" 1761 | ] 1762 | }, 1763 | { 1764 | "cell_type": "code", 1765 | "execution_count": 30, 1766 | "metadata": {}, 1767 | "outputs": [], 1768 | "source": [ 1769 | "model = Autoencoder(len(tokenizer.vocab)).to(device)" 1770 | ] 1771 | }, 1772 | { 1773 | "cell_type": "code", 1774 | "execution_count": 12, 1775 | "metadata": {}, 1776 | "outputs": [], 1777 | "source": [ 1778 | "bs=32" 1779 | ] 1780 | }, 1781 | { 1782 | "cell_type": "code", 1783 | "execution_count": 36, 1784 | "metadata": {}, 1785 | "outputs": [ 1786 | { 1787 | "data": { 1788 | "text/plain": [ 1789 | "((25000, 30522), (25000, 30522), numpy.ndarray)" 1790 | ] 1791 | }, 1792 | "execution_count": 36, 1793 | "metadata": {}, 1794 | "output_type": "execute_result" 1795 | } 1796 | ], 1797 | "source": [ 1798 | "X_train_c = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in X_train_counts])\n", 1799 | "X_val_c = np.array([np.array(np.log10(1. + c.todense())).flatten() for c in X_val_counts])\n", 1800 | "X_train_c.shape, X_val_c.shape, type(X_train_c)" 1801 | ] 1802 | }, 1803 | { 1804 | "cell_type": "code", 1805 | "execution_count": 37, 1806 | "metadata": {}, 1807 | "outputs": [ 1808 | { 1809 | "data": { 1810 | "text/plain": [ 1811 | "((48000, 30522), (2000, 30522))" 1812 | ] 1813 | }, 1814 | "execution_count": 37, 1815 | "metadata": {}, 1816 | "output_type": "execute_result" 1817 | } 1818 | ], 1819 | "source": [ 1820 | "tr = np.concatenate((X_train_c, X_val_c), axis=0); \n", 1821 | "vl = tr[48000:50000,:]\n", 1822 | "tr = tr[:48000,:]\n", 1823 | "tr.shape, vl.shape" 1824 | ] 1825 | }, 1826 | { 1827 | "cell_type": "code", 1828 | "execution_count": null, 1829 | "metadata": {}, 1830 | "outputs": [], 1831 | "source": [] 1832 | }, 1833 | { 1834 | "cell_type": "code", 1835 | "execution_count": 38, 1836 | "metadata": { 1837 | "colab": {}, 1838 | "colab_type": "code", 1839 | "id": "7JWVVefohvY3" 1840 | }, 1841 | "outputs": [], 1842 | "source": [ 1843 | "t = torch.Tensor(tr)\n", 1844 | "t2 = torch.Tensor(vl)\n", 1845 | "tr_d = TensorDataset(t,t)\n", 1846 | "val_d = TensorDataset(t2,t2)\n", 1847 | "tr_dl = DataLoader(tr_d, bs, shuffle=True)\n", 1848 | "val_dl = DataLoader(val_d, bs, shuffle=True)\n", 1849 | "data = DataBunch(tr_dl, val_dl)" 1850 | ] 1851 | }, 1852 | { 1853 | "cell_type": "code", 1854 | "execution_count": 31, 1855 | "metadata": { 1856 | "colab": {}, 1857 | "colab_type": "code", 1858 | "id": "PXov4VZghvY8" 1859 | }, 1860 | "outputs": [], 1861 | "source": [ 1862 | "learn = Learner(data=data, model=model, loss_func=custom_loss)\n", 1863 | "if n_gpu > 1: learn.model = torch.nn.DataParallel(learn.model)" 1864 | ] 1865 | }, 1866 | { 1867 | "cell_type": "code", 1868 | "execution_count": 47, 1869 | "metadata": { 1870 | "colab": {}, 1871 | "colab_type": "code", 1872 | "id": "JaL5XnevhvZA", 1873 | "outputId": "c02d8e70-c534-4f96-d769-152b8f2b0018" 1874 | }, 1875 | "outputs": [ 1876 | { 1877 | "data": { 1878 | "text/html": [], 1879 | "text/plain": [ 1880 | "" 1881 | ] 1882 | }, 1883 | "metadata": {}, 1884 | "output_type": "display_data" 1885 | }, 1886 | { 1887 | "name": "stdout", 1888 | "output_type": "stream", 1889 | "text": [ 1890 | "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" 1891 | ] 1892 | }, 1893 | { 1894 | "data": { 1895 | "image/png": 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\n", 1896 | "text/plain": [ 1897 | "
" 1898 | ] 1899 | }, 1900 | "metadata": { 1901 | "image/png": { 1902 | "height": 263, 1903 | "width": 384 1904 | }, 1905 | "needs_background": "light" 1906 | }, 1907 | "output_type": "display_data" 1908 | } 1909 | ], 1910 | "source": [ 1911 | "learn.lr_find()\n", 1912 | "learn.recorder.plot()" 1913 | ] 1914 | }, 1915 | { 1916 | "cell_type": "code", 1917 | "execution_count": 56, 1918 | "metadata": { 1919 | "colab": {}, 1920 | "colab_type": "code", 1921 | "id": "AdKUZ1E-hvZC", 1922 | "outputId": "f376366c-ee27-4a98-c44d-13c2f1d9187d" 1923 | }, 1924 | "outputs": [ 1925 | { 1926 | "data": { 1927 | "text/html": [ 1928 | "\n", 1929 | " \n", 1930 | " \n", 1931 | " \n", 1932 | " \n", 1933 | " \n", 1934 | " \n", 1935 | " \n", 1936 | " \n", 1937 | " \n", 1938 | " \n", 1939 | " \n", 1940 | " \n", 1941 | " \n", 1942 | " \n", 1943 | " \n", 1944 | " \n", 1945 | " \n", 1946 | " \n", 1947 | " \n", 1948 | " \n", 1949 | " \n", 1950 | " \n", 1951 | " \n", 1952 | " \n", 1953 | " \n", 1954 | " \n", 1955 | " \n", 1956 | " \n", 1957 | " \n", 1958 | " \n", 1959 | " \n", 1960 | " \n", 1961 | " \n", 1962 | " \n", 1963 | " \n", 1964 | " \n", 1965 | " \n", 1966 | " \n", 1967 | " \n", 1968 | " \n", 1969 | "
epochtrain_lossvalid_losstime
05.6776556.75426401:46
15.7290726.79996501:46
25.8275286.73724001:46
35.6387406.70770201:46
45.4788926.70534301:46
" 1970 | ], 1971 | "text/plain": [ 1972 | "" 1973 | ] 1974 | }, 1975 | "metadata": {}, 1976 | "output_type": "display_data" 1977 | } 1978 | ], 1979 | "source": [ 1980 | "learn.fit_one_cycle(5, slice(1e-03), wd=0.1)" 1981 | ] 1982 | }, 1983 | { 1984 | "cell_type": "code", 1985 | "execution_count": 49, 1986 | "metadata": { 1987 | "colab": {}, 1988 | "colab_type": "code", 1989 | "id": "uGCakUfjhvZE", 1990 | "outputId": "31da8f55-2ecf-477d-c3e4-94969925ab49" 1991 | }, 1992 | "outputs": [ 1993 | { 1994 | "data": { 1995 | "image/png": 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\n", 1996 | "text/plain": [ 1997 | "
" 1998 | ] 1999 | }, 2000 | "metadata": { 2001 | "image/png": { 2002 | "height": 263, 2003 | "width": 393 2004 | }, 2005 | "needs_background": "light" 2006 | }, 2007 | "output_type": "display_data" 2008 | } 2009 | ], 2010 | "source": [ 2011 | "learn.recorder.plot_losses()" 2012 | ] 2013 | }, 2014 | { 2015 | "cell_type": "code", 2016 | "execution_count": 77, 2017 | "metadata": {}, 2018 | "outputs": [], 2019 | "source": [ 2020 | "x_train_a = transform_data_with_autoencoder(learn.model.module, X_train_c)\n", 2021 | "x_val_a = transform_data_with_autoencoder(learn.model.module, X_val_c)" 2022 | ] 2023 | }, 2024 | { 2025 | "cell_type": "code", 2026 | "execution_count": 84, 2027 | "metadata": { 2028 | "colab": {}, 2029 | "colab_type": "code", 2030 | "id": "rS9FiP48hvZM", 2031 | "outputId": "f75840f9-10d6-48c8-8de6-171c7009840f" 2032 | }, 2033 | "outputs": [ 2034 | { 2035 | "name": "stderr", 2036 | "output_type": "stream", 2037 | "text": [ 2038 | "/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:13: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n", 2039 | "Invalid limit will be ignored.\n" 2040 | ] 2041 | }, 2042 | { 2043 | "data": { 2044 | "image/png": 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\n", 2045 | "text/plain": [ 2046 | "
" 2047 | ] 2048 | }, 2049 | "metadata": { 2050 | "image/png": { 2051 | "height": 265, 2052 | "width": 401 2053 | }, 2054 | "needs_background": "light" 2055 | }, 2056 | "output_type": "display_data" 2057 | } 2058 | ], 2059 | "source": [ 2060 | "hist3 = get_acc_on_samples(x_train_a, x_val_a)\n", 2061 | "plot_new_hist(hist3, \"vanilla-auto\")" 2062 | ] 2063 | }, 2064 | { 2065 | "cell_type": "markdown", 2066 | "metadata": {}, 2067 | "source": [ 2068 | "## Transformer" 2069 | ] 2070 | }, 2071 | { 2072 | "cell_type": "code", 2073 | "execution_count": 3, 2074 | "metadata": {}, 2075 | "outputs": [], 2076 | "source": [ 2077 | "def prints(s):\n", 2078 | " current_time = time.strftime(\"%Y-%m-%d-%H:%M:%S\", time.gmtime())\n", 2079 | " with open(\"logs.txt\", \"a\") as text_file:\n", 2080 | " print(f\"{current_time}: {s}\", file=text_file)\n", 2081 | " print(s)\n", 2082 | " \n", 2083 | "import copy\n", 2084 | "class EncoderDecoder(nn.Module):\n", 2085 | " \"\"\"\n", 2086 | " A standard Encoder-Decoder architecture. Base for this and many \n", 2087 | " other models.\n", 2088 | " \"\"\"\n", 2089 | " def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):\n", 2090 | " super(EncoderDecoder, self).__init__()\n", 2091 | " self.encoder = encoder\n", 2092 | " self.decoder = decoder\n", 2093 | " self.src_embed = src_embed\n", 2094 | " self.tgt_embed = tgt_embed\n", 2095 | " self.generator = generator\n", 2096 | " \n", 2097 | " def forward(self, src, tgt, src_mask, tgt_mask):\n", 2098 | " \"Take in and process masked src and target sequences.\"\n", 2099 | " return self.decode(self.encode(src, src_mask), src_mask,\n", 2100 | " tgt, tgt_mask)\n", 2101 | " \n", 2102 | " def encode(self, src, src_mask):\n", 2103 | " return self.encoder(self.src_embed(src), src_mask)\n", 2104 | " \n", 2105 | " def decode(self, memory, src_mask, tgt, tgt_mask):\n", 2106 | " return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)\n", 2107 | " \n", 2108 | "class Generator(nn.Module):\n", 2109 | " \"Define standard linear + softmax generation step.\"\n", 2110 | " def __init__(self, d_model, vocab):\n", 2111 | " super(Generator, self).__init__()\n", 2112 | " self.proj = nn.Linear(d_model, vocab)\n", 2113 | "\n", 2114 | " def forward(self, x):\n", 2115 | " return F.log_softmax(self.proj(x), dim=-1)\n", 2116 | " \n", 2117 | "def clones(module, N):\n", 2118 | " \"Produce N identical layers.\"\n", 2119 | " return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])\n", 2120 | " \n", 2121 | "class Encoder(nn.Module):\n", 2122 | " \"Core encoder is a stack of N layers\"\n", 2123 | " def __init__(self, layer, N):\n", 2124 | " super(Encoder, self).__init__()\n", 2125 | " self.layers = clones(layer, N)\n", 2126 | " self.norm = LayerNorm(layer.size)\n", 2127 | " \n", 2128 | " def forward(self, x, mask):\n", 2129 | " \"Pass the input (and mask) through each layer in turn.\"\n", 2130 | " for layer in self.layers:\n", 2131 | " x = layer(x, mask)\n", 2132 | " res = self.norm(x) # ([30, 10, 512])\n", 2133 | " res = res[:,0].unsqueeze_(1).expand(res.size()) # ([30, 10, 512])\n", 2134 | " return res\n", 2135 | " \n", 2136 | "class LayerNorm(nn.Module):\n", 2137 | " \"Construct a layernorm module (See citation for details).\"\n", 2138 | " def __init__(self, features, eps=1e-6):\n", 2139 | " super(LayerNorm, self).__init__()\n", 2140 | " self.a_2 = nn.Parameter(torch.ones(features))\n", 2141 | " self.b_2 = nn.Parameter(torch.zeros(features))\n", 2142 | " self.eps = eps\n", 2143 | "\n", 2144 | " def forward(self, x):\n", 2145 | " mean = x.mean(-1, keepdim=True)\n", 2146 | " std = x.std(-1, keepdim=True)\n", 2147 | " return self.a_2 * (x - mean) / (std + self.eps) + self.b_2\n", 2148 | " \n", 2149 | "class SublayerConnection(nn.Module):\n", 2150 | " \"\"\"\n", 2151 | " A residual connection followed by a layer norm.\n", 2152 | " Note for code simplicity the norm is first as opposed to last.\n", 2153 | " \"\"\"\n", 2154 | " def __init__(self, size, dropout):\n", 2155 | " super(SublayerConnection, self).__init__()\n", 2156 | " self.norm = LayerNorm(size)\n", 2157 | " self.dropout = nn.Dropout(dropout)\n", 2158 | "\n", 2159 | " def forward(self, x, sublayer):\n", 2160 | " \"Apply residual connection to any sublayer with the same size.\"\n", 2161 | " return x + self.dropout(sublayer(self.norm(x)))\n", 2162 | " \n", 2163 | "class EncoderLayer(nn.Module):\n", 2164 | " \"Encoder is made up of self-attn and feed forward (defined below)\"\n", 2165 | " def __init__(self, size, self_attn, feed_forward, dropout):\n", 2166 | " super(EncoderLayer, self).__init__()\n", 2167 | " self.self_attn = self_attn\n", 2168 | " self.feed_forward = feed_forward\n", 2169 | " self.sublayer = clones(SublayerConnection(size, dropout), 2)\n", 2170 | " self.size = size\n", 2171 | "\n", 2172 | " def forward(self, x, mask):\n", 2173 | " \"Follow Figure 1 (left) for connections.\"\n", 2174 | " x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))\n", 2175 | " return self.sublayer[1](x, self.feed_forward)\n", 2176 | " \n", 2177 | "class Decoder(nn.Module):\n", 2178 | " \"Generic N layer decoder with masking.\"\n", 2179 | " def __init__(self, layer, N):\n", 2180 | " super(Decoder, self).__init__()\n", 2181 | " self.layers = clones(layer, N)\n", 2182 | " self.norm = LayerNorm(layer.size)\n", 2183 | " \n", 2184 | " def forward(self, x, memory, src_mask, tgt_mask):\n", 2185 | " for layer in self.layers:\n", 2186 | " x = layer(x, memory, src_mask, tgt_mask)\n", 2187 | " return self.norm(x)\n", 2188 | " \n", 2189 | "class DecoderLayer(nn.Module):\n", 2190 | " \"Decoder is made of self-attn, src-attn, and feed forward (defined below)\"\n", 2191 | " def __init__(self, size, self_attn, src_attn, feed_forward, dropout):\n", 2192 | " super(DecoderLayer, self).__init__()\n", 2193 | " self.size = size\n", 2194 | " self.self_attn = self_attn\n", 2195 | " self.src_attn = src_attn\n", 2196 | " self.feed_forward = feed_forward\n", 2197 | " self.sublayer = clones(SublayerConnection(size, dropout), 3)\n", 2198 | " \n", 2199 | " def forward(self, x, memory, src_mask, tgt_mask):\n", 2200 | " \"Follow Figure 1 (right) for connections.\"\n", 2201 | " m = memory\n", 2202 | " x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))\n", 2203 | " x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))\n", 2204 | " return self.sublayer[2](x, self.feed_forward)\n", 2205 | "\n", 2206 | "def subsequent_mask(size):\n", 2207 | " \"Mask out subsequent positions.\"\n", 2208 | " attn_shape = (1, size, size)\n", 2209 | " subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')\n", 2210 | " return torch.from_numpy(subsequent_mask) == 0\n", 2211 | " \n", 2212 | "def attention(query, key, value, mask=None, dropout=None):\n", 2213 | " \"Compute 'Scaled Dot Product Attention'\"\n", 2214 | " d_k = query.size(-1)\n", 2215 | " scores = torch.matmul(query, key.transpose(-2, -1)) \\\n", 2216 | " / math.sqrt(d_k)\n", 2217 | " if mask is not None:\n", 2218 | " scores = scores.masked_fill(mask == 0, -1e9)\n", 2219 | " p_attn = F.softmax(scores, dim = -1)\n", 2220 | " if dropout is not None:\n", 2221 | " p_attn = dropout(p_attn)\n", 2222 | " return torch.matmul(p_attn, value), p_attn\n", 2223 | " \n", 2224 | "class MultiHeadedAttention(nn.Module):\n", 2225 | " def __init__(self, h, d_model, dropout=0.1):\n", 2226 | " \"Take in model size and number of heads.\"\n", 2227 | " super(MultiHeadedAttention, self).__init__()\n", 2228 | " assert d_model % h == 0\n", 2229 | " # We assume d_v always equals d_k\n", 2230 | " self.d_k = d_model // h\n", 2231 | " self.h = h\n", 2232 | " self.linears = clones(nn.Linear(d_model, d_model), 4)\n", 2233 | " self.attn = None\n", 2234 | " self.dropout = nn.Dropout(p=dropout)\n", 2235 | " \n", 2236 | " def forward(self, query, key, value, mask=None):\n", 2237 | " \"Implements Figure 2\"\n", 2238 | " if mask is not None:\n", 2239 | " # Same mask applied to all h heads.\n", 2240 | " mask = mask.unsqueeze(1)\n", 2241 | " nbatches = query.size(0)\n", 2242 | " \n", 2243 | " # 1) Do all the linear projections in batch from d_model => h x d_k \n", 2244 | " query, key, value = \\\n", 2245 | " [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n", 2246 | " for l, x in zip(self.linears, (query, key, value))]\n", 2247 | " \n", 2248 | " # 2) Apply attention on all the projected vectors in batch. \n", 2249 | " x, self.attn = attention(query, key, value, mask=mask, \n", 2250 | " dropout=self.dropout)\n", 2251 | " \n", 2252 | " # 3) \"Concat\" using a view and apply a final linear. \n", 2253 | " x = x.transpose(1, 2).contiguous() \\\n", 2254 | " .view(nbatches, -1, self.h * self.d_k)\n", 2255 | " return self.linears[-1](x)\n", 2256 | " \n", 2257 | "class PositionwiseFeedForward(nn.Module):\n", 2258 | " \"Implements FFN equation.\"\n", 2259 | " def __init__(self, d_model, d_ff, dropout=0.1):\n", 2260 | " super(PositionwiseFeedForward, self).__init__()\n", 2261 | " self.w_1 = nn.Linear(d_model, d_ff)\n", 2262 | " self.w_2 = nn.Linear(d_ff, d_model)\n", 2263 | " self.dropout = nn.Dropout(dropout)\n", 2264 | "\n", 2265 | " def forward(self, x):\n", 2266 | " return self.w_2(self.dropout(F.relu(self.w_1(x))))\n", 2267 | "\n", 2268 | "class Embeddings(nn.Module):\n", 2269 | " def __init__(self, d_model, vocab):\n", 2270 | " super(Embeddings, self).__init__()\n", 2271 | " self.lut = nn.Embedding(vocab, d_model)\n", 2272 | " self.d_model = d_model\n", 2273 | "\n", 2274 | " def forward(self, x):\n", 2275 | " return self.lut(x) * math.sqrt(self.d_model)\n", 2276 | " \n", 2277 | "class PositionalEncoding(nn.Module):\n", 2278 | " \"Implement the PE function.\"\n", 2279 | " def __init__(self, d_model, dropout, max_len=5000):\n", 2280 | " super(PositionalEncoding, self).__init__()\n", 2281 | " self.dropout = nn.Dropout(p=dropout)\n", 2282 | " \n", 2283 | " # Compute the positional encodings once in log space.\n", 2284 | " pe = torch.zeros(max_len, d_model)\n", 2285 | " position = torch.arange(0., max_len).unsqueeze(1)\n", 2286 | " div_term = torch.exp(torch.arange(0., d_model, 2) * -(math.log(10000.0) / d_model))\n", 2287 | " pe[:, 0::2] = torch.sin(position * div_term)\n", 2288 | " pe[:, 1::2] = torch.cos(position * div_term)\n", 2289 | " pe = pe.unsqueeze(0)\n", 2290 | " self.register_buffer('pe', pe)\n", 2291 | " \n", 2292 | " def forward(self, x):\n", 2293 | " x = x + Variable(self.pe[:, :x.size(1)], \n", 2294 | " requires_grad=False)\n", 2295 | " return self.dropout(x)\n", 2296 | "\n", 2297 | "def make_model(src_vocab, tgt_vocab, N=6, \n", 2298 | " d_model=512, d_ff=2048, h=8, dropout=0.1):\n", 2299 | " \"Helper: Construct a model from hyperparameters.\"\n", 2300 | " c = copy.deepcopy\n", 2301 | " attn = MultiHeadedAttention(h, d_model)\n", 2302 | " ff = PositionwiseFeedForward(d_model, d_ff, dropout)\n", 2303 | " position = PositionalEncoding(d_model, dropout)\n", 2304 | " model = EncoderDecoder(\n", 2305 | " Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),\n", 2306 | " Decoder(DecoderLayer(d_model, c(attn), c(attn), \n", 2307 | " c(ff), dropout), N),\n", 2308 | " nn.Sequential(Embeddings(d_model, src_vocab), c(position)),\n", 2309 | " nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),\n", 2310 | " Generator(d_model, tgt_vocab))\n", 2311 | " \n", 2312 | " # This was important from their code. \n", 2313 | " # Initialize parameters with Glorot / fan_avg.\n", 2314 | " for p in model.parameters():\n", 2315 | " if p.dim() > 1:\n", 2316 | " nn.init.xavier_uniform_(p)\n", 2317 | " return model\n", 2318 | "\n", 2319 | "class Batch:\n", 2320 | " \"Object for holding a batch of data with mask during training.\"\n", 2321 | " def __init__(self, src, trg=None, pad=0):\n", 2322 | " self.src = src\n", 2323 | " self.src_mask = (src != pad).unsqueeze(-2)\n", 2324 | " if trg is not None:\n", 2325 | " self.trg = trg[:, :-1]\n", 2326 | " self.trg_y = trg[:, 1:]\n", 2327 | " self.trg_mask = \\\n", 2328 | " self.make_std_mask(self.trg, pad)\n", 2329 | " self.ntokens = (self.trg_y != pad).data.sum()\n", 2330 | " \n", 2331 | " @staticmethod\n", 2332 | " def make_std_mask(tgt, pad):\n", 2333 | " \"Create a mask to hide padding and future words.\"\n", 2334 | " tgt_mask = (tgt != pad).unsqueeze(-2)\n", 2335 | " tgt_mask = tgt_mask & Variable(\n", 2336 | " subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))\n", 2337 | " return tgt_mask\n", 2338 | " \n", 2339 | "class MyIterator(data.Iterator):\n", 2340 | " def create_batches(self):\n", 2341 | " if self.train:\n", 2342 | " def pool(d, random_shuffler):\n", 2343 | " for p in data.batch(d, self.batch_size * 100):\n", 2344 | " p_batch = data.batch(\n", 2345 | " sorted(p, key=self.sort_key),\n", 2346 | " self.batch_size, self.batch_size_fn)\n", 2347 | " for b in random_shuffler(list(p_batch)):\n", 2348 | " yield b\n", 2349 | " self.batches = pool(self.data(), self.random_shuffler)\n", 2350 | " \n", 2351 | " else:\n", 2352 | " self.batches = []\n", 2353 | " for b in data.batch(self.data(), self.batch_size,\n", 2354 | " self.batch_size_fn):\n", 2355 | " self.batches.append(sorted(b, key=self.sort_key))\n", 2356 | " \n", 2357 | "global max_src_in_batch\n", 2358 | "def batch_size_fn(new, count, sofar):\n", 2359 | " \"Keep augmenting batch and calculate total number of tokens + padding.\"\n", 2360 | " global max_src_in_batch\n", 2361 | " if count == 1:\n", 2362 | " max_src_in_batch = 0\n", 2363 | " max_src_in_batch = max(max_src_in_batch, len(new.text)+2)\n", 2364 | " src_elements = count * max_src_in_batch\n", 2365 | " return src_elements\n", 2366 | "\n", 2367 | "def run_epoch(data_iter, model, loss_compute):\n", 2368 | " \"Standard Training and Logging Function\"\n", 2369 | " start = time.time()\n", 2370 | " total_tokens = 0\n", 2371 | " total_loss = 0\n", 2372 | " tokens = 0\n", 2373 | " for i, batch in enumerate(data_iter):\n", 2374 | " out = model.forward(batch.src, batch.trg, \n", 2375 | " batch.src_mask, batch.trg_mask)\n", 2376 | " loss = loss_compute(out, batch.trg_y, batch.ntokens)\n", 2377 | " total_loss += loss\n", 2378 | " total_tokens += batch.ntokens\n", 2379 | " tokens += batch.ntokens\n", 2380 | " if i % 100 == 1:\n", 2381 | " elapsed = time.time() - start\n", 2382 | " s = \"Epoch Step: %d Loss: %f Tokens per Sec: %f\" % (i, loss / batch.ntokens, tokens / elapsed)\n", 2383 | " start = time.time()\n", 2384 | " prints(s)\n", 2385 | " tokens = 0\n", 2386 | " return total_loss / total_tokens\n", 2387 | "\n", 2388 | "class NoamOpt:\n", 2389 | " \"Optim wrapper that implements rate.\"\n", 2390 | " def __init__(self, model_size, factor, warmup, optimizer):\n", 2391 | " self.optimizer = optimizer\n", 2392 | " self._step = 0\n", 2393 | " self.warmup = warmup\n", 2394 | " self.factor = factor\n", 2395 | " self.model_size = model_size\n", 2396 | " self._rate = 0\n", 2397 | " \n", 2398 | " def step(self):\n", 2399 | " \"Update parameters and rate\"\n", 2400 | " self._step += 1\n", 2401 | " rate = self.rate()\n", 2402 | " for p in self.optimizer.param_groups:\n", 2403 | " p['lr'] = rate\n", 2404 | " self._rate = rate\n", 2405 | " self.optimizer.step()\n", 2406 | " \n", 2407 | " def rate(self, step = None):\n", 2408 | " \"Implement `lrate` above\"\n", 2409 | " if step is None:\n", 2410 | " step = self._step\n", 2411 | " return self.factor * \\\n", 2412 | " (self.model_size ** (-0.5) *\n", 2413 | " min(step ** (-0.5), step * self.warmup ** (-1.5)))\n", 2414 | " \n", 2415 | "def get_std_opt(model):\n", 2416 | " return NoamOpt(model.src_embed[0].d_model, 2, 4000,\n", 2417 | " torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))\n", 2418 | " \n", 2419 | "class LabelSmoothing(nn.Module):\n", 2420 | " \"Implement label smoothing.\"\n", 2421 | " def __init__(self, size, padding_idx, smoothing=0.0):\n", 2422 | " super(LabelSmoothing, self).__init__()\n", 2423 | " self.criterion = nn.KLDivLoss(reduction='sum')\n", 2424 | " self.padding_idx = padding_idx\n", 2425 | " self.confidence = 1.0 - smoothing\n", 2426 | " self.smoothing = smoothing\n", 2427 | " self.size = size\n", 2428 | " self.true_dist = None\n", 2429 | " \n", 2430 | " def forward(self, x, target):\n", 2431 | " assert x.size(1) == self.size\n", 2432 | " true_dist = x.data.clone()\n", 2433 | " true_dist.fill_(self.smoothing / (self.size - 2))\n", 2434 | " true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)\n", 2435 | " true_dist[:, self.padding_idx] = 0\n", 2436 | " mask = torch.nonzero(target.data == self.padding_idx)\n", 2437 | " if mask.dim() > 0:\n", 2438 | " true_dist.index_fill_(0, mask.squeeze(), 0.0)\n", 2439 | " self.true_dist = true_dist\n", 2440 | " return self.criterion(x, Variable(true_dist, requires_grad=False))\n", 2441 | "\n", 2442 | "class SimpleLossCompute:\n", 2443 | " \"A simple loss compute and train function.\"\n", 2444 | " def __init__(self, generator, criterion, opt=None):\n", 2445 | " self.generator = generator\n", 2446 | " self.criterion = criterion\n", 2447 | " self.opt = opt\n", 2448 | " \n", 2449 | " def __call__(self, x, y, norm):\n", 2450 | " x = self.generator(x)\n", 2451 | " loss = self.criterion(x.contiguous().view(-1, x.size(-1)), \n", 2452 | " y.contiguous().view(-1)) / norm\n", 2453 | " loss.backward()\n", 2454 | " if self.opt is not None:\n", 2455 | " self.opt.step()\n", 2456 | " self.opt.optimizer.zero_grad()\n", 2457 | " return loss * norm\n", 2458 | "\n", 2459 | "class MultiGPULossCompute:\n", 2460 | " \"A multi-gpu loss compute and train function.\"\n", 2461 | " def __init__(self, generator, criterion, devices, opt=None, chunk_size=5):\n", 2462 | " # Send out to different gpus.\n", 2463 | " self.generator = generator\n", 2464 | " self.criterion = nn.parallel.replicate(criterion, \n", 2465 | " devices=devices)\n", 2466 | " self.opt = opt\n", 2467 | " self.devices = devices\n", 2468 | " self.chunk_size = chunk_size\n", 2469 | " \n", 2470 | " def __call__(self, out, targets, normalize):\n", 2471 | " total = 0.0\n", 2472 | " out_scatter = nn.parallel.scatter(out, self.devices)\n", 2473 | " generator = nn.parallel.replicate(self.generator, list(range(len(out_scatter))))\n", 2474 | " out_grad = [[] for _ in out_scatter]\n", 2475 | " targets = nn.parallel.scatter(targets, \n", 2476 | " target_gpus=self.devices)\n", 2477 | " \n", 2478 | " # Divide generating into chunks.\n", 2479 | " chunk_size = self.chunk_size\n", 2480 | " for i in range(0, out_scatter[0].size(1), chunk_size):\n", 2481 | " # Predict distributions\n", 2482 | " out_column = [[Variable(o[:, i:i+chunk_size].data, \n", 2483 | " requires_grad=self.opt is not None)] \n", 2484 | " for o in out_scatter]\n", 2485 | " \n", 2486 | " gen = nn.parallel.parallel_apply(generator, out_column)\n", 2487 | "\n", 2488 | " # Compute loss. \n", 2489 | " y = [(g.contiguous().view(-1, g.size(-1)), \n", 2490 | " t[:, i:i+chunk_size].contiguous().view(-1)) \n", 2491 | " for g, t in zip(gen, targets)]\n", 2492 | " loss = nn.parallel.parallel_apply(self.criterion[:len(out_scatter)], y) \n", 2493 | " l = nn.parallel.gather(loss, \n", 2494 | " target_device=self.devices[0])\n", 2495 | " l = l.sum() / normalize\n", 2496 | " total += l.data\n", 2497 | "\n", 2498 | " # Backprop loss to output of transformer\n", 2499 | " if self.opt is not None:\n", 2500 | " l.backward()\n", 2501 | " for j, l in enumerate(loss):\n", 2502 | " out_grad[j].append(out_column[j][0].grad.data.clone())\n", 2503 | "\n", 2504 | " # Backprop all loss through transformer. \n", 2505 | " if self.opt is not None:\n", 2506 | " out_grad = [Variable(torch.cat(og, dim=1)) for og in out_grad]\n", 2507 | " o1 = out\n", 2508 | " o2 = nn.parallel.gather(out_grad, \n", 2509 | " target_device=self.devices[0])\n", 2510 | " o1.backward(gradient=o2)\n", 2511 | " self.opt.step()\n", 2512 | " self.opt.optimizer.zero_grad()\n", 2513 | " return total * normalize\n", 2514 | "\n", 2515 | "def greedy_decode(model, src, src_mask, max_len, start_symbol):\n", 2516 | " memory = model.encode(src, src_mask)\n", 2517 | " ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)\n", 2518 | " for i in range(max_len-1):\n", 2519 | " out = model.decode(memory, src_mask, \n", 2520 | " Variable(ys), \n", 2521 | " Variable(subsequent_mask(ys.size(1))\n", 2522 | " .type_as(src.data)))\n", 2523 | " prob = model.generator(out[:, -1])\n", 2524 | " _, next_word = torch.max(prob, dim = 1)\n", 2525 | " next_word = next_word.data[0]\n", 2526 | " ys = torch.cat([ys, \n", 2527 | " torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)\n", 2528 | " return ys\n", 2529 | " \n", 2530 | "def test_quality(model):\n", 2531 | " l = sum(1 for _ in valid_iter)\n", 2532 | " for i, batch in islice(enumerate(valid_iter), l-4, l-1):\n", 2533 | " print(\"True:\", end=\"\\t\")\n", 2534 | " for i in range(1, batch.text.size(1)):\n", 2535 | " sym = TEXT.vocab.itos[batch.text.data[0, i]]\n", 2536 | " print(sym, end =\" \")\n", 2537 | " if sym == \"[SEP]\": break\n", 2538 | " print()\n", 2539 | "\n", 2540 | " src = batch.text[:1]\n", 2541 | " src_mask = (src != TEXT.vocab.stoi[\"[PAD]\"]).unsqueeze(-2)\n", 2542 | " out = greedy_decode(model, src, src_mask, \n", 2543 | " max_len=256, start_symbol=TEXT.vocab.stoi[\"[CLS]\"])\n", 2544 | " print(\"Predicted:\", end=\"\\t\")\n", 2545 | " for i in range(1, out.size(1)):\n", 2546 | " sym = TEXT.vocab.itos[out[0, i]]\n", 2547 | " print(sym, end =\" \")\n", 2548 | " if sym == \"[SEP]\": break\n", 2549 | " print()\n", 2550 | " print(\"------------------------------\")\n", 2551 | " \n", 2552 | "def data_gen(V, batch, nbatches):\n", 2553 | " \"Generate random data for a src-tgt copy task.\"\n", 2554 | " for i in range(nbatches):\n", 2555 | " data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))\n", 2556 | " data[:, 0] = 1\n", 2557 | " src = Variable(data, requires_grad=False)\n", 2558 | " tgt = Variable(data, requires_grad=False)\n", 2559 | " yield Batch(src, tgt, 0)\n", 2560 | " \n", 2561 | "def plot_lengths(dataset):\n", 2562 | " plt.hist([len(e.text) for e in dataset.examples], bins=100, range=(0, 1000))\n", 2563 | " " 2564 | ] 2565 | }, 2566 | { 2567 | "cell_type": "code", 2568 | "execution_count": 5, 2569 | "metadata": {}, 2570 | "outputs": [], 2571 | "source": [ 2572 | "# V = 11\n", 2573 | "# criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)\n", 2574 | "# model = make_model(V, V, N=2)\n", 2575 | "# model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,\n", 2576 | "# torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))\n", 2577 | "\n", 2578 | "# for epoch in range(2):\n", 2579 | "# model.train()\n", 2580 | "# run_epoch(data_gen(V, 30, 20), model, \n", 2581 | "# SimpleLossCompute(model.generator, criterion, model_opt))\n", 2582 | "# model.eval()\n", 2583 | "# print(run_epoch(data_gen(V, 30, 5), model, \n", 2584 | "# SimpleLossCompute(model.generator, criterion, None)))" 2585 | ] 2586 | }, 2587 | { 2588 | "cell_type": "code", 2589 | "execution_count": 4, 2590 | "metadata": {}, 2591 | "outputs": [ 2592 | { 2593 | "data": { 2594 | "text/plain": [ 2595 | "22365" 2596 | ] 2597 | }, 2598 | "execution_count": 4, 2599 | "metadata": {}, 2600 | "output_type": "execute_result" 2601 | }, 2602 | { 2603 | "data": { 2604 | "image/png": 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o63s9sBnwQGZ2++LRrWZYEXHrMJPeOJp6SZIkaTqYzD3/ZwFvBq7JzG+29W8A/gqYC2xfHgfQnCx8IPDtEvhbZpd27TDLafVvN8EaSZIkaaBNyp7/iPgEzQm6PwY+1D4tMx8FPttRsioiDqE5EfetwMeA8yZj3cYjM+d26y+/COw9xasjSZIkjUvP9/xHxPE0wf1O4B2Z+eRo6srhOV8uT/dvm9TaSz+b7lr9T0+wRpIkSRpoPQ3/EXEicAHNtfrfUa74MxaPlfbXh/1k5nrg58A2EbFzl5rdS3tPW9/9wAvAbuXcg9HUSJIkSQOtZ+E/Ij4FnAv8kCb4PzqO2exX2gc6+leW9tAuNYd1jKFc2nM1sDXwh6OpkSRJkgZdT8J/RHyG5gTfW2luqPX4CGP3joiXLDciDgZOKk8v65jcul/AaRGxfVvNHOA44Hngko6aL5X2jHLpz1bNvjR3+X2Ml954TJIkSRpYEz7hNyIWAJ+jOczmRuATEdE5bCgzl5Z/nwPsHhGrgYdK31785pr7n8nM1e3Fmbk6Is4BTgZuj4jlwBY0IX4H4ISOu/sCLKO5s/B84LaIWAHsWGo2A47OzGfG+7olSZKkmaYXV/vZtbSbAScOM+Y7wNLy70uB9wP70hx+83LgF8DXgCWZ2e2mXGTmwoj4Ec2e/mOAF4E1wNmZeXWX8RkRf0xz+M9HgROA54BVwBmdXzAkSZKkQTfh8J+Zi4BFYxj/FeAr41zWUn7zJWI04zfSnIdw7niWJ0mSJA2SybzJlyRJkqRpxPAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVYsLhPyJ2jIiPRcSVEXFfRDwbEWsj4qaI+LOI6LqMiJgXEddExJOl5vaIODEiNhthWYdHxA1l/usi4vsRsWAT67cgIm4u49eW+sMn+rolSZKkmaYXe/4/CFwMvBX4PvBF4ArgzcCXga9FRLQXRMR7gVXA/sCVwBJgC+BcYFm3hUTE8cCKMt/LyjJfCyyNiMXD1CwGlgI7l/GXAb8HrCjzkyRJkqqxeQ/mcQ/wHuAbmfliqzMiTgVuBo4APkDzhYCI2JYmiL8AHJiZt5T+zwArgfkRcVRmLmub1xxgMfAksE9mDpX+zwE/ABZGxBWZ+b22mnnAQuB+YN/MfKr0nw3cCiyOiKtb85IkSZIG3YT3/Gfmysxc0R78S/8jwEXl6YFtk+YDOwHLWsG/jH8OOL08/XjHYj4KbAksaQ/rJdB/vjw9tqOm9fzMVvAvNUPAhWV+H9n0K5QkSZIGw2Sf8Ptvpd3Y1ndQaa/rMn4VsAGYFxFbjrLm2o4xE6mRJEmSBtakhf+I2Bz4cHnaHsDfUNp7OmsycyPwIM3hSLuNsuZhYD2wS0RsXZY9C3gdsK5M73RvafcY1YuRJEmSBkAvjvkfzlk0J+dek5nfbOufXdq1w9S1+rcbY82sMm7DOJcxrIi4dZhJbxxNvSRJkjQdTMqe/4j4BM3Jtj8GPjQZy5AkSZI0Nj3f818uoXkecCdwcGY+2TGktdd9Nt21+p/uqHllmfbECDVrO9qxLGNYmTm3W3/5RWDv0cxDkiRJ6ree7vmPiBOBC4A7gHeUK/50uru0LznevpwnsCvNCcIPjLJmZ5pDfh7KzA0Ambke+DmwTZneaffSvuQcAkmSJGlQ9Sz8R8SnaG7S9UOa4P/oMENXlvbQLtP2B7YGVmfm86OsOaxjzERqJEmSpIHVk/BfbtB1Fs3Nsw7OzMdHGL4ceBw4KiL2aZvHVsAZ5emXOmouAZ4Hji83/GrVbA+cWp5e1FHTen5aGdeqmQMcV+Z3ycivTJIkSRocEz7mPyIWAJ+juWPvjcAnIqJz2FBmLgXIzGci4miaLwE3RMQymjv3vofmkp7LgcvbizPzwYj4JHA+cEtEXA78iuaGYbsAX2i/u2+pWR0R5wAnA7dHxHJgC+BIYAfgBO/uK0mSpJr04oTfXUu7GXDiMGO+AyxtPcnMqyLiAOA04AhgK+A+mqB+fmZm5wwy84KIGAJOobl/wMtoTio+PTO/2m2hmbkwIn5Es6f/GOBFYA1wdmZePbaXKUmSJM1sEw7/mbkIWDSOuu8C7xxjzQpgxRhrltL2xUOSJEmq1aTd4VeSJEnS9GL4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkiph+JckSZIqYfiXJEmSKmH4lyRJkirRk/AfEfMj4oKIuDEinomIjIjLhhk7p0wf7rFshOUsiIibI2JdRKyNiBsi4vARxm8WESdFxO0R8WxEPBkR10TEvF68bkmSJGkm2bxH8zkd+I/AOuAh4I2jqPkX4Kou/Xd0GxwRi4GFZf4XA1sARwErIuKEzFzSMT6AZcB84G5gCbADcCSwKiKOyMyvj2I9JUmSpIHQq/B/Ek0ovw84ALh+FDU/zMxFo5l52VO/ELgf2Dcznyr9ZwO3Aosj4urMHGorO4om+K8GDs7M50rNRcBNwMURsTIzfzmadZAkSZJmup4c9pOZ12fmvZmZvZhfF8eW9sxW8C/LHQIuBLYEPtJR8/HSnt4K/qXmB8DlwE40Xw4kSZKkKvTzhN/XRsSfR8Sppd1rhLEHlfa6LtOu7RhDRGwFzAM2ADeOpkaSJEkadL067Gc8/qg8fi0ibgAWZOZP2/pmAa8D1mXmw13mc29p92jrez2wGfBAZm4cZc2wIuLWYSaN5twGSZIkaVrox57/DcBfAXOB7cujdZ7AgcC3S+BvmV3atcPMr9W/3QRrJEmSpIE25Xv+M/NR4LMd3asi4hCaE3HfCnwMOG+q1204mTm3W3/5RWDvKV4dSZIkaVymzU2+yuE5Xy5P92+b1NpLP5vuWv1PT7BGkiRJGmjTJvwXj5X214f9ZOZ64OfANhGxc5ea3Ut7T1vf/cALwG4R0e3XjW41kiRJ0kCbbuF/v9I+0NG/srSHdqk5rGMM5dKeq4GtgT8cTY0kSZI06KY8/EfE3hHxkuVGxME0NwsDuKxj8kWlPS0itm+rmQMcBzwPXNJR86XSnlEu/dmq2ZfmLr+PAVeM71VIkiRJM09PTviNiPcB7ytPX1Pat0XE0vLvxzPzlPLvc4DdI2I1zV2BAfbiN9fc/0xmrm6ff2aujohzgJOB2yNiObAFTYjfATih4+6+AMuAD9DcyOu2iFgB7FhqNgOOzsxnxv+qJUmSpJmlV1f7+X1gQUffbuUB8BOgFf4vBd4P7Etz+M3LgV8AXwOWZGa3m3KRmQsj4kc0e/qPAV4E1gBnZ+bVXcZnRPwxzeE/HwVOAJ4DVgFndH7BkCRJkgZdT8J/Zi4CFo1y7FeAr4xzOUuBpWMYvxE4tzwkSZKkqk23E34lSZIkTRLDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVInN+70C0kw259Pf6No/dNa7pnhNJEmSNs09/5IkSVIl3PMvjcJwe/glSZJmEvf8S5IkSZUw/EuSJEmVMPxLkiRJlTD8S5IkSZUw/EuSJEmVMPxLkiRJlTD8S5IkSZXwOv/SJPDOv5IkaTpyz78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVInN+70CUk3mfPobXfuHznrXFK+JJEmqkXv+JUmSpEoY/iVJkqRK9CT8R8T8iLggIm6MiGciIiPisk3UzIuIayLiyYh4NiJuj4gTI2KzEWoOj4gbImJtRKyLiO9HxIJNLGdBRNxcxq8t9YeP97VKkiRJM1Wv9vyfDhwP/D7w800Njoj3AquA/YErgSXAFsC5wLJhao4HVgBvBi4DLgZeCyyNiMXD1CwGlgI7l/GXAb8HrCjzkyRJkqrRq/B/ErAHsC3w8ZEGRsS2NEH8BeDAzPyzzPwkzReH7wHzI+Kojpo5wGLgSWCfzDwuM08C9gLuBxZGxNs6auYBC8v0vTLzpMw8Dphb5rO4zFeSJEmqQk/Cf2Zen5n3ZmaOYvh8YCdgWWbe0jaP52h+QYCXfoH4KLAlsCQzh9pqngI+X54e21HTen5mGdeqGQIuLPP7yCjWV5IkSRoI/Tjh96DSXtdl2ipgAzAvIrYcZc21HWMmUiNJkiQNrH5c5/8Npb2nc0JmboyIB4E3AbsBd42i5uGIWA/sEhFbZ+aGiJgFvA5Yl5kPd1mHe0u7x2hWOCJuHWbSG0dTL0mSJE0H/djzP7u0a4eZ3urfbhw1szvasSxDkiRJGmje4XcUMnNut/7yi8DeU7w6kiRJ0rj0Y89/5176Tq3+p8dRs7ajHcsyJEmSpIHWj/B/d2lfcrx9RGwO7ApsBB4YZc3OwCzgoczcAJCZ62nuN7BNmd5p99K+5BwCSZIkaVD1I/yvLO2hXabtD2wNrM7M50dZc1jHmInUSJIkSQOrH8f8Lwf+BjgqIi5oXes/IrYCzihjvtRRcwnw34HjI+KS1rX+I2J74NQy5qKOmouADwGnRcRVrWv9lxt7HQc8X+Yr9d2cT3+ja//QWe+a4jWRJEmDrCfhPyLeB7yvPH1Nad8WEUvLvx/PzFMAMvOZiDia5kvADRGxjOaOu++huaTncuDy9vln5oMR8UngfOCWiLgc+BXNDcN2Ab6Qmd/rqFkdEecAJwO3R8RyYAvgSGAH4IT2G4ZJkiRJg65Xe/5/H1jQ0bdbeQD8BDilNSEzr4qIA4DTgCOArYD7aIL6+d3uFJyZF0TEUJnPh2kOWboTOD0zv9ptpTJzYUT8iGZP/zHAi8Aa4OzMvHp8L1WSJEmamXoS/jNzEbBojDXfBd45xpoVwIox1iwFlo6lRpIkSRpEXudfKoY77l6SJGlQ9ONqP5IkSZL6wPAvSZIkVcLwL0mSJFXC8C9JkiRVwhN+pWnMm39JkqRecs+/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJw78kSZJUCcO/JEmSVAnDvyRJklQJb/IlzUDe/EuSJI2He/4lSZKkShj+JUmSpEp42I80QDwcSJIkjcQ9/5IkSVIlDP+SJElSJQz/kiRJUiUM/5IkSVIlDP+SJElSJQz/kiRJUiUM/5IkSVIlDP+SJElSJQz/kiRJUiUM/5IkSVIlDP+SJElSJTbv9wpImnxzPv2Nrv1DZ71ritdEkiT1k3v+JUmSpEoY/iVJkqRKGP4lSZKkShj+JUmSpEoY/iVJkqRKGP4lSZKkSnipT6liXgJUkqS6uOdfkiRJqoThX5IkSaqE4V+SJEmqhOFfkiRJqkTfTviNiCHg3w8z+ReZ+ZouNfOA04H9gFcA9wJ/B1yQmS8Ms5zDgVOAtwCbAf8/8L8y86sTfQ3SoPJEYEmSBlO/r/azFvhil/51nR0R8V7gCuBPZzYqAAAPI0lEQVQ54HLgSeDdwLnA24EPdqk5HrgAeAK4DPgVMB9YGhG/l5mn9OZlSJIkSdNfv8P/05m5aFODImJb4GLgBeDAzLyl9H8GWAnMj4ijMnNZW80cYDHNl4R9MnOo9H8O+AGwMCKuyMzv9fIFSYNsuF8EwF8FJEmaCWbKMf/zgZ2AZa3gD5CZz9EcBgTw8Y6ajwJbAktawb/UPAV8vjw9drJWWJIkSZpu+r3nf8uI+BPg3wHrgduBVV2O3z+otNd1mccqYAMwLyK2zMznR1FzbccYSZIkaeD1O/y/Bri0o+/BiPhIZn6nre8Npb2ncwaZuTEiHgTeBOwG3DWKmocjYj2wS0RsnZkbRlrJiLh1mElvHKlOkiRJmk76Gf4vAW6kufrOL2mC+/HAMcC1EfG2zPyXMnZ2adcOM69W/3ZtfaOpmVXGjRj+NVhGOm5dkiRpkPUt/Gfm/+zougM4NiLWAQuBRcD7p3q9usnMud36yy8Ce0/x6kiSJEnjMh1P+L2otPu39bX23s+mu1b/0+OoGe6XAUmSJGmg9PuY/24eK+2str67gX2APYDfOv4+IjYHdgU2Ag901Lyy1Hyvo2bnMv+HNnW8v6TR8cZgkiRNf9Nxz/9+pW0P8itLe2iX8fsDWwOr2670s6mawzrGSJIkSQOvL+E/IvaMiFld+ucAS8rTy9omLQceB46KiH3axm8FnFGefqljdpcAzwPHl/m2arYHTi1PL0KSJEmqRL8O+zmS5g67q4Cf0Fzt5/XAu4CtgGto7s4LQGY+ExFH03wJuCEiltHcufc9NJf0XA5c3r6AzHwwIj4JnA/cEhGXA7+iuWHYLsAXvLuv1D8eJiRJ0tTrV/i/nia0vwV4O83x908DN9Fc9//SzMz2gsy8KiIOAE4DjqD5knAfcDJwfuf4UnNBRAwBpwAfpvml407g9Mz86uS8NEmSJGl66kv4Lzfw+s4mB7607rvAO8dYswJYMdZlSZIkSYNmOl7tR9IA8aZqkiRNH9Pxaj+SJEmSJoF7/iVNK54ILEnS5HHPvyRJklQJw78kSZJUCQ/70aQb6wmfHt4hSZI0OdzzL0mSJFXCPf+SZoReXTLUX5YkSTVzz78kSZJUCff8a9rp1TkC3lxKkiTpt7nnX5IkSaqEe/7VM+5plyRJmt4M/5rx/NKhsRjrHYS947AkaZB42I8kSZJUCcO/JEmSVAkP+5EkPHxMklQHw78kjcNIXxY8H0CSNF152I8kSZJUCff8a8w8PEKSJGlmMvxLUo95eVBJ0nRl+JekPvPeA5KkqWL4l6Qp4iFzkqR+M/xL0oDwFwFJ0qYY/iVpmvKXAklSr3mpT0mSJKkShn9JkiSpEh72I0mV8hwBSaqP4V+SBlyvzh0Y63z8EiFJ04/hX5L0W6biRGNPZpak/vCYf0mSJKkShn9JkiSpEh72I0maFB7aI0nTj3v+JUmSpEoY/iVJkqRKGP4lSZKkShj+JUmSpEp4wq+G5cl6kiRJg8U9/5IkSVIlDP+SJElSJQz/kiRJUiUGPvxHxC4R8XcR8a8R8XxEDEXEFyNi+36vmyRJkjSVBvqE34h4PbAaeBXwdeDHwB8A/w04NCLenplP9HEVJUmSpCkz6Hv+/xdN8P9EZr4vMz+dmQcB5wJvAM7s69pJkiRJU2hg9/yXvf6HAEPAhR2T/xI4BvhQRCzMzPVTvHrThpfzlCRJqscg7/l/R2m/lZkvtk/IzF8C3wW2Bvab6hWTJEmS+mFg9/zTHNYDcM8w0++l+WVgD+DbU7JGfeQefkmSJA1y+J9d2rXDTG/1b7epGUXErcNM+o933XUXc+fOHeu6TZo7fj7cy5WkmWvuP32236sgSX1z1113AczpxbwGOfxPhReeffbZtWvWrBma4uW+sbQ/nuLlamq5nevgdh6FNb/o9xpMiNu4Dm7nOvRrO88BnunFjAY5/Ld2gc8eZnqr/+lNzSgzp8+ufX7zS8R0Wy/1ltu5Dm7nwec2roPbuQ6DsJ0H+YTfu0u7xzDTdy/tcOcESJIkSQNlkMP/9aU9JCJ+63VGxO8Abwc2AP9nqldMkiRJ6oeBDf+ZeT/wLZpjpI7rmPw/gVnApTVf41+SJEl1GeRj/gH+AlgNnB8RBwN3AW+luQfAPcBpfVw3SZIkaUoN7J5/+PXe/32ApTShfyHweuA8YL/MfKJ/aydJkiRNrcjMfq+DJEmSpCkw0Hv+JUmSJP2G4V+SJEmqhOFfkiRJqoThX5IkSaqE4V+SJEmqhOFfkiRJqoThX5IkSaqE4X8GiYhdIuLvIuJfI+L5iBiKiC9GxPb9Xjf9tojYMSI+FhFXRsR9EfFsRKyNiJsi4s8iout7LyLmRcQ1EfFkqbk9Ik6MiM1GWNbhEXFDmf+6iPh+RCyYvFenkUTEn0RElsfHhhkz5m0WEQsi4uYyfm2pP3xyXoWGExEHl/f1I+Vz+F8j4psR8c4uY30/zzAR8a6I+FZEPFS22QMR8Y8R8bZhxruNp6GImB8RF0TEjRHxTPk8vmwTNVOyLafFZ3lm+pgBD5o7E/8CSOAq4CxgZXn+Y2DHfq+jj9/aXseWbfOvwD8Afw38HfB06V9OucleW817gY3AOuArwNll2ybwj8Ms5/gy/XHgQuBc4Gelb3G//w61PYDfLdv4l2UbfKwX2wxYXKb/rIy/EHii9B3f79ddywP427bt8L+BzwMXA2uAv+0Y6/t5hj2Av2n7+3+5/H92OfAr4EXgT9zGM+MB/LD8TX8J3FX+fdkI46dkW06Xz/K+byAfo9xQ8M3yH8cJHf3nlP6L+r2OPn5ruxwEvBt4WUf/a4Cflm12RFv/tsCjwPPAPm39WwGry/ijOuY1B3iufHDMaevfHriv1Lyt33+LWh5AAP8M3F/+x/GS8D+ebQbMK/33Adt3zOuJMr85k/W6fPz673102Q5LgS26TH952799P8+wR/lsfgF4BHhVx7R3lL//A27jmfEo22z38rl8ICOE/6naltPps9zDfmaAiHg9cAgwRPMtsd1fAuuBD0XErCleNQ0jM1dm5orMfLGj/xHgovL0wLZJ84GdgGWZeUvb+OeA08vTj3cs5qPAlsCSzBxqq3mKZo8kNL9AaGp8guZL30do3pPdjGebtZ6fWca1aoZoPg+2LMvUJImILYEzab64H5OZv+ock5n/1vbU9/PM8+9pDoX+fmY+2j4hM6+n2YO8U1u323gay8zrM/PeLOl6E6ZqW06bz3LD/8zwjtJ+q0uY/CXwXWBrYL+pXjGNSyskbGzrO6i013UZvwrYAMwrIWQ0Ndd2jNEkiog9aQ4ROC8zV40wdDzbzO3cf39EEw7+X+DFclz4pyLivw1zLLjv55nnXprDe/4gIl7ZPiEi9gd+h+aXvRa38eCYqm05bba/4X9meENp7xlm+r2l3WMK1kUTEBGbAx8uT9s/AIbdxpm5EXgQ2BzYbZQ1D9Psfd4lIrae4GprBGWbXkqzV/jUTQwf0zYrv+a9DlhXpnfyvT819i3tc8BtwNU0X/a+CKyOiO9ERPteYd/PM0xmPgl8Cng1cGdE/O+I+OuI+BrwLeCfgD9vK3EbD45J35bT7bPc8D8zzC7t2mGmt/q3m4J10cScBbwZuCYzv9nWP55tPNqa2cNMV298FngL8KeZ+ewmxo51m/nenx5eVdpP0hyz+4c0e4L3ogmG+wP/2Dbe9/MMlJlfBD5AE/SOBj4NfJDm5MylHYcDuY0Hx1Rsy2n1WW74l6ZIRHwCWEhzBYEP9Xl11AMR8Vaavf1fyMzv9Xt9NGla/6/cCLwnM2/KzHWZ+SPg/cBDwAHDXQ5SM0NE/Heaq/sspbnC3ixgLvAA8A8R8bf9Wzupdwz/M8Om9ga0+p+egnXROETE8cB5wJ3AO8pPzO3Gs41HWzPcngZNQDnc5+9pfvb9zCjLxrrNfO9PD62/723tJ/cBZOYGmquxAfxBaX0/zzARcSDNpT7/v8w8OTMfyMwNmbmG5gvez4GFEdE69MNtPDimYltOq89yw//McHdphzsWbPfSDndOgPooIk4ELgDuoAn+j3QZNuw2LiFzV5q9jg+MsmZnmr1WD5Vwot7bhuZvvyfwXNuNvZLmKlwAF5e+L5bnY9pmmbmeJnRsU6Z38r0/NVrbbbj/Mbeu3PGKjvG+n2eO1k2Wru+cUP7mN9NkpreUbrfx4Jj0bTndPssN/zND68PokOi4M2xE/A7wdpqz0f/PVK+YRhYRn6K5kccPaYL/o8MMXVnaQ7tM25/mak6rM/P5UdYc1jFGvfc8zc1guj1uK2NuKs9bhwSNZ5u5nfvv2zTH+v8/nZ/BxZtL+2BpfT/PPK0ruew0zPRWf+syr27jwTFV23L6bP+puJmAj4k/8CZfM+5BcyhIArcAO2xi7LbAY4ztJiO74g1jpuUDWET3m3yNeZsxjW4MU/MD+HrZDid19B9Cc/fXp4DZpc/38wx7AP+1/I0fAV7XMe2wso2fBXZ0G8+sB6O7ydekb8vp9FkeZcGa5sqNvlbTXHXi6zS3q34rzT0A7gHmZeYT/VtDtYuIBTQnjb1Ac8hPt2M4hzJzaVvN+2hONnsOWAY8CbyH5pJiy4H/mh1v2Ig4ATif5oPjcpq9UvOBXWhOQj2ll69LoxMRi2gO/Tk6M7/cMW3M2ywivgCcTHNi6XJgC+BIYEeaHQJLJu3FCICI2IXmM/h3aX4JuI0mALyP34SDK9rG+36eQcovOt8E/jPNDb2upPkisCfNIUEBnJiZ57XVuI2nqbJt3leevgb4LzSH7dxY+h5v/1tP1bacNp/l/f5G5mP0D5r/6VwCPFz+I/sJzXWmt+/3uvl4ybZaRBMIRnrc0KXu7cA1NHsRnwV+BJwEbDbCst4NfIfmf1jrgR8AC/r9N6j5wTB7/ieyzYA/LePWl7rvAIf3+7XW9KA59OOC8tn7K+BxmpD4B8OM9/08gx7Ay4ETaQ6hfYbmOO9Hae7rcIjbeOY8RvH/4KF+bcvp8Fnunn9JkiSpEp7wK0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRVwvAvSZIkVcLwL0mSJFXC8C9JkiRV4v8CaFGF2bWsbwUAAAAASUVORK5CYII=\n", 2605 | "text/plain": [ 2606 | "
" 2607 | ] 2608 | }, 2609 | "metadata": { 2610 | "image/png": { 2611 | "height": 250, 2612 | "width": 383 2613 | }, 2614 | "needs_background": "light" 2615 | }, 2616 | "output_type": "display_data" 2617 | } 2618 | ], 2619 | "source": [ 2620 | "MAX_LEN = 512\n", 2621 | "TEXT = data.Field(tokenize=tokenizer.tokenize, \n", 2622 | " batch_first=True,\n", 2623 | " init_token='[CLS]',\n", 2624 | " eos_token='[SEP]', \n", 2625 | " pad_token='[PAD]',\n", 2626 | " unk_token='[UNK]', \n", 2627 | " preprocessing=lambda x: x[0:MAX_LEN-2])\n", 2628 | "LABEL = data.Field(sequential=False)\n", 2629 | "\n", 2630 | "train, val = datasets.IMDB.splits(text_field=TEXT, \n", 2631 | " label_field=LABEL)\n", 2632 | "\n", 2633 | "MIN_FREQ = 3\n", 2634 | "TEXT.build_vocab(train, min_freq=MIN_FREQ)\n", 2635 | "LABEL.build_vocab(train)\n", 2636 | "\n", 2637 | "plot_lengths(train)\n", 2638 | "len(TEXT.vocab) # 22365" 2639 | ] 2640 | }, 2641 | { 2642 | "cell_type": "code", 2643 | "execution_count": 13, 2644 | "metadata": {}, 2645 | "outputs": [], 2646 | "source": [ 2647 | "orig_train_examples = copy.deepcopy(train.examples)\n", 2648 | "orig_val_examples = copy.deepcopy(val.examples)" 2649 | ] 2650 | }, 2651 | { 2652 | "cell_type": "code", 2653 | "execution_count": 9, 2654 | "metadata": {}, 2655 | "outputs": [], 2656 | "source": [ 2657 | "def get_auto_datasets(size=512):\n", 2658 | " tr = [ex for ex in orig_train_examples if len(ex.text) < size-1]\n", 2659 | " vl = [ex for ex in orig_val_examples if len(ex.text) < size-1]\n", 2660 | " exs = tr + vl\n", 2661 | " X_tr, X_vl = train_test_split(exs, test_size=0.1, random_state=42)\n", 2662 | " train.examples = X_tr\n", 2663 | " val.examples = X_vl\n", 2664 | " return train, val" 2665 | ] 2666 | }, 2667 | { 2668 | "cell_type": "code", 2669 | "execution_count": 11, 2670 | "metadata": {}, 2671 | "outputs": [ 2672 | { 2673 | "data": { 2674 | "text/plain": [ 2675 | "(3312, 368)" 2676 | ] 2677 | }, 2678 | "execution_count": 11, 2679 | "metadata": {}, 2680 | "output_type": "execute_result" 2681 | }, 2682 | { 2683 | "data": { 2684 | "image/png": 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\n", 2685 | "text/plain": [ 2686 | "
" 2687 | ] 2688 | }, 2689 | "metadata": { 2690 | "image/png": { 2691 | "height": 250, 2692 | "width": 377 2693 | }, 2694 | "needs_background": "light" 2695 | }, 2696 | "output_type": "display_data" 2697 | } 2698 | ], 2699 | "source": [ 2700 | "f_train, f_val = get_auto_datasets(100)\n", 2701 | "plot_lengths(f_train)\n", 2702 | "plot_lengths(f_val)\n", 2703 | "len(f_train), len(f_val)" 2704 | ] 2705 | }, 2706 | { 2707 | "cell_type": "code", 2708 | "execution_count": 8, 2709 | "metadata": {}, 2710 | "outputs": [], 2711 | "source": [ 2712 | "pad_idx = TEXT.vocab.stoi[\"[PAD]\"]\n", 2713 | "\n", 2714 | "criterion = LabelSmoothing(size=len(TEXT.vocab), padding_idx=pad_idx, smoothing=0.1)\n", 2715 | "criterion.to(device)\n", 2716 | "\n", 2717 | "model = torch.load('./trauto_2019-05-14-01:21:08_520.pth')\n", 2718 | "# model = make_model(len(TEXT.vocab), len(TEXT.vocab), N=6)\n", 2719 | "model.to(device) \n", 2720 | "if n_gpu > 1: \n", 2721 | " model_par = nn.DataParallel(model).to(device)\n", 2722 | "\n", 2723 | "model_opt = NoamOpt(model.src_embed[0].d_model, 1, 2000,\n", 2724 | " torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))" 2725 | ] 2726 | }, 2727 | { 2728 | "cell_type": "code", 2729 | "execution_count": 13, 2730 | "metadata": {}, 2731 | "outputs": [], 2732 | "source": [ 2733 | "BATCH_SIZE = 10000\n", 2734 | "train_iter = MyIterator(f_train, batch_size=BATCH_SIZE, device=device,\n", 2735 | " repeat=False, sort_key=lambda x: (len(x.text)),\n", 2736 | " batch_size_fn=batch_size_fn, train=True)\n", 2737 | "valid_iter = MyIterator(f_val, batch_size=BATCH_SIZE, device=device,\n", 2738 | " repeat=False, sort_key=lambda x: (len(x.text)),\n", 2739 | " batch_size_fn=batch_size_fn, train=False)" 2740 | ] 2741 | }, 2742 | { 2743 | "cell_type": "code", 2744 | "execution_count": 5, 2745 | "metadata": {}, 2746 | "outputs": [], 2747 | "source": [ 2748 | "tr_losses = []\n", 2749 | "vl_losses = []" 2750 | ] 2751 | }, 2752 | { 2753 | "cell_type": "code", 2754 | "execution_count": 9, 2755 | "metadata": {}, 2756 | "outputs": [ 2757 | { 2758 | "name": "stdout", 2759 | "output_type": "stream", 2760 | "text": [ 2761 | "Epoch: 1\n" 2762 | ] 2763 | }, 2764 | { 2765 | "name": "stderr", 2766 | "output_type": "stream", 2767 | "text": [ 2768 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/nn/parallel/_functions.py:61: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", 2769 | " warnings.warn('Was asked to gather along dimension 0, but all '\n" 2770 | ] 2771 | }, 2772 | { 2773 | "name": "stdout", 2774 | "output_type": "stream", 2775 | "text": [ 2776 | "Epoch Step: 1 Loss: 1.378235 Tokens per Sec: 1592.000000\n", 2777 | "Epoch Step: 101 Loss: 1.870785 Tokens per Sec: 6227.000000\n", 2778 | "Epoch Step: 301 Loss: 1.819553 Tokens per Sec: 6266.000000\n", 2779 | "Epoch Step: 401 Loss: 1.803898 Tokens per Sec: 6214.000000\n", 2780 | "Epoch Step: 501 Loss: 3.167712 Tokens per Sec: 6162.000000\n", 2781 | "Epoch Step: 601 Loss: 2.758060 Tokens per Sec: 6205.000000\n", 2782 | "Epoch Step: 701 Loss: 1.498593 Tokens per Sec: 6217.000000\n", 2783 | "Epoch Step: 801 Loss: 1.354508 Tokens per Sec: 6194.000000\n", 2784 | "True:\tjohn wayne is without a doubt one of the most popular and loved actors of all time . his career stretched over forty years , and within that time he starred in films such as \" angel and the bad ##man \" , \" the green be ##ret ##s \" , \" sands of i ##wo jim ##a \" , \" rio bravo \" , \" north to alaska \" , and \" the [UNK] \" . < br / > < br / > the film ' s listed above are hailed as some of his best , unlike this 1934 effort \" randy rides alone \" , which has been pretty much forgotten about as time ' s gone on , which is un ##sur ##pr ##ising , as it ' s nothing memorable apart from its very short running time of just 53 minutes . < br / > < br / > a young john wayne plays randy bow ##ers , who for reasons never really explained , arrives at a saloon in the middle of nowhere and finds that everyone inside has been killed . while looking around , a posse arrives and finds randy there and they arrest him , accusing him of being a gang member and demand to know where the rest of his gang is . he is put in jail accused of the murders . sally rogers , whose uncle owned the saloon and was murdered , arrives at the jail to see randy in order to clarify that he was one of the gang members ( she was hiding in a secret room when the shooting took place ) . sally doesn ' t believe that randy is a killer , and doesn ' t recognise him , so while the sheriff is out , she slips him the keys and randy escapes . while running away from the sheriff and his posse , randy convenient ##ly stumble ##s into the gang ' s hideout in a cave who were responsible for the murders . randy sets out to clear his name , and also to bring the gang to justice . < br / > < br / > \" randy rides alone \" can be a fun film to watch , especially if you ' re a john wayne fan . but at the same time it has far too many flaws that are impossible to ignore . the film is also extremely dated , as you would expect ; we have the terrible camera shooting which makes everyone look like they are moving in super - fast motion , and the dialogue is terrible . the acting isn ' t great either , and wayne ' s character is very wooden and he , along with the rest of the cast , look like wooden puppets who are being conducted by someone ( in this case it ' s by director harry fraser ) . harry fraser is at the helm , and does a good [SEP] \n", 2785 | "Predicted:\tjohn wayne ' s \" the trouble \" is that john ford , who , as a fan of his work , is one of the most under ##rated actors of all time . he ' s been in the \" home \" and \" the last great \" films , and i ' ve seen him in \" the rain \" and \" the incredible bad guy \" . < br / > < br / > the film begins with john wayne , and his brother , randy ( randy ) , who is also a pretty good looking guy , and has been told by a gang of thugs . he is about to be at the top of his game , which is apparently un ##com ##men ##dable , and his brother , randy , and his sister , and his sister , and brother , randy . they are also very much in place of the gang , which looks like a lot of people are being held apart by the gang . < br / > < br / > the rest of the cast are pretty much wasted in such films as \" randy \" , \" randy rides \" , and \" the gang \" . this is a very hard time capsule of the original film , which is about as far as i can tell . it ' s not that anyone else has ever been in a film that looks like a randy qu \n", 2786 | "------------------------------\n", 2787 | "True:\tthis is slightly less sick ##ening than the first two films , but otherwise it ' s business as usual : a sc ##uz ##zy , sl ##ea ##zy and un ##balance ##d slice of disease ##d cinema . charles bro ##nson is back , blasting into action when his friend is killed by yo ##bs terror ##ising the neighbourhood . crime , you see , is up 11 % in the south belmont area . . . so what ' s to be done ? a stronger police presence ? tough ##er jail ##s ? harsh ##er sentences ? nope , the only solution is to send in a loose cannon like bro ##nson to met ##e out blood ##thi ##rst ##y revenge or , as the writers would have it , justice : this time he ' s the personal killing machine of police chief ed lau ##ter . < br / > < br / > the writers bend over backwards to make ke ##rse ##y the hero , sending the useless cops into the area only to con ##fi ##sca ##te a weapon from an elderly resident who keeps it for protection , and supplying a scene in which ke ##rse ##y has his camera stolen and shoots the thief right in the back , to applause from the watching crowd . capital punishment for theft ? well , okay . the attitude of everyone in the film is that this is a solution , and the dish ##ones ##t twisting of the characters into cipher ##s who exist only to cheer ke ##rse ##y on or back him up is app ##all ##ing . < br / > < br / > sure , these villains are sc ##um , but shouldn ' t the film leave the audience to make up its mind , rather than slant ##ing the entire thing towards ke ##rse ##y and his mind ##less answer ? fun ##ni ##ly enough the bel ##ea ##gue ##red residents don ' t fear gang rep ##ris ##als or blame ke ##rse ##y for any of the violence , which is odd as one character is killed precisely because of ke ##rse ##y ' s involvement . at the end of the film they all take guns from their sock drawers and glee ##fully join in with the massacre , never stopping to think things through or struggle with the thought of having to kill another human being . < br / > < br / > the at ##ro ##cious ##ly shallow performances don ' t help bro ##nson has literally one facial expression throughout and can ' t even put in ##fle ##ction on the right words . new heights of stupidity are reached here a machine gun ? a rocket launcher ? ! and new low ##s of mis ##ogy ##ny : the movie con ##tri ##ves to des ##ec ##rate every female character in sight , whether by rape , explosion or throat - slash ##ing ; and it [SEP] \n", 2788 | "Predicted:\tthis is a crime movie , but it is only the second time in its execution . the plot is pure exploitation . the action is pure and un ##in ##vo ##lved for a sl ##ea ##zy , and un ##in ##hi ##bit ##ed , and the violence is rather than death wish . the film is set in new york city , which is why it has to be taken as a whole , so why would you want to make a movie like this ? the first thing is , it ' s a lo ##ony crime scene , which is actually quite right from the start . < br / > < br / > the film opens with a bunch of people who are watching a bunch of lo ##bot ##omi ##zed by the police , who ' s having fun watching him . the police don ' t take the time to kill , because of his own , or at least , when they ' re being attacked by the police ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? well , the villains are all rep ##ulsive , especially when they ' re not as lo ##ath ##some as the villains . the villains are more than menacing , and the villains are more than menacing . one of the villains is the villains who ' ve got \n", 2789 | "------------------------------\n", 2790 | "True:\tlos angeles physician tom reed ( vincent vent ##res ##ca ) survives a tragic auto accident but ends up going to prison in the high desert of california . when his time is up he lands in a small , wind - swept town named pu ##rga ##tory flats . his first stop is a bar , where he quickly slams a beer and gets hired as a bartender . so much for ex - con ##s having a tough time finding work . this is the first in a long line of absurd ##ities that make up the plot of writer / director harris done ' s silly attempt at modern , desert - set , film noir . < br / > < br / > his first night on the job tom meets a sexy femme fatal named sunny ( alexandra holden ) , who hangs out with a family of bad boys : the me ##ck ##lins , consisting of uncle dean ( gregg henry ) and his two nephew ##s , the drug addicted owen ( kevin alejandro ) , who is sunny ' s husband , and aw ##ol soldier randy ( \" 90 ##21 ##0 \" s brian austin greer ) . after his shift is finished there ' s a shooting , and dr . tom just happens to be nearby . he agrees to treat one of the wounded and , most importantly , not tell the cops . i ' m not sure that ' s a good move for a guy fresh out of the pen , but this script ( co - written by diane fine ) has very little to do with logic . < br / > < br / > tom makes a series of poor decisions that get him further and further en ##tangled with the criminal ##ly inclined me ##ck ##lin boys , including stealing medical supplies and hopping into the sack with sunny . everybody in the theater is screaming , \" don ' t do it ! walk away \" , but tom does it anyway . < br / > < br / > in a classic film noir like \" body heat \" or \" double ind ##em ##nity \" we root for and em ##path ##ize with william hurt and fred mc ##mur ##ray as they get sucked into the web of bright , sexy , devi ##ous femme fatal ##s like kathleen turner and barbara stan ##wy ##ck . it ' s not their fault . we ' d probably be tempted by all that money or that particular dame , too . but dr . tom ' s weakness seems to stem from stupidity more than circumstances . sunny is sexy but not a very compelling character , and there ' s no money to te ##mpt him . you ' re left wondering if he attended the same medical school as dr . nick river ##ia from \" the simpsons \" . < br / [SEP] \n" 2791 | ] 2792 | }, 2793 | { 2794 | "name": "stdout", 2795 | "output_type": "stream", 2796 | "text": [ 2797 | "Predicted:\tfirst , tom brown ( tom mc ##int ##ire ) is a tough , tough , tough - guy who is a loser . he is a tough guy who is a victim of a small town in the desert . he ' s a tough guy , but he ' s not a bad writer . his partner , tom ( tom mc ##int ##ire ) , is left to live with him and his partner ( robert eng ##lund ) . his sister is left in jail for the first time . when he meets the young couple , he decides to make a job of finding out that the writer and director of \" the left me in trouble \" . < br / > < br / > so , we get a big mistake . this is a very good example of how the film opens up with a series of flashbacks and no one left in the desert . the two meet at the end of the film , where the two cops and their daughter get a job . < br / > < br / > but that ' s not the case . this is a very med ##io ##cre film that shows the tough ##ness of the first film . it ' s not easy to tell . but it ' s just a big mistake . < br / > < br / > tom brown ( as tom sk ##er ##rit ##t ) \n", 2798 | "------------------------------\n", 2799 | "Epoch Step: 1 Loss: 0.816386 Tokens per Sec: 4294967295.000000\n", 2800 | "Epoch Step: 101 Loss: 1.110516 Tokens per Sec: 18208.000000\n", 2801 | "Epoch Step: 201 Loss: 1.353794 Tokens per Sec: 17860.000000\n", 2802 | "Epoch Step: 301 Loss: 1.542023 Tokens per Sec: 17830.000000\n", 2803 | "Epoch Step: 401 Loss: 1.974018 Tokens per Sec: 16623.000000\n", 2804 | "Epoch Step: 501 Loss: 2.386497 Tokens per Sec: 16226.000000\n", 2805 | "Epoch Step: 601 Loss: 2.921898 Tokens per Sec: 15519.000000\n", 2806 | "Epoch Step: 701 Loss: 3.273592 Tokens per Sec: 16009.000000\n", 2807 | "Epoch Step: 801 Loss: 3.188971 Tokens per Sec: 15642.000000\n", 2808 | "VALIDATION LOSS: 2.162333\n", 2809 | "Epoch: 2\n", 2810 | "Epoch Step: 1 Loss: 2.722145 Tokens per Sec: 7865.000000\n", 2811 | "Epoch Step: 101 Loss: 2.581414 Tokens per Sec: 6211.000000\n", 2812 | "Epoch Step: 201 Loss: 2.105173 Tokens per Sec: 6204.000000\n", 2813 | "Epoch Step: 301 Loss: 1.778284 Tokens per Sec: 6283.000000\n", 2814 | "Epoch Step: 401 Loss: 3.200924 Tokens per Sec: 6157.000000\n", 2815 | "Epoch Step: 501 Loss: 2.187073 Tokens per Sec: 6222.000000\n", 2816 | "Epoch Step: 601 Loss: 1.960883 Tokens per Sec: 6161.000000\n", 2817 | "Epoch Step: 701 Loss: 3.372905 Tokens per Sec: 6213.000000\n", 2818 | "Epoch Step: 801 Loss: 1.785080 Tokens per Sec: 6144.000000\n", 2819 | "Epoch: 3\n", 2820 | "Epoch Step: 1 Loss: 1.922140 Tokens per Sec: 7819.000000\n", 2821 | "Epoch Step: 101 Loss: 3.165004 Tokens per Sec: 6124.000000\n", 2822 | "Epoch Step: 201 Loss: 3.373371 Tokens per Sec: 6220.000000\n", 2823 | "Epoch Step: 301 Loss: 3.152642 Tokens per Sec: 6154.000000\n", 2824 | "Epoch Step: 401 Loss: 1.921576 Tokens per Sec: 6222.000000\n", 2825 | "Epoch Step: 501 Loss: 2.082068 Tokens per Sec: 6109.000000\n", 2826 | "Epoch Step: 601 Loss: 2.416815 Tokens per Sec: 6155.000000\n", 2827 | "Epoch Step: 701 Loss: 2.246686 Tokens per Sec: 6261.000000\n", 2828 | "Epoch Step: 801 Loss: 2.041450 Tokens per Sec: 6207.000000\n", 2829 | "Epoch: 4\n", 2830 | "Epoch Step: 1 Loss: 1.489060 Tokens per Sec: 7781.000000\n", 2831 | "Epoch Step: 101 Loss: 1.749454 Tokens per Sec: 5976.000000\n", 2832 | "Epoch Step: 201 Loss: 1.775020 Tokens per Sec: 6025.000000\n", 2833 | "Epoch Step: 301 Loss: 3.134117 Tokens per Sec: 6222.000000\n", 2834 | "Epoch Step: 401 Loss: 2.777619 Tokens per Sec: 6144.000000\n", 2835 | "Epoch Step: 501 Loss: 3.150565 Tokens per Sec: 6167.000000\n", 2836 | "Epoch Step: 601 Loss: 3.433340 Tokens per Sec: 6195.000000\n", 2837 | "Epoch Step: 701 Loss: 2.639482 Tokens per Sec: 6214.000000\n", 2838 | "Epoch Step: 801 Loss: 3.146263 Tokens per Sec: 6158.000000\n", 2839 | "Epoch: 5\n", 2840 | "Epoch Step: 1 Loss: 1.802351 Tokens per Sec: 7850.000000\n", 2841 | "Epoch Step: 101 Loss: 1.762089 Tokens per Sec: 6030.000000\n", 2842 | "Epoch Step: 201 Loss: 3.023382 Tokens per Sec: 6019.000000\n", 2843 | "Epoch Step: 301 Loss: 3.073139 Tokens per Sec: 6229.000000\n", 2844 | "Epoch Step: 401 Loss: 2.404180 Tokens per Sec: 6259.000000\n", 2845 | "Epoch Step: 501 Loss: 2.722541 Tokens per Sec: 6196.000000\n", 2846 | "Epoch Step: 601 Loss: 3.099750 Tokens per Sec: 6210.000000\n", 2847 | "Epoch Step: 701 Loss: 3.217110 Tokens per Sec: 6111.000000\n", 2848 | "Epoch Step: 801 Loss: 3.350076 Tokens per Sec: 6207.000000\n", 2849 | "Epoch: 6\n", 2850 | "Epoch Step: 1 Loss: 1.626878 Tokens per Sec: 7834.000000\n", 2851 | "Epoch Step: 101 Loss: 2.725226 Tokens per Sec: 6141.000000\n", 2852 | "Epoch Step: 201 Loss: 2.504176 Tokens per Sec: 6214.000000\n", 2853 | "Epoch Step: 301 Loss: 3.014816 Tokens per Sec: 6149.000000\n", 2854 | "Epoch Step: 401 Loss: 3.088159 Tokens per Sec: 6180.000000\n", 2855 | "Epoch Step: 501 Loss: 2.008882 Tokens per Sec: 6159.000000\n", 2856 | "Epoch Step: 601 Loss: 1.785432 Tokens per Sec: 6218.000000\n", 2857 | "Epoch Step: 701 Loss: 2.967357 Tokens per Sec: 6216.000000\n", 2858 | "Epoch Step: 801 Loss: 1.798384 Tokens per Sec: 6225.000000\n", 2859 | "True:\tjohn wayne is without a doubt one of the most popular and loved actors of all time . his career stretched over forty years , and within that time he starred in films such as \" angel and the bad ##man \" , \" the green be ##ret ##s \" , \" sands of i ##wo jim ##a \" , \" rio bravo \" , \" north to alaska \" , and \" the [UNK] \" . < br / > < br / > the film ' s listed above are hailed as some of his best , unlike this 1934 effort \" randy rides alone \" , which has been pretty much forgotten about as time ' s gone on , which is un ##sur ##pr ##ising , as it ' s nothing memorable apart from its very short running time of just 53 minutes . < br / > < br / > a young john wayne plays randy bow ##ers , who for reasons never really explained , arrives at a saloon in the middle of nowhere and finds that everyone inside has been killed . while looking around , a posse arrives and finds randy there and they arrest him , accusing him of being a gang member and demand to know where the rest of his gang is . he is put in jail accused of the murders . sally rogers , whose uncle owned the saloon and was murdered , arrives at the jail to see randy in order to clarify that he was one of the gang members ( she was hiding in a secret room when the shooting took place ) . sally doesn ' t believe that randy is a killer , and doesn ' t recognise him , so while the sheriff is out , she slips him the keys and randy escapes . while running away from the sheriff and his posse , randy convenient ##ly stumble ##s into the gang ' s hideout in a cave who were responsible for the murders . randy sets out to clear his name , and also to bring the gang to justice . < br / > < br / > \" randy rides alone \" can be a fun film to watch , especially if you ' re a john wayne fan . but at the same time it has far too many flaws that are impossible to ignore . the film is also extremely dated , as you would expect ; we have the terrible camera shooting which makes everyone look like they are moving in super - fast motion , and the dialogue is terrible . the acting isn ' t great either , and wayne ' s character is very wooden and he , along with the rest of the cast , look like wooden puppets who are being conducted by someone ( in this case it ' s by director harry fraser ) . harry fraser is at the helm , and does a good [SEP] \n", 2860 | "Predicted:\t\" the incredible melting man \" is one of john saxon ' s finest films . he has a small role as a young and pretty much the same actor , and he has been in such a short time for his \" live action \" films , which he has been in some of his films as \" the rest of the cast \" , which is at least one of his most appealing performances . < br / > < br / > however , the film is also pretty bad . the film is about a group of gang boss , who is murdered by a gang of hood ##lum ##s , and is in love with the gang of villains , including john wayne , and the gang that includes peter weston ( al clive ##r ) . they are also being held into the house , and they are quickly killed by a gang of villains who are being held at home by a gang of villains . < br / > < br / > this film is very hard to tell , and you can tell that there are no such reasons as \" the naked city \" and \" the naked city \" . it ' s hard to tell which this film was made , and that ' s not all that great . < br / > < br / > the film is shot in black and white , which looks like a lot \n", 2861 | "------------------------------\n", 2862 | "True:\tthis is slightly less sick ##ening than the first two films , but otherwise it ' s business as usual : a sc ##uz ##zy , sl ##ea ##zy and un ##balance ##d slice of disease ##d cinema . charles bro ##nson is back , blasting into action when his friend is killed by yo ##bs terror ##ising the neighbourhood . crime , you see , is up 11 % in the south belmont area . . . so what ' s to be done ? a stronger police presence ? tough ##er jail ##s ? harsh ##er sentences ? nope , the only solution is to send in a loose cannon like bro ##nson to met ##e out blood ##thi ##rst ##y revenge or , as the writers would have it , justice : this time he ' s the personal killing machine of police chief ed lau ##ter . < br / > < br / > the writers bend over backwards to make ke ##rse ##y the hero , sending the useless cops into the area only to con ##fi ##sca ##te a weapon from an elderly resident who keeps it for protection , and supplying a scene in which ke ##rse ##y has his camera stolen and shoots the thief right in the back , to applause from the watching crowd . capital punishment for theft ? well , okay . the attitude of everyone in the film is that this is a solution , and the dish ##ones ##t twisting of the characters into cipher ##s who exist only to cheer ke ##rse ##y on or back him up is app ##all ##ing . < br / > < br / > sure , these villains are sc ##um , but shouldn ' t the film leave the audience to make up its mind , rather than slant ##ing the entire thing towards ke ##rse ##y and his mind ##less answer ? fun ##ni ##ly enough the bel ##ea ##gue ##red residents don ' t fear gang rep ##ris ##als or blame ke ##rse ##y for any of the violence , which is odd as one character is killed precisely because of ke ##rse ##y ' s involvement . at the end of the film they all take guns from their sock drawers and glee ##fully join in with the massacre , never stopping to think things through or struggle with the thought of having to kill another human being . < br / > < br / > the at ##ro ##cious ##ly shallow performances don ' t help bro ##nson has literally one facial expression throughout and can ' t even put in ##fle ##ction on the right words . new heights of stupidity are reached here a machine gun ? a rocket launcher ? ! and new low ##s of mis ##ogy ##ny : the movie con ##tri ##ves to des ##ec ##rate every female character in sight , whether by rape , explosion or throat - slash ##ing ; and it [SEP] \n" 2863 | ] 2864 | }, 2865 | { 2866 | "name": "stdout", 2867 | "output_type": "stream", 2868 | "text": [ 2869 | "Predicted:\tthis is a film that is so bad , it ' s hard to watch . but the only reason why ? because the film is so un ##watch ##able is the only reason for watching it . the first hour or so are the usual suspects : 1 . the second , the protagonist , a sc ##um ##onic , is a lo ##ony who would rather be in the same league as the original , and the subsequent fight scenes , which make up the entire movie . < br / > < br / > the protagonist , a young man who has been murdered by his gang , is forced to live in a small town in new york . but his own brother , who has been murdered by the gang of thugs , and his gang of thugs , and his gang of thugs , take over the entire city . this is a rather sc ##um ##my , and un ##sca ##ry piece of cinema . < br / > < br / > the film opens with a gun taking up from a gang of thugs , and the gang of thugs , and the gang of thugs , to help him . as the police come to the rescue , the cops are forced to shoot the cops , and shoot themselves into the car , and shoot him in the back of the car , and then shoot him in the back of his own \n", 2870 | "------------------------------\n", 2871 | "True:\tlos angeles physician tom reed ( vincent vent ##res ##ca ) survives a tragic auto accident but ends up going to prison in the high desert of california . when his time is up he lands in a small , wind - swept town named pu ##rga ##tory flats . his first stop is a bar , where he quickly slams a beer and gets hired as a bartender . so much for ex - con ##s having a tough time finding work . this is the first in a long line of absurd ##ities that make up the plot of writer / director harris done ' s silly attempt at modern , desert - set , film noir . < br / > < br / > his first night on the job tom meets a sexy femme fatal named sunny ( alexandra holden ) , who hangs out with a family of bad boys : the me ##ck ##lins , consisting of uncle dean ( gregg henry ) and his two nephew ##s , the drug addicted owen ( kevin alejandro ) , who is sunny ' s husband , and aw ##ol soldier randy ( \" 90 ##21 ##0 \" s brian austin greer ) . after his shift is finished there ' s a shooting , and dr . tom just happens to be nearby . he agrees to treat one of the wounded and , most importantly , not tell the cops . i ' m not sure that ' s a good move for a guy fresh out of the pen , but this script ( co - written by diane fine ) has very little to do with logic . < br / > < br / > tom makes a series of poor decisions that get him further and further en ##tangled with the criminal ##ly inclined me ##ck ##lin boys , including stealing medical supplies and hopping into the sack with sunny . everybody in the theater is screaming , \" don ' t do it ! walk away \" , but tom does it anyway . < br / > < br / > in a classic film noir like \" body heat \" or \" double ind ##em ##nity \" we root for and em ##path ##ize with william hurt and fred mc ##mur ##ray as they get sucked into the web of bright , sexy , devi ##ous femme fatal ##s like kathleen turner and barbara stan ##wy ##ck . it ' s not their fault . we ' d probably be tempted by all that money or that particular dame , too . but dr . tom ' s weakness seems to stem from stupidity more than circumstances . sunny is sexy but not a very compelling character , and there ' s no money to te ##mpt him . you ' re left wondering if he attended the same medical school as dr . nick river ##ia from \" the simpsons \" . < br / [SEP] \n", 2872 | "Predicted:\tfirst of all , this is a pretty bad film . but it ' s not a good film . the plot is set in new york city , where the characters live in a small town , and the two of them live together . a series of mis ##fi ##ts that make us believe that a small town is set in the early 1970s . < br / > < br / > tom ( robert mitch ##um ) is a young tom ( tom everett scott ) , a former high school student who has been sleeping with his uncle , a high - school boss ( robert eng ##lund ) , and his daughter jenny ( kathy baker ) . he ' s a doctor who has been en ##tic ##ing his own ne ##rdy high - school boss , a former student who is a former roommate who is also a suspect . he ' s a former student of the first \" big \" type of guy , and he ' s up to his job . but when he gets into trouble , he gets a job and gets into a job that is going to be left alone for him . < br / > < br / > but there ' s no more to this story than the first two minutes of the film . we get to see the \" sexy \" guy ( robert eng ##lund ) , a tough guy who looks \n", 2873 | "------------------------------\n", 2874 | "Epoch Step: 1 Loss: 1.513008 Tokens per Sec: 13743.000000\n", 2875 | "Epoch Step: 101 Loss: 2.129503 Tokens per Sec: 18208.000000\n", 2876 | "Epoch Step: 201 Loss: 2.412512 Tokens per Sec: 17860.000000\n", 2877 | "Epoch Step: 301 Loss: 2.429930 Tokens per Sec: 17434.000000\n", 2878 | "Epoch Step: 401 Loss: 2.640728 Tokens per Sec: 16623.000000\n", 2879 | "Epoch Step: 501 Loss: 2.995038 Tokens per Sec: 15895.000000\n", 2880 | "Epoch Step: 601 Loss: 3.302456 Tokens per Sec: 15835.000000\n", 2881 | "Epoch Step: 701 Loss: 3.615057 Tokens per Sec: 15682.000000\n", 2882 | "Epoch Step: 801 Loss: 3.474561 Tokens per Sec: 15968.000000\n", 2883 | "VALIDATION LOSS: 2.839061\n", 2884 | "Epoch: 7\n", 2885 | "Epoch Step: 1 Loss: 1.133549 Tokens per Sec: 7642.000000\n", 2886 | "Epoch Step: 101 Loss: 2.903978 Tokens per Sec: 6148.000000\n", 2887 | "Epoch Step: 201 Loss: 2.025905 Tokens per Sec: 6263.000000\n", 2888 | "Epoch Step: 301 Loss: 2.917012 Tokens per Sec: 6218.000000\n", 2889 | "Epoch Step: 401 Loss: 1.610366 Tokens per Sec: 6171.000000\n", 2890 | "Epoch Step: 501 Loss: 2.082399 Tokens per Sec: 6164.000000\n", 2891 | "Epoch Step: 601 Loss: 2.872767 Tokens per Sec: 6172.000000\n", 2892 | "Epoch Step: 701 Loss: 1.825423 Tokens per Sec: 6207.000000\n", 2893 | "Epoch Step: 801 Loss: 2.296348 Tokens per Sec: 6272.000000\n", 2894 | "Epoch: 8\n", 2895 | "Epoch Step: 1 Loss: 1.366967 Tokens per Sec: 7664.000000\n", 2896 | "Epoch Step: 101 Loss: 1.555836 Tokens per Sec: 6170.000000\n", 2897 | "Epoch Step: 201 Loss: 2.823003 Tokens per Sec: 6202.000000\n", 2898 | "Epoch Step: 301 Loss: 2.913805 Tokens per Sec: 6224.000000\n", 2899 | "Epoch Step: 401 Loss: 1.527506 Tokens per Sec: 6153.000000\n", 2900 | "Epoch Step: 501 Loss: 2.917202 Tokens per Sec: 6222.000000\n", 2901 | "Epoch Step: 601 Loss: 2.701970 Tokens per Sec: 6203.000000\n", 2902 | "Epoch Step: 701 Loss: 2.151023 Tokens per Sec: 6169.000000\n", 2903 | "Epoch Step: 801 Loss: 1.452205 Tokens per Sec: 6161.000000\n", 2904 | "Epoch: 9\n", 2905 | "Epoch Step: 1 Loss: 2.744883 Tokens per Sec: 7665.000000\n", 2906 | "Epoch Step: 101 Loss: 2.333969 Tokens per Sec: 6167.000000\n", 2907 | "Epoch Step: 201 Loss: 2.664581 Tokens per Sec: 6164.000000\n", 2908 | "Epoch Step: 301 Loss: 2.387236 Tokens per Sec: 6157.000000\n", 2909 | "Epoch Step: 401 Loss: 2.851847 Tokens per Sec: 6210.000000\n", 2910 | "Epoch Step: 501 Loss: 2.730557 Tokens per Sec: 6208.000000\n", 2911 | "Epoch Step: 601 Loss: 2.911171 Tokens per Sec: 6163.000000\n", 2912 | "Epoch Step: 701 Loss: 1.779993 Tokens per Sec: 6224.000000\n", 2913 | "Epoch Step: 801 Loss: 2.861305 Tokens per Sec: 6193.000000\n", 2914 | "Epoch: 10\n", 2915 | "Epoch Step: 1 Loss: 2.380349 Tokens per Sec: 7825.000000\n", 2916 | "Epoch Step: 101 Loss: 1.334219 Tokens per Sec: 5972.000000\n", 2917 | "Epoch Step: 201 Loss: 1.693183 Tokens per Sec: 6056.000000\n", 2918 | "Epoch Step: 301 Loss: 2.246971 Tokens per Sec: 5972.000000\n", 2919 | "Epoch Step: 401 Loss: 2.063526 Tokens per Sec: 6167.000000\n", 2920 | "Epoch Step: 501 Loss: 2.856549 Tokens per Sec: 6259.000000\n", 2921 | "Epoch Step: 601 Loss: 2.717936 Tokens per Sec: 6163.000000\n", 2922 | "Epoch Step: 701 Loss: 1.543654 Tokens per Sec: 6273.000000\n", 2923 | "Epoch Step: 801 Loss: 2.622971 Tokens per Sec: 6162.000000\n", 2924 | "Epoch: 11\n", 2925 | "Epoch Step: 1 Loss: 2.607197 Tokens per Sec: 7887.000000\n", 2926 | "Epoch Step: 101 Loss: 2.636989 Tokens per Sec: 6254.000000\n", 2927 | "Epoch Step: 201 Loss: 1.629218 Tokens per Sec: 6266.000000\n", 2928 | "Epoch Step: 301 Loss: 1.980191 Tokens per Sec: 6314.000000\n", 2929 | "Epoch Step: 401 Loss: 2.183221 Tokens per Sec: 6157.000000\n", 2930 | "Epoch Step: 501 Loss: 2.624758 Tokens per Sec: 6114.000000\n", 2931 | "Epoch Step: 601 Loss: 1.443795 Tokens per Sec: 6277.000000\n", 2932 | "Epoch Step: 701 Loss: 2.746735 Tokens per Sec: 6119.000000\n", 2933 | "Epoch Step: 801 Loss: 2.718351 Tokens per Sec: 6100.000000\n", 2934 | "True:\tjohn wayne is without a doubt one of the most popular and loved actors of all time . his career stretched over forty years , and within that time he starred in films such as \" angel and the bad ##man \" , \" the green be ##ret ##s \" , \" sands of i ##wo jim ##a \" , \" rio bravo \" , \" north to alaska \" , and \" the [UNK] \" . < br / > < br / > the film ' s listed above are hailed as some of his best , unlike this 1934 effort \" randy rides alone \" , which has been pretty much forgotten about as time ' s gone on , which is un ##sur ##pr ##ising , as it ' s nothing memorable apart from its very short running time of just 53 minutes . < br / > < br / > a young john wayne plays randy bow ##ers , who for reasons never really explained , arrives at a saloon in the middle of nowhere and finds that everyone inside has been killed . while looking around , a posse arrives and finds randy there and they arrest him , accusing him of being a gang member and demand to know where the rest of his gang is . he is put in jail accused of the murders . sally rogers , whose uncle owned the saloon and was murdered , arrives at the jail to see randy in order to clarify that he was one of the gang members ( she was hiding in a secret room when the shooting took place ) . sally doesn ' t believe that randy is a killer , and doesn ' t recognise him , so while the sheriff is out , she slips him the keys and randy escapes . while running away from the sheriff and his posse , randy convenient ##ly stumble ##s into the gang ' s hideout in a cave who were responsible for the murders . randy sets out to clear his name , and also to bring the gang to justice . < br / > < br / > \" randy rides alone \" can be a fun film to watch , especially if you ' re a john wayne fan . but at the same time it has far too many flaws that are impossible to ignore . the film is also extremely dated , as you would expect ; we have the terrible camera shooting which makes everyone look like they are moving in super - fast motion , and the dialogue is terrible . the acting isn ' t great either , and wayne ' s character is very wooden and he , along with the rest of the cast , look like wooden puppets who are being conducted by someone ( in this case it ' s by director harry fraser ) . harry fraser is at the helm , and does a good [SEP] \n" 2935 | ] 2936 | }, 2937 | { 2938 | "name": "stdout", 2939 | "output_type": "stream", 2940 | "text": [ 2941 | "Predicted:\tjohn saxon is one of the greatest actors of all time , and he ' s always been a big fan of his work . however , \" the night time \" is a very small one , which has been the best of his films , and \" the night time \" , which is also one of the best of his films . < br / > < br / > as such , \" the night at the time \" is a pretty forget ##table look at the film . it ' s hard to believe that john savage , who was in charge of the film , was in trouble for a film that was filmed in black and white , and has a great look to it . as such , \" randy \" is a pretty bad film , and you ' ll be wishing that someone would put them in a studio . < br / > < br / > anyway , the rest of the cast is pretty much wasted . randy qu ##aid , whose character , \" randy \" , is called \" randy from \" the naked mile \" , is just about as bad as he looks and looks like he ' s in the film . < br / > < br / > anyway , the rest of the cast are pretty much wasted . randy qu ##aid , whose character , \" randy \" , is about as wooden \n", 2942 | "------------------------------\n", 2943 | "True:\tthis is slightly less sick ##ening than the first two films , but otherwise it ' s business as usual : a sc ##uz ##zy , sl ##ea ##zy and un ##balance ##d slice of disease ##d cinema . charles bro ##nson is back , blasting into action when his friend is killed by yo ##bs terror ##ising the neighbourhood . crime , you see , is up 11 % in the south belmont area . . . so what ' s to be done ? a stronger police presence ? tough ##er jail ##s ? harsh ##er sentences ? nope , the only solution is to send in a loose cannon like bro ##nson to met ##e out blood ##thi ##rst ##y revenge or , as the writers would have it , justice : this time he ' s the personal killing machine of police chief ed lau ##ter . < br / > < br / > the writers bend over backwards to make ke ##rse ##y the hero , sending the useless cops into the area only to con ##fi ##sca ##te a weapon from an elderly resident who keeps it for protection , and supplying a scene in which ke ##rse ##y has his camera stolen and shoots the thief right in the back , to applause from the watching crowd . capital punishment for theft ? well , okay . the attitude of everyone in the film is that this is a solution , and the dish ##ones ##t twisting of the characters into cipher ##s who exist only to cheer ke ##rse ##y on or back him up is app ##all ##ing . < br / > < br / > sure , these villains are sc ##um , but shouldn ' t the film leave the audience to make up its mind , rather than slant ##ing the entire thing towards ke ##rse ##y and his mind ##less answer ? fun ##ni ##ly enough the bel ##ea ##gue ##red residents don ' t fear gang rep ##ris ##als or blame ke ##rse ##y for any of the violence , which is odd as one character is killed precisely because of ke ##rse ##y ' s involvement . at the end of the film they all take guns from their sock drawers and glee ##fully join in with the massacre , never stopping to think things through or struggle with the thought of having to kill another human being . < br / > < br / > the at ##ro ##cious ##ly shallow performances don ' t help bro ##nson has literally one facial expression throughout and can ' t even put in ##fle ##ction on the right words . new heights of stupidity are reached here a machine gun ? a rocket launcher ? ! and new low ##s of mis ##ogy ##ny : the movie con ##tri ##ves to des ##ec ##rate every female character in sight , whether by rape , explosion or throat - slash ##ing ; and it [SEP] \n", 2944 | "Predicted:\tthis is a new entry in the series of films , which are only 10 times better than this . it is a crime against crime , but it is fun to watch , and is so un ##for ##get ##table that it is hard to make a film like this . < br / > < br / > the protagonist , roy sc ##hei ##der , is the protagonist of the new york city , and is the only person who has the right to call the police , as well as the writers , the protagonist , is a lo ##vable mis ##guide ##d scientist who keeps his teeth from turning into a lo ##ony drug addict . the opening scene is rather jar ##ring , but the rest of the film is shot in the back of the day . < br / > < br / > spoil ##ers : in the opening scene , the protagonist ' s character is killed by a fire ##fighter , and an angry sh ##red of his own pre ##text or the gang ' s own sc ##um . the gang ' s gang at the first time , as is the police force , and the gang ' s gang ' s gang leader , as usual , is killed off by a gang of thugs . why would they want to shoot him ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? \n", 2945 | "------------------------------\n", 2946 | "True:\tlos angeles physician tom reed ( vincent vent ##res ##ca ) survives a tragic auto accident but ends up going to prison in the high desert of california . when his time is up he lands in a small , wind - swept town named pu ##rga ##tory flats . his first stop is a bar , where he quickly slams a beer and gets hired as a bartender . so much for ex - con ##s having a tough time finding work . this is the first in a long line of absurd ##ities that make up the plot of writer / director harris done ' s silly attempt at modern , desert - set , film noir . < br / > < br / > his first night on the job tom meets a sexy femme fatal named sunny ( alexandra holden ) , who hangs out with a family of bad boys : the me ##ck ##lins , consisting of uncle dean ( gregg henry ) and his two nephew ##s , the drug addicted owen ( kevin alejandro ) , who is sunny ' s husband , and aw ##ol soldier randy ( \" 90 ##21 ##0 \" s brian austin greer ) . after his shift is finished there ' s a shooting , and dr . tom just happens to be nearby . he agrees to treat one of the wounded and , most importantly , not tell the cops . i ' m not sure that ' s a good move for a guy fresh out of the pen , but this script ( co - written by diane fine ) has very little to do with logic . < br / > < br / > tom makes a series of poor decisions that get him further and further en ##tangled with the criminal ##ly inclined me ##ck ##lin boys , including stealing medical supplies and hopping into the sack with sunny . everybody in the theater is screaming , \" don ' t do it ! walk away \" , but tom does it anyway . < br / > < br / > in a classic film noir like \" body heat \" or \" double ind ##em ##nity \" we root for and em ##path ##ize with william hurt and fred mc ##mur ##ray as they get sucked into the web of bright , sexy , devi ##ous femme fatal ##s like kathleen turner and barbara stan ##wy ##ck . it ' s not their fault . we ' d probably be tempted by all that money or that particular dame , too . but dr . tom ' s weakness seems to stem from stupidity more than circumstances . sunny is sexy but not a very compelling character , and there ' s no money to te ##mpt him . you ' re left wondering if he attended the same medical school as dr . nick river ##ia from \" the simpsons \" . < br / [SEP] \n", 2947 | "Predicted:\ttom ( tom bar ##man ) is a small town writer who is working in a small town where he ' s going to work for a long time . he ' s the first of his career , but he ' s out of jail and gets a job with a big job . < br / > < br / > after a long time , tom ( paul rudd ) and his girlfriend ( tom ( ed me ##y ) , a crazy , but big jerk ##y , a small town . the first victim is killed by a car that ' s not sure of . the film starts out like a series of \" the usual suspects \" , but it gets very much in the first place . < br / > < br / > the film begins with a series of events that make up for a series of coincidence ##s and events that occur in the first place . tom turns up at a car crash and gets into a van - like \" tom , \" tom , \" tom , and his buddy . the two cops at the bar , and there ' s no fuel in the middle of the night . tom is a jerk ##y , self - conscious and mis ##ogy ##ny , and he ' s just not sure what to do . < br / > < br / > so we get to see tom \n", 2948 | "------------------------------\n", 2949 | "Epoch Step: 1 Loss: 1.616261 Tokens per Sec: 4294967295.000000\n", 2950 | "Epoch Step: 101 Loss: 2.287065 Tokens per Sec: 17794.000000\n", 2951 | "Epoch Step: 201 Loss: 2.584965 Tokens per Sec: 17463.000000\n", 2952 | "Epoch Step: 301 Loss: 2.565032 Tokens per Sec: 17830.000000\n", 2953 | "Epoch Step: 401 Loss: 2.764774 Tokens per Sec: 16623.000000\n", 2954 | "Epoch Step: 501 Loss: 3.119474 Tokens per Sec: 16226.000000\n" 2955 | ] 2956 | }, 2957 | { 2958 | "name": "stdout", 2959 | "output_type": "stream", 2960 | "text": [ 2961 | "Epoch Step: 601 Loss: 3.404990 Tokens per Sec: 15835.000000\n", 2962 | "Epoch Step: 701 Loss: 3.727908 Tokens per Sec: 16009.000000\n", 2963 | "Epoch Step: 801 Loss: 3.577969 Tokens per Sec: 16308.000000\n", 2964 | "VALIDATION LOSS: 2.967480\n", 2965 | "Epoch: 12\n", 2966 | "Epoch Step: 1 Loss: 1.634545 Tokens per Sec: 7782.000000\n", 2967 | "Epoch Step: 101 Loss: 1.236310 Tokens per Sec: 6253.000000\n", 2968 | "Epoch Step: 201 Loss: 1.848471 Tokens per Sec: 6272.000000\n", 2969 | "Epoch Step: 301 Loss: 2.022540 Tokens per Sec: 6218.000000\n", 2970 | "Epoch Step: 401 Loss: 2.116828 Tokens per Sec: 6108.000000\n", 2971 | "Epoch Step: 501 Loss: 1.854738 Tokens per Sec: 6177.000000\n", 2972 | "Epoch Step: 601 Loss: 2.192439 Tokens per Sec: 6105.000000\n", 2973 | "Epoch Step: 701 Loss: 1.825527 Tokens per Sec: 6157.000000\n", 2974 | "Epoch Step: 801 Loss: 2.387137 Tokens per Sec: 6162.000000\n", 2975 | "Epoch: 13\n", 2976 | "Epoch Step: 1 Loss: 2.383227 Tokens per Sec: 7715.000000\n", 2977 | "Epoch Step: 101 Loss: 1.378927 Tokens per Sec: 6147.000000\n", 2978 | "Epoch Step: 201 Loss: 2.516225 Tokens per Sec: 6101.000000\n", 2979 | "Epoch Step: 301 Loss: 2.101686 Tokens per Sec: 6218.000000\n", 2980 | "Epoch Step: 401 Loss: 2.543607 Tokens per Sec: 6123.000000\n", 2981 | "Epoch Step: 501 Loss: 1.956255 Tokens per Sec: 6105.000000\n", 2982 | "Epoch Step: 601 Loss: 2.256653 Tokens per Sec: 6159.000000\n", 2983 | "Epoch Step: 701 Loss: 1.669684 Tokens per Sec: 6166.000000\n", 2984 | "Epoch Step: 801 Loss: 2.555591 Tokens per Sec: 6127.000000\n", 2985 | "Epoch: 14\n", 2986 | "Epoch Step: 1 Loss: 2.659044 Tokens per Sec: 7716.000000\n", 2987 | "Epoch Step: 101 Loss: 1.941702 Tokens per Sec: 6164.000000\n", 2988 | "Epoch Step: 201 Loss: 2.620811 Tokens per Sec: 6161.000000\n", 2989 | "Epoch Step: 301 Loss: 2.589362 Tokens per Sec: 6171.000000\n", 2990 | "Epoch Step: 401 Loss: 2.001449 Tokens per Sec: 6204.000000\n", 2991 | "Epoch Step: 501 Loss: 1.472722 Tokens per Sec: 6249.000000\n", 2992 | "Epoch Step: 601 Loss: 2.719321 Tokens per Sec: 6217.000000\n", 2993 | "Epoch Step: 701 Loss: 2.654023 Tokens per Sec: 6324.000000\n", 2994 | "Epoch Step: 801 Loss: 2.533460 Tokens per Sec: 6065.000000\n", 2995 | "Epoch: 15\n", 2996 | "Epoch Step: 1 Loss: 2.537005 Tokens per Sec: 7770.000000\n", 2997 | "Epoch Step: 101 Loss: 1.265391 Tokens per Sec: 6210.000000\n", 2998 | "Epoch Step: 201 Loss: 1.229011 Tokens per Sec: 6215.000000\n", 2999 | "Epoch Step: 301 Loss: 1.765888 Tokens per Sec: 6204.000000\n", 3000 | "Epoch Step: 401 Loss: 2.439212 Tokens per Sec: 6268.000000\n", 3001 | "Epoch Step: 501 Loss: 2.609391 Tokens per Sec: 6161.000000\n", 3002 | "Epoch Step: 601 Loss: 1.569594 Tokens per Sec: 6206.000000\n", 3003 | "Epoch Step: 701 Loss: 1.985846 Tokens per Sec: 6212.000000\n", 3004 | "Epoch Step: 801 Loss: 1.156709 Tokens per Sec: 6214.000000\n", 3005 | "Epoch: 16\n", 3006 | "Epoch Step: 1 Loss: 1.699267 Tokens per Sec: 7874.000000\n", 3007 | "Epoch Step: 101 Loss: 2.180212 Tokens per Sec: 6076.000000\n", 3008 | "Epoch Step: 201 Loss: 1.445212 Tokens per Sec: 6123.000000\n", 3009 | "Epoch Step: 301 Loss: 2.181925 Tokens per Sec: 6161.000000\n", 3010 | "Epoch Step: 401 Loss: 2.318261 Tokens per Sec: 6208.000000\n", 3011 | "Epoch Step: 501 Loss: 2.208717 Tokens per Sec: 6218.000000\n", 3012 | "Epoch Step: 601 Loss: 2.105663 Tokens per Sec: 6310.000000\n", 3013 | "Epoch Step: 701 Loss: 2.285606 Tokens per Sec: 6152.000000\n", 3014 | "Epoch Step: 801 Loss: 2.741899 Tokens per Sec: 6209.000000\n", 3015 | "True:\tjohn wayne is without a doubt one of the most popular and loved actors of all time . his career stretched over forty years , and within that time he starred in films such as \" angel and the bad ##man \" , \" the green be ##ret ##s \" , \" sands of i ##wo jim ##a \" , \" rio bravo \" , \" north to alaska \" , and \" the [UNK] \" . < br / > < br / > the film ' s listed above are hailed as some of his best , unlike this 1934 effort \" randy rides alone \" , which has been pretty much forgotten about as time ' s gone on , which is un ##sur ##pr ##ising , as it ' s nothing memorable apart from its very short running time of just 53 minutes . < br / > < br / > a young john wayne plays randy bow ##ers , who for reasons never really explained , arrives at a saloon in the middle of nowhere and finds that everyone inside has been killed . while looking around , a posse arrives and finds randy there and they arrest him , accusing him of being a gang member and demand to know where the rest of his gang is . he is put in jail accused of the murders . sally rogers , whose uncle owned the saloon and was murdered , arrives at the jail to see randy in order to clarify that he was one of the gang members ( she was hiding in a secret room when the shooting took place ) . sally doesn ' t believe that randy is a killer , and doesn ' t recognise him , so while the sheriff is out , she slips him the keys and randy escapes . while running away from the sheriff and his posse , randy convenient ##ly stumble ##s into the gang ' s hideout in a cave who were responsible for the murders . randy sets out to clear his name , and also to bring the gang to justice . < br / > < br / > \" randy rides alone \" can be a fun film to watch , especially if you ' re a john wayne fan . but at the same time it has far too many flaws that are impossible to ignore . the film is also extremely dated , as you would expect ; we have the terrible camera shooting which makes everyone look like they are moving in super - fast motion , and the dialogue is terrible . the acting isn ' t great either , and wayne ' s character is very wooden and he , along with the rest of the cast , look like wooden puppets who are being conducted by someone ( in this case it ' s by director harry fraser ) . harry fraser is at the helm , and does a good [SEP] \n", 3016 | "Predicted:\tjohn wayne ' s \" the night of the dead \" , starring robert reed as one of his best actors , \" the incredible trail \" , and \" the night of the dead \" , starring robert reed as jeff clark , who has a small role in \" the naked mile \" . < br / > < br / > however , despite being a pretty good film , it is also pretty much a one that has to be seen to be believed . the rest of the cast is pretty bad , especially jeff ##ery tam ##bor , who is so under ##used and looks like a 16 year old from \" the time of the dead \" and \" the gang \" who killed him . he and his gang are all un ##lika ##ble , and they are both very funny and honest . < br / > < br / > when you think about it , it ' s hard to believe that john wayne and ruby keel ##er ( who was never a sports star ) , and they are just in trouble for being there . however , despite being a very good actor , jeff ##ery tam ##bly ##n is in some of his best performances . he ' s a very small time actor and is in some of his films , such as \" the one in which he and ruby keel ##er ( who is always a great actor \n", 3017 | "------------------------------\n", 3018 | "True:\tthis is slightly less sick ##ening than the first two films , but otherwise it ' s business as usual : a sc ##uz ##zy , sl ##ea ##zy and un ##balance ##d slice of disease ##d cinema . charles bro ##nson is back , blasting into action when his friend is killed by yo ##bs terror ##ising the neighbourhood . crime , you see , is up 11 % in the south belmont area . . . so what ' s to be done ? a stronger police presence ? tough ##er jail ##s ? harsh ##er sentences ? nope , the only solution is to send in a loose cannon like bro ##nson to met ##e out blood ##thi ##rst ##y revenge or , as the writers would have it , justice : this time he ' s the personal killing machine of police chief ed lau ##ter . < br / > < br / > the writers bend over backwards to make ke ##rse ##y the hero , sending the useless cops into the area only to con ##fi ##sca ##te a weapon from an elderly resident who keeps it for protection , and supplying a scene in which ke ##rse ##y has his camera stolen and shoots the thief right in the back , to applause from the watching crowd . capital punishment for theft ? well , okay . the attitude of everyone in the film is that this is a solution , and the dish ##ones ##t twisting of the characters into cipher ##s who exist only to cheer ke ##rse ##y on or back him up is app ##all ##ing . < br / > < br / > sure , these villains are sc ##um , but shouldn ' t the film leave the audience to make up its mind , rather than slant ##ing the entire thing towards ke ##rse ##y and his mind ##less answer ? fun ##ni ##ly enough the bel ##ea ##gue ##red residents don ' t fear gang rep ##ris ##als or blame ke ##rse ##y for any of the violence , which is odd as one character is killed precisely because of ke ##rse ##y ' s involvement . at the end of the film they all take guns from their sock drawers and glee ##fully join in with the massacre , never stopping to think things through or struggle with the thought of having to kill another human being . < br / > < br / > the at ##ro ##cious ##ly shallow performances don ' t help bro ##nson has literally one facial expression throughout and can ' t even put in ##fle ##ction on the right words . new heights of stupidity are reached here a machine gun ? a rocket launcher ? ! and new low ##s of mis ##ogy ##ny : the movie con ##tri ##ves to des ##ec ##rate every female character in sight , whether by rape , explosion or throat - slash ##ing ; and it [SEP] \n" 3019 | ] 3020 | }, 3021 | { 3022 | "name": "stdout", 3023 | "output_type": "stream", 3024 | "text": [ 3025 | "Predicted:\tthis is a crime against crime against the system , but it is only the third time in the film . it is set in new york city , which is rather than the usual suspects , but it is so much fun to watch . < br / > < br / > the film has a strong protagonist , robert blake , a sc ##am , who ' s been blackmail ##ed by the gang , is killed by a gang of thugs who are sc ##um ##ming up the city for a killing sp ##ree ##ching him . the first clue is to what the heck is going on and what ' s up with him ? ? ? ? ? ? ? ? ? the lo ##vable , mean spirited , and un ##in ##formed . the two cops , make their own or re ##tar ##ded , turn into lo ##vable and lo ##vable as the one in this movie . the villains are so un ##bel ##ie ##va ##bly ugly that you almost feel sorry for them . the villains , especially the villains , are only as worthy as they seem to be in the first place . the villains , the villains , the villains , the villains , the villains , the villains , the villains , the villains , the villains , the villains , the sc ##umb ##ag , the villains , the villains , the villains , the villains , the villains , \n", 3026 | "------------------------------\n", 3027 | "True:\tlos angeles physician tom reed ( vincent vent ##res ##ca ) survives a tragic auto accident but ends up going to prison in the high desert of california . when his time is up he lands in a small , wind - swept town named pu ##rga ##tory flats . his first stop is a bar , where he quickly slams a beer and gets hired as a bartender . so much for ex - con ##s having a tough time finding work . this is the first in a long line of absurd ##ities that make up the plot of writer / director harris done ' s silly attempt at modern , desert - set , film noir . < br / > < br / > his first night on the job tom meets a sexy femme fatal named sunny ( alexandra holden ) , who hangs out with a family of bad boys : the me ##ck ##lins , consisting of uncle dean ( gregg henry ) and his two nephew ##s , the drug addicted owen ( kevin alejandro ) , who is sunny ' s husband , and aw ##ol soldier randy ( \" 90 ##21 ##0 \" s brian austin greer ) . after his shift is finished there ' s a shooting , and dr . tom just happens to be nearby . he agrees to treat one of the wounded and , most importantly , not tell the cops . i ' m not sure that ' s a good move for a guy fresh out of the pen , but this script ( co - written by diane fine ) has very little to do with logic . < br / > < br / > tom makes a series of poor decisions that get him further and further en ##tangled with the criminal ##ly inclined me ##ck ##lin boys , including stealing medical supplies and hopping into the sack with sunny . everybody in the theater is screaming , \" don ' t do it ! walk away \" , but tom does it anyway . < br / > < br / > in a classic film noir like \" body heat \" or \" double ind ##em ##nity \" we root for and em ##path ##ize with william hurt and fred mc ##mur ##ray as they get sucked into the web of bright , sexy , devi ##ous femme fatal ##s like kathleen turner and barbara stan ##wy ##ck . it ' s not their fault . we ' d probably be tempted by all that money or that particular dame , too . but dr . tom ' s weakness seems to stem from stupidity more than circumstances . sunny is sexy but not a very compelling character , and there ' s no money to te ##mpt him . you ' re left wondering if he attended the same medical school as dr . nick river ##ia from \" the simpsons \" . < br / [SEP] \n", 3028 | "Predicted:\ttom is a long - time writer who ' s been in a small town , a tough guy with a bad hair ##head ( tom hank ##s ) . he ' s not a bad guy , but he ' s the first victim of his life . when he discovers that his ex - wife ( gene tierney ) is driving home to drive him to the small room . after driving a car , he meets a sexy hip ##pie named lyle ( paul douglas ) , a trio of thieves and keep taking advantage of his work . < br / > < br / > first , this time , tom hank ##s is the most appealing and bel ##ie ##vable man in the world . tom turns out to be a high - school bully , but is so much more interested in the script and the script . there ' s a very good deal of tension and tension . < br / > < br / > the film starts out like a series of long wind ##ed , where the two cops make up their holiday and run into a van that is so easy to get into . but it gets no more than a few good lines . first , the ex - vietnam vet ##s are killed by a mysterious psychic ( robert mc ##lag ##len ) and tom ( as the tough - guy who has a job as a tough guy \n", 3029 | "------------------------------\n", 3030 | "Epoch Step: 1 Loss: 1.684819 Tokens per Sec: 4294967295.000000\n", 3031 | "Epoch Step: 101 Loss: 2.416137 Tokens per Sec: 18208.000000\n", 3032 | "Epoch Step: 201 Loss: 2.719902 Tokens per Sec: 17463.000000\n", 3033 | "Epoch Step: 301 Loss: 2.670963 Tokens per Sec: 17434.000000\n", 3034 | "Epoch Step: 401 Loss: 2.847700 Tokens per Sec: 16985.000000\n", 3035 | "Epoch Step: 501 Loss: 3.213319 Tokens per Sec: 16226.000000\n", 3036 | "Epoch Step: 601 Loss: 3.480537 Tokens per Sec: 16165.000000\n", 3037 | "Epoch Step: 701 Loss: 3.842853 Tokens per Sec: 16009.000000\n", 3038 | "Epoch Step: 801 Loss: 3.651115 Tokens per Sec: 16308.000000\n", 3039 | "VALIDATION LOSS: 3.069046\n", 3040 | "CPU times: user 12h 27min 22s, sys: 4h 34min, total: 17h 1min 22s\n", 3041 | "Wall time: 5h 25min 37s\n" 3042 | ] 3043 | } 3044 | ], 3045 | "source": [ 3046 | "%%time\n", 3047 | "# for i in range(100,550,20):\n", 3048 | "# print(i)\n", 3049 | "# f_train, f_val = get_auto_datasets(515)\n", 3050 | "BATCH_SIZE = 8000\n", 3051 | "train_iter = MyIterator(train, batch_size=BATCH_SIZE, device=device,\n", 3052 | " repeat=False, sort_key=lambda x: (len(x.text)),\n", 3053 | " batch_size_fn=batch_size_fn, train=True)\n", 3054 | "valid_iter = MyIterator(val, batch_size=BATCH_SIZE, device=device,\n", 3055 | " repeat=False, sort_key=lambda x: (len(x.text)),\n", 3056 | " batch_size_fn=batch_size_fn, train=False)\n", 3057 | "for epoch in range(16):\n", 3058 | " prints(\"Epoch: %s\" % (epoch+1))\n", 3059 | " model_par.train()\n", 3060 | " loss = run_epoch((Batch(b.text, b.text, pad_idx) for b in train_iter), \n", 3061 | " model_par, \n", 3062 | " MultiGPULossCompute(model.generator, criterion, \n", 3063 | " devices=[0,1,2,3], opt=model_opt, chunk_size=514))\n", 3064 | " tr_losses.append(loss)\n", 3065 | " model_par.eval()\n", 3066 | " if epoch % 5 == 0:\n", 3067 | " with torch.no_grad():\n", 3068 | " test_quality(model)\n", 3069 | " loss = run_epoch((Batch(b.text, b.text, pad_idx) for b in valid_iter), \n", 3070 | " model_par, \n", 3071 | " MultiGPULossCompute(model.generator, criterion, \n", 3072 | " devices=[0,1,2,3], opt=None, chunk_size=514))\n", 3073 | " prints(\"VALIDATION LOSS: %f\" % loss.item())\n", 3074 | " vl_losses.append(loss)\n", 3075 | " \n", 3076 | "# if i % 100 == 0:\n", 3077 | "# name = str(i)\n", 3078 | "# current_time = time.strftime(\"%Y-%m-%d-%H:%M:%S\", time.gmtime())\n", 3079 | "# torch.save(model, \"./trauto_%s_%s.pth\" % (current_time, name))" 3080 | ] 3081 | }, 3082 | { 3083 | "cell_type": "code", 3084 | "execution_count": 96, 3085 | "metadata": {}, 3086 | "outputs": [ 3087 | { 3088 | "data": { 3089 | "text/plain": [ 3090 | "[tensor(7.8959, device='cuda:0'),\n", 3091 | " tensor(6.6865, device='cuda:0'),\n", 3092 | " tensor(4.6938, device='cuda:0'),\n", 3093 | " tensor(4.3410, device='cuda:0'),\n", 3094 | " tensor(4.1935, device='cuda:0'),\n", 3095 | " tensor(4.3383, device='cuda:0'),\n", 3096 | " tensor(4.7120, device='cuda:0'),\n", 3097 | " tensor(3.3147, device='cuda:0'),\n", 3098 | " tensor(3.7002, device='cuda:0'),\n", 3099 | " tensor(4.2839, device='cuda:0'),\n", 3100 | " tensor(2.2390, device='cuda:0'),\n", 3101 | " tensor(2.8114, device='cuda:0'),\n", 3102 | " tensor(3.3543, device='cuda:0')]" 3103 | ] 3104 | }, 3105 | "execution_count": 96, 3106 | "metadata": {}, 3107 | "output_type": "execute_result" 3108 | } 3109 | ], 3110 | "source": [ 3111 | "vl_losses" 3112 | ] 3113 | }, 3114 | { 3115 | "cell_type": "code", 3116 | "execution_count": 10, 3117 | "metadata": {}, 3118 | "outputs": [ 3119 | { 3120 | "name": "stderr", 3121 | "output_type": "stream", 3122 | "text": [ 3123 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type EncoderDecoder. It won't be checked for correctness upon loading.\n", 3124 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3125 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Encoder. It won't be checked for correctness upon loading.\n", 3126 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3127 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type EncoderLayer. It won't be checked for correctness upon loading.\n", 3128 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3129 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type MultiHeadedAttention. It won't be checked for correctness upon loading.\n", 3130 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3131 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type PositionwiseFeedForward. It won't be checked for correctness upon loading.\n", 3132 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3133 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type SublayerConnection. It won't be checked for correctness upon loading.\n", 3134 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3135 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type LayerNorm. It won't be checked for correctness upon loading.\n", 3136 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3137 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Decoder. It won't be checked for correctness upon loading.\n", 3138 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3139 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type DecoderLayer. It won't be checked for correctness upon loading.\n", 3140 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3141 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Embeddings. It won't be checked for correctness upon loading.\n", 3142 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3143 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type PositionalEncoding. It won't be checked for correctness upon loading.\n", 3144 | " \"type \" + obj.__name__ + \". It won't be checked \"\n", 3145 | "/anaconda/envs/py36/lib/python3.6/site-packages/torch/serialization.py:251: UserWarning: Couldn't retrieve source code for container of type Generator. It won't be checked for correctness upon loading.\n", 3146 | " \"type \" + obj.__name__ + \". It won't be checked \"\n" 3147 | ] 3148 | } 3149 | ], 3150 | "source": [ 3151 | "name = \"520-16\"\n", 3152 | "current_time = time.strftime(\"%Y-%m-%d-%H:%M:%S\", time.gmtime())\n", 3153 | "torch.save(model, \"./trauto_%s_%s.pth\" % (current_time, name))\n", 3154 | "# net.load_state_dict(torch.load('./net.pth'))" 3155 | ] 3156 | }, 3157 | { 3158 | "cell_type": "code", 3159 | "execution_count": 24, 3160 | "metadata": {}, 3161 | "outputs": [ 3162 | { 3163 | "data": { 3164 | "text/plain": [ 3165 | "([tensor(8.4402, device='cuda:0'),\n", 3166 | " tensor(8.4993, device='cuda:0'),\n", 3167 | " tensor(8.0315, device='cuda:0'),\n", 3168 | " tensor(7.4370, device='cuda:0'),\n", 3169 | " tensor(6.6916, device='cuda:0'),\n", 3170 | " tensor(6.0129, device='cuda:0'),\n", 3171 | " tensor(5.6707, device='cuda:0'),\n", 3172 | " tensor(5.5260, device='cuda:0'),\n", 3173 | " tensor(5.1802, device='cuda:0'),\n", 3174 | " tensor(4.8976, device='cuda:0'),\n", 3175 | " tensor(4.7065, device='cuda:0'),\n", 3176 | " tensor(4.5639, device='cuda:0'),\n", 3177 | " tensor(4.4391, device='cuda:0'),\n", 3178 | " tensor(4.4793, device='cuda:0'),\n", 3179 | " tensor(4.3287, device='cuda:0'),\n", 3180 | " tensor(4.1916, device='cuda:0'),\n", 3181 | " tensor(4.0643, device='cuda:0'),\n", 3182 | " tensor(3.9462, device='cuda:0'),\n", 3183 | " tensor(3.8135, device='cuda:0'),\n", 3184 | " tensor(4.0398, device='cuda:0'),\n", 3185 | " tensor(3.8437, device='cuda:0'),\n", 3186 | " tensor(3.6698, device='cuda:0'),\n", 3187 | " tensor(3.4946, device='cuda:0'),\n", 3188 | " tensor(3.3382, device='cuda:0'),\n", 3189 | " tensor(3.1766, device='cuda:0'),\n", 3190 | " tensor(3.5171, device='cuda:0'),\n", 3191 | " tensor(3.2971, device='cuda:0'),\n", 3192 | " tensor(3.0783, device='cuda:0'),\n", 3193 | " tensor(2.8765, device='cuda:0'),\n", 3194 | " tensor(2.7002, device='cuda:0'),\n", 3195 | " tensor(2.5246, device='cuda:0'),\n", 3196 | " tensor(2.8960, device='cuda:0'),\n", 3197 | " tensor(2.6964, device='cuda:0'),\n", 3198 | " tensor(2.5395, device='cuda:0'),\n", 3199 | " tensor(2.3993, device='cuda:0'),\n", 3200 | " tensor(2.2754, device='cuda:0'),\n", 3201 | " tensor(2.1594, device='cuda:0'),\n", 3202 | " tensor(2.5364, device='cuda:0'),\n", 3203 | " tensor(2.3870, device='cuda:0'),\n", 3204 | " tensor(2.2818, device='cuda:0'),\n", 3205 | " tensor(2.1870, device='cuda:0'),\n", 3206 | " tensor(2.0967, device='cuda:0'),\n", 3207 | " tensor(2.0198, device='cuda:0'),\n", 3208 | " tensor(2.3417, device='cuda:0'),\n", 3209 | " tensor(2.2287, device='cuda:0'),\n", 3210 | " tensor(2.1468, device='cuda:0'),\n", 3211 | " tensor(2.0806, device='cuda:0'),\n", 3212 | " tensor(2.0170, device='cuda:0'),\n", 3213 | " tensor(1.9589, device='cuda:0'),\n", 3214 | " tensor(2.2168, device='cuda:0'),\n", 3215 | " tensor(2.1337, device='cuda:0'),\n", 3216 | " tensor(2.0735, device='cuda:0'),\n", 3217 | " tensor(2.0168, device='cuda:0'),\n", 3218 | " tensor(1.9727, device='cuda:0'),\n", 3219 | " tensor(1.9259, device='cuda:0'),\n", 3220 | " tensor(2.1583, device='cuda:0'),\n", 3221 | " tensor(2.0888, device='cuda:0'),\n", 3222 | " tensor(2.0379, device='cuda:0'),\n", 3223 | " tensor(1.9936, device='cuda:0'),\n", 3224 | " tensor(1.9538, device='cuda:0'),\n", 3225 | " tensor(1.9192, device='cuda:0'),\n", 3226 | " tensor(2.1177, device='cuda:0'),\n", 3227 | " tensor(2.0585, device='cuda:0'),\n", 3228 | " tensor(2.0155, device='cuda:0'),\n", 3229 | " tensor(1.9798, device='cuda:0'),\n", 3230 | " tensor(1.9463, device='cuda:0'),\n", 3231 | " tensor(1.9180, device='cuda:0'),\n", 3232 | " tensor(2.0867, device='cuda:0'),\n", 3233 | " tensor(2.0369, device='cuda:0'),\n", 3234 | " tensor(2.0014, device='cuda:0'),\n", 3235 | " tensor(1.9705, device='cuda:0'),\n", 3236 | " tensor(1.9416, device='cuda:0'),\n", 3237 | " tensor(1.9172, device='cuda:0'),\n", 3238 | " tensor(2.0702, device='cuda:0'),\n", 3239 | " tensor(2.0278, device='cuda:0'),\n", 3240 | " tensor(1.9949, device='cuda:0'),\n", 3241 | " tensor(1.9684, device='cuda:0'),\n", 3242 | " tensor(1.9441, device='cuda:0'),\n", 3243 | " tensor(1.9215, device='cuda:0'),\n", 3244 | " tensor(2.0615, device='cuda:0'),\n", 3245 | " tensor(2.0231, device='cuda:0'),\n", 3246 | " tensor(1.9944, device='cuda:0'),\n", 3247 | " tensor(1.9704, device='cuda:0'),\n", 3248 | " tensor(1.9484, device='cuda:0'),\n", 3249 | " tensor(1.9280, device='cuda:0'),\n", 3250 | " tensor(2.0616, device='cuda:0'),\n", 3251 | " tensor(2.0260, device='cuda:0'),\n", 3252 | " tensor(2.0009, device='cuda:0'),\n", 3253 | " tensor(1.9784, device='cuda:0'),\n", 3254 | " tensor(1.9589, device='cuda:0'),\n", 3255 | " tensor(1.9408, device='cuda:0'),\n", 3256 | " tensor(2.0565, device='cuda:0'),\n", 3257 | " tensor(2.0240, device='cuda:0'),\n", 3258 | " tensor(2.0010, device='cuda:0'),\n", 3259 | " tensor(1.9812, device='cuda:0'),\n", 3260 | " tensor(1.9635, device='cuda:0'),\n", 3261 | " tensor(1.9475, device='cuda:0'),\n", 3262 | " tensor(2.0562, device='cuda:0'),\n", 3263 | " tensor(2.0263, device='cuda:0'),\n", 3264 | " tensor(2.0052, device='cuda:0'),\n", 3265 | " tensor(1.9871, device='cuda:0'),\n", 3266 | " tensor(1.9711, device='cuda:0'),\n", 3267 | " tensor(1.9561, device='cuda:0'),\n", 3268 | " tensor(2.0610, device='cuda:0'),\n", 3269 | " tensor(2.0343, device='cuda:0'),\n", 3270 | " tensor(2.0152, device='cuda:0'),\n", 3271 | " tensor(1.9978, device='cuda:0'),\n", 3272 | " tensor(1.9828, device='cuda:0'),\n", 3273 | " tensor(1.9686, device='cuda:0'),\n", 3274 | " tensor(2.0582, device='cuda:0'),\n", 3275 | " tensor(2.0322, device='cuda:0'),\n", 3276 | " tensor(2.0152, device='cuda:0'),\n", 3277 | " tensor(1.9993, device='cuda:0'),\n", 3278 | " tensor(1.9857, device='cuda:0'),\n", 3279 | " tensor(1.9732, device='cuda:0'),\n", 3280 | " tensor(2.0601, device='cuda:0'),\n", 3281 | " tensor(2.0356, device='cuda:0'),\n", 3282 | " tensor(2.0191, device='cuda:0'),\n", 3283 | " tensor(2.0044, device='cuda:0'),\n", 3284 | " tensor(1.9914, device='cuda:0'),\n", 3285 | " tensor(1.9794, device='cuda:0'),\n", 3286 | " tensor(2.0607, device='cuda:0'),\n", 3287 | " tensor(2.0381, device='cuda:0'),\n", 3288 | " tensor(2.0222, device='cuda:0'),\n", 3289 | " tensor(2.0088, device='cuda:0'),\n", 3290 | " tensor(1.9968, device='cuda:0'),\n", 3291 | " tensor(1.9860, device='cuda:0'),\n", 3292 | " tensor(2.5128, device='cuda:0'),\n", 3293 | " tensor(2.4721, device='cuda:0'),\n", 3294 | " tensor(2.4477, device='cuda:0'),\n", 3295 | " tensor(2.4283, device='cuda:0'),\n", 3296 | " tensor(2.4117, device='cuda:0'),\n", 3297 | " tensor(2.3970, device='cuda:0'),\n", 3298 | " tensor(2.3831, device='cuda:0'),\n", 3299 | " tensor(2.3702, device='cuda:0'),\n", 3300 | " tensor(2.3586, device='cuda:0'),\n", 3301 | " tensor(2.3483, device='cuda:0'),\n", 3302 | " tensor(2.3374, device='cuda:0'),\n", 3303 | " tensor(2.3278, device='cuda:0')],\n", 3304 | " [tensor(8.1753, device='cuda:0'),\n", 3305 | " tensor(8.2121, device='cuda:0'),\n", 3306 | " tensor(5.6380, device='cuda:0'),\n", 3307 | " tensor(5.3670, device='cuda:0'),\n", 3308 | " tensor(4.5743, device='cuda:0'),\n", 3309 | " tensor(4.4048, device='cuda:0'),\n", 3310 | " tensor(4.1665, device='cuda:0'),\n", 3311 | " tensor(3.9326, device='cuda:0'),\n", 3312 | " tensor(3.8588, device='cuda:0'),\n", 3313 | " tensor(3.4387, device='cuda:0'),\n", 3314 | " tensor(3.5454, device='cuda:0'),\n", 3315 | " tensor(2.7719, device='cuda:0'),\n", 3316 | " tensor(3.0922, device='cuda:0'),\n", 3317 | " tensor(2.2784, device='cuda:0'),\n", 3318 | " tensor(2.6855, device='cuda:0'),\n", 3319 | " tensor(2.0504, device='cuda:0'),\n", 3320 | " tensor(2.4754, device='cuda:0'),\n", 3321 | " tensor(1.9140, device='cuda:0'),\n", 3322 | " tensor(2.3159, device='cuda:0'),\n", 3323 | " tensor(1.8427, device='cuda:0'),\n", 3324 | " tensor(2.2275, device='cuda:0'),\n", 3325 | " tensor(1.7557, device='cuda:0'),\n", 3326 | " tensor(2.1277, device='cuda:0'),\n", 3327 | " tensor(1.7653, device='cuda:0'),\n", 3328 | " tensor(2.1099, device='cuda:0'),\n", 3329 | " tensor(1.7105, device='cuda:0'),\n", 3330 | " tensor(2.0463, device='cuda:0'),\n", 3331 | " tensor(1.6892, device='cuda:0'),\n", 3332 | " tensor(2.0110, device='cuda:0'),\n", 3333 | " tensor(1.6745, device='cuda:0'),\n", 3334 | " tensor(1.9939, device='cuda:0'),\n", 3335 | " tensor(1.6453, device='cuda:0'),\n", 3336 | " tensor(1.9480, device='cuda:0'),\n", 3337 | " tensor(1.6494, device='cuda:0'),\n", 3338 | " tensor(1.9437, device='cuda:0'),\n", 3339 | " tensor(1.6476, device='cuda:0'),\n", 3340 | " tensor(1.9268, device='cuda:0'),\n", 3341 | " tensor(1.6661, device='cuda:0'),\n", 3342 | " tensor(1.9406, device='cuda:0'),\n", 3343 | " tensor(1.6442, device='cuda:0'),\n", 3344 | " tensor(1.9116, device='cuda:0'),\n", 3345 | " tensor(1.6579, device='cuda:0'),\n", 3346 | " tensor(1.9229, device='cuda:0'),\n", 3347 | " tensor(2.1860, device='cuda:0'),\n", 3348 | " tensor(2.3678, device='cuda:0'),\n", 3349 | " tensor(2.3918, device='cuda:0'),\n", 3350 | " tensor(2.4828, device='cuda:0')])" 3351 | ] 3352 | }, 3353 | "execution_count": 24, 3354 | "metadata": {}, 3355 | "output_type": "execute_result" 3356 | } 3357 | ], 3358 | "source": [ 3359 | "tr_losses,vl_losses" 3360 | ] 3361 | }, 3362 | { 3363 | "cell_type": "markdown", 3364 | "metadata": {}, 3365 | "source": [ 3366 | "# Final" 3367 | ] 3368 | }, 3369 | { 3370 | "cell_type": "code", 3371 | "execution_count": 11, 3372 | "metadata": {}, 3373 | "outputs": [ 3374 | { 3375 | "name": "stdout", 3376 | "output_type": "stream", 3377 | "text": [ 3378 | "True:\tthis is one of the worst sandra bullock movie since speed 2 but not quite that bad . i really lost it with those out of the blue not so \" special effect \" . guys , if you ' re an ins ##om ##nia ##c go with your girl to see this movie . i give it three sleep ##ies ! [SEP] \n", 3379 | "Predicted:\tthis is one of the worst movies i ' ve ever seen . not since the very bad guys \" with 2 guys that really ins ##om ##nia ##c \" , but it is so camp ##y that it bog ##s down an ins ##om ##nia ##c ' s with 2 guys sitting in a pile of really hot . i give this movie 2 out of 10 . [SEP] \n", 3380 | "---------------\n", 3381 | "True:\ti think the film makes a sub ##tile reference to rouge of ki ##es ##low ##ski , as the whole atmosphere gives me a feeling of red . it seems to be that a lot of the backgrounds contain red , think of the tea - room f . e . i also think this is one of the greatest movies of the last years . [SEP] \n", 3382 | "Predicted:\ti think of the last of the film , a red haired red ##eem ##ing features of a lot of the previous films , a sub - titles that seems to be red ##eem ##ed , gives the whole movie a feeling of amusement . also i think this is one of the greatest films i have ever seen . it seems to me that it is as strong as the god awful red ##unda ##ncy . [SEP] \n", 3383 | "---------------\n", 3384 | "True:\tpublicity got me to the theatre < br / > < br / > advice will take you away from this waist of time . < br / > < br / > very bad everything . < br / > < br / > do you really want to see a monkey talking with a technological device ? < br / > < br / > x [SEP] \n", 3385 | "Predicted:\tgot to the video rental place . < br / > < br / > take a very bad advice from you will want to see this . < br / > < br / > flight got everything with a very difficult talking nonsense and talking animals talking about the windows of course . < br / > < br / > do you want to see a total nonsense ? < br / > < br / > [SEP] \n", 3386 | "---------------\n" 3387 | ] 3388 | } 3389 | ], 3390 | "source": [ 3391 | "for i, batch in islice(enumerate(valid_iter), 3, 6):\n", 3392 | " print(\"True:\", end=\"\\t\")\n", 3393 | " for i in range(1, batch.text.size(1)):\n", 3394 | " sym = TEXT.vocab.itos[batch.text.data[0, i]]\n", 3395 | " print(sym, end =\" \")\n", 3396 | " if sym == \"[SEP]\": break\n", 3397 | " print()\n", 3398 | "\n", 3399 | " src = batch.text[:1]\n", 3400 | " src_mask = (src != TEXT.vocab.stoi[\"[PAD]\"]).unsqueeze(-2)\n", 3401 | " with torch.no_grad():\n", 3402 | " out = greedy_decode(model, src, src_mask, \n", 3403 | " max_len=256, start_symbol=TEXT.vocab.stoi[\"[CLS]\"])\n", 3404 | " print(\"Predicted:\", end=\"\\t\")\n", 3405 | " for i in range(1, out.size(1)):\n", 3406 | " sym = TEXT.vocab.itos[out[0, i]]\n", 3407 | " print(sym, end =\" \")\n", 3408 | " if sym == \"[SEP]\": break\n", 3409 | " print()\n", 3410 | " print(\"---------------\")" 3411 | ] 3412 | }, 3413 | { 3414 | "cell_type": "code", 3415 | "execution_count": null, 3416 | "metadata": {}, 3417 | "outputs": [], 3418 | "source": [ 3419 | "# with open('VOCAB.pkl', 'wb') as output:\n", 3420 | "# pickle.dump(TEXT.vocab, output, pickle.HIGHEST_PROTOCOL)\n", 3421 | "\n", 3422 | "with open('VOCAB.pkl', 'rb') as input:\n", 3423 | " vocab = pickle.load(input)\n", 3424 | " print(len(vocab)) # 22365" 3425 | ] 3426 | }, 3427 | { 3428 | "cell_type": "code", 3429 | "execution_count": 22, 3430 | "metadata": {}, 3431 | "outputs": [], 3432 | "source": [ 3433 | "model = torch.load('./trauto_2019-05-14-01:21:08_520.pth')" 3434 | ] 3435 | }, 3436 | { 3437 | "cell_type": "code", 3438 | "execution_count": 23, 3439 | "metadata": {}, 3440 | "outputs": [ 3441 | { 3442 | "name": "stdout", 3443 | "output_type": "stream", 3444 | "text": [ 3445 | "0\n", 3446 | "1000\n", 3447 | "2000\n", 3448 | "3000\n", 3449 | "4000\n", 3450 | "5000\n", 3451 | "6000\n", 3452 | "7000\n", 3453 | "8000\n", 3454 | "9000\n", 3455 | "10000\n", 3456 | "11000\n", 3457 | "12000\n", 3458 | "13000\n", 3459 | "14000\n", 3460 | "15000\n", 3461 | "16000\n", 3462 | "17000\n", 3463 | "18000\n", 3464 | "19000\n", 3465 | "20000\n", 3466 | "21000\n", 3467 | "22000\n", 3468 | "23000\n", 3469 | "24000\n", 3470 | "0\n", 3471 | "1000\n", 3472 | "2000\n", 3473 | "3000\n", 3474 | "4000\n", 3475 | "5000\n", 3476 | "6000\n", 3477 | "7000\n", 3478 | "8000\n", 3479 | "9000\n", 3480 | "10000\n", 3481 | "11000\n", 3482 | "12000\n", 3483 | "13000\n", 3484 | "14000\n", 3485 | "15000\n", 3486 | "16000\n", 3487 | "17000\n", 3488 | "18000\n", 3489 | "19000\n", 3490 | "20000\n", 3491 | "21000\n", 3492 | "22000\n", 3493 | "23000\n", 3494 | "24000\n" 3495 | ] 3496 | } 3497 | ], 3498 | "source": [ 3499 | "def get_auto_hid(exs):\n", 3500 | " x_auto = torch.zeros(25000, 512)\n", 3501 | " y_auto = []\n", 3502 | " model.eval()\n", 3503 | "\n", 3504 | " with torch.no_grad():\n", 3505 | " for i, ex in enumerate(exs):\n", 3506 | " src = torch.tensor([TEXT.vocab.stoi[\"[CLS]\"]] + [TEXT.vocab.stoi[t] for t in ex.text] + [TEXT.vocab.stoi[\"[SEP]\"]]).unsqueeze(-2)\n", 3507 | " src_mask = torch.ones(src.size())\n", 3508 | " hid = model.encode(src.to(device), src_mask.to(device))\n", 3509 | " x_auto[i] = hid[0,0]\n", 3510 | " y_auto.append(ex.label)\n", 3511 | " if i % 1000 == 0: print(i)\n", 3512 | " return x_auto, y_auto \n", 3513 | "\n", 3514 | "x_tr_auto, y_tr_auto = get_auto_hid(orig_train_examples)\n", 3515 | "x_vl_auto, y_vl_auto = get_auto_hid(orig_val_examples)" 3516 | ] 3517 | }, 3518 | { 3519 | "cell_type": "code", 3520 | "execution_count": 24, 3521 | "metadata": {}, 3522 | "outputs": [ 3523 | { 3524 | "name": "stderr", 3525 | "output_type": "stream", 3526 | "text": [ 3527 | "/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:37: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n", 3528 | "Invalid limit will be ignored.\n" 3529 | ] 3530 | }, 3531 | { 3532 | "data": { 3533 | "image/png": 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\n", 3534 | "text/plain": [ 3535 | "
" 3536 | ] 3537 | }, 3538 | "metadata": { 3539 | "image/png": { 3540 | "height": 265, 3541 | "width": 401 3542 | }, 3543 | "needs_background": "light" 3544 | }, 3545 | "output_type": "display_data" 3546 | } 3547 | ], 3548 | "source": [ 3549 | "hist3 = get_acc_on_samples(x_tr_auto.cpu().numpy(), y_tr_auto, x_vl_auto.cpu().numpy(), y_vl_auto)\n", 3550 | "plot_new_hist(hist3, \"tr-512\")" 3551 | ] 3552 | }, 3553 | { 3554 | "cell_type": "code", 3555 | "execution_count": 25, 3556 | "metadata": {}, 3557 | "outputs": [ 3558 | { 3559 | "data": { 3560 | "text/plain": [ 3561 | "[0.75752, 0.79372, 0.84776, 0.85912, 0.8792, 0.90064, 0.90316, 0.91012]" 3562 | ] 3563 | }, 3564 | "execution_count": 25, 3565 | "metadata": {}, 3566 | "output_type": "execute_result" 3567 | } 3568 | ], 3569 | "source": [ 3570 | "hist3" 3571 | ] 3572 | }, 3573 | { 3574 | "cell_type": "code", 3575 | "execution_count": null, 3576 | "metadata": {}, 3577 | "outputs": [], 3578 | "source": [] 3579 | } 3580 | ], 3581 | "metadata": { 3582 | "accelerator": "GPU", 3583 | "colab": { 3584 | "name": "kursa4.ipynb", 3585 | "provenance": [], 3586 | "toc_visible": true, 3587 | "version": "0.3.2" 3588 | }, 3589 | "kernelspec": { 3590 | "display_name": "Python 3", 3591 | "language": "python", 3592 | "name": "python3" 3593 | }, 3594 | "language_info": { 3595 | "codemirror_mode": { 3596 | "name": "ipython", 3597 | "version": 3 3598 | }, 3599 | "file_extension": ".py", 3600 | "mimetype": "text/x-python", 3601 | "name": "python", 3602 | "nbconvert_exporter": "python", 3603 | "pygments_lexer": "ipython3", 3604 | "version": "3.7.1" 3605 | }, 3606 | "pycharm": { 3607 | "stem_cell": { 3608 | "cell_type": "raw", 3609 | "metadata": { 3610 | "collapsed": false 3611 | }, 3612 | "source": [] 3613 | } 3614 | } 3615 | }, 3616 | "nbformat": 4, 3617 | "nbformat_minor": 1 3618 | } 3619 | --------------------------------------------------------------------------------