goit_python_ds_hw_08.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lexxai/goit_python_ds_hw_08/blob/main/goit_python_ds_hw_08.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Модуль 8. Глибоке навчання. Tensorflow.\n",
        "\n",
        "*З циклу [домашніх завдань Python Data Science](https://github.com/lexxai/goit_python_data_sciense_homework).*\n",
        "\n",
        "# Домашнє завдання"
      ],
      "metadata": {
        "id": "SuEHs0R0iEJC"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0Yf4NBJUSNM"
      },
      "source": [
        "# Створення нейронної мережі\n",
        "\n",
        "У цьому завданні ми створимо повнозв'язну нейронну мережу, використовуючи при цьому низькорівневі механізми tensorflow.\n",
        "\n",
        "Архітектура нейромережі представлена на наступному малюнку. Як бачиш, у ній є один вхідний шар, два приховані, а також вихідний шар. В якості активаційної функції у прихованих шарах буде використовуватись сигмоїда. На вихідному шарі ми використовуємо softmax.\n",
        "\n",
        "Частина коду зі створення мережі вже написана, тобі потрібно заповнити пропуски у вказаних місцях."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01rZWUu0USNQ"
      },
      "source": [
        "## Архітектура нейронної мережі\n",
        "\n",
        "<img src=\"http://cs231n.github.io/assets/nn1/neural_net2.jpeg\" alt=\"nn\" style=\"width: 400px;\"/>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLvIZ705Qw_V"
      },
      "source": [
        "## Про датасет MNIST\n",
        "\n",
        "Дану нейромережу ми будемо вивчати на датасеті MNIST. Цей датасет являє собою велику кількість зображень рукописних цифр розміром $28 \\times 28$ пікселів. Кожен піксель приймає значення від 0 до 255.\n",
        "\n",
        "Як і раніше, датасет буде розділений на навчальну та тестову вибірки. При цьому ми виконаємо нормалізацію всіх зображень, щоб значення пікселів знаходилось у проміжку від 0 до 1, розділивши яскравість кожного пікселя на 255.\n",
        "\n",
        "Окрім того, архітектура нейронної мережі очікує на вхід вектор. У нашому ж випадку кожен об'єкт вибірки являє собою матрицю. Що ж робити? У цьому завданні ми \"розтягнемо\" матрицю $28 \\times 28$, отримавши при цьому вектор, що складається з 784 елементів.\n",
        "\n",
        "![MNIST Dataset](https://www.researchgate.net/profile/Steven-Young-5/publication/306056875/figure/fig1/AS:393921575309346@1470929630835/Example-images-from-the-MNIST-dataset.png)\n",
        "\n",
        "Більше інформації про датасет можна знайти [тут](http://yann.lecun.com/exdb/mnist/)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Модель"
      ],
      "metadata": {
        "id": "srKzy_8szszJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "il_0_5OyUSNR"
      },
      "outputs": [],
      "source": [
        "from enum import Enum, auto\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import keras as K\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "cd-1_abTUSNS"
      },
      "outputs": [],
      "source": [
        "num_classes = 10 # загальна кількість класів, у нашому випадку це цифри від 0 до 9\n",
        "num_features = 784 # кількість атрибутів вхідного вектора 28 * 28 = 784\n",
        "\n",
        "learning_rate = 0.001 # швидкість навчання нейронної мережі\n",
        "training_steps = 3000 # максимальне число кроків навчання для однієї епохи\n",
        "batch_size = 256 # перераховувати ваги мережі ми будемо не на всій вибірці, а на її випадковій підмножині з batch_size елементів\n",
        "display_step = 100 # кожні 100 ітерацій ми будемо показувати поточне значення функції втрат і точності\n",
        "\n",
        "n_hidden_1 = 128 # кількість нейронів 1-го шару\n",
        "n_hidden_2 = 256 # кількість нейронів 2-го шару\n",
        "\n",
        "class Activations(Enum):\n",
        "  softmax = auto()\n",
        "  sigmoid = auto()\n",
        "\n",
        "activation_funcs = {\n",
        "      Activations.softmax: tf.nn.softmax,\n",
        "      Activations.sigmoid: tf.nn.sigmoid,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "pGTXiRyTUSNT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "105c9dc9-98f0-4a44-e173-0411db6d9781"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# from tensorflow.keras.datasets import mnist\n",
        "from keras.datasets import mnist\n",
        "\n",
        "# Завантажуємо датасет\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "#print(y_train.dtype)\n",
        "\n",
        "# Перетворюємо цілочисельні пікселі на тип float32\n",
        "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n",
        "\n",
        "# Перетворюємо матриці розміром 28x28 пікселів у вектор з 784 елементів\n",
        "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n",
        "\n",
        "# Нормалізуємо значення пікселів\n",
        "x_train, x_test = x_train / 255., x_test / 255.\n",
        "\n",
        "# Перемішаємо тренувальні дані\n",
        "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
        "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FkRmCQjnUSNV"
      },
      "outputs": [],
      "source": [
        "# Створимо нейронну мережу\n",
        "\n",
        "class DenseLayer(tf.Module):\n",
        "    def __init__(self, in_features, out_features, name=None):\n",
        "        super().__init__(name=name)\n",
        "        self.w = tf.Variable(\n",
        "            tf.random.normal([in_features, out_features]), name=\"w\"\n",
        "        )\n",
        "        self.b = tf.Variable(tf.zeros([out_features]), name=\"b\")\n",
        "\n",
        "    def __call__(self, x, activation=Activations.sigmoid):\n",
        "        y = tf.matmul(x, self.w) + self.b\n",
        "        fun = activation_funcs.get(activation)\n",
        "        if fun is not None:\n",
        "          return fun(y)\n",
        "        return y\n",
        "\n",
        "class NN(tf.Module):\n",
        "  def __init__(self, name=None):\n",
        "    super().__init__(name=name)\n",
        "    # Перший шар, який складається з 128 нейронів\n",
        "    self.layer_1 = DenseLayer(in_features=num_features, out_features=n_hidden_1, name=\"DL1\")\n",
        "    # Другий шар, який складається з 256 нейронів\n",
        "    self.layer_2 = DenseLayer(in_features=n_hidden_1, out_features=n_hidden_2, name=\"DL2\")\n",
        "    # Вихідний шар\n",
        "    self.layer_3 = DenseLayer(in_features=n_hidden_2, out_features=num_classes, name=\"EL\")\n",
        "\n",
        "\n",
        "\n",
        "  def __call__(self, x):\n",
        "    # Передача даних через перші два шари та вихідний шар з функцією активації softmax\n",
        "    x = self.layer_1(x)\n",
        "    x = self.layer_2(x)\n",
        "    return self.layer_3(x, activation=Activations.softmax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "LIf3o7VAUSNV"
      },
      "outputs": [],
      "source": [
        "# В якості функції помилки (loss) в даному випадку зручно взяти крос-ентропію\n",
        "def cross_entropy(y_pred, y_true):\n",
        "    # Закодувати label в one hot vector\n",
        "    y_true_h = tf.one_hot(tf.cast(y_true, tf.int32), depth=num_classes, dtype=tf.float32)\n",
        "    # print(f\"cross_entropy {y_true=}\")\n",
        "\n",
        "    # Значення передбачення, щоб уникнути помилки log(0).\n",
        "    y_pred = tf.cast(y_pred, tf.float32)\n",
        "    y_pred = tf.clip_by_value(y_pred, 1e-9, 1.0)\n",
        "    #print(f\"{y_true_h.shape=}\")\n",
        "    #print(f\"{y_pred.shape=}\")\n",
        "    # Обчислення крос-ентропії\n",
        "    return tf.reduce_mean(-tf.reduce_sum(y_true_h * tf.math.log(y_pred)))\n",
        "\n",
        "# Як метрику якості використовуємо точність\n",
        "def accuracy(y_pred, y_true):\n",
        "    # Місце для вашого коду\n",
        "    # Convert predicted probabilities to predicted labels\n",
        "    #print(f\"{y_pred=}\")\n",
        "    y_pred_labels = tf.argmax(y_pred, axis=1)\n",
        "\n",
        "    # Compare predicted labels with true labels\n",
        "    y_true = tf.cast(y_true, tf.float32)\n",
        "    y_pred_labels = tf.cast(y_pred_labels, tf.float32)\n",
        "    correct_predictions = tf.equal(y_pred_labels, y_true)\n",
        "\n",
        "    # Compute accuracy\n",
        "    accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))\n",
        "\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "MQeT1yatUSNW"
      },
      "outputs": [],
      "source": [
        "# Функція навчання нейромережі\n",
        "def train(model, input_x, output_y, learning_rate=learning_rate):\n",
        "  # Для налаштування вагів мережі будемо використовувати стохастичний градієнтний спуск\n",
        "  optimizer = tf.optimizers.Adam(learning_rate)\n",
        "\n",
        "  # Активація автоматичного диференціювання\n",
        "  with tf.GradientTape() as g:\n",
        "    pred = model(input_x)\n",
        "    loss = cross_entropy(pred, output_y)\n",
        "\n",
        "  # Отримаємо градієнти відносно ваг\n",
        "  gradients = g.gradient(loss, model.trainable_variables)\n",
        "  # Оновлення ваг за допомогою градієнтного спуску\n",
        "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "  # # Модифікуємо параметри\n",
        "  # model.trainable_variables[0].assign_sub(learning_rate * gradients[0])\n",
        "  # model.trainable_variables[1].assign_sub(learning_rate * gradients[1])\n",
        "\n",
        "  # Або одна команда:\n",
        "  #optimizer.minimize(loss, model.trainable_variables)\n",
        "  return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Тренування"
      ],
      "metadata": {
        "id": "cyzqv0Bw02Gr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "fnyns9lBfpQZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d4c9259-693f-4343-bc5b-f09e44cc22dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step  100, Loss:  254.61, Accuracy: 75.39%\n",
            "Step  200, Loss:  142.41, Accuracy: 86.72%\n",
            "Step  300, Loss:   96.56, Accuracy: 89.45%\n",
            "Step  400, Loss:   56.63, Accuracy: 92.58%\n",
            "Step  500, Loss:   78.03, Accuracy: 90.62%\n",
            "Step  600, Loss:   62.17, Accuracy: 91.80%\n",
            "Step  700, Loss:   31.74, Accuracy: 97.66%\n",
            "Step  800, Loss:   87.36, Accuracy: 95.70%\n",
            "Step  900, Loss:   39.37, Accuracy: 95.70%\n",
            "Step 1000, Loss:   56.55, Accuracy: 93.75%\n",
            "Step 1100, Loss:   39.49, Accuracy: 95.31%\n",
            "Step 1200, Loss:   28.93, Accuracy: 97.27%\n",
            "Step 1300, Loss:   27.03, Accuracy: 97.66%\n",
            "Step 1400, Loss:   30.80, Accuracy: 96.48%\n",
            "Step 1500, Loss:   32.72, Accuracy: 96.88%\n",
            "Step 1600, Loss:   14.11, Accuracy: 98.83%\n",
            "Step 1700, Loss:   15.98, Accuracy: 98.44%\n",
            "Step 1800, Loss:   12.21, Accuracy: 98.83%\n",
            "Step 1900, Loss:   41.47, Accuracy: 96.88%\n",
            "Step 2000, Loss:    6.96, Accuracy: 99.22%\n",
            "Break by accuracy\n"
          ]
        }
      ],
      "source": [
        "# Створимо екземпляр нейронної мережі\n",
        "neural_net = NN(name=\"mnist\")\n",
        "\n",
        "# Тренування мережі\n",
        "loss_history = []  # кожні display_step кроків зберігай в цьому списку поточну помилку нейромережі\n",
        "accuracy_history = [] # кожні display_step кроків зберігай в цьому списку поточну точність нейромережі\n",
        "\n",
        "# У цьому циклі ми будемо проводити навчання нейронної мережі\n",
        "# із тренувального датасета train_data вилучи випадкову підмножину, на якій\n",
        "# відбудеться тренування. Використовуй метод take, доступний для тренувального датасета.\n",
        "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n",
        "    # Оновлюємо ваги нейронної мережі\n",
        "    # print(batch_x)\n",
        "    # print(f\"{batch_y.dtype=}\")\n",
        "    train(neural_net, batch_x, batch_y, learning_rate=0.002)\n",
        "    if step % display_step == 0:\n",
        "        pred = neural_net(batch_x)\n",
        "        # print(batch_y.shape)\n",
        "        # print(pred.shape)\n",
        "        current_loss = cross_entropy(pred, batch_y)\n",
        "        acc = accuracy(pred, batch_y)\n",
        "        accuracy_history.append(acc)\n",
        "        loss_history.append(current_loss)\n",
        "        print(f\"Step {step:4}, Loss: {current_loss:7.2f}, Accuracy: {acc:.2%}\")\n",
        "        if acc > 0.99:\n",
        "          print(\"Break by accuracy\")\n",
        "          break"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Візуалізація моделі"
      ],
      "metadata": {
        "id": "zG-yyZp0z-5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import plot_model\n",
        "import tensorflow as tf\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.Input(shape=x_train.shape,  name=\"INPUT\"),\n",
        "    #tf.keras.layers.Dense(n_hidden_1, activation='relu', input_shape=x_train.shape, name=\"Hidden_Layer_1\"), #Input\n",
        "    tf.keras.layers.Dense(n_hidden_1, activation='relu',  name=\"Hidden_Layer_1\"), # Hidden_1\n",
        "    tf.keras.layers.Dense(n_hidden_2, activation='relu',  name=\"Hidden_Layer_2\"), # Hidden_2\n",
        "    tf.keras.layers.Dense(num_classes, activation='softmax', name=\"OUTPUT\") # Output\n",
        "])\n",
        "\n",
        "\n",
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "h2mGtvqLE0fs",
        "outputId": "816f9a2d-6a6a-4067-d74e-674b64d6e086"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Графіки"
      ],
      "metadata": {
        "id": "frnZCflrzUVV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "_yCBfG6MbQB2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "outputId": "00ea52e1-1662-40ef-bc07-b9d8b4c6c351"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Виведіть графіки залежності зміни точності і втрат від кроку\n",
        "# Якщо все зроблено правильно, то точність повинна зростати, а втрати зменшуватись\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(12,4))\n",
        "# Виведіть графік функції втрат\n",
        "\n",
        "plt.plot(loss_history, \"r\")\n",
        "plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: int((x + 1) * display_step)))\n",
        "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{int(y):5}'))\n",
        "plt.xlabel(\"Кроки\")\n",
        "plt.title(\"Втрати\")\n",
        "plt.grid()\n",
        "#plt.show()\n",
        "# Виведіть графік точності\n",
        "# Місце для вашого коду\n",
        "fig = plt.figure(figsize=(12,4))\n",
        "plt.plot(accuracy_history)\n",
        "plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: int((x + 1) * display_step)))\n",
        "plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0%}'))\n",
        "plt.title(\"Точність\")\n",
        "plt.xlabel(\"Кроки\")\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax1 = plt.subplots()\n",
        "fig.set_size_inches((12,6))\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Step')\n",
        "ax1.set_ylabel('Loss', color=color)\n",
        "ax1.plot(loss_history, color=color, label=\"Loss\")\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "ax1.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: int((x + 1) * display_step)))\n",
        "ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{int(y):4}'))\n",
        "ax1.set_xticks(np.arange(0, len(loss_history)+1, step=2))  # Set label locations.\n",
        "#ax1.legend()\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Accuracy', color=color)  # we already handled the x-label with ax1\n",
        "p2=ax2.plot(accuracy_history, color=color, label=\"Accuracy\")\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0%}'))\n",
        "#ax2.legend()\n",
        "#ax1.grid()\n",
        "ax2.grid()\n",
        "ax1.xaxis.grid(True)\n",
        "\n",
        "# Combine the legends from ax1 and ax2\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax2.legend(lines + lines2, labels + labels2, loc='best')\n",
        "\n",
        "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
        "plt.title(\"NN Loss / Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "q8JvNdi1WeJu",
        "outputId": "d1abd4e8-d1a9-4e13-e36a-0dd99a293669"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Точність"
      ],
      "metadata": {
        "id": "33weNqXqzdT-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "LE3g4gDyUSNY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b91302e-b88d-4ff9-c62b-ec1df4b02970"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc_mean=94.257814%\n",
            "test_acc=93.980002%\n"
          ]
        }
      ],
      "source": [
        "# Обчисліть точність навченої нейромережі\n",
        "acc_mean = np.mean(accuracy_history)\n",
        "print(f\"{acc_mean=:%}\")\n",
        "# Тестування моделі на тестових даних\n",
        "test_pred = neural_net(x_test)\n",
        "test_acc = accuracy(test_pred, y_test).numpy()\n",
        "test_pred_values = np.argmax(test_pred, axis=1)\n",
        "print(f\"{test_acc=:%}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "_EEHAubOUSNY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d2119b5-2da6-4514-8867-190be558b4da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "testing_select=10, test_acc_10=90.00%\n",
            "sub_tests=100 by 5: mean_acc=95.40%\n",
            "selected imnage idx: [3444  809 8196 9519 4681]\n",
            "test_values: [4 1 6 0 9]\n",
            "pred_values: [4 1 0 0 9]\n",
            "accuraccy : 80.00%\n"
          ]
        }
      ],
      "source": [
        "# Протестуйте навчену нейромережу на 10 зображеннях. З тестової вибірки візьміть 5\n",
        "# випадкових зображень і передайте їх у нейронну мережу.\n",
        "# Виведіть зображення та випишіть  поруч відповідь нейромережі.\n",
        "# Зробіть висновок про те, чи помиляється твоя нейронна мережа, і якщо так, то як часто?\n",
        "\n",
        "# Тестування моделі на тестових даних\n",
        "testing_select = 10\n",
        "test_pred_10 = neural_net(x_test[:testing_select])\n",
        "test_acc_10 = accuracy(test_pred_10, y_test[:testing_select]).numpy()\n",
        "print(f\"{testing_select=}, {test_acc_10=:.2%}\")\n",
        "\n",
        "\n",
        "img_selected = 5\n",
        "sub_tests = 100\n",
        "acc_list = []\n",
        "for i in range(sub_tests):\n",
        "  random_img_idx = np.random.randint(0, x_test.shape[0], size=img_selected)\n",
        "  pred = neural_net(x_test[random_img_idx])\n",
        "  acc = accuracy(pred, y_test[random_img_idx]).numpy()\n",
        "  acc_list.append(acc)\n",
        "\n",
        "mean_acc = np.mean(acc_list)\n",
        "print(f\"{sub_tests=} by {img_selected}: {mean_acc=:.2%}\")\n",
        "print(f\"selected imnage idx: {random_img_idx}\")\n",
        "print(\"test_values:\", y_test[random_img_idx])\n",
        "\n",
        "pred_values = np.argmax(pred, axis=1)\n",
        "print(\"pred_values:\", pred_values)\n",
        "print(f\"accuraccy : {acc:.2%}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = [ f\"Number: {i}\" for i in range(0, num_classes)]\n",
        "print(classification_report(y_test, test_pred_values, target_names=target_names))\n",
        "cr = classification_report(y_test, test_pred_values, target_names=target_names, output_dict=True)\n",
        "print(cr[\"accuracy\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exap9VoKrPKj",
        "outputId": "ca0b3f40-10af-4ceb-c45f-be64b444dba8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "   Number: 0       0.96      0.98      0.97       980\n",
            "   Number: 1       0.98      0.98      0.98      1135\n",
            "   Number: 2       0.94      0.93      0.94      1032\n",
            "   Number: 3       0.92      0.92      0.92      1010\n",
            "   Number: 4       0.94      0.94      0.94       982\n",
            "   Number: 5       0.93      0.92      0.93       892\n",
            "   Number: 6       0.95      0.95      0.95       958\n",
            "   Number: 7       0.94      0.94      0.94      1028\n",
            "   Number: 8       0.92      0.91      0.92       974\n",
            "   Number: 9       0.91      0.92      0.92      1009\n",
            "\n",
            "    accuracy                           0.94     10000\n",
            "   macro avg       0.94      0.94      0.94     10000\n",
            "weighted avg       0.94      0.94      0.94     10000\n",
            "\n",
            "0.9398\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, test_pred_values, labels=range(10))\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3mV0DPIcFSB",
        "outputId": "c00991fb-3fdd-49fd-e4ee-434cb58ab466"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 957,    0,    1,    1,    1,    3,    9,    4,    3,    1],\n",
              "       [   0, 1107,    9,    1,    0,    3,    4,    2,    9,    0],\n",
              "       [   9,    1,  960,   16,    8,    2,    7,   11,   15,    3],\n",
              "       [   0,    2,   17,  933,    2,   18,    0,    9,   21,    8],\n",
              "       [   1,    3,    1,    3,  922,    3,   10,    3,    5,   31],\n",
              "       [   5,    0,    3,   29,    6,  820,   11,    3,    9,    6],\n",
              "       [  11,    2,    3,    1,    9,    8,  910,    2,    9,    3],\n",
              "       [   1,    6,   13,    7,    1,    1,    0,  968,    3,   28],\n",
              "       [   5,    3,   13,   18,    8,   14,    6,    7,  891,    9],\n",
              "       [   6,    6,    1,    9,   26,    7,    1,   18,    5,  930]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, test_pred_values  )\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "h5cLNCYHdQAz",
        "outputId": "1dc4bf4c-d336-4cea-be1d-3e45ef2b5972"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Зображення"
      ],
      "metadata": {
        "id": "fLRtar1Czh8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the images\n",
        "colors = [(1, 1, 1), (1, 0, 0), (1, 0, 0)]  # white, red, red\n",
        "custom_cmap = plt.cm.colors.LinearSegmentedColormap.from_list(\"CustomRed\", colors, N=256)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "img_size = int(x_test.shape[1]**(1/2))\n",
        "for i, idx in enumerate(random_img_idx, start=1):\n",
        "    plt.subplot(1, img_selected, i)\n",
        "    correct = y_test[idx] == pred_values[i-1]\n",
        "    cmap = 'gray_r' if correct else custom_cmap\n",
        "    color = \"black\" if correct else \"red\"\n",
        "    plt.imshow(x_test[idx].reshape(img_size, img_size), cmap=cmap)\n",
        "    plt.title(f'REAL: {y_test[idx]} / PR: {pred_values[i-1]}', color=color)\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.suptitle(f\"Accuracy: {acc:.2%}\")\n",
        "plt.subplots_adjust(top=2.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "eOxPSpUrgkQo",
        "outputId": "e78dabdc-a07e-4c4c-8402-2a9146717f0c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 5 Axes>"
            ],
            "image/png": 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z52L27Nll8rthmSUbbdq0sf5S07dvX0RERODpp5/Gzz//7FRDd+3ahfPnz2Pt2rW6E3HMZrNhnWz+/Pm4ffs2hgwZYh1zmJ6eDsAyjj01NRUhISHF/nK1bds2jB492uFjNmnSxO4/PMDyuLiCct27d4e/vz8mTJiADh06oH///g4fr8D06dNRt25dREVFWc/1woULAIDLly8jNTUV9evXd7oT30mVqQ+WhW3btqFr164Ozzcq3LeK4+/vby3XpUsXNG/eHD179sSiRYswefLkUrXZpbVp8+e4/759LUOenn4a+Plnyy/pJbVrl2WIz9q1lpcts9m4ZCMkxPJfvS/JwEAgJ8cyv6SoR/UClvkaJbgGokkT+wkYYBlGVlCue3fL5PAJEyxJmRPXQOvwqYLhVIWdP//nZ1HBVabrX8GQovM6P5Pz58/jnnvuKfKX2AKu8h1s71xDXKT/AZWrDwJA3bp18fXXX+PkyZO4cOECmjRpgjp16iAkJARNmza1u7+r9EEACAsLw/Hjx3HixAlcuXIFLVu2hLe3NyZNmoTIyEin6izMkN8g3dzc8P777+PcuXNYvHixU3WYzWYEBgZiw4YNymvYsGGIi4vDzZs3y7jlFmlpabhy5QrCwsLQsGFDNGzY0PpUhZiYGDRs2BAnTpwocv/jx48jLS3N4XF6pfH888+jcePGePvtt2G5w1YyaWlpOHXqFBo1amQ912HDhgEAXnzxRTRs2BDXrl0r62YbztX7YGldvXoV+/fvvyN9sEePHoiMjERMTEyJFzuqtNzcLOtonDtnmajsDLPZ8ov9hg3qa9gwyyRvo/pfSIhlYTu9sbrnzlnWDalevej9jx8H0tIcn69RGs8/DzRuDLz9tmVgVUndf7/lDs2332rjt29b1t8ovMaHi3D161/dunUREBCAb21/JrA8uCXczs/Elb6D77//fri7uyvnevv2bRw9etTuuVZUrt4HC2vSpAnat2+POnXq4MSJEzh//rzdpMCV+mABk8mEsLAwRERE4J577kFCQgLy8/MdSoDsKu04rKKerywi0qZNGwkKCpKbN29aY448XzkrK0uqV68uY8aM0X3/m2++EQCydu1aawwOjNW7evWqJCUlydWrV4st991330lcXJzm9dFHHwkAiY6Olri4uGLrmDNnjgQFBUl+fn6xxxEp2VyQos5x6dKlAkDi4uKsscuXL0tSUpLcuHGj2Dr37dunnOt7770nAOS1116TuLg4uX37tt22lafK2AdtlXQew7p168Td3d3h4zjSdhH9dTZERLZt2yYA5G9/+5s1dqfOtdwVtc6GiEibNpb5FYX6n0PrbGRliVSvLlJE/5NvvrEcs1D/c2jOxtWrlnkhjvxMJk601Pnvf/8Zu3zZMpeie/fi950zx3LeDlwDi1xnQ09R57h0qeW9QtdAuXzZcq52roEiItK1q2WdjWvX/owtX26pc/t2+/uXo8p6/XvhhRc08yZFRP7zn/8IAFm2bFmx+7rSd7CISNeuXSU4OFiuFep/y5cvFwCyvYL3P5HK2wdt5eXlSY8ePcTHx0ez/oseV+uDtrKysqR169ZKv3RWmQ2j0vPqq69i0KBBWLVqFV544QVrPDMzE5999pnuPsOHD8fWrVvxxx9/oHfv3rpl2rVrh4CAAJjNZgwZMsQa//bbbzFr1iylfFRUFCIiIhAXF4fRo0dj5cqV1ln+elq3bo3WrVtrYgVDjMLCwtC3b98i9wVgXXHZmcflOiM6OhrTp0/HnDlzrG1bvHgxZs6ciYSEBERFRRW5r96zn/38/AAAjz76qN1zrehctQ8CwNmzZ/GPf/zDWi8Aa92hoaEYMWJEkfvGx8cjIiKiyNVYy1q3bt1w//33Y+HChRg/fjw8PDzu2LlWaK++apmHsGoVUKj/ITPTMu9Bz/DhwNatwB9/AEX0P7RrZ3kylNkMFOp/+PZbQKf/ISrKMqwrLs4ytGnlyj+f9lSUN96wPOZ2wABg8mTLkKkPP7QMoYqJKX7f+HigWzfgDl0DER0NTJ8OzJljGcIGWO4ozZwJJCRYzr84s2cDjz8OREZa5o6kp1vmxXTuDHTtanDjjePK178333wTGzZsQIcOHTBx4kRcv34d8+bNwwMPPGB3aIorfQcDwOzZs/H4448jMjISY8eORXp6OhYsWIDOnTujqwv3P8C1++DEiRORnZ2N8PBw5OTkYM2aNdYlEQqv/6LH1frg4MGDERISgpYtW+LatWtYsWIFTp8+jfj4eFQv7i62o0qbrRSX0ebl5Unjxo2lcePG1qcGREZG6s6mL3iJiPTq1Uu8vLyKzcaio6PFw8NDMjIyRER/hn7B67333tO01d5qynoczTyvXr0q7u7usn79+jKtV6T4rH3GjBkC/LlK5TvvvKPZLglXfRpVZeuDBT8HvVdkZGSR++Xn50tgYKDMnTvX7jEKFNe3CivqzoaIyKpVqzTndifOtUIo7s5GXp5I48aWV8GTUyIj9Z+qVPASEenVS8TLq/i/ykdHW57E9N/+V2yd/+1/1rY6eg1MSRHp189yN8PbW6RjRxF7Tze7elXE3V3EwWtgmdzZELGsrI5Cq5W/84791csL27dP5PHHLZ97QIDlOGXwFz2jVdbrn4jI8ePHpXPnzuLj4yN+fn7yzDPP2H2yj6t+B+/bt08ef/xx8fLykoCAABk/fnyZ/EX5TqisfXDlypXSqlUrqVatmlSvXl2efPJJ2bVrl939XLEPzpkzR5o3by5eXl5Sq1Yt6d27txw5csSh9jvCJFKKAV6kWL9+PZ555hlkZGTcsb8qExWWmJiItm3b4qeffkLLli3Luzl0t1m/HnjmGctaGLwG0h3G72Aqb+yDqor/iCEX4+fnhw8++IAdjMpVTEwMEw0qH35+wAcfMNGgcsHvYCpv7IMq3tkgIiIiIiJD8M4GEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZgskGEREREREZwr28G0BERHeJ69fV2Pr1auzZZ+3XdeaMGmvQoMRNIiIiY/HOBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGcIkIlLejSAiIheSkaHGduxQYyNGGN+Wwvh15tKOHj2qxDp37qzEMnT63969e5VYREREmbSLiEqHdzaIiIiIiMgQTDaIiIiIiMgQTDaIiIiIiMgQTDaIiIiIiMgQXEGcqAJLS0tTYtHR0Urss88+U2IhISFGNIkqs+xsNaa3wveoUca3xVYVm7+NnT9/59tAZSo3N1ezvXr1aqXMlStXlJibm5sSGzRokBL75z//qdnet2+fUmbr1q122wkAu3btcqgcEal4Z4OIiIiIiAzBZIOIiIiIiAzBZIOIiIiIiAzBZIOIiIiIiAxRKSeInzx5UokdOXJEiQ0ePPhONOeOSUhIUGKffPKJEjtw4IAS2759u2a7efPmZdcwctry5cuV2OHDh5XY//t//0+JcYI42WUzgRa9et35NjzwgBqzbRcA1K9vfFvojlq8eLFmOzY21um6Ll68qMRmz56t2f7yyy+drn/Lli2a7b59+zpdF5Udve++559/XomlpqYqsaSkJLv116lTR4kdPHhQiYWGhtqt627GOxtERERERGQIJhtERERERGQIJhtERERERGSISjlno3fv3krs7NmzSuz27dua7eHDhxvWptK6dOmSZnvPnj1KmfHjxyuxjIwMJXb//fcrsdq1a5eidWSUmzdvKrHr168rsaVLlyqxtm3bGtImclF6i5f16WPsMdu3127v3Wvs8ajC2rhxoxJ7++23DT3msGHDNNsNGjRQyjg6T2TkyJF29xtVHotdVmL5+fma7X/84x9KmcmTJysxvYUg9ZhMJrtl9OYCDR06VIm99NJLSuzpp592qB13A97ZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQ7j8BPH169crsZSUFCWWm5urxPQm/lRUq1ev1my/9tprTtdVrVo1JRYQEOB0fURUjrKz1diKFWpM5wESZWrqVDX2178ae0xyGR988IESu3Xrlt399BaYffXVV5WYiCixJ554QrPdpEkTpcyaNWuU2G+//abEbB/M8e677yplOEG8bNk+xGf06NFlWr+Hh4dmu1WrVkoZvQWhDx06pMS6du1adg2rhHhng4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDOFSE8R//PFHJaa36rfeZHBXsmPHDiX25ptvOlXXk08+qcS6dOniVF1Ucen9O6BKSG8yeEyMGnvvvbI7Zni4GtOZNEl3pwsXLiixwYMHK7F9+/YpMdsVnMN1+trOnTuVWO3atUvQwj8FBQUpsREjRiixL774QomdPHlSs207YRwAjh49qsT0zokcs3Tp0jKrS2/y9/bt2zXbeg/K6devnxL75z//qcSSk5Odapfevx8fHx8lVqNGDafqryh4Z4OIiIiIiAzBZIOIiIiIiAzBZIOIiIiIiAzBZIOIiIiIiAzhUhPE9VYIdfXJ4ImJiUpMbxVSR87TdrIdAKSnpyux3r17O9g6IipXNivoolo1tUx+vvP1266o/MsvztdFdyW9ibEHDhxQYnrfT25ubprtvn37KmWcnQzuqHnz5ikxvbbOnz9fs/37778rZVavXq3E7r//fiXm7u5Sv3q5HL3fcZYtW6bE6tSpo9m+cuWKUkZvMriesLAwu2VSU1OVWKdOnZSY3kr3a9eu1Wy72oRx3tkgIiIiIiJDMNkgIiIiIiJDMNkgIiIiIiJDMNkgIiIiIiJDuNQspY0bNzq9b2BgoBKLjIwsTXPKxPLly5XY5cuXnapLbyXUVatWOVUXVVze3t5KLDg4uBxaQoaLiNBul2Yy+MyZamz6dOfrI4L+atuO8vPz02xXhO9kAJg7d64Ss50gric2NlaJTZw4UYmFhoY617C7zMqVK+2W0ZtsrzcZ3JHvSC8vLyX20EMPKbFfdB6k4eHhYbd+vd/RUlJSHIqlpaVptvUePFCR8c4GEREREREZgskGEREREREZgskGEREREREZokLP2Xj55Zc12x9++KFD++mNrRw7dqwS8/X1da5hTtJbPMiRMYl6unfvrsSeffZZp+qiiuv06dPl3QS6U3bsUGOHDztX16FDaqxNG+fqortWdna2ZvuNN95QyuiNj9dju4AaAHz22Wea7YoyZ0NvoUK689q1a6fZ/umnn5Qy7du3V2LOLgSpNx/yu+++c6ouPdevXy+zulwN72wQEREREZEhmGwQEREREZEhmGwQEREREZEhmGwQEREREZEhKvQEcdsFckwmk0P7jR8/Xol5enqWSZuKkpWVpcROnTql2f7kk0+UMnl5eQ7VP3jwYM32ihUrlDI+Pj4O1UWu49y5c0qsZs2aSszVFvghHd26Obffc8+psYo6Gfzvf1djkyaVXf16n8UHH6gxncW7SHXhwgXN9uLFi52uq2vXrkqsY8eOTtdnJL1F/ejOa9Gihd0yCQkJSmz//v1KLCoqqiyaVCI//vijZltvsT5H2f4+6Wrf+byzQUREREREhmCyQUREREREhmCyQUREREREhmCyQUREREREhqjQE8Qd0atXLyXm7l52p6W3wrfeqs4nT55UYuvXry+zdoSGhmq2ORn87pCamlreTaCK7uOPy7sFjivLyeB6dB7CoRvbtEmN9e9f9u2pZBx9oIkeESnDlpSd3bt3K7FVq1YpMdv2630WM2bMUGK2391kvNmzZyux8pggfvnyZc12aVYQ37p1q2a7T58+ShlHH6JUHnhng4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDOHyE8RDQkKUWHx8vFN1TZw4UYnZrqAKALdu3XKqfj2BgYFKTG9Sevv27cvsmOTaWrZsWd5NIHJMRZ2wOGCAGqugE5jL05w5czTbbm5uTtf1+uuvl7Y5hsjIyFBieudpOyG8bt26SplRo0aVXcNI9/cjR+zZs0eJ6T2wZ/DgwU7VrycnJ0eJ2f77KQ3bhxZ89NFHShkPD48yO15Z450NIiIiIiIyBJMNIiIiIiIyBJMNIiIiIiIyBJMNIiIiIiIyhMtPENebJKMXq6geeughJdatW7dyaAm5iocffri8m0AVyY4daqxr1zvfjqNHy66uI0fUWHi4dvvUKbVMkyZl1wbCzz//7NR+vXv3VmLNmjUrbXMM8dprrzm1n97kYq4WXraefvppzfbmzZuVMlu2bFFiubm5SuzZZ59VYl9++aVm+5VXXlHKtGrVyl4zAej/3vmf//zHoX3vBryzQUREREREhmCyQUREREREhmCyQUREREREhqjQczZ69uyp2XZ2sb6KrHv37uXdBKrARGehsZ07d5ZDS6jC0pvjVR4L1Dk718xsVmO28zP03HefGtMbM3/2bImbRKXTp0+f8m4CAOCozTyiTz/9VClz7tw5h+qaNm2aZvuNN95wul3kmCpVtH8P79evn1Jm//79Suy3335TYjdu3FBiZptrz8aNG5UyXl5edtsJANnZ2Q6Vc9ajjz6q2bb9bCo612otERERERG5DCYbRERERERkCCYbRERERERkCCYbRERERERkiAo9Qfzzzz/XbL/wwgtKmTVr1jhdv+0iQ6dPn1bKNG3aVIn99NNPDtUfGBio2U5MTFTKhISEOFQX3Z1MJpMS69SpUzm0hFyKTr8xfNL4hQv2y+zbp8YiIsq+LXTX+frrr5XY0KFDNdsXL150uv6ZM2c6vS+VjREjRjgUmz59uhKbNWuW3fpv3brlUKws6f0OqPfQgt9//93QdhiNdzaIiIiIiMgQTDaIiIiIiMgQTDaIiIiIiMgQTDaIiIiIiMgQFXqCuK+vr2Z78eLFSpmbN28qsYSEBCUWFBSkxL744gvNdlJSklJm69atSszRCeLVqlXTbNevX9+h/YjoLvTrr2rs6ae123v2OF+/3qRxW/37q7FNmxyrf/RoNfb229rtRo0cq8sRUVFqjKuFlymxeahAXl6eQ/tFR0crsZEjRzrVhgs6Dx4YPHiwEtun8/ABvQds2KpTp44SW79+vYOto4ronXfeUWI1a9ZUYvHx8Zrt3bt3O31MvZXGu3Xrptm2XYUeAPr06eNQ/Q899JBmmyuIExERERERgckGEREREREZhMkGEREREREZgskGEREREREZwiS2M8BIo0uXLkps586dSmz8+PFKbNy4cZrtli1bll3D6K4QHBysxPRWTJ07d+6daA7dafn52m29Cdw2D7pwOaGhaszoid7nz6sxnYnCd7uOHTtqtvUmYTvq9ddfV2K2k2Nfe+01pczVq1eV2LFjx5SY3uR1Nzc3zbafn59SZt26dUrM9rypcsrNzdVs661Cf+jQIYfq8vHxUWIvvPCCZtvDw0Mp8+qrryqxBQsWKDHbff/9738rZSIjI+22s7zwzgYRERERERmCyQYRERERERmCyQYRERERERmiQi/qd6ctWrRIie3fv9+hffUWawkMDCx1m+juoTcGU29BK7qL2C7ctGWLWubaNTWmt4BaRZ3bwfkZFZbtnIrSzNmYPXu2EvvrX//qdH2OsJ2jsXHjRqVMRR7nTsZyd9f+Chyls1CoXqwsherNWdORk5Oj2dZbcLoi92Xe2SAiIiIiIkMw2SAiIiIiIkMw2SAiIiIiIkMw2SAiIiIiIkPc1Yv6rVmzRrM9ZswYpczt27eVmN7CLOnp6UosICCgFK2ju43eBHG9RSX1Jlq++eabhrSJKhHbBQIBIDvb/n4HD6qxJ58sfXtKKilJu92okVqmatU705a7hO0DKv7yl78oZdLS0hyqy5FF9xzVvHlzJab3ffv2229rtrlYH1U0er871q9f3+5+DRo0UGKnT58uiyYZgnc2iIiIiIjIEEw2iIiIiIjIEEw2iIiIiIjIEEw2iIiIiIjIEHf1CuJHjhzRbOtNBq9Ro4YSW79+vRLjZHAqrfj4eCUWHh6uxCZNmnQHWkOVju1q5ADg42N/P71JtXfvc0XuKnVsVlrftGmTUkbv+3DevHll1ga9yeBbtmxRYo0bNy6zYxLdKTVr1lRiDzzwgBI7duzYnWiOYXhng4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDMFkg4iIiIiIDHHXTBBPTU1VYqtXr7a739///ncl1rlz5zJoEZHWlStXlNjw4cOVmLe3951oDhGRht4DK/RiMTExxjeGqBKoXr26EtN72AEniBMREREREelgskFERERERIZgskFERERERIZgskFERERERIa4ayaI+/n5KbHWrVtrtn///XelTKdOnYxqEpGGIw8sICIiosrrwQcfVGIpKSma7f79+9+p5pQJ3tkgIiIiIiJDMNkgIiIiIiJDMNkgIiIiIiJDmEREyrsRRERERERU+fDOBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhERERERGYLJBhHdFZYuXQqTyYS2bduWd1MqhfXr16Ndu3bw8/ND7dq1ERkZifj4eKVcfn4+5s6di4YNG8LLywsPPvggPv/8c4ePc/XqVYwdOxYBAQGoVq0aOnTogO+//1637NatW9G6dWt4eXmhfv36eOedd5Cbm6spc+LECbRv3x7Vq1fHI488ggMHDij1LFy4EGFhYcq+RERUckw2iOiuYDab0aBBAyQmJuLUqVPl3RyXFhsbiyFDhsDf3x9//etfMW3aNGRmZqJnz57YvHmzpuxbb72FqVOnolOnToiNjUX9+vXx9NNPY+3atXaPk5+fjx49emDNmjWYMGEC5s6di0uXLiEqKgonT57UlN2+fTv69u0LPz8/xMbGom/fvpg1axZeeukla5m8vDz0798feXl5mDdvHgIDA9GnTx9cu3bNWubSpUt499138be//Q3u7u6l/KSIiAhCRFTJnT59WgDI5s2bJSAgQGbMmFHeTSrS9evXy7sJdjVp0kQeffRRyc/Pt8YyMzPF19dXevfubY2lp6eLh4eHjB8/3hrLz8+X9u3bS7169SQ3N7fY46xbt04AyIYNG6yxS5cuiZ+fnwwbNkxTtmXLltKqVSvJycmxxt566y0xmUySlJQkIiJJSUkCQM6ePSsiIjdu3BBvb2/ZsWOHdZ9nn31WevXqVZKPg4iIisE7G0RU6ZnNZtSqVQs9evTAwIEDYTabdctdvXoVkyZNQoMGDeDp6Yl69eph5MiRyMjIsJbJzs7GjBkz0LRpU3h5eSE4OBj9+/dHSkoKAGD37t0wmUzYvXu3pu7U1FSYTCasWrXKGouOjoavry9SUlLQvXt3VK9eHc888wwAYN++fRg0aBDq168PT09P3HvvvZg0aRJu3ryptDs5ORmDBw9GQEAAvL290axZM7z11lsAgISEBJhMJsTFxSn7rVmzBiaTCQcOHEBmZiaSk5ORmZlp9/O8du0aAgMDYTKZrLEaNWrA19cX3t7e1tgXX3yBnJwcvPjii9aYyWTCuHHjkJ6erjuEqbCNGzciKCgI/fv3t8YCAgIwePBgfPHFF7h16xYAy9CoEydOYOzYsZq7ES+++CJEBBs3bgQA62dXq1YtAICPjw+8vb2RlZUFAPj+++9hNpuxcOFCu58BERE5hskGEVV6ZrMZ/fv3R9WqVTFs2DCcPHkShw8f1pS5fv062rdvj9jYWHTu3BmLFi3CCy+8gOTkZKSnpwOwDMPp2bMnZs6ciYcffhgLFizAxIkTkZmZiePHjzvVttzcXHTp0gWBgYGYP38+BgwYAADYsGEDsrKyMG7cOMTGxqJLly6IjY3FyJEjNfv/+OOPaNu2LXbt2oXnnnsOixYtQt++ffHll18CAKKionDvvffqJlhmsxmNGzfGY489hri4OLRo0UI3KbEVFRWFHTt2IDY2FqmpqUhOTsb48eORmZmJiRMnWssdOXIE1apVQ4sWLTT7t2nTxvp+cY4cOYLWrVujShXtV1WbNm2QlZWFX375RVPPI488oikXEhKCevXqWd9v2rQpatasiRkzZuDs2bOYN28erl27htatWwMAXn75ZUyYMAH33Xef3c+AiIgcVN63VoiIjPTtt98KANm5c6eIWIbx1KtXTyZOnKgpN336dOtQK1sFw4VWrFghAGThwoVFlklISBAAkpCQoHn/zJkzAkBWrlxpjY0aNUoAyOuvv67Ul5WVpcTef/99MZlM1mFAIiJPPPGEVK9eXRMr3B4RkTfeeEM8PT3l6tWr1tilS5fE3d1d3nnnHRERWblypdK+oly8eFGefPJJAWB9+fv7y/79+zXlevToIY0aNVL2v3HjRpHnXVi1atVkzJgxSjw+Pl4AWIc/zZs3TwBIWlqaUvbRRx+Vdu3aWbfXrFkj3t7eAkDc3Nxk/vz5IiJiNpslKChIMjMz7Z4/ERE5jnc2iKhSM5vNCAoKQocOHQBYhvEMGTIEa9euRV5enrXcpk2b0KpVK/Tr10+po2C40KZNm+Dv76+ZdGxbxhnjxo1TYoWHI924cQMZGRl4/PHHISLWv9RfvnwZe/fuxZgxY1C/fv0i2zNy5EjcunXLOpwIANatW4fc3FwMHz4cgGVIl4ggOjrabnt9fHzQrFkzjBo1Chs2bMCKFSusw8kKT76/efMmPD09lf29vLys7xfH0f0L/ltU2cLHGTZsGH799VccOHAAv/76K6ZMmYKsrCxMnToVs2fPhq+vL2bOnIlGjRrhwQcfdOhODxERFY3JBhFVWnl5eVi7di06dOiAM2fO4NSpUzh16hTatm2Lixcv4quvvrKWTUlJwf33319sfSkpKWjWrFmZPqXI3d0d9erVU+JpaWmIjo7GPffcA19fXwQEBCAyMhIArPMqTp8+DQB22928eXM8+uijmqFUZrMZ7dq1c2rI0KBBg5CWloZVq1Zh4MCBGD16NHbv3o3bt29b54oAloSpYF5FYdnZ2db3i+Po/gX/Laqs7XFq1aqFdu3aISgoCADw/vvvIzAwEKNHj8aKFSvw4YcfYvny5fjf//1fDBkyhE8vIyIqBSYbRFRp7dq1C+fPn8fatWvRpEkT62vw4MEAUORE8dIo6g5H4bsohXl6eipzEvLy8tCpUyfEx8dj6tSp2LJlC3bu3GmdXJ6fn1/ido0cORJ79uxBeno6UlJScPDgQetdjZI4ffo0duzYgd69e2vi99xzDyIiIvDNN99YY8HBwbhw4QJERFP2/PnzACxzKooTHBxsLVvc/sHBwZq4bdnijpOamooFCxZg0aJFqFKlCj7//HM8//zz6NixI8aMGYPHHnvMocf0EhGRPj5EnIgqLbPZjMDAQCxZskR5b/PmzYiLi8OHH34Ib29vNG7c2O4k78aNG+PQoUPIycmBh4eHbpmCJx1dvXpVEz979qzD7T527Bh++eUXfPrpp5oJ4Tt37tSUa9SoEQA4NDl96NChmDx5Mj7//HPcvHkTHh4eGDJkiMNtKnDx4kUA+slTTk6OZiG88PBwLF++HElJSWjZsqU1fujQIev7xQkPD8e+ffuQn5+vScgOHToEHx8fNG3aVFPPt99+a518DgDnzp1Deno6xo4dW+QxXnnlFfTu3RsRERHWfQonJyEhIfj111+LbScRERWNdzaIqFK6efMmNm/ejJ49e2LgwIHKa8KECfjjjz+wdetWAMCAAQPwww8/6I7RL/jL/IABA5CRkYHFixcXWSY0NBRubm7Yu3ev5v2lS5c63HY3NzdNnQX/v2jRIk25gIAAPPHEE1ixYgXS0tJ021PA398f3bp1w2effQaz2YyuXbvC39/f+r6jj7697777UKVKFaxbt05zjPT0dOzbtw8PPfSQNdanTx94eHhozl1E8OGHH6Ju3bp4/PHHrfHz588jOTkZOTk51tjAgQNx8eJFzUKBGRkZ2LBhA3r16mWdoxEWFobmzZvj448/1iRBy5Ytg8lkwsCBA3XPJSEhAdu2bcPcuXOtsaCgICQnJ1u3k5KSUKdOnWI/EyIiKkZ5zUwnIjLS2rVrBYBs2bJF9/28vDwJCAiwLuD2xx9/SMuWLcXNzU2ee+45+fDDDyUmJkbatWsnR48eFRGR3NxciYqKEgAydOhQWbJkicydO1c6d+6sOc7QoUPF3d1dJk+eLEuWLJFu3brJww8/rPs0qmrVqiltu337tjRu3Fj8/f1l9uzZEhsbK1FRUdKqVSuljqNHj4qvr6/Url1b3njjDfn444/lzTfflFatWin1bty40fr0qHXr1mneK8nTqP7nf/5HAEiHDh0kNjZWYmJipF69euLm5iZ79uzRlH311VcFgIwdO1Y++eQT6dGjhwAQs9msKVfwZK4zZ85YY7m5udKuXTvx9fWVmTNnypIlSyQsLEyqV68uycnJmv2//PJLMZlM0rFjR/n444/l5ZdflipVqshzzz2new65ubny4IMPyvTp0zXx2NhY8fb2lpiYGHn++eelSpUqcvz4cbufCRER6WOyQUSVUq9evcTLy0tu3LhRZJno6Gjx8PCQjIwMERH57bffZMKECVK3bl2pWrWq1KtXT0aNGmV9X8TySNq33npLGjZsKB4eHlKnTh0ZOHCgpKSkWMtcvnxZBgwYID4+PlKrVi15/vnn5fjx4w4nGyIiJ06ckKeeekp8fX3F399fnnvuOfnhhx90E4Ljx49Lv379xM/PT7y8vKRZs2Yybdo0pc5bt25JrVq1pGbNmnLz5k3NeyVJNnJyciQ2NlbCw8PF19dXfH19pUOHDrJr1y6lbF5ensTExEhoaKhUrVpVwsLC5LPPPlPK6SUbIiK///67PPvss1K7dm3x8fGRyMhIOXz4sG674uLiJDw8XDw9PaVevXry9ttvy+3bt3XLLlmyROrVq6f0j5ycHJk8ebL4+/tLaGiofPrpp3Y/DyIiKppJxOZeOxERVUq5ubkICQlBr1698H//93/l3RwiIroLcM4GEdFdYsuWLbh8+bKyCjkREZFReGeDiKiSO3ToEH788Ue899578Pf3x/fff1/eTSIiorsE72wQEVVyy5Ytw7hx4xAYGIjVq1eXd3OIiOguwjsbRERERERkCN7ZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQzDZICIiIiIiQ/x/7WLy8H1wwzEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Висновок"
      ],
      "metadata": {
        "id": "l8kmYGCKo34F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"При тестуванні {x_test.shape[0]} тестових зображень середня похибка склала: {1-cr['accuracy']:.2%}\")\n",
        "print(f\"На вибірці з {img_selected} зображень на {sub_tests} тестуваннях середня похибка склала: {1-mean_acc:.2%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kA4IKeb8o-zL",
        "outputId": "ad991da9-8e81-4bf1-c95d-a200feb7e0a1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "При тестуванні 10000 тестових зображень середня похибка склала: 6.02%\n",
            "На вибірці з 5 зображень на 100 тестуваннях середня похибка склала: 4.60%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Загальна точнічть роботи моделі на основі трестувань 10'000 зображень за f1 склала 94%, найгірша якість знаходжнеея цифри \"8\" (92%), найкарща для цифри \"1\" (98%)."
      ],
      "metadata": {
        "id": "2Ojqn87DxVFx"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jHkzE5kUrb1G"
      },
      "execution_count": 183,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}