customModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Embedding, LSTM, SpatialDropout1D, Input\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive</td>\n",
       "      <td>I am coming to the borders and I will kill you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will kill you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im coming on borderlands and i will murder you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting into borderlands and i can murder y...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sentiment                                            content\n",
       "0  Positive  I am coming to the borders and I will kill you...\n",
       "1  Positive  im getting on borderlands and i will kill you ...\n",
       "2  Positive  im coming on borderlands and i will murder you...\n",
       "3  Positive  im getting on borderlands 2 and i will murder ...\n",
       "4  Positive  im getting into borderlands and i can murder y..."
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrain = pd.read_csv('./data/twitter_train.csv')\n",
    "dataTrain.columns = [\"tweet id\", \"entity\", \"sentiment\", \"content\"]\n",
    "dataTrain.drop([\"tweet id\", \"entity\"],inplace=True, axis=1)\n",
    "\n",
    "dataTrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment      0\n",
       "content      686\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrain.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrain.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment    0\n",
       "content      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrain.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment\n",
       "Negative      22358\n",
       "Positive      20654\n",
       "Neutral       18108\n",
       "Irrelevant    12875\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrain[\"sentiment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrain = dataTrain[dataTrain.sentiment != \"Irrelevant\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distr = dataTrain[\"sentiment\"].value_counts()\n",
    "plot = distr.plot(kind=\"bar\")\n",
    "plot.set_ylabel('Number of tweets')\n",
    "plot.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41308\n",
      "36216\n",
      "44716\n"
     ]
    }
   ],
   "source": [
    "dataTrain['content'] = dataTrain['content'].apply(lambda x: x.lower())\n",
    "dataTrain['content'] = dataTrain['content'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))\n",
    "\n",
    "print(dataTrain[ dataTrain['sentiment'] == 'Positive'].size)\n",
    "print(dataTrain[ dataTrain['sentiment'] == 'Neutral'].size)\n",
    "print(dataTrain[ dataTrain['sentiment'] == 'Negative'].size)\n",
    "\n",
    "for idx,row in dataTrain.iterrows():\n",
    "    row.iloc[0] = row.iloc[0].replace('rt',' ')\n",
    "    \n",
    "maxFeatures = 2000\n",
    "tokenizer = Tokenizer(num_words=maxFeatures, split=' ')\n",
    "tokenizer.fit_on_texts(dataTrain['content'].values)\n",
    "X = tokenizer.texts_to_sequences(dataTrain['content'].values)\n",
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_3 (Embedding)     (None, 166, 128)          256000    \n",
      "                                                                 \n",
      " spatial_dropout1d_3 (Spati  (None, 166, 128)          0         \n",
      " alDropout1D)                                                    \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 196)               254800    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 591       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 511391 (1.95 MB)\n",
      "Trainable params: 511391 (1.95 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "embed_dim = 128\n",
    "lstm_out = 196\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(X.shape[1],)))\n",
    "model.add(Embedding(2000, embed_dim))\n",
    "model.add(SpatialDropout1D(0.4))\n",
    "model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(3,activation='softmax'))\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False  True]\n",
      " [False False  True]\n",
      " [False False  True]\n",
      " ...\n",
      " [False False  True]\n",
      " [False False  True]\n",
      " [False False  True]]\n",
      "(48896, 166) (48896, 3)\n",
      "(12224, 166) (12224, 3)\n"
     ]
    }
   ],
   "source": [
    "Y = pd.get_dummies(dataTrain['sentiment']).values\n",
    "print(Y)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[55], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1735\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1736\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1739\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1740\u001b[0m ):\n\u001b[0;32m   1741\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1742\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1744\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    854\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    855\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    856\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 857\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    859\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    860\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    861\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m    146\u001b[0m   (concrete_function,\n\u001b[0;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs)\u001b[0m\n\u001b[0;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1351\u001b[0m     args,\n\u001b[0;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1353\u001b[0m     executing_eagerly)\n\u001b[0;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1472\u001b[0m   )\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "model.fit(X_train, Y_train, epochs = 10, batch_size=batch_size, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[57], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m pos_correct, neg_correct, neut_correct \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(X_test)):\n\u001b[1;32m----> 6\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m      8\u001b[0m     predicted_class \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(result)\n\u001b[0;32m      9\u001b[0m     true_class \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(Y_test[x])\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\training.py:2554\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   2552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[0;32m   2553\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m-> 2554\u001b[0m     tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2555\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   2556\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:864\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    862\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    863\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 864\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[0;32m    866\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    867\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m    146\u001b[0m   (concrete_function,\n\u001b[0;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs)\u001b[0m\n\u001b[0;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1351\u001b[0m     args,\n\u001b[0;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1353\u001b[0m     executing_eagerly)\n\u001b[0;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1472\u001b[0m   )\n",
      "File \u001b[1;32mc:\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Variables to count instances and correct predictions\n",
    "pos_cnt, neg_cnt, neut_cnt = 0, 0, 0\n",
    "pos_correct, neg_correct, neut_correct = 0, 0, 0\n",
    "\n",
    "for x in range(len(X_test)):\n",
    "    result = model.predict(X_test[x].reshape(1, X_test.shape[1]), batch_size=1, verbose=1)[0]\n",
    "    \n",
    "    predicted_class = np.argmax(result)\n",
    "    true_class = np.argmax(Y_test[x])\n",
    "    \n",
    "    # Check if prediction is correct\n",
    "    if predicted_class == true_class:\n",
    "        if true_class == 0:\n",
    "            neg_correct += 1\n",
    "        elif true_class == 1:\n",
    "            neut_correct += 1\n",
    "        elif true_class == 2:\n",
    "            pos_correct += 1\n",
    "    \n",
    "    # Increment the respective counters\n",
    "    if true_class == 0:\n",
    "        neg_cnt += 1\n",
    "    elif true_class == 1:\n",
    "        neut_cnt += 1\n",
    "    elif true_class == 2:\n",
    "        pos_cnt += 1\n",
    "\n",
    "# Calculate and print accuracies\n",
    "print(\"pos_acc:\", pos_correct / pos_cnt * 100, \"%\")\n",
    "print(\"neg_acc:\", neg_correct / neg_cnt * 100, \"%\")\n",
    "print(\"neut_acc:\", neut_correct / neut_cnt * 100, \"%\")"
   ]
  }
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