NNscratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5678554d",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Intro-and-setup\" data-toc-modified-id=\"Intro-and-setup-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Intro and setup</a></span></li><li><span><a href=\"#Implementing-a-Neural-Network\" data-toc-modified-id=\"Implementing-a-Neural-Network-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Implementing a Neural Network</a></span></li><li><span><a href=\"#Training-the-network\" data-toc-modified-id=\"Training-the-network-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Training the network</a></span><ul class=\"toc-item\"><li><span><a href=\"#Training-with-ReLu-activation\" data-toc-modified-id=\"Training-with-ReLu-activation-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Training with ReLu activation</a></span></li><li><span><a href=\"#Training-with-sigmoid-activation\" data-toc-modified-id=\"Training-with-sigmoid-activation-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Training with sigmoid activation</a></span></li><li><span><a href=\"#Training-with-leaky-ReLu\" data-toc-modified-id=\"Training-with-leaky-ReLu-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Training with leaky ReLu</a></span></li><li><span><a href=\"#Training-with-tanh\" data-toc-modified-id=\"Training-with-tanh-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>Training with <code>tanh</code></a></span></li></ul></li><li><span><a href=\"#Mini-batches\" data-toc-modified-id=\"Mini-batches-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Mini batches</a></span></li><li><span><a href=\"#Evaluating-models\" data-toc-modified-id=\"Evaluating-models-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Evaluating models</a></span></li><li><span><a href=\"#PyTorch-simple-implementation\" data-toc-modified-id=\"PyTorch-simple-implementation-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>PyTorch simple implementation</a></span></li><li><span><a href=\"#Using-my-own-image\" data-toc-modified-id=\"Using-my-own-image-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Using my own image</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b606d6df",
   "metadata": {},
   "source": [
    "# Building a Neural Network from Scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d71b4f",
   "metadata": {},
   "source": [
    "## Intro and setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083acb34",
   "metadata": {},
   "source": [
    "The purpose of this notebook is simple. I want to build a simple Artificial Neural Network without using any framework such as `scikit-learn`, `PyTorch` or `TensorFlow`.\n",
    "I believe this will help me gain a deeper understanding of the mathematics involved and will help that understanding sediment in my memory better.\n",
    "First of all let's import some libraries. Since we aren't going to use any deep learning or machine learning frameworks, the number of imports will be limited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a843a2e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List\n",
    "import pickle # To store data and trained models\n",
    "from tqdm import tqdm # To visualize progress bars during training\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039a70f4",
   "metadata": {},
   "source": [
    "We will be training and testing our network using the [MNIST_784 dataset](https://www.openml.org/d/554). We can download that as a `python` dictionary using `scikit-learn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67af3d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml(name=\"mnist_784\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "145324fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"mnist.pickle\", 'wb') as f:\n",
    "    pickle.dump(mnist, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "438cfccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"mnist.pickle\", 'rb') as f:\n",
    "    mnist = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "4e2e9af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = mnist.data\n",
    "labels = mnist.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5f94a005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4aea5af8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43545\n"
     ]
    }
   ],
   "source": [
    "# Let's select a random datapoint.\n",
    "# np.random.seed(42)\n",
    "n = np.random.choice(np.arange(data.shape[0]+1))\n",
    "print(n)\n",
    "test_img = data.iloc[n].values\n",
    "test_label = mnist.target.iloc[n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ba9fa108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784,)\n"
     ]
    }
   ],
   "source": [
    "print(test_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "991d8dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image label: 6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 231,
       "width": 231
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "side_length = int(np.sqrt(test_img.shape))\n",
    "reshaped_test_img = test_img.reshape(side_length, side_length)\n",
    "print(\"Image label: \" + str(test_label))\n",
    "plt.imshow(reshaped_test_img, cmap=\"Greys\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d587aa6",
   "metadata": {},
   "source": [
    "So we know that image is a vector of shape 784, which is a flattened 28 x 28 2D matrix.\n",
    "If we split our dataset into a training set with e.g. 60.000 datapoints and a test set with 10.000 datapoints we will end up with the following: $input\\;layer\\;size = m*n$, where $m$ is the number of samples and $n$ the number of features. Hence in our case the network's input layer with have size (60.000 x 784).<br>Let's start by coding a simple NN with just one hidden layer.\n",
    "If the hidden layer has say 4 nodes: how do we control the mapping from the input layer to the hidden layer? In other words, what should be the size of the **weight matrix**?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1431abc9",
   "metadata": {},
   "source": [
    "Each node in the input layer will connect to each input in the hidden layer. So we will need 784 * 4 = 3136 weights. And each node in the hidden layer will add a bias to each weighted sum so we'll need 4 biases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f789bb",
   "metadata": {},
   "source": [
    "As a rule: if layer $L_n$ has $s_n$ nodes and layer $L_{n+1}$ has $s_{n+1\n",
    "}$ nodes then the $\\Theta_n$ matrix mapping fron $L_n$ to $L_{n+1}$ will be of size $(s_{n+1},\\;s_{n})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7f2ad74b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 70000)\n"
     ]
    }
   ],
   "source": [
    "w1 = np.ones((4, 784)) * 0.01\n",
    "z1 = np.dot(w1, data.T)\n",
    "print(z1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "61344ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 70000)\n"
     ]
    }
   ],
   "source": [
    "w2 = np.ones((10, 4))\n",
    "z2 = np.dot(w2, z1)\n",
    "print(z2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f775ec9",
   "metadata": {},
   "source": [
    "(But remember: we need to add *biases* to each node in the hidden layer.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0235029d",
   "metadata": {},
   "source": [
    "## Implementing a Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164d8552",
   "metadata": {},
   "source": [
    "Let's start by defining some **activation functions**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c9f3d80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z: np.ndarray) -> np.ndarray:\n",
    "    return 1.0 / (1.0 + np.exp(-z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "009ae73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(z: np.ndarray) -> np.ndarray:\n",
    "    return np.maximum(0, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1cd1bcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanh(z: np.ndarray) -> np.ndarray:\n",
    "    return np.tanh(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d94d983e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaky_relu(z: np.ndarray) -> np.ndarray:\n",
    "    return np.where(z > 0, z, z * 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba3d2ea",
   "metadata": {},
   "source": [
    "For the final layer we will use **softmax** given that this is a multiclass classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6c57a9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(z: np.ndarray) -> np.ndarray:\n",
    "    e = np.exp(z - np.max(z))\n",
    "    return e / np.sum(e, axis=0, keepdims=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7de2b08",
   "metadata": {},
   "source": [
    "We are also going to need some pre-processing functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9cecef72",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(x: np.ndarray) -> np.ndarray:\n",
    "    return (x - np.min(x)) / (np.max(x) - np.min(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4bbb9352",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode(x: np.ndarray, num_labels: int) -> np.ndarray:\n",
    "    return np.eye(num_labels)[x]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9fe4c4",
   "metadata": {},
   "source": [
    "Finally, we will need derivatives for the activation functions to perform gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cfa0e5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def derivative(function_name: str, z: np.ndarray) -> np.ndarray:\n",
    "    if function_name == \"sigmoid\":\n",
    "        return sigmoid(z) * (1 - sigmoid(z))\n",
    "    if function_name == \"tanh\":\n",
    "        return 1 - np.square(tanh(z))\n",
    "    if function_name == \"relu\":\n",
    "        y = (z > 0) * 1\n",
    "        return y\n",
    "    if function_name == \"leaky_relu\":\n",
    "        return  np.where(z > 0, 1, 0.01)\n",
    "    return \"No such activation\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7d6693",
   "metadata": {},
   "source": [
    "We now have everything we need for our deep neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7fc5eeeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NN(object):\n",
    "    def __init__(self, X: np.ndarray, y: np.ndarray, X_test: np.ndarray, y_test: np.ndarray, activation: str, num_labels: int, architecture: List[int]):\n",
    "        self.X = normalize(X) # normalize training data in range 0,1\n",
    "        assert np.all((self.X >= 0) | (self.X <= 1)) # test that normalize succeded\n",
    "        self.X, self.X_test = X.copy(), X_test.copy()\n",
    "        self.y, self.y_test = y.copy(), y_test.copy()\n",
    "        self.layers = {} # define dict to store results of activation\n",
    "        self.architecture = architecture # size of hidden layers as array  \n",
    "        self.activation = activation # activation function\n",
    "        assert self.activation in [\"relu\", \"tanh\", \"sigmoid\", \"leaky_relu\"]\n",
    "        self.parameters = {}\n",
    "        self.num_labels = num_labels\n",
    "        self.m = X.shape[1]\n",
    "        self.architecture.append(self.num_labels)\n",
    "        self.num_input_features = X.shape[0]\n",
    "        self.architecture.insert(0, self.num_input_features)\n",
    "        self.L = len(architecture) \n",
    "        assert self.X.shape == (self.num_input_features, self.m)\n",
    "        assert self.y.shape == (self.num_labels, self.m)\n",
    "        \n",
    "    def initialize_parameters(self):\n",
    "        for i in range(1, self.L):\n",
    "            print(f\"Initializing parameters for layer: {i}.\")\n",
    "            self.parameters[\"w\"+str(i)] = np.random.randn(self.architecture[i], self.architecture[i-1]) * 0.01\n",
    "            self.parameters[\"b\"+str(i)] = np.zeros((self.architecture[i], 1))\n",
    "    \n",
    "    \n",
    "    def forward(self):\n",
    "        params=self.parameters\n",
    "        self.layers[\"a0\"] = self.X\n",
    "        for l in range(1, self.L-1):\n",
    "            self.layers[\"z\" + str(l)] = np.dot(params[\"w\" + str(l)], \n",
    "                                               self.layers[\"a\"+str(l-1)]) + params[\"b\"+str(l)]\n",
    "            self.layers[\"a\" + str(l)] = eval(self.activation)(self.layers[\"z\"+str(l)])\n",
    "            assert self.layers[\"a\"+str(l)].shape == (self.architecture[l], self.m)\n",
    "        self.layers[\"z\" + str(self.L-1)] = np.dot(params[\"w\" + str(self.L-1)],\n",
    "                                                  self.layers[\"a\"+str(self.L-2)]) + params[\"b\"+str(self.L-1)]\n",
    "        self.layers[\"a\"+str(self.L-1)] = softmax(self.layers[\"z\"+str(self.L-1)])\n",
    "        self.output = self.layers[\"a\"+str(self.L-1)]\n",
    "        assert self.output.shape == (self.num_labels, self.m)\n",
    "        assert all([s for s in np.sum(self.output, axis=1)])        \n",
    "        \n",
    "        cost = - np.sum(self.y * np.log(self.output + 0.000000001))\n",
    "\n",
    "        return cost, self.layers\n",
    "\n",
    "\n",
    "    def backpropagate(self):\n",
    "        derivatives = {}\n",
    "        dZ = self.output - self.y\n",
    "        assert dZ.shape == (self.num_labels, self.m)\n",
    "        dW = np.dot(dZ, self.layers[\"a\" + str(self.L-2)].T) / self.m\n",
    "        db = np.sum(dZ, axis=1, keepdims=True) / self.m\n",
    "        dAPrev = np.dot(self.parameters[\"w\" + str(self.L-1)].T, dZ)\n",
    "        derivatives[\"dW\" + str(self.L-1)] = dW\n",
    "        derivatives[\"db\" + str(self.L-1)] = db\n",
    "        \n",
    "        for l in range(self.L-2, 0, -1):\n",
    "            \n",
    "            dZ = dAPrev * derivative(self.activation, self.layers[\"z\" + str(l)])  \n",
    "            dW = 1. / self.m * np.dot(dZ, self.layers[\"a\" + str(l-1)].T)\n",
    "            db = 1. / self.m * np.sum(dZ, axis=1, keepdims=True)\n",
    "            if l > 1:\n",
    "                dAPrev = np.dot(self.parameters[\"w\" + str(l)].T, (dZ))\n",
    "            derivatives[\"dW\" + str(l)] = dW\n",
    "            derivatives[\"db\" + str(l)] = db\n",
    "        self.derivatives = derivatives\n",
    "        \n",
    "        return self.derivatives\n",
    "\n",
    "    \n",
    "    def fit(self, lr=0.01, epochs=1000):\n",
    "        self.costs = [] \n",
    "        self.initialize_parameters()\n",
    "        self.accuracies = {\"train\": [], \"test\": []}\n",
    "        for epoch in tqdm(range(epochs), colour=\"BLUE\"):\n",
    "            cost, cache = self.forward()\n",
    "            self.costs.append(cost)\n",
    "            derivatives = self.backpropagate()            \n",
    "            for layer in range(1, self.L):\n",
    "                self.parameters[\"w\"+str(layer)] = self.parameters[\"w\"+str(layer)] - lr * derivatives[\"dW\" + str(layer)]\n",
    "                self.parameters[\"b\"+str(layer)] = self.parameters[\"b\"+str(layer)] - lr * derivatives[\"db\" + str(layer)]            \n",
    "            train_accuracy = self.accuracy(self.X, self.y)\n",
    "            test_accuracy = self.accuracy(self.X_test, self.y_test)\n",
    "            if epoch % 10 == 0:\n",
    "                print(f\"Epoch: {epoch:3d} | Cost: {cost:.3f} | Accuracy: {train_accuracy:.3f}\")\n",
    "            self.accuracies[\"train\"].append(train_accuracy)\n",
    "            self.accuracies[\"test\"].append(test_accuracy)\n",
    "        print(\"Training terminated\")\n",
    "\n",
    "\n",
    "    def predict(self, x):\n",
    "        params = self.parameters\n",
    "        n_layers = self.L - 1\n",
    "        values = [x]\n",
    "        for l in range(1, n_layers):\n",
    "            z = np.dot(params[\"w\" + str(l)], values[l-1]) + params[\"b\" + str(l)]\n",
    "            a = eval(self.activation)(z)\n",
    "            values.append(a)\n",
    "        z = np.dot(params[\"w\"+str(n_layers)], values[n_layers-1]) + params[\"b\"+str(n_layers)]\n",
    "        a = softmax(z)\n",
    "        if x.shape[1]>1:\n",
    "            ans = np.argmax(a, axis=0)\n",
    "        else:\n",
    "            ans = np.argmax(a)\n",
    "        return ans\n",
    "    \n",
    "    \n",
    "    def accuracy(self, X, y):\n",
    "        P = self.predict(X)\n",
    "        return sum(np.equal(P, np.argmax(y, axis=0))) / y.shape[1]*100\n",
    "    \n",
    "    \n",
    "    def pickle_model(self, name: str):\n",
    "        with open(\"fitted_model_\"+ name + \".pickle\", \"wb\") as modelFile:\n",
    "            pickle.dump(self, modelFile)\n",
    "    \n",
    "    \n",
    "    def plot_counts(self):\n",
    "        counts = np.unique(np.argmax(self.output, axis=0), return_counts=True)\n",
    "        plt.bar(counts[0], counts[1], color=\"navy\")\n",
    "        plt.ylabel(\"Counts\")\n",
    "        plt.xlabel(\"y_hat\")\n",
    "        plt.title(\"Distribution of predictions\")\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    def plot_cost(self, lr):\n",
    "        plt.figure(figsize=(8, 4))\n",
    "        plt.plot(np.arange(0, len(self.costs)), self.costs, lw=1, color=\"orange\")\n",
    "        plt.title(f\"Learning rate: {lr}\\nFinal Cost: {self.costs[-1]:.5f}\", fontdict={\n",
    "        \"family\":\"sans-serif\", \n",
    "        \"size\": \"12\"})\n",
    "        plt.xlabel(\"Epoch\")\n",
    "        plt.ylabel(\"Cost\")\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    def plot_accuracies(self, lr):\n",
    "        acc = self.accuracies\n",
    "        fig = plt.figure(figsize=(6,4))\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(acc[\"train\"], label=\"train\")\n",
    "        ax.plot(acc[\"test\"], label=\"test\")\n",
    "        plt.legend(loc=\"lower right\")\n",
    "        ax.set_title(\"Accuracy\")\n",
    "        ax.annotate(f\"Train: {acc['train'][-1]:.2f}\", (len(acc[\"train\"])+4, acc[\"train\"][-1]+2), color=\"blue\")\n",
    "        ax.annotate(f\"Test: {acc['test'][-1]:.2f}\", (len(acc[\"test\"])+4, acc[\"test\"][-1]-2), color=\"orange\")\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        plt.show()\n",
    "    \n",
    "    def __str__(self):                \n",
    "        return str(self.architecture)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fb7848",
   "metadata": {},
   "source": [
    "## Training the network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb1cf53",
   "metadata": {},
   "source": [
    "Let's split the data into a training and a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "29185f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reloading data\n",
    "with open(\"mnist.pickle\", 'rb') as f:\n",
    "    mnist = pickle.load(f)\n",
    "data = mnist.data\n",
    "labels = mnist.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "974b7d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_split_no = 60000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "aaa23aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.values[:train_test_split_no].T\n",
    "y_train = labels[:train_test_split_no].values.astype(int)\n",
    "y_train = one_hot_encode(y_train, 10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "3de8c91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = data.values[train_test_split_no:].T\n",
    "y_test = labels[train_test_split_no:].values.astype(int)\n",
    "y_test = one_hot_encode(y_test, 10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "eed6b659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((784, 60000), (784, 10000))"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f78ed62",
   "metadata": {},
   "source": [
    "### Training with ReLu activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "6e3a0cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAMS = [X_train, y_train, X_test, y_test, \"relu\", 10, [512, 300]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "19169bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_relu = NN(*PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "584e1653",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs_relu = 50\n",
    "lr_relu = 0.003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "53ab9575",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|\u001b[34m          \u001b[0m| 0/50 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing parameters for layer: 1.\n",
      "Initializing parameters for layer: 2.\n",
      "Initializing parameters for layer: 3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|\u001b[34m▏         \u001b[0m| 1/50 [00:04<03:52,  4.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   0 | Cost: 146154.837 | Accuracy: 18.590\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|\u001b[34m██▏       \u001b[0m| 11/50 [00:54<03:17,  5.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  10 | Cost: 77131.526 | Accuracy: 75.060\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|\u001b[34m████▏     \u001b[0m| 21/50 [01:46<02:32,  5.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  20 | Cost: 47151.615 | Accuracy: 82.263\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|\u001b[34m██████▏   \u001b[0m| 31/50 [02:38<01:38,  5.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  30 | Cost: 35998.465 | Accuracy: 85.198\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|\u001b[34m████████▏ \u001b[0m| 41/50 [14:11<03:04, 20.55s/it] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  40 | Cost: 30510.154 | Accuracy: 86.967\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[34m██████████\u001b[0m| 50/50 [14:53<00:00, 17.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training terminated\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "nn_relu.fit(lr=lr_relu, epochs=epochs_relu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "6198bcf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 291,
       "width": 519
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_relu.plot_cost(lr_relu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d1e136c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_relu.plot_accuracies(lr_relu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "381366d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_relu.pickle_model(\"relu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "04fae464",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"fitted_model_relu.pickle\", \"rb\") as f:\n",
    "    nn_relu = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757bee47",
   "metadata": {},
   "source": [
    "### Training with sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "f227bc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAMS_sigmoid = [X_train, y_train, X_test, y_test, \"sigmoid\", 10, [512, 300]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "fd1d95b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_sigmoid = 0.01\n",
    "epochs_sigmoid = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "ae173643",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_sigmoid = NN(*PARAMS_sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cdcf927",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|\u001b[34m          \u001b[0m| 0/150 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing parameters for layer: 1.\n",
      "Initializing parameters for layer: 2.\n",
      "Initializing parameters for layer: 3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  1%|\u001b[34m          \u001b[0m| 1/150 [00:06<16:18,  6.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   0 | Cost: 138685.114 | Accuracy: 9.737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|\u001b[34m▋         \u001b[0m| 11/150 [01:09<14:37,  6.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  10 | Cost: 138125.949 | Accuracy: 9.737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|\u001b[34m█▍        \u001b[0m| 21/150 [02:14<13:58,  6.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  20 | Cost: 137980.568 | Accuracy: 11.237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|\u001b[34m██        \u001b[0m| 31/150 [03:19<13:07,  6.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  30 | Cost: 137912.456 | Accuracy: 11.237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|\u001b[34m██▋       \u001b[0m| 40/150 [04:20<12:22,  6.75s/it]"
     ]
    }
   ],
   "source": [
    "nn_sigmoid.fit(lr=lr_sigmoid, epochs=epochs_sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "94a65390",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_sigmoid.pickle_model(\"sigmoid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9a6bb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"fitted_model_sigmoid.pickle\", \"rb\") as f:\n",
    "    nn_sigmoid = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6f76b10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 291,
       "width": 513
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_sigmoid.plot_cost(lr_sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c787219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 428
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_sigmoid.plot_accuracies(lr_sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a57216b",
   "metadata": {},
   "source": [
    "### Training with leaky ReLu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0188f5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAMS_leaky = [X_train, y_train, X_test, y_test, \"leaky_relu\", 10, [128, 32]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "18163f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_leaky = NN(*PARAMS_leaky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "769d8245",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_leaky = 0.003\n",
    "epochs_leaky = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fabfef2c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|\u001b[34m          \u001b[0m| 0/200 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing parameters for layer: 1.\n",
      "Initializing parameters for layer: 2.\n",
      "Initializing parameters for layer: 3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|\u001b[34m          \u001b[0m| 1/200 [00:01<04:26,  1.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   0 | Cost: 138393.976 | Accuracy: 8.328\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|\u001b[34m▌         \u001b[0m| 11/200 [00:13<03:56,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  10 | Cost: 135399.276 | Accuracy: 22.795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|\u001b[34m█         \u001b[0m| 21/200 [00:26<03:46,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  20 | Cost: 130679.610 | Accuracy: 37.838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|\u001b[34m█▌        \u001b[0m| 31/200 [00:38<03:31,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  30 | Cost: 121661.152 | Accuracy: 48.312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|\u001b[34m██        \u001b[0m| 41/200 [00:51<03:19,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  40 | Cost: 105656.536 | Accuracy: 57.497\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|\u001b[34m██▌       \u001b[0m| 51/200 [01:04<03:07,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  50 | Cost: 86155.206 | Accuracy: 65.415\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|\u001b[34m███       \u001b[0m| 61/200 [01:16<02:55,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  60 | Cost: 69437.813 | Accuracy: 71.528\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|\u001b[34m███▌      \u001b[0m| 71/200 [01:29<02:42,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  70 | Cost: 57214.183 | Accuracy: 75.895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|\u001b[34m████      \u001b[0m| 81/200 [01:41<02:29,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  80 | Cost: 48812.132 | Accuracy: 78.590\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|\u001b[34m████▌     \u001b[0m| 91/200 [01:54<02:20,  1.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  90 | Cost: 43022.844 | Accuracy: 80.557\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|\u001b[34m█████     \u001b[0m| 101/200 [02:12<02:51,  1.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100 | Cost: 38870.653 | Accuracy: 82.018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|\u001b[34m█████▌    \u001b[0m| 111/200 [02:25<01:54,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 110 | Cost: 35757.852 | Accuracy: 83.222\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|\u001b[34m██████    \u001b[0m| 121/200 [02:38<01:38,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 120 | Cost: 33334.466 | Accuracy: 84.337\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|\u001b[34m██████▌   \u001b[0m| 131/200 [02:50<01:26,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 130 | Cost: 31388.424 | Accuracy: 85.223\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|\u001b[34m███████   \u001b[0m| 141/200 [03:03<01:14,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 140 | Cost: 29786.601 | Accuracy: 85.925\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|\u001b[34m███████▌  \u001b[0m| 151/200 [03:16<01:01,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 150 | Cost: 28443.348 | Accuracy: 86.587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|\u001b[34m████████  \u001b[0m| 161/200 [03:28<00:48,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 160 | Cost: 27301.659 | Accuracy: 87.080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|\u001b[34m████████▌ \u001b[0m| 171/200 [03:41<00:36,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 170 | Cost: 26328.781 | Accuracy: 87.465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|\u001b[34m█████████ \u001b[0m| 181/200 [03:53<00:23,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 180 | Cost: 25653.037 | Accuracy: 87.608\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|\u001b[34m█████████▌\u001b[0m| 191/200 [04:07<00:11,  1.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 190 | Cost: 27710.532 | Accuracy: 84.880\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[34m██████████\u001b[0m| 200/200 [04:18<00:00,  1.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training terminated\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "nn_leaky.fit(X_train, y_train, lr=lr_leaky, epochs=epochs_leaky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a7ed568e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 291,
       "width": 519
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_leaky.plot_cost(lr=lr_leaky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af8da2f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 420
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_leaky.plot_accuracies(lr_leaky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "03bb02fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_leaky.pickle_model(\"leaky\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "47fac8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"fitted_model_leaky.pickle\", \"rb\") as f:\n",
    "    nn_leaky = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "152d4962",
   "metadata": {},
   "source": [
    "### Training with `tanh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "20d6b7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAMS_tanh = [X_train, y_train, X_test, y_test, \"relu\", 10, [64, 10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "555b6bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_tanh = NN(*PARAMS_tanh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "01df1b08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|\u001b[34m          \u001b[0m| 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing parameters for layer: 1.\n",
      "Initializing parameters for layer: 2.\n",
      "Initializing parameters for layer: 3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  1%|\u001b[34m          \u001b[0m| 1/100 [00:00<00:56,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   0 | Cost: 23043.854 | Accuracy: 11.450\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|\u001b[34m██        \u001b[0m| 21/100 [00:11<00:45,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  20 | Cost: 22831.969 | Accuracy: 17.270\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|\u001b[34m████      \u001b[0m| 41/100 [00:21<00:32,  1.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  40 | Cost: 22245.258 | Accuracy: 15.060\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 61%|\u001b[34m██████    \u001b[0m| 61/100 [00:33<00:21,  1.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  60 | Cost: 20813.700 | Accuracy: 23.370\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|\u001b[34m████████  \u001b[0m| 81/100 [00:43<00:10,  1.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  80 | Cost: 17567.665 | Accuracy: 42.770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|\u001b[34m██████████\u001b[0m| 100/100 [00:55<00:00,  1.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training terminated\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "nn_tanh.fit(X_train, y_train, lr=0.003, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "0425ef7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 291,
       "width": 513
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_tanh.plot_cost(lr=0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "80b27d76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 428
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_tanh.plot_accuracies(lr=0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "be5096e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_tanh.pickle_model(\"tanh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "f85a0abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"fitted_model_tanh.pickle\", \"rb\") as f:\n",
    "    nn_tanh = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f4d00c",
   "metadata": {},
   "source": [
    "## Mini batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "1200aade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Not yet implemented\n",
    "\n",
    "def iterate_mini_batches(X, y, batch_size, shuffle=False):    \n",
    "    no_of_samples = X.shape[1]\n",
    "    if shuffle:\n",
    "        ind_list = np.array([i for i in range(no_of_samples)])\n",
    "        np.random.shuffle(ind_list)\n",
    "        X  = X[:, ind_list]\n",
    "        y = y[:, ind_list]\n",
    "    for start_idx in range(0, no_of_samples, batch_size):\n",
    "        if start_idx + batch_size <= no_of_samples:\n",
    "            end_idx = start_idx+batch_size -1\n",
    "        else:\n",
    "            end_idx = (no_of_samples) % batch_size + start_idx + 1\n",
    "        print(start_idx, end_idx)\n",
    "        yield X[:, start_idx:end_idx], y[:, start_idx:end_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a4056d",
   "metadata": {},
   "source": [
    "## Evaluating models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17b91711",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabulate import tabulate\n",
    "def print_accuracies(models, X_train, y_train, X_test, y_test):\n",
    "    data = [[model.activation, model.architecture[1:-1], model.accuracy(X_train, y_train), model.accuracy(X_test, y_test)] for model in models]\n",
    "    print(tabulate(data, headers=[\"Activation\", \"Architecture\",\"Train\", \"Test\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6cbfa8b3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nn_sigmoid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-f9d9e0e598f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_accuracies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnn_relu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnn_sigmoid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnn_leaky\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'nn_sigmoid' is not defined"
     ]
    }
   ],
   "source": [
    "print_accuracies([nn_relu, nn_sigmoid, nn_leaky], X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7ba56f16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 231
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results for nn_relu\n",
    "example_index = 2132\n",
    "plt.imshow(X_test[:, example_index].reshape(28,28), cmap=\"Greys\")\n",
    "prediction = nn_relu.predict(X_test[:, example_index].reshape((X_test.shape[0], 1)))\n",
    "plt.title(\"Prediction: \" + str(prediction))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "946c8c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[:, example_index].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f3d445a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_predictions(model, X_test, labels, rows, cols):\n",
    "    indexes = np.random.choice(range(X_test.shape[1]), rows * cols)\n",
    "    fig, ax = plt.subplots(rows, cols, figsize=(5, 4))\n",
    "    n = 0\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            ix = indexes[n]\n",
    "            example = X_test[:, ix].reshape(28,28)\n",
    "            prediction = model.predict(X_test[:, ix].reshape((X_test[:, ix].shape[0], 1)))        \n",
    "            subplot = ax[i, j]\n",
    "            subplot.set_title(f\"Prediction: {prediction}\\nLabel: {np.argmax(labels[:, ix])}\")\n",
    "            subplot.imshow(example, cmap=\"Greys\")\n",
    "            subplot.axis('off')\n",
    "            n += 1\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "db2dd58e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 310
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(nn_relu, X_test, y_test, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "08d2c898",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 395
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_relu.plot_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15766b97",
   "metadata": {},
   "source": [
    "## PyTorch simple implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0ac5b2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor, Lambda, Compose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3cc79055",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = datasets.MNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "03620cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = datasets.MNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7c54ef5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X [N, C, H, W]:  torch.Size([64, 1, 28, 28])\n",
      "Shape of y:  torch.Size([64]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "\n",
    "# Create data loaders.\n",
    "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
    "\n",
    "for X, y in test_dataloader:\n",
    "    print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
    "    print(\"Shape of y: \", y.shape, y.dtype)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6d7672e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "NeuralNetwork(\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (linear_relu_stack): Sequential(\n",
      "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Get cpu or gpu device for training.\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))\n",
    "\n",
    "# Define model\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(28*28, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        return logits\n",
    "\n",
    "model = NeuralNetwork().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eed8f725",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f425fe36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    model.train()\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 100 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "50e8af38",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "adca96ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 2.300775  [    0/60000]\n",
      "loss: 2.301840  [ 6400/60000]\n",
      "loss: 2.282770  [12800/60000]\n",
      "loss: 2.284122  [19200/60000]\n",
      "loss: 2.284169  [25600/60000]\n",
      "loss: 2.283376  [32000/60000]\n",
      "loss: 2.264910  [38400/60000]\n",
      "loss: 2.276175  [44800/60000]\n",
      "loss: 2.258247  [51200/60000]\n",
      "loss: 2.246940  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 43.2%, Avg loss: 2.255026 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 2.252551  [    0/60000]\n",
      "loss: 2.250427  [ 6400/60000]\n",
      "loss: 2.243942  [12800/60000]\n",
      "loss: 2.221015  [19200/60000]\n",
      "loss: 2.234735  [25600/60000]\n",
      "loss: 2.232835  [32000/60000]\n",
      "loss: 2.203316  [38400/60000]\n",
      "loss: 2.231014  [44800/60000]\n",
      "loss: 2.195654  [51200/60000]\n",
      "loss: 2.178700  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 58.6%, Avg loss: 2.189478 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 2.186056  [    0/60000]\n",
      "loss: 2.177417  [ 6400/60000]\n",
      "loss: 2.184645  [12800/60000]\n",
      "loss: 2.127757  [19200/60000]\n",
      "loss: 2.158648  [25600/60000]\n",
      "loss: 2.153749  [32000/60000]\n",
      "loss: 2.105645  [38400/60000]\n",
      "loss: 2.153380  [44800/60000]\n",
      "loss: 2.093971  [51200/60000]\n",
      "loss: 2.066463  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 62.5%, Avg loss: 2.079785 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss: 2.076205  [    0/60000]\n",
      "loss: 2.053771  [ 6400/60000]\n",
      "loss: 2.080588  [12800/60000]\n",
      "loss: 1.974402  [19200/60000]\n",
      "loss: 2.026093  [25600/60000]\n",
      "loss: 2.013709  [32000/60000]\n",
      "loss: 1.939237  [38400/60000]\n",
      "loss: 2.015360  [44800/60000]\n",
      "loss: 1.920931  [51200/60000]\n",
      "loss: 1.878904  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 67.8%, Avg loss: 1.891260 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss: 1.891540  [    0/60000]\n",
      "loss: 1.842802  [ 6400/60000]\n",
      "loss: 1.895721  [12800/60000]\n",
      "loss: 1.732353  [19200/60000]\n",
      "loss: 1.796990  [25600/60000]\n",
      "loss: 1.773157  [32000/60000]\n",
      "loss: 1.676817  [38400/60000]\n",
      "loss: 1.790881  [44800/60000]\n",
      "loss: 1.653640  [51200/60000]\n",
      "loss: 1.599851  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 72.2%, Avg loss: 1.604094 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "loss: 1.617017  [    0/60000]\n",
      "loss: 1.535026  [ 6400/60000]\n",
      "loss: 1.610565  [12800/60000]\n",
      "loss: 1.420208  [19200/60000]\n",
      "loss: 1.473720  [25600/60000]\n",
      "loss: 1.446976  [32000/60000]\n",
      "loss: 1.347097  [38400/60000]\n",
      "loss: 1.498165  [44800/60000]\n",
      "loss: 1.344015  [51200/60000]\n",
      "loss: 1.287269  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 76.3%, Avg loss: 1.281475 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "loss: 1.314274  [    0/60000]\n",
      "loss: 1.211389  [ 6400/60000]\n",
      "loss: 1.291645  [12800/60000]\n",
      "loss: 1.133055  [19200/60000]\n",
      "loss: 1.163226  [25600/60000]\n",
      "loss: 1.139735  [32000/60000]\n",
      "loss: 1.050502  [38400/60000]\n",
      "loss: 1.220205  [44800/60000]\n",
      "loss: 1.088493  [51200/60000]\n",
      "loss: 1.035495  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 79.5%, Avg loss: 1.023502 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "loss: 1.074040  [    0/60000]\n",
      "loss: 0.966777  [ 6400/60000]\n",
      "loss: 1.031672  [12800/60000]\n",
      "loss: 0.927516  [19200/60000]\n",
      "loss: 0.941415  [25600/60000]\n",
      "loss: 0.917859  [32000/60000]\n",
      "loss: 0.841664  [38400/60000]\n",
      "loss: 1.011760  [44800/60000]\n",
      "loss: 0.913980  [51200/60000]\n",
      "loss: 0.865425  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 81.3%, Avg loss: 0.848545 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "loss: 0.909316  [    0/60000]\n",
      "loss: 0.804406  [ 6400/60000]\n",
      "loss: 0.850043  [12800/60000]\n",
      "loss: 0.791530  [19200/60000]\n",
      "loss: 0.794096  [25600/60000]\n",
      "loss: 0.769899  [32000/60000]\n",
      "loss: 0.701501  [38400/60000]\n",
      "loss: 0.867900  [44800/60000]\n",
      "loss: 0.796538  [51200/60000]\n",
      "loss: 0.753488  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 82.8%, Avg loss: 0.731063 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "loss: 0.795697  [    0/60000]\n",
      "loss: 0.694111  [ 6400/60000]\n",
      "loss: 0.725075  [12800/60000]\n",
      "loss: 0.700761  [19200/60000]\n",
      "loss: 0.691807  [25600/60000]\n",
      "loss: 0.669715  [32000/60000]\n",
      "loss: 0.602781  [38400/60000]\n",
      "loss: 0.768667  [44800/60000]\n",
      "loss: 0.713293  [51200/60000]\n",
      "loss: 0.677674  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 83.9%, Avg loss: 0.648860 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
    "    train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test(test_dataloader, model, loss_fn)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c5a708a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "732be9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_as_mnist(image_path):\n",
    "    \"\"\"\n",
    "    Assume this is a color or grey scale image of a digit which has not so far been preprocessed\n",
    "\n",
    "    Black and White\n",
    "    Resize to 20 x 20 (digit in center ideally)\n",
    "    Sharpen\n",
    "    Add white border to make it 28 x 28\n",
    "    Convert to white on black\n",
    "    \"\"\"\n",
    "    # open as greyscale\n",
    "    image = cv2.imread(image_path, 0)\n",
    "\n",
    "    # crop to contour with largest area\n",
    "    cropped = do_cropping(image)\n",
    "\n",
    "    # resizing the image to 20 x 20\n",
    "    resized20 = cv2.resize(cropped, (20, 20), interpolation=cv2.INTER_CUBIC)\n",
    "\n",
    "    cv2.imwrite('1_resized.jpg', resized20)\n",
    "\n",
    "    # gaussian filtering\n",
    "    blurred = cv2.GaussianBlur(resized20, (3, 3), 0)\n",
    "\n",
    "    # white digit on black background\n",
    "    ret, thresh = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "\n",
    "    padded = to20by20(thresh)\n",
    "\n",
    "\n",
    "    resized28 = padded_image(padded, 28)\n",
    "\n",
    "    # normalize the image values to fit in the range [0,1]\n",
    "    norm_image = np.asarray(resized28, dtype=np.float32) / 255.\n",
    "\n",
    "    # cv2.imshow('image', norm_image)\n",
    "    # cv2.waitKey(0)\n",
    "\n",
    "    # # Flatten the image to a 1-D vector and return\n",
    "    flat = norm_image.reshape(1, 28 * 28)\n",
    "    # return flat\n",
    "\n",
    "    # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\n",
    "    tva = [(255 - x) * 1.0 / 255.0 for x in flat]\n",
    "    return tva\n",
    "\n",
    "\n",
    "\n",
    "def padded_image(image, tosize):\n",
    "    \"\"\"\n",
    "    This method adds padding to the image and makes it to a tosize x tosize array,\n",
    "    without losing the aspect ratio.\n",
    "    Assumes desired image is square\n",
    "\n",
    "    :param image: the input image as numpy array\n",
    "    :param tosize: the final dimensions\n",
    "    \"\"\"\n",
    "\n",
    "    # image dimensions\n",
    "    image_height, image_width = image.shape\n",
    "\n",
    "\n",
    "    # if not already square then pad to square\n",
    "    if image_height != image_width:\n",
    "\n",
    "        # Add padding\n",
    "        # The aim is to make an image of different width and height to a sqaure image\n",
    "        # For that first the biggest attribute among width and height are determined.\n",
    "        max_index = np.argmax([image_height, image_width])\n",
    "\n",
    "\n",
    "        # if height is the biggest one, then add padding to width until width becomes\n",
    "        # equal to height\n",
    "        if max_index == 0:\n",
    "            #order of padding is: top, bottom, left, right\n",
    "            left = int((image_height - image_width) / 2)\n",
    "            right = image_height - image_width - left\n",
    "            padded_img = cv2.copyMakeBorder(image, 0, 0,\n",
    "                                            left,\n",
    "                                            right,\n",
    "                                            cv2.BORDER_CONSTANT)\n",
    "\n",
    "        # else if width is the biggest one, then add padding to height until height becomes\n",
    "        # equal to width\n",
    "        else:\n",
    "            top = int((image_width - image_height) / 2)\n",
    "            bottom = image_width - image_height - top\n",
    "            padded_img = cv2.copyMakeBorder(image, top, bottom, 0, 0,  cv2.BORDER_CONSTANT)\n",
    "    else:\n",
    "        padded_img = image\n",
    "\n",
    "\n",
    "    # now that it's a square, add any additional padding required\n",
    "    image_height, image_width = padded_img.shape\n",
    "    padding = tosize - image_height\n",
    "\n",
    "    # need to handle where padding is not divisiable by 2\n",
    "    left = top = int(padding/2)\n",
    "    right = bottom = padding - left\n",
    "    resized = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT)\n",
    "\n",
    "\n",
    "    return resized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9b706c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.NN at 0x7fab50f38a30>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_relu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4817d75f",
   "metadata": {},
   "source": [
    "## Using my own image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7aa89d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "41256a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread(\"for_mnist2.jpeg\", cv2.IMREAD_GRAYSCALE) # flag 0 imports to grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "529d639a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fab51137b50>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7ztV7w4YN2bUXX3yxq/dRRx2VXTtv3jxX7+effz67dsuWLa7ec+fOza59+umnXb3b2tpc9ccff3x27cc+9jFX7xNOOCG7dsqUKa7ense1rq4uV2/P48sRRxzh6r1z505XfaPhyB0AgGAIdwAAgiHcAQAIhnAHACAYwh0AgGAIdwAAgiHcAQAIhnAHACAYwh0AgGAIdwAAgiHcAQAIhnAHACAYwh0AgGAIdwAAgiHcAQAIJuw8d8k3k72vr6+GKxmZSZMmZdd6193b25tdu3DhQlfvo48+OrvWO5/72Wefza4988wzXb2vuuqq7Nr777/f1fvHP/5xdu2sWbNcva+99lpX/eTJk7Nrp0+f7urtmWvu+RmTfPPgW1paXL337t2bXdvR0eHq3dzcnF2bUnL19iirN0fuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABBM2JGvZuYandrT05NdO2GC73cmz0hHr+7u7uzatrY2V2/PCNHvf//7rt7f/OY3s2vvvfdeV2/PKMy1a9e6ek+dOjW79oorrnD1Pvjgg131nhGg+/btc/X21Ht+xiTf2FXvyNempqbSentGp3rHrnr3lzJw5A4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEE3aee6PyzFuWfLPoe3t7Xb137tyZXTt9+nRX7+XLl2fXPvbYY67ea9asya5duHChq7dnzvXs2bNdvbdt2+aqb29vz67t6+tz9Z4yZUoptZJvnruXZ655mTPRvb09j22eWs+6OXIHACCYmoS7mZ1jZjea2aNmtsPMkpndWeW+c4rbq73dVYs1AQDQqGr1Z/nPSjpB0i5JL0s6kL8V/krSPUNc/0yN1gQAQEOqVbh/QpVQf1HSWyQ9eAA1T6aUVtSoPwAAKNQk3FNKvw9zM6vFpwQAAJnKfLb8a8zsIkkHSdoi6WcppadG8gnMbFWVm3xPIQYAoI6VGe5nFm+/Z2YPSTo/pbS+lBUBABBAGeHeKek6VZ5Mt7a47nhJKySdIel+MzsxpbR7f58opXTSUNcXR/SLarFYAADqzZi/zj2ltCmldE1K6fGU0vbi7RFJyyT9XNLrJV041usCACCKcXMSm5RSr6SvFx8uLnMtAADUs3ET7oVXi8vWUlcBAEAdG2/hfkpxuXbYewEAgKrGPNzNbJGZ/VFfM1uqyslwJGnIU9cCAID9q8mz5c1suaTlxYeHF5d/YWa3F+9vTil9qnj/eknzzWylKme1kyrPll9SvH91SmllLdYFAEAjqtVL4U6UdP6g6+YVb5L0G0n94X6HpLMl/Zmkt0uaJGmjpH+TdFNK6dEarQkAgIZUq9PPrlDldeoHct/bJN1Wi777U9b8YO+s6J6enuzaiRN939LJkydn13pmg0vStGnTsmubm5tdvT/96U9n13Z3d7t6e1x33XWu+iuuuCK79oILLnD1vummm1z1u3btyq71/pxMmjQpu7apqcnV21PvfWwqc5a85/FlwgTff6A9329PrWfd4+0JdQAAwIlwBwAgGMIdAIBgCHcAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIIh3AEACIZwBwAgmFrNcx93Ukqu8YQtLS3ZtWaWXStJnZ2d2bW9vb2u3p7RqZ51S9KmTZuya7/xjW+4entG3Z588smu3h/4wAeyazds2ODq7Rm7eskll7h6X3311a76q666Krv2iCOOcPX2/Jx5f0Y9I6F3797t6n3kkUdm127ZssXV2zMS2vuY7Bm96untqeXIHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCIdwBAAgm7Dx3L8/M5b6+PldvT71nHrvX1q1bXfWPP/54du2vf/1rV+9LL700u3bx4sWu3p7tNmvWLFfv9evXZ9fecsstrt5nn322q/5LX/pSdu3FF1/s6n300Udn16aUXL09s+i3bdvm6u2ZJd/W1ubqvW/fPld9o+HIHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCIdwBAAiGcAcAIBjCHQCAYAh3AACCCTvy1czU1NSUXe8ZbWhm2bWSNGXKFFe9x6ZNm7Jr16xZ4+q9cePG7NrPf/7zrt5HHnlkdq33+93e3p5d29nZ6eo9bdq07Nrt27e7et96662u+ptvvjm79qabbnL19uxvLS0trt4bNmworbd3X/fwjOGeMMF3HOsZ0+vZZp6+HLkDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwYSd555Scs3C9cxUnzjRt1k96/bO2H7llVdKqZWk7u7u7NrZs2e7envmXE+ePNnV+9VXX82u9cxjl6Smpqbs2h07drh6t7a2uuqXLVuWXXvbbbe5ej/22GPZtccee6yr92GHHZZd63lskaTNmzdn13rWLfnW7v26Pcrq7T5yN7ODzOxCM/uOmb1oZl1m1mFmPzGzC8xsyB5mdqqZ3WdmW4uap8zsMjPLf7QBAAA1OXI/V9ItkjZIelDSekmHSXqvpK9LeruZnZsG/PpiZu+R9G1J3ZK+JWmrpHdJ+oqk04rPCQAAMtQi3NdIerek76WU9vVfaWZXSfoPSe9TJei/XVzfJumfJfVJOj2l9Mvi+qslPSDpHDM7L6V0Vw3WBgBAw3H/WT6l9EBK6d6BwV5c/4qkW4sPTx9w0zmSDpF0V3+wF/fvlvTZ4sOPe9cFAECjGu1ny+8tLnsHXLekuPzBEPd/RFKnpFPNzPcsJQAAGtSoPVvezCZK+nDx4cAgP6a4XDO4JqXUa2YvSTpO0jxJz+6nx6oqNy0c2WoBAIhjNI/cvyjpDZLuSyn9cMD1M4rLjip1/dfPHKV1AQAQ2qgcuZvZpZIul/ScpA+NRg9JSimdVKX/KkmLRqsvAADjWc2P3M3sEkk3SFot6YyU0tZBd+k/Mp+hofVfv73WawMAoBHUNNzN7DJJN0p6RpVgH+qUZc8XlwuGqJ8oaa4qT8BbW8u1AQDQKGoW7mZ2pSonoXlSlWDfVOWuDxSXZw1x22JJUyWtTCn11GptAAA0kpqEe3ECmi9KWiVpaUppuBMQ3y1ps6TzzOxNAz7HFEmfKz68pRbrAgCgEbmfUGdm50v6B1XOOPeopEvNbPDd1qWUbpeklNIOM/sbVUL+ITO7S5XTz75blZfJ3a3KKWkBAECGWjxbfm5x2STpsir3eVjS7f0fpJTuMbO3SPqMKqennSLpRUmflPS1VOYIHwAA6pw73FNKKyStyKj7qaS/9Pavxsxc4yw9tb29vfu/0zB2796dXTvEX01G5Oijj86u9f5O5hkh+t3vftfV2zM+9OCDD3b19oxt3bJli6v3EUcckV07a9YsV+/f/va3rvo3vvGN2bWekc6StHLlyuzaJUuW7P9Ow9i7d+/+71TFvn379n+nYcyZMye7dv369a7eM2ZUe4HV/k2Y4PsPtKfe85jsqR3t088CAIAxRrgDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDDuee7jmWcWrmcme2dnZ3at5JvX7Jl5LPnmXDc3N7t6e75fL774oqv3F77whezaa665xtW7paUlu/aQQw5x9e7r68uu3bBhg6v3zJkzXfWbN2/Orl26dKmr98MPP5xdu3HjRldvz8+4d577zp07s2vb29tdvVNK2bWex5Za1JeBI3cAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIIh3AEACCb0yFfPeMMJE/J/75k+fXp2rZdnhKfkG+noGRcrSYsWLcqu3b17t6v37Nmzs2tvuOEGV++Ojo7s2osuusjV27OvtrW1uXqvXr3aVX/99ddn1x566KGu3vPmzcuuLfPxwTt2ddu2bdm13nHUXV1drvp65Blzy5E7AADBEO4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEEzoee5mll3rmYvumSMv+WbJe2olqbm52VVfllNPPdVVf+yxx2bXPv30067eDz30UHbtl7/8ZVfviRPzHwI8PyOSbza4JE2bNi27dv78+a7ey5Yty65taWlx9fb8jO/atau03p2dna7ertnmzsdFT++ycOQOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMGEHfmaUiptbKtn1Ky33jua0PN1e8cqenp7v+729vbs2je/+c2u3ieffLKr3mPSpEnZtd6Rr93d3a761tbW7FrPqFupvHHSkm+79fb2uno3NTVl13q3uefr9j42eevLUH8rBgAAw3KHu5kdZGYXmtl3zOxFM+sysw4z+4mZXWBmEwbdf46ZpWHe7vKuCQCARlaLP8ufK+kWSRskPShpvaTDJL1X0tclvd3Mzk1//HfTX0m6Z4jP90wN1gQAQMOqRbivkfRuSd9LKf3+n6ZmdpWk/5D0PlWC/tuD6p5MKa2oQX8AADCA+8/yKaUHUkr3Dgz24vpXJN1afHi6tw8AADgwo/1s+b3F5VBP0XyNmV0k6SBJWyT9LKX01CivBwCA8EYt3M1soqQPFx/+YIi7nFm8Dax5SNL5KaX1B9hjVZWbFh7gMgEACGc0Xwr3RUlvkHRfSumHA67vlHSdpJMktRdvb1HlyXinS7rfzPJfwAoAQIMblSN3M7tU0uWSnpP0oYG3pZQ2SbpmUMkjZrZM0k8knSzpQkk37K9PSumkKv1XSVo08pUDAFD/an7kbmaXqBLMqyWdkVLaeiB1KaVeVV46J0mLa70uAAAaRU3D3cwuk3SjKq9VP6N4xvxIvFpc8md5AAAy1SzczexKSV+R9KQqwb4p49OcUlyurdW6AABoNDUJdzO7WpUn0K2StDSltHmY+y4afEra4vqlkj5RfHhnLdYFAEAjcj+hzszOl/QPkvokPSrp0iEmJq1LKd1evH+9pPlmtlLSy8V1x0taUrx/dUpppXddAAA0qlo8W35ucdkk6bIq93lY0u3F+3dIOlvSn0l6u6RJkjZK+jdJN6WUHq3BmgAAaFjucC/OD79iBPe/TdJt3r4H2Gss2tRcmesus3eZM7I9X7dn3ZJvpvq+ffv2f6dh7N27d/93qsK7r3i+bkmaPHlydq13rnmZM9U9+1tzc7Ort2cmu/fnxLOvl/m45nls8qybee4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEAzhDgBAMIQ7AADBEO4AAARDuAMAEEwt5rmPS2ampqam7HrXqL0Jvt+ZvKMRy+rt/bo936/W1lZX77LGMnrrvb09YzS940OnTJniqu/s7Myu9Y7K9Wz3MseuenlGBO/Zs8fV2/PY5Hls8fKOo87FkTsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABGPekZHjkZltaWlpmbVgwYKyl4IR8Ix09I7JrdefgzLHzXq3ubfeO7a1LGWOdPYqczyxh3cctYfn637hhRfU1dW1NaV00Ehro4b7S5LaJK2rcpeFxeVzY7KgGNhmedhuedhuI8c2yzOet9scSTtSSnNHWhgy3PfHzFZJUkrppLLXUi/YZnnYbnnYbiPHNssTdbvxP3cAAIIh3AEACIZwBwAgGMIdAIBgCHcAAIJpyGfLAwAQGUfuAAAEQ7gDABAM4Q4AQDCEOwAAwRDuAAAEQ7gDABAM4Q4AQDANFe5mdqSZ/YuZ/ZeZ9ZjZOjP7qpm1l7228arYRqnK2ytlr68sZnaOmd1oZo+a2Y5ie9y5n5pTzew+M9tqZl1m9pSZXWZmTWO17rKNZLuZ2Zxh9r1kZneN9frLYGYHmdmFZvYdM3ux2Hc6zOwnZnaBmQ35ON7o+9tIt1u0/W1i2QsYK2b2OkkrJR0q6buqzO79c0l/J+ksMzstpbSlxCWOZx2SvjrE9bvGeB3jyWclnaDKNnhZ/z0Tekhm9h5J35bULelbkrZKepekr0g6TdK5o7nYcWRE263wK0n3DHH9M7Vb1rh2rqRbJG2Q9KCk9ZIOk/ReSV+X9HYzOzcNOCMZ+5ukjO1WiLG/pZQa4k3SDyUlSf9r0PXXF9ffWvYax+ObpHWS1pW9jvH2JukMSfMlmaTTi33ozir3bZO0SVKPpDcNuH6KKr9wJknnlf01jcPtNqe4/fay113yNluiSjBPGHT94aoEVpL0vgHXs7/lbbdQ+1tD/Fm+OGpfpkpQ3Tzo5r+XtFvSh8ysdYyXhjqVUnowpfRCKh4V9uMcSYdIuiul9MsBn6NblSNZSfr4KCxz3BnhdoOklNIDKaV7U0r7Bl3/iqRbiw9PH3AT+5uytlsojfJn+TOKyx8N8Y3eaWY/VSX8T5F0/1gvrg5MNrO/lnSUKr8IPSXpkZRSX7nLqhtLissfDHHbI5I6JZ1qZpNTSj1jt6y68Rozu0jSQZK2SPpZSumpktc0XuwtLnsHXMf+tn9Dbbd+Ifa3Rgn3Y4rLNVVuf0GVcF8gwn0oh0u6Y9B1L5nZR1NKD5exoDpTdf9LKfWa2UuSjpM0T9KzY7mwOnFm8fZ7ZvaQpPNTSutLWdE4YGYTJX24+HBgkLO/DWOY7dYvxP7WEH+WlzSjuOyocnv/9TNHfyl1518lLVUl4Fsl/amkf1Ll/1PfN7MTylta3WD/y9Mp6TpJJ0lqL97eosqTo06XdH+D/yvti5LeIOm+lNIPB1zP/ja8atst1P7WKOGOTCmla4v/XW1MKXWmlJ5JKf2tKk9EbJG0otwVIqqU0qaU0jUppcdTStuLt0dU+SvbzyW9XtKF5a6yHGZ2qaTLVXnVz4dKXk7dGG67RdvfGiXc+39TnVHl9v7rt4/+UsLof0LK4lJXUR/Y/2oopdSrykuZpAbc/8zsEkk3SFot6YyU0tZBd2F/G8IBbLch1ev+1ijh/nxxuaDK7fOLy2r/k8cfe7W4rJs/U5Wo6v5X/P9vripP7Fk7louqcw25/5nZZZJuVOU112cUz/wejP1tkAPcbsOpu/2tUcL9weJy2RBnJZquykkdOiU9NtYLq2OnFJcN8wDh8EBxedYQty2WNFXSygZ+5nKOhtv/zOxKVU5C86QqAbWpyl3Z3wYYwXYbTt3tbw0R7iml/5T0I1WeBHbxoJuvVeW3sTtSSrvHeGnjmpn9yVBPIDGzOZJuKj4c9pSrkCTdLWmzpPPM7E39V5rZFEmfKz68pYyFjWdmtmioU6ua2VJJnyg+bIj9z8yuVuWJYKskLU0pbR7m7uxvhZFst2j7mzXKuSSGOP3ss5JOVuU18GsknZo4/ewfMLMVqjz55BFJv5G0U9LrJL1DlbNd3Sfp7JTSnrLWWBYzWy5pefHh4ZLepspv9Y8W121OKX1q0P3vVuV0oHepcjrQd6vysqW7Jf1VI5zYZSTbrXj50XxVfm5fLm4/Xv/9Ou6rU0r9YRWWmZ0v6XZJfar8aXmoZ8GvSyndPqBmuRp8fxvpdgu3v5V9iryxfJP0WlVe2rVB0h5VAuurktrLXtt4fFPlZSDfVOWZpdtVOfHDq5L+nyqvE7Wy11jitlmhyqkqq72tG6LmNFV+IdomqUvS06ocETSV/fWMx+0m6QJJ/67KmSV3qXI61fWqnCv9f5T9tYyjbZYkPcT+5ttu0fa3hjlyBwCgUTTE/9wBAGgkhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAwhDsAAMEQ7gAABEO4AwAQDOEOAEAw/x91GEG80jNdLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 251
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img, cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "1ca3ab2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped = np.asarray(img).reshape((X_test.shape[0], 1)) * 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e09dccfb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 45,  43,  46,  52,  53,  51,  57,  66,  58,  45,  45,  66,  46,\n",
       "         55,  53,  50,  51,  55,  48,  59,  59,  47,  53,  49,  53,  54,\n",
       "         55,  57],\n",
       "       [ 48,  46,  46,  48,  49,  50,  54,  58,  57,  64,  52,  46,  27,\n",
       "         52,  56,  53,  50,  62,  58,  62,  58,  49,  58,  54,  43,  44,\n",
       "         47,  50],\n",
       "       [ 53,  51,  48,  45,  47,  52,  52,  49,  50,  54,  49,  70,  64,\n",
       "         60,  45,  61,  51,  65,  58,  57,  55,  51,  52,  33,  61,  59,\n",
       "         56,  52],\n",
       "       [ 53,  54,  50,  45,  48,  53,  51,  44,  61,  54,  42,  92, 138,\n",
       "        160, 146, 162, 197, 140,  75,  63,  54,  38,  60,  82,  49,  49,\n",
       "         49,  49],\n",
       "       [ 47,  50,  49,  46,  47,  51,  53,  52,  38, 127, 167, 162, 132,\n",
       "        133, 126, 141, 153, 174, 185, 187, 142,  74,  47,  36,  46,  46,\n",
       "         46,  46],\n",
       "       [ 42,  43,  46,  47,  44,  47,  60,  75, 170, 154,  99,  69,  52,\n",
       "         66,  48,  40,  61,  59,  93, 178, 204, 143,  87,  54,  62,  59,\n",
       "         55,  50],\n",
       "       [ 45,  43,  47,  50,  45,  47,  75, 110, 133,  72,  26,  49,  51,\n",
       "         55,  45,  53,  44, 184, 239, 188, 160, 184, 156,  58,  50,  49,\n",
       "         48,  48],\n",
       "       [ 51,  47,  50,  55,  48,  50,  89, 138,  83,  55,  45,  69,  50,\n",
       "         52,  50,  54,  48, 116, 162, 184, 186, 169, 126,  60,  52,  50,\n",
       "         47,  43],\n",
       "       [ 60,  43,  47,  48,  54,  37, 131, 134,  47,  67,  55,  33,  64,\n",
       "         49,  53,  55,  63,  53,  73, 115, 196, 181,  70,  57,  50,  51,\n",
       "         53,  54],\n",
       "       [ 44,  48,  53,  39,  52,  56, 130, 100,  59,  43,  61,  67,  50,\n",
       "         62,  63,  55,  47,  64, 117, 159, 201, 163,  60,  49,  47,  48,\n",
       "         48,  49],\n",
       "       [ 37,  48,  54,  38,  49,  63, 135, 117,  65,  49,  43,  64,  60,\n",
       "         63,  43,  97, 153, 153, 187, 199, 196, 148,  64,  49,  43,  42,\n",
       "         42,  42],\n",
       "       [ 48,  42,  48,  47,  49,  50, 133, 169,  34, 135, 176, 173, 168,\n",
       "        169, 155, 183, 207, 187, 185, 151, 101,  65,  46,  63,  45,  45,\n",
       "         44,  43],\n",
       "       [ 55,  41,  44,  51,  50,  41, 103, 153, 170, 146, 149, 181, 188,\n",
       "        162, 190, 181, 176, 101,  51,  39,  49,  60,  45,  27,  49,  49,\n",
       "         49,  49],\n",
       "       [ 50,  47,  46,  45,  49,  48,  62,  73, 197, 199, 148,  82, 136,\n",
       "        189, 177,  71,  63,  48,  46,  46,  36,  34,  43,  51,  43,  44,\n",
       "         45,  47],\n",
       "       [ 43,  52,  50,  38,  44,  55,  44,  37,  42, 126, 187, 160, 187,\n",
       "        183,  84,  48,  37,  41,  45,  55,  52,  39,  37,  37,  39,  41,\n",
       "         44,  47],\n",
       "       [ 43,  52,  50,  38,  40,  53,  50,  61,  44,  54, 170, 189, 158,\n",
       "        159,  54,  68,  48,  51,  40,  45,  51,  40,  41,  45,  45,  48,\n",
       "         53,  57],\n",
       "       [ 46,  52,  54,  49,  43,  43,  45,  46,  35, 166, 177, 189,  40,\n",
       "        165, 187,  65,  62,  57,  44,  40,  40,  45,  51,  51,  53,  50,\n",
       "         47,  46],\n",
       "       [ 52,  39,  38,  52,  54,  47,  59,  83, 158, 161, 123,  35,  68,\n",
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       "         42,  40],\n",
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     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "3d33772f",
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