MNIST_NNs_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic\n",
    "Option 2: Implement a neural network from scratch.\n",
    "\n",
    "Write a `NeuralNetwork` class in Python that implements a neural network model with dense layers and ReLU activation. \n",
    "\n",
    "This class should contain the following methods:\n",
    "\n",
    "1. `__init__`: Declares a neural network with a list of integers indicating the size of each layer. \n",
    "2. `fit`: Trains the neural network model using the Stochastic Gradient Descent method.\n",
    "3. `predict`: Calculates the output values for a list of input data.\n",
    "\n",
    "It is up to you to decide the details, but your implementation cannot use neural network classes from any existing library. Please refer to the slides on neural networks and this online textbook for the formulas and algorithms you should use. After completing the implementation, apply the model on a dataset (for example, the Iris dataset) to see whether it gives reasonable results.\n",
    "\n",
    "Instructions: All code should be executed, and results should be displayed in the notebook. Writing is as important as coding. Clearly describe every step that you take."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # for calculus\n",
    "from pandas import read_csv  # for data loading\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import os # os system managment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description\n",
    "__Full neural network construction with only math and numpy__\n",
    "\n",
    "For this short project, I will create a fully connected two layer neural network to predict the numbers associated with the images in the MNIST data set. The network will take in the 784 pixels in an image as input. The first hidden layer will contain 10 neurons with ReLU activation, followed by a 10 neuron output layer with sigmoid activation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, to make it possible, i'll use a trainning and testing dataset form `kaggle` from __`mnist-in-csv`__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test & Train data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "script_dir = os.getcwd() # get the current working directory\n",
    "# define the custom name and extension of train an test dataset\n",
    "# in our case ext mime is csv.\n",
    "train_file = \"mnist_train.csv\"  \n",
    "test_file = \"mnist_test.csv\"\n",
    "# compute the absolute files path\n",
    "abs_train_file_path = os.path.join(script_dir, train_file)\n",
    "abs_test_file_path = os.path.join(script_dir, test_file)\n",
    "# load each fiile\n",
    "train = read_csv(abs_train_file_path)\n",
    "test = read_csv(abs_test_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Less exploration of data to pull out somes details we said before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> show head data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
       "0      5    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
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       "\n",
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       "\n",
       "[5 rows x 785 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it's there a data set of 784 pixels of different images showing a number between `0` to `9`. each row content a label which is the number show by the image and 784 pixels's value belong to it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> show train information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 785 entries, label to 28x28\n",
      "dtypes: int64(785)\n",
      "memory usage: 359.3 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the dataset `don't have missing values or noise values`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Test data pretreatment\n",
    "in the goal of building the NN, we need to get each pixel to image as input of our first or entry layer of our NN, thus we will change the type data from `DataFrame` to `ndarray`, next transpose each dataset as modeling to be trust by the model nn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dtype changing\n",
    "train = np.array(train)\n",
    "test = np.array(test)\n",
    "# split train label and features before transpose the array\n",
    "X_train = train[:, 1:].T\n",
    "Y_train = train[:, 0].T\n",
    "# split test label and features before transpose the array\n",
    "X_test = test[:, 1:].T\n",
    "Y_test = test[:, 0].T\n",
    "# normalize each feature value to involve image computation\n",
    "X_train = X_train / 255.\n",
    "X_test = X_test / 255.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN Logic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our NN will have a simple two-layer architecture. \n",
    "\n",
    "Input layer $a^{[0]}$ will have 784 units corresponding to the 784 pixels in each 28x28 input image. \n",
    "\n",
    "A hidden layer $a^{[1]}$ will have 10 units with ReLU activation, and \n",
    "\n",
    "finally our output layer $a^{[2]}$ will have 10 units corresponding to the ten digit classes with softmax activation.\n",
    "\n",
    "**Forward propagation**\n",
    "\n",
    "$$Z^{[1]} = W^{[1]} X + b^{[1]}$$\n",
    "$$A^{[1]} = g_{\\text{ReLU}}(Z^{[1]}))$$\n",
    "$$Z^{[2]} = W^{[2]} A^{[1]} + b^{[2]}$$\n",
    "$$A^{[2]} = g_{\\text{softmax}}(Z^{[2]})$$\n",
    "\n",
    "**Backward propagation**\n",
    "\n",
    "$$dZ^{[2]} = A^{[2]} - Y$$\n",
    "$$dW^{[2]} = \\frac{1}{m} dZ^{[2]} A^{[1]T}$$\n",
    "$$dB^{[2]} = \\frac{1}{m} \\Sigma {dZ^{[2]}}$$\n",
    "$$dZ^{[1]} = W^{[2]T} dZ^{[2]} .* g^{[1]\\prime} (z^{[1]})$$\n",
    "$$dW^{[1]} = \\frac{1}{m} dZ^{[1]} A^{[0]T}$$\n",
    "$$dB^{[1]} = \\frac{1}{m} \\Sigma {dZ^{[1]}}$$\n",
    "\n",
    "**Parameter updates**\n",
    "\n",
    "$$W^{[2]} := W^{[2]} - \\alpha dW^{[2]}$$\n",
    "$$b^{[2]} := b^{[2]} - \\alpha db^{[2]}$$\n",
    "$$W^{[1]} := W^{[1]} - \\alpha dW^{[1]}$$\n",
    "$$b^{[1]} := b^{[1]} - \\alpha db^{[1]}$$\n",
    "\n",
    "**Vars and shapes**\n",
    "\n",
    "Forward prop\n",
    "\n",
    "- $A^{[0]} = X$: 784 x m\n",
    "- $Z^{[1]} \\sim A^{[1]}$: 10 x m\n",
    "- $W^{[1]}$: 10 x 784 (as $W^{[1]} A^{[0]} \\sim Z^{[1]}$)\n",
    "- $B^{[1]}$: 10 x 1\n",
    "- $Z^{[2]} \\sim A^{[2]}$: 10 x m\n",
    "- $W^{[1]}$: 10 x 10 (as $W^{[2]} A^{[1]} \\sim Z^{[2]}$)\n",
    "- $B^{[2]}$: 10 x 1\n",
    "\n",
    "Backprop\n",
    "\n",
    "- $dZ^{[2]}$: 10 x m ($~A^{[2]}$)\n",
    "- $dW^{[2]}$: 10 x 10\n",
    "- $dB^{[2]}$: 10 x 1\n",
    "- $dZ^{[1]}$: 10 x m ($~A^{[1]}$)\n",
    "- $dW^{[1]}$: 10 x 10\n",
    "- $dB^{[1]}$: 10 x 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN MNIST IMPLEMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class mnist_nn:\n",
    "    \"\"\" \n",
    "    mnist neural network build from scratch within numpy, pandas and matplotlib\n",
    "    \n",
    "    Args:\n",
    "        features (ndarray): the features content all examples features values based for train ....\n",
    "        labels (ndarray): the labels content all real labels of all examples features...\n",
    "        alpha (Numeric): the alpha is an update coeficient...\n",
    "        iterations (Numeric): the iterations is the number of epochs of training stage...\n",
    "        \n",
    "    Attributes:\n",
    "        features (ndarray): where store the features content all examples features values based for train ....\n",
    "        labels (ndarray): where store the labels content all real labels of all examples features...\n",
    "        alpha (Numeric): where store the alpha is an update coeficient...\n",
    "        iterations (Numeric): where store the iterations is the number of epochs of training stage...\n",
    "        w1 (ndarray): the normally randomly initialized weights from the input layer to the first hidden layer...\n",
    "        b1 (ndarray): the biais from the input layers to the first hidden layer...\n",
    "        w2 (ndarray): the normally randomly initialized weights from the first hidden layer to the second hidden layer...\n",
    "        b2 (ndarray): the biais from the first hidden layers to the second hidden layer...\n",
    "        \n",
    "    \"\"\"\n",
    "    def __init__(self, features, labels, alpha, iterations):\n",
    "        ''' Return an instance of mnist_nn with all attributes and methods '''\n",
    "        self.features = features\n",
    "        self.labels = labels\n",
    "        self.alpha = alpha\n",
    "        self.iterations = iterations\n",
    "        self.size, self.m = self.features.shape\n",
    "        self.W1 = np.random.normal(size=(10, self.size)) * np.sqrt(1./(self.size))\n",
    "        self.b1 = np.random.normal(size=(10, 1)) * np.sqrt(1./10)\n",
    "        self.W2 = np.random.normal(size=(10, 10)) * np.sqrt(1./20)\n",
    "        self.b2 = np.random.normal(size=(10, 1)) * np.sqrt(1./(784))\n",
    "        self.Z1, self.A1, self.Z2, self.A2 = 0,0,0,0\n",
    "        self.dW1, self.db1, self.dW2, self.db2 = 0,0,0,0\n",
    "        \n",
    "    def ReLU(self,Z):\n",
    "        \"\"\"\n",
    "        ReLU method\n",
    "        \n",
    "        Args:\n",
    "            Z (ndarray): there is a computed features\n",
    "            \n",
    "        Return:\n",
    "            Z (ndarray) max between Zi and 0\n",
    "        \"\"\"\n",
    "        return np.maximum(Z,0)\n",
    "\n",
    "    def derivative_ReLU(self,Z):\n",
    "        \"\"\"\n",
    "        derivative_ReLU method\n",
    "        \n",
    "        Args:\n",
    "            Z (ndarray): there is a computed features\n",
    "            \n",
    "        Return:\n",
    "            Z (ndarray) with true if zi > 0 and false else\n",
    "        \"\"\"\n",
    "        return Z > 0\n",
    "\n",
    "    def softmax(self,Z):\n",
    "        \"\"\"\n",
    "        softmax method\n",
    "        \n",
    "        Args:\n",
    "            Z (ndarray): there is a computed features\n",
    "            \n",
    "        Return:\n",
    "            (ndarray) computed softmax values for each sets of scores in Z.\n",
    "        \"\"\"\n",
    "        exp = np.exp(Z - np.max(Z))\n",
    "        return exp / exp.sum(axis=0)\n",
    "\n",
    "    def forward_propagation(self,X):\n",
    "        \"\"\"\n",
    "            forward_propagation method\n",
    "            \n",
    "            make a propagation from input layer to output layer\n",
    "        \"\"\"\n",
    "        self.Z1 = self.W1.dot(X) + self.b1 #10, m\n",
    "        self.A1 = self.ReLU(self.Z1) # 10,m\n",
    "        self.Z2 = self.W2.dot(self.A1) + self.b2 #10,m\n",
    "        self.A2 = self.softmax(self.Z2) #10,m\n",
    "\n",
    "    def one_hot(self,Y):\n",
    "        ''' return an 0 vector with 1 only in the position correspondind to the value in Y'''\n",
    "        one_hot_Y = np.zeros((Y.max()+1,Y.size))\n",
    "        one_hot_Y[Y,np.arange(Y.size)] = 1\n",
    "        return one_hot_Y\n",
    "\n",
    "    def backward_propagation(self):\n",
    "        \"\"\"\n",
    "            backward_propagation method\n",
    "            \n",
    "            computed the derivatives of forwarding process\n",
    "        \"\"\"\n",
    "        one_hot_Y = self.one_hot(self.labels)\n",
    "        self.dZ2 = 2*(self.A2 - one_hot_Y) #10,m\n",
    "        self.dW2 = 1/self.m * (self.dZ2.dot(self.A1.T)) # 10 , 10\n",
    "        self.db2 = 1/self.m * np.sum(self.dZ2,1) # 10, 1\n",
    "        self.dZ1 = self.W2.T.dot(self.dZ2)*self.derivative_ReLU(self.Z1) # 10, m\n",
    "        self.dW1 = 1/self.m * (self.dZ1.dot(self.features.T))  # 10, 784\n",
    "        self.db1 = 1/self.m * np.sum(self.dZ1,1) # 10, 1\n",
    "\n",
    "    def update_params(self):\n",
    "        \"\"\"\n",
    "            update_params method\n",
    "            \n",
    "            use the the learning rate or update rate to update the weights and biais based on the above formula\n",
    "        \"\"\"\n",
    "        self.W1 -= self.alpha * self.dW1\n",
    "        self.b1 -= self.alpha * np.reshape(self.db1, (10,1))\n",
    "        self.W2 -= self.alpha * self.dW2\n",
    "        self.b2 -= self.alpha * np.reshape(self.db2, (10,1))\n",
    "    \n",
    "    def fit(self):\n",
    "        \"\"\"\n",
    "            fit method\n",
    "            \n",
    "            for the number of epochs or itérations make a chain of forward, backward propagation and weights updates to try to reach the best weight of units in each layers\n",
    "        \"\"\"\n",
    "        for i in range(self.iterations):\n",
    "            self.forward_propagation(self.features)\n",
    "            self.backward_propagation()\n",
    "            self.update_params()\n",
    "        \n",
    "    def get_predictions(self):\n",
    "        \"\"\"\n",
    "            get_predictions method\n",
    "            \n",
    "            To know the predicted label, use the argmax method of numpy to get the node in the output layer with the greatest value\n",
    "        \"\"\"\n",
    "        return np.argmax(self.A2, 0)\n",
    "\n",
    "    def make_predictions(self,X):\n",
    "        \"\"\"\n",
    "            make_predictions method\n",
    "            \n",
    "            from an ndarray, try to make forward propagation in the NN and use the latest layer values of this forwarding to determine which label has been predict for each entries\n",
    "        \"\"\"\n",
    "        self.forward_propagation(X)\n",
    "        return self.get_predictions()\n",
    "    \n",
    "    def show_prediction(self,index, X, Y):\n",
    "        \"\"\"\n",
    "            show_prediction method\n",
    "            \n",
    "            from user index, set of features and labels, use the current black box model to try to predict the label of the index element base of the vector of features belong to its\n",
    "        \"\"\"\n",
    "        # get the vector of features to predict\n",
    "        vect_X = X[:, index, None]\n",
    "        # use the black box to predic\n",
    "        prediction = self.make_predictions(vect_X)\n",
    "        # get the target label previous to have like output\n",
    "        label = Y[index]\n",
    "        # show current and espected output\n",
    "        print(\"Prediction: \", prediction)\n",
    "        print(\"Label: \", label)\n",
    "        # reshape the image to be able to plot or show\n",
    "        current_image = vect_X.reshape((28, 28)) * 255\n",
    "        # define the tendance background\n",
    "        plt.gray()\n",
    "        # set image to show with nearest interpolation\n",
    "        plt.imshow(current_image, interpolation='nearest')\n",
    "        # show the image\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metric Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_accuracy(predictions, Y):\n",
    "    return np.sum(predictions == Y)/Y.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RUNNER OF OUR CNN MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **MAIN EXECUTION CELL**\n",
    ">\n",
    "> STEPS:\n",
    "> \n",
    "> - INSTANCIATE mnist_nn by given some argments to `mnist_nn(...)` such as:\n",
    ">   - Training Features -> features\n",
    ">   - Training Labels -> labels\n",
    ">   - Update Rate -> alpha\n",
    ">   - Iteration | Epoch -> iterations\n",
    "> - TRAIN THE CCN mnist by calling the `fit()` method\n",
    "> - Make a Prediction by sending:\n",
    ">   - Test Features -> X\n",
    "> - Show the prediction of the Test dataset by calling `show_prediction()`\n",
    "> - Save the model we Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library for model saving\n",
    "import joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9078\n",
      "Prediction:  [7]\n",
      "Label:  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [2]\n",
      "Label:  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [1]\n",
      "Label:  1\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [0]\n",
      "Label:  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [4]\n",
      "Label:  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instanciation\n",
    "lrate = 0.15\n",
    "epochs = 200\n",
    "# the testing label and features was already decompose on top of this notebook as Y_train and X_train respectively \n",
    "model = mnist_nn(X_train,Y_train,lrate,epochs)\n",
    "\n",
    "# fit the model\n",
    "model.fit()\n",
    "\n",
    "# test the model using the test label and test train name as early Y_test and X_test\n",
    "predictions = model.make_predictions(X_test)\n",
    "\n",
    "# pull out the accuracy of the model by calling our metric method get_accuracy()\n",
    "acc_test = get_accuracy(predictions, Y_test)\n",
    "print(f\"Test Accuracy: {acc_test}\")\n",
    "\n",
    "# show the prediction of 5 first element in X_test\n",
    "for i in range(5):\n",
    "    model.show_prediction(i, X_test, Y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mnist_nn.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save knn model\n",
    "joblib.dump(model, 'mnist_nn.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load knn models\n",
    "mnist_nn_model = joblib.load('mnist_nn.pkl', mmap_mode='r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [7]\n",
      "Label:  7\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [2]\n",
      "Label:  2\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [1]\n",
      "Label:  1\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [0]\n",
      "Label:  0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [4]\n",
      "Label:  4\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the prediction of 5 first element in X_test on the load model\n",
    "for i in range(5):\n",
    "    mnist_nn_model.show_prediction(i, X_test, Y_test)\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
  },
  "kernelspec": {
   "display_name": "Python 3.9.2 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}