model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import Conv1D, LSTM, MaxPooling1D, Flatten\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('mouse_data.csv')\n",
    "\n",
    "labels = data.iloc[:, -1].values  # Extract labels\n",
    "data = data.drop(columns = 'label')\n",
    "data = data.to_numpy()\n",
    "\n",
    "x_data = data[:, :300]  # Extract 'x' coordinates\n",
    "y_data = data[:, 300:]  # Extract 'y' coordinates\n",
    "\n",
    "x = np.stack((x_data, y_data), axis=-1)\n",
    "y = labels\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 300, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(x[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 300, 2)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000,)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "25/25 [==============================] - 6s 169ms/step - loss: 0.7044 - accuracy: 0.5969 - val_loss: 0.5597 - val_accuracy: 0.7450\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.5359 - accuracy: 0.7325 - val_loss: 0.4995 - val_accuracy: 0.7550\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.4883 - accuracy: 0.7569 - val_loss: 0.4620 - val_accuracy: 0.7625\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.4400 - accuracy: 0.7894 - val_loss: 0.4226 - val_accuracy: 0.8125\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.4083 - accuracy: 0.8188 - val_loss: 0.4127 - val_accuracy: 0.8225\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.3664 - accuracy: 0.8319 - val_loss: 0.4269 - val_accuracy: 0.8150\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.3501 - accuracy: 0.8431 - val_loss: 0.3569 - val_accuracy: 0.8425\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.3257 - accuracy: 0.8619 - val_loss: 0.3383 - val_accuracy: 0.8525\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.2985 - accuracy: 0.8800 - val_loss: 0.3578 - val_accuracy: 0.8300\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.2791 - accuracy: 0.8894 - val_loss: 0.3234 - val_accuracy: 0.8600\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.2401 - accuracy: 0.9044 - val_loss: 0.3147 - val_accuracy: 0.8500\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.2275 - accuracy: 0.9125 - val_loss: 0.3001 - val_accuracy: 0.8750\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.2042 - accuracy: 0.9244 - val_loss: 0.3018 - val_accuracy: 0.8650\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 4s 153ms/step - loss: 0.2065 - accuracy: 0.9187 - val_loss: 0.2915 - val_accuracy: 0.8725\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.1870 - accuracy: 0.9244 - val_loss: 0.2840 - val_accuracy: 0.8900\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 4s 161ms/step - loss: 0.1666 - accuracy: 0.9331 - val_loss: 0.2774 - val_accuracy: 0.8800\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.1577 - accuracy: 0.9444 - val_loss: 0.2916 - val_accuracy: 0.8825\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.1343 - accuracy: 0.9525 - val_loss: 0.2910 - val_accuracy: 0.8750\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.1292 - accuracy: 0.9538 - val_loss: 0.2713 - val_accuracy: 0.8825\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.1253 - accuracy: 0.9600 - val_loss: 0.2844 - val_accuracy: 0.8825\n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.1101 - accuracy: 0.9625 - val_loss: 0.2763 - val_accuracy: 0.8775\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.1009 - accuracy: 0.9669 - val_loss: 0.2770 - val_accuracy: 0.8750\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0988 - accuracy: 0.9675 - val_loss: 0.3070 - val_accuracy: 0.8800\n",
      "Epoch 24/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0964 - accuracy: 0.9681 - val_loss: 0.2583 - val_accuracy: 0.8950\n",
      "Epoch 25/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0844 - accuracy: 0.9744 - val_loss: 0.2481 - val_accuracy: 0.8925\n",
      "Epoch 26/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0830 - accuracy: 0.9762 - val_loss: 0.2574 - val_accuracy: 0.8975\n",
      "Epoch 27/50\n",
      "25/25 [==============================] - 4s 157ms/step - loss: 0.0817 - accuracy: 0.9781 - val_loss: 0.2641 - val_accuracy: 0.8925\n",
      "Epoch 28/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0928 - accuracy: 0.9663 - val_loss: 0.2625 - val_accuracy: 0.8950\n",
      "Epoch 29/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0734 - accuracy: 0.9812 - val_loss: 0.2697 - val_accuracy: 0.8975\n",
      "Epoch 30/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0769 - accuracy: 0.9744 - val_loss: 0.2638 - val_accuracy: 0.8925\n",
      "Epoch 31/50\n",
      "25/25 [==============================] - 4s 154ms/step - loss: 0.0721 - accuracy: 0.9750 - val_loss: 0.2419 - val_accuracy: 0.9050\n",
      "Epoch 32/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0673 - accuracy: 0.9819 - val_loss: 0.2751 - val_accuracy: 0.9050\n",
      "Epoch 33/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0532 - accuracy: 0.9862 - val_loss: 0.2465 - val_accuracy: 0.9100\n",
      "Epoch 34/50\n",
      "25/25 [==============================] - 4s 157ms/step - loss: 0.0449 - accuracy: 0.9900 - val_loss: 0.2352 - val_accuracy: 0.9100\n",
      "Epoch 35/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0397 - accuracy: 0.9931 - val_loss: 0.2658 - val_accuracy: 0.9125\n",
      "Epoch 36/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0446 - accuracy: 0.9906 - val_loss: 0.2605 - val_accuracy: 0.9100\n",
      "Epoch 37/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0409 - accuracy: 0.9906 - val_loss: 0.2590 - val_accuracy: 0.9175\n",
      "Epoch 38/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0517 - accuracy: 0.9856 - val_loss: 0.2663 - val_accuracy: 0.9000\n",
      "Epoch 39/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0387 - accuracy: 0.9894 - val_loss: 0.2369 - val_accuracy: 0.9200\n",
      "Epoch 40/50\n",
      "25/25 [==============================] - 4s 157ms/step - loss: 0.0328 - accuracy: 0.9931 - val_loss: 0.2190 - val_accuracy: 0.9200\n",
      "Epoch 41/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0325 - accuracy: 0.9944 - val_loss: 0.2601 - val_accuracy: 0.9075\n",
      "Epoch 42/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0330 - accuracy: 0.9944 - val_loss: 0.2551 - val_accuracy: 0.9125\n",
      "Epoch 43/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0335 - accuracy: 0.9931 - val_loss: 0.2418 - val_accuracy: 0.9100\n",
      "Epoch 44/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0309 - accuracy: 0.9969 - val_loss: 0.2594 - val_accuracy: 0.9175\n",
      "Epoch 45/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0280 - accuracy: 0.9931 - val_loss: 0.2893 - val_accuracy: 0.8975\n",
      "Epoch 46/50\n",
      "25/25 [==============================] - 4s 157ms/step - loss: 0.0319 - accuracy: 0.9919 - val_loss: 0.2690 - val_accuracy: 0.9075\n",
      "Epoch 47/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0251 - accuracy: 0.9937 - val_loss: 0.2892 - val_accuracy: 0.9100\n",
      "Epoch 48/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.2904 - val_accuracy: 0.9150\n",
      "Epoch 49/50\n",
      "25/25 [==============================] - 4s 156ms/step - loss: 0.0206 - accuracy: 0.9956 - val_loss: 0.2955 - val_accuracy: 0.9175\n",
      "Epoch 50/50\n",
      "25/25 [==============================] - 4s 155ms/step - loss: 0.0177 - accuracy: 0.9987 - val_loss: 0.2867 - val_accuracy: 0.9175\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7fbd7c746fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D, MaxPooling1D, LSTM, Flatten, Dense, Dropout\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(64, 3, activation='relu', input_shape=(300, 2)))\n",
    "model.add(MaxPooling1D())\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train, epochs=50, batch_size=64, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: cursor_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: cursor_model/assets\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model.save('cursor_model')  # Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbd29237730>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "arr = x[1003]\n",
    "x_axis = []\n",
    "y_axis = []\n",
    "for i in arr:\n",
    "    x_axis.append(i[0])\n",
    "    y_axis.append(i[1])\n",
    "\n",
    "plt.plot(x_axis, y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 311ms/step\n",
      "[[0.07669341]]\n"
     ]
    }
   ],
   "source": [
    "single_row = x_test[1].reshape(1, 300, 2)\n",
    "prediction = model.predict(single_row)\n",
    "print(prediction)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}