From 1d0184c4cc2d71cdaed0e531215f227cad1bbcb4 Mon Sep 17 00:00:00 2001
From: Matvey <KiseliovMI@mpei.ru>
Date: Wed, 17 Dec 2025 22:06:28 +0300
Subject: [PATCH] lr4

---
 labworks/LW4/1.png           | Bin 0 -> 14156 bytes
 labworks/LW4/2.png           | Bin 0 -> 21180 bytes
 labworks/LW4/Untitled4.ipynb |   1 +
 labworks/LW4/report4.md      | 315 +++++++++++++++++++++++++++++++++++
 4 files changed, 316 insertions(+)
 create mode 100644 labworks/LW4/1.png
 create mode 100644 labworks/LW4/2.png
 create mode 100644 labworks/LW4/Untitled4.ipynb
 create mode 100644 labworks/LW4/report4.md

diff --git a/labworks/LW4/1.png b/labworks/LW4/1.png
new file mode 100644
index 0000000000000000000000000000000000000000..875a23adb2e36b613f56234d0d17580b181312cb
GIT binary patch
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diff --git a/labworks/LW4/Untitled4.ipynb b/labworks/LW4/Untitled4.ipynb
new file mode 100644
index 0000000..3b9990e
--- /dev/null
+++ b/labworks/LW4/Untitled4.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1dQiaK4Y4D4eYkkKYx5IBKLvpkudanxQO","authorship_tag":"ABX9TyORSDet3l9scspyGr9Z+1WD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"id":"rASa9J7lykKr","executionInfo":{"status":"ok","timestamp":1765983141737,"user_tz":-180,"elapsed":41,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}}},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab4')"]},{"cell_type":"code","source":["from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.models import Sequential\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","import tensorflow as tf\n","device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n","  raise SystemError('GPU device not found')\n","print('Found GPU at: {}'.format(device_name))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ivnaOo__zEWA","executionInfo":{"status":"ok","timestamp":1765983184600,"user_tz":-180,"elapsed":9392,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"382aaeea-c183-4225-bd05-15447035225a"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Found GPU at: /device:GPU:0\n"]}]},{"cell_type":"code","source":["from keras.datasets import imdb\n","\n","vocabulary_size = 5000\n","index_from = 3\n","\n","(X_train, y_train), (X_test, y_test) = imdb.load_data(\n","    path=\"imdb.npz\",\n","    num_words=vocabulary_size,\n","    skip_top=0,\n","    maxlen=None,\n","    seed=23,\n","    start_char=1,\n","    oov_char=2,\n","    index_from=index_from\n","    )\n","\n","print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)\n","print('Shape of X test:', X_test.shape)\n","print('Shape of y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vEvCcnAazRYl","executionInfo":{"status":"ok","timestamp":1765983273528,"user_tz":-180,"elapsed":6591,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"c030c415-4ee5-418f-958d-5105ea420484"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n","\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n","Shape of X train: (25000,)\n","Shape of y train: (25000,)\n","Shape of X test: (25000,)\n","Shape of y test: (25000,)\n"]}]},{"cell_type":"code","source":["# создание словаря для перевода индексов в слова\n","# заргузка словаря \"слово:индекс\"\n","word_to_id = imdb.get_word_index()\n","# уточнение словаря\n","word_to_id = {key:(value + index_from) for key,value in word_to_id.items()}\n","word_to_id[\"<PAD>\"] = 0\n","word_to_id[\"<START>\"] = 1\n","word_to_id[\"<UNK>\"] = 2\n","word_to_id[\"<UNUSED>\"] = 3\n","# создание обратного словаря \"индекс:слово\"\n","id_to_word = {value:key for key,value in word_to_id.items()}\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zMtAST4d0VaM","executionInfo":{"status":"ok","timestamp":1765983530313,"user_tz":-180,"elapsed":2232,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"48cd3621-3c3d-41b0-8d77-c42c32e47c75"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n","\u001b[1m1641221/1641221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1us/step\n"]}]},{"cell_type":"code","source":["print(X_train[23])\n","print('len:',len(X_train[23]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KnfAYV9e0mym","executionInfo":{"status":"ok","timestamp":1765983620380,"user_tz":-180,"elapsed":39,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"165ae9e5-07c1-45f6-81ff-6a54f631277b"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["[1, 14, 20, 9, 290, 149, 48, 25, 358, 2, 120, 318, 302, 50, 26, 49, 221, 2057, 10, 10, 1212, 39, 15, 45, 801, 2, 2, 363, 2396, 7, 2, 209, 2327, 283, 8, 4, 425, 10, 10, 45, 24, 290, 3613, 972, 4, 65, 198, 40, 3462, 1224, 2, 23, 6, 4457, 225, 24, 76, 50, 8, 895, 19, 45, 164, 204, 5, 24, 55, 318, 38, 92, 140, 11, 18, 4, 65, 33, 32, 43, 168, 33, 4, 302, 10, 10, 17, 47, 77, 1046, 12, 188, 6, 117, 2, 33, 4, 130, 2, 4, 2, 7, 87, 3709, 2199, 7, 35, 2504, 5, 33, 211, 320, 2504, 132, 190, 48, 25, 2754, 4, 1273, 2, 45, 6, 1682, 8, 2, 42, 24, 8, 2, 10, 10, 32, 11, 32, 45, 6, 542, 3709, 22, 290, 319, 18, 15, 1288, 5, 15, 584]\n","len: 146\n"]}]},{"cell_type":"code","source":["review_as_text = ' '.join(id_to_word[id] for id in X_train[23])\n","print(review_as_text)\n","print('len:',len(review_as_text))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eFf0XX2b0_Na","executionInfo":{"status":"ok","timestamp":1765983653187,"user_tz":-180,"elapsed":8,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"49af7bce-a738-4b08-a35a-667b0cd699f8"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["<START> this movie is worth watching if you enjoy <UNK> over special effects there are some interesting visuals br br aside from that it's typical <UNK> <UNK> hollywood fare of <UNK> without substance true to the title br br it's not worth picking apart the story that's like performing brain <UNK> on a dinosaur there's not much there to begin with it's nothing original and not very special so don't go in for the story at all just look at the effects br br as has been mentioned it got a little <UNK> at the end <UNK> the <UNK> of great fx treatment of an invisible and at times half invisible man however if you ignore the standard <UNK> it's a sight to <UNK> or not to <UNK> br br all in all it's a decent fx film worth seeing for that purpose and that alone\n","len: 763\n"]}]},{"cell_type":"code","source":["print('MAX Len: ',len(max(X_train, key=len)))\n","print('MIN Len: ',len(min(X_train, key=len)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yqXFOWGa1E0j","executionInfo":{"status":"ok","timestamp":1765983671908,"user_tz":-180,"elapsed":9,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"ab5edfa3-f814-4f1f-b09c-61d1027a655b"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["MAX Len:  2494\n","MIN Len:  11\n"]}]},{"cell_type":"code","source":["# предобработка данных\n","from tensorflow.keras.utils import pad_sequences\n","max_words = 500\n","X_train = pad_sequences(X_train, maxlen=max_words, value=0, padding='pre', truncating='post')\n","X_test = pad_sequences(X_test, maxlen=max_words, value=0, padding='pre', truncating='post')"],"metadata":{"id":"o89GWgu51KkF","executionInfo":{"status":"ok","timestamp":1765983696841,"user_tz":-180,"elapsed":1297,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["print('MAX Len: ',len(max(X_train, key=len)))\n","print('MIN Len: ',len(min(X_train, key=len)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t-HAPdSq1MxI","executionInfo":{"status":"ok","timestamp":1765983703497,"user_tz":-180,"elapsed":2,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"57f5f920-e872-4d8b-ba8d-b01e60089253"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["MAX Len:  500\n","MIN Len:  500\n"]}]},{"cell_type":"code","source":["print(X_train[23])\n","print('len:',len(X_train[23]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pFijUmIL1N3o","executionInfo":{"status":"ok","timestamp":1765983717854,"user_tz":-180,"elapsed":15,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"7fde05fe-3d14-4946-b2c9-61495d2cc387"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["[   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0   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2754    4 1273    2   45    6 1682\n","    8    2   42   24    8    2   10   10   32   11   32   45    6  542\n"," 3709   22  290  319   18   15 1288    5   15  584]\n","len: 500\n"]}]},{"cell_type":"code","source":["review_as_text = ' '.join(id_to_word[id] for id in X_train[23])\n","print(review_as_text)\n","print('len:',len(review_as_text))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SJ0r-p8l1Sv4","executionInfo":{"status":"ok","timestamp":1765983767943,"user_tz":-180,"elapsed":15,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"42d68b4c-dcdc-43cb-81a6-34a6570d9aaa"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> this movie is worth watching if you enjoy <UNK> over special effects there are some interesting visuals br br aside from that it's typical <UNK> <UNK> hollywood fare of <UNK> without substance true to the title br br it's not worth picking apart the story that's like performing brain <UNK> on a dinosaur there's not much there to begin with it's nothing original and not very special so don't go in for the story at all just look at the effects br br as has been mentioned it got a little <UNK> at the end <UNK> the <UNK> of great fx treatment of an invisible and at times half invisible man however if you ignore the standard <UNK> it's a sight to <UNK> or not to <UNK> br br all in all it's a decent fx film worth seeing for that purpose and that alone\n","len: 2887\n"]}]},{"cell_type":"code","source":["# вывод данных\n","print('X train: \\n',X_train)\n","print('X train: \\n',X_test)\n","\n","# вывод размерностей\n","print('Shape of X train:', X_train.shape)\n","print('Shape of X test:', X_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hsbobKXb1dL0","executionInfo":{"status":"ok","timestamp":1765983790872,"user_tz":-180,"elapsed":19,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"9813b826-988d-46f1-aa98-9a82faa8fae4"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["X train: \n"," [[   0    0    0 ...    6   52  106]\n"," [   0    0    0 ...   87   22  231]\n"," [   0    0    0 ...    6  158  158]\n"," ...\n"," [   0    0    0 ... 1005    4 1630]\n"," [   0    0    0 ...    9    6  991]\n"," [   0    0    0 ...    7   32   58]]\n","X train: \n"," [[   0    0    0 ...    4    2    2]\n"," [   0    0    0 ...    6    2  123]\n"," [   0    0    0 ...    2   11  831]\n"," ...\n"," [   1   14  402 ...  819   45  131]\n"," [   0    0    0 ...   17 1540    2]\n"," [   1   17    6 ... 1026  362   37]]\n","Shape of X train: (25000, 500)\n","Shape of X test: (25000, 500)\n"]}]},{"cell_type":"code","source":["embed_dim = 32\n","lstm_units = 64\n","\n","model = Sequential()\n","model.add(layers.Embedding(input_dim=vocabulary_size, output_dim=embed_dim, input_length=max_words, input_shape=(max_words,)))\n","model.add(layers.LSTM(lstm_units))\n","model.add(layers.Dropout(0.5))\n","model.add(layers.Dense(1, activation='sigmoid'))\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"cgVZk2R81nLO","executionInfo":{"status":"ok","timestamp":1765983814420,"user_tz":-180,"elapsed":1762,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"95e38ad5-7969-415d-9fd3-ad36221ee7ce"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n","  warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:100: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n","  super().__init__(**kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"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;34m500\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │       \u001b[38;5;34m160,000\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m24,832\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\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 (\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"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │       <span style=\"color: #00af00; text-decoration-color: #00af00\">160,000</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">24,832</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m184,897\u001b[0m (722.25 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">184,897</span> (722.25 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m184,897\u001b[0m (722.25 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">184,897</span> (722.25 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["# компилируем и обучаем модель\n","batch_size = 64\n","epochs = 3\n","model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n","model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GTLaDqUc1qES","executionInfo":{"status":"ok","timestamp":1765983854527,"user_tz":-180,"elapsed":24070,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"524a1871-22c0-4d7b-bc92-3c35695e458a"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/3\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 22ms/step - accuracy: 0.6255 - loss: 0.6261 - val_accuracy: 0.8294 - val_loss: 0.3905\n","Epoch 2/3\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 21ms/step - accuracy: 0.8643 - loss: 0.3334 - val_accuracy: 0.8626 - val_loss: 0.3467\n","Epoch 3/3\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.8877 - loss: 0.2905 - val_accuracy: 0.8722 - val_loss: 0.3317\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.history.History at 0x78f347b1fb30>"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["test_loss, test_acc = model.evaluate(X_test, y_test)\n","print(f\"\\nTest accuracy: {test_acc}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x1suf0Ja1zHn","executionInfo":{"status":"ok","timestamp":1765983869364,"user_tz":-180,"elapsed":7401,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"a7802332-5719-4270-f626-1526a64531b9"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8640 - loss: 0.3313\n","\n","Test accuracy: 0.8622000217437744\n"]}]},{"cell_type":"code","source":["#значение метрики качества классификации на тестовых данных\n","print(f\"\\nTest accuracy: {test_acc}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2v7LKydR2Bjs","executionInfo":{"status":"ok","timestamp":1765983940438,"user_tz":-180,"elapsed":48,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"8235b522-9545-4313-f5a1-59ef89873352"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Test accuracy: 0.8622000217437744\n"]}]},{"cell_type":"code","source":["#отчет о качестве классификации тестовой выборки\n","y_score = model.predict(X_test)\n","y_pred = [1 if y_score[i,0]>=0.5 else 0 for i in range(len(y_score))]\n","\n","from sklearn.metrics import classification_report\n","print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Negative', 'Positive']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vvZxAYYg2Hr1","executionInfo":{"status":"ok","timestamp":1765983962172,"user_tz":-180,"elapsed":10700,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"0951f090-e463-4ea3-afd5-edc55e4b2bc8"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 12ms/step\n","              precision    recall  f1-score   support\n","\n","    Negative       0.83      0.91      0.87     12500\n","    Positive       0.90      0.81      0.86     12500\n","\n","    accuracy                           0.86     25000\n","   macro avg       0.87      0.86      0.86     25000\n","weighted avg       0.87      0.86      0.86     25000\n","\n"]}]},{"cell_type":"code","source":["#построение ROC-кривой и AUC ROC\n","from sklearn.metrics import roc_curve, auc\n","\n","fpr, tpr, thresholds = roc_curve(y_test, y_score)\n","plt.plot(fpr, tpr)\n","plt.grid()\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('ROC')\n","plt.show()\n","print('AUC ROC:', auc(fpr, tpr))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"7IGX_vWY20SC","executionInfo":{"status":"ok","timestamp":1765984128331,"user_tz":-180,"elapsed":618,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"c6f239e9-1b43-4ac5-9f8b-22950df9bf9e"},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUihJREFUeJzt3Xl4E+X6PvA7e7q3ULpSLIsslR2EA4gIFooLylGBI/wAUfG4VDlWlEWg4kJVFOEoHlTECoogrnwFgYKigCgIFFGgCC2WpS2U0qZrkmbe3x9pA7EFmpJkmuT+XBdXk8nM5MlDJbcz78yrEEIIEBEREXkJpdwFEBERETkTww0RERF5FYYbIiIi8ioMN0RERORVGG6IiIjIqzDcEBERkVdhuCEiIiKvwnBDREREXoXhhoiIiLwKww0RERF5FYYbImpS0tPToVAobH/UajViY2Nx33334dSpU3XWF0JgxYoVuPHGGxEaGgp/f3906dIFzz//PMrLyy/5Pl9++SVuueUWhIeHQ6vVIiYmBqNHj8Z3333nyo9HRG6g4NxSRNSUpKenY9KkSXj++efRunVrVFVV4eeff0Z6ejri4+Px+++/Q6/XAwAsFgvGjh2LTz/9FAMHDsRdd90Ff39/bNu2DStXrkRCQgI2b96MyMhI2/6FELj//vuRnp6OHj164J577kFUVBTy8vLw5ZdfYs+ePdixYwf69+8vVwuI6GoJIqIm5IMPPhAAxO7du+2WT5s2TQAQq1evti2bN2+eACCmTp1aZz9r164VSqVSDB8+3G75/PnzBQDxn//8R0iSVGe75cuXi19++cVJn4aI5MDTUkTkEQYOHAgAOHbsGACgsrIS8+fPR/v27ZGWllZn/REjRmDixInYsGEDfv75Z9s2aWlp6NixI1577TUoFIo6240fPx59+vRx4SchIldjuCEij3D8+HEAQFhYGABg+/btOH/+PMaOHQu1Wl3vNhMmTAAAfPPNN7ZtioqKMHbsWKhUKtcXTUSyqP9fBCIimZWUlKCwsBBVVVX45ZdfMHfuXOh0Otx+++0AgIMHDwIAunXrdsl91L526NAhu59dunRxZelEJDOGGyJqkhITE+2ex8fH46OPPkLLli0BAKWlpQCAoKCgS+6j9jWDwWD383LbEJHnY7ghoiZp8eLFaN++PUpKSrBs2TL8+OOP0Ol0ttdrA0ptyKnP3wNQcHDwFbchIs/HMTdE1CT16dMHiYmJuPvuu7F27Vp07twZY8eORVlZGQCgU6dOAIDffvvtkvuofS0hIQEA0LFjRwDAgQMHXFk6EcmM4YaImjyVSoW0tDScPn0ab731FgDghhtuQGhoKFauXAmLxVLvdsuXLwcA2zidG264AWFhYfjkk08uuQ0ReT6GGyLyCDfddBP69OmDhQsXoqqqCv7+/pg6dSqysrLw7LPP1ll/3bp1SE9PR1JSEv7xj38AAPz9/TFt2jQcOnQI06ZNg6jnHqYfffQRdu3a5fLPQ0SuwzE3ROQxnn76aYwaNQrp6el4+OGHMX36dOzbtw+vvPIKdu7cibvvvht+fn7Yvn07PvroI3Tq1AkffvhhnX388ccfeP311/H999/b7lCcn5+Pr776Crt27cJPP/0k0yckImfg9AtE1KTUTr+we/du9O7d2+41SZLQvn17AEBWVhZUKhUkScLy5cuxdOlSHDhwACaTCW3btsXo0aPx1FNPISAgoN73+fzzz/Huu+/i119/hcFgQIsWLXDjjTfikUcewaBBg1z+OYnIdRhuiIiIyKtwzA0RERF5FYYbIiIi8ioMN0RERORVGG6IiIjIqzDcEBERkVdhuCEiIiKv4nM38ZMkCadPn0ZQUBAUCoXc5RAREVEDCCFQWlqKmJgYKJWXPzbjc+Hm9OnTiIuLk7sMIiIiaoQTJ06gZcuWl13H58JNUFAQAGtzgoODnbpvs9mMTZs2YdiwYdBoNE7dN13APrsH++we7LP7sNfu4ao+GwwGxMXF2b7HL8fnwk3tqajg4GCXhBt/f38EBwfzPxwXYp/dg312D/bZfdhr93B1nxsypIQDiomIiMirMNwQERGRV2G4ISIiIq/CcENEREReheGGiIiIvArDDREREXkVhhsiIiLyKgw3RERE5FUYboiIiMirMNwQERGRV5E13Pz4448YMWIEYmJioFAo8NVXX11xm61bt6Jnz57Q6XRo164d0tPTXV4nEREReQ5Zw015eTm6deuGxYsXN2j9nJwc3HbbbRg8eDAyMzPxn//8Bw8++CA2btzo4kqJiIjIU8g6ceYtt9yCW265pcHrL1myBK1bt8brr78OAOjUqRO2b9+ON954A0lJSa4qk4iIyCcIISAEIAkBAUAIQMC67OLHF79ukQTMFsm23GQ2o8Qk7+fwqFnBd+7cicTERLtlSUlJ+M9//nPJbYxGI4xGo+25wWAAYJ211Gw2O7W+2v05e79kj312D/bZPdhn15FqvnTNkkC1RaDSaEKxETiSVwKFSgVJErAIAUlCzU/rc4skcL7CDK1aaV0mCUhCoFq6eB3gRFEFmgVqIUkXvwZYJAkWCThSUIqYUD9US1JNALDu67eTJWgdHmB7T6kmFNS+j6Vm2f6TJWjXIuBCyLgoUNSGCwjrugLWZbjoce16AHC+wvr7pVMra/ZRE1hqHkvCub1vHaTCPS76jm0Ijwo3+fn5iIyMtFsWGRkJg8GAyspK+Pn51dkmLS0Nc+fOrbN806ZN8Pf3d0mdGRkZLtkv2WOf3YN9dg9v6LMQgFkCyqoBk8X6uNSsgAKABYBFAqSanyUmQKkAqgVQLSlwshxopgMsAjVf9tZ1JQGcKFMgWAsAAhahgCSs650sV6C5TtRZv/aPSQIEFH+rUg3s/cW9jalHzrmKBq139Gy5U9/XWC05dX8XUyqs3VYCUCmE03+nKyoa1jPAw8JNY8yYMQMpKSm25waDAXFxcRg2bBiCg4Od+l5msxkZGRkYOnQoNBqNU/dNF7DP7sE+u4dcfRY1RwgqzRZUmCwwVFajzFiNE+croVQAJosEs0Xg2NlyhPhpcOxsGRRQQKUEKs0S9vx1HjqNCufKjAjWa1BcaXbpF+eZKgB1ggpwzlh32eUoFQJCKCAANA/QQqVUQKlAzU+F7ackBI6fq0D3uBCoFAoolQqoFKj5aX2uAJBbVIHOMSFQKQGVUgmVElAqFFArreucOl+JTtFBUCmVUCsVUKus71FUbkKrZv62fSkVuOh9FFDW7MdYLSHMXwOFAlBAAYXCulxR047ax0qF9TVc9Lh2ee16khDw16qgAKC4aB2FbR8Aah7Xvp9SgZr91r73hTqsi63ra9UXhvC66ne69sxLQ3hUuImKikJBQYHdsoKCAgQHB9d71AYAdDoddDpdneUajcZl/5C4ct90AfvsHuyze1zc5yqzBYZKM4oqTKgyS7BIEqprTmlUSwLFlWZYJAmSZA0hx8+Vw1+jRrUkodxowdkyI3YcLUR0iB4FBiMKy4wI0qtt21tq/jhLpdlY7/KoYD20aiVyiyrQrWUIVEoF1ColNCoFVEplzTo6aNVKqJVK5JdUoUNUENRKBVQqa0BQKZVQKaynVdq0CIBWpYRapYRapYBGqYRFCIT6aaBSWkNDbahQ17xXsF4NjcoaLCRLNb799lvceuut/J12A2f/2+HIvjwq3PTr1w/r16+3W5aRkYF+/frJVBERUV2magkFhiqcLq7E6ZJK/H7KgACdGvtPFCMyWAezpWYsiEXC7uNFCFGqMGXnJjQP0MJYLaHMWO2UOorKL4zqLK26/D6DdGqUGqvRPjIQJ4oq0ad1M2hUSujUSpwsrkSvVmGoNFejRaAOzQK00KpVsAiBlqF+CAvQIlCngk6tQliAFgFaFRQKx46ouINZano1kWvIGm7Kyspw9OhR2/OcnBxkZmaiWbNmaNWqFWbMmIFTp05h+fLlAICHH34Yb731Fp555hncf//9+O677/Dpp59i3bp1cn0EIvJC1RYJ58pNOFtqRLUkUG2RYLJIOGMwQqlU4GhBKQSsAz6zz5ahwmRBUbkJQXr1FUNEfYpqTrecK7e/xESnVsJYLeGa5v7WIxFKZc3RD+vpjlbN/dE8QAudWoVTxZVIiAlGkE6NIL0a0SHWo9ktw/ygVSsRpFdDq1JddETEeuRDp7EGmKYYRogaS9Zw8+uvv2Lw4MG257VjYyZOnIj09HTk5eUhNzfX9nrr1q2xbt06PPnkk1i0aBFatmyJpUuX8jJwIgJgvSS1pNKMM6VVMFRW42ypEZVmC0zV1qMkRwpK0TxAi6pqCftyz8Nfq0ZxhQnGagmH80sRpFOj0mxBdSNP2dQXbHq2CkVksB7nykzocU0oCktN6BgVBI3KetpECYG9+3/DrQOvh16rQUSwHmH+GjQL0DJwEDWSrOHmpptughCX/kekvrsP33TTTdi3b58LqyKipqLKbMHJ8xU4UVQJs0VCdmE5yo3VKCwz4s+CMgT7abA39zyaBWhRabLgTKnxqsaSlNZzOkijUqBlmL81jCiVOH6uHP3bNsfZUiMGtAtHbJgfwvy1CK85XeOvVcFPo0KInwZK5ZXDidlsRkDBfgy8NpzjQIicxKPG3BCRZ5IkgcJyI0qrqnG6uBIllWZUmCzIPlsOAYGT5ysRoFVhV04RKkwWSAIoLKt/kGp9iivs738RpFMjLECLYD81mgdYB6xqawayni6uQre4EOg1KlSaLOgcG4LmgVr4aVTQqpUI89fCT6tCmL/1Shoi8jwMN0TUaOfLTcgtqkBhmRHnykzIPFmMAK0Kh/NLkVNYDr1GhaNnyqBSKhp9REWvUaJ5gA5nSqvQt3Vz5Buq0LVlCAK0aoT4adAuIhAqpQItw/zQPECHqBC93WWpROR7GG6IqF5CCBQbgaNnylBUaT09VGW2jk35OfsccgobfnOx2mBTO9g1MkSPYL0GGpUCxRVmdIoOhoBA2xaBsEgC8c0DENfMH+GBWoT5axt0eoeIqBbDDZEPKjdWo6TSjNPFlThXbsIZQxWKK8w4X2FGaZUZ3x0+U3PljhrY+9MV93dtRCBaBOkQ5q/F2VIj+rdrDgBoHqhDQnQQwgN1iA7x4xEVInILhhsiLySEwImiSuw7cR4HTxtQLQkcKShFZm5xvYNmLydQp4Zeo0S50YJmAVoMTYhEp+ggxIb6o3NsMEL9tS76FEREjcNwQ+RhLJLAn2dKceBkCSpMFhSWGXG+woSfjp6DWqXAkYKyBu+r9sZtQzpGoFmAFkF6NZoHaBHir0W7cD+cOrATd94+jFfxEJFHYbghasIMVWb8erwIe/46j/0nSrD9aKHD+2gRpENCdDASEyIR39wfzQN0aNnMD8H6ywcWs9mMM380tnIiIvkw3BDJyFhtwflyM/46V47ME8XIK6nCuXIT/jpXjpzC8ive7TaumR/6tWmO5oE6NA/QonmgFq3DAxGsV6NlmD/HuBCRT2K4IXIhIQTOV5hRWGaEodKMAoMR6w6cRl5JFfblFjdoHxFBOnRtGYK4Zv5o5q/Fje1boHNsCO/BQkR0CQw3RE5krLbgh6yz+O7wGWz7sxCGSnODB/AG69XQqlXo0zoM7SKC0D0uBO0jg9AyzN/FVRMReReGG6KrYJEEDp424JPduVj5S+4l16sduNs6PAA6tXWiwju6x2JYQiRiQv14FIaIyIkYboiuwFQtIa+kEpsPnUFxhQn5JVXIN1Qhp7AcJ89X1rtNx6ggDOkYga4tQ3FThxbQa1RurpqIyHcx3BBdpKhmOoHss2X47WQJMg4W4FRx/QGmllalRJ/WzTCiWzR6tApDuxaBvKMuEZGMGG7IZ5mqJWz78yx25RThnR+zoVUpYbJIl1w/vrk/zBaBu3rGIibUD62a+eOa5v6IDNZDo+JVSURETQXDDfmUw/kGbP+zEAdOleDrzNN2r9UGm2C9GoaqanSODcaAtuG4u1dLXBsRCIWCR2OIiDwBww15NUkS+OHIWXzzWx5+O1mMP8/UvXtveKAWj9zUDtfFBKNryxD4a/mfBRGRJ+O/4uR1yozVeH9bDj799QTKTdUorjDXWWdYQiTu7dsKA9uFQ81TSkREXoXhhrxCpcmCzYcK8MGOHOyt5+Z4t3aJwpCOkUiIDkbHqCAO+CUi8mIMN+SRJAF8vOsElu34C2qVAtlny+us07d1MwzpGIGxfVsh6ArzKBERkfdguCGPIYTAvhPFWPXLX/h0jxrAIbvXw/w1SLouCn3bNMOtXaKhU/PeMkREvojhhpo0IQSOFJRh7f5TWPz9sTqvtw4PwIhuMRhzfRxiQvS8oomIiBhuqGkyVJnxRsYRfHsgH/mGqjqv39vWgqljEtE8mPMuERGRPYYbalJKq8xI+XQ/Mg4W2C2/Pj4Md3SLwe1dYxCoVWD9+vUI9uM4GiIiqovhhpqEkkoznvlsPzb+YR9qZt3WCff2aYUA3YVfVbO57qXdREREtRhuSFY/HjmLqWv240yp0W75ff3jMePWjhwUTEREDmO4IVkUV5gw5+s/sHa//RQIaXd1waheLXljPSIiajSGG3KrClM1lvyQjf9u+dO2rGNUEMb94xqM7t2SR2qIiOiqMdyQW1gkgS/2nsT0Lw7AIgkAgFqpwPxRXfHPHi1lro6IiLwJww25lCQJfLI7F89++bttWai/Brd2icb0WzoimHcOJiIiJ2O4IZcQQuD97Tl4I+MIyk0W2/Kk6yLxxpjunHmbiIhcht8w5HQrfv4Lr23MQknlhUu27+3TCs8kdUBYgFbGyoiIyBcw3JDTHCkoxeyvfscvOUW2Zf8e1AbJg9tx4koiInIbhhu6an+cLsHjK/chu/DCzNx94pvh1Xu6Ij48QMbKiIjIFzHc0FX5dPcJPPP5b7bnbcIDMHtEAgZ3iJCxKiIi8mUMN9Qo+SVVeHD5bvx+ymBbtubhfrg+vpmMVRERETHcUCOUVpkx9I0fUFpVDQAY1L4F3hnfC3oNb8BHRETyY7ihBpMkgc/2nLQ7DZV2Vxfc26eVjFURERHZY7ihBik3VqPnCxkwVksAgCCdGrNvT8Do6+NkroyIiMgeww1dUfbZMgx5/Qfb84HXhuOd8b14Iz4iImqS+O1El5VxsACTl/9qe548uB2eGtYeCoVCxqqIiIgujeGGLunrzFOYsirT9vyD+67H4I68xJuIiJo2hhuqo9oiYfoXB/DZnpO2Zbtm3oyIYL2MVRERETUMww3V8ejHe7HpYAEAoGerUCwY3Z3BhoiIPAbDDdl567s/bcEmsVMklk7sLXNFREREjmG4IZsFGUfw3y1/ArDODcVgQ0REnkgpdwHUNFSZLbZgAwAfT+4rYzVERESNx3BDAIDklXttj3c/mwiNir8aRETkmfgNRli0+U9sPnQGAPDO+F5oEaSTuSIiIqLGY7jxcWt+PYE3Nh8BANw/oDWSrouSuSIiIqKrwwHFPmzmlwew8pdcAEBMiB4zb+0oc0VERERXj+HGRz239g9bsIkN9cOPzwyGSskpFYiIyPPxtJQPWvPrCaT/dBwA0DEqCNsYbIiIyIsw3PiYc2VGPP3ZbwCsR2zWPzEQSgYbIiLyIgw3PuaOt3bYHq974gYGGyIi8joMNz5k0x/5OFVcCQB4Ykg7hPprZa6IiIjI+RhufMTvp0rw0Io9AKzjbJ4c2l7mioiIiFxD9nCzePFixMfHQ6/Xo2/fvti1a9dl11+4cCE6dOgAPz8/xMXF4cknn0RVVZWbqvVMZcZq3P7mdgCAWqnAqof+AYWCp6OIiMg7yRpuVq9ejZSUFKSmpmLv3r3o1q0bkpKScObMmXrXX7lyJaZPn47U1FQcOnQI77//PlavXo2ZM2e6uXLPIUkCd9QEGwD48P4+PB1FREReTdZws2DBAkyePBmTJk1CQkIClixZAn9/fyxbtqze9X/66ScMGDAAY8eORXx8PIYNG4Z77733ikd7fNlb3x9FdmE5AGDWbZ0woF24zBURERG5lmw38TOZTNizZw9mzJhhW6ZUKpGYmIidO3fWu03//v3x0UcfYdeuXejTpw+ys7Oxfv16jB8//pLvYzQaYTQabc8NBgMAwGw2w2w2O+nTwLbPi3/KzSIJLMiwTq0wtFMEJv4jrsnUdjWaWp+9FfvsHuyz+7DX7uGqPjuyP9nCTWFhISwWCyIjI+2WR0ZG4vDhw/VuM3bsWBQWFuKGG26AEALV1dV4+OGHL3taKi0tDXPnzq2zfNOmTfD397+6D3EJGRkZLtmvo97PUqL24NzggNNYv/60vAU5WVPps7djn92DfXYf9to9nN3nioqKBq/rUdMvbN26FfPmzcPbb7+Nvn374ujRo5gyZQpeeOEFzJ49u95tZsyYgZSUFNtzg8GAuLg4DBs2DMHBwU6tz2w2IyMjA0OHDoVGo3Hqvh319f48/LbzAADg8cFtMGpIO1nrcaam1Gdvxj67B/vsPuy1e7iqz7VnXhpCtnATHh4OlUqFgoICu+UFBQWIiqp/ZurZs2dj/PjxePDBBwEAXbp0QXl5OR566CE8++yzUCrrDiHS6XTQ6XR1lms0Gpf9crty3w1RVG7C1M+sweaObjF4KqmTbLW4ktx99hXss3uwz+7DXruHs/vsyL5kG1Cs1WrRq1cvbNmyxbZMkiRs2bIF/fr1q3ebioqKOgFGpVIBAIQQrivWw3ywIwcAoFEpMH9UV5mrISIici9ZT0ulpKRg4sSJ6N27N/r06YOFCxeivLwckyZNAgBMmDABsbGxSEtLAwCMGDECCxYsQI8ePWynpWbPno0RI0bYQo6vW7UrF29+dxQAcP+A1tCp2RciIvItsoabMWPG4OzZs5gzZw7y8/PRvXt3bNiwwTbIODc31+5IzaxZs6BQKDBr1iycOnUKLVq0wIgRI/DSSy/J9RGalP0nijH9C+vpqNhQP96FmIiIfJLsA4qTk5ORnJxc72tbt261e65Wq5GamorU1FQ3VOZZJEngzsUXJsVc/e9/QK/hURsiIvI9sk+/QM6RcejCwOx1T9yAlmGuucydiIioqWO48RLpO44DsE6KeV1MiLzFEBERyYjhxgtUmKqxM/scAODfg9rIXA0REZG8GG68wNvfH7M9HtE1RsZKiIiI5Mdw4wW+3HcKADD8uiioVfwrJSIi38ZvQg+3K6cIp4orAQBTkzrIXA0REZH8GG483Mpf/rI9bhcRKGMlRERETQPDjQfLK6nEV5nWmb7T7uoiczVERERNA8ONB7tv2W4AQKtm/ri3TyuZqyEiImoaGG481F/nypFVUAoAmH17gszVEBERNR0MNx7qk10nAABqpQJDEyJlroaIiKjpYLjxQCeKKrDkB+u9bVKGcXJMIiKiizHceKCl27IBAM0DtHjghtYyV0NERNS0MNx4mJPnK/DRL7kAgP8kXgudmjN/ExERXYzhxsNMXLYLFkkgOkTPK6SIiIjqwXDjQQ6eNuDY2XIAwLO3deJUC0RERPXgt6MHeWrNftvj2zlBJhERUb0YbjxEldmCrHwDAOC/9/aQuRoiIqKmi+HGQ2z4PR+SsD6+rUu0vMUQERE1YQw3HmLpduvl3zd3jIBKqZC5GiIioqaL4cYD5BSW4/dT1lNS18WGyFwNERFR08Zw4wEWbT5iezzl5mtlrISIiKjpY7hp4iySwFeZpwEAz41I4CkpIiKiK2C4aeJ25RTZHo/te42MlRAREXkGhpsm7u2tRwEAHSKDoFXzr4uIiOhK+G3ZxFWYLACAIL1a5kqIiIg8A8NNE1ZUbsKev84DAFJHXCdzNURERJ6B4aYJ+++WP22PO8cGy1gJERGR52C4acK2HC4AALQJD4BCwaukiIiIGoLhpomqtkg4UVQJAHhmeAeZqyEiIvIcDDdN1Bd7TwEA9BolhiZEyVwNERGR52C4aaJ+O1UMAIgI0vPGfURERA5guGmi1v2WBwAY27eVzJUQERF5lqsKN1VVVc6qgy5SYarG+QozAOss4ERERNRwDocbSZLwwgsvIDY2FoGBgcjOzgYAzJ49G++//77TC/RFD3+0FwCgVABtWwTKXA0REZFncTjcvPjii0hPT8err74KrVZrW965c2csXbrUqcX5qh+PnAUA9GgVBiXH2xARETnE4XCzfPlyvPvuuxg3bhxUKpVtebdu3XD48GGnFueLvj98xvb4nfG9ZKyEiIjIMzkcbk6dOoV27drVWS5JEsxms1OK8mUvrT8EAFApFQgP1MlcDRERkedxONwkJCRg27ZtdZZ/9tln6NGjh1OK8mX+WuvRsNu7RstcCRERkWdyeKrpOXPmYOLEiTh16hQkScIXX3yBrKwsLF++HN98840ravQZlSYLDuUZAAAP3dhG5mqIiIg8k8NHbu6880783//9HzZv3oyAgADMmTMHhw4dwv/93/9h6NChrqjRZ2w5XACzRSBAq0JCNCfKJCIiagyHj9wAwMCBA5GRkeHsWnxe8sp9AIC4Zv6cKJOIiKiRHD5y06ZNG5w7d67O8uLiYrRpw1MpjVVYZrQ9nja8o4yVEBEReTaHw83x48dhsVjqLDcajTh16pRTivJFO44W2h4P5l2JiYiIGq3Bp6XWrl1re7xx40aEhITYnlssFmzZsgXx8fFOLc6XHC+sAAC0j+QdiYmIiK5Gg8PNyJEjAQAKhQITJ060e02j0SA+Ph6vv/66U4vzJd8dLgAADL8uSuZKiIiIPFuDw40kSQCA1q1bY/fu3QgPD3dZUb5GkgT2nywBAHSODbnC2kRERHQ5Dl8tlZOT44o6fNqX+y6MVbqpA8fbEBERXY1GXQpeXl6OH374Abm5uTCZTHavPfHEE04pzJd8d9F8Ulq1w2O8iYiI6CIOh5t9+/bh1ltvRUVFBcrLy9GsWTMUFhbC398fERERDDeNICAAAHf1iJW5EiIiIs/n8GGCJ598EiNGjMD58+fh5+eHn3/+GX/99Rd69eqF1157zRU1er3DeaUAgP7tOI6JiIjoajkcbjIzM/HUU09BqVRCpVLBaDQiLi4Or776KmbOnOmKGr3a+XITsgvLAQAdo4JkroaIiMjzORxuNBoNlErrZhEREcjNzQUAhISE4MSJE86tzges3X/a9vi6GM4nRUREdLUcHnPTo0cP7N69G9deey0GDRqEOXPmoLCwECtWrEDnzp1dUaNXyz5bZnvM+aSIiIiunsNHbubNm4fo6GgAwEsvvYSwsDA88sgjOHv2LN555x2nF+jtcs5Z70w8qH0LmSshIiLyDg4fuendu7ftcUREBDZs2ODUgnzNzmPWOaX6tG4mcyVERETewWk3Vdm7dy9uv/12h7dbvHgx4uPjodfr0bdvX+zateuy6xcXF+Oxxx5DdHQ0dDod2rdvj/Xr1ze2bFnlFJbDbLFeBt6vbXOZqyEiIvIODoWbjRs3YurUqZg5cyays7MBAIcPH8bIkSNx/fXX26ZoaKjVq1cjJSUFqamp2Lt3L7p164akpCScOXOm3vVNJhOGDh2K48eP47PPPkNWVhbee+89xMZ65v1htv951va4Z6swGSshIiLyHg0+LfX+++9j8uTJaNasGc6fP4+lS5diwYIFePzxxzFmzBj8/vvv6NSpk0NvvmDBAkyePBmTJk0CACxZsgTr1q3DsmXLMH369DrrL1u2DEVFRfjpp5+g0WgAwKNnIv/hiPWU1G1domWuhIiIyHs0ONwsWrQIr7zyCp5++ml8/vnnGDVqFN5++20cOHAALVu2dPiNTSYT9uzZgxkzZtiWKZVKJCYmYufOnfVus3btWvTr1w+PPfYYvv76a7Ro0QJjx47FtGnToFKp6t3GaDTCaDTanhsMBgCA2WyG2Wx2uO7Lqd1fQ/e7+ZB1JvBWYXqn1+LNHO0zNQ777B7ss/uw1+7hqj47sr8Gh5tjx45h1KhRAIC77roLarUa8+fPb1SwAYDCwkJYLBZERkbaLY+MjMThw4fr3SY7Oxvfffcdxo0bh/Xr1+Po0aN49NFHYTabkZqaWu82aWlpmDt3bp3lmzZtgr+/f6Nqv5KMjIwrriMEoIAKAgqoC//E+vV/uqQWb9aQPtPVY5/dg312H/baPZzd54qKigav2+BwU1lZaQsDCoUCOp3Odkm4u0iShIiICLz77rtQqVTo1asXTp06hfnz518y3MyYMQMpKSm25waDAXFxcRg2bBiCg5170zyz2YyMjAwMHTrUdtrsUgyVZoifvwcA3H/XMATqGjWHqU9ypM/UeOyze7DP7sNeu4er+lx75qUhHPpGXbp0KQIDAwEA1dXVSE9PR3i4/XxIDZ04Mzw8HCqVCgUFBXbLCwoKEBUVVe820dHR0Gg0dqegOnXqhPz8fJhMJmi12jrb6HQ66HS6Oss1Go3Lfrkbsu8/coptj0MD9LyBXyO48u+QLmCf3YN9dh/22j2c3WdH9tXgcNOqVSu89957tudRUVFYsWKF3ToKhaLB4Uar1aJXr17YsmULRo4cCcB6ZGbLli1ITk6ud5sBAwZg5cqVkCTJNgXEkSNHEB0dXW+wacrKjNUAAD+NisGGiIjIiRocbo4fP+70N09JScHEiRPRu3dv9OnTBwsXLkR5ebnt6qkJEyYgNjYWaWlpAIBHHnkEb731FqZMmYLHH38cf/75J+bNm9fgQNWUHD9nnSzzH2148z4iIiJnknWgx5gxY3D27FnMmTMH+fn56N69OzZs2GAbZJybm2s7QgMAcXFx2LhxI5588kl07doVsbGxmDJlCqZNmybXR2i0b/bnAQBahwfKXAkREZF3kX0Ua3Jy8iVPQ23durXOsn79+uHnn392cVWudzDPOjAqPMizTqcRERE1dU6bfoEarrTqwrX6SdfVP3iaiIiIGofhRgYHT1+4nK1tC56WIiIiciaGGxlkFZQC4EzgRERErtCocHPs2DHMmjUL9957r22Sy2+//RZ//PGHU4vzVit/yQUAJEQ79yaCRERE1Ihw88MPP6BLly745Zdf8MUXX6CsrAwAsH///kveJZjs5RuqAADtI4NkroSIiMj7OBxupk+fjhdffBEZGRl2N84bMmSIV1zF5GpVZgtKq6w38OsdHyZzNURERN7H4XBz4MAB/POf/6yzPCIiAoWFhU4pypsVlhlhkQQA4NoIDiYmIiJyNofDTWhoKPLy8uos37dvH2JjY51SlDc7U2q0Pea0C0RERM7ncLj517/+hWnTpiE/Px8KhQKSJGHHjh2YOnUqJkyY4IoavcqJIuuU7VHBepkrISIi8k4Oh5t58+ahY8eOiIuLQ1lZGRISEnDjjTeif//+mDVrlitq9CrCekYKJZXmy69IREREjeLw9AtarRbvvfceZs+ejd9//x1lZWXo0aMHrr32WlfU53UqzRYAwIB24TJXQkRE5J0cDjfbt2/HDTfcgFatWqFVq1auqMmr1d6dWK/h/ROJiIhcweFv2CFDhqB169aYOXMmDh486IqavJql5ryUTq2SuRIiIiLv5HC4OX36NJ566in88MMP6Ny5M7p374758+fj5MmTrqjP6+SXWG/g1yWWdycmIiJyBYfDTXh4OJKTk7Fjxw4cO3YMo0aNwocffoj4+HgMGTLEFTV6ldPFlQCA2DB/mSshIiLyTlc18KN169aYPn06Xn75ZXTp0gU//PCDs+ryWrXhJjqEl4ITERG5QqPDzY4dO/Doo48iOjoaY8eORefOnbFu3Tpn1uZ1jNUWGGqmXojkfW6IiIhcwuGrpWbMmIFVq1bh9OnTGDp0KBYtWoQ777wT/v48zXIlJ4qsR220aiXCA7VXWJuIiIgaw+Fw8+OPP+Lpp5/G6NGjER7Oe7U4YvfxIgBAiJ+GUy8QERG5iMPhZseOHa6owydk5hYDAMqN1fIWQkRE5MUaFG7Wrl2LW265BRqNBmvXrr3sunfccYdTCvNG+06cBwAM6RghcyVERETeq0HhZuTIkcjPz0dERARGjhx5yfUUCgUsFouzavM6aqV1/HbXliEyV0JEROS9GhRuJEmq9zE55mCedeqFdhGBMldCRETkvRy+FHz58uUwGo11lptMJixfvtwpRXmrmJp72wRoHR7qRERERA3kcLiZNGkSSkpK6iwvLS3FpEmTnFKUtzpdM/VCc14GTkRE5DIOhxshRL2XMZ88eRIhIRxLcinVlgun84L0GhkrISIi8m4NPj/So0cPKBQKKBQK3HzzzVCrL2xqsViQk5OD4cOHu6RIb1BUbrI9bh7AIzdERESu0uBwU3uVVGZmJpKSkhAYeGFQrFarRXx8PO6++26nF+gtCssuhBu16qqm9CIiIqLLaHC4SU1NBQDEx8djzJgx0Os5N5IjcosqAABRnFOKiIjIpRy+bGfixImuqMPrmWrG3JgsvJSeiIjIlRoUbpo1a4YjR44gPDwcYWFhl50XqaioyGnFeZMzBuuVUr2vCZO5EiIiIu/WoHDzxhtvICgoyPaYkz467tfj1qkXYkL9ZK6EiIjIuzUo3Fx8Kuq+++5zVS1eTaW0BkKeliIiInIthy/b2bt3Lw4cOGB7/vXXX2PkyJGYOXMmTCbTZbb0bVkFpQCAnq14WoqIiMiVHA43//73v3HkyBEAQHZ2NsaMGQN/f3+sWbMGzzzzjNML9AbVFglHz5QBADpGBclcDRERkXdzONwcOXIE3bt3BwCsWbMGgwYNwsqVK5Geno7PP//c2fV5hfyawcQA0IHhhoiIyKUaNf1C7czgmzdvxq233goAiIuLQ2FhoXOr8xKH80ptjzW8gR8REZFLOfxN27t3b7z44otYsWIFfvjhB9x2220AgJycHERGRjq9QG9QZqwGAPAiMyIiItdzONwsXLgQe/fuRXJyMp599lm0a9cOAPDZZ5+hf//+Ti/QG/x5xnrkZuC1LWSuhIiIyPs5fIfirl272l0tVWv+/PlQqVROKcrbBOqss4AXlhplroSIiMj7ORxuau3ZsweHDh0CACQkJKBnz55OK8rbVJotAICe14TKWwgREZEPcDjcnDlzBmPGjMEPP/yA0NBQAEBxcTEGDx6MVatWoUULnnr5u4OnDQAAvZpHtoiIiFzN4TE3jz/+OMrKyvDHH3+gqKgIRUVF+P3332EwGPDEE0+4okaPt/9kMQBAo+aVUkRERK7m8JGbDRs2YPPmzejUqZNtWUJCAhYvXoxhw4Y5tThv4aexHrGJb+4vcyVERETez+FDCZIkQaPR1Fmu0Whs978he7lFFQCAdhGBMldCRETk/RwON0OGDMGUKVNw+vRp27JTp07hySefxM033+zU4ryBsdpiexwZrJexEiIiIt/gcLh56623YDAYEB8fj7Zt26Jt27Zo3bo1DAYD3nzzTVfU6NFOna+0PY4N9ZOxEiIiIt/g8JibuLg47N27F1u2bLFdCt6pUyckJiY6vThvsCunCADQJjwACt6imIiIyOUcCjerV6/G2rVrYTKZcPPNN+Pxxx93VV1e48CpEgCARQiZKyEiIvINDQ43//vf//DYY4/h2muvhZ+fH7744gscO3YM8+fPd2V9Hk+qCTXtIzkbOBERkTs0eMzNW2+9hdTUVGRlZSEzMxMffvgh3n77bVfW5hX+b38eAODGa8NlroSIiMg3NDjcZGdnY+LEibbnY8eORXV1NfLy8lxSmLcwWayXx3eICpa5EiIiIt/Q4HBjNBoREBBwYUOlElqtFpWVlZfZyrdZJAFTtTXc8B43RERE7uHQgOLZs2fD3//CXXZNJhNeeuklhISE2JYtWLDAedV5uLMXzQIeoOO8UkRERO7Q4HBz4403Iisry25Z//79kZ2dbXvOS53tna8w2R7rOGkmERGRWzQ43GzdutWFZXin2iM3tXNLERERkes1iWmqFy9ejPj4eOj1evTt2xe7du1q0HarVq2CQqHAyJEjXVtgI9Xe20at4hEtIiIid5E93KxevRopKSlITU3F3r170a1bNyQlJeHMmTOX3e748eOYOnUqBg4c6KZKHfdXYTkAoGMU73FDRETkLrKHmwULFmDy5MmYNGkSEhISsGTJEvj7+2PZsmWX3MZisWDcuHGYO3cu2rRp48ZqHaNWWdtbOys4ERERuZ6s4cZkMmHPnj1281IplUokJiZi586dl9zu+eefR0REBB544AF3lNlolSbrjOD/aNNc5kqIiIh8h8MTZzpTYWEhLBYLIiMj7ZZHRkbi8OHD9W6zfft2vP/++8jMzGzQexiNRhiNFy7JNhgMAACz2Qyz2dy4wi+hdn+1P8+WWu8B5K9ROv29fNnf+0yuwT67B/vsPuy1e7iqz47sr1HhZtu2bXjnnXdw7NgxfPbZZ4iNjcWKFSvQunVr3HDDDY3ZZYOUlpZi/PjxeO+99xAe3rDpDNLS0jB37tw6yzdt2mR3zx5nysjIAADsP6oEoERx3l9Yv/64S97Ll9X2mVyLfXYP9tl92Gv3cHafKyoaPsTD4XDz+eefY/z48Rg3bhz27dtnOypSUlKCefPmYf369Q3eV3h4OFQqFQoKCuyWFxQUICoqqs76x44dw/HjxzFixAjbMkmy3gFYrVYjKysLbdu2tdtmxowZSElJsT03GAyIi4vDsGHDEBzs3CkRzGYzMjIyMHToUGg0GkyZvQkA0KtrAm7td41T38uX/b3P5Brss3uwz+7DXruHq/pce+alIRwONy+++CKWLFmCCRMmYNWqVbblAwYMwIsvvujQvrRaLXr16oUtW7bYLueWJAlbtmxBcnJynfU7duyIAwcO2C2bNWsWSktLsWjRIsTFxdXZRqfTQafT1Vmu0Whc9stdu+9rmvvjr3MV0GnU/A/JBVz5d0gXsM/uwT67D3vtHs7usyP7cjjcZGVl4cYbb6yzPCQkBMXFxY7uDikpKZg4cSJ69+6NPn36YOHChSgvL8ekSZMAABMmTEBsbCzS0tKg1+vRuXNnu+1DQ0MBoM7ypsBcM69U15ah8hZCRETkQxwON1FRUTh69Cji4+Ptlm/fvr1Rl2WPGTMGZ8+exZw5c5Cfn4/u3btjw4YNtkHGubm5UCplv2K9UWpnBNdpPLN+IiIiT+RwuJk8eTKmTJmCZcuWQaFQ4PTp09i5cyemTp2K2bNnN6qI5OTkek9DAVee9iE9Pb1R7+kOhWXWuaW0KoYbIiIid3E43EyfPh2SJOHmm29GRUUFbrzxRuh0OkydOhWPP/64K2r0SFVmi+1xoE7WK+6JiIh8isPfugqFAs8++yyefvppHD16FGVlZUhISEBgYKAr6vNYeSVVtsctguoOaCYiIiLXaPQhBa1Wi4SEBGfW4lVKqy7cbEih4MSZRERE7uJwuBk8ePBlv6y/++67qyrIW5hqrpSKb+6aGwUSERFR/RwON927d7d7bjabkZmZid9//x0TJ050Vl0ez1gTbnRqlcyVEBER+RaHw80bb7xR7/LnnnsOZWVlV12Qtygss9652U/LcENEROROTrtG+f/9v/+HZcuWOWt3Hq+0qhoABxMTERG5m9PCzc6dO6HX6521O4+XeaIYABDAIzdERERu5fBpqbvuusvuuRACeXl5+PXXXxt9Ez9vVHu11Llyk8yVEBER+RaHw01ISIjdc6VSiQ4dOuD555/HsGHDnFaYpzNbBACgQ2SQzJUQERH5FofCjcViwaRJk9ClSxeEhYW5qiavoFNbz/iFBWhlroSIiMi3ODTmRqVSYdiwYY2a/dvX1B65CfNnuCEiInInhwcUd+7cGdnZ2a6oxatUS9b73KhVvDsxERGROzkcbl588UVMnToV33zzDfLy8mAwGOz+kFV1zZEbDcMNERGRWzV4zM3zzz+Pp556CrfeeisA4I477rCbhkEIAYVCAYvFcqld+JRfcs4BANRKp11tT0RERA3Q4HAzd+5cPPzww/j+++9dWY/XaBcRhEN5BlgkIXcpREREPqXB4UYI65f0oEGDXFaMN6kyW49gxYT6yVwJERGRb3HonMnlZgMnezmF5QAAvYanpYiIiNzJofvctG/f/ooBp6io6KoK8gYXn4pqxvvcEBERuZVD4Wbu3Ll17lBMdV085UJ0CE9LERERuZND4eZf//oXIiIiXFWL16gdb6NVK6FS8lQeERGROzV4QAjH2zRcSaV10sxAncNTdxEREdFVanC4qb1aiq6ssubITRFnBCciInK7Bh9akGqmE6ArM1Zbe8UZwYmIiNyP1ym7QHGF9bSUv04lcyVERES+h+HGBWrDjaFm7A0RERG5D8ONC9TeuE+v4ZEbIiIid2O4cQFzzYzgLcN4jxsiIiJ3Y7hxgdo7FHNGcCIiIvfjt68LVNeGGxXvDURERORuDDcuUF1z2TzvTkxEROR+DDcuUGW2hhs1ww0REZHbMdy4wInzlQCA8ECdzJUQERH5HoYbF8jKLwUAhPhpZK6EiIjI9zDcuMCpYuuRG62a7SUiInI3fvu6QEllNQCgZZi/zJUQERH5HoYbF2odznBDRETkbgw3Tma5aPJ0DigmIiJyP4YbJzNfFG44txQREZH7Mdw4WYXlwmMdBxQTERG5Hb99ncx4UbhRKHgTPyIiIndjuHEyg8kaaDgjOBERkTwYbpysZs5MnKy5SzERERG5F8ONk1lqwk23uFBZ6yAiIvJVDDdOVhtuNJw0k4iISBYMN05WG27UKoYbIiIiOTDcOJntyI2KrSUiIpIDv4Gd7GyV9YiNkpeBExERyYLhxsn81dZDNyeKKmSuhIiIyDcx3DhZdc30Cz2vCZO3ECIiIh/FcONkpWbr6ShOvUBERCQPfgM7mcFk/SkJIW8hREREPorhxsn0tonAOaCYiIhIDgw3TlZVM3Fm2xYB8hZCRETkoxhunKy82vrTX6uWtxAiIiIfxXDjZNWS9XRUqL9G5kqIiIh8U5MIN4sXL0Z8fDz0ej369u2LXbt2XXLd9957DwMHDkRYWBjCwsKQmJh42fXdreZKcKg4txQREZEsZA83q1evRkpKClJTU7F3715069YNSUlJOHPmTL3rb926Fffeey++//577Ny5E3FxcRg2bBhOnTrl5srrJ9XOLcVwQ0REJAvZw82CBQswefJkTJo0CQkJCViyZAn8/f2xbNmyetf/+OOP8eijj6J79+7o2LEjli5dCkmSsGXLFjdXXr/auaWUDDdERESykDXcmEwm7NmzB4mJibZlSqUSiYmJ2LlzZ4P2UVFRAbPZjGbNmrmqTIcIHrkhIiKSlayX9BQWFsJisSAyMtJueWRkJA4fPtygfUybNg0xMTF2AeliRqMRRqPR9txgMAAAzGYzzGZzIyuvn9lsth25gSQ5ff9kVdtX9te12Gf3YJ/dh712D1f12ZH9efT1yi+//DJWrVqFrVu3Qq/X17tOWloa5s6dW2f5pk2b4O/v7/Sa8iutLd2162cUNSyfUSNlZGTIXYJPYJ/dg312H/baPZzd54qKhk9ILWu4CQ8Ph0qlQkFBgd3ygoICREVFXXbb1157DS+//DI2b96Mrl27XnK9GTNmICUlxfbcYDDYBiEHBwdf3Qf4G7PZjOBfv4PBrMCAfv3Qi5NnuoTZbEZGRgaGDh0KjYaX3LsK++we7LP7sNfu4ao+1555aQhZw41Wq0WvXr2wZcsWjBw5EgBsg4OTk5Mvud2rr76Kl156CRs3bkTv3r0v+x46nQ46na7Oco1G45Jf7uqa01LhwX78j8fFXPV3SPbYZ/dgn92HvXYPZ/fZkX3JfloqJSUFEydORO/evdGnTx8sXLgQ5eXlmDRpEgBgwoQJiI2NRVpaGgDglVdewZw5c7By5UrEx8cjPz8fABAYGIjAwEDZPkctS82NbrQq1eVXJCIiIpeQPdyMGTMGZ8+exZw5c5Cfn4/u3btjw4YNtkHGubm5UCovXNT1v//9DyaTCffcc4/dflJTU/Hcc8+5s/R6GWvuUKxR82opIiIiOcgebgAgOTn5kqehtm7davf8+PHjri+okYxmi+2xn4ZHboiIiOQg+038vInJdh04oGe4ISIikgXDjRNVS5LtsUbF1hIREcmB38BOZJEuHLnhDYqJiIjkwXDjROaa01IalQIKBdMNERGRHBhunMhYbR1QzHmliIiI5MNw40TlRmu4qTRLV1iTiIiIXIXhxolMNXfwiwyqe0dkIiIicg+GGyeqqrnPTaC+Sdw+iIiIyCcx3DhRfokRACBddNUUERERuRfDjRNp1dZ2FleaZa6EiIjIdzHcOFF1zZibhOhgmSshIiLyXQw3TlQ7/YJaxUvBiYiI5MJw40S1A4q1nHqBiIhINvwWdqKsgjK5SyAiIvJ5DDdOFFRzCXjtERwiIiJyP4YbJ9qbWwwA6B4XIm8hREREPozhxomiQ/QAgHPlJpkrISIi8l0MN05Ue/O+ayMCZa6EiIjIdzHcOFF1TbhRcVZwIiIi2TDcOJGlJtyoGW6IiIhkw3DjRBYeuSEiIpIdw40TXTgtxbYSERHJhd/CTrQ39zwAnpYiIiKSE8ONE7UODwAAVJh4Ez8iIiK5MNw4Ue2l4LX3uyEiIiL3Y7hxotpZwTWcFZyIiEg2DDdOZLZIAAANZwUnIiKSDb+FnejE+UoAPHJDREQkJ4YbJwrQqgAASgXDDRERkVwYbpxIURNqAnVqmSshIiLyXQw3TiSEdUAxD9wQERHJh+HGiaSacMPTUkRERPJhuHGimtvcgDcoJiIikg/DjRNJttNSTDdERERyYbhxIsEjN0RERLJjuHEijrkhIiKSH8ONE3HMDRERkfwYbpyk9jJwALwWnIiISEYMN04iXZRteOSGiIhIPgw3TiJddOSGY26IiIjkw3DjJPbhRsZCiIiIfBzDjZNI0oXHvM8NERGRfBhunKTCVG17rFezrURERHLht7CTXDSeGGoV20pERCQXfgs7Se2QG4VdzCEiIiJ3Y7hxErv73BAREZFsGG6cpDbacCgxERGRvBhunOTCjOAyF0JEROTjGG6chGeliIiImgaGGyexzQgucx1ERES+jt/FTiI46IaIiKhJYLhxkguXghMREZGcGG6cRNRcL8VwQ0REJC+GGyeRao/cMN0QERHJiuHGSWpv4sdsQ0REJC+GGyeReCk4ERFRk8Bw4zS8iR8REVFT0CTCzeLFixEfHw+9Xo++ffti165dl11/zZo16NixI/R6Pbp06YL169e7qdJLk3i1FBERUZMge7hZvXo1UlJSkJqair1796Jbt25ISkrCmTNn6l3/p59+wr333osHHngA+/btw8iRIzFy5Ej8/vvvbq7cHi8FJyIiahpkDzcLFizA5MmTMWnSJCQkJGDJkiXw9/fHsmXL6l1/0aJFGD58OJ5++ml06tQJL7zwAnr27Im33nrLzZXbqzJbrA+YboiIiGSllvPNTSYT9uzZgxkzZtiWKZVKJCYmYufOnfVus3PnTqSkpNgtS0pKwldffVXv+kajEUaj0fbcYDAAAMxmM8xm81V+ggsO5xUDsGYbZ+6X6qrtL/vsWuyze7DP7sNeu4er+uzI/mQNN4WFhbBYLIiMjLRbHhkZicOHD9e7TX5+fr3r5+fn17t+Wloa5s6dW2f5pk2b4O/v38jK66mrFNAoVegZLpCRkeG0/dKlsc/uwT67B/vsPuy1ezi7zxUVFQ1eV9Zw4w4zZsywO9JjMBgQFxeHYcOGITg42KnvNdlsRkZGBoYOHQqNRuPUfdMFZvbZLdhn92Cf3Ye9dg9X9bn2zEtDyBpuwsPDoVKpUFBQYLe8oKAAUVFR9W4TFRXl0Po6nQ46na7Oco1G47Jfblfumy5gn92DfXYP9tl92Gv3cHafHdmXrAOKtVotevXqhS1bttiWSZKELVu2oF+/fvVu069fP7v1Aeuhr0utT0RERL5F9tNSKSkpmDhxInr37o0+ffpg4cKFKC8vx6RJkwAAEyZMQGxsLNLS0gAAU6ZMwaBBg/D666/jtttuw6pVq/Drr7/i3XfflfNjEBERURMhe7gZM2YMzp49izlz5iA/Px/du3fHhg0bbIOGc3NzoVReOMDUv39/rFy5ErNmzcLMmTNx7bXX4quvvkLnzp3l+ghERETUhMgebgAgOTkZycnJ9b62devWOstGjRqFUaNGubgqIiIi8kSy38SPiIiIyJkYboiIiMirMNwQERGRV2G4ISIiIq/CcENEREReheGGiIiIvArDDREREXkVhhsiIiLyKgw3RERE5FWaxB2K3UkIAcCxqdMbymw2o6KiAgaDgTPOuhD77B7ss3uwz+7DXruHq/pc+71d+z1+OT4XbkpLSwEAcXFxMldCREREjiotLUVISMhl11GIhkQgLyJJEk6fPo2goCAoFAqn7ttgMCAuLg4nTpxAcHCwU/dNF7DP7sE+uwf77D7stXu4qs9CCJSWliImJsZuQu36+NyRG6VSiZYtW7r0PYKDg/kfjhuwz+7BPrsH++w+7LV7uKLPVzpiU4sDiomIiMirMNwQERGRV2G4cSKdTofU1FTodDq5S/Fq7LN7sM/uwT67D3vtHk2hzz43oJiIiIi8G4/cEBERkVdhuCEiIiKvwnBDREREXoXhhoiIiLwKw42DFi9ejPj4eOj1evTt2xe7du267Ppr1qxBx44dodfr0aVLF6xfv95NlXo2R/r83nvvYeDAgQgLC0NYWBgSExOv+PdCVo7+PtdatWoVFAoFRo4c6doCvYSjfS4uLsZjjz2G6Oho6HQ6tG/fnv92NICjfV64cCE6dOgAPz8/xMXF4cknn0RVVZWbqvVMP/74I0aMGIGYmBgoFAp89dVXV9xm69at6NmzJ3Q6Hdq1a4f09HSX1wlBDbZq1Sqh1WrFsmXLxB9//CEmT54sQkNDRUFBQb3r79ixQ6hUKvHqq6+KgwcPilmzZgmNRiMOHDjg5so9i6N9Hjt2rFi8eLHYt2+fOHTokLjvvvtESEiIOHnypJsr9yyO9rlWTk6OiI2NFQMHDhR33nmne4r1YI722Wg0it69e4tbb71VbN++XeTk5IitW7eKzMxMN1fuWRzt88cffyx0Op34+OOPRU5Ojti4caOIjo4WTz75pJsr9yzr168Xzz77rPjiiy8EAPHll19edv3s7Gzh7+8vUlJSxMGDB8Wbb74pVCqV2LBhg0vrZLhxQJ8+fcRjjz1me26xWERMTIxIS0urd/3Ro0eL2267zW5Z3759xb///W+X1unpHO3z31VXV4ugoCDx4YcfuqpEr9CYPldXV4v+/fuLpUuXiokTJzLcNICjff7f//4n2rRpI0wmk7tK9AqO9vmxxx4TQ4YMsVuWkpIiBgwY4NI6vUlDws0zzzwjrrvuOrtlY8aMEUlJSS6sTAielmogk8mEPXv2IDEx0bZMqVQiMTERO3furHebnTt32q0PAElJSZdcnxrX57+rqKiA2WxGs2bNXFWmx2tsn59//nlERETggQcecEeZHq8xfV67di369euHxx57DJGRkejcuTPmzZsHi8XirrI9TmP63L9/f+zZs8d26io7Oxvr16/Hrbfe6paafYVc34M+N3FmYxUWFsJisSAyMtJueWRkJA4fPlzvNvn5+fWun5+f77I6PV1j+vx306ZNQ0xMTJ3/oOiCxvR5+/bteP/995GZmemGCr1DY/qcnZ2N7777DuPGjcP69etx9OhRPProozCbzUhNTXVH2R6nMX0eO3YsCgsLccMNN0AIgerqajz88MOYOXOmO0r2GZf6HjQYDKisrISfn59L3pdHbsirvPzyy1i1ahW+/PJL6PV6ucvxGqWlpRg/fjzee+89hIeHy12OV5MkCREREXj33XfRq1cvjBkzBs8++yyWLFkid2leZevWrZg3bx7efvtt7N27F1988QXWrVuHF154Qe7SyAl45KaBwsPDoVKpUFBQYLe8oKAAUVFR9W4TFRXl0PrUuD7Xeu211/Dyyy9j8+bN6Nq1qyvL9HiO9vnYsWM4fvw4RowYYVsmSRIAQK1WIysrC23btnVt0R6oMb/P0dHR0Gg0UKlUtmWdOnVCfn4+TCYTtFqtS2v2RI3p8+zZszF+/Hg8+OCDAIAuXbqgvLwcDz30EJ599lkolfx/f2e41PdgcHCwy47aADxy02BarRa9evXCli1bbMskScKWLVvQr1+/erfp16+f3foAkJGRccn1qXF9BoBXX30VL7zwAjZs2IDevXu7o1SP5mifO3bsiAMHDiAzM9P254477sDgwYORmZmJuLg4d5bvMRrz+zxgwAAcPXrUFh4B4MiRI4iOjmawuYTG9LmioqJOgKkNlIJTLjqNbN+DLh2u7GVWrVoldDqdSE9PFwcPHhQPPfSQCA0NFfn5+UIIIcaPHy+mT59uW3/Hjh1CrVaL1157TRw6dEikpqbyUvAGcLTPL7/8stBqteKzzz4TeXl5tj+lpaVyfQSP4Gif/45XSzWMo33Ozc0VQUFBIjk5WWRlZYlvvvlGREREiBdffFGuj+ARHO1zamqqCAoKEp988onIzs4WmzZtEm3bthWjR4+W6yN4hNLSUrFv3z6xb98+AUAsWLBA7Nu3T/z1119CCCGmT58uxo8fb1u/9lLwp59+Whw6dEgsXryYl4I3RW+++aZo1aqV0Gq1ok+fPuLnn3+2vTZo0CAxceJEu/U//fRT0b59e6HVasV1110n1q1b5+aKPZMjfb7mmmsEgDp/UlNT3V+4h3H09/liDDcN52iff/rpJ9G3b1+h0+lEmzZtxEsvvSSqq6vdXLXncaTPZrNZPPfcc6Jt27ZCr9eLuLg48eijj4rz58+7v3AP8v3339f7721tbydOnCgGDRpUZ5vu3bsLrVYr2rRpIz744AOX16kQgsffiIiIyHtwzA0RERF5FYYbIiIi8ioMN0RERORVGG6IiIjIqzDcEBERkVdhuCEiIiKvwnBDREREXoXhhojspKenIzQ0VO4yGk2hUOCrr7667Dr33XcfRo4c6ZZ6iMj9GG6IvNB9990HhUJR58/Ro0flLg3p6em2epRKJVq2bIlJkybhzJkzTtl/Xl4ebrnlFgDA8ePHoVAokJmZabfOokWLkJ6e7pT3u5TnnnvO9jlVKhXi4uLw0EMPoaioyKH9MIgROY6zghN5qeHDh+ODDz6wW9aiRQuZqrEXHByMrKwsSJKE/fv3Y9KkSTh9+jQ2btx41fu+0uzxABASEnLV79MQ1113HTZv3gyLxYJDhw7h/vvvR0lJCVavXu2W9yfyVTxyQ+SldDodoqKi7P6oVCosWLAAXbp0QUBAAOLi4vDoo4+irKzskvvZv38/Bg8ejKCgIAQHB6NXr1749ddfba9v374dAwcOhJ+fH+Li4vDEE0+gvLz8srUpFApERUUhJiYGt9xyC5544gls3rwZlZWVkCQJzz//PFq2bAmdTofu3btjw4YNtm1NJhOSk5MRHR0NvV6Pa665BmlpaXb7rj0t1bp1awBAjx49oFAocNNNNwGwPxry7rvvIiYmxm4WbgC48847cf/999uef/311+jZsyf0ej3atGmDuXPnorq6+rKfU61WIyoqCrGxsUhMTMSoUaOQkZFhe91iseCBBx5A69at4efnhw4dOmDRokW215977jl8+OGH+Prrr21HgbZu3QoAOHHiBEaPHo3Q0FA0a9YMd955J44fP37Zeoh8BcMNkY9RKpX473//iz/++AMffvghvvvuOzzzzDOXXH/cuHFo2bIldu/ejT179mD69OnQaDQAgGPHjmH48OG4++678dtvv2H16tXYvn07kpOTHarJz88PkiShuroaixYtwuuvv47XXnsNv/32G5KSknDHHXfgzz//BAD897//xdq1a/Hpp58iKysLH3/8MeLj4+vd765duwAAmzdvRl5eHr744os664waNQrnzp3D999/b1tWVFSEDRs2YNy4cQCAbdu2YcKECZgyZQoOHjyId955B+np6XjppZca/BmPHz+OjRs3QqvV2pZJkoSWLVtizZo1OHjwIObMmYOZM2fi008/BQBMnToVo0ePxvDhw5GXl4e8vDz0798fZrMZSUlJCAoKwrZt27Bjxw4EBgZi+PDhMJlMDa6JyGu5fGpOInK7iRMnCpVKJQICAmx/7rnnnnrXXbNmjWjevLnt+QcffCBCQkJsz4OCgkR6enq92z7wwAPioYceslu2bds2oVQqRWVlZb3b/H3/R44cEe3btxe9e/cWQggRExMjXnrpJbttrr/+evHoo48KIYR4/PHHxZAhQ4QkSfXuH4D48ssvhRBC5OTkCABi3759duv8fUbzO++8U9x///225++8846IiYkRFotFCCHEzTffLObNm2e3jxUrVojo6Oh6axBCiNTUVKFUKkVAQIDQ6/W22ZMXLFhwyW2EEOKxxx4Td9999yVrrX3vDh062PXAaDQKPz8/sXHjxsvun8gXcMwNkZcaPHgw/ve//9meBwQEALAexUhLS8Phw4dhMBhQXV2NqqoqVFRUwN/fv85+UlJS8OCDD2LFihW2Uytt27YFYD1l9dtvv+Hjjz+2rS+EgCRJyMnJQadOneqtraSkBIGBgZAkCVVVVbjhhhuwdOlSGAwGnD59GgMGDLBbf8CAAdi/fz8A6ymloUOHokOHDhg+fDhuv/12DBs27Kp6NW7cOEyePBlvv/02dDodPv74Y/zrX/+CUqm0fc4dO3bYHamxWCyX7RsAdOjQAWvXrkVVVRU++ugjZGZm4vHHH7dbZ/HixVi2bBlyc3NRWVkJk8mE7t27X7be/fv34+jRowgKCrJbXlVVhWPHjjWiA0TeheGGyEsFBASgXbt2dsuOHz+O22+/HY888gheeuklNGvWDNu3b8cDDzwAk8lU75f0c889h7Fjx2LdunX49ttvkZqailWrVuGf//wnysrK8O9//xtPPPFEne1atWp1ydqCgoKwd+9eKJVKREdHw8/PDwBgMBiu+Ll69uyJnJwcfPvtt9i8eTNGjx6NxMREfPbZZ1fc9lJGjBgBIQTWrVuH66+/Htu2bcMbb7xhe72srAxz587FXXfdVWdbvV5/yf1qtVrb38HLL7+M2267DXPnzsULL7wAAFi1ahWmTp2K119/Hf369UNQUBDmz5+PX3755bL1lpWVoVevXnahslZTGTROJCeGGyIfsmfPHkiShNdff912VKJ2fMfltG/fHu3bt8eTTz6Je++9Fx988AH++c9/omfPnjh48GCdEHUlSqWy3m2Cg4MRExODHTt2YNCgQbblO3bsQJ8+fezWGzNmDMaMGYN77rkHw4cPR1FREZo1a2a3v9rxLRaL5bL16PV63HXXXfj4449x9OhRdOjQAT179rS93rNnT2RlZTn8Of9u1qxZGDJkCB555BHb5+zfvz8effRR2zp/P/Ki1Wrr1N+zZ0+sXr0aERERCA4OvqqaiLwRBxQT+ZB27drBbDbjzTffRHZ2NlasWIElS5Zccv3KykokJydj69at+Ouvv7Bjxw7s3r3bdrpp2rRp+Omnn5CcnIzMzEz8+eef+Prrrx0eUHyxp59+Gq+88gpWr16NrKwsTJ8+HZmZmZgyZQoAYMGCBfjkk09w+PBhHDlyBGvWrEFUVFS9Nx6MiIiAn58fNmzYgIKCApSUlFzyfceNG4d169Zh2bJltoHEtebMmYPly5dj7ty5+OOPP3Do0CGsWrUKs2bNcuiz9evXD127dsW8efMAANdeey1+/fVXbNy4EUeOHMHs2bOxe/duu23i4+Px22+/ISsrC4WFhTCbzRg3bhzCw8Nx5513Ytu2bcjJycHWrVvxxBNP4OTJkw7VROSV5B70Q0TOV98g1FoLFiwQ0dHRws/PTyQlJYnly5cLAOL8+fNCCPsBv0ajUfzrX/8ScXFxQqvVipiYGJGcnGw3WHjXrl1i6NChIjAwUAQEBIiuXbvWGRB8sb8PKP47i8UinnvuOREbGys0Go3o1q2b+Pbbb22vv/vuu6J79+4iICBABAcHi5tvvlns3bvX9jouGlAshBDvvfeeiIuLE0qlUgwaNOiS/bFYLCI6OloAEMeOHatT14YNG0T//v2Fn5+fCA4OFn369BHvvvvuJT9Hamqq6NatW53ln3zyidDpdCI3N1dUVVWJ++67T4SEhIjQ0FDxyCOPiOnTp9ttd+bMGVt/AYjvv/9eCCFEXl6emDBhgggPDxc6nU60adNGTJ48WZSUlFyyJiJfoRBCCHnjFREREZHz8LQUEREReRWGGyIiIvIqDDdERETkVRhuiIiIyKsw3BAREZFXYbghIiIir8JwQ0RERF6F4YaIiIi8CsMNEREReRWGGyIiIvIqDDdERETkVRhuiIiIyKv8fz1oc0+wRXZPAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["AUC ROC: 0.9370553728000001\n"]}]}]}
\ No newline at end of file
diff --git a/labworks/LW4/report4.md b/labworks/LW4/report4.md
new file mode 100644
index 0000000..95555bb
--- /dev/null
+++ b/labworks/LW4/report4.md
@@ -0,0 +1,315 @@
+# Отчёт по лабораторной работе №4
+
+### Киселёв Матвей, Мамедов Расул А-01-22
+### Вариант 6
+
+### 1) В среде Google Colab создать новый блокнот (notebook). Настроить блокнот для работы с аппаратным ускорителем GPU.
+```python
+# импорт модулей
+import os
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab4')
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+import matplotlib.pyplot as plt
+import numpy as np
+```
+```python
+import tensorflow as tf
+device_name = tf.test.gpu_device_name()
+if device_name != '/device:GPU:0':
+  raise SystemError('GPU device not found')
+print('Found GPU at: {}'.format(device_name))
+```
+```
+Found GPU at: /device:GPU:0
+```
+
+### 2) Загрузить набор данных IMDb, содержащий оцифрованные отзывы на фильмы, размеченные на два класса: позитивные и негативные. При загрузке набора данных параметр seed выбрать равным (4k – 1), где k – номер бригады. Вывести размеры полученных обучающих и тестовых массивов данных.
+
+```python
+# загрузка датасета
+from keras.datasets import imdb
+
+vocabulary_size = 5000
+index_from = 3
+
+(X_train, y_train), (X_test, y_test) = imdb.load_data(
+    path="imdb.npz",
+    num_words=vocabulary_size,
+    skip_top=0,
+    maxlen=None,
+    seed=23,
+    start_char=1,
+    oov_char=2,
+    index_from=index_from
+    )
+
+# вывод размерностей
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+print('Shape of X test:', X_test.shape)
+print('Shape of y test:', y_test.shape)
+```
+```
+Shape of X train: (25000,)
+Shape of y train: (25000,)
+Shape of X test: (25000,)
+Shape of y test: (25000,)
+```
+
+### 3) Вывести один отзыв из обучающего множества в виде списка индексов слов. Преобразовать список индексов в текст и вывести отзыв в виде текста. Вывести длину отзыва. Вывести метку класса данного отзыва и название класса (1 – Positive, 0 – Negative).
+
+```python
+# создание словаря для перевода индексов в слова
+# заргузка словаря "слово:индекс"
+word_to_id = imdb.get_word_index()
+# уточнение словаря
+word_to_id = {key:(value + index_from) for key,value in word_to_id.items()}
+word_to_id["<PAD>"] = 0
+word_to_id["<START>"] = 1
+word_to_id["<UNK>"] = 2
+word_to_id["<UNUSED>"] = 3
+# создание обратного словаря "индекс:слово"
+id_to_word = {value:key for key,value in word_to_id.items()}
+```
+```python
+print(X_train[23])
+print('len:',len(X_train[23]))
+```
+```
+[1, 14, 20, 9, 290, 149, 48, 25, 358, 2, 120, 318, 302, 50, 26, 49, 221, 2057, 10, 10, 1212, 39, 15, 45, 801, 2, 2, 363, 2396, 7, 2, 209, 2327, 283, 8, 4, 425, 10, 10, 45, 24, 290, 3613, 972, 4, 65, 198, 40, 3462, 1224, 2, 23, 6, 4457, 225, 24, 76, 50, 8, 895, 19, 45, 164, 204, 5, 24, 55, 318, 38, 92, 140, 11, 18, 4, 65, 33, 32, 43, 168, 33, 4, 302, 10, 10, 17, 47, 77, 1046, 12, 188, 6, 117, 2, 33, 4, 130, 2, 4, 2, 7, 87, 3709, 2199, 7, 35, 2504, 5, 33, 211, 320, 2504, 132, 190, 48, 25, 2754, 4, 1273, 2, 45, 6, 1682, 8, 2, 42, 24, 8, 2, 10, 10, 32, 11, 32, 45, 6, 542, 3709, 22, 290, 319, 18, 15, 1288, 5, 15, 584]
+len: 146
+```
+```python
+review_as_text = ' '.join(id_to_word[id] for id in X_train[23])
+print(review_as_text)
+print('len:',len(review_as_text))
+```
+```
+<START> this movie is worth watching if you enjoy <UNK> over special effects there are some interesting visuals br br aside from that it's typical <UNK> <UNK> hollywood fare of <UNK> without substance true to the title br br it's not worth picking apart the story that's like performing brain <UNK> on a dinosaur there's not much there to begin with it's nothing original and not very special so don't go in for the story at all just look at the effects br br as has been mentioned it got a little <UNK> at the end <UNK> the <UNK> of great fx treatment of an invisible and at times half invisible man however if you ignore the standard <UNK> it's a sight to <UNK> or not to <UNK> br br all in all it's a decent fx film worth seeing for that purpose and that alone
+len: 763
+```
+
+
+### 4) Вывести максимальную и минимальную длину отзыва в обучающем множестве.
+
+```python
+print('MAX Len: ',len(max(X_train, key=len)))
+print('MIN Len: ',len(min(X_train, key=len)))
+```
+```
+MAX Len:  2494
+MIN Len:  11
+```
+
+### 5) Провести предобработку данных. Выбрать единую длину, к которой будут приведены все отзывы. Короткие отзывы дополнить спецсимволами, а длинные обрезать до выбранной длины.
+
+```python
+# предобработка данных
+from tensorflow.keras.utils import pad_sequences
+max_words = 500
+X_train = pad_sequences(X_train, maxlen=max_words, value=0, padding='pre', truncating='post')
+X_test = pad_sequences(X_test, maxlen=max_words, value=0, padding='pre', truncating='post')
+```
+
+### 6) Повторить п. 4.
+
+```python
+print('MAX Len: ',len(max(X_train, key=len)))
+print('MIN Len: ',len(min(X_train, key=len)))
+```
+```
+MAX Len:  500
+MIN Len:  500
+```
+
+### 7) Повторить п. 3. Сделать вывод о том, как отзыв преобразовался после предобработки.
+```python
+print(X_train[23])
+print('len:',len(X_train[23]))
+```
+```
+[   0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    0    0    0    0    0    0    0    0    0    0
+    0    0    0    0    1   14   20    9  290  149   48   25  358    2
+  120  318  302   50   26   49  221 2057   10   10 1212   39   15   45
+  801    2    2  363 2396    7    2  209 2327  283    8    4  425   10
+   10   45   24  290 3613  972    4   65  198   40 3462 1224    2   23
+    6 4457  225   24   76   50    8  895   19   45  164  204    5   24
+   55  318   38   92  140   11   18    4   65   33   32   43  168   33
+    4  302   10   10   17   47   77 1046   12  188    6  117    2   33
+    4  130    2    4    2    7   87 3709 2199    7   35 2504    5   33
+  211  320 2504  132  190   48   25 2754    4 1273    2   45    6 1682
+    8    2   42   24    8    2   10   10   32   11   32   45    6  542
+ 3709   22  290  319   18   15 1288    5   15  584]
+len: 500
+```
+
+```python
+review_as_text = ' '.join(id_to_word[id] for id in X_train[23])
+print(review_as_text)
+print('len:',len(review_as_text))
+```
+```
+
+
+<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> this movie is worth watching if you enjoy <UNK> over special effects there are some interesting visuals br br aside from that it's typical <UNK> <UNK> hollywood fare of <UNK> without substance true to the title br br it's not worth picking apart the story that's like performing brain <UNK> on a dinosaur there's not much there to begin with it's nothing original and not very special so don't go in for the story at all just look at the effects br br as has been mentioned it got a little <UNK> at the end <UNK> the <UNK> of great fx treatment of an invisible and at times half invisible man however if you ignore the standard <UNK> it's a sight to <UNK> or not to <UNK> br br all in all it's a decent fx film worth seeing for that purpose and that alone
+len: 2887
+
+```
+### В начало отзыва добавились токены PAD в количестве, необходимом для достижения длины в 500 индексов.
+
+### 8) Вывести предобработанные массивы обучающих и тестовых данных и их размерности.
+
+```python
+# вывод данных
+print('X train: \n',X_train)
+print('X train: \n',X_test)
+
+# вывод размерностей
+print('Shape of X train:', X_train.shape)
+print('Shape of X test:', X_test.shape)
+```
+```
+X train: 
+ [[   0    0    0 ...    6   52  106]
+ [   0    0    0 ...   87   22  231]
+ [   0    0    0 ...    6  158  158]
+ ...
+ [   0    0    0 ... 1005    4 1630]
+ [   0    0    0 ...    9    6  991]
+ [   0    0    0 ...    7   32   58]]
+X train: 
+ [[   0    0    0 ...    4    2    2]
+ [   0    0    0 ...    6    2  123]
+ [   0    0    0 ...    2   11  831]
+ ...
+ [   1   14  402 ...  819   45  131]
+ [   0    0    0 ...   17 1540    2]
+ [   1   17    6 ... 1026  362   37]]
+Shape of X train: (25000, 500)
+Shape of X test: (25000, 500)
+
+```
+
+### 9) Реализовать модель рекуррентной нейронной сети, состоящей из слоев Embedding, LSTM, Dropout, Dense, и обучить ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывести информацию об архитектуре нейронной сети. Добиться качества обучения по метрике accuracy не менее 0.8.
+
+```python
+embed_dim = 32
+lstm_units = 64
+
+model = Sequential()
+model.add(layers.Embedding(input_dim=vocabulary_size, output_dim=embed_dim, input_length=max_words, input_shape=(max_words,)))
+model.add(layers.LSTM(lstm_units))
+model.add(layers.Dropout(0.5))
+model.add(layers.Dense(1, activation='sigmoid'))
+
+model.summary()
+```
+![picture](1.png)
+
+```python
+# компиляция и обучение модели
+batch_size = 64
+epochs = 3
+model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
+model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)
+```
+```
+Epoch 1/3
+313/313 ━━━━━━━━━━━━━━━━━━━━ 11s 22ms/step - accuracy: 0.6255 - loss: 0.6261 - val_accuracy: 0.8294 - val_loss: 0.3905
+Epoch 2/3
+313/313 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.8643 - loss: 0.3334 - val_accuracy: 0.8626 - val_loss: 0.3467
+Epoch 3/3
+313/313 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - accuracy: 0.8877 - loss: 0.2905 - val_accuracy: 0.8722 - val_loss: 0.3317
+
+<keras.src.callbacks.history.History at 0x78f347b1fb30>
+```
+```python
+test_loss, test_acc = model.evaluate(X_test, y_test)
+print(f"\nTest accuracy: {test_acc}")
+```
+```
+782/782 ━━━━━━━━━━━━━━━━━━━━ 7s 9ms/step - accuracy: 0.8640 - loss: 0.3313
+
+Test accuracy: 0.8622000217437744
+```
+
+### 10) Оценить качество обучения на тестовых данных:
+### - вывести значение метрики качества классификации на тестовых данных,
+### - вывести отчет о качестве классификации тестовой выборки.
+### - построить ROC-кривую по результату обработки тестовой выборки и вычислить площадь под ROC-кривой (AUC ROC).
+
+```python
+#значение метрики качества классификации на тестовых данных
+print(f"\nTest accuracy: {test_acc}")
+```
+```
+Test accuracy: 0.8622000217437744
+```
+
+```python
+#отчет о качестве классификации тестовой выборки
+y_score = model.predict(X_test)
+y_pred = [1 if y_score[i,0]>=0.5 else 0 for i in range(len(y_score))]
+
+from sklearn.metrics import classification_report
+print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Negative', 'Positive']))
+```
+```
+              precision    recall  f1-score   support
+
+    Negative       0.83      0.91      0.87     12500
+    Positive       0.90      0.81      0.86     12500
+
+    accuracy                           0.86     25000
+   macro avg       0.87      0.86      0.86     25000
+weighted avg       0.87      0.86      0.86     25000
+```
+
+```python
+#построение ROC-кривой и AUC ROC
+from sklearn.metrics import roc_curve, auc
+
+fpr, tpr, thresholds = roc_curve(y_test, y_score)
+plt.plot(fpr, tpr)
+plt.grid()
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('ROC')
+plt.show()
+print('AUC ROC:', auc(fpr, tpr))
+```
+![picture](2.png)
+```
+AUC ROC: 0.9370553728000001
+```
+
+### 11) Сделать выводы по результатам применения рекуррентной нейронной сети для решения задачи определения тональности текста. 
+
+### Показатель accuracy превышает требуемый порог 0.8, а значение AUC ROC > 0.9 показывает высокую способность модели различать два класса. Таким образом, рекуррентная нейронная сеть хорошо справилась с задачей определения тональности текста.  
\ No newline at end of file