neural_network.ipynb

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 "cells": [
  {
   "source": [
    "# Neural Networks"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Libraries Import"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import keras\n",
    "import matplotlib.pyplot as pyplot\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "source": [
    "## Configuration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "PIXELS_PER_DIMENSION = 28\n",
    "MAX_PIXEL_VALUE = 255\n",
    "CLASSES_NAMES = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
    "IMAGES_TO_DISPLAY = 3\n",
    "VALIDATION_SIZE = 10000\n",
    "BATCH_SIZE = 20\n",
    "EPOCHS_COUNT = 30"
   ]
  },
  {
   "source": [
    "## Dataset Exploration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_image(pixels: list) -> None:\n",
    "    pixels = pixels.reshape([PIXELS_PER_DIMENSION, PIXELS_PER_DIMENSION])\n",
    "\n",
    "    pyplot.gray()\n",
    "    pyplot.imshow(pixels)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image (number 31927 in the dataset) is classified as a 'Sandal'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image (number 1944 in the dataset) is classified as a 'Sneaker'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image (number 18397 in the dataset) is classified as a 'Bag'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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Etq6uLlVf77zzjqru3XffVdWFGlugwYMHh2wPvCtC+8i52traiDWBjxA0CVtqAIxCqAEwCqEGwCiEGgCjEGoAjEKoATAKoQbAKIQaAKMQagCMwh0FYVy+fFlV99vf/ta2vGTJkqC2YcOGqfqaOHGiqu7jjz+OWNPc3Kzqa/369UFt+/fvl0ceecTWlpOTo+pv0aJFEWucTqeqr/b2dlVd4N/j1ltvDWrLzs5W9fWPf/xDVTd9+nRVXbg7Nj4vLS1N1a597sSjjz4asSbccxFMwJYaAKMQagCMQqgBMAqhBsAohBoAoxBqAIxCqAEwCqEGwCgplmVZyRyA2+22XShqWZZ62uL+IHCa6PT09KBpqC9duqTqS1v30EMPRazZuHGjqq+Wlpagtrlz58pbb71la5swYYKqP81Frs8884yqr87OTlXdypUrbctjxowJmo79xIkTqr4qKytVdS+99JKqTrNOjx49GtS2YMECefXVV21t2gugNVOIJ/o7lujvscvlkqamppA/Y0sNgFEi3sPR2dkpy5Ytk1OnTonD4RCXyyVVVVWSlZUlR44ckYqKCrl69aqMHTtW1q1bp74dBQASIeKWWkpKiixevFjq6upk165dMm7cOFm/fr34/X558sknpaKiQurq6qSwsDDkfYQAcCNFDLXMzEyZOXNm7/LUqVPlzJkz0tDQIOnp6VJYWCgiIgsXLpS9e/cmbqQAoNCnEwV+v18efPBBKSoqkpycHHn99ddl27ZtvT+fMmWKHDhwQDIzMxMxVgCIqE9TD61evVqGDh0q9957r/z1r3+NywA4+9n3Os5+2nH20+6LfvZTHWper1eam5tly5YtkpqaKk6n0/bB6ejokNTUVLbSACSV6pKODRs2SENDg1RXV/dOLjd58mTp6uqSw4cPi4jI9u3bZd68eYkbKQAoRNxSO3HihGzdulXcbrcsXLhQRERyc3Olurpa1q5dK5WVlbZLOgAgmSKG2i233CLHjh0L+bOvfe1rsmvXrrgPaiAJPB4yY8aMoLbx48er+tqxY4eqbtq0aRFrAo8phZOaGnpjPfB4iGYKcRGRtra2iDUHDhxQ9bVp0yZVXajfIbDtN7/5jaqv7373u6o67fm1UaNGRazJy8tTtQ8ZMkT1npp1YDLuKABgFEINgFEINQBGIdQAGIVQA2AUQg2AUQg1AEYh1AAYhVADYJQ+zdKBYDfddFPEtp6eHlVf2rsA7rnnnog12hk/fD5fyPa0tLTrLocTasaJQJpZJEREbr75ZlXd8ePHbctf/vKXg9oWLFig6mvs2LGquqeeekpVd/bs2Yg1n81JGNj22muv2dqWLFmies9Tp06p6kzFlhoAoxBqAIxCqAEwCqEGwCiEGgCjEGoAjEKoATAKoQbAKFx8G6NQT88KbLtw4YKqrylTpqjqQl3wG0h78W24x5gFtn/yySeq/iZPnhyx5vMPx76ef//736q6wIuW58yZI/X19ba273//+6q+zp07p6pbs2aNqu7zj38M55///GfI9hEjRtiW09PTVe/Zh0f5GoktNQBGIdQAGIVQA2AUQg2AUQg1AEYh1AAYhVADYBRCDYBRCDUARuGOghi1t7fblnNzc4PaJk2apOpr8ODBqrphw4ZFrAm8Gr2vJk6caFv2+/2q12mnLteYMWOGqi7UXRE//OEPbcvd3d2qvoYPH66qGzJkiKru9ttvj1gT7vPx0EMP2ZazsrJU77lz505VnanYUgNgFEINgFEINQBGIdQAGIVQA2AUQg2AUQg1AEYh1AAYhVADYBTuKIjRG2+8YVueMmVKUNtXv/pVVV/aq/Y1zwvQzrUf6vkJY8aMkePHj9va0tLSVP1pfofUVN3/0nDPTwg0aJD9Y+x2u+V///uf6rWBtM8B0D53oqOjI6px5OTkyMcff2xrC/U8jFD+9re/RfWepmBLDYBRIm6pdXZ2yrJly+TUqVPicDjE5XJJVVWVZGVlSUFBgeTn5/f+5127dq0UFBQkfNAAEE7EUEtJSZHFixf3PtbM6/XK+vXr5dlnnxURke3bt6tusAaAGyHi7mdmZqbtOY1Tp04Nes4iAPQXKVYfnnzq9/vlwQcflKKiIrnvvvukoKBAbrvtNvH5fDJnzhwpLS0Vh8ORyPECwHX1KdRWrVolra2tsnnzZklNTZWWlhZxOp1y8eJFefLJJyU/P1+eeOKJPg3A7XbbnmJtWZb6rFd/UFFRYVtetWqVVFZW2toCl8NpampS1WlWWSxnP++8807Zv3+/rW0gnf2cNWuWHDx4UPXaQPF+Cnq0/+QnT54sDQ0NtrZbbrlF9doFCxZErHnzzTejGpdWor/HLpcr7PdFffbT6/VKc3OzbNy4sfdD6XQ6RUQkIyND5s+fL/X19bGPFgBioAq1DRs2SENDg1RXV/f+5zl//rx0dXWJyKezndbV1YnH40ncSAFAIeLZzxMnTsjWrVvF7XbLwoULReTTKasXL14sFRUVkpKSIj09PTJt2jR57LHHEj5gALiePh1TS4SBfkzt1ltvtS2/++67QVusgcdGwmltbVXVBR5DCkV7bCjUXPvp6ely9epVW5v22NBAWnc3QltbW8SaUM9PGDNmTNBVBtrnTtxxxx0Ra/7zn/+o+orWgDimBgADAaEGwCiEGgCjEGoAjEKoATAKoQbAKIQaAKMQagCMwnTeMXrvvfcitmlvHtbehH7t2rWINdqpwS9fvhzU9stf/lI2b95sa7t06ZKqvytXrkSs6enpUfUV6qLUUHw+n2158+bN8vOf/9zWFngxcTgXL15U1Wn/vpq68+fPB7Xt27dP7r//flub9u+W6Atr+zu21AAYhVADYBRCDYBRCDUARiHUABiFUANgFEINgFEINQBGSfrFt7m5uUFtLpcrCSOJn8DxDx06VPU67cWVmotvtRMah5tFN3CWVc1suyK6GXcDL5YNR/N7husvKyvLtqy9kDfUTMChaP++motvw71nTk6ObVn7+egv359EjiNUbnwm6dN5A0A8sfsJwCiEGgCjEGoAjEKoATAKoQbAKIQaAKMQagCMQqgBMAqhBsAohBoAoyT93s/PnDx5UsrLy+XcuXOSmZkpXq9X3G53soelVlRUJA6Ho/fex7KyMpk9e3aSR3V9Xq9X6urq5KOPPpJdu3ZJfn6+iAycdRFu/ANpXXR2dsqyZcvk1KlT4nA4xOVySVVVlWRlZcmRI0ekoqJCrl69KmPHjpV169ZJdnZ2sodsc73xFxQUSH5+vqSmfrrttHbtWikoKEj8oKx+YtGiRdbOnTsty7KsnTt3WosWLUryiPrmrrvuso4dO5bsYfTJoUOHrDNnzgSNfaCsi3DjH0jrorOz0zp48GDv8nPPPWc99dRTls/ns771rW9Zhw4dsizLsqqrq63y8vJkDTOscOO3LMvKz8+3Ll68eMPH1C92P9vb26WxsVGKi4tFRKS4uFgaGxulo6MjySMzW2FhoTidTlvbQFoXocY/0GRmZsrMmTN7l6dOnSpnzpyRhoYGSU9Pl8LCQhERWbhwoezduzdZwwwr3PiTqV/sfra0tEhOTo6kpaWJiEhaWpqMHj1aWlpagqaQ6c/KysrEsiyZPn26LF26NGj6noGAdZE8fr9fXnnlFSkqKpKWlhYZM2ZM78+ysrLE7/f3HhLojz4//s8sWrRIfD6fzJkzR0pLS8XhcCR8HP1iS80ENTU18uc//1lef/11sSxLqqqqkj2kL6yBui5Wr14tQ4cOlXvvvTfZQ4lK4Pj3798vb7zxhtTU1Mj7778v1dXVN2Qc/SLUnE6ntLa29k725/P55OzZswNq1+KzsTocDikpKZH6+vokjyg6rIvk8Hq90tzcLBs3bpTU1FRxOp223biOjg5JTU3tt1tpgeMX+f/1kJGRIfPnz79h66FfhFp2drZ4PB6pra0VEZHa2lrxeDwDZnfn8uXLcuHCBRH5dEbUPXv2iMfjSfKoosO6uPE2bNggDQ0NUl1d3bt7NnnyZOnq6pLDhw+LiMj27dtl3rx5yRxmWKHGf/78eenq6hKRT2fsrauru2Hrod/MfPvBBx9IeXm5fPLJJzJixAjxer0yYcKEZA9L5cMPP5TS0lLx+Xzi9/slLy9PVqxYIaNHj0720K5rzZo1sm/fPmlra5ORI0dKZmam7N69e8Csi1Dj37Jly4BaFydOnJDi4mJxu92903rn5uZKdXW11NfXS2Vlpe2SjptvvjnJI7YLN/7FixdLRUWFpKSkSE9Pj0ybNk2WL18uw4YNS/iY+k2oAUA89IvdTwCIF0INgFEINQBGIdQAGIVQA2AUQg2AUQg1AEb5P6kPDxFd1e1EAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "indexes = [random.randint(0, len(X_train)) for _ in range(IMAGES_TO_DISPLAY)]\n",
    "for index in indexes:\n",
    "    print(\"The above image (number {} in the dataset) is classified as a '{}'.\".format(index, CLASSES_NAMES[y_train[index]]))\n",
    "\n",
    "    pixels = X_train[index]\n",
    "    display_image(pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(15, 5)})\n",
    "\n",
    "def plot_labels_distribution(labels: np.array) -> None:\n",
    "    string_labels = [CLASSES_NAMES[label] for label in labels]\n",
    "\n",
    "    plot_train = sns.histplot(data=string_labels, shrink=0.9)\n",
    "    pyplot.xlabel(\"Labels\")\n",
    "    pyplot.title(\"Distribution of Labels in Dataset\")\n",
    "    pyplot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot_labels_distribution(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot_labels_distribution(y_test)"
   ]
  },
  {
   "source": [
    "## Preprocessing"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "X_train = X_train / MAX_PIXEL_VALUE\n",
    "X_test = X_test / MAX_PIXEL_VALUE"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 41,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validation = X_train[-VALIDATION_SIZE:]\n",
    "X_train = X_train[:-VALIDATION_SIZE]\n",
    "\n",
    "y_validation = y_train[-VALIDATION_SIZE:]\n",
    "y_train = y_train[:-VALIDATION_SIZE]"
   ]
  },
  {
   "source": [
    "## Network Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential(\n",
    "    [\n",
    "        layers.Flatten(input_shape=(PIXELS_PER_DIMENSION, PIXELS_PER_DIMENSION), name=\"flatten\"),\n",
    "        layers.Dense(300, activation=\"relu\", name=\"dense_1\"),\n",
    "        layers.Dense(100, activation=\"relu\", name=\"dense_2\"),\n",
    "        layers.Dense(len(CLASSES_NAMES), activation=\"softmax\", name=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "model.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nflatten (Flatten)            (None, 784)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 300)               235500    \n_________________________________________________________________\ndense_2 (Dense)              (None, 100)               30100     \n_________________________________________________________________\nsoftmax (Dense)              (None, 10)                1010      \n=================================================================\nTotal params: 266,610\nTrainable params: 266,610\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The weights of the first hidden layer are:\n\n[[-0.00258356 -0.04287695 -0.00080762 ... -0.06088414 -0.01810262\n  -0.00501981]\n [ 0.00840638  0.05068494 -0.05901249 ...  0.03504758 -0.05494433\n   0.05713125]\n [-0.06164733  0.05779707  0.01213291 ... -0.02423042 -0.02070763\n   0.05868194]\n ...\n [-0.05029047 -0.04427202  0.0704903  ...  0.00752795 -0.048204\n  -0.05915596]\n [ 0.05128475 -0.0357127   0.06307554 ... -0.02828469  0.01526793\n   0.00574511]\n [ 0.07132061  0.05258058  0.04134983 ... -0.04279372 -0.02485666\n  -0.01892803]]\n\nThe thresholds of the first hidden layer are:\n\n[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "weights, thresholds = model.layers[1].get_weights()\n",
    "print(\"The weights of the first hidden layer are:\\n\\n{}\\n\".format(weights))\n",
    "print(\"The thresholds of the first hidden layer are:\\n\\n{}\".format(thresholds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.9263 - accuracy: 0.7037 - val_loss: 0.5222 - val_accuracy: 0.8083\n",
      "Epoch 2/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.4762 - accuracy: 0.8344 - val_loss: 0.4983 - val_accuracy: 0.8168\n",
      "Epoch 3/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.4294 - accuracy: 0.8505 - val_loss: 0.4168 - val_accuracy: 0.8526\n",
      "Epoch 4/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3964 - accuracy: 0.8585 - val_loss: 0.3947 - val_accuracy: 0.8604\n",
      "Epoch 5/30\n",
      "2500/2500 [==============================] - 6s 3ms/step - loss: 0.3703 - accuracy: 0.8691 - val_loss: 0.3815 - val_accuracy: 0.8664\n",
      "Epoch 6/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3595 - accuracy: 0.8695 - val_loss: 0.3687 - val_accuracy: 0.8701\n",
      "Epoch 7/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3390 - accuracy: 0.8789 - val_loss: 0.3690 - val_accuracy: 0.8684\n",
      "Epoch 8/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3335 - accuracy: 0.8798 - val_loss: 0.3567 - val_accuracy: 0.8706\n",
      "Epoch 9/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3232 - accuracy: 0.8843 - val_loss: 0.3588 - val_accuracy: 0.8718\n",
      "Epoch 10/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.3106 - accuracy: 0.8880 - val_loss: 0.3462 - val_accuracy: 0.8747\n",
      "Epoch 11/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2991 - accuracy: 0.8915 - val_loss: 0.3693 - val_accuracy: 0.8658\n",
      "Epoch 12/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2916 - accuracy: 0.8950 - val_loss: 0.3396 - val_accuracy: 0.8797\n",
      "Epoch 13/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2786 - accuracy: 0.8985 - val_loss: 0.3396 - val_accuracy: 0.8774\n",
      "Epoch 14/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2829 - accuracy: 0.8978 - val_loss: 0.3452 - val_accuracy: 0.8774\n",
      "Epoch 15/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2671 - accuracy: 0.9036 - val_loss: 0.3305 - val_accuracy: 0.8813\n",
      "Epoch 16/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2669 - accuracy: 0.9033 - val_loss: 0.3413 - val_accuracy: 0.8775\n",
      "Epoch 17/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2542 - accuracy: 0.9068 - val_loss: 0.3314 - val_accuracy: 0.8823\n",
      "Epoch 18/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2501 - accuracy: 0.9091 - val_loss: 0.3332 - val_accuracy: 0.8820\n",
      "Epoch 19/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2413 - accuracy: 0.9131 - val_loss: 0.3220 - val_accuracy: 0.8844\n",
      "Epoch 20/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2325 - accuracy: 0.9159 - val_loss: 0.3111 - val_accuracy: 0.8879\n",
      "Epoch 21/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2315 - accuracy: 0.9171 - val_loss: 0.3251 - val_accuracy: 0.8816\n",
      "Epoch 22/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2335 - accuracy: 0.9152 - val_loss: 0.3121 - val_accuracy: 0.8883\n",
      "Epoch 23/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2223 - accuracy: 0.9186 - val_loss: 0.3161 - val_accuracy: 0.8873\n",
      "Epoch 24/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2154 - accuracy: 0.9217 - val_loss: 0.3550 - val_accuracy: 0.8746\n",
      "Epoch 25/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2151 - accuracy: 0.9229 - val_loss: 0.3069 - val_accuracy: 0.8916\n",
      "Epoch 26/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2075 - accuracy: 0.9259 - val_loss: 0.3101 - val_accuracy: 0.8882\n",
      "Epoch 27/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.2034 - accuracy: 0.9271 - val_loss: 0.3083 - val_accuracy: 0.8904\n",
      "Epoch 28/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.1995 - accuracy: 0.9290 - val_loss: 0.3346 - val_accuracy: 0.8819\n",
      "Epoch 29/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.1980 - accuracy: 0.9296 - val_loss: 0.3100 - val_accuracy: 0.8925\n",
      "Epoch 30/30\n",
      "2500/2500 [==============================] - 5s 2ms/step - loss: 0.1925 - accuracy: 0.9314 - val_loss: 0.3181 - val_accuracy: 0.8877\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
    "history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), epochs=EPOCHS_COUNT, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "source": [
    "## Network Evaluation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "execution_count": 47
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "accuracies_per_epoch = list(zip(history.history[\"accuracy\"], history.epoch))\n",
    "validation_accuracies_per_epoch = list(zip(history.history[\"val_accuracy\"], history.epoch))\n",
    "\n",
    "accuracies_df = pd.DataFrame(accuracies_per_epoch, columns=[\"accuracy\", \"epoch\"])\n",
    "validation_accuracies_df = pd.DataFrame(validation_accuracies_per_epoch, columns=[\"accuracy\", \"epoch\"])\n",
    "\n",
    "first_plot = sns.lineplot(data=accuracies_df, x=\"epoch\", y=\"accuracy\", label=\"Test\")\n",
    "second_plot = sns.lineplot(data=validation_accuracies_df, x=\"epoch\", y=\"accuracy\", label=\"Validation\")\n",
    "pyplot.xlabel(\"Epoch\")\n",
    "pyplot.ylabel(\"Accuracy\")\n",
    "pyplot.title(\"Accuracies in Each Epoch\")\n",
    "pyplot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The model accuracy over the test dataset is: 0.88.\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=False)\n",
    "print(\"The model accuracy over the test dataset is: {:.2f}.\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image is a 'Ankle boot' and is classified as a 'Ankle boot'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image is a 'Pullover' and is classified as a 'Pullover'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The above image is a 'Trouser' and is classified as a 'Trouser'.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "y_pred = model.predict([X_test[:IMAGES_TO_DISPLAY]])\n",
    "\n",
    "for index in range(IMAGES_TO_DISPLAY):\n",
    "    current_y_pred = y_pred[index].argmax()\n",
    "    print(\"The above image is a '{}' and is classified as a '{}'.\".format(CLASSES_NAMES[y_test[index]], CLASSES_NAMES[current_y_pred]))\n",
    "\n",
    "    pixels = X_train[index]\n",
    "    display_image(pixels)"
   ]
  }
 ]
}