handwritten_digit_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88cd533c-65d4-421b-bb01-217d961c0765",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kalya\\AppData\\Roaming\\Python\\Python312\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.8660 - loss: 0.4677\n",
      "Epoch 2/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9675 - loss: 0.1107\n",
      "Epoch 3/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9790 - loss: 0.0692\n",
      "Epoch 4/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9837 - loss: 0.0506\n",
      "Epoch 5/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9871 - loss: 0.0383\n",
      "Epoch 6/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.0271\n",
      "Epoch 7/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0214\n",
      "Epoch 8/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.9941 - loss: 0.0177\n",
      "Epoch 9/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9955 - loss: 0.0140\n",
      "Epoch 10/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9962 - loss: 0.0113\n",
      "Epoch 11/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9969 - loss: 0.0095\n",
      "Epoch 12/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.0100\n",
      "Epoch 13/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9969 - loss: 0.0089\n",
      "Epoch 14/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.9965 - loss: 0.0100\n",
      "Epoch 15/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9976 - loss: 0.0066\n",
      "Epoch 16/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9975 - loss: 0.0076\n",
      "Epoch 17/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.9979 - loss: 0.0069\n",
      "Epoch 18/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9979 - loss: 0.0065\n",
      "Epoch 19/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9968 - loss: 0.0093\n",
      "Epoch 20/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.9982 - loss: 0.0057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n",
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "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: 0.9688 - loss: 0.1899\n",
      "loss : 0.16634920239448547\n",
      "accuracy : 0.9740999937057495\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(x_train,y_train),(x_test,y_test) = mnist.load_data()\n",
    "\n",
    "x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
    "x_test = tf.keras.utils.normalize(x_test, axis = 1)\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(x_train, y_train, epochs=20)\n",
    "\n",
    "model.save('hand_written.h5')\n",
    "\n",
    "model = tf.keras.models.load_model('hand_written.h5')\n",
    "\n",
    "loss,accuracy = model.evaluate(x_test,y_test)\n",
    "print(f'loss : {loss}')\n",
    "print(f'accuracy : {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82b1a8ef-e95a-4812-b816-6807f40c0ca5",
   "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 49ms/step\n",
      "this is the 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAaLUlEQVR4nO3df0zU9x3H8df5g1Nb7hwiHMxT0ba6VWWZU0ZsnZ1EZInR6h/2xx9ijEaHzZR17VhaqdsSWpu4po3TfzZZk6qdSdXUP0gUK6Ybumg1xmwlwnBqFFxNuEMUNPLZH8ZbT/HH4Z3vO3g+km8id98v9+63396zX+/LF49zzgkAgMdsgPUAAID+iQABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATg6wHuFN3d7cuXLig9PR0eTwe63EAADFyzqm9vV25ubkaMODe5zlJF6ALFy4oGAxajwEAeETnzp3TqFGj7vl80gUoPT1d0q3BfT6f8TQAgFiFw2EFg8HI+/m9JCxAmzZt0vvvv6+Wlhbl5+fro48+0vTp0x+43e2/dvP5fAQIAFLYgz5GSchFCJ9++qnKy8tVWVmpr776Svn5+SouLtalS5cS8XIAgBSUkABt3LhRy5cv19KlS/X9739fW7Zs0bBhw/TnP/85ES8HAEhBcQ/Q9evXdezYMRUVFf3/RQYMUFFRkerr6+9av6urS+FwOGoBAPR9cQ/QN998o5s3byo7Ozvq8ezsbLW0tNy1flVVlfx+f2ThCjgA6B/MfxC1oqJCoVAospw7d856JADAYxD3q+AyMzM1cOBAtba2Rj3e2tqqQCBw1/per1derzfeYwAAklzcz4DS0tI0depU1dbWRh7r7u5WbW2tCgsL4/1yAIAUlZCfAyovL9eSJUv0ox/9SNOnT9cHH3ygjo4OLV26NBEvBwBIQQkJ0OLFi/Xf//5X69atU0tLi37wgx+opqbmrgsTAAD9l8c556yH+LZwOCy/369QKMSdEAAgBT3s+7j5VXAAgP6JAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYiHuA3nnnHXk8nqhl4sSJ8X4ZAECKG5SIb/rss89q//79/3+RQQl5GQBACktIGQYNGqRAIJCIbw0A6CMS8hnQ6dOnlZubq3HjxunVV1/V2bNn77luV1eXwuFw1AIA6PviHqCCggJVV1erpqZGmzdvVnNzs55//nm1t7f3uH5VVZX8fn9kCQaD8R4JAJCEPM45l8gXaGtr05gxY7Rx40YtW7bsrue7urrU1dUV+TocDisYDCoUCsnn8yVyNABAAoTDYfn9/ge+jyf86oDhw4frmWeeUWNjY4/Pe71eeb3eRI8BAEgyCf85oCtXrqipqUk5OTmJfikAQAqJe4Bef/111dXV6cyZM/r73/+uF198UQMHDtTLL78c75cCAKSwuP8V3Pnz5/Xyyy/r8uXLGjlypJ577jkdPnxYI0eOjPdLAQBSWNwDtGPHjnh/SwBAH8S94AAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwn/hXRIftXV1b3abv369TFvc+bMmV69FpLf2LFjY96msrIy5m1KS0tj3gbJiTMgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmPA455z1EN8WDofl9/sVCoXk8/msx0k5W7ZsiXmbVatWJWAS4MGGDBkS8zbXrl1LwCSIp4d9H+cMCABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwMch6ANxbTU1NzNtUVFQkYBIgMTo7O61HgCHOgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE9yMNIktXbo05m3a2triP0gcXbt2LeZthgwZkoBJcD8ej8d6BPQDnAEBAEwQIACAiZgDdOjQIc2bN0+5ubnyeDzavXt31PPOOa1bt045OTkaOnSoioqKdPr06XjNCwDoI2IOUEdHh/Lz87Vp06Yen9+wYYM+/PBDbdmyRUeOHNETTzyh4uJifvEUACBKzBchlJSUqKSkpMfnnHP64IMP9NZbb2n+/PmSpI8//ljZ2dnavXu3XnrppUebFgDQZ8T1M6Dm5ma1tLSoqKgo8pjf71dBQYHq6+t73Karq0vhcDhqAQD0fXENUEtLiyQpOzs76vHs7OzIc3eqqqqS3++PLMFgMJ4jAQCSlPlVcBUVFQqFQpHl3Llz1iMBAB6DuAYoEAhIklpbW6Meb21tjTx3J6/XK5/PF7UAAPq+uAYoLy9PgUBAtbW1kcfC4bCOHDmiwsLCeL4UACDFxXwV3JUrV9TY2Bj5urm5WSdOnFBGRoZGjx6tNWvW6Pe//72efvpp5eXl6e2331Zubq4WLFgQz7kBACku5gAdPXpUL7zwQuTr8vJySdKSJUtUXV2tN954Qx0dHVqxYoXa2tr03HPPqaamhvt5AQCieJxzznqIbwuHw/L7/QqFQv3+86BkvyHkypUrY95m8+bNCZgE8ZbMx16SvWWhBw/7Pm5+FRwAoH8iQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACAiZh/HQNwG3e2BvAoOAMCAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZD0AUld1dXXM25SWlsZ9Dms1NTUxb/Pee+/FvM2SJUti3qYv7m/0HZwBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmPM45Zz3Et4XDYfn9foVCIfl8PutxTA0dOjTmbTo7OxMwCZLBkCFDYt7m2rVrvXotj8fTq+0ehyR7y0IPHvZ9nDMgAIAJAgQAMBFzgA4dOqR58+YpNzdXHo9Hu3fvjnq+tLRUHo8napk7d2685gUA9BExB6ijo0P5+fnatGnTPdeZO3euLl68GFm2b9/+SEMCAPqemH8jaklJiUpKSu67jtfrVSAQ6PVQAIC+LyGfAR08eFBZWVmaMGGCVq1apcuXL99z3a6uLoXD4agFAND3xT1Ac+fO1ccff6za2lq99957qqurU0lJiW7evNnj+lVVVfL7/ZElGAzGeyQAQBJ6pJ8D8ng82rVrlxYsWHDPdf79739r/Pjx2r9/v2bPnn3X811dXerq6op8HQ6HFQwG+Tkg8XNAiMbPAd3CzwElv6T5OaBx48YpMzNTjY2NPT7v9Xrl8/miFgBA35fwAJ0/f16XL19WTk5Ool8KAJBCYr4K7sqVK1FnM83NzTpx4oQyMjKUkZGh9evXa9GiRQoEAmpqatIbb7yhp556SsXFxXEdHACQ2mIO0NGjR/XCCy9Evi4vL5ckLVmyRJs3b9bJkyf1l7/8RW1tbcrNzdWcOXP0u9/9Tl6vN35TAwBSHjcjTWLvvvtuzNtUVFQkYBKkqt7+581FCHgUSXMRAgAAPSFAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJmH8dAx6fX//614/ldbiDdmpYuXKl9QhAXHEGBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCY8DjnnPUQ3xYOh+X3+xUKheTz+azHAfolj8djPcI9JdlbFnrwsO/jnAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwMsh4AQOLU1NRYjwDcE2dAAAATBAgAYCKmAFVVVWnatGlKT09XVlaWFixYoIaGhqh1Ojs7VVZWphEjRujJJ5/UokWL1NraGtehAQCpL6YA1dXVqaysTIcPH9a+fft048YNzZkzRx0dHZF11q5dq88//1w7d+5UXV2dLly4oIULF8Z9cABAaovpIoQ7P9Csrq5WVlaWjh07ppkzZyoUCulPf/qTtm3bpp/+9KeSpK1bt+p73/ueDh8+rB//+MfxmxwAkNIe6TOgUCgkScrIyJAkHTt2TDdu3FBRUVFknYkTJ2r06NGqr6/v8Xt0dXUpHA5HLQCAvq/XAeru7taaNWs0Y8YMTZo0SZLU0tKitLQ0DR8+PGrd7OxstbS09Ph9qqqq5Pf7I0swGOztSACAFNLrAJWVlenUqVPasWPHIw1QUVGhUCgUWc6dO/dI3w8AkBp69YOoq1ev1t69e3Xo0CGNGjUq8nggEND169fV1tYWdRbU2tqqQCDQ4/fyer3yer29GQMAkMJiOgNyzmn16tXatWuXDhw4oLy8vKjnp06dqsGDB6u2tjbyWENDg86ePavCwsL4TAwA6BNiOgMqKyvTtm3btGfPHqWnp0c+1/H7/Ro6dKj8fr+WLVum8vJyZWRkyOfz6bXXXlNhYSFXwAEAosQUoM2bN0uSZs2aFfX41q1bVVpaKkn6wx/+oAEDBmjRokXq6upScXGx/vjHP8ZlWABA3+FxzjnrIb4tHA7L7/crFArJ5/NZj9Mv9PaGlatWrYp5mzNnzvTqtYDbkuwtCz142Pdx7gUHADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE736jajoW5YuXdqr7W7/Piigt1auXGk9wn315k7xj+su8WPHjo15G0mqrKyMeZvbv24n3jgDAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMeJxzznqIbwuHw/L7/QqFQvL5fNbj9Asej8d6BPQBEydOjHmbzs7OmLfpzY078X9DhgyJeZtr167FtP7Dvo9zBgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmBhkPQCAvuHrr7+2HgEPobS01HqECM6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAAT3IwUGjJkSK+26+zsjPMkQP+0cuXKmLfZvHlzAiZ5vDgDAgCYIEAAABMxBaiqqkrTpk1Tenq6srKytGDBAjU0NEStM2vWLHk8nqilN6eXAIC+LaYA1dXVqaysTIcPH9a+fft048YNzZkzRx0dHVHrLV++XBcvXowsGzZsiOvQAIDUF9NFCDU1NVFfV1dXKysrS8eOHdPMmTMjjw8bNkyBQCA+EwIA+qRH+gwoFApJkjIyMqIe/+STT5SZmalJkyapoqJCV69evef36OrqUjgcjloAAH1fry/D7u7u1po1azRjxgxNmjQp8vgrr7yiMWPGKDc3VydPntSbb76phoYGffbZZz1+n6qqKq1fv763YwAAUlSvA1RWVqZTp07pyy+/jHp8xYoVkT9PnjxZOTk5mj17tpqamjR+/Pi7vk9FRYXKy8sjX4fDYQWDwd6OBQBIEb0K0OrVq7V3714dOnRIo0aNuu+6BQUFkqTGxsYeA+T1euX1enszBgAghcUUIOecXnvtNe3atUsHDx5UXl7eA7c5ceKEJCknJ6dXAwIA+qaYAlRWVqZt27Zpz549Sk9PV0tLiyTJ7/dr6NChampq0rZt2/Szn/1MI0aM0MmTJ7V27VrNnDlTU6ZMScg/AAAgNcUUoNv3Hpo1a1bU41u3blVpaanS0tK0f/9+ffDBB+ro6FAwGNSiRYv01ltvxW1gAEDfEPNfwd1PMBhUXV3dIw0EAOgfuBs2VFlZ2avtKioq4jwJ8GD99c7RfRE3IwUAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATHjcg25x/ZiFw2H5/X6FQiH5fD7rcQAAMXrY93HOgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZD3AnW7fmi4cDhtPAgDojdvv3w+61WjSBai9vV2SFAwGjScBADyK9vZ2+f3+ez6fdHfD7u7u1oULF5Seni6PxxP1XDgcVjAY1Llz5/r1nbLZD7ewH25hP9zCfrglGfaDc07t7e3Kzc3VgAH3/qQn6c6ABgwYoFGjRt13HZ/P168PsNvYD7ewH25hP9zCfrjFej/c78znNi5CAACYIEAAABMpFSCv16vKykp5vV7rUUyxH25hP9zCfriF/XBLKu2HpLsIAQDQP6TUGRAAoO8gQAAAEwQIAGCCAAEATKRMgDZt2qSxY8dqyJAhKigo0D/+8Q/rkR67d955Rx6PJ2qZOHGi9VgJd+jQIc2bN0+5ubnyeDzavXt31PPOOa1bt045OTkaOnSoioqKdPr0aZthE+hB+6G0tPSu42Pu3Lk2wyZIVVWVpk2bpvT0dGVlZWnBggVqaGiIWqezs1NlZWUaMWKEnnzySS1atEitra1GEyfGw+yHWbNm3XU8rFy50mjinqVEgD799FOVl5ersrJSX331lfLz81VcXKxLly5Zj/bYPfvss7p48WJk+fLLL61HSriOjg7l5+dr06ZNPT6/YcMGffjhh9qyZYuOHDmiJ554QsXFxers7HzMkybWg/aDJM2dOzfq+Ni+fftjnDDx6urqVFZWpsOHD2vfvn26ceOG5syZo46Ojsg6a9eu1eeff66dO3eqrq5OFy5c0MKFCw2njr+H2Q+StHz58qjjYcOGDUYT34NLAdOnT3dlZWWRr2/evOlyc3NdVVWV4VSPX2VlpcvPz7cew5Qkt2vXrsjX3d3dLhAIuPfffz/yWFtbm/N6vW779u0GEz4ed+4H55xbsmSJmz9/vsk8Vi5duuQkubq6OufcrX/3gwcPdjt37oys869//ctJcvX19VZjJtyd+8E5537yk5+4X/ziF3ZDPYSkPwO6fv26jh07pqKioshjAwYMUFFRkerr6w0ns3H69Gnl5uZq3LhxevXVV3X27FnrkUw1NzerpaUl6vjw+/0qKCjol8fHwYMHlZWVpQkTJmjVqlW6fPmy9UgJFQqFJEkZGRmSpGPHjunGjRtRx8PEiRM1evToPn083Lkfbvvkk0+UmZmpSZMmqaKiQlevXrUY756S7makd/rmm2908+ZNZWdnRz2enZ2tr7/+2mgqGwUFBaqurtaECRN08eJFrV+/Xs8//7xOnTql9PR06/FMtLS0SFKPx8ft5/qLuXPnauHChcrLy1NTU5N+85vfqKSkRPX19Ro4cKD1eHHX3d2tNWvWaMaMGZo0aZKkW8dDWlqahg8fHrVuXz4eetoPkvTKK69ozJgxys3N1cmTJ/Xmm2+qoaFBn332meG00ZI+QPi/kpKSyJ+nTJmigoICjRkzRn/961+1bNkyw8mQDF566aXInydPnqwpU6Zo/PjxOnjwoGbPnm04WWKUlZXp1KlT/eJz0Pu5135YsWJF5M+TJ09WTk6OZs+eraamJo0fP/5xj9mjpP8ruMzMTA0cOPCuq1haW1sVCASMpkoOw4cP1zPPPKPGxkbrUczcPgY4Pu42btw4ZWZm9snjY/Xq1dq7d6+++OKLqF/fEggEdP36dbW1tUWt31ePh3vth54UFBRIUlIdD0kfoLS0NE2dOlW1tbWRx7q7u1VbW6vCwkLDyexduXJFTU1NysnJsR7FTF5engKBQNTxEQ6HdeTIkX5/fJw/f16XL1/uU8eHc06rV6/Wrl27dODAAeXl5UU9P3XqVA0ePDjqeGhoaNDZs2f71PHwoP3QkxMnTkhSch0P1ldBPIwdO3Y4r9frqqur3T//+U+3YsUKN3z4cNfS0mI92mP1y1/+0h08eNA1Nze7v/3tb66oqMhlZma6S5cuWY+WUO3t7e748ePu+PHjTpLbuHGjO378uPvPf/7jnHPu3XffdcOHD3d79uxxJ0+edPPnz3d5eXnu2rVrxpPH1/32Q3t7u3v99dddfX29a25udvv373c//OEP3dNPP+06OzutR4+bVatWOb/f7w4ePOguXrwYWa5evRpZZ+XKlW706NHuwIED7ujRo66wsNAVFhYaTh1/D9oPjY2N7re//a07evSoa25udnv27HHjxo1zM2fONJ48WkoEyDnnPvroIzd69GiXlpbmpk+f7g4fPmw90mO3ePFil5OT49LS0tx3v/tdt3jxYtfY2Gg9VsJ98cUXTtJdy5IlS5xzty7Ffvvtt112drbzer1u9uzZrqGhwXboBLjffrh69aqbM2eOGzlypBs8eLAbM2aMW758eZ/7n7Se/vklua1bt0bWuXbtmvv5z3/uvvOd77hhw4a5F1980V28eNFu6AR40H44e/asmzlzpsvIyHBer9c99dRT7le/+pULhUK2g9+BX8cAADCR9J8BAQD6JgIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxP8AdnO9YeGHbbYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "this is the 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAaLUlEQVR4nO3df0zU9x3H8df5g1Nb7hwiHMxT0ba6VWWZU0ZsnZ1EZInR6h/2xx9ijEaHzZR17VhaqdsSWpu4po3TfzZZk6qdSdXUP0gUK6Ybumg1xmwlwnBqFFxNuEMUNPLZH8ZbT/HH4Z3vO3g+km8id98v9+63396zX+/LF49zzgkAgMdsgPUAAID+iQABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATg6wHuFN3d7cuXLig9PR0eTwe63EAADFyzqm9vV25ubkaMODe5zlJF6ALFy4oGAxajwEAeETnzp3TqFGj7vl80gUoPT1d0q3BfT6f8TQAgFiFw2EFg8HI+/m9JCxAmzZt0vvvv6+Wlhbl5+fro48+0vTp0x+43e2/dvP5fAQIAFLYgz5GSchFCJ9++qnKy8tVWVmpr776Svn5+SouLtalS5cS8XIAgBSUkABt3LhRy5cv19KlS/X9739fW7Zs0bBhw/TnP/85ES8HAEhBcQ/Q9evXdezYMRUVFf3/RQYMUFFRkerr6+9av6urS+FwOGoBAPR9cQ/QN998o5s3byo7Ozvq8ezsbLW0tNy1flVVlfx+f2ThCjgA6B/MfxC1oqJCoVAospw7d856JADAYxD3q+AyMzM1cOBAtba2Rj3e2tqqQCBw1/per1derzfeYwAAklzcz4DS0tI0depU1dbWRh7r7u5WbW2tCgsL4/1yAIAUlZCfAyovL9eSJUv0ox/9SNOnT9cHH3ygjo4OLV26NBEvBwBIQQkJ0OLFi/Xf//5X69atU0tLi37wgx+opqbmrgsTAAD9l8c556yH+LZwOCy/369QKMSdEAAgBT3s+7j5VXAAgP6JAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYiHuA3nnnHXk8nqhl4sSJ8X4ZAECKG5SIb/rss89q//79/3+RQQl5GQBACktIGQYNGqRAIJCIbw0A6CMS8hnQ6dOnlZubq3HjxunVV1/V2bNn77luV1eXwuFw1AIA6PviHqCCggJVV1erpqZGmzdvVnNzs55//nm1t7f3uH5VVZX8fn9kCQaD8R4JAJCEPM45l8gXaGtr05gxY7Rx40YtW7bsrue7urrU1dUV+TocDisYDCoUCsnn8yVyNABAAoTDYfn9/ge+jyf86oDhw4frmWeeUWNjY4/Pe71eeb3eRI8BAEgyCf85oCtXrqipqUk5OTmJfikAQAqJe4Bef/111dXV6cyZM/r73/+uF198UQMHDtTLL78c75cCAKSwuP8V3Pnz5/Xyyy/r8uXLGjlypJ577jkdPnxYI0eOjPdLAQBSWNwDtGPHjnh/SwBAH8S94AAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwn/hXRIftXV1b3abv369TFvc+bMmV69FpLf2LFjY96msrIy5m1KS0tj3gbJiTMgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmPA455z1EN8WDofl9/sVCoXk8/msx0k5W7ZsiXmbVatWJWAS4MGGDBkS8zbXrl1LwCSIp4d9H+cMCABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwMch6ANxbTU1NzNtUVFQkYBIgMTo7O61HgCHOgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE9yMNIktXbo05m3a2triP0gcXbt2LeZthgwZkoBJcD8ej8d6BPQDnAEBAEwQIACAiZgDdOjQIc2bN0+5ubnyeDzavXt31PPOOa1bt045OTkaOnSoioqKdPr06XjNCwDoI2IOUEdHh/Lz87Vp06Yen9+wYYM+/PBDbdmyRUeOHNETTzyh4uJifvEUACBKzBchlJSUqKSkpMfnnHP64IMP9NZbb2n+/PmSpI8//ljZ2dnavXu3XnrppUebFgDQZ8T1M6Dm5ma1tLSoqKgo8pjf71dBQYHq6+t73Karq0vhcDhqAQD0fXENUEtLiyQpOzs76vHs7OzIc3eqqqqS3++PLMFgMJ4jAQCSlPlVcBUVFQqFQpHl3Llz1iMBAB6DuAYoEAhIklpbW6Meb21tjTx3J6/XK5/PF7UAAPq+uAYoLy9PgUBAtbW1kcfC4bCOHDmiwsLCeL4UACDFxXwV3JUrV9TY2Bj5urm5WSdOnFBGRoZGjx6tNWvW6Pe//72efvpp5eXl6e2331Zubq4WLFgQz7kBACku5gAdPXpUL7zwQuTr8vJySdKSJUtUXV2tN954Qx0dHVqxYoXa2tr03HPPqaamhvt5AQCieJxzznqIbwuHw/L7/QqFQv3+86BkvyHkypUrY95m8+bNCZgE8ZbMx16SvWWhBw/7Pm5+FRwAoH8iQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACAiZh/HQNwG3e2BvAoOAMCAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZD0AUld1dXXM25SWlsZ9Dms1NTUxb/Pee+/FvM2SJUti3qYv7m/0HZwBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmPM45Zz3Et4XDYfn9foVCIfl8PutxTA0dOjTmbTo7OxMwCZLBkCFDYt7m2rVrvXotj8fTq+0ehyR7y0IPHvZ9nDMgAIAJAgQAMBFzgA4dOqR58+YpNzdXHo9Hu3fvjnq+tLRUHo8napk7d2685gUA9BExB6ijo0P5+fnatGnTPdeZO3euLl68GFm2b9/+SEMCAPqemH8jaklJiUpKSu67jtfrVSAQ6PVQAIC+LyGfAR08eFBZWVmaMGGCVq1apcuXL99z3a6uLoXD4agFAND3xT1Ac+fO1ccff6za2lq99957qqurU0lJiW7evNnj+lVVVfL7/ZElGAzGeyQAQBJ6pJ8D8ng82rVrlxYsWHDPdf79739r/Pjx2r9/v2bPnn3X811dXerq6op8HQ6HFQwG+Tkg8XNAiMbPAd3CzwElv6T5OaBx48YpMzNTjY2NPT7v9Xrl8/miFgBA35fwAJ0/f16XL19WTk5Ool8KAJBCYr4K7sqVK1FnM83NzTpx4oQyMjKUkZGh9evXa9GiRQoEAmpqatIbb7yhp556SsXFxXEdHACQ2mIO0NGjR/XCCy9Evi4vL5ckLVmyRJs3b9bJkyf1l7/8RW1tbcrNzdWcOXP0u9/9Tl6vN35TAwBSHjcjTWLvvvtuzNtUVFQkYBKkqt7+581FCHgUSXMRAgAAPSFAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJmH8dAx6fX//614/ldbiDdmpYuXKl9QhAXHEGBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCY8DjnnPUQ3xYOh+X3+xUKheTz+azHAfolj8djPcI9JdlbFnrwsO/jnAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwMsh4AQOLU1NRYjwDcE2dAAAATBAgAYCKmAFVVVWnatGlKT09XVlaWFixYoIaGhqh1Ojs7VVZWphEjRujJJ5/UokWL1NraGtehAQCpL6YA1dXVqaysTIcPH9a+fft048YNzZkzRx0dHZF11q5dq88//1w7d+5UXV2dLly4oIULF8Z9cABAaovpIoQ7P9Csrq5WVlaWjh07ppkzZyoUCulPf/qTtm3bpp/+9KeSpK1bt+p73/ueDh8+rB//+MfxmxwAkNIe6TOgUCgkScrIyJAkHTt2TDdu3FBRUVFknYkTJ2r06NGqr6/v8Xt0dXUpHA5HLQCAvq/XAeru7taaNWs0Y8YMTZo0SZLU0tKitLQ0DR8+PGrd7OxstbS09Ph9qqqq5Pf7I0swGOztSACAFNLrAJWVlenUqVPasWPHIw1QUVGhUCgUWc6dO/dI3w8AkBp69YOoq1ev1t69e3Xo0CGNGjUq8nggEND169fV1tYWdRbU2tqqQCDQ4/fyer3yer29GQMAkMJiOgNyzmn16tXatWuXDhw4oLy8vKjnp06dqsGDB6u2tjbyWENDg86ePavCwsL4TAwA6BNiOgMqKyvTtm3btGfPHqWnp0c+1/H7/Ro6dKj8fr+WLVum8vJyZWRkyOfz6bXXXlNhYSFXwAEAosQUoM2bN0uSZs2aFfX41q1bVVpaKkn6wx/+oAEDBmjRokXq6upScXGx/vjHP8ZlWABA3+FxzjnrIb4tHA7L7/crFArJ5/NZj9Mv9PaGlatWrYp5mzNnzvTqtYDbkuwtCz142Pdx7gUHADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE736jajoW5YuXdqr7W7/Piigt1auXGk9wn315k7xj+su8WPHjo15G0mqrKyMeZvbv24n3jgDAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMeJxzznqIbwuHw/L7/QqFQvL5fNbj9Asej8d6BPQBEydOjHmbzs7OmLfpzY078X9DhgyJeZtr167FtP7Dvo9zBgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmBhkPQCAvuHrr7+2HgEPobS01HqECM6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAAT3IwUGjJkSK+26+zsjPMkQP+0cuXKmLfZvHlzAiZ5vDgDAgCYIEAAABMxBaiqqkrTpk1Tenq6srKytGDBAjU0NEStM2vWLHk8nqilN6eXAIC+LaYA1dXVqaysTIcPH9a+fft048YNzZkzRx0dHVHrLV++XBcvXowsGzZsiOvQAIDUF9NFCDU1NVFfV1dXKysrS8eOHdPMmTMjjw8bNkyBQCA+EwIA+qRH+gwoFApJkjIyMqIe/+STT5SZmalJkyapoqJCV69evef36OrqUjgcjloAAH1fry/D7u7u1po1azRjxgxNmjQp8vgrr7yiMWPGKDc3VydPntSbb76phoYGffbZZz1+n6qqKq1fv763YwAAUlSvA1RWVqZTp07pyy+/jHp8xYoVkT9PnjxZOTk5mj17tpqamjR+/Pi7vk9FRYXKy8sjX4fDYQWDwd6OBQBIEb0K0OrVq7V3714dOnRIo0aNuu+6BQUFkqTGxsYeA+T1euX1enszBgAghcUUIOecXnvtNe3atUsHDx5UXl7eA7c5ceKEJCknJ6dXAwIA+qaYAlRWVqZt27Zpz549Sk9PV0tLiyTJ7/dr6NChampq0rZt2/Szn/1MI0aM0MmTJ7V27VrNnDlTU6ZMScg/AAAgNcUUoNv3Hpo1a1bU41u3blVpaanS0tK0f/9+ffDBB+ro6FAwGNSiRYv01ltvxW1gAEDfEPNfwd1PMBhUXV3dIw0EAOgfuBs2VFlZ2avtKioq4jwJ8GD99c7RfRE3IwUAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATHjcg25x/ZiFw2H5/X6FQiH5fD7rcQAAMXrY93HOgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZD3AnW7fmi4cDhtPAgDojdvv3w+61WjSBai9vV2SFAwGjScBADyK9vZ2+f3+ez6fdHfD7u7u1oULF5Seni6PxxP1XDgcVjAY1Llz5/r1nbLZD7ewH25hP9zCfrglGfaDc07t7e3Kzc3VgAH3/qQn6c6ABgwYoFGjRt13HZ/P168PsNvYD7ewH25hP9zCfrjFej/c78znNi5CAACYIEAAABMpFSCv16vKykp5vV7rUUyxH25hP9zCfriF/XBLKu2HpLsIAQDQP6TUGRAAoO8gQAAAEwQIAGCCAAEATKRMgDZt2qSxY8dqyJAhKigo0D/+8Q/rkR67d955Rx6PJ2qZOHGi9VgJd+jQIc2bN0+5ubnyeDzavXt31PPOOa1bt045OTkaOnSoioqKdPr0aZthE+hB+6G0tPSu42Pu3Lk2wyZIVVWVpk2bpvT0dGVlZWnBggVqaGiIWqezs1NlZWUaMWKEnnzySS1atEitra1GEyfGw+yHWbNm3XU8rFy50mjinqVEgD799FOVl5ersrJSX331lfLz81VcXKxLly5Zj/bYPfvss7p48WJk+fLLL61HSriOjg7l5+dr06ZNPT6/YcMGffjhh9qyZYuOHDmiJ554QsXFxers7HzMkybWg/aDJM2dOzfq+Ni+fftjnDDx6urqVFZWpsOHD2vfvn26ceOG5syZo46Ojsg6a9eu1eeff66dO3eqrq5OFy5c0MKFCw2njr+H2Q+StHz58qjjYcOGDUYT34NLAdOnT3dlZWWRr2/evOlyc3NdVVWV4VSPX2VlpcvPz7cew5Qkt2vXrsjX3d3dLhAIuPfffz/yWFtbm/N6vW779u0GEz4ed+4H55xbsmSJmz9/vsk8Vi5duuQkubq6OufcrX/3gwcPdjt37oys869//ctJcvX19VZjJtyd+8E5537yk5+4X/ziF3ZDPYSkPwO6fv26jh07pqKioshjAwYMUFFRkerr6w0ns3H69Gnl5uZq3LhxevXVV3X27FnrkUw1NzerpaUl6vjw+/0qKCjol8fHwYMHlZWVpQkTJmjVqlW6fPmy9UgJFQqFJEkZGRmSpGPHjunGjRtRx8PEiRM1evToPn083Lkfbvvkk0+UmZmpSZMmqaKiQlevXrUY756S7makd/rmm2908+ZNZWdnRz2enZ2tr7/+2mgqGwUFBaqurtaECRN08eJFrV+/Xs8//7xOnTql9PR06/FMtLS0SFKPx8ft5/qLuXPnauHChcrLy1NTU5N+85vfqKSkRPX19Ro4cKD1eHHX3d2tNWvWaMaMGZo0aZKkW8dDWlqahg8fHrVuXz4eetoPkvTKK69ozJgxys3N1cmTJ/Xmm2+qoaFBn332meG00ZI+QPi/kpKSyJ+nTJmigoICjRkzRn/961+1bNkyw8mQDF566aXInydPnqwpU6Zo/PjxOnjwoGbPnm04WWKUlZXp1KlT/eJz0Pu5135YsWJF5M+TJ09WTk6OZs+eraamJo0fP/5xj9mjpP8ruMzMTA0cOPCuq1haW1sVCASMpkoOw4cP1zPPPKPGxkbrUczcPgY4Pu42btw4ZWZm9snjY/Xq1dq7d6+++OKLqF/fEggEdP36dbW1tUWt31ePh3vth54UFBRIUlIdD0kfoLS0NE2dOlW1tbWRx7q7u1VbW6vCwkLDyexduXJFTU1NysnJsR7FTF5engKBQNTxEQ6HdeTIkX5/fJw/f16XL1/uU8eHc06rV6/Wrl27dODAAeXl5UU9P3XqVA0ePDjqeGhoaNDZs2f71PHwoP3QkxMnTkhSch0P1ldBPIwdO3Y4r9frqqur3T//+U+3YsUKN3z4cNfS0mI92mP1y1/+0h08eNA1Nze7v/3tb66oqMhlZma6S5cuWY+WUO3t7e748ePu+PHjTpLbuHGjO378uPvPf/7jnHPu3XffdcOHD3d79uxxJ0+edPPnz3d5eXnu2rVrxpPH1/32Q3t7u3v99dddfX29a25udvv373c//OEP3dNPP+06OzutR4+bVatWOb/f7w4ePOguXrwYWa5evRpZZ+XKlW706NHuwIED7ujRo66wsNAVFhYaTh1/D9oPjY2N7re//a07evSoa25udnv27HHjxo1zM2fONJ48WkoEyDnnPvroIzd69GiXlpbmpk+f7g4fPmw90mO3ePFil5OT49LS0tx3v/tdt3jxYtfY2Gg9VsJ98cUXTtJdy5IlS5xzty7Ffvvtt112drbzer1u9uzZrqGhwXboBLjffrh69aqbM2eOGzlypBs8eLAbM2aMW758eZ/7n7Se/vklua1bt0bWuXbtmvv5z3/uvvOd77hhw4a5F1980V28eNFu6AR40H44e/asmzlzpsvIyHBer9c99dRT7le/+pULhUK2g9+BX8cAADCR9J8BAQD6JgIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxP8AdnO9YeGHbbYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "this is the 9\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": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "this is the 9\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": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "this is the 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAaAUlEQVR4nO3dXWxT9xnH8Z95iYE2MQshcTIMBNrCViDTGGQRLaMjAjIJQeGCvlyQCoFgoRpkXbtMLSnbpDAqMdSKwc0Gq1SgQyqgchEJQgnqFpigIIS2RSQLAwQJKxJ2CCQg8t8FwqshvNjYeWzn+5GOROxz4qenR/7G8fGJxznnBABAL+tnPQAAoG8iQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwMQA6wHu1d3drYsXLyozM1Mej8d6HABAlJxzam9vV0FBgfr1e/DrnKQL0MWLFxUIBKzHAAA8ofPnz2vEiBEPvD/pApSZmSnpzuBZWVnG0wAAohUKhRQIBMLP5w+SsABt2rRJH3zwgVpbW1VUVKSPPvpIU6dOfeR2d3/tlpWVRYAAIIU96m2UhJyE8Omnn6qyslLV1dX66quvVFRUpNmzZ+vy5cuJeDgAQApKSIA2bNigpUuX6o033tB3v/tdbdmyRUOGDNGf/vSnRDwcACAFxT1AN2/e1PHjx1VaWvr/B+nXT6WlpWpoaLhv/a6uLoVCoYgFAJD+4h6gr7/+Wrdv31ZeXl7E7Xl5eWptbb1v/ZqaGvl8vvDCGXAA0DeYfxC1qqpKwWAwvJw/f956JABAL4j7WXA5OTnq37+/2traIm5va2uT3++/b32v1yuv1xvvMQAASS7ur4AyMjI0efJk1dXVhW/r7u5WXV2dSkpK4v1wAIAUlZDPAVVWVmrx4sX6wQ9+oKlTp2rjxo3q6OjQG2+8kYiHAwCkoIQEaNGiRfrvf/+rNWvWqLW1Vd/73vdUW1t734kJAIC+y+Occ9ZDfFMoFJLP51MwGORKCACQgh73edz8LDgAQN9EgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATAywHgD2amtrY9puxYoVUW9z9uzZmB4L6Wn06NFRb1NdXR31NuXl5VFvg8TjFRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYMLjnHPWQ3xTKBSSz+dTMBhUVlaW9Th9Qn5+fkzbtba2xnkS4NEGDRoU9TY3btxIwCR4kMd9HucVEADABAECAJiIe4Def/99eTyeiGX8+PHxfhgAQIpLyB+ke/7553XgwIH/P8gA/u4dACBSQsowYMAA+f3+RHxrAECaSMh7QGfOnFFBQYHGjBmj119/XefOnXvgul1dXQqFQhELACD9xT1AxcXF2rZtm2pra7V582a1tLToxRdfVHt7e4/r19TUyOfzhZdAIBDvkQAASSjhnwO6evWqRo0apQ0bNmjJkiX33d/V1aWurq7w16FQSIFAgM8B9SI+B4RUwueAkt/jfg4o4WcHDB06VM8995yampp6vN/r9crr9SZ6DABAkkn454CuXbum5ubmmH/KBgCkp7gH6K233lJ9fb3Onj2rv/3tb3r55ZfVv39/vfrqq/F+KABACov7r+AuXLigV199VVeuXNHw4cP1wgsv6MiRIxo+fHi8HwoAkMLiHqCdO3fG+1siwTiZAKmkvLzcegTECdeCAwCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMJPwP0gFPKsF/tBeAEV4BAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMDrAcA+qLa2tqot1mxYkXU25w9ezbqbZLd6NGjo96muro6pscqLy+PaTs8Hl4BAQBMECAAgImoA3T48GHNnTtXBQUF8ng82rNnT8T9zjmtWbNG+fn5Gjx4sEpLS3XmzJl4zQsASBNRB6ijo0NFRUXatGlTj/evX79eH374obZs2aKjR4/qqaee0uzZs9XZ2fnEwwIA0kfUJyGUlZWprKysx/ucc9q4caPeffddzZs3T5L08ccfKy8vT3v27NErr7zyZNMCANJGXN8DamlpUWtrq0pLS8O3+Xw+FRcXq6Ghocdturq6FAqFIhYAQPqLa4BaW1slSXl5eRG35+Xlhe+7V01NjXw+X3gJBALxHAkAkKTMz4KrqqpSMBgML+fPn7ceCQDQC+IaIL/fL0lqa2uLuL2trS183728Xq+ysrIiFgBA+otrgAoLC+X3+1VXVxe+LRQK6ejRoyopKYnnQwEAUlzUZ8Fdu3ZNTU1N4a9bWlp08uRJZWdna+TIkVq1apV++9vf6tlnn1VhYaHee+89FRQUaP78+fGcGwCQ4qIO0LFjx/TSSy+Fv66srJQkLV68WNu2bdPbb7+tjo4OLVu2TFevXtULL7yg2tpaDRo0KH5TAwBSnsc556yH+KZQKCSfz6dgMMj7Qb3E4/FYj/BQsRyisVzsU+KCn+kq1h+AN2/eHPU2XMD08Z/Hzc+CAwD0TQQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDB1bCR9FfDBlLJ6NGjo96muro66m2S+arbXA0bAJDUCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATXIwUGjx4cEzbdXZ2xnkSxNvy5ctj2m7z5s1xnqRn69ati3qbqqqqBExia9CgQVFvc+PGjQRMEh9cjBQAkNQIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMDrAeAverq6pi2S8eLQsYilgt+9tbFPpPdL3/5y155nGQ/VvvqhX15BQQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmPA455z1EN8UCoXk8/kUDAaVlZVlPQ6APszj8ViP8EBJ9tQd4XGfx3kFBAAwQYAAACaiDtDhw4c1d+5cFRQUyOPxaM+ePRH3l5eXy+PxRCxz5syJ17wAgDQRdYA6OjpUVFSkTZs2PXCdOXPm6NKlS+Flx44dTzQkACD9RP0XUcvKylRWVvbQdbxer/x+f8xDAQDSX0LeAzp06JByc3M1btw4rVixQleuXHngul1dXQqFQhELACD9xT1Ac+bM0ccff6y6ujr97ne/U319vcrKynT79u0e16+pqZHP5wsvgUAg3iMBAJLQE30OyOPxaPfu3Zo/f/4D1/n3v/+tsWPH6sCBA5o5c+Z993d1damrqyv8dSgUUiAQ4HNAAMzxOaDYJM3ngMaMGaOcnBw1NTX1eL/X61VWVlbEAgBIfwkP0IULF3TlyhXl5+cn+qEAACkk6rPgrl27FvFqpqWlRSdPnlR2drays7O1du1aLVy4UH6/X83NzXr77bf1zDPPaPbs2XEdHACQ2qIO0LFjx/TSSy+Fv66srJQkLV68WJs3b9apU6f05z//WVevXlVBQYFmzZql3/zmN/J6vfGbGgCQ8rgYKQA8ACchxCZpTkIAAKAnBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgIkB1gPAXm1tbUzbrVixIuptzp49G9Nj9ZbRo0dHvU11dXXU25SXl0e9DZBueAUEADBBgAAAJqIKUE1NjaZMmaLMzEzl5uZq/vz5amxsjFins7NTFRUVGjZsmJ5++mktXLhQbW1tcR0aAJD6ogpQfX29KioqdOTIEe3fv1+3bt3SrFmz1NHREV5n9erV+vzzz7Vr1y7V19fr4sWLWrBgQdwHBwCktqhOQrj3zept27YpNzdXx48f1/Tp0xUMBvXHP/5R27dv149//GNJ0tatW/Wd73xHR44c0Q9/+MP4TQ4ASGlP9B5QMBiUJGVnZ0uSjh8/rlu3bqm0tDS8zvjx4zVy5Eg1NDT0+D26uroUCoUiFgBA+os5QN3d3Vq1apWmTZumCRMmSJJaW1uVkZGhoUOHRqybl5en1tbWHr9PTU2NfD5feAkEArGOBABIITEHqKKiQqdPn9bOnTufaICqqioFg8Hwcv78+Sf6fgCA1BDTB1FXrlypffv26fDhwxoxYkT4dr/fr5s3b+rq1asRr4La2trk9/t7/F5er1derzeWMQAAKSyqV0DOOa1cuVK7d+/WwYMHVVhYGHH/5MmTNXDgQNXV1YVva2xs1Llz51RSUhKfiQEAaSGqV0AVFRXavn279u7dq8zMzPD7Oj6fT4MHD5bP59OSJUtUWVmp7OxsZWVl6c0331RJSQlnwAEAIkQVoM2bN0uSZsyYEXH71q1bw9e2+v3vf69+/fpp4cKF6urq0uzZs/WHP/whLsMCANKHxznnrIf4plAoJJ/Pp2AwqKysLOtx+oR7f5X6uJL9wqK9ZdCgQVFvc/eHuWhwAdPe5/F4rEd4oCR76o7wuM/jXAsOAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJrgaNrRu3bqYtquqqorzJH1HLFfQvnHjRgImwcNwNezYcDVsAEBSI0AAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMcDFSpKV0vMDq6NGjo96muro6pscqLy+Pabt0w8VIY8PFSAEASY0AAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHFSIFvGDx4cNTbdHZ2JmAS4OGS7Kk7AhcjBQAkNQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxADrAYBkUl1dHfU2VVVVCZgESH+8AgIAmCBAAAATUQWopqZGU6ZMUWZmpnJzczV//nw1NjZGrDNjxgx5PJ6IZfny5XEdGgCQ+qIKUH19vSoqKnTkyBHt379ft27d0qxZs9TR0RGx3tKlS3Xp0qXwsn79+rgODQBIfVGdhFBbWxvx9bZt25Sbm6vjx49r+vTp4duHDBkiv98fnwkBAGnpid4DCgaDkqTs7OyI2z/55BPl5ORowoQJqqqq0vXr1x/4Pbq6uhQKhSIWAED6i/k07O7ubq1atUrTpk3ThAkTwre/9tprGjVqlAoKCnTq1Cm98847amxs1Geffdbj96mpqdHatWtjHQMAkKJiDlBFRYVOnz6tL7/8MuL2ZcuWhf89ceJE5efna+bMmWpubtbYsWPv+z5VVVWqrKwMfx0KhRQIBGIdCwCQImIK0MqVK7Vv3z4dPnxYI0aMeOi6xcXFkqSmpqYeA+T1euX1emMZAwCQwqIKkHNOb775pnbv3q1Dhw6psLDwkducPHlSkpSfnx/TgACA9BRVgCoqKrR9+3bt3btXmZmZam1tlST5fD4NHjxYzc3N2r59u37yk59o2LBhOnXqlFavXq3p06dr0qRJCfkPAACkpqgCtHnzZkl3Pmz6TVu3blV5ebkyMjJ04MABbdy4UR0dHQoEAlq4cKHefffduA0MAEgPUf8K7mECgYDq6+ufaCAAQN/gcY+qSi8LhULy+XwKBoPKysqyHgdIiHXr1kW9DVfdTl+xXK7s7m+kktHjPo9zMVIAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQXIwUAxBUXIwUAJDUCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmBlgPcK+7l6YLhULGkwAAYnH3+ftRlxpNugC1t7dLkgKBgPEkAIAn0d7eLp/P98D7k+5q2N3d3bp48aIyMzPl8Xgi7guFQgoEAjp//nyfvlI2++EO9sMd7Ic72A93JMN+cM6pvb1dBQUF6tfvwe/0JN0roH79+mnEiBEPXScrK6tPH2B3sR/uYD/cwX64g/1wh/V+eNgrn7s4CQEAYIIAAQBMpFSAvF6vqqur5fV6rUcxxX64g/1wB/vhDvbDHam0H5LuJAQAQN+QUq+AAADpgwABAEwQIACACQIEADCRMgHatGmTRo8erUGDBqm4uFh///vfrUfqde+//748Hk/EMn78eOuxEu7w4cOaO3euCgoK5PF4tGfPnoj7nXNas2aN8vPzNXjwYJWWlurMmTM2wybQo/ZDeXn5fcfHnDlzbIZNkJqaGk2ZMkWZmZnKzc3V/Pnz1djYGLFOZ2enKioqNGzYMD399NNauHCh2trajCZOjMfZDzNmzLjveFi+fLnRxD1LiQB9+umnqqysVHV1tb766isVFRVp9uzZunz5svVove7555/XpUuXwsuXX35pPVLCdXR0qKioSJs2berx/vXr1+vDDz/Uli1bdPToUT311FOaPXu2Ojs7e3nSxHrUfpCkOXPmRBwfO3bs6MUJE6++vl4VFRU6cuSI9u/fr1u3bmnWrFnq6OgIr7N69Wp9/vnn2rVrl+rr63Xx4kUtWLDAcOr4e5z9IElLly6NOB7Wr19vNPEDuBQwdepUV1FREf769u3brqCgwNXU1BhO1fuqq6tdUVGR9RimJLndu3eHv+7u7nZ+v9998MEH4duuXr3qvF6v27Fjh8GEvePe/eCcc4sXL3bz5s0zmcfK5cuXnSRXX1/vnLvz/37gwIFu165d4XX++c9/OkmuoaHBasyEu3c/OOfcj370I/ezn/3MbqjHkPSvgG7evKnjx4+rtLQ0fFu/fv1UWlqqhoYGw8lsnDlzRgUFBRozZoxef/11nTt3znokUy0tLWptbY04Pnw+n4qLi/vk8XHo0CHl5uZq3LhxWrFiha5cuWI9UkIFg0FJUnZ2tiTp+PHjunXrVsTxMH78eI0cOTKtj4d798Ndn3zyiXJycjRhwgRVVVXp+vXrFuM9UNJdjPReX3/9tW7fvq28vLyI2/Py8vSvf/3LaCobxcXF2rZtm8aNG6dLly5p7dq1evHFF3X69GllZmZaj2eitbVVkno8Pu7e11fMmTNHCxYsUGFhoZqbm/WrX/1KZWVlamhoUP/+/a3Hi7vu7m6tWrVK06ZN04QJEyTdOR4yMjI0dOjQiHXT+XjoaT9I0muvvaZRo0apoKBAp06d0jvvvKPGxkZ99tlnhtNGSvoA4f/KysrC/540aZKKi4s1atQo/eUvf9GSJUsMJ0MyeOWVV8L/njhxoiZNmqSxY8fq0KFDmjlzpuFkiVFRUaHTp0/3ifdBH+ZB+2HZsmXhf0+cOFH5+fmaOXOmmpubNXbs2N4es0dJ/yu4nJwc9e/f/76zWNra2uT3+42mSg5Dhw7Vc889p6amJutRzNw9Bjg+7jdmzBjl5OSk5fGxcuVK7du3T1988UXEn2/x+/26efOmrl69GrF+uh4PD9oPPSkuLpakpDoekj5AGRkZmjx5surq6sK3dXd3q66uTiUlJYaT2bt27Zqam5uVn59vPYqZwsJC+f3+iOMjFArp6NGjff74uHDhgq5cuZJWx4dzTitXrtTu3bt18OBBFRYWRtw/efJkDRw4MOJ4aGxs1Llz59LqeHjUfujJyZMnJSm5jgfrsyAex86dO53X63Xbtm1z//jHP9yyZcvc0KFDXWtrq/VovernP/+5O3TokGtpaXF//etfXWlpqcvJyXGXL1+2Hi2h2tvb3YkTJ9yJEyecJLdhwwZ34sQJ95///Mc559y6devc0KFD3d69e92pU6fcvHnzXGFhobtx44bx5PH1sP3Q3t7u3nrrLdfQ0OBaWlrcgQMH3Pe//3337LPPus7OTuvR42bFihXO5/O5Q4cOuUuXLoWX69evh9dZvny5GzlypDt48KA7duyYKykpcSUlJYZTx9+j9kNTU5P79a9/7Y4dO+ZaWlrc3r173ZgxY9z06dONJ4+UEgFyzrmPPvrIjRw50mVkZLipU6e6I0eOWI/U6xYtWuTy8/NdRkaG+/a3v+0WLVrkmpqarMdKuC+++MJJum9ZvHixc+7Oqdjvvfeey8vLc16v182cOdM1NjbaDp0AD9sP169fd7NmzXLDhw93AwcOdKNGjXJLly5Nux/Sevrvl+S2bt0aXufGjRvupz/9qfvWt77lhgwZ4l5++WV36dIlu6ET4FH74dy5c2769OkuOzvbeb1e98wzz7hf/OIXLhgM2g5+D/4cAwDARNK/BwQASE8ECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgIn/AWzMA6zgVACzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "this is the 6\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": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "this is the 7\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": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "this is the 3\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": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "this is the 6\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_number =1\n",
    "while os.path.isfile(f\"./images/{image_number}.png\"):\n",
    "    try:\n",
    "        img = cv2.imread(f\"./images/{image_number}.png\")[:,:,0]\n",
    "        img=np.invert(np.array([img]))\n",
    "        prediction = model.predict(img)\n",
    "        print(f\"this is the {np.argmax(prediction)}\")\n",
    "\n",
    "        plt.imshow(img[0],cmap = plt.cm.binary)\n",
    "        plt.show()\n",
    "    except:\n",
    "        print(\"ERROR!\")\n",
    "    finally:\n",
    "        image_number+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4d471c-cc2c-4a82-9a2b-39ed8cfe7c6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57dbd747-ba15-4f7d-9a6c-aedf87dbf5ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf07ddd-f0a8-411d-a012-f1781aa61949",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6230bcb5-31bd-4f60-85a7-5570a527708a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}