{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from gensim.models import Word2Vec\n", "from gensim.utils import simple_preprocess\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, classification_report\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def build_corpus(file_list):\n", " documents = []\n", " for file in file_list:\n", " with open(file, 'r', encoding=\"utf8\") as f:\n", " for line in f:\n", " processed_line = simple_preprocess(line)\n", " documents.append(processed_line)\n", " return documents" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def text_to_vector(text, model):\n", " tokens = simple_preprocess(text)\n", " word_vectors = [model.wv[token] for token in tokens if token in model.wv]\n", " if word_vectors:\n", " return np.mean(word_vectors, axis=0)\n", " else:\n", " return np.zeros(model.vector_size)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def read_text_file(filepath):\n", " lines = []\n", " with open(filepath, 'r', encoding=\"utf8\") as file:\n", " for line in file:\n", " lines.append(line.strip())\n", " return lines" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def write_predictions_to_file(predictions, filepath):\n", " with open(filepath, 'w', encoding=\"utf8\") as file:\n", " for prediction in predictions:\n", " file.write(f\"{prediction[0]}\\n\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\mateu\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:86: 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__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6695 - loss: 0.5541 - val_accuracy: 0.8258 - val_loss: 0.3704\n", "Epoch 2/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8517 - loss: 0.3327 - val_accuracy: 0.8433 - val_loss: 0.3428\n", "Epoch 3/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8496 - loss: 0.3206 - val_accuracy: 0.8313 - val_loss: 0.3492\n", "Epoch 4/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8634 - loss: 0.2961 - val_accuracy: 0.8414 - val_loss: 0.3361\n", "Epoch 5/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8571 - loss: 0.3020 - val_accuracy: 0.8442 - val_loss: 0.3439\n", "Epoch 6/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8655 - loss: 0.2932 - val_accuracy: 0.8368 - val_loss: 0.3387\n", "Epoch 7/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8744 - loss: 0.2790 - val_accuracy: 0.8313 - val_loss: 0.3474\n", "Epoch 8/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8717 - loss: 0.2764 - val_accuracy: 0.8543 - val_loss: 0.3247\n", "Epoch 9/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9022 - loss: 0.2077 - val_accuracy: 0.8827 - val_loss: 0.2680\n", "Epoch 135/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9005 - loss: 0.2125 - val_accuracy: 0.8772 - val_loss: 0.2646\n", "Epoch 136/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9108 - loss: 0.2043 - val_accuracy: 0.8827 - val_loss: 0.2763\n", "Epoch 137/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9061 - loss: 0.2135 - val_accuracy: 0.8873 - val_loss: 0.2587\n", "Epoch 138/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8985 - loss: 0.2229 - val_accuracy: 0.8781 - val_loss: 0.2951\n", "Epoch 139/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9130 - loss: 0.1934 - val_accuracy: 0.8873 - val_loss: 0.2643\n", "Epoch 170/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8920 - loss: 0.2272 - val_accuracy: 0.8863 - val_loss: 0.2647\n", "Epoch 171/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9054 - loss: 0.2128 - val_accuracy: 0.8873 - val_loss: 0.2600\n", "Epoch 172/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9070 - loss: 0.2026 - val_accuracy: 0.8900 - val_loss: 0.2581\n", "Epoch 173/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9001 - loss: 0.2152 - val_accuracy: 0.8845 - val_loss: 0.2585\n", "Epoch 174/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9101 - loss: 0.2082 - val_accuracy: 0.8781 - val_loss: 0.2773\n", "Epoch 175/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9066 - loss: 0.2163 - val_accuracy: 0.8845 - val_loss: 0.2673\n", "Epoch 176/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9156 - loss: 0.1942 - val_accuracy: 0.8818 - val_loss: 0.2736\n", "Epoch 177/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9030 - loss: 0.2067 - val_accuracy: 0.8726 - val_loss: 0.2855\n", "Epoch 178/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9015 - loss: 0.2270 - val_accuracy: 0.8735 - val_loss: 0.2828\n", "Epoch 179/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9068 - loss: 0.2097 - val_accuracy: 0.8753 - val_loss: 0.2844\n", "Epoch 180/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9093 - loss: 0.2045 - val_accuracy: 0.8781 - val_loss: 0.2797\n", "Epoch 181/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9106 - loss: 0.2079 - val_accuracy: 0.8799 - val_loss: 0.2795\n", "Epoch 182/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9077 - loss: 0.2081 - val_accuracy: 0.8808 - val_loss: 0.2617\n", "Epoch 183/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8979 - loss: 0.2151 - val_accuracy: 0.8909 - val_loss: 0.2600\n", "Epoch 184/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9060 - loss: 0.2027 - val_accuracy: 0.8928 - val_loss: 0.2595\n", "Epoch 185/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9071 - loss: 0.1985 - val_accuracy: 0.8909 - val_loss: 0.2628\n", "Epoch 186/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9030 - loss: 0.2134 - val_accuracy: 0.8790 - val_loss: 0.2716\n", "Epoch 187/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9042 - loss: 0.1951 - val_accuracy: 0.8873 - val_loss: 0.2690\n", "Epoch 188/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9136 - loss: 0.2058 - val_accuracy: 0.8653 - val_loss: 0.2986\n", "Epoch 189/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9098 - loss: 0.1989 - val_accuracy: 0.8891 - val_loss: 0.2642\n", "Epoch 220/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9120 - loss: 0.1992 - val_accuracy: 0.8478 - val_loss: 0.3517\n", "Epoch 221/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8967 - loss: 0.2336 - val_accuracy: 0.8918 - val_loss: 0.2637\n", "Epoch 222/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9099 - loss: 0.2053 - val_accuracy: 0.8818 - val_loss: 0.2788\n", "Epoch 223/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9084 - loss: 0.2008 - val_accuracy: 0.8818 - val_loss: 0.2726\n", "Epoch 224/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9126 - loss: 0.1978 - val_accuracy: 0.8937 - val_loss: 0.2584\n", "Epoch 260/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9165 - loss: 0.1918 - val_accuracy: 0.8845 - val_loss: 0.2720\n", "Epoch 261/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9159 - loss: 0.1933 - val_accuracy: 0.8964 - val_loss: 0.2534\n", "Epoch 262/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9131 - loss: 0.1979 - val_accuracy: 0.8983 - val_loss: 0.2582\n", "Epoch 263/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9109 - loss: 0.1932 - val_accuracy: 0.8900 - val_loss: 0.2768\n", "Epoch 264/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9151 - loss: 0.1920 - val_accuracy: 0.8900 - val_loss: 0.2587\n", "Epoch 265/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9077 - loss: 0.2053 - val_accuracy: 0.8882 - val_loss: 0.2654\n", "Epoch 266/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9191 - loss: 0.1924 - val_accuracy: 0.8937 - val_loss: 0.2573\n", "Epoch 267/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9187 - loss: 0.1875 - val_accuracy: 0.8873 - val_loss: 0.2564\n", "Epoch 268/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9049 - loss: 0.2044 - val_accuracy: 0.8891 - val_loss: 0.2667\n", "Epoch 269/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9160 - loss: 0.1881 - val_accuracy: 0.8918 - val_loss: 0.2642\n", "Epoch 270/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9170 - loss: 0.1873 - val_accuracy: 0.8873 - val_loss: 0.2709\n", "Epoch 271/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9082 - loss: 0.1996 - val_accuracy: 0.8973 - val_loss: 0.2614\n", "Epoch 272/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9144 - loss: 0.1837 - val_accuracy: 0.8946 - val_loss: 0.2696\n", "Epoch 273/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9162 - loss: 0.1885 - val_accuracy: 0.8909 - val_loss: 0.2612\n", "Epoch 274/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9187 - loss: 0.1806 - val_accuracy: 0.8900 - val_loss: 0.2727\n", "Epoch 415/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9246 - loss: 0.1657 - val_accuracy: 0.8845 - val_loss: 0.2809\n", "Epoch 416/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9340 - loss: 0.1608 - val_accuracy: 0.8909 - val_loss: 0.2727\n", "Epoch 417/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9211 - loss: 0.1746 - val_accuracy: 0.8937 - val_loss: 0.2638\n", "Epoch 418/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9232 - loss: 0.1713 - val_accuracy: 0.8882 - val_loss: 0.2870\n", "Epoch 419/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9218 - loss: 0.1775 - val_accuracy: 0.8799 - val_loss: 0.2932\n", "Epoch 420/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9025 - loss: 0.2156 - val_accuracy: 0.8983 - val_loss: 0.2720\n", "Epoch 421/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9202 - loss: 0.1733 - val_accuracy: 0.8854 - val_loss: 0.2812\n", "Epoch 422/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9222 - loss: 0.1702 - val_accuracy: 0.8790 - val_loss: 0.2967\n", "Epoch 423/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9113 - loss: 0.1816 - val_accuracy: 0.8928 - val_loss: 0.2691\n", "Epoch 424/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9177 - loss: 0.1718 - val_accuracy: 0.8790 - val_loss: 0.2897\n", "Epoch 455/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9217 - loss: 0.1685 - val_accuracy: 0.8818 - val_loss: 0.2676\n", "Epoch 456/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9187 - loss: 0.1789 - val_accuracy: 0.8863 - val_loss: 0.2734\n", "Epoch 457/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9271 - loss: 0.1665 - val_accuracy: 0.8964 - val_loss: 0.2666\n", "Epoch 458/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9245 - loss: 0.1619 - val_accuracy: 0.8781 - val_loss: 0.2971\n", "Epoch 459/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9298 - loss: 0.1634 - val_accuracy: 0.8882 - val_loss: 0.2706\n", "Epoch 460/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9208 - loss: 0.1681 - val_accuracy: 0.8973 - val_loss: 0.2758\n", "Epoch 461/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9254 - loss: 0.1778 - val_accuracy: 0.8808 - val_loss: 0.2834\n", "Epoch 462/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9203 - loss: 0.1705 - val_accuracy: 0.8799 - val_loss: 0.2810\n", "Epoch 463/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9158 - loss: 0.1889 - val_accuracy: 0.8873 - val_loss: 0.2778\n", "Epoch 464/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - 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accuracy: 0.9235 - loss: 0.1729 - val_accuracy: 0.8717 - val_loss: 0.2747\n", "Epoch 495/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9257 - loss: 0.1711 - val_accuracy: 0.8781 - val_loss: 0.3104\n", "Epoch 496/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9230 - loss: 0.1633 - val_accuracy: 0.8818 - val_loss: 0.2897\n", "Epoch 497/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9263 - loss: 0.1640 - val_accuracy: 0.8735 - val_loss: 0.2944\n", "Epoch 498/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9282 - loss: 0.1590 - val_accuracy: 0.8909 - val_loss: 0.2772\n", "Epoch 499/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9251 - loss: 0.1611 - val_accuracy: 0.8873 - val_loss: 0.2931\n", "Epoch 500/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9259 - loss: 0.1632 - val_accuracy: 0.8918 - val_loss: 0.2766\n", "Epoch 501/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9228 - loss: 0.1631 - val_accuracy: 0.8882 - val_loss: 0.2741\n", "Epoch 502/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9266 - loss: 0.1606 - val_accuracy: 0.8781 - val_loss: 0.2770\n", "Epoch 503/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9217 - loss: 0.1703 - val_accuracy: 0.8946 - val_loss: 0.2816\n", "Epoch 504/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9271 - loss: 0.1706 - val_accuracy: 0.8937 - val_loss: 0.2726\n", "Epoch 505/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9362 - loss: 0.1553 - val_accuracy: 0.8900 - val_loss: 0.2780\n", "Epoch 506/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9290 - loss: 0.1646 - val_accuracy: 0.8863 - val_loss: 0.3070\n", "Epoch 507/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9256 - loss: 0.1696 - val_accuracy: 0.8827 - val_loss: 0.2871\n", "Epoch 508/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9272 - loss: 0.1526 - val_accuracy: 0.8882 - val_loss: 0.2747\n", "Epoch 509/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9284 - loss: 0.1602 - val_accuracy: 0.8891 - val_loss: 0.2868\n", "Epoch 510/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9367 - loss: 0.1571 - val_accuracy: 0.8891 - val_loss: 0.2827\n", "Epoch 511/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9368 - loss: 0.1538 - val_accuracy: 0.8873 - val_loss: 0.2810\n", "Epoch 512/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9132 - loss: 0.1806 - val_accuracy: 0.8900 - val_loss: 0.2780\n", "Epoch 513/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9290 - loss: 0.1563 - val_accuracy: 0.8763 - val_loss: 0.2864\n", "Epoch 514/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - 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accuracy: 0.9512 - loss: 0.1052 - val_accuracy: 0.8689 - val_loss: 0.3619\n", "Epoch 960/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9590 - loss: 0.1040 - val_accuracy: 0.8772 - val_loss: 0.3578\n", "Epoch 961/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9535 - loss: 0.1082 - val_accuracy: 0.8882 - val_loss: 0.3726\n", "Epoch 962/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9481 - loss: 0.1130 - val_accuracy: 0.8753 - val_loss: 0.3845\n", "Epoch 963/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9556 - loss: 0.1067 - val_accuracy: 0.8772 - val_loss: 0.3907\n", "Epoch 964/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9544 - loss: 0.1105 - val_accuracy: 0.8680 - val_loss: 0.3859\n", "Epoch 965/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9518 - loss: 0.1129 - val_accuracy: 0.8726 - val_loss: 0.4016\n", "Epoch 966/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9517 - loss: 0.1123 - val_accuracy: 0.8753 - val_loss: 0.4160\n", "Epoch 967/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9519 - loss: 0.1057 - val_accuracy: 0.8735 - val_loss: 0.3864\n", "Epoch 968/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9538 - loss: 0.1060 - val_accuracy: 0.8753 - val_loss: 0.3817\n", "Epoch 969/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9553 - loss: 0.1117 - val_accuracy: 0.8753 - val_loss: 0.3748\n", "Epoch 970/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9572 - loss: 0.1043 - val_accuracy: 0.8799 - val_loss: 0.3654\n", "Epoch 971/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9516 - loss: 0.1096 - val_accuracy: 0.8772 - val_loss: 0.3756\n", "Epoch 972/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9550 - loss: 0.1066 - val_accuracy: 0.8616 - val_loss: 0.4797\n", "Epoch 973/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9479 - loss: 0.1214 - val_accuracy: 0.8735 - val_loss: 0.3808\n", "Epoch 974/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9584 - loss: 0.1004 - val_accuracy: 0.8680 - val_loss: 0.3958\n", "Epoch 975/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9504 - loss: 0.1210 - val_accuracy: 0.8680 - val_loss: 0.4856\n", "Epoch 976/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9412 - loss: 0.1393 - val_accuracy: 0.8772 - val_loss: 0.4309\n", "Epoch 977/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9545 - loss: 0.1070 - val_accuracy: 0.8781 - val_loss: 0.4104\n", "Epoch 978/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9447 - loss: 0.1352 - val_accuracy: 0.8781 - val_loss: 0.3633\n", "Epoch 979/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9485 - loss: 0.1155 - val_accuracy: 0.8726 - val_loss: 0.4027\n", "Epoch 980/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9441 - loss: 0.1234 - val_accuracy: 0.8735 - val_loss: 0.3668\n", "Epoch 981/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9550 - loss: 0.1070 - val_accuracy: 0.8790 - val_loss: 0.3914\n", "Epoch 982/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9563 - loss: 0.1001 - val_accuracy: 0.8763 - val_loss: 0.3797\n", "Epoch 983/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9519 - loss: 0.1102 - val_accuracy: 0.8799 - val_loss: 0.3767\n", "Epoch 984/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9617 - loss: 0.0968 - val_accuracy: 0.8744 - val_loss: 0.3871\n", "Epoch 985/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9492 - loss: 0.1192 - val_accuracy: 0.8808 - val_loss: 0.3760\n", "Epoch 986/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9507 - loss: 0.1138 - val_accuracy: 0.8836 - val_loss: 0.3957\n", "Epoch 987/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9557 - loss: 0.1014 - val_accuracy: 0.8708 - val_loss: 0.3938\n", "Epoch 988/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9592 - loss: 0.0977 - val_accuracy: 0.8790 - val_loss: 0.3815\n", "Epoch 989/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9491 - loss: 0.1213 - val_accuracy: 0.8854 - val_loss: 0.3666\n", "Epoch 990/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9565 - loss: 0.1018 - val_accuracy: 0.8763 - val_loss: 0.3965\n", "Epoch 991/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9619 - loss: 0.0954 - val_accuracy: 0.8808 - val_loss: 0.3932\n", "Epoch 992/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9502 - loss: 0.1148 - val_accuracy: 0.8708 - val_loss: 0.3778\n", "Epoch 993/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9605 - loss: 0.0996 - val_accuracy: 0.8763 - val_loss: 0.4069\n", "Epoch 994/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9473 - loss: 0.1161 - val_accuracy: 0.8763 - val_loss: 0.3945\n", "Epoch 995/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9588 - loss: 0.0959 - val_accuracy: 0.8744 - val_loss: 0.4135\n", "Epoch 996/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9625 - loss: 0.0901 - val_accuracy: 0.8662 - val_loss: 0.4634\n", "Epoch 997/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9454 - loss: 0.1246 - val_accuracy: 0.8708 - val_loss: 0.4124\n", "Epoch 998/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9574 - loss: 0.1060 - val_accuracy: 0.8836 - val_loss: 0.3864\n", "Epoch 999/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9543 - loss: 0.1107 - val_accuracy: 0.8845 - val_loss: 0.3957\n", "Epoch 1000/1000\n", "\u001b[1m137/137\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9496 - loss: 0.1262 - val_accuracy: 0.8708 - val_loss: 0.3969\n", "\u001b[1m171/171\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n", "\u001b[1m171/171\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 831us/step\n" ] } ], "source": [ "documents = build_corpus(['dev-0/in.tsv', 'test-A/in.tsv'])\n", "word2vec_model = Word2Vec(sentences=documents, vector_size=100, window=5, min_count=1, workers=4)\n", "word2vec_model.save(\"word2vec.model\")\n", "\n", "dev_texts = read_text_file('dev-0/in.tsv')\n", "test_texts = read_text_file('test-A/in.tsv')\n", "\n", "dev_features = np.array([text_to_vector(text, word2vec_model) for text in dev_texts])\n", "test_features = np.array([text_to_vector(text, word2vec_model) for text in test_texts])\n", "\n", "dev_labels = pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None).values.flatten()\n", "X_train, X_valid, y_train, y_valid = train_test_split(dev_features, dev_labels, test_size=0.2, random_state=42)\n", "\n", "neural_network = Sequential([\n", " Dense(64, activation='relu', input_shape=(100,)),\n", " Dense(32, activation='relu'),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "neural_network.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", "\n", "training_history = neural_network.fit(X_train, y_train, epochs=1000, batch_size=32, validation_data=(X_valid, y_valid))\n", "\n", "dev_predictions_raw = neural_network.predict(dev_features)\n", "test_predictions_raw = neural_network.predict(test_features)\n", "\n", "dev_predictions = (dev_predictions_raw > 0.5).astype(int)\n", "test_predictions = (test_predictions_raw > 0.5).astype(int)\n", "\n", "write_predictions_to_file(dev_predictions, 'dev-0/out.tsv')\n", "write_predictions_to_file(test_predictions, 'test-A/out.tsv')\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Evaluation Results ===\n", "Accuracy: 0.9364\n", "\n", "Classification Report:\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.93 0.89 0.91 1983\n", " 1 0.94 0.96 0.95 3469\n", "\n", " accuracy 0.94 5452\n", " macro avg 0.93 0.93 0.93 5452\n", "weighted avg 0.94 0.94 0.94 5452\n", "\n", "==========================\n", "\n" ] } ], "source": [ "dev_pred_labels = pd.read_csv('dev-0/out.tsv', header=None).values.flatten()\n", "expected_labels = dev_labels\n", "\n", "accuracy = accuracy_score(expected_labels, dev_pred_labels)\n", "report = classification_report(expected_labels, dev_pred_labels)\n", "\n", "print(\"=== Evaluation Results ===\")\n", "print(f\"Accuracy: {accuracy:.4f}\")\n", "print(\"\\nClassification Report:\\n\")\n", "print(report)\n", "print(\"==========================\\n\")" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 2 }