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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gensim.models.phrases import Phraser, Phrases\n", + "\n", + "def prep_text(text):\n", + " doc = sp(text)\n", + " lower_sents = []\n", + " for sent in doc.sents:\n", + " lower_sents.append([word.lemma_.lower() for word in sent if not word.is_punct and not word.is_stop and not word.is_space])\n", + " lower_bigram = Phraser(Phrases(lower_sents))\n", + " clean_sents = []\n", + " for sent in lower_sents:\n", + " clean_sents.append(lower_bigram[sent])\n", + " return clean_sents\n", + "\n", + "df[\"prep_text\"] = df.apply(lambda row: prep_text(row[\"text\"]), axis=1)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "763e9011", + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.models.word2vec import Word2Vec\n", + "\n", + "word2vec = Word2Vec(\n", + " sentences=df[\"prep_text\"].explode().tolist(),\n", + " vector_size=64,\n", + " sg=1,\n", + " window=10,\n", + " epochs=5,\n", + " min_count=10,\n", + " workers=4,\n", + " seed=9,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aa8f0e50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'система': 0,\n", + " 'работа': 1,\n", + " 'требование': 2,\n", + " 'база': 3,\n", + " 'пользователь': 4,\n", + " 'разработка': 5,\n", + " 'модель': 6,\n", + " 'информация': 7,\n", + " 'субд': 8,\n", + " 'этап': 9,\n", + " 'ошибка': 10,\n", + " 'функция': 11,\n", + " 'являться': 12,\n", + " 'таблица': 13,\n", + " 'средство': 14,\n", + " 'проект': 15,\n", + " 'сервер': 16,\n", + " 'процесс': 17,\n", + " 'документ': 18,\n", + " 'программа': 19,\n", + " 'использовать': 20,\n", + " 'состояние': 21,\n", + " 'проектирование': 22,\n", + " 'программный': 23,\n", + " 'случай': 24,\n", + " 'создание': 25,\n", + " 'модуль': 26,\n", + " 'время': 27,\n", + " 'объект': 28,\n", + " 'заказчик': 29,\n", + " 'приложение': 30,\n", + " 'реализация': 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xytoken
0-24.734394-7.651334система
1-30.030426-18.815897работа
2-26.8211312.513596требование
3-8.13346024.581547база
4-20.1580373.450009пользователь
............
10386.665592-2.917084некий
103918.3088099.314460самым
104012.663247-1.679625похожий
10417.7626401.464980целесообразный
1042-0.540131-15.037376обследование
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1043 rows × 3 columns

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" + ], + "text/plain": [ + " x y token\n", + "0 -24.734394 -7.651334 система\n", + "1 -30.030426 -18.815897 работа\n", + "2 -26.821131 2.513596 требование\n", + "3 -8.133460 24.581547 база\n", + "4 -20.158037 3.450009 пользователь\n", + "... ... ... ...\n", + "1038 6.665592 -2.917084 некий\n", + "1039 18.308809 9.314460 самым\n", + "1040 12.663247 -1.679625 похожий\n", + "1041 7.762640 1.464980 целесообразный\n", + "1042 -0.540131 -15.037376 обследование\n", + "\n", + "[1043 rows x 3 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import TSNE\n", + "\n", + "tsne = TSNE(n_components=2, max_iter=1000, random_state=9)\n", + "coords_df = pd.DataFrame(\n", + " tsne.fit_transform(word2vec.wv[word2vec.wv.key_to_index]), columns=[\"x\", \"y\"]\n", + ")\n", + "coords_df[\"token\"] = word2vec.wv.key_to_index.keys()\n", + "coords_df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1fa73292", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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const render_items = [{\"docid\":\"681b38e1-4bb7-437b-a4e7-5f18a8a9fdb7\",\"roots\":{\"p1046\":\"bf24c757-431b-4f0d-8197-1fcf9f8a30ad\"},\"root_ids\":[\"p1046\"]}];\n void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1046" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.io import output_notebook\n", + "from bokeh.plotting import show, figure\n", + "\n", + "output_notebook()\n", + "p = figure(width=800, height=800)\n", + "p.text(x=coords_df.x, y=coords_df.y, text=coords_df.token) # type: ignore\n", + "show(p)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lec4-2-nlp-dense.ipynb b/lec4-2-nlp-dense.ipynb new file mode 100644 index 0000000..2a32930 --- /dev/null +++ b/lec4-2-nlp-dense.ipynb @@ -0,0 +1,1610 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "507915ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9.2\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\"\n", + "import keras\n", + "\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e0043e5c", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.datasets import imdb\n", + "import os\n", + "\n", + "unique_words = 5000\n", + "max_length = 100\n", + "\n", + "output_dir = \"tmp\"\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "(X_train, y_train), (X_valid, y_valid) = imdb.load_data(num_words=unique_words, skip_top=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aadc3471", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{34704: 'fawn',\n", + " 52009: 'tsukino',\n", + " 52010: 'nunnery',\n", + " 16819: 'sonja',\n", + " 63954: 'vani',\n", + " 1411: 'woods',\n", + " 16118: 'spiders',\n", + " 2348: 'hanging',\n", + " 2292: 'woody',\n", + " 52011: 'trawling',\n", + " 52012: \"hold's\",\n", + " 11310: 'comically',\n", + " 40833: 'localized',\n", + " 30571: 'disobeying',\n", + " 52013: \"'royale\",\n", + " 40834: \"harpo's\",\n", + " 52014: 'canet',\n", + " 19316: 'aileen',\n", + " 52015: 'acurately',\n", + " 52016: \"diplomat's\",\n", + " 25245: 'rickman',\n", + " 6749: 'arranged',\n", + " 52017: 'rumbustious',\n", + " 52018: 'familiarness',\n", + " 52019: \"spider'\",\n", + " 68807: 'hahahah',\n", + " 52020: \"wood'\",\n", + " 40836: 'transvestism',\n", + " 34705: \"hangin'\",\n", + " 2341: 'bringing',\n", + " 40837: 'seamier',\n", + " 34706: 'wooded',\n", + " 52021: 'bravora',\n", + " 16820: 'grueling',\n", + " 1639: 'wooden',\n", + " 16821: 'wednesday',\n", + " 52022: \"'prix\",\n", + " 34707: 'altagracia',\n", + " 52023: 'circuitry',\n", + " 11588: 'crotch',\n", + " 57769: 'busybody',\n", + " 52024: \"tart'n'tangy\",\n", + " 14132: 'burgade',\n", + " 52026: 'thrace',\n", + " 11041: \"tom's\",\n", + " 52028: 'snuggles',\n", + " 29117: 'francesco',\n", + " 52030: 'complainers',\n", + " 52128: 'templarios',\n", + " 40838: '272',\n", + " 52031: '273',\n", + " 52133: 'zaniacs',\n", + " 34709: '275',\n", + " 27634: 'consenting',\n", + " 40839: 'snuggled',\n", + " 15495: 'inanimate',\n", + " 52033: 'uality',\n", + " 11929: 'bronte',\n", + " 4013: 'errors',\n", + " 3233: 'dialogs',\n", + " 52034: \"yomada's\",\n", + " 34710: \"madman's\",\n", + " 30588: 'dialoge',\n", + " 52036: 'usenet',\n", + " 40840: 'videodrome',\n", + " 26341: \"kid'\",\n", + " 52037: 'pawed',\n", + " 30572: \"'girlfriend'\",\n", + " 52038: \"'pleasure\",\n", + " 52039: \"'reloaded'\",\n", + " 40842: \"kazakos'\",\n", + " 52040: 'rocque',\n", + " 52041: 'mailings',\n", + " 11930: 'brainwashed',\n", + " 16822: 'mcanally',\n", + " 52042: \"tom''\",\n", + " 25246: 'kurupt',\n", + " 21908: 'affiliated',\n", + " 52043: 'babaganoosh',\n", + " 40843: \"noe's\",\n", + " 40844: 'quart',\n", + " 362: 'kids',\n", + " 5037: 'uplifting',\n", + " 7096: 'controversy',\n", + " 21909: 'kida',\n", + " 23382: 'kidd',\n", + " 52044: \"error'\",\n", + " 52045: 'neurologist',\n", + " 18513: 'spotty',\n", + " 30573: 'cobblers',\n", + " 9881: 'projection',\n", + " 40845: 'fastforwarding',\n", + " 52046: 'sters',\n", + " 52047: \"eggar's\",\n", + " 52048: 'etherything',\n", + " 40846: 'gateshead',\n", + " 34711: 'airball',\n", + " 25247: 'unsinkable',\n", + " 7183: 'stern',\n", + " 52049: \"cervi's\",\n", + " 40847: 'dnd',\n", + " 11589: 'dna',\n", + " 20601: 'insecurity',\n", + " 52050: \"'reboot'\",\n", + " 11040: 'trelkovsky',\n", + " 52051: 'jaekel',\n", + " 52052: 'sidebars',\n", + " 52053: \"sforza's\",\n", + " 17636: 'distortions',\n", + " 52054: 'mutinies',\n", + " 30605: 'sermons',\n", + " 40849: '7ft',\n", + " 52055: 'boobage',\n", + " 52056: \"o'bannon's\",\n", + " 23383: 'populations',\n", + " 52057: 'chulak',\n", + " 27636: 'mesmerize',\n", + " 52058: 'quinnell',\n", + " 10310: 'yahoo',\n", + " 52060: 'meteorologist',\n", + " 42580: 'beswick',\n", + " 15496: 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'most\\x85and',\n", + " 3933: 'dinosaurs',\n", + " 355: 'wrong',\n", + " 52073: 'seda',\n", + " 52074: 'stollen',\n", + " 34715: 'sentencing',\n", + " 40856: 'ouroboros',\n", + " 40857: 'assimilates',\n", + " 40858: 'colorfully',\n", + " 27639: 'glenne',\n", + " 52075: 'dongen',\n", + " 4763: 'subplots',\n", + " 52076: 'kiloton',\n", + " 23384: 'chandon',\n", + " 34716: \"effect'\",\n", + " 27640: 'snugly',\n", + " 40859: 'kuei',\n", + " 9095: 'welcomed',\n", + " 30074: 'dishonor',\n", + " 52078: 'concurrence',\n", + " 23385: 'stoicism',\n", + " 14899: \"guys'\",\n", + " 52080: \"beroemd'\",\n", + " 6706: 'butcher',\n", + " 40860: \"melfi's\",\n", + " 30626: 'aargh',\n", + " 20602: 'playhouse',\n", + " 11311: 'wickedly',\n", + " 1183: 'fit',\n", + " 52081: 'labratory',\n", + " 40862: 'lifeline',\n", + " 1930: 'screaming',\n", + " 4290: 'fix',\n", + " 52082: 'cineliterate',\n", + " 52083: 'fic',\n", + " 52084: 'fia',\n", + " 34717: 'fig',\n", + " 52085: 'fmvs',\n", + " 52086: 'fie',\n", 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'adapt',\n", + " 30579: \"russo's\",\n", + " 48249: 'guitarists',\n", + " 10556: 'abbott',\n", + " 40864: 'abbots',\n", + " 17652: 'lanisha',\n", + " 40866: 'magickal',\n", + " 52108: 'mattter',\n", + " 52109: \"'willy\",\n", + " 34719: 'pumpkins',\n", + " 52110: 'stuntpeople',\n", + " 30580: 'estimate',\n", + " 40867: 'ugghhh',\n", + " 11312: 'gameplay',\n", + " 52111: \"wern't\",\n", + " 40868: \"n'sync\",\n", + " 16120: 'sickeningly',\n", + " 40869: 'chiara',\n", + " 4014: 'disturbed',\n", + " 40870: 'portmanteau',\n", + " 52112: 'ineffectively',\n", + " 82146: \"duchonvey's\",\n", + " 37522: \"nasty'\",\n", + " 1288: 'purpose',\n", + " 52115: 'lazers',\n", + " 28108: 'lightened',\n", + " 52116: 'kaliganj',\n", + " 52117: 'popularism',\n", + " 18514: \"damme's\",\n", + " 30581: 'stylistics',\n", + " 52118: 'mindgaming',\n", + " 46452: 'spoilerish',\n", + " 52120: \"'corny'\",\n", + " 34721: 'boerner',\n", + " 6795: 'olds',\n", + " 52121: 'bakelite',\n", + " 27642: 'renovated',\n", + " 27643: 'forrester',\n", + " 52122: \"lumiere's\",\n", + " 52027: 'gaskets',\n", + " 887: 'needed',\n", + " 34722: 'smight',\n", + " 1300: 'master',\n", + " 25908: \"edie's\",\n", + " 40871: 'seeber',\n", + " 52123: 'hiya',\n", + " 52124: 'fuzziness',\n", + " 14900: 'genesis',\n", + " 12610: 'rewards',\n", + " 30582: 'enthrall',\n", + " 40872: \"'about\",\n", + " 52125: \"recollection's\",\n", + " 11042: 'mutilated',\n", + " 52126: 'fatherlands',\n", + " 52127: \"fischer's\",\n", + " 5402: 'positively',\n", + " 34708: '270',\n", + " 34723: 'ahmed',\n", + " 9839: 'zatoichi',\n", + " 13889: 'bannister',\n", + " 52130: 'anniversaries',\n", + " 30583: \"helm's\",\n", + " 52131: \"'work'\",\n", + " 34724: 'exclaimed',\n", + " 52132: \"'unfunny'\",\n", + " 52032: '274',\n", + " 547: 'feeling',\n", + " 52134: \"wanda's\",\n", + " 33269: 'dolan',\n", + " 52136: '278',\n", + " 52137: 'peacoat',\n", + " 40873: 'brawny',\n", + " 40874: 'mishra',\n", + " 40875: 'worlders',\n", + " 52138: 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+ " 40884: 'doesn’t',\n", + " 52152: 'purged',\n", + " 660: 'saying',\n", + " 41098: \"martians'\",\n", + " 23421: 'norliss',\n", + " 27645: 'dickey',\n", + " 52155: 'dicker',\n", + " 52156: \"'sependipity\",\n", + " 8425: 'padded',\n", + " 57795: 'ordell',\n", + " 40885: \"sturges'\",\n", + " 52157: 'independentcritics',\n", + " 5748: 'tempted',\n", + " 34727: \"atkinson's\",\n", + " 25250: 'hounded',\n", + " 52158: 'apace',\n", + " 15497: 'clicked',\n", + " 30587: \"'humor'\",\n", + " 17180: \"martino's\",\n", + " 52159: \"'supporting\",\n", + " 52035: 'warmongering',\n", + " 34728: \"zemeckis's\",\n", + " 21914: 'lube',\n", + " 52160: 'shocky',\n", + " 7479: 'plate',\n", + " 40886: 'plata',\n", + " 40887: 'sturgess',\n", + " 40888: \"nerds'\",\n", + " 20603: 'plato',\n", + " 34729: 'plath',\n", + " 40889: 'platt',\n", + " 52162: 'mcnab',\n", + " 27646: 'clumsiness',\n", + " 3902: 'altogether',\n", + " 42587: 'massacring',\n", + " 52163: 'bicenntinial',\n", + " 40890: 'skaal',\n", + " 14363: 'droning',\n", + " 8779: 'lds',\n", + " 21915: 'jaguar',\n", + " 34730: \"cale's\",\n", + " 1780: 'nicely',\n", + " 4591: 'mummy',\n", + " 18516: \"lot's\",\n", + " 10089: 'patch',\n", + " 50205: 'kerkhof',\n", + " 52164: \"leader's\",\n", + " 27647: \"'movie\",\n", + " 52165: 'uncomfirmed',\n", + " 40891: 'heirloom',\n", + " 47363: 'wrangle',\n", + " 52166: 'emotion\\x85',\n", + " 52167: \"'stargate'\",\n", + " 40892: 'pinoy',\n", + " 40893: 'conchatta',\n", + " 41131: 'broeke',\n", + " 40894: 'advisedly',\n", + " 17639: \"barker's\",\n", + " 52169: 'descours',\n", + " 775: 'lots',\n", + " 9262: 'lotr',\n", + " 9882: 'irs',\n", + " 52170: 'lott',\n", + " 40895: 'xvi',\n", + " 34731: 'irk',\n", + " 52171: 'irl',\n", + " 6890: 'ira',\n", + " 21916: 'belzer',\n", + " 52172: 'irc',\n", + " 27648: 'ire',\n", + " 40896: 'requisites',\n", + " 7696: 'discipline',\n", + " 52964: 'lyoko',\n", + " 11313: 'extend',\n", + " 876: 'nature',\n", + " 52173: \"'dickie'\",\n", + " 40897: 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'much',\n", + " 34733: 'muco',\n", + " 22618: 'wiseguy',\n", + " 27651: \"richie's\",\n", + " 40907: 'tonino',\n", + " 52190: 'unleavened',\n", + " 11590: 'fry',\n", + " 40908: \"'tv'\",\n", + " 40909: 'toning',\n", + " 14364: 'obese',\n", + " 30592: 'sensationalized',\n", + " 40910: 'spiv',\n", + " 6262: 'spit',\n", + " 7367: 'arkin',\n", + " 21918: 'charleton',\n", + " 16826: 'jeon',\n", + " 21919: 'boardroom',\n", + " 4992: 'doubts',\n", + " 3087: 'spin',\n", + " 53086: 'hepo',\n", + " 27652: 'wildcat',\n", + " 10587: 'venoms',\n", + " 52194: 'misconstrues',\n", + " 18517: 'mesmerising',\n", + " 40911: 'misconstrued',\n", + " 52195: 'rescinds',\n", + " 52196: 'prostrate',\n", + " 40912: 'majid',\n", + " 16482: 'climbed',\n", + " 34734: 'canoeing',\n", + " 52198: 'majin',\n", + " 57807: 'animie',\n", + " 40913: 'sylke',\n", + " 14902: 'conditioned',\n", + " 40914: 'waddell',\n", + " 52199: '3\\x85',\n", + " 41191: 'hyperdrive',\n", + " 34735: 'conditioner',\n", + " 53156: 'bricklayer',\n", + " 2579: 'hong',\n", + " 52201: 'memoriam',\n", + " 30595: 'inventively',\n", + " 25252: \"levant's\",\n", + " 20641: 'portobello',\n", + " 52203: 'remand',\n", + " 19507: 'mummified',\n", + " 27653: 'honk',\n", + " 19508: 'spews',\n", + " 40915: 'visitations',\n", + " 52204: 'mummifies',\n", + " 25253: 'cavanaugh',\n", + " 23388: 'zeon',\n", + " 40916: \"jungle's\",\n", + " 34736: 'viertel',\n", + " 27654: 'frenchmen',\n", + " 52205: 'torpedoes',\n", + " 52206: 'schlessinger',\n", + " 34737: 'torpedoed',\n", + " 69879: 'blister',\n", + " 52207: 'cinefest',\n", + " 34738: 'furlough',\n", + " 52208: 'mainsequence',\n", + " 40917: 'mentors',\n", + " 9097: 'academic',\n", + " 20605: 'stillness',\n", + " 40918: 'academia',\n", + " 52209: 'lonelier',\n", + " 52210: 'nibby',\n", + " 52211: \"losers'\",\n", + " 40919: 'cineastes',\n", + " 4452: 'corporate',\n", + " 40920: 'massaging',\n", + " 30596: 'bellow',\n", + " 19509: 'absurdities',\n", + " 53244: 'expetations',\n", + " 40921: 'nyfiken',\n", + " 75641: 'mehras',\n", + " 52212: 'lasse',\n", + " 52213: 'visability',\n", + " 33949: 'militarily',\n", + " 52214: \"elder'\",\n", + " 19026: 'gainsbourg',\n", + " 20606: 'hah',\n", + " 13423: 'hai',\n", + " 34739: 'haj',\n", + " 25254: 'hak',\n", + " 4314: 'hal',\n", + " 4895: 'ham',\n", + " 53262: 'duffer',\n", + " 52216: 'haa',\n", + " 69: 'had',\n", + " 11933: 'advancement',\n", + " 16828: 'hag',\n", + " 25255: \"hand'\",\n", + " 13424: 'hay',\n", + " 20607: 'mcnamara',\n", + " 52217: \"mozart's\",\n", + " 30734: 'duffel',\n", + " 30597: 'haq',\n", + " 13890: 'har',\n", + " 47: 'has',\n", + " 2404: 'hat',\n", + " 40922: 'hav',\n", + " 30598: 'haw',\n", + " 52218: 'figtings',\n", + " 15498: 'elders',\n", + " 52219: 'underpanted',\n", + " 52220: 'pninson',\n", + " 27655: 'unequivocally',\n", + " 23676: \"barbara's\",\n", + " 52222: \"bello'\",\n", + " 13000: 'indicative',\n", + " 40923: 'yawnfest',\n", + " 52223: 'hexploitation',\n", + " 52224: \"loder's\",\n", + " 27656: 'sleuthing',\n", + " 32625: \"justin's\",\n", + " 52225: \"'ball\",\n", + " 52226: \"'summer\",\n", + " 34938: \"'demons'\",\n", + " 52228: \"mormon's\",\n", + " 34740: \"laughton's\",\n", + " 52229: 'debell',\n", + " 39727: 'shipyard',\n", + " 30600: 'unabashedly',\n", + " 40404: 'disks',\n", + " 2293: 'crowd',\n", + " 10090: 'crowe',\n", + " 56437: \"vancouver's\",\n", + " 34741: 'mosques',\n", + " 6630: 'crown',\n", + " 52230: 'culpas',\n", + " 27657: 'crows',\n", + " 53347: 'surrell',\n", + " 52232: 'flowless',\n", + " 52233: 'sheirk',\n", + " 40926: \"'three\",\n", + " 52234: \"peterson'\",\n", + " 52235: 'ooverall',\n", + " 40927: 'perchance',\n", + " 1324: 'bottom',\n", + " 53366: 'chabert',\n", + " 52236: 'sneha',\n", + " 13891: 'inhuman',\n", + " 52237: 'ichii',\n", + " 52238: 'ursla',\n", + " 30601: 'completly',\n", + " 40928: 'moviedom',\n", + " 52239: 'raddick',\n", + " 51998: 'brundage',\n", + " 40929: 'brigades',\n", + " 1184: 'starring',\n", + " 52240: \"'goal'\",\n", + " 52241: 'caskets',\n", + " 52242: 'willcock',\n", + " 52243: \"threesome's\",\n", + " 52244: \"mosque'\",\n", + " 52245: \"cover's\",\n", + " 17640: 'spaceships',\n", + " 40930: 'anomalous',\n", + " 27658: 'ptsd',\n", + " 52246: 'shirdan',\n", + " 21965: 'obscenity',\n", + " 30602: 'lemmings',\n", + " 30603: 'duccio',\n", + " 52247: \"levene's\",\n", + " 52248: \"'gorby'\",\n", + " 25258: \"teenager's\",\n", + " 5343: 'marshall',\n", + " 9098: 'honeymoon',\n", + " 3234: 'shoots',\n", + " 12261: 'despised',\n", + " 52249: 'okabasho',\n", + " 8292: 'fabric',\n", + " 18518: 'cannavale',\n", + " 3540: 'raped',\n", + " 52250: \"tutt's\",\n", + " 17641: 'grasping',\n", + " 18519: 'despises',\n", + " 40931: \"thief's\",\n", + " 8929: 'rapes',\n", + " 52251: 'raper',\n", + " 27659: \"eyre'\",\n", + " 52252: 'walchek',\n", + " 23389: \"elmo's\",\n", + " 40932: 'perfumes',\n", + " 21921: 'spurting',\n", + " 52253: \"exposition'\\x85\",\n", + " 52254: 'denoting',\n", + " 34743: 'thesaurus',\n", + " 40933: \"shoot'\",\n", + " 49762: 'bonejack',\n", + " 52256: 'simpsonian',\n", + " 30604: 'hebetude',\n", + " 34744: \"hallow's\",\n", + " 52257: 'desperation\\x85',\n", + " 34745: 'incinerator',\n", + " 10311: 'congratulations',\n", + " 52258: 'humbled',\n", + " 5927: \"else's\",\n", + " 40848: 'trelkovski',\n", + " 52259: \"rape'\",\n", + " 59389: \"'chapters'\",\n", + " 52260: '1600s',\n", + " 7256: 'martian',\n", + " 25259: 'nicest',\n", + " 52262: 'eyred',\n", + " 9460: 'passenger',\n", + " 6044: 'disgrace',\n", + " 52263: 'moderne',\n", + " 5123: 'barrymore',\n", + " 52264: 'yankovich',\n", + " 40934: 'moderns',\n", + " 52265: 'studliest',\n", + " 52266: 'bedsheet',\n", + " 14903: 'decapitation',\n", + " 52267: 'slurring',\n", + " 52268: \"'nunsploitation'\",\n", + " 34746: \"'character'\",\n", + " 9883: 'cambodia',\n", + " 52269: 'rebelious',\n", + " 27660: 'pasadena',\n", + " 40935: 'crowne',\n", + " 52270: \"'bedchamber\",\n", + " 52271: 'conjectural',\n", + " 52272: 'appologize',\n", + " 52273: 'halfassing',\n", + " 57819: 'paycheque',\n", + " 20609: 'palms',\n", + " 52274: \"'islands\",\n", + " 40936: 'hawked',\n", + " 21922: 'palme',\n", + " 40937: 'conservatively',\n", + " 64010: 'larp',\n", + " 5561: 'palma',\n", + " 21923: 'smelling',\n", + " 13001: 'aragorn',\n", + " 52275: 'hawker',\n", + " 52276: 'hawkes',\n", + " 3978: 'explosions',\n", + " 8062: 'loren',\n", + " 52277: \"pyle's\",\n", + " 6707: 'shootout',\n", + " 18520: \"mike's\",\n", + " 52278: \"driscoll's\",\n", + " 40938: 'cogsworth',\n", + " 52279: \"britian's\",\n", + " 34747: 'childs',\n", + " 52280: \"portrait's\",\n", + " 3629: 'chain',\n", + " 2500: 'whoever',\n", + " 52281: 'puttered',\n", + " 52282: 'childe',\n", + " 52283: 'maywether',\n", + " 3039: 'chair',\n", + " 52284: \"rance's\",\n", + " 34748: 'machu',\n", + " 4520: 'ballet',\n", + " 34749: 'grapples',\n", + " 76155: 'summerize',\n", + " 30606: 'freelance',\n", + " 52286: \"andrea's\",\n", + " 52287: '\\x91very',\n", + " 45882: 'coolidge',\n", + " 18521: 'mache',\n", + " 52288: 'balled',\n", + " 40940: 'grappled',\n", + " 18522: 'macha',\n", + " 21924: 'underlining',\n", + " 5626: 'macho',\n", + " 19510: 'oversight',\n", + " 25260: 'machi',\n", + " 11314: 'verbally',\n", + " 21925: 'tenacious',\n", + " 40941: 'windshields',\n", + " 18560: 'paychecks',\n", + " 3399: 'jerk',\n", + " 11934: \"good'\",\n", + " 34751: 'prancer',\n", + " 21926: 'prances',\n", + " 52289: 'olympus',\n", + " 21927: 'lark',\n", + " 10788: 'embark',\n", + " 7368: 'gloomy',\n", + " 52290: 'jehaan',\n", + " 52291: 'turaqui',\n", + " 20610: \"child'\",\n", + " 2897: 'locked',\n", + " 52292: 'pranced',\n", + " 2591: 'exact',\n", + " 52293: 'unattuned',\n", + " 786: 'minute',\n", + " 16121: 'skewed',\n", + " 40943: 'hodgins',\n", + " 34752: 'skewer',\n", + " 52294: 'think\\x85',\n", + " 38768: 'rosenstein',\n", + " 52295: 'helmit',\n", + " 34753: 'wrestlemanias',\n", + " 16829: 'hindered',\n", + " 30607: \"martha's\",\n", + " 52296: 'cheree',\n", + " 52297: \"pluckin'\",\n", + " 40944: 'ogles',\n", + " 11935: 'heavyweight',\n", + " 82193: 'aada',\n", + " 11315: 'chopping',\n", + " 61537: 'strongboy',\n", + " 41345: 'hegemonic',\n", + " 40945: 'adorns',\n", + " 41349: 'xxth',\n", + " 34754: 'nobuhiro',\n", + " 52301: 'capitães',\n", + " 52302: 'kavogianni',\n", + " 13425: 'antwerp',\n", + " 6541: 'celebrated',\n", + " 52303: 'roarke',\n", + " 40946: 'baggins',\n", + " 31273: 'cheeseburgers',\n", + " 52304: 'matras',\n", + " 52305: \"nineties'\",\n", + " 52306: \"'craig'\",\n", + " 13002: 'celebrates',\n", + " 3386: 'unintentionally',\n", + " 14365: 'drafted',\n", + " 52307: 'climby',\n", + " 52308: '303',\n", + " 18523: 'oldies',\n", + " 9099: 'climbs',\n", + " 9658: 'honour',\n", + " 34755: 'plucking',\n", + " 30077: '305',\n", + " 5517: 'address',\n", + " 40947: 'menjou',\n", + " 42595: \"'freak'\",\n", + " 19511: 'dwindling',\n", + " 9461: 'benson',\n", + " 52310: 'white’s',\n", + " 40948: 'shamelessness',\n", + " 21928: 'impacted',\n", + " 52311: 'upatz',\n", + " 3843: 'cusack',\n", + " 37570: \"flavia's\",\n", + " 52312: 'effette',\n", + " 34756: 'influx',\n", + " 52313: 'boooooooo',\n", + " 52314: 'dimitrova',\n", + " 13426: 'houseman',\n", + " 25262: 'bigas',\n", + " 52315: 'boylen',\n", + " 52316: 'phillipenes',\n", + " 40949: 'fakery',\n", + " 27661: \"grandpa's\",\n", + " 27662: 'darnell',\n", + " 19512: 'undergone',\n", + " 52318: 'handbags',\n", + " 21929: 'perished',\n", + " 37781: 'pooped',\n", + " 27663: 'vigour',\n", + " 3630: 'opposed',\n", + " 52319: 'etude',\n", + " 11802: \"caine's\",\n", + " 52320: 'doozers',\n", + " 34757: 'photojournals',\n", + " 52321: 'perishes',\n", + " 34758: 'constrains',\n", + " 40951: 'migenes',\n", + " 30608: 'consoled',\n", + " 16830: 'alastair',\n", + " 52322: 'wvs',\n", + " 52323: 'ooooooh',\n", + " 34759: 'approving',\n", + " 40952: 'consoles',\n", + " 52067: 'disparagement',\n", + " 52325: 'futureistic',\n", + " 52326: 'rebounding',\n", + " 52327: \"'date\",\n", + " 52328: 'gregoire',\n", + " 21930: 'rutherford',\n", + " 34760: 'americanised',\n", + " 82199: 'novikov',\n", + " 1045: 'following',\n", + " 34761: 'munroe',\n", + " 52329: \"morita'\",\n", + " 52330: 'christenssen',\n", + " 23109: 'oatmeal',\n", + " 25263: 'fossey',\n", + " 40953: 'livered',\n", + " 13003: 'listens',\n", + " 76167: \"'marci\",\n", + " 52333: \"otis's\",\n", + " 23390: 'thanking',\n", + " 16022: 'maude',\n", + " 34762: 'extensions',\n", + " 52335: 'ameteurish',\n", + " 52336: \"commender's\",\n", + " 27664: 'agricultural',\n", + " 4521: 'convincingly',\n", + " 17642: 'fueled',\n", + " 54017: 'mahattan',\n", + " 40955: \"paris's\",\n", + " 52339: 'vulkan',\n", + " 52340: 'stapes',\n", + " 52341: 'odysessy',\n", + " 12262: 'harmon',\n", + " 4255: 'surfing',\n", + " 23497: 'halloran',\n", + " 49583: 'unbelieveably',\n", + " 52342: \"'offed'\",\n", + " 30610: 'quadrant',\n", + " 19513: 'inhabiting',\n", + " 34763: 'nebbish',\n", + " 40956: 'forebears',\n", + " 34764: 'skirmish',\n", + " 52343: 'ocassionally',\n", + " 52344: \"'resist\",\n", + " 21931: 'impactful',\n", + " 52345: 'spicier',\n", + " 40957: 'touristy',\n", + " 52346: \"'football'\",\n", + " 40958: 'webpage',\n", + " 52348: 'exurbia',\n", + " 52349: 'jucier',\n", + " 14904: 'professors',\n", + " 34765: 'structuring',\n", + " 30611: 'jig',\n", + " 40959: 'overlord',\n", + " 25264: 'disconnect',\n", + " 82204: 'sniffle',\n", + " 40960: 'slimeball',\n", + " 40961: 'jia',\n", + " 16831: 'milked',\n", + " 40962: 'banjoes',\n", + " 1240: 'jim',\n", + " 52351: 'workforces',\n", + " 52352: 'jip',\n", + " 52353: 'rotweiller',\n", + " 34766: 'mundaneness',\n", + " 52354: \"'ninja'\",\n", + " 11043: \"dead'\",\n", + " 40963: \"cipriani's\",\n", + " 20611: 'modestly',\n", + " 52355: \"professor'\",\n", + " 40964: 'shacked',\n", + " 34767: 'bashful',\n", + " 23391: 'sorter',\n", + " 16123: 'overpowering',\n", + " 18524: 'workmanlike',\n", + " 27665: 'henpecked',\n", + " 18525: 'sorted',\n", + " 52357: \"jōb's\",\n", + " 52358: \"'always\",\n", + " 34768: \"'baptists\",\n", + " 52359: 'dreamcatchers',\n", + " 52360: \"'silence'\",\n", + " 21932: 'hickory',\n", + " 52361: 'fun\\x97yet',\n", + " 52362: 'breakumentary',\n", + " 15499: 'didn',\n", + " 52363: 'didi',\n", + " 52364: 'pealing',\n", + " 40965: 'dispite',\n", + " 25265: \"italy's\",\n", + " 21933: 'instability',\n", + " 6542: 'quarter',\n", + " 12611: 'quartet',\n", + " 52365: 'padmé',\n", + " 52366: \"'bleedmedry\",\n", + " 52367: 'pahalniuk',\n", + " 52368: 'honduras',\n", + " 10789: 'bursting',\n", + " 41468: \"pablo's\",\n", + " 52370: 'irremediably',\n", + " 40966: 'presages',\n", + " 57835: 'bowlegged',\n", + " 65186: 'dalip',\n", + " 6263: 'entering',\n", + " 76175: 'newsradio',\n", + " 54153: 'presaged',\n", + " 27666: \"giallo's\",\n", + " 40967: 'bouyant',\n", + " 52371: 'amerterish',\n", + " 18526: 'rajni',\n", + " 30613: 'leeves',\n", + " 34770: 'macauley',\n", + " 615: 'seriously',\n", + " 52372: 'sugercoma',\n", + " 52373: 'grimstead',\n", + " 52374: \"'fairy'\",\n", + " 30614: 'zenda',\n", + " 52375: \"'twins'\",\n", + " 17643: 'realisation',\n", + " 27667: 'highsmith',\n", + " 7820: 'raunchy',\n", + " 40968: 'incentives',\n", + " 52377: 'flatson',\n", + " 35100: 'snooker',\n", + " 16832: 'crazies',\n", + " 14905: 'crazier',\n", + " 7097: 'grandma',\n", + " 52378: 'napunsaktha',\n", + " 30615: 'workmanship',\n", + " 52379: 'reisner',\n", + " 61309: \"sanford's\",\n", + " 52380: '\\x91doña',\n", + " 6111: 'modest',\n", + " 19156: \"everything's\",\n", + " 40969: 'hamer',\n", + " 52382: \"couldn't'\",\n", + " 13004: 'quibble',\n", + " 52383: 'socking',\n", + " 21934: 'tingler',\n", + " 52384: 'gutman',\n", + " 40970: 'lachlan',\n", + " 52385: 'tableaus',\n", + " 52386: 'headbanger',\n", + " 2850: 'spoken',\n", + " 34771: 'cerebrally',\n", + " 23493: \"'road\",\n", + " 21935: 'tableaux',\n", + " 40971: \"proust's\",\n", + " 40972: 'periodical',\n", + " 52388: \"shoveller's\",\n", + " 25266: 'tamara',\n", + " 17644: 'affords',\n", + " 3252: 'concert',\n", + " 87958: \"yara's\",\n", + " 52389: 'someome',\n", + " 8427: 'lingering',\n", + " 41514: \"abraham's\",\n", + " 34772: 'beesley',\n", + " 34773: 'cherbourg',\n", + " 28627: 'kagan',\n", + " 9100: 'snatch',\n", + " 9263: \"miyazaki's\",\n", + " 25267: 'absorbs',\n", + " 40973: \"koltai's\",\n", + " 64030: 'tingled',\n", + " 19514: 'crossroads',\n", + " 16124: 'rehab',\n", + " 52392: 'falworth',\n", + " 52393: 'sequals',\n", + " ...}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "word_index = imdb.get_word_index()\n", + "word_index = { k: (v + 3) for k, v in word_index.items() }\n", + "word_index[\"PAD\"] = 0\n", + "word_index[\"START\"] = 1\n", + "word_index[\"UNK\"] = 2\n", + "index_word = { v: k for k, v in word_index.items() }\n", + "index_word" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "059670a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 530,\n", + " 973,\n", + " 1622,\n", + " 1385,\n", + " 65,\n", + " 458,\n", + " 4468,\n", + " 66,\n", + " 3941,\n", + " 2,\n", + " 173,\n", + " 2,\n", + " 256,\n", + " 2,\n", + " 2,\n", + " 100,\n", + " 2,\n", + " 838,\n", + " 112,\n", + " 50,\n", + " 670,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 480,\n", + " 284,\n", + " 2,\n", + " 150,\n", + " 2,\n", + " 172,\n", + " 112,\n", + " 167,\n", + " 2,\n", + " 336,\n", + " 385,\n", + " 2,\n", + " 2,\n", + " 172,\n", + " 4536,\n", + " 1111,\n", + " 2,\n", + " 546,\n", + " 2,\n", + " 2,\n", + " 447,\n", + " 2,\n", + " 192,\n", + " 50,\n", + " 2,\n", + " 2,\n", + " 147,\n", + " 2025,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 1920,\n", + " 4613,\n", + " 469,\n", + " 2,\n", + " 2,\n", + " 71,\n", + " 87,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 530,\n", + " 2,\n", + " 76,\n", + " 2,\n", + " 2,\n", + " 1247,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 515,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 626,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 62,\n", + " 386,\n", + " 2,\n", + " 2,\n", + " 316,\n", + " 2,\n", + " 106,\n", + " 2,\n", + " 2,\n", + " 2223,\n", + " 2,\n", + " 2,\n", + " 480,\n", + " 66,\n", + " 3785,\n", + " 2,\n", + " 2,\n", + " 130,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 619,\n", + " 2,\n", + " 2,\n", + " 124,\n", + " 51,\n", + " 2,\n", + " 135,\n", + " 2,\n", + " 2,\n", + " 1415,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 215,\n", + " 2,\n", + " 77,\n", + " 52,\n", + " 2,\n", + " 2,\n", + " 407,\n", + " 2,\n", + " 82,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 107,\n", + " 117,\n", + " 2,\n", + " 2,\n", + " 256,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 3766,\n", + " 2,\n", + " 723,\n", + " 2,\n", + " 71,\n", + " 2,\n", + " 530,\n", + " 476,\n", + " 2,\n", + " 400,\n", + " 317,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 1029,\n", + " 2,\n", + " 104,\n", + " 88,\n", + " 2,\n", + " 381,\n", + " 2,\n", + " 297,\n", + " 98,\n", + " 2,\n", + " 2071,\n", + " 56,\n", + " 2,\n", + " 141,\n", + " 2,\n", + " 194,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 226,\n", + " 2,\n", + " 2,\n", + " 134,\n", + " 476,\n", + " 2,\n", + " 480,\n", + " 2,\n", + " 144,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 51,\n", + " 2,\n", + " 2,\n", + " 224,\n", + " 92,\n", + " 2,\n", + " 104,\n", + " 2,\n", + " 226,\n", + " 65,\n", + " 2,\n", + " 2,\n", + " 1334,\n", + " 88,\n", + " 2,\n", + " 2,\n", + " 283,\n", + " 2,\n", + " 2,\n", + " 4472,\n", + " 113,\n", + " 103,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 2,\n", + " 178,\n", + " 2]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4c1912fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"UNK UNK UNK UNK UNK brilliant casting location scenery story direction everyone's really suited UNK part UNK played UNK UNK could UNK imagine being there robert UNK UNK UNK amazing actor UNK now UNK same being director UNK father came UNK UNK same scottish island UNK myself UNK UNK loved UNK fact there UNK UNK real connection UNK UNK UNK UNK witty remarks throughout UNK UNK were great UNK UNK UNK brilliant UNK much UNK UNK bought UNK UNK UNK soon UNK UNK UNK released UNK UNK UNK would recommend UNK UNK everyone UNK watch UNK UNK fly UNK UNK amazing really cried UNK UNK end UNK UNK UNK sad UNK UNK know what UNK say UNK UNK cry UNK UNK UNK UNK must UNK been good UNK UNK definitely UNK also UNK UNK UNK two little UNK UNK played UNK UNK UNK norman UNK paul UNK were UNK brilliant children UNK often left UNK UNK UNK UNK list UNK think because UNK stars UNK play them UNK grown up UNK such UNK big UNK UNK UNK whole UNK UNK these children UNK amazing UNK should UNK UNK UNK what UNK UNK done don't UNK think UNK whole story UNK UNK lovely because UNK UNK true UNK UNK someone's life after UNK UNK UNK UNK UNK us UNK\"" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\" \".join(index_word[id] for id in X_train[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "faf79c8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"START this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert redford's is an amazing actor and now the same being director norman's father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for retail and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also congratulations to the two little boy's that played the part's of norman and paul they were just brilliant children are often left out of the praising list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\"" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(textual_X_train, _), _ = imdb.load_data()\n", + "\" \".join(index_word[id] for id in textual_X_train[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "131e125a", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.preprocessing.sequence import pad_sequences\n", + "\n", + "X_train = pad_sequences(X_train, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)\n", + "X_valid = pad_sequences(X_valid, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6a2e7a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding_1 (Embedding)         │ (None, 100, 64)        │       320,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_1 (Flatten)             │ (None, 6400)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (Dense)                 │ (None, 64)             │       409,664 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (Dropout)             │ (None, 64)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ (None, 1)              │            65 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m320,000\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6400\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m409,664\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m65\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 729,729 (2.78 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m729,729\u001b[0m (2.78 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from keras.api.models import Sequential\n", + "from keras.api.layers import Dense, Flatten, Dropout, Embedding, InputLayer\n", + "\n", + "simple_model = Sequential()\n", + "simple_model.add(InputLayer(shape=(max_length,), dtype=\"float32\"))\n", + "simple_model.add(Embedding(unique_words, 64))\n", + "simple_model.add(Flatten())\n", + "simple_model.add(Dense(64, activation=\"relu\"))\n", + "simple_model.add(Dropout(0.5))\n", + "simple_model.add(Dense(1, activation=\"sigmoid\"))\n", + "\n", + "simple_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "52043fc5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 19ms/step - accuracy: 0.5753 - loss: 0.6517 - val_accuracy: 0.8346 - val_loss: 0.3689\n", + "Epoch 2/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - accuracy: 0.8922 - loss: 0.2751 - val_accuracy: 0.8460 - val_loss: 0.3510\n", + "Epoch 3/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.9724 - loss: 0.1080 - val_accuracy: 0.8335 - val_loss: 0.4402\n", + "Epoch 4/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 18ms/step - accuracy: 0.9974 - loss: 0.0224 - val_accuracy: 0.8337 - val_loss: 0.5407\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from keras.api.callbacks import ModelCheckpoint\n", + "\n", + "simple_model.compile(\n", + " loss=\"binary_crossentropy\",\n", + " optimizer=\"adam\",\n", + " metrics=[\"accuracy\"],\n", + ")\n", + "\n", + "simple_model.fit(\n", + " X_train,\n", + " y_train,\n", + " batch_size=128,\n", + " epochs=4,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[ModelCheckpoint(filepath=output_dir + \"/simple_weights.{epoch:02d}.keras\")],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "73443ddb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.8436 - loss: 0.3559\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.3510318398475647, 0.8459600210189819]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simple_model.load_weights(output_dir + \"/simple_weights.02.keras\")\n", + "simple_model.evaluate(X_valid, y_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "069236c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(simple_model.predict(X_valid))\n", + "_ = plt.axvline(x=0.5, color=\"orange\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lec4-3-nlp-conv.ipynb b/lec4-3-nlp-conv.ipynb new file mode 100644 index 0000000..83dbcd5 --- /dev/null +++ b/lec4-3-nlp-conv.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "507915ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9.2\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\"\n", + "import keras\n", + "\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0043e5c", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.datasets import imdb\n", + "import os\n", + "\n", + "unique_words = 5000\n", + "max_length = 400\n", + "\n", + "output_dir = \"tmp\"\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "(X_train, y_train), (X_valid, y_valid) = imdb.load_data(num_words=unique_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "131e125a", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.preprocessing.sequence import pad_sequences\n", + "\n", + "X_train = pad_sequences(X_train, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)\n", + "X_valid = pad_sequences(X_valid, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47f253ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding (Embedding)           │ (None, 400, 64)        │       320,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout1d               │ (None, 400, 64)        │             0 │\n",
+       "│ (SpatialDropout1D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d (Conv1D)                 │ (None, 398, 256)       │        49,408 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_max_pooling1d            │ (None, 256)            │             0 │\n",
+       "│ (GlobalMaxPooling1D)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 256)            │        65,792 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (Dropout)               │ (None, 256)            │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ (None, 1)              │           257 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m320,000\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m398\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m49,408\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_max_pooling1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m65,792\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m257\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 435,457 (1.66 MB)\n",
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 Trainable params: 435,457 (1.66 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m435,457\u001b[0m (1.66 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from keras.api.models import Sequential\n", + "from keras.api.layers import InputLayer, Embedding, SpatialDropout1D, Conv1D, GlobalMaxPooling1D, Dense, Dropout\n", + "\n", + "conv_model = Sequential()\n", + "conv_model.add(InputLayer(shape=(max_length,), dtype=\"float32\"))\n", + "conv_model.add(Embedding(unique_words, 64))\n", + "conv_model.add(SpatialDropout1D(0.2))\n", + "\n", + "# сверточный слой\n", + "conv_model.add(Conv1D(256, 3, activation=\"relu\"))\n", + "\n", + "conv_model.add(GlobalMaxPooling1D())\n", + "\n", + "# полносвязанный слой\n", + "conv_model.add(Dense(256, activation=\"relu\"))\n", + "conv_model.add(Dropout(0.2))\n", + "\n", + "# выходной слой\n", + "conv_model.add(Dense(1, activation=\"sigmoid\"))\n", + "\n", + "conv_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68838a5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 48ms/step - accuracy: 0.6241 - loss: 0.6084 - val_accuracy: 0.8735 - val_loss: 0.3017\n", + "Epoch 2/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 44ms/step - accuracy: 0.8978 - loss: 0.2526 - val_accuracy: 0.8906 - val_loss: 0.2637\n", + "Epoch 3/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 45ms/step - accuracy: 0.9400 - loss: 0.1670 - val_accuracy: 0.8906 - val_loss: 0.2735\n", + "Epoch 4/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 46ms/step - accuracy: 0.9648 - loss: 0.1062 - val_accuracy: 0.8894 - val_loss: 0.3019\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from keras.api.callbacks import ModelCheckpoint\n", + "\n", + "conv_model.compile(\n", + " loss=\"binary_crossentropy\",\n", + " optimizer=\"adam\",\n", + " metrics=[\"accuracy\"],\n", + ")\n", + "\n", + "conv_model.fit(\n", + " X_train,\n", + " y_train,\n", + " batch_size=128,\n", + " epochs=4,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[ModelCheckpoint(filepath=output_dir + \"/conv_weights.{epoch:02d}.keras\")],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6475dab9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8874 - loss: 0.2689\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.2637385427951813, 0.89055997133255]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conv_model.load_weights(output_dir + \"/conv_weights.02.keras\")\n", + "conv_model.evaluate(X_valid, y_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bf2d2d6c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(conv_model.predict(X_valid))\n", + "_ = plt.axvline(x=0.5, color=\"orange\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lec4-4-nlp-rnn.ipynb b/lec4-4-nlp-rnn.ipynb new file mode 100644 index 0000000..1079499 --- /dev/null +++ b/lec4-4-nlp-rnn.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "507915ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9.2\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n", + "import keras\n", + "\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0043e5c", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.datasets import imdb\n", + "import os\n", + "\n", + "unique_words = 10000\n", + "max_length = 100\n", + "\n", + "output_dir = \"tmp\"\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "(X_train, y_train), (X_valid, y_valid) = imdb.load_data(num_words=unique_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "131e125a", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.preprocessing.sequence import pad_sequences\n", + "\n", + "X_train = pad_sequences(X_train, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)\n", + "X_valid = pad_sequences(X_valid, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e3fb0ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding (Embedding)           │ (None, 100, 64)        │       640,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout1d               │ (None, 100, 64)        │             0 │\n",
+       "│ (SpatialDropout1D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ simple_rnn (SimpleRNN)          │ (None, 256)            │        82,176 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 1)              │           257 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m640,000\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ simple_rnn (\u001b[38;5;33mSimpleRNN\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m82,176\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m257\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 722,433 (2.76 MB)\n",
+       "
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 Trainable params: 722,433 (2.76 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m722,433\u001b[0m (2.76 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from keras.api.models import Sequential\n", + "from keras.api.layers import InputLayer, Embedding, SpatialDropout1D, SimpleRNN, Dense\n", + "\n", + "rnn_model = Sequential()\n", + "rnn_model.add(InputLayer(shape=(max_length,), dtype=\"float32\"))\n", + "rnn_model.add(Embedding(unique_words, 64))\n", + "rnn_model.add(SpatialDropout1D(0.2))\n", + "rnn_model.add(SimpleRNN(256, dropout=0.2))\n", + "rnn_model.add(Dense(1, activation=\"sigmoid\"))\n", + "\n", + "rnn_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "11236198", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 68ms/step - accuracy: 0.5207 - loss: 0.6994 - val_accuracy: 0.5872 - val_loss: 0.6700\n", + "Epoch 2/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 64ms/step - accuracy: 0.6188 - loss: 0.6423 - val_accuracy: 0.6368 - val_loss: 0.6183\n", + "Epoch 3/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 64ms/step - accuracy: 0.7102 - loss: 0.5539 - val_accuracy: 0.6463 - val_loss: 0.6441\n", + "Epoch 4/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 65ms/step - accuracy: 0.7746 - loss: 0.4737 - val_accuracy: 0.7338 - val_loss: 0.5681\n", + "Epoch 5/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 65ms/step - accuracy: 0.8127 - loss: 0.4065 - val_accuracy: 0.6766 - val_loss: 0.6422\n", + "Epoch 6/16\n", + "\u001b[1m196/196\u001b[0m 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val_accuracy: 0.7359 - val_loss: 0.7074\n", + "Epoch 11/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 69ms/step - accuracy: 0.9418 - loss: 0.1523 - val_accuracy: 0.7127 - val_loss: 0.8376\n", + "Epoch 12/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 68ms/step - accuracy: 0.9440 - loss: 0.1462 - val_accuracy: 0.7288 - val_loss: 0.8534\n", + "Epoch 13/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 69ms/step - accuracy: 0.9344 - loss: 0.1649 - val_accuracy: 0.7157 - val_loss: 0.8279\n", + "Epoch 14/16\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 70ms/step - accuracy: 0.9201 - loss: 0.1998 - val_accuracy: 0.6386 - val_loss: 1.1343\n", + "Epoch 15/16\n", + "\u001b[1m196/196\u001b[0m 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}, + { + "cell_type": "code", + "execution_count": 6, + "id": "94987771", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.7307 - loss: 0.7206\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.7074107527732849, 0.7359200119972229]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rnn_model.load_weights(output_dir + \"/rnn_weights.10.keras\")\n", + "rnn_model.evaluate(X_valid, y_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8965a612", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(rnn_model.predict(X_valid))\n", + "_ = plt.axvline(x=0.5, color=\"orange\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lec4-5-nlp-lstm.ipynb b/lec4-5-nlp-lstm.ipynb new file mode 100644 index 0000000..ad95b6b --- /dev/null +++ b/lec4-5-nlp-lstm.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "507915ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9.2\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n", + "import keras\n", + "\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0043e5c", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.datasets import imdb\n", + "import os\n", + "\n", + "unique_words = 10000\n", + "max_length = 100\n", + "\n", + "output_dir = \"tmp\"\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "(X_train, y_train), (X_valid, y_valid) = imdb.load_data(num_words=unique_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "131e125a", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.preprocessing.sequence import pad_sequences\n", + "\n", + "X_train = pad_sequences(X_train, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)\n", + "X_valid = pad_sequences(X_valid, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e3fb0ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
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\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding (Embedding)           │ (None, 100, 64)        │       640,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout1d               │ (None, 100, 64)        │             0 │\n",
+       "│ (SpatialDropout1D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm (LSTM)                     │ (None, 256)            │       328,704 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 1)              │           257 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Trainable params: 968,961 (3.70 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m968,961\u001b[0m (3.70 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from keras.api.models import Sequential\n", + "from keras.api.layers import InputLayer, Embedding, SpatialDropout1D, LSTM, Dense\n", + "\n", + "lstm_model = Sequential()\n", + "lstm_model.add(InputLayer(shape=(max_length,), dtype=\"float32\"))\n", + "lstm_model.add(Embedding(unique_words, 64))\n", + "lstm_model.add(SpatialDropout1D(0.2))\n", + "lstm_model.add(LSTM(256, dropout=0.2))\n", + "lstm_model.add(Dense(1, activation=\"sigmoid\"))\n", + "\n", + "lstm_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "11236198", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 214ms/step - accuracy: 0.6435 - loss: 0.6105 - val_accuracy: 0.8497 - val_loss: 0.3466\n", + "Epoch 2/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 231ms/step - accuracy: 0.8819 - loss: 0.2947 - val_accuracy: 0.8527 - val_loss: 0.3380\n", + "Epoch 3/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 273ms/step - accuracy: 0.9121 - loss: 0.2282 - val_accuracy: 0.8472 - val_loss: 0.3587\n", + "Epoch 4/4\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 325ms/step - accuracy: 0.9299 - loss: 0.1847 - val_accuracy: 0.8332 - val_loss: 0.3998\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from keras.api.callbacks import ModelCheckpoint\n", + "\n", + "lstm_model.compile(\n", + " loss=\"binary_crossentropy\",\n", + " optimizer=\"adam\",\n", + " metrics=[\"accuracy\"],\n", + ")\n", + "\n", + "lstm_model.fit(\n", + " X_train,\n", + " y_train,\n", + " batch_size=128,\n", + " epochs=4,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[ModelCheckpoint(filepath=output_dir + \"/lstm_weights.{epoch:02d}.keras\")],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "94987771", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 50ms/step - accuracy: 0.8509 - loss: 0.3421\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.33803924918174744, 0.8527200222015381]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lstm_model.load_weights(output_dir + \"/lstm_weights.02.keras\")\n", + "lstm_model.evaluate(X_valid, y_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8965a612", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 47ms/step\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(lstm_model.predict(X_valid))\n", + "_ = plt.axvline(x=0.5, color=\"orange\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lec4-6-nlp-blstm.ipynb b/lec4-6-nlp-blstm.ipynb new file mode 100644 index 0000000..18fc6eb --- /dev/null +++ b/lec4-6-nlp-blstm.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "507915ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9.2\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"jax\"\n", + "import keras\n", + "\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0043e5c", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.datasets import imdb\n", + "import os\n", + "\n", + "unique_words = 10000\n", + "max_length = 100\n", + "\n", + "output_dir = \"tmp\"\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "(X_train, y_train), (X_valid, y_valid) = imdb.load_data(num_words=unique_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "131e125a", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.api.preprocessing.sequence import pad_sequences\n", + "\n", + "X_train = pad_sequences(X_train, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)\n", + "X_valid = pad_sequences(X_valid, maxlen=max_length, padding=\"pre\", truncating=\"pre\", value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e3fb0ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding (Embedding)           │ (None, 100, 64)        │       640,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout1d               │ (None, 100, 64)        │             0 │\n",
+       "│ (SpatialDropout1D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bidirectional (Bidirectional)   │ (None, 512)            │       657,408 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 1)              │           513 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m640,000\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m657,408\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m513\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 1,297,921 (4.95 MB)\n",
+       "
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 Trainable params: 1,297,921 (4.95 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,297,921\u001b[0m (4.95 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from keras.api.models import Sequential\n", + "from keras.api.layers import InputLayer, Embedding, SpatialDropout1D, LSTM, Bidirectional, Dense\n", + "\n", + "blstm_model = Sequential()\n", + "blstm_model.add(InputLayer(shape=(max_length,), dtype=\"float32\"))\n", + "blstm_model.add(Embedding(unique_words, 64))\n", + "blstm_model.add(SpatialDropout1D(0.2))\n", + "blstm_model.add(Bidirectional(LSTM(256, dropout=0.2)))\n", + "blstm_model.add(Dense(1, activation=\"sigmoid\"))\n", + "\n", + "blstm_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "11236198", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m134s\u001b[0m 682ms/step - accuracy: 0.6565 - loss: 0.6039 - val_accuracy: 0.8432 - val_loss: 0.3756\n", + "Epoch 2/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 848ms/step - accuracy: 0.8841 - loss: 0.2820 - val_accuracy: 0.8425 - val_loss: 0.3577\n", + "Epoch 3/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 902ms/step - accuracy: 0.9148 - loss: 0.2238 - val_accuracy: 0.8459 - val_loss: 0.3929\n", + "Epoch 4/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 875ms/step - accuracy: 0.9375 - loss: 0.1744 - val_accuracy: 0.8434 - val_loss: 0.3572\n", + "Epoch 5/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m155s\u001b[0m 790ms/step - accuracy: 0.9466 - loss: 0.1520 - val_accuracy: 0.8385 - val_loss: 0.4029\n", + "Epoch 6/6\n", + "\u001b[1m196/196\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m158s\u001b[0m 807ms/step - accuracy: 0.9584 - loss: 0.1172 - val_accuracy: 0.8337 - val_loss: 0.4419\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from keras.api.callbacks import ModelCheckpoint\n", + "\n", + "blstm_model.compile(\n", + " loss=\"binary_crossentropy\",\n", + " optimizer=\"adam\",\n", + " metrics=[\"accuracy\"],\n", + ")\n", + "\n", + "blstm_model.fit(\n", + " X_train,\n", + " y_train,\n", + " batch_size=128,\n", + " epochs=6,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[ModelCheckpoint(filepath=output_dir + \"/blstm_weights.{epoch:02d}.keras\")],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "94987771", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 110ms/step - accuracy: 0.8449 - loss: 0.3976\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.3929494023323059, 0.8458799719810486]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blstm_model.load_weights(output_dir + \"/blstm_weights.03.keras\")\n", + "blstm_model.evaluate(X_valid, y_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8965a612", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 108ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(blstm_model.predict(X_valid))\n", + "_ = plt.axvline(x=0.5, color=\"orange\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.10)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}