04_01_CNN-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.4.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfds.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset, metadata = tfds.load('mnist', as_supervised=True, with_info=True)\n",
    "train_dataset, test_dataset = dataset['train'], dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(images, labels):\n",
    "    images = tf.cast(images, tf.float32)\n",
    "    images /= 255\n",
    "    return images, labels\n",
    "\n",
    "train_dataset =  train_dataset.map(normalize)\n",
    "test_dataset  =  test_dataset.map(normalize)\n",
    "\n",
    "#train_dataset =  train_dataset.cache()\n",
    "#test_dataset  =  test_dataset.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image, label in test_dataset.take(1):\n",
    "    break\n",
    "image = image.numpy().reshape((28,28))\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(image, cmap=plt.cm.binary)\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "i = 0\n",
    "for (image, label) in test_dataset.take(25):\n",
    "    image = image.numpy().reshape((28,28))\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(image, cmap=plt.cm.binary)\n",
    "    i += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "train_dataset = train_dataset.cache().repeat().shuffle(60000).batch(BATCH_SIZE)\n",
    "test_dataset = test_dataset.cache().batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<BatchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>\n"
     ]
    }
   ],
   "source": [
    "print (train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu, input_shape=(28, 28, 1)),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
    "\n",
    "    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
    "\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3136)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               401536    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 421,642\n",
      "Trainable params: 421,642\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 39s 19ms/step - loss: 0.1162 - accuracy: 0.9639\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 39s 21ms/step - loss: 0.0419 - accuracy: 0.9871\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0254 - accuracy: 0.9923\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 34s 18ms/step - loss: 0.0199 - accuracy: 0.9937\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 38s 20ms/step - loss: 0.0155 - accuracy: 0.9949\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(60000/BATCH_SIZE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step - loss: 0.0321 - accuracy: 0.9899\n",
      "Accuracy on test dataset: 0.9898999929428101\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(10000/BATCH_SIZE))\n",
    "print('Accuracy on test dataset:', test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xlabel('Epoch Number')\n",
    "plt.plot(hist.history['loss']);\n",
    "plt.plot(hist.history['accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAtI0lEQVR4nO3deXxV9Z3/8deH7CxJgIQ17JuiIkqk7igKg7XjgrZqa6vVGduOtq4z1Zn5dVqnjtOOS9VaW1uwOtZaa0fLOFpBwAVxC5VFRDBsshO2hCV7Pr8/zglcQ5YbyM3J8n4+HvfBud9zvud+7gn3fu73+z3ne8zdERERiVeXqAMQEZH2RYlDRESaRYlDRESaRYlDRESaRYlDRESaJTnqAFpDTk6ODx06NOowRETalUWLFu1w99y65Z0icQwdOpSCgoKowxARaVfMbH195eqqEhGRZlHiEBGRZlHiEBGRZlHiEBGRZlHiEBGRZklo4jCzaWa20swKzezOetYPMbO5ZrbUzF43s7yYdT8xs4/CxxUx5b81s7Vmtjh8jE/kexARkc9LWOIwsyTgUeACYCxwlZmNrbPZfcBT7j4OuBu4N6x7IXAyMB74AnCHmWXG1PtHdx8fPhYn6j2IiMjhEnkdx0Sg0N3XAJjZs8DFwMcx24wFbguX5wMvxpS/6e5VQJWZLQWmAc8lMF4RkXarqrqGon3lbCspZ2txGdv3lrG1uIxvTRpBVkZKi75WIhPHQGBDzPONBK2HWEuA6cBDwKVADzPrHZb/m5ndD3QFzuXzCeceM/sBMBe4093L6764md0A3AAwePDgFnlDIiKtzd3Zc6CSrSVlbDv4KGdrSRnbY5Z37Cun7u2VkroYF48f2K4SRzzuAH5uZtcCbwKbgGp3n21mpwALgSLgHaA6rHMXsBVIBR4Hvk/QzfU57v54uJ78/HzdrUpE2pwDFVWfayFsKylja3E52/aWsa24LPi3pJyKqprD6vbqlkqfHmn0y0pnbP9M+mal0zczjX6Z6fQNH727pdKli7V43IlMHJuAQTHP88Kyg9x9M0GLAzPrDlzm7nvCdfcA94TrngFWheVbwurlZvYEQfIREWkzKqtrKNpbfrB1UNtSCFoJ5QdbD3vLqg6r2zU1iX6Z6fTJTGPC4J4Hk0DfzHT6ZaXRp0ewLi05KYJ3Fkhk4vgAGGVmwwgSxpXAV2M3MLMcYJe71xC0JGaG5UlAtrvvNLNxwDhgdriuv7tvMTMDLgE+SuB7EBE5yN3ZfaAyJgkc3kLYWlzOzv2HdxsldzH69Eijb1Y6I3O7c+bIHPrUaSH0zUyje1oywddb25WwxOHuVWZ2E/AqkATMdPflZnY3UODus4BzgHvNzAm6qm4Mq6cAb4UHrwS4OhwoB/idmeUCBiwGvp2o9yAinceBiiq2FjfeQtheUk5F9eHdRr27pdInM51+mWkcPyDrsBZCv6x0enVNTLdRFMzrpsUOKD8/3zU7rkjnVNttdKiFUMa2veWfG0PYVlzG3vLDu426pSYFYwfhl399LYQ+PdJJTe6Y11Kb2SJ3z69bHvXguIjIEXF3du2vOKyFsK2kPEgQ4XJ93UYpSUafHsEX/6g+QbdRbQuhb4/0cKA5ne5p+oqsj46KiLQ5NTXOjn3lbC4uY/OeUjbvKWVLcRlbiksPnoVUtLf+bqOc7qkHu4fG5cV0G2UeajH07EDdRlFQ4hCRVuXulJRWsWlPKVuKSw8mhy17guUtxaVsLS6jsvrzzYSuqUn0ywoSwBeG9To4ptA381ALIbd7WoftNmpLlDhEpEWVVlSzubiULXvK2FwcthZil4vLOFBR/bk6yV2MflnpDMjOYMLgnvTPzmBAdgYDstLpn5XBwOwMMjPa/tlGnYUSh4jErbK6hm0lZWw52IUUtBA27wlbDcWl7D5QeVi93B5pDMjOYHTfHkwa3YcB2UGS6J+VzsDsDHK6p6nrqB1R4hARIBhX2Lm/IkwEdZJC2ILYvreMmjoDzZnpyUHrIDuDkwZnh8uHWgp9MzvuWUedlRKHSCdRUlYZdBntKT3UlVS7XFzGlj1lhw02p6d0YUBWBv2z0zlrVE7QhZSVTv/sDAaGyaGbzjzqdPQXF+kAyiqr2VrbfVRcFg40f77VsK/OdQpJXYx+men0z0rnxLxsph2fzoCsjINdSAOyM+jZNUXjCnIYJQ6RNq6quobte8vrjCV8vuWwc3/FYfVyuqfSPyuDYTndOH1ETsy4QtCV1KdHOkkaV5AjoMQhEqHai9i21LleIfYU1W17y6muM7DQIy2Z/mEiOGFgNgPCFkL/7KDV0C8rnfSU6CbBk45NiUOklbk7H6zbzYwFa3hjVRFllZ8fV0hN7hJ0FWVlcOqI3oe6j8Kk0D87ncz0lr2/gkhzKHGItJKKqhpeXraFGQvWsmxTMdldU7gifxBDc7od7D4akJ1B726pGleQNk2JQyTBdu+v4Jn3P+Opd9axraScEbnduOfS45l+Uh4ZqepOkvZHiUMkQQq372Pm22v5n79upKyyhrNG5fCfl41j0qhcXewm7ZoSh0gLcncWFO5gxoK1vL6yiNTkLlw6fiDXnTmMMf16RB2eSItQ4hBpAWWV1fx58SZmLljHym17yemexq3nj+Zrpw4mp3ta1OGJtCglDpGjULS3nP9+dz2/e3c9O/dXcEy/HvzX5eO4aPyASO8JLZJIShwiR2DFlhJmLFjLrMWbqaiu4bxj+nD9mcM4bURvnRElHZ4Sh0icamqc+Su3M2PBWhau3klGShJXnDKIb54xlOG53aMOT6TVKHGINOFARRV/WrSRJ95ex5od++mXmc73px3DVRMHkd01NerwRFqdEodIA7YUl/LkwvX8/v3PKC6t5MS8LB66cjxfPKE/KUmaJlw6LyUOkTqWbNjDjAVreXnZFmrc+Zvj+nH9mcOYMKSnxi9EUOIQAaC6xpm9fCszFqylYP1uuqclc83pQ7n29KEM6tU16vBE2pSEJg4zmwY8BCQBv3H3/6yzfggwE8gFdgFXu/vGcN1PgAvDTf/d3f8Qlg8DngV6A4uAr7v74XNKi8Rhb1klf/hgA79duI6Nu0sZ1CuD//elsXwlP48emkhQpF4JSxxmlgQ8CkwBNgIfmNksd/84ZrP7gKfc/UkzmwzcC3zdzC4ETgbGA2nA62b2iruXAD8BHnT3Z83sl8D1wGOJeh/SMW3YdYAn3l7HcwUb2FdexSlDe/KvFx7LlLH9dI8KkSYkssUxESh09zUAZvYscDEQmzjGAreFy/OBF2PK33T3KqDKzJYC08zsj8Bk4Kvhdk8CP0SJQ+Lg7hSs382Mt9Yy++OtdDHjwnH9uf7MYYzLy446PJF2I5GJYyCwIeb5RuALdbZZAkwn6M66FOhhZr3D8n8zs/uBrsC5BAmnN7AnTCi1+xxY34ub2Q3ADQCDBw9uifcj7VRl9aHpzJduLCYrI4VvTRrBNacNpV9WetThibQ7UQ+O3wH83MyuBd4ENgHV7j7bzE4BFgJFwDtAdXN27O6PA48D5OfnexObSwe050A4nfnC9WwtKWN4Tjf+/ZLjuezkgXRNjfq/vkj7lchPzyZgUMzzvLDsIHffTNDiwMy6A5e5+55w3T3APeG6Z4BVwE4g28ySw1bHYfsUWV20jyfeXsufFm2itLKaM0b25j+mH885o/toOnORFpDIxPEBMCo8C2oTcCWHxiYAMLMcYJe71wB3EZxhVTuwnu3uO81sHDAOmO3ubmbzgcsJzqy6BvhzAt+DtBPuzsLVO5mxYC3zPtlOalIXLh4/gOvOHMax/TOjDk+kQ0lY4nD3KjO7CXiV4HTcme6+3MzuBgrcfRZwDnCvmTlBV9WNYfUU4K3wYqsSgtN0a8c1vg88a2Y/Bj4EZiTqPUjbV1ZZzawlm5m5YC2fbN1L726p3HzeKK4+dQi5PTSduUgimHvH7/7Pz8/3goKCqMOQFrRjXzlPv7uep99dz459wXTm150xjIvGDyA9RdOZi7QEM1vk7vl1yzVCKO3KJ1tLmLlgLS8u3kxFVQ3njsnl+jOHc8ZITWcu0lqUOKTNq6lx3lhVxIwFa1lQuIP0lC58eUIe3zxjGCP7aDpzkdamxCFtVmlFNX/660Zmvr2WNUX76ZuZxj9NG8NVpwymZzdNZy4SFSUOaXO2Fpfx1DvreOb9z9hzoJJxms5cpE1R4pA2Y9nGYmYsWMNLS4PpzKeO7cf1Zw0jX9OZi7QpShwSqeoaZ87H25i5YC3vr9tF97RkvnFaMJ354N6azlykLVLikEjsK6/iuXA68892HSCvZwb/euGxfOWUQWRqOnORNk2JQ1rVhl0HeHLhOv7wwQb2lleRP6Qnd11wDFPG9iVZ4xci7YIShyScu7No/W5mvr2Wv3y0FTPjwhP6c92Zwxg/KDvq8ESkmZQ4JGFqpzOfuWAtSzYWk5mezA1nj+Ca04fQPysj6vBE5AgpcUiLKz5QGUxn/s46thSXMSynG/9+8XFcNiFP05mLdAD6FEuL2V5SxiPzCnl+0UZKK6s5fURvfnzJ8Zw7RtOZi3QkShzSItydm575kMUb9nDR+AFcd8Ywxg7QdOYiHZESh7SItz7dwfvrdvHvlxzP108dEnU4IpJAOv9Rjpq7c//slQzMzuCK/EFNVxCRdk2JQ47aayu2s2RjMTefP4rUZP2XEuno9CmXo1JTE7Q2huV0Y/pJA6MOR0RagRKHHJX/W7aFT7bu5ZbzR+nKb5FOQp90OWJV1TU8+NoqxvTtwd+OGxB1OCLSSpQ45Ii9uHgza4r2c+uU0bpOQ6QTUeKQI1JRVcNDc1dx/MBM/ua4vlGHIyKtSIlDjsgfF21gw65Sbp86RjdZEulkEpo4zGyama00s0Izu7Oe9UPMbK6ZLTWz180sL2bdT81suZmtMLOHLfx2CrdbaWaLw0efRL4HOVxZZTWPzC1kwpCenDM6N+pwRKSVJSxxmFkS8ChwATAWuMrMxtbZ7D7gKXcfB9wN3BvWPR04AxgHHA+cAkyKqfc1dx8fPrYn6j1I/Z557zO2lpRx+9TRam2IdEJNJg4LXG1mPwifDzaziXHseyJQ6O5r3L0CeBa4uM42Y4F54fL8mPUOpAOpQBqQAmyL4zUlwQ5UVPGL1ws5fURvTh+RE3U4IhKBeFocvwBOA64Kn+8laEk0ZSCwIeb5xrAs1hJgerh8KdDDzHq7+zsEiWRL+HjV3VfE1Hsi7Kb6f6afvK3qyYXr2bGvgtunjo46FBGJSDyJ4wvufiNQBuDuuwlaAi3hDmCSmX1I0BW1Cag2s5HAsUAeQbKZbGZnhXW+5u4nAGeFj6/Xt2Mzu8HMCsysoKioqIXC7dxKyir55RurOXdMLhOG9Io6HBGJSDyJozIcr3AAM8sFauKotwmInfEuLyw7yN03u/t0dz8J+JewbA9B6+Ndd9/n7vuAVwhaPbj7pvDfvcAzBF1ih3H3x909393zc3M1gNsSZi5YS3FpJbdNGRN1KCISoXgSx8PAC0AfM7sHWAD8Rxz1PgBGmdkwM0sFrgRmxW5gZjlmVhvDXcDMcPkzgpZIspmlELRGVoTPc8K6KcCXgI/iiEWO0u79Fcx4ay3TjuvHCXlZUYcjIhFq8n4c7v47M1sEnAcYcEmd8YaG6lWZ2U3Aq0ASMNPdl5vZ3UCBu88CzgHuNTMH3gRuDKs/D0wGlhG0dP7i7v9rZt2AV8OkkQS8Bvy6We9Yjsjjb61hX0UVt07R2IZIZ2fuXv8Ks0Y7sd19V0IiSoD8/HwvKCiIOox2q2hvOWf/dD5Tj+vLQ1eeFHU4ItJKzGyRu+fXLW+sxbGI4Ne+AYOB3eFyNkFX0rCWD1PaosdeX01FdQ23nK/Whog0Msbh7sPcfThBd9DfunuOu/cmGFeY3VoBSrS2FJfy9HvrufzkPIbldIs6HBFpA+IZHD/V3V+ufeLurwCnJy4kaUsemVeIu/Pd80ZGHYqItBFNDo4Dm83sX4Gnw+dfAzYnLiRpKz7beYDnPtjAV78wmLyeXaMOR0TaiHhaHFcBuQSn5L4A9OHQVeTSgT0091OSuhg3nqvWhogcEs/puLuAm1shFmlDCrfv44UPN3L9mcPom5kedTgi0oY0mTjMbD7hVeOx3H1yQiKSNuFnr60iPSWJb08aEXUoItLGxDPGcUfMcjpwGVCVmHCkLVixpYSXlm7hpnNH0rt7WtThiEgbE09X1aI6RW+b2fsJikfagAfmrKJHejJ/f9bwqEMRkTYonq6q2CvIuwATAE1W1EEt2bCHOR9v4/Ypo8nqmhJ1OCLSBsXTVRV7BXkVsBa4PpFBSXTun7OKnl1T+OaZmhhAROoXT+I41t3LYgvMTB3fHdD7a3fx5qoi/vmLx9A9LZ7/GiLSGcVzHcfCesreaelAJFruzn2zV5LbI42vnzo06nBEpA1r8GelmfUjuPtehpmdRNBVBZAJ6DLiDubtwp28v3YXP7roODJSk6IOR0TasMb6I/4GuJbgzn0PxJTvBf45gTFJK6ttbQzISufKiYOariAinVqDicPdnwSeNLPL3P1PrRiTtLJ5n2xn8YY9/Of0E0hLVmtDRBrXWFfV1e7+NDDUzG6ru97dH6inmrQzNTXO/bNXMaR3Vy6bkBd1OCLSDjTWVVV784XurRGIROMvy7fy8ZYSHrziRFKS4jlXQkQ6u8a6qn4V/vuj1gtHWlN1jfPAnFWM7NOdi04cGHU4ItJOxHPleC7w98DQ2O3d/brEhSWt4c+LN1G4fR+/+NrJJHWxpiuIiBDfBYB/Bt4iuIVsdWLDkdZSWV3Dz177lOMGZDLtuH5RhyMi7Ug8iaOru38/4ZFIq3p+0UY+23WAmdfm00WtDRFphnhGQ18ysy8mPBJpNWWV1Tw891NOGpzNuWP6RB2OiLQz8SSOmwmSR6mZlZjZXjMriWfnZjbNzFaaWaGZ3VnP+iFmNtfMlprZ62aWF7Pup2a23MxWmNnDZmZh+QQzWxbu82C5xO/Z9z9jS3EZd0wdgw6fiDRXk4nD3Xu4exd3z3D3zPB5ZlP1zCwJeBS4ABgLXGVmY+tsdh/wlLuPA+4G7g3rng6cAYwDjgdOASaFdR4jGKwfFT6mNf02pVZpRTU/n7+aU4f34vQRvaMOR0TaoXjOqjq5nuJiYL27N3YnwIlAobuvCffzLHAx8HHMNmOB2osL5wMvhstOcLfBVII5slKAbWbWH8h093fDfT4FXAK80tT7kMBT76xjx75yHrv6ZLU2ROSIxNNV9QvgXeDX4eNd4I/ASjOb2ki9gcCGmOcbw7JYS4Dp4fKlQA8z6+3u7xAkki3h41V3XxHW39jEPgEwsxvMrMDMCoqKipp+l53A3rJKfvnGaiaNzuWUob2ariAiUo94Esdm4CR3n+DuE4DxwBpgCvDTo3z9O4BJZvYhQVfUJqDazEYCxxJMsDgQmGxmZzVnx+7+uLvnu3t+bm7uUYbZMTzx9jp2H6jk9qmjow5FRNqxeBLHaHdfXvvE3T8GjqntgmrEJiB2qtW8sOwgd9/s7tPd/STgX8KyPQStj3fdfZ+77yPoijotrJ/X2D6lfnsOVPDrN9cwdWxfxuVlRx2OiLRj8SSO5Wb2mJlNCh+/AD4O7wJY2Ui9D4BRZjbMzFKBK4FZsRuYWY6Z1cZwFzAzXP6MoCWSbGYpBK2RFe6+BSgxs1PDs6m+QXCBojTh12+tYV9FFbeptSEiRymexHEtUAjcEj7WhGWVwLkNVQoHzm8CXgVWAM+5+3Izu9vMLgo3O4dgrGQV0Be4Jyx/HlgNLCMYB1ni7v8brvsH4DdhTKvRwHiTduwr54m31/GlcQM4pl+TJ8SJiDTK3D3qGBIuPz/fCwoKog4jMj9+6WNmvr2WObdNYkSuJjsWkfiY2SJ3z69bHs/puKMIrq8YS3CKLADuPrxFI5SE2Fpcxn+/u57pJ+cpaYhIi4inq+oJgovuqgi6pp4Cnk5kUNJyHp1fSHWNc/N5o6IORUQ6iHgSR4a7zyXo1lrv7j8ELkxsWNISNuw6wLMffMYVpwxiUK+uUYcjIh1EPLPjlodnPn1qZjcRnP6qPo924OG5n2Jm3DR5ZNShiEgHEu8kh12B7wETgK8D1yQyKDl6a4r28ae/buTqLwyhf1ZG1OGISAfSZIvD3T8IF/cB30xsONJSfvbap6QlJ/Gdc0ZEHYqIdDANJg4zm9XQOgB3v6ix9RKdT7aW8L9LN/PtSSPI7ZEWdTgi0sE01uI4jWCSwt8D7xHMUivtwINzVtE9NZlvna0zpkWk5TWWOPoRTGR4FfBV4P+A38fOWyVtz7KNxby6fBu3nj+a7K6pUYcjIh1Qg4Pj7l7t7n9x92uAUwmm+Hg9PLNK2qj756wku2sK1505NOpQRKSDanRwPJzI8EKCVsdQ4GHghcSHJUeiYN0uXl9ZxJ0XHEOP9JSowxGRDqqxwfGnCG7b+jLwI3f/qNWikiNy/+xV5HRP4xunDYk6FBHpwBq7juNqgnt63wwsNLOS8LHXzEpaJzyJ18LCHbyzZic3njuCrqnxXNcpInJkGvyGcfd4Lg6UNsDduW/2SvpnpXPVxMFRhyMiHZySQwfw+soi/vrZHr47eRTpKUlRhyMiHZwSRztX29oY3KsrX87Pa7qCiMhRUuJo515dvpXlm0u4+bxRpCTpzykiidfkN42Zdau9L7iZjTazi8L7gEvEqmucB+asYkRuNy45aWDU4YhIJxHPT9Q3gXQzGwjMJpgd97eJDEri89LSzazato9bp4wmqYtmhBGR1hFP4jB3PwBMB37h7l8GjktsWNKUquoaHpyzimP69eCLx/ePOhwR6UTiShxmdhrwNYL5qgB06k7E/vTXjazbeYDbp46hi1obItKK4kkctwB3AS+4+3IzGw7MT2hU0qjyqmoenlvIiXlZnH9sn6jDEZFOJp4bOb0BvAEQDpLvcPfvJTowadgfPtjApj2l3Dv9BMzU2hCR1hXPWVXPmFmmmXUDPgI+NrN/jGfnZjbNzFaaWaGZ3VnP+iFmNtfMlprZ62aWF5afa2aLYx5lZnZJuO63ZrY2Zt345rzh9q60oppH5hUycWgvzhqVE3U4ItIJxdNVNdbdS4BLgFeAYQRnVjXKzJKAR4ELgLHAVWY2ts5m9wFPufs44G7gXgB3n+/u4919PDAZOEBwRletf6xd7+6L43gPHcbT766naG85t08drdaGiEQinsSREl63cQkwy90rAY+j3kSg0N3XuHsF8CxwcZ1txgLzwuX59awHuBx4JTyzq1PbV17FY2+s5qxROXxheO+owxGRTiqexPErYB3QDXjTzIYA8cyOO5Dg1rO1NoZlsZYQnOYLcCnQw8zqfiNeSXD72lj3hN1bD4b3DDmMmd1gZgVmVlBUVBRHuG3fb99ey679Fdw+dUzUoYhIJ9Zk4nD3h919oLt/0QPrgXNb6PXvACaZ2YfAJGATUF270sz6AycAr8bUuQs4BjgF6AV8v4G4H3f3fHfPz83NbaFwo1N8oJJfvbmG84/ty/hB2VGHIyKdWDyD41lm9kDtr3czu5+g9dGUTcCgmOd5YdlB7r7Z3ae7+0nAv4Rle2I2+QrBacCVMXW2hAmsHHiCoEusw/vNgjXsLavitimjow5FRDq5eLqqZgJ7Cb7Ev0LQTfVEHPU+AEaZ2TAzSyXocpoVu4GZ5dTOg0XQkphZZx9XUaebKmyFYMHI8CUEZ3p1aDv3lTNzwVouHNefsQMyow5HRDq5eG4VN8LdL4t5/iMzW9xUJXevMrObCLqZkoCZ4QWEdwMF7j4LOAe418ycYE6sG2vrm9lQghbLG3V2/TszywUMWAx8O4730K796s01lFZWc+v5o6IORUQkrsRRamZnuvsCADM7AyiNZ+fu/jLBPctjy34Qs/w88HwDdddx+GA67j45ntfuKLaXlPHkwnVcctJARvbpEXU4IiJxJY5vA0+ZWVb4fDdwTeJCkliPzi+kusa5+Ty1NkSkbYhnypElwIlmlhk+LzGzW4ClCY6t09u4+wDPvP8ZX84fxJDe8ZyPICKSeHHfMs7dS8IryAFuS1A8EuPn8woxjO9OHhl1KCIiBx3pvUY110WCrduxnz8u2shXvzCYAdkZUYcjInLQkSaOeKYckaPw0NxPSUky/uHcEVGHIiLyOQ2OcZjZXupPEAboJ3ACrdq2lxcXb+KGs4fTp0d61OGIiHxOg4nD3XXuZ0QenLOKbqnJfPtstTZEpO050q4qSZCPNhXzykdbue7MYfTslhp1OCIih1HiaGMemLOKrIwUrj9zWNShiIjUS4mjDVm0fjfzPtnODWcPJysjJepwRETqpcTRhjwwZyW9u6Vy7elDow5FRKRBShxtxMLVO3i7cCffOWcE3dLimQlGRCQaShxtgLvzwOxV9M1M4+pTh0QdjohIo5Q42oA3VhVRsH433508ivSUpKjDERFplBJHxNyd+2evIq9nBl/JH9R0BRGRiClxRGz2x9tYtqmYm88bRWqy/hwi0vbpmypCNTXB2MbwnG5cetJh96wSEWmTlDgi9NKyLazctpdbpowmOUl/ChFpH/RtFZGq6hp+NmcVY/r24Esn9I86HBGRuClxROSFDzexZsd+bps6mi5ddHsTEWk/lDgiUFFVw0NzP+WEgVlMHds36nBERJpFiSMCzxVsYOPuUm6fOhoztTZEpH1R4mhlZZXVPDLvU/KH9GTS6NyowxERabaEJg4zm2ZmK82s0MzurGf9EDOba2ZLzex1M8sLy881s8UxjzIzuyRcN8zM3gv3+Qcza1c3rXj63fVsKynn9qlj1NoQkXYpYYnDzJKAR4ELgLHAVWY2ts5m9wFPufs44G7gXgB3n+/u4919PDAZOADMDuv8BHjQ3UcCu4HrE/UeWtr+8ioee301Z4zszWkjekcdjojIEUlki2MiUOjua9y9AngWuLjONmOBeeHy/HrWA1wOvOLuByz4iT4ZeD5c9yRwSUsHnii/XbiOnfsruG3KmKhDERE5YolMHAOBDTHPN4ZlsZYA08PlS4EeZlb3p/iVwO/D5d7AHnevamSfAJjZDWZWYGYFRUVFR/gWWk5xaSW/emM1k4/pw4QhPaMOR0TkiEU9OH4HMMnMPgQmAZuA6tqVZtYfOAF4tbk7dvfH3T3f3fNzc6MfhJ6xYC0lZVXcNmV01KGIiByVRN4xaBMQO91rXlh2kLtvJmxxmFl34DJ33xOzyVeAF9y9Mny+E8g2s+Sw1XHYPtuiXfsrmLlgLRcc34/jB2ZFHY6IyFFJZIvjA2BUeBZUKkGX06zYDcwsx8xqY7gLmFlnH1dxqJsKd3eCsZDLw6JrgD8nIPYW9as3V7O/oopb1doQkQ4gYYkjbBHcRNDNtAJ4zt2Xm9ndZnZRuNk5wEozWwX0Be6prW9mQwlaLG/U2fX3gdvMrJBgzGNGot5DS9i+t4wnF67j4hMHMLpvj6jDERE5agm9ubW7vwy8XKfsBzHLz3PoDKm6dddRz8C3u68hOGOrXfjF/NVUVju3nK/Whoh0DFEPjndom/eU8sx7n/HlCXkMzekWdTgiIi1CiSOBHplXCMB3zxsVcSQiIi1HiSNB1u/czx8LNnDVxEEMzM6IOhwRkRajxJEgD839lKQuxo3njow6FBGRFqXEkQCF2/fy4oebuOb0ofTJTI86HBGRFqXEkQAPvvYpGSlJfOvs4VGHIiLS4pQ4WtjHm0v4v6VbuO7MYfTunhZ1OCIiLU6Jo4U9MGclmenJ/N1Zam2ISMekxNGCPvxsN6+t2M4NZw8nKyMl6nBERBJCiaMFPTBnFb26pXLtGcOiDkVEJGGUOFrIu2t28tanO/jOpBF0T0voTC4iIpFS4mgB7s4Ds1fRp0caV586JOpwREQSSomjBbz16Q7eX7eLmyaPJCM1KepwREQSSonjKLk7989eycDsDK44ZVDTFURE2jkljqP02ortLNlYzPfOG0laslobItLxKXEchZqaoLUxtHdXpp+cF3U4IiKtQonjKLz80RY+2bqXW84fTUqSDqWIdA76tjtCVdU1PDBnFaP6dOdvTxwQdTgiIq1GieMI/XnxZtYU7ee2KaNJ6mJRhyMi0mqUOI5AZXUNP5u7iuMGZDLt+H5RhyMi0qqUOI7AHws2smFXKXdMHYOZWhsi0rkocTRTWWU1j8z7lJMHZ3POmNyowxERaXVKHM30+/c/Y0txmVobItJpJTRxmNk0M1tpZoVmdmc964eY2VwzW2pmr5tZXsy6wWY228xWmNnHZjY0LP+tma01s8XhY3wi30OsAxVVPDp/NacN783pI3Na62VFRNqUhCUOM0sCHgUuAMYCV5nZ2Dqb3Qc85e7jgLuBe2PWPQX8l7sfC0wEtses+0d3Hx8+FifqPdT15ML17NhXzu1TR7fWS4qItDmJbHFMBArdfY27VwDPAhfX2WYsMC9cnl+7Pkwwye4+B8Dd97n7gQTG2qSSskp++cZqzhmTS/7QXlGGIiISqUQmjoHAhpjnG8OyWEuA6eHypUAPM+sNjAb2mNn/mNmHZvZfYQum1j1h99aDZlbvjb3N7AYzKzCzgqKioqN+MzMXrKW4tJLbp4w56n2JiLRnUQ+O3wFMMrMPgUnAJqAaSAbOCtefAgwHrg3r3AUcE5b3Ar5f347d/XF3z3f3/Nzcozv7aff+Cma8tZa/Oa4vJ+RlHdW+RETau0Qmjk1A7DzjeWHZQe6+2d2nu/tJwL+EZXsIWieLw26uKuBF4ORw/RYPlANPEHSJJdTjb61hX0UVt07R2IaISCITxwfAKDMbZmapwJXArNgNzCzHzGpjuAuYGVM328xqmwqTgY/DOv3Dfw24BPgoge+Bor3l/PbtdfztuAEc0y8zkS8lItIuJCxxhC2Fm4BXgRXAc+6+3MzuNrOLws3OAVaa2SqgL3BPWLeaoJtqrpktAwz4dVjnd2HZMiAH+HGi3gPAY6+vpryqmlvOH5XIlxERaTeSE7lzd38ZeLlO2Q9ilp8Hnm+g7hxgXD3lk1s4zAZtKS7l6ffWc9nJeQzP7d5aLysi0qZFPTjepv18XiHuzvfOU2tDRKSWEkcjBvfqyt+dNZxBvbpGHYqISJuR0K6q9u5bk0ZEHYKISJujFoeIiDSLEoeIiDSLEoeIiDSLEoeIiDSLEoeIiDSLEoeIiDSLEoeIiDSLEoeIiDSLuXvUMSScmRUB64+weg6wowXDaSmKq3kUV/MorubpqHENcffDbmjUKRLH0TCzAnfPjzqOuhRX8yiu5lFczdPZ4lJXlYiINIsSh4iINIsSR9MejzqABiiu5lFczaO4mqdTxaUxDhERaRa1OEREpFmUOEREpFmUOEJmNs3MVppZoZndWc/6NDP7Q7j+PTMb2kbiutbMisxscfj4u1aIaaaZbTezjxpYb2b2cBjzUjM7OdExxRnXOWZWHHOsftBKcQ0ys/lm9rGZLTezm+vZptWPWZxxtfoxM7N0M3vfzJaEcf2onm1a/fMYZ1yt/nmMee0kM/vQzF6qZ13LHi937/QPIAlYDQwHUoElwNg62/wD8Mtw+UrgD20krmuBn7fy8TobOBn4qIH1XwReAQw4FXivjcR1DvBSBP+/+gMnh8s9gFX1/B1b/ZjFGVerH7PwGHQPl1OA94BT62wTxecxnrha/fMY89q3Ac/U9/dq6eOlFkdgIlDo7mvcvQJ4Fri4zjYXA0+Gy88D55mZtYG4Wp27vwnsamSTi4GnPPAukG1m/dtAXJFw9y3u/tdweS+wAhhYZ7NWP2ZxxtXqwmOwL3yaEj7qnsXT6p/HOOOKhJnlARcCv2lgkxY9XkocgYHAhpjnGzn8A3RwG3evAoqB3m0gLoDLwu6N581sUIJjike8cUfhtLCr4RUzO661XzzsIjiJ4NdqrEiPWSNxQQTHLOx2WQxsB+a4e4PHqxU/j/HEBdF8Hn8G/BNQ08D6Fj1eShzt3/8CQ919HDCHQ78q5HB/JZh750TgEeDF1nxxM+sO/Am4xd1LWvO1G9NEXJEcM3evdvfxQB4w0cyOb43XbUoccbX659HMvgRsd/dFiX6tWkocgU1A7C+DvLCs3m3MLBnIAnZGHZe773T38vDpb4AJCY4pHvEcz1bn7iW1XQ3u/jKQYmY5rfHaZpZC8OX8O3f/n3o2ieSYNRVXlMcsfM09wHxgWp1VUXwem4wros/jGcBFZraOoDt7spk9XWebFj1eShyBD4BRZjbMzFIJBo9m1dlmFnBNuHw5MM/DkaYo46rTD34RQT911GYB3wjPFDoVKHb3LVEHZWb9avt1zWwiwf//hH/ZhK85A1jh7g80sFmrH7N44orimJlZrpllh8sZwBTgkzqbtfrnMZ64ovg8uvtd7p7n7kMJviPmufvVdTZr0eOVfKQVOxJ3rzKzm4BXCc5kmunuy83sbqDA3WcRfMD+28wKCQZgr2wjcX3PzC4CqsK4rk10XGb2e4KzbXLMbCPwbwQDhbj7L4GXCc4SKgQOAN9MdExxxnU58B0zqwJKgStbIflD8Ivw68CysH8c4J+BwTGxRXHM4okrimPWH3jSzJIIEtVz7v5S1J/HOONq9c9jQxJ5vDTliIiINIu6qkREpFmUOEREpFmUOEREpFmUOEREpFmUOEREpFmUOKRTMbPqmJlLF1s9Mw4fxb6HWgMz89bZ7odmdsDM+sSU7WusTkvHIHI0dB2HdDal4ZQRUdsB3A58P+pAYplZcjiXkUiD1OIQAcxsnZn91MyWWXDPhZFh+VAzmxdOWjfXzAaH5X3N7IVw8r8lZnZ6uKskM/u1BfdrmB1eYVyfmcAVZtarThyfazGY2R1m9sNw+XUze9DMCsxshZmdYmb/Y2afmtmPY3aTbGa/C7d53sy6hvUnmNkbZrbIzF6tvco53O/PzKwAOOyeHCJ1KXFIZ5NRp6vqiph1xe5+AvBzgtlGIZjY78lw0rrfAQ+H5Q8Db4ST/50MLA/LRwGPuvtxwB7gsgbi2EeQPJr7RV3h7vnAL4E/AzcCxwPXmlntbKdjgF+4+7FACfAPFsxJ9QhwubtPCF/7npj9prp7vrvf38x4pBNSV5V0No11Vf0+5t8Hw+XTgOnh8n8DPw2XJwPfgGDGVKDYzHoCa919cbjNImBoI7E8DCw2s/uaEX/tXGXLgOW181mZ2RqCSez2ABvc/e1wu6eB7wF/IUgwc8Kpp5KA2Lmw/tCMGKSTU+IQOcQbWG6O8pjlaqChrircfY+ZPUPQaqhVxed7AtIb2H9Nndeq4dDnuW7sTnD3uuXufloD4exvKE6RutRVJXLIFTH/vhMuL+TQhHBfA94Kl+cC34GDN/fJOsLXfAD4Foe+9LcBfcyst5mlAV86gn0ONrPaBPFVYAGwEsitLTezFIvgRlbSMShxSGdTd4zjP2PW9TSzpQTjDreGZd8FvhmWf51DYxI3A+ea2TKCLqmxRxKMu+8AXgDSwueVwN3A+wQ3Aqo7nXg8VgI3mtkKoCfwWHjr4cuBn5jZEmAxcHrDuxBpmGbHFSE4qwrID7/IRaQRanGIiEizqMUhIiLNohaHiIg0ixKHiIg0ixKHiIg0ixKHiIg0ixKHiIg0y/8HSLYAKTZAPV8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel(\"Loss Magnitude\")\n",
    "plt.plot(hist.history['accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cnn_mnist_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (microsoft store)",
   "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": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "81aa396d7ed62602242c01b807f9762de13f9c3a404432573a99be8bc4dd23e0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}