main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from data import get_mnist_data\n",
    "from preprocessing import SequentialImageDataGenerator\n",
    "\n",
    "from model import NextImagePredictor\n",
    "\n",
    "from test_model import test_model_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train images shape : (60000, 28, 28)\n",
      "Shape of single image : (28, 28)\n"
     ]
    }
   ],
   "source": [
    "df = get_mnist_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max number of images is 6742 for label :  1\n",
      "Min number of images is 5421 for label :  5\n"
     ]
    }
   ],
   "source": [
    "print('Max number of images is' , max(df['labels'].value_counts()), 'for label : ', df['labels'].value_counts().idxmax())\n",
    "print('Min number of images is' , min(df['labels'].value_counts()), 'for label : ', df['labels'].value_counts().idxmin())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = SequentialImageDataGenerator(df)\n",
    "\n",
    "X_train, X_test, y_train, y_test = generator.train_test_split_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape : (26357, 4, 784)\n",
      "y_train shape : (26357, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape : {}\".format(X_train.shape))\n",
    "print(\"y_train shape : {}\".format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ekinc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = NextImagePredictor(X_train=X_train, y_train=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - loss: 0.1100 - mse: 0.1100\n",
      "Epoch 2/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.1076 - mse: 0.1076\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimizer = SGD(Stochastic Gradient Descent)\n",
    "\n",
    "model.train('sgd', 2)\n",
    "\n",
    "for i in [0, 3, 30, 117, 125, 12]:\n",
    "    test_model_prediction(model, X_test, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - loss: 0.0694 - mse: 0.0694\n",
      "Epoch 2/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.0600 - mse: 0.0600\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimizer = RMSPROP(Root Mean Squared Propagation)\n",
    "\n",
    "model.train(\"rmsprop\", 2)\n",
    "\n",
    "for i in [0, 3, 30, 117, 125, 12]:\n",
    "    test_model_prediction(model, X_test, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - loss: 0.0556 - mse: 0.0556\n",
      "Epoch 2/2\n",
      "\u001b[1m824/824\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.0542 - mse: 0.0542\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimizer = Adam\n",
    "\n",
    "model.train(\"adam\", 2)\n",
    "\n",
    "for i in [0, 3, 30, 117, 125, 12]:\n",
    "    test_model_prediction(model, X_test, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}