mnist_deep_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset classification\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import to_categorical\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same set of numbers\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load 2 tuples one for training the model and one for testing, each with the datasets and their respective labels\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n",
      "60000\n"
     ]
    }
   ],
   "source": [
    "# shape of train and test data sets are the same --> 28 x 28 pixels with varying intensity values\n",
    "# train dataset is much larger with 60000 and test with 10000 --> larger datasets reduce overfitting so more dynamic and better at fitting dataset\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assert function used to validate a condition based on the single input arguament\n",
    "# ensure all data in dataset has a corresponding label\n",
    "# if condition is not met then code terminates\n",
    "assert(X_train.shape[0] == y_train.shape[0]), \"the number of images is not equal to the number of labels.\"\n",
    "assert(X_test.shape[0] == y_test.shape[0]), \"the number of images is not equal to the number of labels.\"\n",
    "# ensure images are 28x28 pixels\n",
    "assert(X_train.shape[1:] == (28,28)), \"image dimensions are not 28x28\"\n",
    "assert(X_test.shape[1:] == (28,28)), \"image dimensions are not 28x28\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_samples = []\n",
    "# display a sample of images from mnist dataset in grid form\n",
    "cols = 5\n",
    "num_classes = 10\n",
    "\n",
    "fig, axs = plt.subplots(nrows = num_classes, ncols = cols, figsize = (5, 10))\n",
    "# make sure figures don't overlap\n",
    "fig.tight_layout()\n",
    "# loop through each class\n",
    "for i in range(cols):\n",
    "    # loop through each sample in the class\n",
    "    for j in range(num_classes):\n",
    "        # get a random sample from the specific j class\n",
    "        x_selected = X_train[y_train == j]\n",
    "        # plot the random image from the size of the training dataset\n",
    "        axs[j][i].imshow(x_selected[random.randint(0, len(x_selected - 1))], cmap = 'gray')\n",
    "        axs[j][i].axis(\"off\")\n",
    "        # if in the middle of the grid title the row with the sample number\n",
    "        if i == 2:\n",
    "            axs[j][i].set_title(str(j))\n",
    "            # identify the number of samples for each class i.e. number \n",
    "            number_of_samples.append(len(x_selected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of images')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of samples in each class in bar graph style\n",
    "print(number_of_samples)\n",
    "plt.figure(figsize = (12, 4))\n",
    "plt.bar(range(0, num_classes), number_of_samples)\n",
    "plt.title(\"Distribution of the training dataset\")\n",
    "plt.xlabel(\"Class number\")\n",
    "plt.ylabel(\"Number of images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data for training\n",
    "# one hot encode data labels as is a multiclass dataset\n",
    "# input arguaments for to_categorical is labelled dataset and number of classes \n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise datasets by 255 so max intensity pixel value is 1 and min is 0 and range is normalised\n",
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reformat datasets from 28x28 to a single row of 784 pixels in one image for matrix operations\n",
    "num_pixels = 784\n",
    "X_train = X_train.reshape(X_train.shape[0], num_pixels)\n",
    "X_test = X_test.reshape(X_test.shape[0], num_pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create neural network\n",
    "def create_model():\n",
    "    model = Sequential()\n",
    "    # require 784 input nodes on input layer for each pixel and 10 output nodes in output layer for each class and a hidden layer with relu non linear activation function\n",
    "    # input layer \n",
    "    model.add(Dense(10, input_dim = num_pixels, activation = 'relu'))\n",
    "    # hidden layers\n",
    "    model.add(Dense(10, activation = 'relu'))\n",
    "    model.add(Dense(10, activation = 'relu'))\n",
    "    # output layer\n",
    "    # softmax function used to match the data to the class of highest probability\n",
    "    model.add(Dense(num_classes, activation = 'softmax'))\n",
    "    # configure model\n",
    "    model.compile(Adam(learning_rate = 0.01), loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\44780\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,180</span> (31.95 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,180\u001b[0m (31.95 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,180</span> (31.95 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,180\u001b[0m (31.95 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6956 - loss: 0.9262 - val_accuracy: 0.9258 - val_loss: 0.2671\n",
      "Epoch 2/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9059 - loss: 0.3254 - val_accuracy: 0.9330 - val_loss: 0.2330\n",
      "Epoch 3/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9175 - loss: 0.2830 - val_accuracy: 0.9262 - val_loss: 0.2430\n",
      "Epoch 4/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9195 - loss: 0.2737 - val_accuracy: 0.9317 - val_loss: 0.2339\n",
      "Epoch 5/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9244 - loss: 0.2561 - val_accuracy: 0.9348 - val_loss: 0.2246\n",
      "Epoch 6/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9255 - loss: 0.2500 - val_accuracy: 0.9390 - val_loss: 0.2179\n",
      "Epoch 7/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9283 - loss: 0.2470 - val_accuracy: 0.9342 - val_loss: 0.2267\n",
      "Epoch 8/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9265 - loss: 0.2459 - val_accuracy: 0.9363 - val_loss: 0.2304\n",
      "Epoch 9/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9299 - loss: 0.2379 - val_accuracy: 0.9397 - val_loss: 0.2104\n",
      "Epoch 10/10\n",
      "\u001b[1m270/270\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9302 - loss: 0.2322 - val_accuracy: 0.9423 - val_loss: 0.2054\n"
     ]
    }
   ],
   "source": [
    "# train model with training dataset, labels and split the training dataset into 2 with validation dataset to provide optimal fitting \n",
    "history = model.fit(X_train, y_train, validation_split = 0.1, epochs = 10, batch_size = 200, verbose = 1, shuffle = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training loss and validation loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.title('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training and validation accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.legend(['accuracy', 'val_accuracy'])\n",
    "plt.title('accuracy')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score/Loss: 0.24661771953105927\n",
      "Test Accuracy: 0.9289000034332275\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model accuracy and error/loss\n",
    "score = model.evaluate(X_test, y_test, verbose = 0)\n",
    "print(\"Test Score/Loss:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2592582adf0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain a test image for testing the model using a get request\n",
    "import requests\n",
    "from PIL import Image\n",
    "url = 'https://colah.github.io/posts/2014-10-Visualizing-MNIST/img/mnist_pca/MNIST-p1815-4.png'\n",
    "response = requests.get(url, stream = True)\n",
    "print(response)\n",
    "img = Image.open(response.raw)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x259259a3460>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Z8+e9a5ZuHChd83x48e9a0pKSrxrJOnPf/6zd83gwf4/TkaNGtUr57l8+bJ3DXoHKyEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmuIFpL7l48aJ3zblz57xrHnrooV6pSVZHR4d3zalTp7xr3nnnHe8aSfrtb3/rXbN///6kztUb9u7dm1Tdiy++6F2zbt0675o777zTu+bEiRPeNY8++qh3jSQ1NzcnVYdbx0oIAGCGEAIAmPEKocrKSj3wwAMKhULKycnRvHnzdPLkyYQxzjlVVFQoLy9Pw4YNU3FxcVLLZwBA/+cVQnV1dVq2bJnq6+tVU1Ojy5cvq6SkROfPn4+PWbdundavX68NGzboyJEjikQimj17dlLPBQAA+jevFybs3r074XFVVZVycnJ09OhRzZw5U845vfLKK1q9erXKysokSZs2bVJubq62bt2qZ555JnWdAwAy3m09J9Te3i5Jys7OliQ1NTWppaUl4SOFg8GgHn74YR0+fLjbr9HV1aVYLJawAQAGhqRDyDmn8vJyzZgxQxMmTJAktbS0SJJyc3MTxubm5saPfVVlZaXC4XB8y8/PT7YlAECGSTqEli9fruPHj+uPf/zjdccCgUDCY+fcdfuuWbVqldrb2+Mbr8sHgIEjqTerrlixQjt37tSBAwc0ZsyY+P5IJCLp6oooGo3G97e2tl63OromGAwqGAwm0wYAIMN5rYScc1q+fLneeust7du3T4WFhQnHCwsLFYlEVFNTE9936dIl1dXVqaioKDUdAwD6Da+V0LJly7R161b96U9/UigUij/PEw6HNWzYMAUCAa1cuVJr167VuHHjNG7cOK1du1bDhw/X008/nZZvAACQubxCaOPGjZKk4uLihP1VVVVavHixJOmFF17QxYsX9eyzz+rcuXOaOnWq9u7dq1AolJKGAQD9h1cIOeduOiYQCKiiokIVFRXJ9tQvtbW1ede8//773jUTJ070rulNTU1N3jXJ3HHjZz/7mXeNJDU2NiZVh94zduxY7xp+Ce67uHccAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMBMwN3KrbF7USwWUzgctm6jTxgyZIh3TXZ2dho6SZ2Ojg7vmgsXLqShE/Rk9OjR3jV33XVX6hvpxmeffeZdc/LkyaTOdfny5aTq+pvjx497je/s7FRRUZHa29uVlZXV41hWQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwMtm4AN/a///3Pu+Y///lPGjrBQPPvf/+7V2qQGQKBQNrGsxICAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYMYrhCorK/XAAw8oFAopJydH8+bN08mTJxPGLF68WIFAIGGbNm1aSpsGAPQPXiFUV1enZcuWqb6+XjU1Nbp8+bJKSkp0/vz5hHFz5szRmTNn4tuuXbtS2jQAoH8Y7DN49+7dCY+rqqqUk5Ojo0ePaubMmfH9wWBQkUgkNR0CAPqt23pOqL29XZKUnZ2dsL+2tlY5OTm65557tGTJErW2tt7wa3R1dSkWiyVsAICBIekQcs6pvLxcM2bM0IQJE+L7S0tLtWXLFu3bt08vv/yyjhw5okceeURdXV3dfp3KykqFw+H4lp+fn2xLAIAME3DOuWQKly1bpurqah06dEhjxoy54bgzZ86ooKBA27ZtU1lZ2XXHu7q6EgIqFosRRADQhzQ0NHiN7+zs1PTp09Xe3q6srKwex3o9J3TNihUrtHPnTh04cKDHAJKkaDSqgoICNTY2dns8GAwqGAwm0wYAIMN5hZBzTitWrNDbb7+t2tpaFRYW3rSmra1Nzc3NikajSTcJAOifvJ4TWrZsmd544w1t3bpVoVBILS0tamlp0cWLFyVdXYI9//zz+tvf/qaPP/5YtbW1mjt3rkaOHKn58+en5RsAAGQur5XQxo0bJUnFxcUJ+6uqqrR48WINGjRIDQ0N2rx5sz777DNFo1HNmjVL27dvVygUSlnTAID+wfvPcT0ZNmyY9uzZc1sNAQAGDu4dBwAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwk9QnqwIABo6JEyem7WuzEgIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAmT4XQs456xYAAClwKz/P+1wIdXR0WLcAAEiBW/l5HnB9bOlx5coVnT59WqFQSIFAIOFYLBZTfn6+mpublZWVZdShPa7DVVyHq7gOV3EdruoL18E5p46ODuXl5emOO3pe6/S5j3K44447NGbMmB7HZGVlDehJdg3X4Squw1Vch6u4DldZX4dwOHxL4/rcn+MAAAMHIQQAMJNRIRQMBrVmzRoFg0HrVkxxHa7iOlzFdbiK63BVpl2HPvfCBADAwJFRKyEAQP9CCAEAzBBCAAAzhBAAwExGhdCrr76qwsJC3XnnnZo8ebIOHjxo3VKvqqioUCAQSNgikYh1W2l34MABzZ07V3l5eQoEAtqxY0fCceecKioqlJeXp2HDhqm4uFgnTpywaTaNbnYdFi9efN38mDZtmk2zaVJZWakHHnhAoVBIOTk5mjdvnk6ePJkwZiDMh1u5DpkyHzImhLZv366VK1dq9erVOnbsmB566CGVlpbq1KlT1q31qvHjx+vMmTPxraGhwbqltDt//rwmTZqkDRs2dHt83bp1Wr9+vTZs2KAjR44oEolo9uzZ/e4+hDe7DpI0Z86chPmxa9euXuww/erq6rRs2TLV19erpqZGly9fVklJic6fPx8fMxDmw61cBylD5oPLEA8++KBbunRpwr57773X/eQnPzHqqPetWbPGTZo0yboNU5Lc22+/HX985coVF4lE3EsvvRTf9/nnn7twOOx+85vfGHTYO756HZxzbtGiRe673/2uST9WWltbnSRXV1fnnBu48+Gr18G5zJkPGbESunTpko4ePaqSkpKE/SUlJTp8+LBRVzYaGxuVl5enwsJCLVy4UB999JF1S6aamprU0tKSMDeCwaAefvjhATc3JKm2tlY5OTm65557tGTJErW2tlq3lFbt7e2SpOzsbEkDdz589TpckwnzISNC6OzZs/riiy+Um5ubsD83N1ctLS1GXfW+qVOnavPmzdqzZ49ee+01tbS0qKioSG1tbdatmbn2/z/Q54YklZaWasuWLdq3b59efvllHTlyRI888oi6urqsW0sL55zKy8s1Y8YMTZgwQdLAnA/dXQcpc+ZDn7uLdk+++tEOzrnr9vVnpaWl8X9PnDhR06dP1ze/+U1t2rRJ5eXlhp3ZG+hzQ5IWLFgQ//eECRM0ZcoUFRQUqLq6WmVlZYadpcfy5ct1/PhxHTp06LpjA2k+3Og6ZMp8yIiV0MiRIzVo0KDrfpNpbW297jeegWTEiBGaOHGiGhsbrVsxc+3VgcyN60WjURUUFPTL+bFixQrt3LlT+/fvT/jol4E2H250HbrTV+dDRoTQ0KFDNXnyZNXU1CTsr6mpUVFRkVFX9rq6uvTBBx8oGo1at2KmsLBQkUgkYW5cunRJdXV1A3puSFJbW5uam5v71fxwzmn58uV66623tG/fPhUWFiYcHyjz4WbXoTt9dj4YvijCy7Zt29yQIUPc66+/7t5//323cuVKN2LECPfxxx9bt9ZrnnvuOVdbW+s++ugjV19f7x5//HEXCoX6/TXo6Ohwx44dc8eOHXOS3Pr1692xY8fcv/71L+eccy+99JILh8Purbfecg0NDe6pp55y0WjUxWIx485Tq6fr0NHR4Z577jl3+PBh19TU5Pbv3++mT5/uRo8e3a+uww9/+EMXDoddbW2tO3PmTHy7cOFCfMxAmA83uw6ZNB8yJoScc+7Xv/61KygocEOHDnX3339/wssRB4IFCxa4aDTqhgwZ4vLy8lxZWZk7ceKEdVtpt3//fifpum3RokXOuasvy12zZo2LRCIuGAy6mTNnuoaGBtum06Cn63DhwgVXUlLiRo0a5YYMGeLGjh3rFi1a5E6dOmXddkp19/1LclVVVfExA2E+3Ow6ZNJ84KMcAABmMuI5IQBA/0QIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMDM/wGlqCEopd1RGAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reformat into 28x28 grayscale image using cv2 library\n",
    "import cv2\n",
    "\n",
    "img_array = np.asarray(img)\n",
    "resized = cv2.resize(img_array, (28, 28))\n",
    "grayscale = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)\n",
    "# invert grayscale in line with training dataset\n",
    "image = cv2.bitwise_not(grayscale)\n",
    "plt.imshow(image, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise input image\n",
    "image = image/255\n",
    "image = image.reshape(1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "probabilities: [[1.1648087e-08 2.9539529e-05 9.9935335e-01 4.4031025e-04 5.2416243e-07\n",
      "  7.2541246e-05 3.4221923e-06 7.2409780e-09 1.0028426e-04 6.4124627e-08]]\n",
      "Predicted class: 2\n"
     ]
    }
   ],
   "source": [
    "# obtain probabilities for each class\n",
    "prediction = model.predict(image)\n",
    "print(\"probabilities:\", str(prediction))\n",
    "# predict the class\n",
    "predicted_class = prediction.argmax(axis=-1)\n",
    "print(\"Predicted class:\", predicted_class[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}