Module_3_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Распознавание рукописных цифр"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся выборкой из уже подготовленной БД изображений:\n",
    "\n",
    "MNIST – (Modified National Institute of Standards and Technology)\n",
    "\n",
    "60 000 изображений в обучающей выборке и 10 000 – в тестовой.\n",
    "Каждое изображение имеет размер 28х28 пикселей и представлено в градациях серого, т.е. каждый пиксел имеет значение от 0 до 255 (0 – черный цвет, 255 – белый):"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* x_train – изображения цифр обучающей выборки;\n",
    "* y_train – вектор соответствующих значений цифр (например, если на i-м изображении нарисована 5, то  y_train[i] = 5);\n",
    "* x_test – изображения цифр тестовой выборки;\n",
    "* y_test – вектор соответствующих значений цифр для тестовой выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPooling2D\n",
    "\n",
    "from tensorflow.keras.datasets import mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "# отображение первых 25 изображений из обучающей выборки\n",
    "plt.figure(figsize=(10,5))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# отображение первых 25 изображений из обучающей выборки\n",
    "plt.figure(figsize=(10,5))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Соберем обычную полносвязную ИНС с\n",
    "* 28 x 28 = 784 входами;\n",
    "* 128 нейронами скрытого слоя + bias;\n",
    "* 10 нейронами выходного слоя + bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\git\\image_processing\\.venv\\lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: 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__(**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\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\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",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<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\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<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\">1,290</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",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m100,480\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\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\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 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\">101,770</span> (397.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 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 = keras.Sequential([\n",
    "    Flatten(input_shape=(28, 28, 1)),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    " \n",
    "print(model.summary())     # вывод структуры НС в консоль"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cтандартизация входных данных\n",
    "каждое значение тензоров x_train и x_test будет делиться на максимальное число 255, которое они могут принимать. На выходе получим вещественные величины от 0 до 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train / 255\n",
    "x_test = x_test / 255\n",
    "\n",
    "y_train_cat = keras.utils.to_categorical(y_train, 10)\n",
    "y_test_cat = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train_cat, batch_size=32, epochs=10, validation_split=0.2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На выходе ожидаем вектор с 1 на месте соответствующего числа, т.к. наша НС имеет 10 выходов, и каждый выход будет соответствовать определенной цифре: от 0 до 9.\n",
    "\n",
    "Воспользуемся стандартной функцией Keras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_cat = keras.utils.to_categorical(y_train, 10)\n",
    "y_test_cat = keras.utils.to_categorical(y_test, 10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для задачи классификации стоит выбрать функцию потерь категориальной кросс-энтропии - categorical_crossentropy.\n",
    "\n",
    "Построить алгоритм минимизации процента ошибок распознавания цифр сложно (если вообще возможно), поэтому мы привязываемся к более простому с точки зрения математики критерию – категориальной кросс-энтропии (S), минимизируя которую, надеемся, что будет уменьшаться и процент ошибок. \n",
    "$${Q(S(y), L)=-\\sum_i(L_i*log(S_i))}$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим модель\n",
    "* batch_size = 32 – это размер батча (32 картинки), после которых будет выполняться корректировка весов;\n",
    "* validation_split = 0,2 – разбиение обучающей выборки на собственно обучающую и проверочную. Значение 0,2 определяет, что для каждой эпохи 20% случайных картинок из обучающей выборки будут помещаться в выборку валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.8584 - loss: 0.4887 - val_accuracy: 0.9562 - val_loss: 0.1575\n",
      "Epoch 2/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9592 - loss: 0.1388 - val_accuracy: 0.9657 - val_loss: 0.1197\n",
      "Epoch 3/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9727 - loss: 0.0924 - val_accuracy: 0.9693 - val_loss: 0.1014\n",
      "Epoch 4/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9808 - loss: 0.0661 - val_accuracy: 0.9697 - val_loss: 0.1025\n",
      "Epoch 5/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9849 - loss: 0.0502 - val_accuracy: 0.9698 - val_loss: 0.1000\n",
      "Epoch 6/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9871 - loss: 0.0407 - val_accuracy: 0.9688 - val_loss: 0.1045\n",
      "Epoch 7/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9911 - loss: 0.0290 - val_accuracy: 0.9714 - val_loss: 0.1032\n",
      "Epoch 8/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9917 - loss: 0.0266 - val_accuracy: 0.9750 - val_loss: 0.0921\n",
      "Epoch 9/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9948 - loss: 0.0185 - val_accuracy: 0.9762 - val_loss: 0.0908\n",
      "Epoch 10/10\n",
      "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9960 - loss: 0.0160 - val_accuracy: 0.9734 - val_loss: 0.1042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x297b423bb50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train_cat, batch_size=32, epochs=10, validation_split=0.2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9677 - loss: 0.1178\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1001734733581543, 0.9731000065803528]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test_cat)\n",
    "# Контроль по примеру\n",
    "n = 3\n",
    "plt.imshow(x_test[n], cmap=plt.cm.binary)\n",
    "plt.show()\n",
    "\n",
    "x = np.expand_dims(x_test[n], axis=0)\n",
    "res = model.predict(x)\n",
    "print( res )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Контроль по примеру"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 3\n",
    "plt.imshow(x_test[n], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 94ms/step\n",
      "[[9.99994874e-01 3.44603382e-12 5.06633796e-06 1.30963754e-10\n",
      "  1.07035165e-10 5.50752055e-10 1.02183568e-08 1.33082045e-09\n",
      "  5.85256930e-12 3.72570086e-10]]\n"
     ]
    }
   ],
   "source": [
    "x = np.expand_dims(x_test[n], axis=0)\n",
    "res = model.predict(x)\n",
    "print( res )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print( np.argmax(res) )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропустим через НС всю тестовую выборку и векторы выходных значений преобразуем в числа от 0 до 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
      "(10000,)\n",
      "[7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4]\n",
      "[7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(x_test)\n",
    "pred = np.argmax(pred, axis=1)\n",
    " \n",
    "print(pred.shape)\n",
    " \n",
    "print(pred[:20])\n",
    "print(y_test[:20])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Все ли картинки распознаются верно?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Затем, сформируем маску, которая будет содержать True для верных вариантов и False – для неверных. И с помощью этой маски выделим из тестовой выборки все неверные результаты:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True False  True]\n",
      "(269, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "mask = pred == y_test\n",
    "print(mask[:10])\n",
    " \n",
    "x_false = x_test[~mask]\n",
    "y_false = x_test[~mask]\n",
    " \n",
    "print(x_false.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И выведем первые 5 из них на экран:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение сети: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zs2apqakp6j4nT55UVVWVrr76al111VWaPXu2Ojo64ro0ACD1eQpQfX29qqqqtH37dr3zzjvq6enRjBkz1NXVFbnPww8/rE2bNunNN99UfX29Dh48qDvvvDPuiwMAUpunDyFs2bIl6us1a9YoOztbjY2Nmjp1qkKhkH77299q7dq1+ta3viVJWr16tb785S9r+/bt+sY3vhG/zQEAKe2S3gMKhUKSpMzMTElSY2Ojenp6VFZWFrnPhAkTNGrUKDU0NPT5Pbq7uxUOh6MuAICBL+YA9fb2atGiRbr55ptVVFQkSWpvb1daWppGjBgRdd+cnBy1t7f3+X1qamoUCAQil4KCglhXAgCkkJgDVFVVpb179+r111+/pAWqq6sVCoUil7a2tkv6fgCA1BDTX0RduHChNm/erG3btik/Pz9yfTAY1KlTp3T06NGoV0EdHR0KBoN9fi+/3y+/3x/LGgCAFObpFZBzTgsXLtT69eu1detWFRYWRt0+adIkDR06VLW1tZHrmpqatH//fpWWlsZnYwDAgODpFVBVVZXWrl2rjRs3Kj09PfK+TiAQ0LBhwxQIBPTAAw9o8eLFyszMVEZGhh566CGVlpbyCTgAQBRPAXrxxRclSdOmTYu6fvXq1Zo7d64kacWKFRo0aJBmz56t7u5ulZeX6ze/+U1clgUADBw+55yzXuKzwuGwAoGAQqGQMjIyrNdBijpx4kRMcz/60Y88z6xbty6mx0pmQ4Z4f3v4u9/9rueZP/7xj55nhg8f7nkG/etif45zLjgAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYiOk3ogLJbtiwYTHNrVy50vNMZ2en55nGxkbPMx0dHZ5nrr32Ws8zkvTDH/7Q88zSpUtjeixcvngFBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCY4GSkwGfk5OR4ntm8ebPnmT/84Q+eZxoaGjzPxHqC0Ozs7JjmAC94BQQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmPA555z1Ep8VDocVCAQUCoWUkZFhvQ4AwKOL/TnOKyAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgwlOAampqNHnyZKWnpys7O1uzZs1SU1NT1H2mTZsmn88XdZk/f35clwYApD5PAaqvr1dVVZW2b9+ud955Rz09PZoxY4a6urqi7jdv3jwdOnQoclm+fHlclwYApL4hXu68ZcuWqK/XrFmj7OxsNTY2aurUqZHrr7zySgWDwfhsCAAYkC7pPaBQKCRJyszMjLr+1VdfVVZWloqKilRdXa3jx4+f93t0d3crHA5HXQAAA5+nV0Cf1dvbq0WLFunmm29WUVFR5Pp7771Xo0ePVl5envbs2aPHH39cTU1Neuutt/r8PjU1NVq2bFmsawAAUpTPOediGVywYIHefvttvf/++8rPzz/v/bZu3arp06erublZY8eOPef27u5udXd3R74Oh8MqKChQKBRSRkZGLKsBAAyFw2EFAoEL/hyP6RXQwoULtXnzZm3btu0L4yNJJSUlknTeAPn9fvn9/ljWAACkME8Bcs7poYce0vr161VXV6fCwsILzuzevVuSlJubG9OCAICByVOAqqqqtHbtWm3cuFHp6elqb2+XJAUCAQ0bNkwtLS1au3atvvOd7+jqq6/Wnj179PDDD2vq1KmaOHFiQv4FAACpydN7QD6fr8/rV69erblz56qtrU0/+MEPtHfvXnV1damgoEB33HGHnnzyyYt+P+di/+wQAJCcEvIe0IVaVVBQoPr6ei/fEgBwmeJccAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE0OsF/g855wkKRwOG28CAIjF2Z/fZ3+en0/SBaizs1OSVFBQYLwJAOBSdHZ2KhAInPd2n7tQovpZb2+vDh48qPT0dPl8vqjbwuGwCgoK1NbWpoyMDKMN7XEczuA4nMFxOIPjcEYyHAfnnDo7O5WXl6dBg87/Tk/SvQIaNGiQ8vPzv/A+GRkZl/UT7CyOwxkchzM4DmdwHM6wPg5f9MrnLD6EAAAwQYAAACZSKkB+v19LliyR3++3XsUUx+EMjsMZHIczOA5npNJxSLoPIQAALg8p9QoIADBwECAAgAkCBAAwQYAAACZSJkCrVq3StddeqyuuuEIlJSX68MMPrVfqd0uXLpXP54u6TJgwwXqthNu2bZtmzpypvLw8+Xw+bdiwIep255yefvpp5ebmatiwYSorK9O+fftslk2gCx2HuXPnnvP8qKiosFk2QWpqajR58mSlp6crOztbs2bNUlNTU9R9Tp48qaqqKl199dW66qqrNHv2bHV0dBhtnBgXcxymTZt2zvNh/vz5Rhv3LSUCtG7dOi1evFhLlizRRx99pOLiYpWXl+vw4cPWq/W7G264QYcOHYpc3n//feuVEq6rq0vFxcVatWpVn7cvX75czz//vF566SXt2LFDw4cPV3l5uU6ePNnPmybWhY6DJFVUVEQ9P1577bV+3DDx6uvrVVVVpe3bt+udd95RT0+PZsyYoa6ursh9Hn74YW3atElvvvmm6uvrdfDgQd15552GW8ffxRwHSZo3b17U82H58uVGG5+HSwFTpkxxVVVVka9Pnz7t8vLyXE1NjeFW/W/JkiWuuLjYeg1Tktz69esjX/f29rpgMOieffbZyHVHjx51fr/fvfbaawYb9o/PHwfnnJszZ467/fbbTfaxcvjwYSfJ1dfXO+fO/G8/dOhQ9+abb0bu889//tNJcg0NDVZrJtznj4Nzzn3zm990P/nJT+yWughJ/wro1KlTamxsVFlZWeS6QYMGqaysTA0NDYab2di3b5/y8vI0ZswY3Xfffdq/f7/1SqZaW1vV3t4e9fwIBAIqKSm5LJ8fdXV1ys7O1vjx47VgwQIdOXLEeqWECoVCkqTMzExJUmNjo3p6eqKeDxMmTNCoUaMG9PPh88fhrFdffVVZWVkqKipSdXW1jh8/brHeeSXdyUg/79NPP9Xp06eVk5MTdX1OTo7+9a9/GW1lo6SkRGvWrNH48eN16NAhLVu2TLfeeqv27t2r9PR06/VMtLe3S1Kfz4+zt10uKioqdOedd6qwsFAtLS164oknVFlZqYaGBg0ePNh6vbjr7e3VokWLdPPNN6uoqEjSmedDWlqaRowYEXXfgfx86Os4SNK9996r0aNHKy8vT3v27NHjjz+upqYmvfXWW4bbRkv6AOH/KisrI/88ceJElZSUaPTo0XrjjTf0wAMPGG6GZHD33XdH/vnGG2/UxIkTNXbsWNXV1Wn69OmGmyVGVVWV9u7de1m8D/pFznccHnzwwcg/33jjjcrNzdX06dPV0tKisWPH9veafUr6P4LLysrS4MGDz/kUS0dHh4LBoNFWyWHEiBEaN26cmpubrVcxc/Y5wPPjXGPGjFFWVtaAfH4sXLhQmzdv1nvvvRf161uCwaBOnTqlo0ePRt1/oD4fzncc+lJSUiJJSfV8SPoApaWladKkSaqtrY1c19vbq9raWpWWlhpuZu/YsWNqaWlRbm6u9SpmCgsLFQwGo54f4XBYO3bsuOyfHwcOHNCRI0cG1PPDOaeFCxdq/fr12rp1qwoLC6NunzRpkoYOHRr1fGhqatL+/fsH1PPhQsehL7t375ak5Ho+WH8K4mK8/vrrzu/3uzVr1rh//OMf7sEHH3QjRoxw7e3t1qv1q5/+9Keurq7Otba2ur/+9a+urKzMZWVlucOHD1uvllCdnZ1u165dbteuXU6Se+6559yuXbvcv//9b+ecc88884wbMWKE27hxo9uzZ4+7/fbbXWFhoTtx4oTx5vH1Rcehs7PTPfLII66hocG1tra6d999133ta19z119/vTt58qT16nGzYMECFwgEXF1dnTt06FDkcvz48ch95s+f70aNGuW2bt3qdu7c6UpLS11paanh1vF3oePQ3Nzsfvazn7mdO3e61tZWt3HjRjdmzBg3depU482jpUSAnHPuhRdecKNGjXJpaWluypQpbvv27dYr9bu77rrL5ebmurS0NHfNNde4u+66yzU3N1uvlXDvvfeek3TOZc6cOc65Mx/Ffuqpp1xOTo7z+/1u+vTprqmpyXbpBPii43D8+HE3Y8YMN3LkSDd06FA3evRoN2/evAH3H2l9/ftLcqtXr47c58SJE+7HP/6x+9KXvuSuvPJKd8cdd7hDhw7ZLZ0AFzoO+/fvd1OnTnWZmZnO7/e76667zj366KMuFArZLv45/DoGAICJpH8PCAAwMBEgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJv4HCgkHkkWgp0UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение сети: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение сети: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение сети: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение сети: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tr7/+umpqatTS0sIvkAIAnMLThxA2bdoU9XVlZaUyMjK0fft2TZkyRaFQSL/73e+0bt063XzzzZKkNWvW6KqrrlJ9fb2+/e1vx29yAEC/dl7vAYVCIUlSWlqaJGn79u06duyYioqKIvuMGzdOOTk5qqur6/F7dHV1KRwOR20AgIEv5gB1d3dr8eLFmjx5ssaPHy9JamtrU3JysoYNGxa1b2Zmptra2nr8PhUVFfL7/ZEtOzs71pEAAP1IzAEqLS3Vrl279Morr5zXAOXl5QqFQpGtubn5vL4fAKB/iOkfoi5atEhvvfWWamtrNXLkyMjjgUBAR48e1cGDB6Ougtrb2xUIBHr8Xj6fTz6fL5YxAAD9mKcrIOecFi1apPXr12vLli3Kzc2Nen7ixIkaMmSIqqqqIo81NDRo3759KiwsjM/EAIABwdMVUGlpqdatW6eNGzcqJSUl8r6O3+/X0KFD5ff7de+996qsrExpaWlKTU3VAw88oMLCQj4BBwCI4ilAq1evliRNnTo16vE1a9Zo/vz5kqSnn35agwYN0pw5c9TV1aXi4mI9//zzcRkWADBwJDnnnPUQXxYOh+X3+xUKhZSammo9Ds7gwIEDntdMnjzZ85qv3m3jXIwbN87zGkl64IEHPK8JBoOe18yaNcvzmt708MMPe17zm9/8xvOaq666yvOa+vp6z2v4WdK7zvXnOPeCAwCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgImYfiMqIEnDhw/3vObPf/6z5zXTp0/3vGb37t2e10gnfueVV0OGDPG85rLLLvO8pjd1dHT0ynHKyso8r+HO1gMHV0AAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAluRopelZOT43nN5s2bPa9ZunSp5zWSVFlZ6XnNsWPHPK/573//63lNXzd+/HjPa77zne8kYBL0F1wBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmkpxzznqILwuHw/L7/QqFQkpNTbUeB/1UrC/r//3vf57X/PGPf/S8pqmpyfOaNWvWeF6Tl5fneY0kXX311Z7XrFixwvOaESNGeF6Dvu9cf45zBQQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmOBmpACAuOJmpACAPo0AAQBMeApQRUWFrrvuOqWkpCgjI0OzZs1SQ0ND1D5Tp05VUlJS1Hb//ffHdWgAQP/nKUA1NTUqLS1VfX29Nm/erGPHjmn69Onq7OyM2m/BggVqbW2NbCtXrozr0ACA/u8iLztv2rQp6uvKykplZGRo+/btmjJlSuTxSy65RIFAID4TAgAGpPN6DygUCkmS0tLSoh5fu3at0tPTNX78eJWXl+vw4cOn/R5dXV0Kh8NRGwBg4PN0BfRl3d3dWrx4sSZPnqzx48dHHr/rrrs0atQoBYNB7dy5U48++qgaGhr0xhtv9Ph9KioqtHz58ljHAAD0UzH/O6CFCxfqnXfe0fvvv6+RI0eedr8tW7Zo2rRpamxs1OjRo095vqurS11dXZGvw+GwsrOz+XdAANBPneu/A4rpCmjRokV66623VFtbe8b4SFJBQYEknTZAPp9PPp8vljEAAP2YpwA55/TAAw9o/fr1qq6uVm5u7lnX7NixQ5KUlZUV04AAgIHJU4BKS0u1bt06bdy4USkpKWpra5Mk+f1+DR06VHv37tW6det0yy23aPjw4dq5c6eWLFmiKVOmaMKECQn5DwAA9E+e3gNKSkrq8fE1a9Zo/vz5am5u1ne/+13t2rVLnZ2dys7O1m233abHHnvsnN/P4V5wANC/JeQ9oLO1Kjs7WzU1NV6+JQDgAsW94AAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJi6yHuCrnHOSpHA4bDwJACAWJ39+n/x5fjp9LkAdHR2SpOzsbONJAADno6OjQ36//7TPJ7mzJaqXdXd3q6WlRSkpKUpKSop6LhwOKzs7W83NzUpNTTWa0B7n4QTOwwmchxM4Dyf0hfPgnFNHR4eCwaAGDTr9Oz197gpo0KBBGjly5Bn3SU1NvaBfYCdxHk7gPJzAeTiB83CC9Xk405XPSXwIAQBgggABAEz0qwD5fD4tW7ZMPp/PehRTnIcTOA8ncB5O4Dyc0J/OQ5/7EAIA4MLQr66AAAADBwECAJggQAAAEwQIAGCi3wRo1apVuvzyy3XxxReroKBAH3zwgfVIve6JJ55QUlJS1DZu3DjrsRKutrZWt956q4LBoJKSkrRhw4ao551zWrp0qbKysjR06FAVFRVpz549NsMm0NnOw/z58095fcyYMcNm2ASpqKjQddddp5SUFGVkZGjWrFlqaGiI2ufIkSMqLS3V8OHDddlll2nOnDlqb283mjgxzuU8TJ069ZTXw/333280cc/6RYBeffVVlZWVadmyZfrwww+Vn5+v4uJi7d+/33q0XnfNNdeotbU1sr3//vvWIyVcZ2en8vPztWrVqh6fX7lypZ599lm98MIL2rp1qy699FIVFxfryJEjvTxpYp3tPEjSjBkzol4fL7/8ci9OmHg1NTUqLS1VfX29Nm/erGPHjmn69Onq7OyM7LNkyRK9+eabev3111VTU6OWlhbNnj3bcOr4O5fzIEkLFiyIej2sXLnSaOLTcP3ApEmTXGlpaeTr48ePu2Aw6CoqKgyn6n3Lli1z+fn51mOYkuTWr18f+bq7u9sFAgH35JNPRh47ePCg8/l87uWXXzaYsHd89Tw459y8efPczJkzTeaxsn//fifJ1dTUOOdO/G8/ZMgQ9/rrr0f2+cc//uEkubq6OqsxE+6r58E552688Ub34IMP2g11Dvr8FdDRo0e1fft2FRUVRR4bNGiQioqKVFdXZziZjT179igYDCovL09333239u3bZz2SqaamJrW1tUW9Pvx+vwoKCi7I10d1dbUyMjI0duxYLVy4UAcOHLAeKaFCoZAkKS0tTZK0fft2HTt2LOr1MG7cOOXk5Azo18NXz8NJa9euVXp6usaPH6/y8nIdPnzYYrzT6nM3I/2qzz77TMePH1dmZmbU45mZmdq9e7fRVDYKCgpUWVmpsWPHqrW1VcuXL9cNN9ygXbt2KSUlxXo8E21tbZLU4+vj5HMXihkzZmj27NnKzc3V3r179dOf/lQlJSWqq6vT4MGDrceLu+7ubi1evFiTJ0/W+PHjJZ14PSQnJ2vYsGFR+w7k10NP50GS7rrrLo0aNUrBYFA7d+7Uo48+qoaGBr3xxhuG00br8wHCF0pKSiJ/njBhggoKCjRq1Ci99tpruvfeew0nQ19wxx13RP587bXXasKECRo9erSqq6s1bdo0w8kSo7S0VLt27bog3gc9k9Odh/vuuy/y52uvvVZZWVmaNm2a9u7dq9GjR/f2mD3q838Fl56ersGDB5/yKZb29nYFAgGjqfqGYcOG6corr1RjY6P1KGZOvgZ4fZwqLy9P6enpA/L1sWjRIr311lt67733on59SyAQ0NGjR3Xw4MGo/Qfq6+F056EnBQUFktSnXg99PkDJycmaOHGiqqqqIo91d3erqqpKhYWFhpPZO3TokPbu3ausrCzrUczk5uYqEAhEvT7C4bC2bt16wb8+Pv30Ux04cGBAvT6cc1q0aJHWr1+vLVu2KDc3N+r5iRMnasiQIVGvh4aGBu3bt29AvR7Odh56smPHDknqW68H609BnItXXnnF+Xw+V1lZ6T7++GN33333uWHDhrm2tjbr0XrVj3/8Y1ddXe2amprcX/7yF1dUVOTS09Pd/v37rUdLqI6ODvfRRx+5jz76yElyTz31lPvoo4/cJ5984pxz7le/+pUbNmyY27hxo9u5c6ebOXOmy83NdZ9//rnx5PF1pvPQ0dHhHnroIVdXV+eamprcu+++6775zW+6K664wh05csR69LhZuHCh8/v9rrq62rW2tka2w4cPR/a5//77XU5OjtuyZYvbtm2bKywsdIWFhYZTx9/ZzkNjY6NbsWKF27Ztm2tqanIbN250eXl5bsqUKcaTR+sXAXLOueeee87l5OS45ORkN2nSJFdfX289Uq+bO3euy8rKcsnJye7rX/+6mzt3rmtsbLQeK+Hee+89J+mUbd68ec65Ex/Ffvzxx11mZqbz+Xxu2rRprqGhwXboBDjTeTh8+LCbPn26GzFihBsyZIgbNWqUW7BgwYD7P2k9/fdLcmvWrIns8/nnn7sf/ehH7mtf+5q75JJL3G233eZaW1vthk6As52Hffv2uSlTpri0tDTn8/ncmDFj3MMPP+xCoZDt4F/Br2MAAJjo8+8BAQAGJgIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxP8DsOoJS4cyhP8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "  print(\"Значение сети: \"+str(y_test[i]))\n",
    "  plt.imshow(x_false[i], cmap=plt.cm.binary)\n",
    "  plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}