AI2020_Lab3.ipynb

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      "source": [
        "* Χατζηθεοδώρου Ιάσονας ΑΜ: 03117089 <br>\r\n",
        "* Ανδρέας Βεζάκης ΑΜ: 03117186"
      ]
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      "cell_type": "markdown",
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      "source": [
        "# Τεχνητή Νοημοσύνη: Εργαστηριακή Άσκηση 3 \r\n",
        "Στην άσκηση αυτή θα υλοποιηθούν διάφοροι (σχετικά απλοί) αλγόριθμοι μηχανικής μάθησης για την αναγνώριση ψηφίων από εικόνα στο dataset MNIST. Συγκεκριμένα, θα δίνονται δύο σύνολα δεδομένων $$Ζ_{train}=\\{(x_1,y_1),\\dots,(x_n,y_n)\\}$$ $$Z_{test} = \\{(x_j,y_j),\\dots{,(x_k,y_k)}\\}$$ όπου κάθε $x_i\\in{\\mathbb{R}^p}$ είναι ένα διάνυσμα με τα pixels της κάθε εικόνας και $y_i$ είναι η ετικέτα της εικόνας - ένας ακέραιος στο $[0,9]$. Σε κάθε περίπτωση καλείστε να σχεδιάσετε έναν ταξινομητή, δηλαδή μια απεικόνιση $$f:\\mathbb{R}^p\\rightarrow{[0,9]}$$\r\n",
        "\r\n",
        "## 1ο Μέρος: Αξιολόγηση\r\n",
        "Στο πρώτο μέρος της άσκησης θα υλοποιηθούν συναρτήσεις που θα χρησιμοποιηθούν για την αξιολόγηση των ταξινομητών που θα υλοποιηθούν στα επόμενα μέρη.\r\n",
        "\r\n",
        "## 2ο Μέρος: ΚΝΝ\r\n",
        "Στο δεύτερο μέρος της άσκησης θα υλοποιήσετε τον αλγόριθμο KNN για ταξινόμηση. Υπενθυμίζεται από τις διαφάνειες το πλάνο σχεδιασμού για τον ταξινομητή k κοντινότερων γειτόνων:\r\n",
        "- Αποθηκεύουμε όλα τα δεδομένα ($Z_{train}$) στη μνήμη\r\n",
        "  - Τα δεδομένα μπορούν αποθηκευτούν σε έναν πίνακα $n\\times{p}$ με χρήση του numpy\r\n",
        "- Συγκρίνουμε την είσοδο με τα δεδομένα και βρίσκουμε τα k κοντινότερα ($k<n$) με βάση κάποια απόσταση.\r\n",
        "  - Όταν μας δίνεται μια \"φρέσκια\" εικόνα ως διάνυσμα από \r\n",
        "  pixels $x_i$ χρειαζόμαστε μια συνάρτηση που να υπολογίζει την απόσταση $d(x_i,x_j)$, όπου $x_j$ είναι το διάνυσμα που αντιστιχεί στα pixels μιας εικόνας από τα δεδομένα εκπαίδευσης. Θα πειραματιστείτε με την ευκλείδια απόσταση και την απόσταση συνημιτόνου. Στη συνέχεια ταξινομούνται τα δεδομένα εκπαίδευσης ως προς την απόστασή τους από το $x_i$ και επιλέγονται τα $k$ κοντινότερα\r\n",
        "- Δίνουμε στην έξοδο την κλάση στην οποία ανήκει η πλειοψηφία των k κοντινότερων δεδομένων.\r\n",
        "\r\n",
        "Αφού κατασκευαστεί ο ταξινομητής θα αξιολογήσετε την επίδοσή του στις 100 πρώτες εικόνες του $Z_{test}$ για κάποιες τιμές του k που θα επιλέξετε εσείς, ξεκινώντας από $k=1$.\r\n",
        "\r\n",
        "Στην πράξη πολύ σπάνια θα χρειαστεί να υλοποιήσετε έναν αλγόριθμο μηχανικής μάθησης από το μηδέν, αφού υπάρχουν έτοιμες υλοποιήσεις, π.χ. σε πακέτα της python, οι οποίες είναι βελτιστοποιημένες και εύχρηστες. Το τελευταίο ζητούμενο  στο 1ο μέρος είναι να επαναλάβετε το παραπάνω πείραμα με την έτοιμη υλοποίηση του KNN που παρέχει η βιβλιοθήκη sklearn. Καλείστε να συγκρίνετε τα αποτελέσματα και τους χρόνους εκτέλεσης.\r\n",
        "\r\n",
        "## 3ο Μέρος: Naive Bayes\r\n",
        "Στο τρίτο μέρος της άσκησης θα υλοποιήσετε τον αλγόριθμο Naive Bayes. Ας θυμηθούμε από τις διαφάνειες:\r\n",
        "\r\n",
        "**Υποθέσεις:**\r\n",
        "- Τα χαρακτηριστικά είναι boolean, δηλαδή παίρνουν δύο τιμές\r\n",
        "  - Για το συγκεκριμένο πρόβλημα μπορούμε να στρογγυλοποιήσουμε τις τιμές των pixels από το $[0.0,1.0]\\rightarrow{\\{0,1\\}}$\r\n",
        "- Η πιθανότητα ένα στοιχείο με χαρακτηριστικά x να ανήκει στην κλάση i δίνεται από τον τύπο:\r\n",
        "$$p(i|x)=\\frac{p(i)\\cdot{\\prod_{k=1}^p}p(x^{(k)}|i)}{\\sum_{j=1}^pp(x^{(k)}|j)}$$\r\n",
        "- Για να ταξινομήσουμε ένα διάνυσμα χαρακτηρστικών x σε μια κλάση i επιλέγουμε την κλάση που μεγιστοποιεί την παραπάνω πιθανότητα\r\n",
        "  - Μπορούμε για τη σύγκριση να αγνοήσουμε τον παρονομαστή, αφού για όλες τις κλάσεις θα είναι ίδιος\r\n",
        "\r\n",
        "## 4ο Μέρος: Perceptron\r\n",
        "\r\n",
        "Στο τέταρτο μέρος της άσκησης θα κατασκευάσετε ένα πολυεπίπεδο νευρωνικό δίκτυο. Ο ταξινομητής αυτός θα εκπαιδευτεί στο να ταξινομεί τις εικόνες του mnist σε μια από τις 10 διαφορετικές κλάσεις (από 0 μέχρι 9). Αρχικά θα υλοποιήσετε το μοντέλο αυτό χρησιμοποιώντας αποκλειστικά την βιβλιοθήκη numpy ενώ στην συνέχεια θα χρησιμοποιήσετε μια έτοιμη κλάση για την κατασκευή του ίδιου μοντέλου.  \r\n",
        "\r\n",
        "Ας θυμηθούμε από τις διαφάνειες: \r\n",
        "\r\n",
        "Κάθε MLP αποτελείται από επίπεδα όπου το κάθε ένα από αυτά χωρίζεται στα παρακάτω μέρη: \r\n",
        "\r\n",
        "$$ z(x) = w^Τx + b $$ \r\n",
        "$$ f(x) = a(z(x))$$ \r\n",
        "\r\n",
        "όπου $w$, $b$ είναι τα βάρη του επιπέδου.  Η έξοδος z(x)είναι η απόκριση κάθε νευρώνα πριν την συνάρτηση ενεργοποίησης ενώ η f(x) μετά.  Κάθε επίπεδο συνδέεται με ένα επόμενο του οποίου η είσοδος του αποτελεί την έξοδο (με την συνάρτηση ενεργοποίησης) του προηγούμενου. \r\n",
        "\r\n",
        "Στο μέρος αυτό καλείστε να συμπληρώσετε σε ορισμένα σημεία κώδικα ώστε να επιτυγχάνεται αυτή η λειτουργικότητα. Στην συνέχεια θα υλοποιήσετε το ίδιο ακριβώς μοντέλο χρησιμοποιώντας όμως μια έτοιμη βιβλιοθήκη και θα συγκρίνετε τα αποτελέσματά τους (χρόνο, σκορ κ.α.). "
      ]
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        "id": "Rvm0mywsZmit"
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      "source": [
        "# 1ο Μέρος: Αξιολόγηση\r\n",
        "\r\n",
        "Παρακάτω σας δίνεται η κλάση Evaluate, η οποία υπολογίζει διάφορες μετρικές στη μέθοδο get_metrics, εντοπίζει αντικείμενα που ταξινομήθηκαν λάθος και τα εμφανίζει (μέθοδος get_sample_of_wrong) και υπολογίζει και τον πίνακα σύγχυσης (confusion matrix) όπου οπτικοποιούνται ανά κατηγορία οι προβλέψεις του ταξινομητή.\r\n",
        "\r\n",
        "Για το μέρος αυτό καλείστε να υλοποιήσετε στη μέθοδο my_accuracy τη μετρική accuracy, η οποία ορίζεται ως:\r\n",
        "$$accuracy = \\frac{\\#σωστών\\_προβλέψεων}{\\#δεδομένα}$$\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlTj48uFZhyJ"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score, accuracy_score\r\n",
        "from matplotlib import pyplot as plt\r\n",
        "import numpy as np\r\n",
        "import random\r\n",
        "\r\n",
        "class Evaluate:\r\n",
        "    def __init__(self,y_true,y_pred,x):\r\n",
        "      self.y_true = y_true\r\n",
        "      self.y_pred = y_pred\r\n",
        "      self.x = x\r\n",
        "\r\n",
        "    def my_accuracy(self):\r\n",
        "      y_true = self.y_true\r\n",
        "      y_pred = self.y_pred\r\n",
        "      \r\n",
        "      ##################\r\n",
        "      ## Your code below\r\n",
        "      correct = 0\r\n",
        "      size = len(y_true)\r\n",
        "\r\n",
        "      for i in range(size):\r\n",
        "        if y_true[i] == y_pred[i]:\r\n",
        "          correct += 1\r\n",
        "\r\n",
        "      acc = correct / size\r\n",
        "      ## Your code above\r\n",
        "      ##################\r\n",
        "      return acc\r\n",
        "\r\n",
        "    def get_metrics(self):\r\n",
        "        precision = precision_score(self.y_true, self.y_pred, average = \"macro\")\r\n",
        "        recall = recall_score(self.y_true, self.y_pred, average = \"macro\")\r\n",
        "        f1 = f1_score(self.y_true, self.y_pred, average = \"macro\")\r\n",
        "        results = {\"precision\" : precision, \"recall\": recall, \"f1\": f1,\"accuracy\":self.my_accuracy()}\r\n",
        "        return results   \r\n",
        "    \r\n",
        "    def confusion_matrix(self):\r\n",
        "        cm = confusion_matrix(self.y_true, self.y_pred)\r\n",
        "        return cm \r\n",
        "\r\n",
        "    def get_sample_of_wrong(self, rows = 5, lines = 4):\r\n",
        "        wrong_indexes = [index for index in range(len(self.y_true)) if self.y_true[index] != self.y_pred[index]]\r\n",
        "        plt.figure(figsize=(10, 10))\r\n",
        "\r\n",
        "        random.shuffle(wrong_indexes)\r\n",
        "        for i, index in enumerate(wrong_indexes[: rows*lines]):\r\n",
        "            plt.subplot(rows, lines, i + 1)\r\n",
        "            image = self.x[index]\r\n",
        "            plt.imshow(image.reshape((28, 28)))\r\n",
        "            plt.title(\"Real: {}, Pred: {}\".format(self.y_true[index], self.y_pred[index]))\r\n",
        "            plt.axis('off')\r\n",
        "\r\n",
        "    def get_evaluation_report(self):\r\n",
        "       metrics = self.get_metrics()\r\n",
        "       for m in metrics:\r\n",
        "         print(m+' : '+str(metrics[m]))\r\n",
        "       cm = self.confusion_matrix()\r\n",
        "       print(\"Confusion matrix: \")\r\n",
        "       print(cm)\r\n",
        "       print(\"Some images with wrong predictions\")\r\n",
        "       self.get_sample_of_wrong()"
      ],
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qcSvLy6ZpzB"
      },
      "source": [
        "Παράδειγμα χρήσης της κλάσης. Κανονικά στο x θα υπάρχουν οι εικόνες από το dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "S5hRZ9L1ZoNJ",
        "outputId": "57a7d243-f2de-48f8-f138-8a188d7ffd62"
      },
      "source": [
        "y_true = [1,0,2,0,0,1,1,0]\r\n",
        "y_pred = [0,0,2,0,1,1,0,0]\r\n",
        "x = np.random.rand(8,28*28)\r\n",
        "\r\n",
        "eval = Evaluate(y_true,y_pred,x)\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision : 0.7000000000000001\n",
            "recall : 0.6944444444444443\n",
            "f1 : 0.6888888888888888\n",
            "accuracy : 0.625\n",
            "Confusion matrix: \n",
            "[[3 1 0]\n",
            " [2 1 0]\n",
            " [0 0 1]]\n",
            "Some images with wrong predictions\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+VNwxgrUFDoc8ZX0f98d1Ly/H3SrPNW1POI1Iv5zOccwv217IBypD13a6j+cWVRTy56Kpy5ifizU++2x8UW1vqV2e+dU7HqztXUvQV/ZOncf8OkyClPCsDB7txI67md+K+ZAqzFkViIjsQtGP7vTHNc5vu5H5JaQioqxHqfNZph+1OjFSP82dpbj02WIQMjdM/2aNtr8Y1I/5Wftdhu1s6id2fCwK7oTMAT0+4NWIfadC8n7UHB3M9jmPjrKJw633G4L+0Pqz8cwvrgD8bExd/sqk5cxvZwwyVaxtgKjSpB/4uVt/jIiqgI48iqzYFER6jbkN2XzYyV7M73LTb7Sdu9Crk7TKzEMQBEGwGHl5CIIgCBYjLw9BEATBYjJd82j47le68cC6FawtIBkhrl2XQMdLrs0LsKTcQ0bRknMQbxbSoyzzcz2O0MQndfOwtihEadLSTtitvO85Dym73x7rIaErsCYTcy0v80s1yZMvXXAdX9bjayPrvDy0XeI8NPL7Dbm+6XsbGTRjUvl6iJWCZFjpEML8Sq3mu94LL0Ko54Yaa7OMVk1E1MKht+5Hse/yAjXZ90CHjemCwk7Zw7jmH+2O+19zFHbVuts/Y36/Pi2F35ntxtoiKkAfdv8Ou3Ttt/PvgJsDCpXdGoEsqLaBT5if8RJrbqlbcezoJF6EKuoYQknXf4gQ4VlB7zG/mUWg5Y+9x1Pi3r2Ga1nTdiXOSfHQ3F6HP9D2gyETskw/KroeY9Hgar+ytl/rYl2110WMMZ8e78D82lTB2uTN0Rh/cs8JYn4XbmDAUXZ824BtEJ5zUj48f8e7fNdDgVY4ZtJihNY+8+Z+HXoiu6/vqne0ndqUZx/IsRlrGU57sf7X6Te+vcCc9aD2TL7D/EVp9HOnEjh+mXy8X08shC0KlYoEyZqHIAiC8PqQl4cgCIJgMZmH6k5EqG6OED59e/oeavv2KIvQ2ho5eL3wHRHYUdnSFG6Z1zqG+c24ix3sm7y/Z21DKqPteQuE7TrvvcH8wjZhephrGaZ5ny5by/yGnkUyvqJr8f4MGsylJLvfILm9rAE5rsgCPpOLmY5rcerNZbuSByCBHDyK3dRO5fgO4m3lcY4l3EOyjNxARFTz0CTdj/IM5jWhY9citFQtgOxof/Im88t7BH53vsVO2e1f8LDYgGSENFop3mdnDEIiO5tjv2n79iq+69cqFrJkajYcI98FLldemAUpt+w3CBdNzsm/UyXnmrYH58c1xhfLzfz2rkGIZJ2FY1mb2wGEwMd5IMy0yFRew3p24Z/Q5pZ1+lGLnH31TXX5mUs/j6ZhTLCOh5RUcylPGrjpFHbjG6bCbz5N1zE/3+fIEpGYyj+rnwskswvxkLeW+LV45bl7TYe01PMkT5z6KAkS/d4ZCPd+ac8fnU0v7CTPlx1h21dv82JQKh7jmfc8XlAtsSTkvciSkFbbDD/O/K5HmbIq1F0hspUgCILw+pCXhyAIgmAxmcpW2+9W1Y3LP+TRHxEVMO2JqYqtkbYB9szPAbkQKa4R5J1Cq3lESpIjJIGE3PydlmKHmZN3D8gZc9z3Mr88Vphi1l2AaX/hQ3xXc2xxTPunfo0dqZ/efp/5hd100XbZqoHaTpzgwvxmboXkNGnoh6zN/jFkrPIbITEUsOW1kVftQJI9/6ljsozcQERU5/DHuh+tzSBJ2pu2gQ/uhHsXMplHEcUEQY4quh9SklUSl6ZWr0NN9KYnePLNS42wa7faD6b+UTmE+aWsQP0Yc98Lrce/K+ZknsUmIWNA8HierNDdF9EsgV1wbA8fXrMkZiNk0vwOXP7Mawep4voC1IAIaZEhEeJLnO+DQROzTD+6F+Sqb/6qZ/z+XhqC+5HkhGioqnO5ROT3DaL5EnPj1kR788i+bI74eVyFo6xt/p522vbYh3Hvm008S0HwS+zuPhCFCMPzk3i9o+D6kGOtSmN8LNKFJ059MAPnnrcG+lPIXT4WKSecu00gH4uL+eL4obVxfl5dbzG/kqZko7Mr7BLZShAEQXh9yMtDEARBsBh5eQiCIAgWk+maR7FNs3WjkcKlL6+Jj7Sd7Ilsubs3853onT0RfqbssM6x/to+5tdqBnap59/Nww/vTkCBqfoNUeDkzpdlmF+hidg1HDkcYbuGLX9HPm4Mvc8cVrm393zm5xeH3coPE7FLfefu+szP/TD06BclHVibeTf0vS64T/fm8IyXyY+wI/7+6HFZRqsmIqoydKG+yVEleFvR/QjdfTIOtuv7PFT39rfIOqwcoPN7TXjE/Ixo6Lr+83gW3HwX0A/yXkGW6P7b9jO/z7b30LaNF9amig7ltcn7nIKmvisc4Z2d8mcIEQ2BXn33KUJ1nX/IyfyqfILjHdvOw4cdH5rWeUzJpEPe43q9lQ36c0D3yVmmHzVphLHIKpFn01amXeXR+xC6GhyYj/kdarlI2562+L6V+p6vU5athdDapC58fLw9EeG5qdnQZp2XZ5YosBNjXYcZKJZ3OIwXFItbgjHh0fu4LptQni3XnEggyQU/lPmCr9f9MRXjXtE9rIkcAtGXk1xw/WNWb2J+4y9jfdu/41RZ8xAEQRBeH/LyEARBECwm0xrmblsQRvb98oWsrbXPYG2npGDqnEJ8mtf6IhJ1bXqIqXi73/swv2jU3KHE1QVZ2+2akMKKH0E97PZTuTxwNtxD27YFIR/VnM2LS1nVw5SNUjBV9OtYivn92AiyR7G92Cle4CLfiW4/B6FzXfNfYW3JBkKQbbfjs27drMj8sj/Juu/xJsMQxno8hNcmNw4hBLeAIySnqpd5CO6LECRA7O1xTturO7Vhfj7jEao7tXNx1uY/CH0iWzdIZNfi+C7dhs3wDINisQt8+oWDzC8yBcf7odhhbacSP/cvfKHVGXUgl+X6nWcZGOPyi7av+FdmbeEV8FWt3AKSXsjF0szPq+IDyooYpgSjSc48zD94OsYVD1uMN2XmZqjv/RukcccuKOSVWohnPWjmAhnM3o9/1+s7oIDXqHIIr49uziX0XYsxXn4eCun+bkh+5udqhevK7o/rMngyA7r+IbIPdLzbUttzT+5ifqOboLBTYDdX1pZaE7vZbWJNocop2Zmf9UWTpN6RXknWHbEEQRCE/xjy8hAEQRAsRl4egiAIgsVkuuaxZwUK11TdM461mUPTbG9A+218iGcDde+JsLen57CWkejC0yqUNm2dp8/vsraz/lgr8BoPTfNKea4LJxVFeFvfhT9qe3dVD+Z3ZwbSBeQxZQF4mBjL/AxHrI1cmYNjPK/IBcknYUgRsGoVT3FibcqGGXIbfpc7LGZ+DU/z+5uVMKdkCH+Xd7mxPpu1vXxMF22PWLGZ+e3ZWU/bvu8hBLNgeV4Mqm9DrIm5uHEt93YbrJ21bQVt+IdhtZjfsLrHtD0yP+wJzXsxv9QArC8cOY8+mnFdJ7EG+vbSitu0fWqbJ/NLMPC3XPYf+TqdbYna2r7wAAuEnuV5IaMXS0zrNw0oyxDQGeuvJX/gaxSdW5/V9taD6Ce5eUQ92T/DWlRyCr7DNYsHMj/fAY203XP9T6ytw29DtJ2vPsa97xcuYH7d+43S9uDlWJc4GFyF+T0vhbWHLt38tH2uMV/3rRw3XNsXxmFdr9aXE5ifaoo15zMf8HN65yuMMc5tsOaztl9b5hc7kId/vwqZeQiCIAgWIy8PQRAEwWIyla16lkUo2vorPqzNwwZT8aYPMHVybsQLkDzYijDFxv2xg/bwCV5/3H8wMkCqJC5HzaiIELakGpiWPyvNQ/aym6alrXLc1na2S1wim+FXTtsp9pi+XqzOZY7k+gjTfO4JvxWDljO/uFScx+NyvMDPog2oo5zTFPVXad8o5mdb5NU7/d924gpAcrAP4xtWJ51FLKCbDdquJvG68x7bEA5tZMdzCprOJcQx3j9re1MSrxHexh/T82ez0Sfsz9kyP9/Pmmr75xFe2n5ZyIn5ZbNF377RHJmbw2c7M7+Cv+AcF87vpu331p9gfgM+geTrXOEFa8sVCFksoTr6+YtEnjk1z2/BlBWpUxlZJyJGx7G29k4o7HUgALJVJN/MTQUQ4U2PAiB9Oq7k9zBmDsa2VZ/wWNWWn53Xtm9tyJ19xnDZud83kM2n7sIzn92R7+be0Bx1y3ekNtR27np8zLJ/hvHhSQqWDPLc5DvbU+wxH+jcle+cLzgTMqt5Z7thzWWq0suQLZgG0CuRmYcgCIJgMfLyEARBECwmU9kq9h1M2Q9HPWdtnvZIyHW8J+pIdx7Lp28JXVHU5r3c2Lnbrf1Z5jenKiIcyCUPaxty6bK2J22G5JT7Ft/JW2kcjl//6Ghtj691iPnVr4jiJ4EHcI0B3/F5bkoo5IzaNbHr9NuQRszv0TzsTLd7ynekusVAfpiwY4u2Z47g88Ekx6wrW0WaNkGXXMmjg+7Ohcz3CAofXYrzYH4512KHf6Vc2Jm9/ie+U//HUYhmsc/P5Y07j7G7t+BeSI03F3MZsnnHvjg/847gNjxZ3fy2iJzy6dha296LIpmf8RBSktNhfK6TNY/usx9gSnLXgUeRdd8cqO2vf8RnRcbz5IoJfMN9lsF/Fb6bi/154aXJAeg4Gycjwmhc8978ID6QY8r0wd/N7Q7zolF76qDD3vyKy6e++yFVFZ+FcemTP/h4NmEaJCPPgYHanr6hJ/MbfQDZC8+9wDM/do2PRQ73Iem2WTBR24XCeT/x2bda22Mf8MjP+B6Q55w3PtR26LpizC85J0/u+ipk5iEIgiBYjLw8BEEQBIuRl4cgCIJgMZmueeQ4c0/be7bw7Zr5GkPHnfFHJ21bV+OhmCUnQ9ddntxc27c/LMz8PMph1+i2jMXkTYWoCvshrOyZFw/VfdAB4XdNdyDzaFX7QOa3fRxCkO2jcDz77Dxk7VrXDdpuWRrX/3A4LzJU1O+Gtm/O4VlOR9VHptSF7+BzQ4byW58/QxbZrESKHdZz7g10Y232p/FsU4rgHqy5Wof5pcZi/alqXei1+Ss9YX5hTd21naM7L5RT0R67/R1GY21q6fOizM9mdri2rc6g7Zsuq5nfmLXILJ3rK5xH7nE8fDg1DmsvHVyQ0uBKLP/cByEmfX0W32Hsfx670W15FCejfr8Lr258i8lzE+PIqFkfsbaqQ7HWWdwW/STZNRfzi/wO61eJXdDvtg3jmW5t47G+Sal8PEt2Qh+tfAbPdfS8DGGxAxAW+9QHz9nZ4N/zeXvbabvUelOW5XEZ1kCrYe305UWEjKdeu8Xcgl5iveJ6cCHWZvs1wr1Tj+IY307la34zBg+kv4LMPARBEASLkZeHIAiCYDGZylY3Z2IHbYFTfLqVfSKmR/kqYGr37N145qeiMd3cdBFhae0HjGB+MZNRX7drhp2RQc2RoLDnYiSqW3esIfOLHQbJ4ssCO7TdYwVP1hjbFvN+7+mQ33Kv9mB+o92R0K/FWYSYbv2iLPN73A8/e354hrUtXQCpat9JFIh5ksJDLEc+/4CyKsNboojSit0tWVt8QUzPbV/gb5lDnZcyvxZrEZ44IjdkwmMV+H3MaYOa5o/a8OJeIed4cZw/2TjQj/18oAvCMZf8uFbbCx82Z36ppm9PxHMU0EloyEMda2+AzDZ/NuqjO/vzUGIydYE8V/nfdc8b4fvnUAFh8zVcefGnea6nKStifR/14/OlFGBtV5YiW0X4LNQLP7hpDfNrWwcSEaXgfpb6kUufbZwRgju/nQdr+3o/jtltDopLPa/MtURXUyiwQylIpFXm87DgX5ajr90cicwERXdw2coqGWG2wQ3QVukyc6NZNVvgc7vw70a+q+hv1mexU3543DDm5/Y7T0z7KmTmIQiCIFiMvDwEQRAEi8lUtrrfbqW2R1avztriU7DbNnizaZfvI55c8Elr1JHeEY2djFFFeDK61V5IGPbJFb6D274ypqVDciMJ2pl5vEY1meocD72GHebuvleZW2h/nK9REBEuIXV4lEzsclzzLzlx7A2zFzG/ojaIYtg4mO8MHei0RNudKmPabL+Tn/rpseYa8WMoK7FrMuQeoyZvm9YBu7S/+KGrtoe0G8r87EzBfh89aqLtlr/zHeuHGyMqKTY0B2v7sgt2+C+eic86HMf74qTsrjsAABSeSURBVJoDkCYGVsEzK7yPJyuMCIZ8MLLHPm1vXMoTMh4qif5b4g5k3cKLAphfJydEN664wHcHu+eHVFUhD5KPhibwiKIWNzpr+8T/r9K9lTzugRopUV48i0OZ2ZAqlz9FlN7Pi+oyv3m/oJ5L+Evctxnf8jot5vrxfodXsbYS25BB4/oU1NXo1Kgb86uxD8/2RDj+Rr8wpRrzsxqOhJrOP6Hez+gl3zG/hQHNtF1sAqJM98XzqMRsePzk+JhLaY9GmZJr9kOfzB7I3KjlMX/6K8jMQxAEQbAYeXkIgiAIFiMvD0EQBMFilGG8Opvr6cDiurGQDQ/BbWiqae45DvFi/isqMD/PwVijiG+HNYSNSxYyvz4fQOe3i+A1ir1XYLf4kUDs4E65wfXe+i2uaTv4fYRO5tjONdILdzy0XXQb3p8P2vHdpF7jEBJawg/64cVwd+Z3thLCgssvzBD2thy7X1tdgFb9U3W+w/5ZR6zDXNgwlp/IW86mOzV0P5q2jWvDZd9BWODlP7Amlj2IL8e9NGUdTskG21zgh4jIVAacHAbywkhB53HPS2xF5ttVe7mu3ewcQsU9+mIdIqk2X8+Kd8FaSUQlPLK5Hb9nfp9ex7pJYgJ+x9q0VkZE5LYCbR/67GBtEw5217bf+8gc2/lTXsP6aUXcm4Ax47JMP+p1bqC+sNDxfK2zxjKMMdt+QjGonA+ZG0U1wBjmvgH9q/ac88zv3FMP/PClC2uLKG/KamEaOt128pDpx+2xq7zQJuwCfzDEi/nFF0IfGPSOn7b3LGjM/AoOuK/tqK8w/qgMiSlsTRkztm5dwdo690Yd9GfeuI4tk+Yzv9Zn0P/vdvnslX1IZh6CIAiCxcjLQxAEQbCYTEN1Z9ZFyGFKBC86UqAzZjP+yyC5eC2JZn7hA7GD8mlVTNEabx/P/FI6Q1q60PRb1uZkhd2VZQ9D+krOx6f9j+OQ7Cu0HaaNuRN4XXWnS5iyqVRM8wp7PGV+SbVMhaJ6IUwzbAQvENOyA67Rim+Op7W3Dmu7fwdsIY7swHd/htXl15KV+HY0EmcWfcF3VSfUg1STLQKh0h5beVLDZ0vQlrwHUkKkJ59VuzeEVhF4hsuLtrHwfdQCRagWhTdgfi2KQyY92QP9zWUj384b1QMhndZxOPbyPrzudWx/hLV7z0VopuvGcOZ3ugEk3wSDhw87BuD6zbvtXYN5DWufGeYkd7ww29tM0HSEYIeN5RL60fkIybUqhefwvCqXq3+sg3GlfQq+qA4duQwWNBgxzte/W8Layn83Er8Xgs/KsYVL7ck/wU6NQk30qm1/Z34nz5eB/RQZPfJd5OOtwxCMU0lhuH6b+XzMunEP0mz9ZXyMLTcL8pl1XRz/q74tmF8pU7YP6kKvRGYegiAIgsXIy0MQBEGwmExlKyMZ0z7rgjznfblR17XtbAoMeFnNk/mZk8e5Hse7KlsUl2mKfHZH29X38x3Wdk9wkKatkFhsaSGeBK7qHEQTZGsDSaC3G68vfLEPonrufoCdq4FBXI7qt/CUts+/h9/J6RbF/EIGo262UwDf1dlqLiQG68oIz4j2YG50rKU5+oxH0LztRBfB80vIw+WYVD/sAk8qZJIZVnE5Ju4FJMlC6xBdk1KLJ6m02osIvILuvI9FD4b0GBmEJHT3YnhEze8X8Kx9PkGWhZmPed35szNRd6bjXSR8XDl4F/OLTcVz/6Mxas7Us3/O/OrFQ7aavb4ra8t3D/2qy1cHtL1U8aLlnwehvvkeXi7krcbuGCIps5Xiu7S7fQKNaOkRJCL18Ahjfj2/QYLUjwciSeue2HLMzzoBclS1xaNYW0pFSEZJccim0asAT4i66BKksAebECH6cJ8j8/NaChnrWVtIWHW/4xFgnXOjTkvvsai3UWQmr9lhXx8SaWwJLttFtYVtXRrjXmJqBPPz//CvpSaQmYcgCIJgMfLyEARBECxGXh6CIAiCxWS6w7zYptm60XMuD48jK7x3wmpCj+438ifmtnIDwn2XDkWo3KV4D+a3YyYyrxYfxevy/r4FWmDh3aZtozY8C+7TutDqIlohdC7HOV6cxzD9mutCrJv08ecZWhf4N9X29xXWa/tGEq8v/dl2FPg535fvnK+5Fjpr8a0Ij0v1v8f8Epoh7PPE/olZZmcwEVGrEyN1P5rnwdMJDx+IomB2F7DuVfxnvubhH4U1t1QDt8fOmq8x3bxv0oAV79sqFmsvnuOw8z/lJ76e912pzdpuch7h1R+VOc78llxH9ucSI1Gs6GnzEswv7z7057srED58qd5K5hecgjWa9j583evzfhu1fTkOixme9jykedrx9tp+MDjr9KMqgxfqh+nQJZS1RScg9D76DtayHAP538Z5b2BMqLwAYddH19Rmfo6P0KeSHfgxwt5Dvyzoi88Nr5Kh1nleHKN7NaRB2HKaf9bUJru17W6LsNvxC3lW6WgPU9G0GHxWgQt8XSM5B87X6TJf80n2wTmNKPKztsft7Mv8CldBnzrRdJ7sMBcEQRBeH/LyEARBECwm01Dd/PsxLTMW8ynQshJbtd30ZxRe2vw5r1HtfglTzAFl++F4yfy9pUx1eR3rRLK22Lloc9qCmuhhU4oxv0JDIAXl7Qup6o8J2ZjfJ41QuGdORYT2/dCa7zRN6IrQ3SUFEY98eVkl5ucSi+xkDcv1Z20Fa2EKmLMxdt+HxvLdzzk6cakuKxH6g4e2x1zh0/EF27AjetRISFj7r/KMb15L8dzjC5l251/iCemKVEe/iizBu3eeW9ilG/xRVW0fKjWX+XW4gWl851KQN3xu12N+ZV3xbOOjcX6OQVxyu/klaqnnOwTNtFIKT0fgfAyZFIpu5zuR1+yErJtzDUJ8jyzg55SzAy9YlVVoMhyhsNuvVmVt9ndNyQqdTf0mgyJ/fwD+I/x7yEdxRbhj9qfoQ6HNuCy6rvYGbQ+IHIyGVH6MwTVPaPujPJBIL2zh5z7vMbIvrBz0jbar9eEF7G7NQThxaC2cn00cD0ff/C0K1fXzeIe1NXLBTvc5n/XRtmrHsz50dvuN/goy8xAEQRAsRl4egiAIgsVkKls570M9i6ofcynJLw47FL2WYNqjQvhuxXtLEJnUtiR2ibZx5knmRl5BnQdVvTxrK7kJU/EzuU2fG8GTMK4shl2jDbsgWmVwvZ+Z3w8TsQs371BcV8RiHr1VJtdt+NlClrBJ5FPUMbMRnbO2eUPW9mgxdlDPKomdx6fjSjG/Y8VrUFbF/jmkhIAMJSaepkJezBaJyBGXX+2Z3ye7cY9ndeuNBkdepzy8Mrp0xkgUm1hIEIk1IWE18ZnI/Oq3Q9/s44xImfYVLzG/ye/2NP2E/uGzgSfT+6AP5Lgk0wbjokV4dN+lyojSsk7mO+fzbMM5XbyERKS7Pl/M/KbUQe0QakdZhpa5MHZElsnO2iY1OqLtacGttL2uux/zK+eDDBTRxSD32BbgkaTW53D8Qgf5EDlvKmR5p2UYl9yd+fh4JcpN242/geQd1Zq5UeNGeK5ThkPSdbjNx9FuvohiTUxFloa1z99lfo1Xoy9/c8eHtQ09h++N5yHUKXfy5TLrsrHIWjDic3olMvMQBEEQLEZeHoIgCILFyMtDEARBsJhMd5hXGYpdnXaRPHQyvCreO0YRaIbZf+O7uRt2RzbIOo6oV72uL88GGl4J2nXBrTxsdcpvWLOY1QKhbTfH5GN+3jMCtR1d15QF9yce9vayGoo8hVfB+eYM5mFvNT/BubvbY3f4oRq8/ni7C/jcZy95kaeidtAuK9k90nZoCvebPH2ItrNaDfNV/vV1P9o6iGu0Ns+wVvCB735tL/cuw/z8V0Hnz3UNoZnzPuL1xz+5iR3Wz57wGvdkuqu24dCNHR7z2114P2qfR69Am1tOrmsvcN+r7a/CG+JcB/LM0nQrQJuJDRByeWz9aubW4EP0gS8X8YJokaa1oUnXO2jbdT4PQ388Fus8N9tPzzL9qNQsjEW57vO2KFOEfen6aGyd/xrzW3EbRb9mlPHV9rLSvDa9VXast730zcPaPHJiHPhtHbIgZ3/Gx1GHEKwjPGyB49l484zcddwCtX12O/p4ofk8Y3h0NxScy30cvxO7ga8NPghEhmjPEjz7QEwy+kpkLNZ1EuJ4H8p9HMe8tPLVY5HMPARBEASLkZeHIAiCYDGZylbvOg/UjW3P8kR+vg0g/Uw9jzrdScTDXT9aPkzbeZtDDqCveQGex+8gJC7XXdZEOUMgJ8WZQmvzfxDL/B59A5nC0R7TRutlXN5KtcFMbOCXSExWyY6HTp5PgPQ1xAnn7v3tMOZnUxnnZJx1Zm0Oobi/1YabQkDznmJ+PU8N0vb9npOzjNxARNSiyjR9E1QQT2rnfRj37tb7SGp4cxZPVlhsPW6J1RRTtoNpvIDXo6aQd4qv48/TbTskxCfx6CsbS/zI/Np3x87v4PqY3ue5yWVNm2G4loc3kJQzeyj/m6x2e8imJ/dDmmjbnksTTRwRGj9pwSDWVr0/dinf+ApyydNufHdwscFIHHrw2eos04+KL1qAPuTK64WXWIgQ7IhKiIV2COfP61FT3A7PspCQXyRy6cfdEX3y6yK8bzQ+h77RuzQKNk3O58/8WjWCvE6mMfZ+jwLMz8Y0hNmYHqVDGD93c5LDoA7oa226n2R+P25DxgHz2ENElDMYkmZobUhVqTbczyYW9+nmrDEiWwmCIAivD3l5CIIgCBYjLw9BEATBYjJNT/LTLWSGfGfIENY24xzCDI/HYv2jcvZA5hfjhTQQtZzCtR09mYesjc1/VttHX/DUDLdG4ecc4xAynFiK64eO9tAqSztDI3wY6cT84qcgrcDnZxEyXMeTr+uETfDQ9pc9oRFeGDyP+V1MRDjfRw94Vl3rBLyf71WHVuv2kKdEWFNng+mnyZSV6LrlqLbN95uIqG32g9oefwr9rdVlrvnHumItKfooMhK7n7vI/DxikL7mfi+eufjmLayzOf4Gnbt8xeHMzz0vnplzA6xrlGwbzvwu7EYanUK3oVGHVeMa8ilfrHPkDEabd/Zg5vfBKaSPcH3Kj3HuexQLazIZ35UDu2sxv4zZXbMKder8oe2Hn5dmbVaxWDjI7Y8Q7NDaPI1J6cnXtf1yD/rTiQrbmF+ZjegPA1u1Ym0O3RFif/AHZK3d/wEfsxYdRDqdA1F4/hEL+Vpe/v6B2t5dCtm+vY7x/h/SEL+XHcs1dKUXDzNOGITx0fVsEmtLscN6tHUFjIHFJvE1pIg6/Bxfhcw8BEEQBIuRl4cgCIJgMZmG6saHoHBumW0jWNu+9qjV/d5xTPO8J/Gp+OATCCVb2Rq7ixOK8pDWLWu+1vaU4Oas7eZzyFMLS6MIVdejPGQ2/69Q4axNmW9Hfr6V+TV1wLyvxRfjta14dBw9rY4QQI/dpoZxvDDWWs8ftD2kF79PKXZ4P9/viGmjyzke0hxbyBQe98Wrw+PeRk4HFtcPY2YdLgM86ItMsm4LIUHtDTzD/CaFVte2ebf/jqAqzO/dQpA31v/OJZ1sNxHGm5gb0/uS4y8wvwoX0RYUn1vbz8e5Mb9nZZAVIa4AHlmXbn7Mb0cAiofl2owQ4ZAG/LvnPR87go0cXHKhJIRZ5tmA678bycPQd5Rbr+0ibiFZph/VbztP36ywqlxtf5kD9/FlXtynPtV4Hzr6BcJYnY4iY3ZNvyfM73wLyJ0RzXmBuHy/YnwbehjZfEcf7M388p/DrV8yc6m2e5wZzP18IZ/mGPxY20mLXJlfcAOMF6XWIeQ8YAYPM6ZbkNWKb33Gmm73R18uegD3KaYw32GebEpUfWWZ7DAXBEEQXiPy8hAEQRAsJlPZ6v2Tw3TjMqbbEPXpDXnmmRemTvWHcAnA9zRq9hY8iRlQZEn+3or3wDRKZeP6kecS7BYPq4Fpf/YInqwxqS9qO3vnRZTMqXM8yV7JMSjwE7QdURL9vfg09+fekD2s7kPqujeOH69H2+PaPvRlA9YWXQTXaWWquWKq50JERDlCcS3nvxuXZeQGIqKG736l+9E8n+Ws7duwRto+eg331fsrXgzHCIa08HIvdpUnLuHT+08XrNP26HVcIjjz4QJt11k2Tts5H/PvgDJFLJnlzyc1+WMptR7RfUGfQ1aoWYjXVR9VENFmA2aN0XZyTn68KG98B6xfcGnGyg3bj52OQH47MGM+8+v7DgpUHbg7L8v0o6IrIVt5L3zK2h7PhezyVTkUXNv9jEual76FfPjDNNy30S15hOSyA2u13fjgGNaWpxCilBJPQjJM5coPze7znbZ9OiHCcNO+Ncyv1cSx2na6gxrj6ibP/tj+In7eMQiyfkgdnog2KRf6a7E9PKI12RnjdGAbDEDOJbi8NdYTiWh7lzorspUgCILw+pCXhyAIgmAx8vIQBEEQLCbTHeYT3bD7d+aTpqztwYdYlxhW/oC2t87ixX52zUII7sDro7WdrwEvVBJxEtp1Qgm+lmEdAe2u2RBkHj0X7sH8gh9DCz95H2FpY5odYH67mzXTtp0fxEq/4SWZX1wtUyhmFejxZ/stYH49G/fSduinfFen52IsdESVRsbPl725bptjQYbCRVmIyOLQV/usHM3acph3XPuiCFjAaL5ztugB3LuehdEvt5+rzPyymeKti/z0grXVqzZQ2x/0QuGpAw1KML+ohqW0vWEhnnXTw1z/rvw9woL35P9N29XO92V+7U/jmr0OItOvkd2O+SU5ISS9+Gq+buI/F7vjX9pDhq6+n59Tpx18zTGr4PAAQ9XsQ5tYW+etuL9L+tTUdqXjvHiXTTz6WvuLyJjxzgaeWaLZtgnaLnyOj0W20fiePv0Qa6yFHaOZ35hfu2m7TBjWS5OJr6/l+dXUHxyxfhE8sCLzW3wDGb4H+SATw/bZfFtDUgOsX7TpyNdw7RXW1DZMaov/387XTQ7NxzowD0DmyMxDEARBsBh5eQiCIAgWk2moriAIgiD8f8jMQxAEQbAYeXkIgiAIFiMvD0EQBMFi5OUhCIIgWIy8PARBEASLkZeHIAiCYDH/B8Tul7O2dAE4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9Sex6aEZtt9"
      },
      "source": [
        "# Dataset\r\n",
        "\r\n",
        "To MNIST είναι από τα πιο διαδεδομένα σύνολα δεδομένων στη μηχανική μάθηση. Για όσους δουλεύουν στο Google Colab, το κελί που ακολουθεί φορτώνει τα δεδομένα εκπαίδευσης σε έναν πίνακα x_train με τις ετικέτες των δεδομένων στη λίστα y_train, και τα testing δεδομένα αντίστοιχα στα x_test και y_test.\r\n",
        "\r\n",
        "\r\n",
        "Για όσους δεν δουλεύουν στο colab μπορούν να αντικαταστήσουν τις δύο πρώτες γραμμές του κελιού (train_dset = ... και test_dset = ... ) με τον παρακάτω κώδικα:\r\n",
        "```\r\n",
        "mnist_dataframe = pd.read_csv(\r\n",
        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\r\n",
        "  sep=\",\",\r\n",
        "  header=None)\r\n",
        "mnist_dataframe = mnist_dataframe.head(10000)\r\n",
        "\r\n",
        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\r\n",
        "train_dset = mnist_dataframe[:7500]\r\n",
        "test_dset = mnist_dataframe[7500:10000]\r\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvbVV9isZrwZ"
      },
      "source": [
        "import pandas as pd\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "from matplotlib import pyplot as plt##\r\n",
        "\r\n",
        "train_dset = pd.read_csv('/content/sample_data/mnist_train_small.csv',header=None)\r\n",
        "test_dset = pd.read_csv('/content/sample_data/mnist_test.csv',header=None)\r\n",
        "\r\n",
        "## From https://colab.research.google.com/notebooks/mlcc/multi-class_classification_of_handwritten_digits.ipynb#scrollTo=JfFWWvMWDFrR\r\n",
        "def parse_labels_and_features(dataset):\r\n",
        "  \"\"\"Extracts labels and features.\r\n",
        "  \r\n",
        "  This is a good place to scale or transform the features if needed.\r\n",
        "  \r\n",
        "  Args:\r\n",
        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\r\n",
        "      monochrome pixel values on the remaining columns, in row major order.\r\n",
        "  Returns:\r\n",
        "    A `tuple` `(labels, features)`:\r\n",
        "      labels: A Pandas `Series`.\r\n",
        "      features: A Pandas `DataFrame`.\r\n",
        "  \"\"\"\r\n",
        "  labels = dataset[0]\r\n",
        "\r\n",
        "  # DataFrame.loc index ranges are inclusive at both ends.\r\n",
        "  features = dataset.loc[:,1:784]\r\n",
        "  # Scale the data to [0, 1] by dividing out the max value, 255.\r\n",
        "  features = features / 255\r\n",
        "\r\n",
        "  return labels, np.array(features)\r\n",
        "\r\n",
        "def show_image(features):\r\n",
        "  img = features.reshape((28,28))\r\n",
        "  plt.imshow(img)\r\n",
        "  plt.show()\r\n",
        "\r\n",
        "y_train,x_train = parse_labels_and_features(train_dset)\r\n",
        "y_test,x_test = parse_labels_and_features(test_dset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4MX_WMeZwRu"
      },
      "source": [
        "Ποια είναι η μορφή των δεδομένων για μια εικόνα;"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhDzmllUZvyp",
        "outputId": "48d06a55-febf-4653-a23b-7f26fc5dbf21"
      },
      "source": [
        "print(\"Shape of an image: \"+str(x_train[0].shape))\r\n",
        "print(\"Max value of pixel: \"+str(np.max(x_train[0])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of an image: (784,)\n",
            "Max value of pixel: 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9B0hgyBZ3Vm"
      },
      "source": [
        "Φορτώνουμε μια τυχαία εικόνα και την εμφανίζουμε, μαζί με την ετικέτα της"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "r3eLiC1nZ00c",
        "outputId": "b3e62a2d-5f8b-4c92-b4e7-0a1da76142bd"
      },
      "source": [
        "i = np.random.randint(0,x_train.shape[0])\r\n",
        "show_image(x_train[i])\r\n",
        "print(\"Label = \" + str(y_train[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label = 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYMke4uRZ8Ae"
      },
      "source": [
        "# 2o Μέρος: Υλοποίηση KNN\r\n",
        "\r\n",
        " Σας δίνεται η κλάση KNN η οποία αρχικοποιείται με ένα σύνολο από δεδομένα x, ετικέτες y και το k για τον αλγόριθμο. Καλείστε να συμπληρώσετε τον κώδικα που λείπει στις μεθόδους distance, get_knn και classify.\r\n",
        "\r\n",
        "Η απόσταση συνημιτόνου μεταξύ δύο διανυσμάτων u,v ορίζεται ως: $$d(u,v)= 1 - \\frac{u\\cdot{v}}{||u||_2||v||_2}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMPfT1jTZ51F"
      },
      "source": [
        "from collections import Counter\r\n",
        "## Simple KNN\r\n",
        "\r\n",
        "class KNN:\r\n",
        "  def __init__(self,x,y,k,distance=\"euclidian\"):\r\n",
        "    self.x = x\r\n",
        "    self.y = y\r\n",
        "    self.k = k\r\n",
        "    self.distance = distance\r\n",
        "  \r\n",
        "  \r\n",
        "  ## Compute the distance between the two vectors img1 and img2\r\n",
        "  # hint: use np.linalg.norm for eucledian\r\n",
        "  # hint: use equation given above for cosine\r\n",
        "  def get_distance(self,img1,img2):\r\n",
        "    if self.distance=='euclidian':\r\n",
        "      ##################\r\n",
        "      ## Your code below\r\n",
        "\r\n",
        "      diff = np.subtract(img1, img2)\r\n",
        "      \r\n",
        "      dist = np.linalg.norm(diff)\r\n",
        "\r\n",
        "      ## Your code above\r\n",
        "      ##################\r\n",
        "      \r\n",
        "    elif self.distance=='cosine':\r\n",
        "      ##################\r\n",
        "      ## Your code below\r\n",
        "\r\n",
        "      norm1 = np.linalg.norm(img1)\r\n",
        "      norm2 = np.linalg.norm(img2)\r\n",
        "      product = np.inner(img1, img2)\r\n",
        "      dist = 1 - product / (norm1 * norm2)\r\n",
        "\r\n",
        "      ## Your code above\r\n",
        "      ##################\r\n",
        "      pass\r\n",
        "\r\n",
        "    return dist\r\n",
        "\r\n",
        "\r\n",
        "  ## Given an image as a vector, returns indexes of k nearest neighbors\r\n",
        "  def get_knn(self,img):\r\n",
        "    distances = list()\r\n",
        "    x = self.x\r\n",
        "    k = self.k\r\n",
        "    \r\n",
        "    ##################\r\n",
        "    ## Your code below - populate the distances list\r\n",
        "    # hint: you can use a for loop\r\n",
        "\r\n",
        "    distances = [self.get_distance(img, u) for u in x]\r\n",
        "\r\n",
        "    ## Your code above\r\n",
        "    ##################\r\n",
        "\r\n",
        "    # Sort distances, and return the indexes of k first elements\r\n",
        "    ans_indexes = np.argsort(distances)[:k]\r\n",
        "    return ans_indexes\r\n",
        "\r\n",
        "\r\n",
        "  ## Given an image as a vector, classify it according to KNN\r\n",
        "  # hint: we have a list of k labels and want to return the most common one\r\n",
        "  def classify(self,img):\r\n",
        "    y = self.y\r\n",
        "    nn_labels = [y[i] for i in self.get_knn(img)]\r\n",
        "    \r\n",
        "    ##################\r\n",
        "    ## Your code below\r\n",
        "\r\n",
        "    c = Counter(nn_labels)\r\n",
        "    prediction = c.most_common(1)[0][0]\r\n",
        "\r\n",
        "    ## Your code above\r\n",
        "    ###################\r\n",
        "    return prediction\r\n",
        "\r\n",
        "\r\n",
        "knn = KNN(x_train,y_train,k=4,distance='euclidian')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8-o5tn3aH0-"
      },
      "source": [
        "Τώρα που είναι έτοιμος ο ταξινομητής ας δούμε τι προβλέπει σε μεμονομένες εικόνες"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "GTj_vqD5Z-Ex",
        "outputId": "54cd221d-0265-493b-f9fa-c88e15c690a9"
      },
      "source": [
        "img = x_test[np.random.randint(0,x_test.shape[0])]\r\n",
        "print(\"Input image: \")\r\n",
        "show_image(img)\r\n",
        "print('Prediction : '+str(knn.classify(img)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input image: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction : 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCiEi1UUaLzE"
      },
      "source": [
        "## Αξιολόγηση του KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKw0oa1jaJei"
      },
      "source": [
        "## Παίρνουμε όλες τις προβλέψεις στο test set (πρώτες 100 εικόνες)\r\n",
        "## ..υπομονή μερικά λεπτά\r\n",
        "preds = [knn.classify(x_test[i]) for i in range(100)]\r\n",
        "labels = [y_test[i] for i in range(100)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "yNVygiKCaNag",
        "outputId": "e6763457-b5d2-48e0-da19-f94d443fbe77"
      },
      "source": [
        "eval = Evaluate(labels,preds,x_test[:100])\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision : 0.9870833333333333\n",
            "recall : 0.9784090909090908\n",
            "f1 : 0.9818973462577467\n",
            "accuracy : 0.98\n",
            "Confusion matrix: \n",
            "[[ 8  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 14  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  7  0  0  0  0  1  0  0]\n",
            " [ 0  0  0 11  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 14  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  7  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 10  0  0  0]\n",
            " [ 0  0  0  0  0  0  0 15  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  2  0]\n",
            " [ 0  0  0  0  1  0  0  0  0 10]]\n",
            "Some images with wrong predictions\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7LP247HaU6m"
      },
      "source": [
        "## Έτοιμος ΚΝΝ classifier\r\n",
        "\r\n",
        "Όπως και με τους περισσότερους αλγορίθμους μηχανικής μάθησης, υπάρχουν έτοιμες βελτιστοποιημένες υλοποιήσεις. Παρακάτω δείχνουμε ένα παράδειγμα χρήσης του ταξινομητή ΚΝΝ που παρέχει η βιβλιοθήκη sklearn: [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "Os0WCL-QaOnr",
        "outputId": "371caf2f-2a79-4af2-e3b3-6e403d1e0aa1"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "\r\n",
        "k = 4\r\n",
        "knc = KNeighborsClassifier(n_neighbors=k)\r\n",
        "knc.fit(x_train, y_train)\r\n",
        "y_pred = knc.predict(x_test[:100])\r\n",
        "\r\n",
        "eval = Evaluate(y_test[:100],y_pred,x_test[:100])\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision : 0.9749019607843138\n",
            "recall : 0.9568181818181818\n",
            "f1 : 0.962567733990148\n",
            "accuracy : 0.96\n",
            "Confusion matrix: \n",
            "[[ 8  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 14  0  0  0  0  0  0  0  0]\n",
            " [ 0  1  6  0  0  0  0  1  0  0]\n",
            " [ 0  0  0 11  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 14  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  7  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 10  0  0  0]\n",
            " [ 0  0  0  0  0  0  0 15  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  2  0]\n",
            " [ 0  0  0  0  1  0  0  1  0  9]]\n",
            "Some images with wrong predictions\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcUBX2XMaZUd"
      },
      "source": [
        "## Σύκγριση υλοποιήσεων\r\n",
        "\r\n",
        "Στα παρακάτω κελιά πειραματιστείτε με τις δύο υλοποιήσεις (τη δική σας και την έτοιμη). Βεβαιωθείτε πως προκύπτουν τα ίδια αποτελέσματα για διάφορες τιμές του k (για ευκλείδια απόσταση) και μετρήστε τους χρόνους εκτέλεσης."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPCmqbbUIlUc"
      },
      "source": [
        "neighbors = 4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Yz_Y_cpaWor",
        "outputId": "248f62b9-c2f0-4a94-a98a-0999f9641b0c"
      },
      "source": [
        "%%time\r\n",
        "knn = KNN(x_train,y_train,k=neighbors,distance='euclidian')\r\n",
        "preds = [knn.classify(x_test[i]) for i in range(100)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 23.1 s, sys: 128 ms, total: 23.2 s\n",
            "Wall time: 23.5 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77s6o3ZkabCY",
        "outputId": "84938803-f850-4e5c-d672-c13de4f3292a"
      },
      "source": [
        "%%time\r\n",
        "knc = KNeighborsClassifier(n_neighbors=neighbors)\r\n",
        "knc.fit(x_train, y_train)\r\n",
        "y_pred = knc.predict(x_test[:100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 6.83 s, sys: 20.9 ms, total: 6.85 s\n",
            "Wall time: 6.89 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7o4cjvWfbDOm",
        "outputId": "9a611f5b-c374-4ba6-ac5e-725d2fdc4767"
      },
      "source": [
        "# Check whether the two arrays are the same\r\n",
        "print(np.array_equal(preds, y_pred))\r\n",
        "\r\n",
        "print(preds)\r\n",
        "print(list(y_pred))\r\n",
        "for i in range(len(preds)):\r\n",
        "  if preds[i] != y_pred[i]:\r\n",
        "    print(\"Different prediction on image\", i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n",
            "[7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 4, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3, 7, 4, 6, 4, 3, 0, 7, 0, 2, 9, 1, 7, 3, 7, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 4, 3, 1, 4, 1, 7, 6, 9]\n",
            "[7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 4, 7, 2, 7, 1, 2, 1, 1, 7, 4, 1, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3, 7, 4, 6, 4, 3, 0, 7, 0, 2, 7, 1, 7, 3, 7, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 4, 3, 1, 4, 1, 7, 6, 9]\n",
            "Different prediction on image 43\n",
            "Different prediction on image 73\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bc9ySKoRaek-"
      },
      "source": [
        "## Σχολιασμός\r\n",
        "- Η δικιά μας υλοποίηση είχε χρόνους εκτέλεσης 21 s με 22 s ενώ ο έτοιμος Classifier περίπου 6.53 s. Αυτό είναι αναμενόμενο καθώς στη δικιά μας υλοποίηση κάναμε κάθε φορά quicksort για να βρούμε τις k κοντινότερες εικόνες ενώ θα μπορούσαμε να χρησιμοποιούμε quickselect. \r\n",
        "- Ο χρόνος εκτέλεσης δεν επηρεαζόταν από την τιμή του k, κάτι που επίσης είναι λογικό, εφόσον για κάθε δεδομένη εικόνα, κάναμε sort όλες τις αποστάσεις της από τις υπόλοιπες. \r\n",
        "- Ως προς τα\r\n",
        "αποτελέσματα γενικά ταυτίζονταν, εκτός από δύο περιπτώσεις που παρατηρήθηκαν, για k=4 και k=6 που υπήρχαν διαφορές σε δύο και μία εικόνα αντίστοιχα. Όπως φαίνεται όμως στα παραπάνω κελιά, για k=4 ο έτοιμος ΚΝΝ κάνει 4 λάθη ενώ η δικιά μας υλοποίηση μόνο 2. Οπότε ίσως ο έτοιμος KNN να μειώνει το χρόνο εκτέλεσης και με κάποια μέθοδο που το αφαιρεί ακρίβεια"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xe17UwwLag5Y"
      },
      "source": [
        "# 3ο Μέρος: Naive Bayes\r\n",
        "\r\n",
        "Αρχικά στρογγυλοποιούμε τις τιμές των pixels για να παίρνουν διακριτές τιμές στο $\\{0,1\\}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "GNbBhsg2ach8",
        "outputId": "d5b9813d-8d5b-4cc6-f672-2f1e9ad4e6d2"
      },
      "source": [
        "## Στρογγυλοποίηση:\r\n",
        "x_train_r = np.round(x_train)\r\n",
        "x_test_r = np.round(x_test)\r\n",
        "show_image(x_test_r[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFORd6XJakZl"
      },
      "source": [
        "Παρακάτω σας δίνεται η κλάση NaiveBayes που υλοποιεί τον αλγόριθμο. Καλείστε αρχικά να υπολογίσετε την πιθανότητα $p(x^{(k)}|i)$ για διάνυσμα χαρακτηριστκών $x$ και κατηγορία $i$ στη μέθοδο compute_probabilities. Στη συνέχεια θα υπολογίσετε την πιθανότητα $p(i|x)$ στη μέθοδο predict."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufKo5f5yaiqH"
      },
      "source": [
        "class NaiveBayes:\r\n",
        "  def __init__(self,x,y):\r\n",
        "    self.x = x\r\n",
        "    self.y = y\r\n",
        "    ## pC is a vector with the probability of each class\r\n",
        "    self.pC = np.zeros((10,))\r\n",
        "    ## pxC is an array with all probabilities p(xi|C)\r\n",
        "    self.pxC = np.zeros((x.shape[-1],10))\r\n",
        "    ## Compute the probabilities\r\n",
        "    self.compute_probabilities()\r\n",
        "\r\n",
        "  def compute_probabilities(self):\r\n",
        "    ## Compute p(C) for each class\r\n",
        "    for label in self.y:\r\n",
        "      self.pC[label]+=1\r\n",
        "    self.pC = self.pC/self.y.shape[0]\r\n",
        "\r\n",
        "    ## Compute p(xi|C) for each feature xi and class C\r\n",
        "    # hint: you can use one or more for loops\r\n",
        "    ###################\r\n",
        "    ## Your code below\r\n",
        "\r\n",
        "    p = self.x.shape[-1]\r\n",
        "    num_of_x_vectors = self.x.shape[0]\r\n",
        "\r\n",
        "    for c in range(10):\r\n",
        "      # Pass through all vectors with the given label\r\n",
        "      for i in range(num_of_x_vectors):\r\n",
        "        if self.y[i] == c :\r\n",
        "          # Pass through the given vector \r\n",
        "          # and increment the frequency \r\n",
        "          # for every characteristic\r\n",
        "          for k in range(p):\r\n",
        "            self.pxC[k][c] += self.x[i][k]\r\n",
        "      \r\n",
        "      # Divide a column of pxC with the amount of vectors\r\n",
        "      # that have the same label\r\n",
        "      num_of_vectors_with_label_c = self.pC[c] * self.y.shape[0]\r\n",
        "      for k in range(p):\r\n",
        "        self.pxC[k][c] /= num_of_vectors_with_label_c\r\n",
        "\r\n",
        "    ## Your code above\r\n",
        "    ##################\r\n",
        "\r\n",
        "  def predict(self,x):\r\n",
        "    ## ~Probability of x belonging to each class\r\n",
        "    ## (not actucal probability since we ignore denominator)\r\n",
        "    pcX = np.ones((10,))\r\n",
        "\r\n",
        "    p = x.shape[-1]\r\n",
        "    for i in range(10):\r\n",
        "      # hint: We have probabilities p({x_j=1}|i) in self.pxC\r\n",
        "      # We also need p({x_j=0}|i) for computing p(x|i)\r\n",
        "      #################\r\n",
        "      ## Your code below\r\n",
        "\r\n",
        "      pcX[i] = self.pC[i]\r\n",
        "      for j in range(p):\r\n",
        "        # curr_p is p({x_j = 1| i}) or 1 - p({x_j = 1| i}) \r\n",
        "        # Depends on the value of x_j\r\n",
        "        curr_p = self.pxC[j][i] if x[j] == 1 else 1 - self.pxC[j][i]\r\n",
        "        pcX[i] *= curr_p\r\n",
        "\r\n",
        "      ## Your code above\r\n",
        "      ##################\r\n",
        "    return np.argmax(pcX)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cO9h_Q8bbAgF"
      },
      "source": [
        "## Αξιολόγηση του Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "id": "2PzuaP48amDv",
        "outputId": "d8774d3d-5948-4f30-d902-248afeddd0ea"
      },
      "source": [
        "nb = NaiveBayes(x_train_r,y_train)\r\n",
        "preds = [ nb.predict(i) for i in x_test_r[:100]]\r\n",
        "eval = Evaluate(y_test[:100], preds, x_test_r[:100])\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision : 0.8400974025974026\n",
            "recall : 0.850108225108225\n",
            "f1 : 0.8261529454950507\n",
            "accuracy : 0.85\n",
            "Confusion matrix: \n",
            "[[ 7  0  0  0  0  0  0  0  1  0]\n",
            " [ 0 13  0  1  0  0  0  0  0  0]\n",
            " [ 0  0  7  1  0  0  0  0  0  0]\n",
            " [ 0  0  1 10  0  0  0  0  0  0]\n",
            " [ 1  0  0  0 12  0  0  0  0  1]\n",
            " [ 0  0  0  2  1  4  0  0  0  0]\n",
            " [ 0  0  2  0  0  0  8  0  0  0]\n",
            " [ 0  0  1  0  0  0  0 13  0  1]\n",
            " [ 0  0  0  0  0  0  0  0  2  0]\n",
            " [ 0  0  0  0  1  0  0  0  1  9]]\n",
            "Some images with wrong predictions\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMUHg-dubFa2"
      },
      "source": [
        "## Έτοιμος Naive Bayes\r\n",
        "\r\n",
        "Όπως με τους περισσότερους αλγορίθμους μηχανικής μάθησης, υπάρχουν έτοιμες βελτιστοποιημένες υλοποιήσεις για τον Naive Bayes. Παρακάτω ο Gaussian Naive Bayes από το SKLearn. Σε αντίθεση με τη δική μας υλοποίηση, ο συγκεκριμένος δουλεύει και με συνεχή δεδομένα, αφού πρώτα κάνει την υπόθεση πως κάθε χαρακτηριστικό ακολουθεί κανονική κατανομή: [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 942
        },
        "id": "Lc7CCDfSbCPh",
        "outputId": "3ad85b9f-1b18-485f-cf6d-e2eb4a8416c1"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\r\n",
        "\r\n",
        "gnb = GaussianNB()\r\n",
        "y_pred = gnb.fit(x_train, y_train).predict(x_test[:100])\r\n",
        "\r\n",
        "eval = Evaluate(y_test[:100], y_pred, x_test[:100])\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "precision : 0.5992985558502799\n",
            "recall : 0.5716774891774892\n",
            "f1 : 0.5081873844349185\n",
            "accuracy : 0.6\n",
            "Confusion matrix: \n",
            "[[ 7  0  0  0  0  0  0  0  0  1]\n",
            " [ 0 14  0  0  0  0  0  0  0  0]\n",
            " [ 3  0  2  0  0  0  2  0  0  1]\n",
            " [ 0  0  1  3  0  0  1  0  5  1]\n",
            " [ 1  1  0  0  4  0  0  0  0  8]\n",
            " [ 2  0  0  0  1  0  0  0  3  1]\n",
            " [ 0  0  0  0  0  0 10  0  0  0]\n",
            " [ 0  0  1  0  0  0  0  8  0  6]\n",
            " [ 1  0  0  0  0  0  0  0  1  0]\n",
            " [ 0  0  0  0  0  0  0  0  0 11]]\n",
            "Some images with wrong predictions\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaecLreybI-3"
      },
      "source": [
        "## Σύγκριση υλοποιήσεων\r\n",
        "\r\n",
        "Όμοια με πριν θα συγκρίνετε τα αποτελέσματα και τους χρόνους εκτέλεσης για τις δύο υλοποιήσεις. Για την έτοιμη υλοποίηση του sklearn δοκιμάστε και το σύνολο x_train_r όπου είναι στρογγυλοποιήμενα τα pixels και το σύνολο x_train οπου παίρνουν συνεχείς τιμές. Σχολιάστε την επίδοση σε κάθε περίπτωση. Ποιες από τις παραδοχές που κάναμε δεν ισχύουν;"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lz-k1DL-bG9v",
        "outputId": "dcd4044e-779a-4afe-818e-e02a295e7038"
      },
      "source": [
        "%%time\r\n",
        "nb = NaiveBayes(x_train_r,y_train)\r\n",
        "preds = [ nb.predict(i) for i in x_test_r[:100]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 19.2 s, sys: 21.6 ms, total: 19.2 s\n",
            "Wall time: 19.6 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHoJZC5RbK60",
        "outputId": "2381a4e5-f50e-4301-aed7-6c606ad73fef"
      },
      "source": [
        "%%time\r\n",
        "gnb = GaussianNB()\r\n",
        "y_pred = gnb.fit(x_train_r, y_train).predict(x_test_r[:100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 339 ms, sys: 0 ns, total: 339 ms\n",
            "Wall time: 340 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-B-NIEbo3kT",
        "outputId": "769bf86e-b74a-40e0-be96-58a5af86d80f"
      },
      "source": [
        "np.array_equal(preds, y_pred)\r\n",
        "\r\n",
        "count = 0\r\n",
        "for i in range(len(preds)):\r\n",
        "  if preds[i] != y_pred[i]:\r\n",
        "    count+=1\r\n",
        "print(count) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAmP4BrCbOB9"
      },
      "source": [
        "## Σχολιασμός\r\n",
        "- Ο χρόνος εκτέλεσης της έτοιμης υλοποίησης είναι κοντά στο 0.5 s ενώ η δικιά μας υλοποίηση απαιτεί 18 s. \r\n",
        "- Στα παραπάνω κελιά που χρησιμοποιήθηκε η κλάση Evaluate φαίνεται πως η δικιά μας υλοποίηση πετυχαίνει πολύ καλύτερο accuracy από την έτοιμη. Ο λόγος που συμβαίνει αυτό είναι πως υποθέτει κανονική κατανομή των χαρακτηριστικών, ενώ στη δικιά μας υλοποίηση εφαρμόζουμε στρογγυλοποίηση και δεν υποθέτουμε τίποτα για την κατανομή.\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vvw-OUkjbRb9"
      },
      "source": [
        "#4ο Μέρος: Multi-Layer Perceptron \r\n",
        "\r\n",
        "Σε αυτό το σημείο της άσκησης θα επιλύσετε το παραπάνω πρόβλημα κατασκευάζοντας ένα πολύεπίπεδο νευρωνικό δίκτυο. Αρχικά θα υλοποιήσετε το νευρωνικό χωρίς να χρησιμοποιήσετε κάποια έτοιμη κλάση κάποιας βιβλιοθήκης (όπως sckit-learn, keras), ενώ στην συνέχεια θα κατασκευάσετε το ίδιο σύστημα με την χρήση της βιβλιοθήκης sckit-learn. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vovDwSIPbTiE"
      },
      "source": [
        "Στο παρακάτω κελί κώδικα σας δίνεται η βασική δομή του επιπέδου ενός πολυεπίπεδου νευρωνικού δικτύου. Η παρακάτω κλάση δεν υλοποιεί κάποιο πραγματικό επίπεδο (όπως Dense) αλλά αυτή χρησιμοποιείται για την παρουσίαση των λειτουργιών κάθε επιπέδου.\r\n",
        "\r\n",
        "Ουσιαστικά κάθε επίπεδο ενός νευρωνικού δικτύου πρέπει να είναι σε θέση να κάνει: \r\n",
        "\r\n",
        "\r\n",
        "1.   Για μια είσοδο να υπολογίζει την έξοδο κάθε νευρώνα. Αυτό επιτυγχάνεται μέσω της μεθόδου forward η όποια δέχεται ως όρισμα μια είσοδο  και επιστρέφει έναν πίνακα με τις εξόδους κάθε νευρώνα του επιπέδου. \r\n",
        "\r\n",
        "2.   Να υπολογίζει τις μεταβολές οι όποιες πρέπει να γίνουν στα βάρη κάθε επιπέδου, ανάλογα με το πόσο καλά- κοντινά ήταν τα αποτελέσματα του επιπέδου στα πραγματικά. Η λειτουργία αυτή θα μας βοηθήσει την ανανέωση των βαρών του δικτύου και συνεπώς στην σωστή εκπαίδευσή του. Η λειτουργικότητα αυτή επιτυγχάνεται μέσω της μεθόδου backward. \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Η λειτουργικότητα συνεπώς κάθε επιπέδου καθορίζεται από την συνάρτηση που υλοποιείται στην μέθοδο forward. Ένα instance της παρακάτω κλάσης συνεπώς  επιστρέφει ως έξοδο την είσοδο κάθε νευρώνα (ταυτοτική συνάρτηση) όποτε δεν προσφέρει κάποια υψηλή λειτουργικότητα. Στην παρακάτω κλάση δεν έχετε να προσθέσετε κάτι, απλά να μελετήσετε και να καταλάβετε την δομή που πρέπει να έχει ένα επίπεδο. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgx1VIMmbMQP"
      },
      "source": [
        "class Layer:\r\n",
        "    def __init__(self):\r\n",
        "        \"\"\"Here we can initialize layer parameters (if any) and auxiliary stuff.\"\"\"\r\n",
        "        # A dummy layer does nothing\r\n",
        "        pass\r\n",
        "    \r\n",
        "    def forward(self, input):\r\n",
        "        \"\"\"\r\n",
        "        Takes input data of shape [batch, input_units], returns output data [batch, output_units]\r\n",
        "        \"\"\"\r\n",
        "        # A dummy layer just returns whatever it gets as input.\r\n",
        "        return input\r\n",
        "\r\n",
        "    def backward(self, input, grad_output):\r\n",
        "        # The gradient of a dummy layer is precisely grad_output, but we'll write it more explicitly\r\n",
        "        num_units = input.shape[1]\r\n",
        "        \r\n",
        "        d_layer_d_input = np.eye(num_units)\r\n",
        "        \r\n",
        "        return np.dot(grad_output, d_layer_d_input) # chain rule"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1iQCroAbV-F"
      },
      "source": [
        "Στο σημείο αυτό αξίζει να αναφερθεί ότι για την σωστή εκπαίδευση του δικτύου (σε πρακτικό επίπεδο) πρέπει να διαχωριστεί η έξοδος κάθε νευρώνα πριν και μετά την συνάρτηση ενεργοποίησης. Έτσι η παραπάνω μέθοδος forward της κλάσης layer πρέπει να υπολογίζει την έξοδο του επιπέδου χωρίς την συνάρτηση ενεργοποίησης και κάποια άλλη κλάση να υπολογίζει το αποτέλεσμα με αυτή.  \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Έκτος όμως από την εκπαίδευση του δικτύου, ο διαχωρισμός αυτός μας βοηθά σημαντικά και κατά την φάση σχεδιασμού της  αρχιτεκτονικής μιας και μας δίνει την δυνατότητα να αλλάζουμε την συνάρτηση ενεργοποίησης χωρίς κάθε φόρα να πρέπει να αλλάξουμε ολόκληρη την κλάση layer. Για τους παραπάνω λόγους θα χειριζόμαστε την συνάρτηση ενεργοποίησης σαν ένα ξεχωριστό επίπεδο με τις δικές της μεθόδους: forward, backward.  \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Παρακάτω παρουσιάζεται η κλάση η όποια υλοποιεί την λειτουργικότητα της συνάρτησης ενεργοποίησης [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)).  \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Με την ίδια λογική μπορούμε να υλοποιήσουμε οποιαδήποτε άλλη συνάρτηση ενεργοποίησης θέλουμε π.χ. sigmoid, tanh κ.ο.κ. και επιπλέον μπορούμε να τις εναλλάσσουμε μεταξύ επιπέδων χωρίς δυσκολία. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P33228VbYIf"
      },
      "source": [
        "class ReLU(Layer):\r\n",
        "    def __init__(self):\r\n",
        "        \"\"\"ReLU layer simply applies elementwise rectified linear unit to all inputs\"\"\"\r\n",
        "        pass\r\n",
        "    \r\n",
        "    def forward(self, input):\r\n",
        "        \"\"\"Apply elementwise ReLU to [batch, input_units] matrix\"\"\"\r\n",
        "        relu_forward = np.maximum(0,input)\r\n",
        "        return relu_forward\r\n",
        "    \r\n",
        "    def backward(self, input, grad_output):\r\n",
        "        \"\"\"Compute gradient of loss w.r.t. ReLU input\"\"\"\r\n",
        "        relu_grad = input > 0\r\n",
        "        return grad_output*relu_grad"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rrrub_76ba19"
      },
      "source": [
        "Η κλάση Dense υλοποιεί ένα επίπεδο dense όπου η έξοδος κάθε νευρώνα (χωρίς τη  συνάρτηση ενεργοποίησης) υπολογίζεται από την παρακάτω εξίσωση: \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "$$ z(x) = w^Τx + b $$ \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "όπου $w$, $b$ είναι τα βάρη του επιπέδου.   \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Συνεπώς το δίκτυο είναι απαραίτητοί να διατηρεί 2 τους δυο πίνακες με τα βάρη οι όποιο στην μέθοδο forward θα χρησιμοποιούνται για τον υπολογισμό της εξόδου και θα ανανεώνονται από την μέθοδο backward. Οι πίνακες αυτοί δημιουργούνται κατά την κατασκευή κάθε στιγμιότυπου και αρχικοποιούνται, ο πρώτος τυχαία και ο δεύτερος με μηδενικά.  Στο σημείο αυτό καλείστε να συμπληρώσετε την μέθοδο forward με κατάλληλο τρόπο ώστε να επιτυγχάνεται επιθυμητή λειτουργικότητα. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEnuIuOHbZge"
      },
      "source": [
        "class Dense(Layer):\r\n",
        "    def __init__(self, input_units, output_units, learning_rate=0.1):\r\n",
        "        \"\"\"\r\n",
        "        Weight Initialization\r\n",
        "        \"\"\"\r\n",
        "        self.input_units = input_units\r\n",
        "        self.output_units = output_units\r\n",
        "        \r\n",
        "        self.learning_rate = learning_rate\r\n",
        "        self.weights = np.random.normal(loc=0.0, \r\n",
        "                                        scale = np.sqrt(2/(input_units+output_units)), \r\n",
        "                                        size = (input_units,output_units))\r\n",
        "        self.biases = np.zeros(output_units)\r\n",
        "        \r\n",
        "    def forward(self,input):\r\n",
        "        \"\"\"\r\n",
        "        Perform an affine transformation:\r\n",
        "        f(x) = <W*x> + b\r\n",
        "        \r\n",
        "        input shape: [number of inputs, input units]\r\n",
        "        output shape: [number of inputs, output units]\r\n",
        "        \"\"\"\r\n",
        "        ###################\r\n",
        "        ## Your code below\r\n",
        "        ## hint: numpy.dot\r\n",
        "        \r\n",
        "        output = np.dot(input,self.weights) + self.biases\r\n",
        "\r\n",
        "        ## Your code above\r\n",
        "        ##################\r\n",
        "        return output\r\n",
        "    \r\n",
        "    def backward(self,input,grad_output):\r\n",
        "        # compute d f / d x = d f / d dense * d dense / d x\r\n",
        "        # where d dense/ d x = weights transposed\r\n",
        "        grad_input = np.dot(grad_output, self.weights.T)\r\n",
        "        \r\n",
        "        # compute gradient w.r.t. weights and biases\r\n",
        "        grad_weights = np.dot(input.T, grad_output)\r\n",
        "        grad_biases = grad_output.mean(axis=0)*input.shape[0]\r\n",
        "        \r\n",
        "        assert grad_weights.shape == self.weights.shape and grad_biases.shape == self.biases.shape\r\n",
        "        \r\n",
        "        # Here we perform a stochastic gradient descent step. \r\n",
        "        self.weights = self.weights - self.learning_rate * grad_weights\r\n",
        "        self.biases = self.biases - self.learning_rate * grad_biases\r\n",
        "        \r\n",
        "        return grad_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ml2jvLeybeRe"
      },
      "source": [
        "Οι παρακάτω συναρτήσεις χρησιμοποιούνται για να μπορεί το δίκτυο να ελέγχει πόσο κοντά βρίσκονται τα αποτελέσματα του στα πραγματικά (Loss function). Όπως είναι λογικό υπάρχουν διαφορετικές τέτοιες συναρτήσεις ανάλογα το πρόβλημα που καλείται να λύσει το δίκτυο. Η παρακάτω συνάρτηση ονομάζεται [softmax](https://en.wikipedia.org/wiki/Softmax_function) και χρησιμοποιείται κατά κύριο λόγο σε προβλήματα ταξινόμησης όπως το συγκεκριμένο. Η softmax δέχεται σαν είσοδο τις ενεργοποιήσεις του τελευταίου επιπέδου και επιστρέφει μια κατανομή πιθανοτήτων για κάθε μια από τις κλάσεις εξόδου (π.χ. κλάση 0 έχει πιθανότητα 0.001,  η κλάση 1 έχει 0.9 κ.ο.κ.).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qT7nl_1Gbc0B"
      },
      "source": [
        "def softmax_crossentropy_with_logits(logits,reference_answers):\r\n",
        "    \"\"\"Compute crossentropy from logits[batch,n_classes] and ids of correct answers\"\"\"\r\n",
        "    logits_for_answers = logits[np.arange(len(logits)),reference_answers]\r\n",
        "    xentropy = - logits_for_answers + np.log(np.sum(np.exp(logits),axis=-1))\r\n",
        "    return xentropy\r\n",
        "\r\n",
        "def grad_softmax_crossentropy_with_logits(logits,reference_answers):\r\n",
        "    \"\"\"Compute crossentropy gradient from logits[batch,n_classes] and ids of correct answers\"\"\"\r\n",
        "    ones_for_answers = np.zeros_like(logits)\r\n",
        "    ones_for_answers[np.arange(len(logits)),reference_answers] = 1\r\n",
        "    \r\n",
        "    softmax = np.exp(logits) / np.exp(logits).sum(axis=-1,keepdims=True)\r\n",
        "    \r\n",
        "    return (- ones_for_answers + softmax) / logits.shape[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XnSJjxYbhdX"
      },
      "source": [
        "Έχοντας υλοποιήσει τις κλάσεις Dense και ReLU μπορούμε πλέον να κατασκευάσουμε μια κλάση η όποια θα ορίζει ένα πολυεπίπεδο νευρωνικό δίκτυο (MLP). Το δίκτυο αυτό ουσιαστικά αποτελείται από μια ακολουθία Dense επιπέδων όπου το κάθε ένα (εκτός του τελευταίου) ακολουθείται από μια μη-γραμμική συνάρτηση ενεργοποίησης (ReLU). Όμοια με πριν, η κλάση αυτή πρέπει να περιέχει μια μέθοδο forward η όποια θα δέχεται μια είσοδο (εδώ μια εικόνα flatten) και θα επιστρέφει μια έξοδο (εδώ μια κατανομή 10 πιθανοτήτων). Παράλληλα πρέπει να περιέχει και μια μέθοδο fit, η όποια θα εκπαιδεύει το δίκτυο δεδομένου ενός τέτοιου συνόλου (εδώ του x_train). Στο σημείο αυτό χρησιμοποιούνται οι μέθοδοι backward που έχουν οριστεί για κάθε ένα επίπεδο (δεν χρειάζεται να συμπληρώσετε κάτι).  Τέλος θα ήταν βοηθητικό να έχουμε και μια μέθοδο η όποια θα μετατρέπει την κατανομή εξόδου στην επιστρεφόμενη κλάση (predict) για κάποιο ή κάποια στιγμιότυπα του συνόλου δεδομένων.   \r\n",
        "\r\n",
        "Το δίκτυο όπως αναφέρθηκε και προηγουμένως αποτελείται από έναν αριθμό Dense επιπέδων κάθε ένα από τα όποια ακολουθείται από μια συνάρτηση ReLU. Η κατασκευή των επιπέδων γίνεται κατά την στιγμή δημιουργίας του δικτύου, όπου δίνεται ως είσοδος μια λίστα με το μέγεθος κάθε επιπέδου, μαζί με το μέγεθος εισόδου (εδώ 28*28). Έτσι για παράδειγμα η παρακάτω γραμμή κώδικα:  \r\n",
        "``` \r\n",
        "net = MLP([100, 200, 100, 10], 784)  \r\n",
        "```  \r\n",
        "κατασκευάζει ένα MLP το όποιο αποτελείται από 4 επίπεδα με μέγεθος 100, 200, 100, 10. Ο αριθμός των επιπέδων καθώς και του μεγέθους καθενός από αυτά είναι ελεύθερος να οριστεί από τον χρήστη.   \r\n",
        "Στον constructor της κλάσης ουσιαστικά ορίζεται μια λίστα η όποια περιέχει κάθε ένα από τα επίπεδα που πρέπει να οριστούν, π.χ. για το παραπάνω παράδειγμα η μεταβλητή net.network περιέχει τα εξής στιγμιότυπα των κλάσεων:  \r\n",
        "\r\n",
        "``` \r\n",
        "[Dense(100), ReLU(), Dense(200), ReLU(), Dense(100), ReLU(), Dense(10)]  \r\n",
        "```  \r\n",
        "\r\n",
        "Συνεπώς η λειτουργικότητα του δικτύου όπως και πριν πρέπει να οριστεί στην μέθοδο forward. Στο σημείο αυτό καλείστε να συμπληρώσετε την μέθοδο αυτή έτσι ώστε το δίκτυο να λειτουργεί όπως πρέπει, δηλαδή στο παράδειγμά μας η είσοδος να περνά από το επίπεδο Dense(100), μετά από το ReLU(), στην συνέχεια από το Dense(200) κ.ο.κ. μέχρι και το τελευταίο επίπεδο.  Ο αλγόριθμος αυτός παρουσιάζεται και σε ψευδοκώδικα στην διαφάνεια 33 του μαθήματος. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHHcg1unbf21"
      },
      "source": [
        "class MLP:\r\n",
        "    def __init__(self, shapes, input_dim):\r\n",
        "        '''\r\n",
        "        Κατασκευή του MLP. Τα επίπεδα και το μέγεθός τους ορίζεται από την μεταβλήτη shape, ενώ\r\n",
        "        το μέγεθος της εισόδου ορίζεται από την μεταβλήτη input_dim\r\n",
        "        '''\r\n",
        "        self.shapes = shapes\r\n",
        "        self.network = [Dense(input_dim, shapes[0])]\r\n",
        "        self.network.append(ReLU())\r\n",
        "        for i in range(1, len(self.shapes) - 1):\r\n",
        "            self.network.append(Dense(shapes[i-1],shapes[i]))\r\n",
        "            self.network.append(ReLU())\r\n",
        "        self.network.append(Dense(shapes[i],shapes[-1]))\r\n",
        "\r\n",
        "    def forward(self, X):\r\n",
        "        \"\"\"\r\n",
        "        Αγόριθμος διφάνειας 33\r\n",
        "        \"\"\"\r\n",
        "        activations = []\r\n",
        "        input = X\r\n",
        "        # Looping through each layer\r\n",
        "        for l in self.network:\r\n",
        "            ###################\r\n",
        "            ## Your code below\r\n",
        "            # hint: τροφοδοτούμε την έξοδο κάθε επιπέδου στο επόμενο\r\n",
        "            activations.append(l.forward(input))\r\n",
        "            input = activations[-1]\r\n",
        "            pass\r\n",
        "\r\n",
        "            ## Your code above\r\n",
        "            ##################        \r\n",
        "        assert len(activations) == len(self.network)\r\n",
        "        return activations\r\n",
        "\r\n",
        "    def predict(self,X):\r\n",
        "        \"\"\"\r\n",
        "        Προβλέπει την έξοδο του δικτύου για ένα ή περισσότερα στιγμιότυπα εισόδου\r\n",
        "        \"\"\"\r\n",
        "        logits = self.forward(X)[-1]\r\n",
        "        return logits.argmax(axis=-1)\r\n",
        "\r\n",
        "    def fit(self, X, y):\r\n",
        "        # Get the layer activations\r\n",
        "        layer_activations = self.forward(X)\r\n",
        "        layer_inputs = [X]+layer_activations  #layer_input[i] is an input for network[i]\r\n",
        "        logits = layer_activations[-1]\r\n",
        "        \r\n",
        "        # Compute the loss and the initial gradient\r\n",
        "        loss = softmax_crossentropy_with_logits(logits,y)\r\n",
        "        loss_grad = grad_softmax_crossentropy_with_logits(logits,y)\r\n",
        "        \r\n",
        "        # Propagate gradients through the network\r\n",
        "        # Reverse propogation as this is backprop\r\n",
        "        for layer_index in range(len(self.network))[::-1]:\r\n",
        "            layer = self.network[layer_index]\r\n",
        "            loss_grad = layer.backward(layer_inputs[layer_index],loss_grad) #grad w.r.t. input, also weight updates\r\n",
        "        return np.mean(loss)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zCBcKqvbm4t"
      },
      "source": [
        "## Αξιολόγηση ενός Multi-Layer Perceptron\r\n",
        "\r\n",
        "Αφού έχουμε κατασκευάσει τα παραπάνω είμαστε πλέον σε θέση να εκπαιδεύσουμε το MLP. Αυτό γίνεται καλώντας την μέθοδο fit. Στο παρακάτω κελί κώδικα ορίζεται το MLP του παραπάνω παραδείγματος και εκπαιδεύεται για 25 εποχές. Στο τέλος κάθε εποχής παρουσιάζονται τα αποτελέσματα του μαζί με μια γραφική των train και test accuracy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jw-8qN6bjQG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "be36b68e-9707-42d7-e8e9-a1dd16b04d5b"
      },
      "source": [
        "%%time\r\n",
        "from IPython.display import clear_output\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "network = MLP([100, 200, 100, 10], 28*28)\r\n",
        "\r\n",
        "train_log = []\r\n",
        "val_log = []\r\n",
        "\r\n",
        "#Αυτά πρέπει να τρέχουν αφού έχετε φτιάξει σωστά τον κώδικα της MLP\r\n",
        "for epoch in range(25):\r\n",
        "     network.fit(x_train,y_train)\r\n",
        "    \r\n",
        "     train_log.append(np.mean(network.predict(x_train)==y_train))\r\n",
        "     val_log.append(np.mean(network.predict(x_test)==y_test))\r\n",
        "    \r\n",
        "     clear_output()\r\n",
        "     print(\"Epoch\",epoch)\r\n",
        "     print(\"Train accuracy:\",train_log[-1])\r\n",
        "     print(\"Val accuracy:\",val_log[-1])\r\n",
        "     plt.plot(train_log,label='train accuracy')\r\n",
        "     plt.plot(val_log,label='val accuracy')\r\n",
        "     plt.legend(loc='best')\r\n",
        "     plt.grid()\r\n",
        "     plt.show()\r\n",
        "  \r\n",
        "y_pred = network.predict(x_test)\r\n",
        "\r\n",
        "eval = Evaluate(y_test, y_pred, x_test)\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 24\n",
            "Train accuracy: 0.68935\n",
            "Val accuracy: 0.6856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "precision : 0.7067491112958907\n",
            "recall : 0.6775945124075171\n",
            "f1 : 0.6550349856856786\n",
            "accuracy : 0.6856\n",
            "Confusion matrix: \n",
            "[[ 921    1   10    3    0    0   28    7   10    0]\n",
            " [   0 1110    0    6    2    0    6    0   11    0]\n",
            " [  68  104  539  103   21    0  138   31   18   10]\n",
            " [  34    7   16  829    1   11   21   25   39   27]\n",
            " [   3   20    5    6  597    1   51  133   21  145]\n",
            " [ 226   16   18  165   20  179   55   75  131    7]\n",
            " [  49   10   20    7    6    1  854    5    5    1]\n",
            " [   2   43    8    6    6    1    5  922    7   28]\n",
            " [  28   45    4  130    9   25   64   43  596   30]\n",
            " [  11   14    8   12   83    5    9  524   34  309]]\n",
            "Some images with wrong predictions\n",
            "CPU times: user 1min 5s, sys: 12.2 s, total: 1min 18s\n",
            "Wall time: 43.2 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygud3NX3b-wN"
      },
      "source": [
        "## Έτοιμο Multi-Layer Perceptron \r\n",
        "\r\n",
        "Όπως και με τις τεχνικές των παραπάνω ερωτημάτων έτσι και εδώ υπάρχει έτοιμη η παραπάνω κλάση σε διάφορες βιβλιοθήκες. Έτσι στο δεύτερο μέρος του ερωτήματος αυτού θα κατασκευάσετε το ίδιο MLP χρησιμοποιώντας όμως την έτοιμη κλάση [MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)  της βιβλιοθήκης scikit-learn. Παρακάτω παρουσιάζεται ένα παράδειγμα χρήσης της κλάσης αυτής. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "id": "hltXBE2Vbojo",
        "outputId": "3e92d4a0-6816-4c80-e5ad-4dd498776f9e"
      },
      "source": [
        "%%time\r\n",
        "from sklearn.neural_network import MLPClassifier\r\n",
        "\r\n",
        "epochs = 25\r\n",
        "mlp = MLPClassifier(hidden_layer_sizes=(100, 200, 100), max_iter = epochs)\r\n",
        "\r\n",
        "mlp.fit(x_train,y_train)\r\n",
        "\r\n",
        "y_pred = mlp.predict(x_test)\r\n",
        "eval = Evaluate(y_test, y_pred, x_test)\r\n",
        "eval.get_evaluation_report()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (25) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "precision : 0.9694460468740212\n",
            "recall : 0.9694425635387377\n",
            "f1 : 0.9694260141013663\n",
            "accuracy : 0.9697\n",
            "Confusion matrix: \n",
            "[[ 970    0    1    1    1    0    3    0    2    2]\n",
            " [   0 1123    4    1    0    0    3    1    3    0]\n",
            " [   3    3 1003    4    3    0    3    7    5    1]\n",
            " [   0    0    8  976    1    9    0    4   11    1]\n",
            " [   1    0    3    1  950    1    6    3    2   15]\n",
            " [   5    0    0    4    4  865    6    2    5    1]\n",
            " [   9    3    2    2    8    5  927    2    0    0]\n",
            " [   1    4   10    5    1    1    0  991    1   14]\n",
            " [   6    1    5   11    7    5    3    3  928    5]\n",
            " [   3    4    2    7   12    7    1    1    8  964]]\n",
            "Some images with wrong predictions\n",
            "CPU times: user 46.3 s, sys: 18.4 s, total: 1min 4s\n",
            "Wall time: 33.5 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOQYML9wcDBk"
      },
      "source": [
        "## Σύγκριση υλοποιήσεων \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Στα παρακάτω κελιά πειραματιστείτε με τις δύο υλοποιήσεις (τη δική σας και την έτοιμη). Συγκρίνετε τα αποτελέσματά σας τόσο ως προς τους χρόνους εκτέλεσης αλλά και ως προς τα αποτελέσματα. Η διαφορά των αποτελεσμάτων προκύπτει από το ότι στην έτοιμη κλάση έχουν γίνει αρκετές βελτιστοποιήσεις στην λειτουργία, στον τρόπο εκπαίδευσης κ.α. με αποτέλεσμα να προκύπτουν καλύτερα αποτέλεσμα. Παρόλα αυτά στην δική μας κλάση έχουμε καλύτερο έλεγχο και έχουμε την δυνατότητα να σχεδιάσουμε πιο σύνθετες αρχιτεκτονικές καθώς να αλλάξουμε τις τιμές παραμέτρων που στην έτοιμη κλάση μπορεί να μην μας δίνεται η δυνατότητα. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-mkiNTxdhk1"
      },
      "source": [
        "\r\n",
        "\r\n",
        "*   Ο χρόνος εκτέλεσης της δικιάς μας υλοποίησης είναι 43.2sec, ενώ της έτοιμης είναι 33.5sec. \r\n",
        "*   Η διαφορά των αποτελεσμάτων που προκύπτει οφείλεται στο ότι η έτοιμη κλάση έχει αρκετές βελτιστοποιήσεις στην λειτουργία και στον τρόπο εκπαίδευσης  με αποτέλεσμα να προκύπτουν καλύτερα αποτέλεσμα.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfrx064gcCr8"
      },
      "source": [
        "# Αξιολόγηση- Συμπεράσματα \r\n",
        "\r\n",
        "  \r\n",
        "\r\n",
        "Τέλος στο σημείο αυτό καλείστε να αξιολογήσετε τις διάφορες τεχνικές ταξινόμησης (KNN, Naive Bayes, MLP), τα αποτελέσματά τους, τους χρόνους καθώς και να παραθέσετε παρατηρήσεις καθώς και οτιδήποτε σας φάνηκε ενδιαφέρον ή ιδιαίτερο. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_lMne2FeGZ9"
      },
      "source": [
        "Η τεχνική που διαρκούσε το περισσότερο χρόνο ήταν η MLP\r\n",
        "\r\n",
        "*   Η τεχνική που διαρκούσε το περισσότερο χρόνο ήταν η MLP με 43.2sec\r\n",
        "*   Η τεχνική με το καλύτερο accuracy ήταν η KNN με 98% accuracy\r\n",
        "*   Οι έτοιμες έχουν αρκετά πιο γρήγορη υλοποίηση ειδικά στην περίπτωση του Bayes, όπου κάνουν παραδοχές για την κατονομή των δεδομένων. \r\n",
        "*   Η δικιά μας υλόποιησης στο KNN είχε καλύτερο accuracy σε σχέση με την έτοιμη KNN, παρόλο που δεν θα έπρεπε να υπάρχει διαφορά στα αποτελέσματα.\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    }
  ]
}