CIFAR in Tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "colab_type": "code",
    "id": "ZFVgD4BIS5Dj",
    "outputId": "35a1eb9b-1ee4-468e-faa8-ee821e41da63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow-gpu==2.0.0-beta1 in /usr/local/lib/python3.6/dist-packages (2.0.0b1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\n",
      "Requirement already satisfied: tb-nightly<1.14.0a20190604,>=1.14.0a20190603 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0a20190603)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.33.6)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (3.7.1)\n",
      "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.16.5)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.1.7)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.8.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.12.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.8.0)\n",
      "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.2.2)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.11.2)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.15.0)\n",
      "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0.dev2019060501)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.0.8)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (3.1.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (41.2.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (0.15.6)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-beta1) (2.8.0)\n",
      "Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.6/dist-packages (1.2.0)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (3.7.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.12.0)\n",
      "Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.3.0)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.16.5)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (5.4.8)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (4.28.1)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.16.0)\n",
      "Requirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.1.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (2.21.0)\n",
      "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.14.0)\n",
      "Requirement already satisfied: promise in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (2.2.1)\n",
      "Requirement already satisfied: wrapt in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.11.2)\n",
      "Requirement already satisfied: absl-py in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.8.0)\n",
      "Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (19.1.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-datasets) (41.2.0)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2019.6.16)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->tensorflow-datasets) (1.24.3)\n",
      "Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tensorflow-datasets) (1.6.0)\n"
     ]
    }
   ],
   "source": [
    "# tensorflow 2.0 beta and tensorflow datasets\n",
    "!pip install tensorflow-gpu==2.0.0-beta1\n",
    "!pip install tensorflow-datasets\n",
    "\n",
    "# tensorflow\n",
    "import tensorflow as     tf\n",
    "from   tensorflow import keras\n",
    "\n",
    "# tensorflow datasets\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "# additional libraries\n",
    "import math\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "dSJkRk4Sd_zq",
    "outputId": "06d8f37d-2831-4bd9-bc67-8a4444141be9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.\n"
     ]
    }
   ],
   "source": [
    "dataset_train, info = tfds.load(\"downsampled_imagenet/64x64\", split=tfds.Split.TRAIN, with_info=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UjpMfoaLy2PL"
   },
   "outputs": [],
   "source": [
    "dataset_test,  info = tfds.load(\"downsampled_imagenet/64x64\", split='validation',  with_info=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "d4ZC1eWsksrn",
    "outputId": "e338bf65-35fe-4ad7-b3cf-e0535faa2c03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "keras_layer_1 (KerasLayer)   multiple                  3540265   \n",
      "=================================================================\n",
      "Total params: 3,540,265\n",
      "Trainable params: 0\n",
      "Non-trainable params: 3,540,265\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "model = tf.keras.Sequential([\n",
    "    hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\", output_shape=[1001])\n",
    "])\n",
    "model.build([None, 224, 224, 3])  # Batch input shape.\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K4qD9MUi02cN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "38NcoYnuxY5h",
    "outputId": "f34105dc-d590-4600-850e-8fcc53f38d6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished 1, now overall length = 32\n",
      "finished 1, now overall length = 64\n",
      "finished 1, now overall length = 96\n",
      "finished 1, now overall length = 128\n",
      "finished 1, now overall length = 160\n",
      "finished 1, now overall length = 192\n"
     ]
    }
   ],
   "source": [
    "IMAGE_RES_mobilenet = 224\n",
    "TRAINING_BATCH_SIZE = 32\n",
    "\n",
    "# get labels for dataset_train\n",
    "train_batches = dataset_train.batch(TRAINING_BATCH_SIZE).prefetch(2)\n",
    "\n",
    "overall_label_arr = []\n",
    "it1 = iter(train_batches)\n",
    "\n",
    "cnt= 0\n",
    "while True:\n",
    "  try:\n",
    "    train_batch = next(it1)\n",
    "  except:\n",
    "    break\n",
    "  image_batch = train_batch['image'].numpy()\n",
    "\n",
    "  image_batch = tf.image.resize(\n",
    "      image_batch,\n",
    "      (224, 224),\n",
    "      method=tf.compat.v2.image.ResizeMethod.BILINEAR\n",
    "  )\n",
    "\n",
    "  predicted_batch = model.predict(image_batch)\n",
    "  predicted_batch = tf.squeeze(predicted_batch).numpy()\n",
    "\n",
    "  predict_labels = np.argmax(predicted_batch, axis=1)\n",
    "  overall_label_arr.extend(predict_labels)\n",
    "  print(\"finished 1, now overall length = \" + str(len(overall_label_arr)))\n",
    "  cnt += 1\n",
    "  if cnt > 5:\n",
    "    break\n",
    "\n",
    "d2 = tf.data.Dataset.from_tensor_slices(overall_label_arr)\n",
    "train_dataset_labelled = tf.data.Dataset.zip((dataset_train, d2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "qeiuM9zothgo",
    "outputId": "2853fe61-6d7e-459e-baf1-5dc5846b02b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished 1, now overall length = 32\n",
      "finished 1, now overall length = 64\n",
      "finished 1, now overall length = 96\n",
      "finished 1, now overall length = 128\n",
      "finished 1, now overall length = 160\n",
      "finished 1, now overall length = 192\n"
     ]
    }
   ],
   "source": [
    "# get labels for dataset_test\n",
    "test_batches = dataset_test.batch(TRAINING_BATCH_SIZE).prefetch(2)\n",
    "\n",
    "overall_label_arr = []\n",
    "it1 = iter(test_batches)\n",
    "\n",
    "cnt= 0\n",
    "while True:\n",
    "  try:\n",
    "    test_batch = next(it1)\n",
    "  except:\n",
    "    break\n",
    "  image_batch = test_batch['image'].numpy()\n",
    "\n",
    "  image_batch = tf.image.resize(\n",
    "      image_batch,\n",
    "      (224, 224),\n",
    "      method=tf.compat.v2.image.ResizeMethod.BILINEAR\n",
    "  )\n",
    "\n",
    "  predicted_batch = model.predict(image_batch)\n",
    "  predicted_batch = tf.squeeze(predicted_batch).numpy()\n",
    "\n",
    "  predict_labels = np.argmax(predicted_batch, axis=1)\n",
    "  overall_label_arr.extend(predict_labels)\n",
    "  print(\"finished 1, now overall length = \" + str(len(overall_label_arr)))\n",
    "  cnt += 1\n",
    "  if cnt > 5:\n",
    "    break\n",
    "\n",
    "d2 = tf.data.Dataset.from_tensor_slices(overall_label_arr)\n",
    "test_dataset_labelled = tf.data.Dataset.zip((dataset_test, d2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iN5nme-g4KKT"
   },
   "outputs": [],
   "source": [
    "from keras.regularizers import l2\n",
    "\n",
    "IMAGE_ROWS = 224\n",
    "IMAGE_COLS = 224\n",
    "CHANNELS = 3\n",
    "\n",
    "DATA_NUM_CLASSES = 1001\n",
    "TRAINING_LR_MAX = 0.001\n",
    "\n",
    "def create_model(level_0_repeats, level_1_repeats, level_2_repeats):\n",
    "  \n",
    "    # encoder - input\n",
    "    model_input = keras.Input(shape=(IMAGE_ROWS, IMAGE_COLS, CHANNELS), name='input_image')\n",
    "    x           = model_input\n",
    "    \n",
    "    # encoder - level 0\n",
    "    for n0 in range(level_0_repeats):\n",
    "        x = keras.layers.Conv2D(32, 3, strides=1, padding='same', activation=None, use_bias=False)(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "    x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n",
    "\n",
    "    # encoder - level 1\n",
    "    for n1 in range(level_1_repeats):\n",
    "        x = keras.layers.Conv2D(64, 3, strides=1, padding='same', activation=None, use_bias=False)(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "    x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n",
    "\n",
    "    # encoder - level 2\n",
    "    for n2 in range(level_2_repeats):\n",
    "        x = keras.layers.Conv2D(128, 3, strides=1, padding='same', activation=None, use_bias=False)(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "\n",
    "    # encoder - output\n",
    "    encoder_output = x\n",
    "\n",
    "    # decoder\n",
    "    y              = keras.layers.GlobalAveragePooling2D()(encoder_output)\n",
    "    decoder_output = keras.layers.Dense(DATA_NUM_CLASSES, activation='softmax')(y)\n",
    "    \n",
    "    # forward path\n",
    "    model = keras.Model(inputs=model_input, outputs=decoder_output, name='cifar_model')\n",
    "\n",
    "    # loss, backward path (implicit) and weight update\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(TRAINING_LR_MAX), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "MODEL_LEVEL_0_BLOCKS    = 4\n",
    "MODEL_LEVEL_1_BLOCKS    = 6\n",
    "MODEL_LEVEL_2_BLOCKS    = 3\n",
    "model = create_model(MODEL_LEVEL_0_BLOCKS, MODEL_LEVEL_1_BLOCKS, MODEL_LEVEL_2_BLOCKS)\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# print(model.metrics_names) # ['loss', 'accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "NMUTgaQa6p0b",
    "outputId": "3eabbde5-c545-40ad-dbfd-e49501fec2d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ZipDataset shapes: ({image: (None, None, 3)}, ()), types: ({image: tf.uint8}, tf.int32)>\n",
      "5/5 [==============================] - 4s 776ms/step - loss: 6.1151 - accuracy: 0.0625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7feaa20d27f0>"
      ]
     },
     "execution_count": 174,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the previous training set/image labels on the new model to train it\n",
    "def resizer(img, label):\n",
    "  img = tf.image.resize(\n",
    "      img['image'],\n",
    "      (224, 224),\n",
    "      method=tf.compat.v2.image.ResizeMethod.BILINEAR\n",
    "  )\n",
    "  return img, label\n",
    "EPOCHS = 5\n",
    "itr1 = train_dataset_labelled.batch(TRAINING_BATCH_SIZE).map(resizer).prefetch(2)\n",
    "print(train_dataset_labelled)\n",
    "model.fit(itr1, steps_per_epoch=5, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "lgqBt7I9Q8fA",
    "outputId": "3bb63f53-7171-468f-e4ac-d50dabfb949f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ZipDataset shapes: ({image: (None, None, 3)}, ()), types: ({image: tf.uint8}, tf.int32)>\n",
      "      6/Unknown - 2s 323ms/step - loss: 0.0147 - accuracy: 1.0000"
     ]
    }
   ],
   "source": [
    "# now test using the previous validation set/image labels on the new model\n",
    "\n",
    "def resizer(img, label):\n",
    "  img = tf.image.resize(\n",
    "      img['image'],\n",
    "      (224, 224),\n",
    "      method=tf.compat.v2.image.ResizeMethod.BILINEAR\n",
    "  )\n",
    "  return img, label\n",
    "\n",
    "EPOCHS = 5\n",
    "itr1 = test_dataset_labelled.batch(TRAINING_BATCH_SIZE).map(resizer).prefetch(2)\n",
    "print(train_dataset_labelled)\n",
    "\n",
    "test_loss, test_accuracy = model.evaluate(itr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eNoQJm8RQ8hw",
    "outputId": "24b6ec05-bd0d-465e-9816-c3dc25fddfa3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.014695137118299803 and accuracy = 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"loss: \" + str(test_loss) + \" and accuracy = \" + str(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AezShL0XQ8lV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YYsU0iXhuL6-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b0CTLVhHuL-Y"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eB18eHBbd0u7"
   },
   "outputs": [],
   "source": [
    "# tensorflow 2.0 beta and tensorflow datasets\n",
    "!pip install tensorflow-gpu==2.0.0-beta1\n",
    "!pip install tensorflow-datasets\n",
    "\n",
    "# tensorflow\n",
    "import tensorflow as     tf\n",
    "from   tensorflow import keras\n",
    "\n",
    "# tensorflow datasets\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow_datasets.image import downsampled_imagenet\n",
    "\n",
    "# additional libraries\n",
    "import math\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LzWDYMi1T_U9"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "#\n",
    "# PARAMETERS\n",
    "#\n",
    "################################################################################\n",
    "\n",
    "# data\n",
    "DATA_NUM_CLASSES        = 10\n",
    "DATA_CHANNELS           = 3\n",
    "DATA_ROWS               = 32\n",
    "DATA_COLS               = 32\n",
    "DATA_CROP_ROWS          = 28\n",
    "DATA_CROP_COLS          = 28\n",
    "DATA_MEAN               = np.array([[[125.30691805, 122.95039414, 113.86538318]]]) # CIFAR10\n",
    "DATA_STD_DEV            = np.array([[[ 62.99321928,  62.08870764,  66.70489964]]]) # CIFAR10\n",
    "\n",
    "# model\n",
    "MODEL_LEVEL_0_REPEATS   = 3\n",
    "MODEL_LEVEL_1_REPEATS   = 3\n",
    "MODEL_LEVEL_2_REPEATS   = 3\n",
    "\n",
    "# training\n",
    "TRAINING_BATCH_SIZE      = 32\n",
    "TRAINING_SHUFFLE_BUFFER  = 5000\n",
    "TRAINING_LR_MAX          = 0.001\n",
    "# TRAINING_LR_SCALE        = 0.1\n",
    "# TRAINING_LR_EPOCHS       = 2\n",
    "TRAINING_LR_INIT_SCALE   = 0.01\n",
    "TRAINING_LR_INIT_EPOCHS  = 5\n",
    "TRAINING_LR_FINAL_SCALE  = 0.01\n",
    "TRAINING_LR_FINAL_EPOCHS = 25\n",
    "\n",
    "# training (derived)\n",
    "TRAINING_NUM_EPOCHS = TRAINING_LR_INIT_EPOCHS + TRAINING_LR_FINAL_EPOCHS\n",
    "TRAINING_LR_INIT    = TRAINING_LR_MAX*TRAINING_LR_INIT_SCALE\n",
    "TRAINING_LR_FINAL   = TRAINING_LR_MAX*TRAINING_LR_FINAL_SCALE\n",
    "\n",
    "# saving\n",
    "SAVE_MODEL_PATH = './save/model/'\n",
    "!mkdir -p \"$SAVE_MODEL_PATH\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "BEvSIiq2UG2k",
    "outputId": "7dc60004-a49e-4a2d-d3d7-30a26eea2c48"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.\n"
     ]
    }
   ],
   "source": [
    "################################################################################\n",
    "#\n",
    "# DATA\n",
    "#\n",
    "################################################################################\n",
    "\n",
    "# pre processing for training data\n",
    "def pre_processing_train(example):\n",
    "\n",
    "    # extract image and label from example\n",
    "    image = example[\"image\"]\n",
    "    label = example[\"label\"]\n",
    "  \n",
    "    # image is cast to float32, normalized, augmented and random cropped\n",
    "    # label is cast to int32\n",
    "    image = tf.math.divide(tf.math.subtract(tf.dtypes.cast(image, tf.float32), DATA_MEAN), DATA_STD_DEV)\n",
    "    image = tf.image.random_flip_left_right(image)\n",
    "    image = tf.image.random_flip_up_down(image)\n",
    "    image = tf.image.random_hue(image, 0.08)\n",
    "    image = tf.image.random_saturation(image, 0.6, 1.6)\n",
    "    image = tf.image.random_brightness(image, 0.05)\n",
    "    image = tf.image.random_contrast(image, 0.7, 1.3)\n",
    "    image = tf.image.random_crop(image, size=[DATA_CROP_ROWS, DATA_CROP_COLS, 3])\n",
    "    label = tf.dtypes.cast(label, tf.int32)\n",
    "    \n",
    "    # return image and label\n",
    "    return image, label\n",
    "\n",
    "# pre processing for testing data\n",
    "def pre_processing_test(example):\n",
    "\n",
    "    # extract image and label from example\n",
    "    image = example[\"image\"]\n",
    "    label = example[\"label\"]\n",
    "\n",
    "    # image is cast to float32, normalized, augmented and center cropped\n",
    "    # label is cast to int32\n",
    "    image = tf.math.divide(tf.math.subtract(tf.dtypes.cast(image, tf.float32), DATA_MEAN), DATA_STD_DEV)\n",
    "    image = tf.image.crop_to_bounding_box(image, (DATA_ROWS - DATA_CROP_ROWS) // 2, (DATA_COLS - DATA_CROP_COLS) // 2, DATA_CROP_ROWS, DATA_CROP_COLS)\n",
    "    label = tf.dtypes.cast(label, tf.int32)\n",
    "    \n",
    "    # return image and label\n",
    "    return image, label\n",
    "\n",
    "# download data and split into training and testing datasets\n",
    "dataset_train, info = tfds.load(\"cifar10\", split=tfds.Split.TRAIN, with_info=True)\n",
    "dataset_test,  info = tfds.load(\"cifar10\", split=tfds.Split.TEST,  with_info=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bsSTUbnaUS91"
   },
   "outputs": [],
   "source": [
    "# transform training dataset\n",
    "dataset_train = dataset_train.map(pre_processing_train, num_parallel_calls=4)\n",
    "dataset_train = dataset_train.shuffle(buffer_size=TRAINING_SHUFFLE_BUFFER)\n",
    "dataset_train = dataset_train.batch(TRAINING_BATCH_SIZE)\n",
    "dataset_train = dataset_train.prefetch(buffer_size=1)\n",
    "\n",
    "# transform testing dataset\n",
    "dataset_test = dataset_test.map(pre_processing_test, num_parallel_calls=4)\n",
    "dataset_test = dataset_test.batch(TRAINING_BATCH_SIZE)\n",
    "dataset_test = dataset_test.prefetch(buffer_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vSWhdddDUde4"
   },
   "outputs": [],
   "source": [
    "from keras.regularizers import l2\n",
    "\n",
    "# create and compile model\n",
    "def create_model(level_0_repeats, level_1_repeats, level_2_repeats):\n",
    "\n",
    "    # encoder - input\n",
    "    model_input = keras.Input(shape=(DATA_CROP_ROWS, DATA_CROP_COLS, DATA_CHANNELS), name='input_image')\n",
    "    x           = model_input\n",
    "    \n",
    "    # encoder - level 0\n",
    "    for n0 in range(level_0_repeats):\n",
    "#         x = keras.layers.Conv2D(32, 3, strides=1, padding='same', activation='relu', use_bias=True)(x)\n",
    "        x = keras.layers.Conv2D(32, 3, strides=1, padding='same', activation=None, use_bias=False, kernel_regularizer = l2(0.001))(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "    x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n",
    "\n",
    "    # encoder - level 1\n",
    "    for n1 in range(level_1_repeats):\n",
    "#         x = keras.layers.Conv2D(64, 3, strides=1, padding='same', activation='relu', use_bias=True)(x)\n",
    "        x = keras.layers.Conv2D(64, 3, strides=1, padding='same', activation=None, use_bias=False, kernel_regularizer = l2(0.001))(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "    x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)\n",
    "        \n",
    "    # encoder - level 2\n",
    "    for n2 in range(level_2_repeats):\n",
    "#         x = keras.layers.Conv2D(128, 3, strides=1, padding='same', activation='relu', use_bias=True)(x)\n",
    "        x = keras.layers.Conv2D(128, 3, strides=1, padding='same', activation=None, use_bias=False, kernel_regularizer = l2(0.001))(x)\n",
    "        x = keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(x)\n",
    "        x = keras.layers.ReLU()(x)\n",
    "\n",
    "    # encoder - output\n",
    "    encoder_output = x\n",
    "\n",
    "    # decoder\n",
    "    y              = keras.layers.GlobalAveragePooling2D()(encoder_output)\n",
    "    decoder_output = keras.layers.Dense(DATA_NUM_CLASSES, activation='softmax', kernel_regularizer = l2(0.001))(y)\n",
    "    \n",
    "    # forward path\n",
    "    model = keras.Model(inputs=model_input, outputs=decoder_output, name='cifar_model')\n",
    "\n",
    "    # loss, backward path (implicit) and weight update\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(TRAINING_LR_MAX), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # return model\n",
    "    return model\n",
    "\n",
    "# create and compile model\n",
    "model = create_model(MODEL_LEVEL_0_REPEATS, MODEL_LEVEL_1_REPEATS, MODEL_LEVEL_2_REPEATS)\n",
    "\n",
    "# model description and figure\n",
    "model.summary()\n",
    "keras.utils.plot_model(model, 'cifar_model.png', show_shapes=True)\n",
    "\n",
    "# debug - model metrics (values returned from model.evaluate)\n",
    "# print(model.metrics_names) # ['loss', 'accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "2cmCri6HUsdz",
    "outputId": "965e3ba9-932a-47f6-ed20-3420f888d7f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "   1563/Unknown - 95s 61ms/step - loss: 2.5599 - accuracy: 0.2806\n",
      "Epoch 00001: val_loss improved from inf to 2.34505, saving model to ./save/model/model_1.h5\n",
      "1563/1563 [==============================] - 101s 64ms/step - loss: 2.5599 - accuracy: 0.2806 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.9885 - accuracy: 0.4772\n",
      "Epoch 00002: val_loss improved from 2.34505 to 1.97263, saving model to ./save/model/model_2.h5\n",
      "1563/1563 [==============================] - 96s 61ms/step - loss: 1.9882 - accuracy: 0.4772 - val_loss: 1.9726 - val_accuracy: 0.4812\n",
      "Epoch 3/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.6187 - accuracy: 0.5728\n",
      "Epoch 00003: val_loss improved from 1.97263 to 1.96908, saving model to ./save/model/model_3.h5\n",
      "1563/1563 [==============================] - 96s 61ms/step - loss: 1.6187 - accuracy: 0.5728 - val_loss: 1.9691 - val_accuracy: 0.4687\n",
      "Epoch 4/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.4259 - accuracy: 0.6157\n",
      "Epoch 00004: val_loss improved from 1.96908 to 1.59740, saving model to ./save/model/model_4.h5\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.4261 - accuracy: 0.6156 - val_loss: 1.5974 - val_accuracy: 0.5481\n",
      "Epoch 5/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.3499 - accuracy: 0.6329\n",
      "Epoch 00005: val_loss did not improve from 1.59740\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 1.3498 - accuracy: 0.6330 - val_loss: 1.6814 - val_accuracy: 0.5104\n",
      "Epoch 6/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.3417 - accuracy: 0.6350\n",
      "Epoch 00006: val_loss did not improve from 1.59740\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 1.3418 - accuracy: 0.6349 - val_loss: 2.4673 - val_accuracy: 0.4303\n",
      "Epoch 7/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.3039 - accuracy: 0.6517\n",
      "Epoch 00007: val_loss improved from 1.59740 to 1.46993, saving model to ./save/model/model_7.h5\n",
      "1563/1563 [==============================] - 96s 62ms/step - loss: 1.3039 - accuracy: 0.6518 - val_loss: 1.4699 - val_accuracy: 0.5921\n",
      "Epoch 8/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.2707 - accuracy: 0.6606\n",
      "Epoch 00008: val_loss did not improve from 1.46993\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.2708 - accuracy: 0.6606 - val_loss: 1.6351 - val_accuracy: 0.5553\n",
      "Epoch 9/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.2441 - accuracy: 0.6719\n",
      "Epoch 00009: val_loss did not improve from 1.46993\n",
      "1563/1563 [==============================] - 96s 61ms/step - loss: 1.2446 - accuracy: 0.6718 - val_loss: 1.5057 - val_accuracy: 0.5699\n",
      "Epoch 10/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.2123 - accuracy: 0.6800\n",
      "Epoch 00010: val_loss did not improve from 1.46993\n",
      "1563/1563 [==============================] - 96s 61ms/step - loss: 1.2119 - accuracy: 0.6800 - val_loss: 1.6982 - val_accuracy: 0.5596\n",
      "Epoch 11/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.1923 - accuracy: 0.6860\n",
      "Epoch 00011: val_loss improved from 1.46993 to 1.33446, saving model to ./save/model/model_11.h5\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.1922 - accuracy: 0.6860 - val_loss: 1.3345 - val_accuracy: 0.6268\n",
      "Epoch 12/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.1626 - accuracy: 0.6972\n",
      "Epoch 00012: val_loss improved from 1.33446 to 1.20800, saving model to ./save/model/model_12.h5\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.1626 - accuracy: 0.6972 - val_loss: 1.2080 - val_accuracy: 0.6779\n",
      "Epoch 13/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.1506 - accuracy: 0.6978\n",
      "Epoch 00013: val_loss did not improve from 1.20800\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.1504 - accuracy: 0.6978 - val_loss: 1.2326 - val_accuracy: 0.6577\n",
      "Epoch 14/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.1228 - accuracy: 0.7062\n",
      "Epoch 00014: val_loss did not improve from 1.20800\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.1224 - accuracy: 0.7063 - val_loss: 1.2823 - val_accuracy: 0.6562\n",
      "Epoch 15/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.1047 - accuracy: 0.7109\n",
      "Epoch 00015: val_loss improved from 1.20800 to 1.20424, saving model to ./save/model/model_15.h5\n",
      "1563/1563 [==============================] - 96s 62ms/step - loss: 1.1046 - accuracy: 0.7109 - val_loss: 1.2042 - val_accuracy: 0.6695\n",
      "Epoch 16/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.0806 - accuracy: 0.7185\n",
      "Epoch 00016: val_loss improved from 1.20424 to 1.07901, saving model to ./save/model/model_16.h5\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 1.0805 - accuracy: 0.7185 - val_loss: 1.0790 - val_accuracy: 0.7152\n",
      "Epoch 17/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.0596 - accuracy: 0.7248\n",
      "Epoch 00017: val_loss did not improve from 1.07901\n",
      "1563/1563 [==============================] - 95s 61ms/step - loss: 1.0598 - accuracy: 0.7248 - val_loss: 1.2319 - val_accuracy: 0.6482\n",
      "Epoch 18/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 1.0383 - accuracy: 0.7286\n",
      "Epoch 00018: val_loss did not improve from 1.07901\n",
      "1563/1563 [==============================] - 96s 61ms/step - loss: 1.0383 - accuracy: 0.7286 - val_loss: 1.1240 - val_accuracy: 0.6920\n",
      "Epoch 19/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 1.0073 - accuracy: 0.7365\n",
      "Epoch 00019: val_loss did not improve from 1.07901\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 1.0076 - accuracy: 0.7364 - val_loss: 1.1520 - val_accuracy: 0.6875\n",
      "Epoch 20/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.9850 - accuracy: 0.7440\n",
      "Epoch 00020: val_loss did not improve from 1.07901\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 0.9853 - accuracy: 0.7439 - val_loss: 1.5287 - val_accuracy: 0.5922\n",
      "Epoch 21/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.9557 - accuracy: 0.7527\n",
      "Epoch 00021: val_loss improved from 1.07901 to 1.02567, saving model to ./save/model/model_21.h5\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 0.9557 - accuracy: 0.7527 - val_loss: 1.0257 - val_accuracy: 0.7286\n",
      "Epoch 22/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 0.9300 - accuracy: 0.7591\n",
      "Epoch 00022: val_loss did not improve from 1.02567\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 0.9298 - accuracy: 0.7592 - val_loss: 1.0654 - val_accuracy: 0.7166\n",
      "Epoch 23/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 0.9032 - accuracy: 0.7636\n",
      "Epoch 00023: val_loss did not improve from 1.02567\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 0.9030 - accuracy: 0.7636 - val_loss: 1.0594 - val_accuracy: 0.7163\n",
      "Epoch 24/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.8712 - accuracy: 0.7726\n",
      "Epoch 00024: val_loss improved from 1.02567 to 0.85836, saving model to ./save/model/model_24.h5\n",
      "1563/1563 [==============================] - 97s 62ms/step - loss: 0.8710 - accuracy: 0.7727 - val_loss: 0.8584 - val_accuracy: 0.7729\n",
      "Epoch 25/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.8360 - accuracy: 0.7823\n",
      "Epoch 00025: val_loss improved from 0.85836 to 0.83679, saving model to ./save/model/model_25.h5\n",
      "1563/1563 [==============================] - 98s 63ms/step - loss: 0.8364 - accuracy: 0.7822 - val_loss: 0.8368 - val_accuracy: 0.7790\n",
      "Epoch 26/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 0.7999 - accuracy: 0.7926\n",
      "Epoch 00026: val_loss improved from 0.83679 to 0.79702, saving model to ./save/model/model_26.h5\n",
      "1563/1563 [==============================] - 96s 62ms/step - loss: 0.8001 - accuracy: 0.7925 - val_loss: 0.7970 - val_accuracy: 0.7913\n",
      "Epoch 27/30\n",
      "1561/1563 [============================>.] - ETA: 0s - loss: 0.7624 - accuracy: 0.8025\n",
      "Epoch 00027: val_loss improved from 0.79702 to 0.75242, saving model to ./save/model/model_27.h5\n",
      "1563/1563 [==============================] - 105s 67ms/step - loss: 0.7623 - accuracy: 0.8025 - val_loss: 0.7524 - val_accuracy: 0.8026\n",
      "Epoch 28/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.7256 - accuracy: 0.8152\n",
      "Epoch 00028: val_loss improved from 0.75242 to 0.73468, saving model to ./save/model/model_28.h5\n",
      "1563/1563 [==============================] - 100s 64ms/step - loss: 0.7255 - accuracy: 0.8153 - val_loss: 0.7347 - val_accuracy: 0.8096\n",
      "Epoch 29/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.6859 - accuracy: 0.8262\n",
      "Epoch 00029: val_loss improved from 0.73468 to 0.68944, saving model to ./save/model/model_29.h5\n",
      "1563/1563 [==============================] - 99s 63ms/step - loss: 0.6858 - accuracy: 0.8262 - val_loss: 0.6894 - val_accuracy: 0.8203\n",
      "Epoch 30/30\n",
      "1562/1563 [============================>.] - ETA: 0s - loss: 0.6626 - accuracy: 0.8334\n",
      "Epoch 00030: val_loss improved from 0.68944 to 0.66262, saving model to ./save/model/model_30.h5\n",
      "1563/1563 [==============================] - 96s 62ms/step - loss: 0.6625 - accuracy: 0.8334 - val_loss: 0.6626 - val_accuracy: 0.8331\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAHwCAYAAAChTMYRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "#\n",
    "# TRAIN AND VALIDATE\n",
    "#\n",
    "################################################################################\n",
    "\n",
    "# learning rate schedule\n",
    "def lr_schedule(epoch):\n",
    "\n",
    "    # staircase\n",
    "    # lr = TRAINING_LR_MAX*math.pow(TRAINING_LR_SCALE, math.floor(epoch/TRAINING_LR_EPOCHS))\n",
    "\n",
    "    # linear warmup followed by cosine decay\n",
    "    if epoch < TRAINING_LR_INIT_EPOCHS:\n",
    "        lr = (TRAINING_LR_MAX - TRAINING_LR_INIT)*(float(epoch)/TRAINING_LR_INIT_EPOCHS) + TRAINING_LR_INIT\n",
    "    else:\n",
    "        lr = (TRAINING_LR_MAX - TRAINING_LR_FINAL)*max(0.0, math.cos(((float(epoch) - TRAINING_LR_INIT_EPOCHS)/(TRAINING_LR_FINAL_EPOCHS - 1.0))*(math.pi/2.0))) + TRAINING_LR_FINAL\n",
    "\n",
    "    # debug - learning rate display\n",
    "    # print(epoch)\n",
    "    # print(lr)\n",
    "\n",
    "    return lr\n",
    "\n",
    "# plot training accuracy and loss curves\n",
    "def plot_training_curves(history):\n",
    "\n",
    "    # training and validation data accuracy\n",
    "    acc     = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "\n",
    "    # training and validation data loss\n",
    "    loss     = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    # plot accuracy\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(acc, label='Training Accuracy')\n",
    "    plt.plot(val_acc, label='Validation Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.ylim([min(plt.ylim()), 1])\n",
    "    plt.title('Training and Validation Accuracy')\n",
    "\n",
    "    # plot loss\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.plot(loss, label='Training Loss')\n",
    "    plt.plot(val_loss, label='Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.ylabel('Cross Entropy')\n",
    "    plt.ylim([0, 2.0])\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.show()\n",
    "\n",
    "# callbacks (learning rate schedule, model checkpointing during training)\n",
    "callbacks = [keras.callbacks.LearningRateScheduler(lr_schedule),\n",
    "             keras.callbacks.ModelCheckpoint(filepath=SAVE_MODEL_PATH+'model_{epoch}.h5', save_best_only=True, monitor='val_loss', verbose=1)]\n",
    "\n",
    "# training\n",
    "initial_epoch_num = 0\n",
    "\n",
    "history = model.fit(x=dataset_train, epochs=TRAINING_NUM_EPOCHS, verbose=1, callbacks=callbacks, validation_data=dataset_test, initial_epoch=initial_epoch_num)\n",
    "\n",
    "# example of restarting training after a crash from the last saved checkpoint\n",
    "# model             = create_model(MODEL_LEVEL_0_REPEATS, MODEL_LEVEL_1_REPEATS, MODEL_LEVEL_2_REPEATS)\n",
    "# model.load_weights(SAVE_MODEL_PATH+'model_X.h5') # replace X with the last saved checkpoint number\n",
    "# initial_epoch_num = X                            # replace X with the last saved checkpoint number\n",
    "# history           = model.fit(x=dataset_train, epochs=TRAINING_NUM_EPOCHS, verbose=1, callbacks=callbacks, validation_data=dataset_test, initial_epoch=initial_epoch_num)\n",
    "\n",
    "# plot accuracy and loss curves\n",
    "plot_training_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Q_6TAynnsqdw",
    "outputId": "43c21677-939b-492f-cca8-27ad2b91ea25"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found GPU at: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('Found GPU at: {}'.format(device_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "PVLcuRrfU36M",
    "outputId": "b9c041bd-9756-499f-ab3b-da3e8db0b07a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    313/Unknown - 7s 22ms/step - loss: 0.6626 - accuracy: 0.8331Test loss:      0.6626186326098519\n",
      "Test accuracy:  0.8331\n"
     ]
    }
   ],
   "source": [
    "################################################################################\n",
    "#\n",
    "# TEST\n",
    "#\n",
    "################################################################################\n",
    "\n",
    "# test\n",
    "test_loss, test_accuracy = model.evaluate(x=dataset_test)\n",
    "print('Test loss:     ', test_loss)\n",
    "print('Test accuracy: ', test_accuracy)\n",
    "\n",
    "# example of saving and loading the model in Keras H5 format\n",
    "# this saves both the model and the weights\n",
    "# model.save('./save/model/model.h5')\n",
    "# new_model       = keras.models.load_model('./save/model/model.h5')\n",
    "# predictions     = model.predict(x=dataset_test)\n",
    "# new_predictions = new_model.predict(x=dataset_test)\n",
    "# np.testing.assert_allclose(predictions, new_predictions, atol=1e-6)\n",
    "\n",
    "# example of saving and loading the model in TensorFlow SavedModel format\n",
    "# this saves both the model and the weights\n",
    "# keras.experimental.export_saved_model(model, './save/model/')\n",
    "# new_model       = keras.experimental.load_from_saved_model('./save/model/')\n",
    "# predictions     = model.predict(x=dataset_test)\n",
    "# new_predictions = new_model.predict(x=dataset_test)\n",
    "# np.testing.assert_allclose(predictions, new_predictions, atol=1e-6)\n",
    "\n",
    "# example of getting a list of all feature maps\n",
    "# feature_map_list = [layer.output for layer in model.layers]\n",
    "# print(feature_map_list)\n",
    "\n",
    "# example of creating a model encoder\n",
    "# replace X with the layer number of the encoder output\n",
    "# model_encoder    = keras.Model(inputs=model.input, outputs=model.layers[X].output)\n",
    "# model_encoder.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "r22ovnVfU9J0",
    "outputId": "e89aeacd-5842-4d86-a93d-61bb265ba799"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 2 and actual label: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhUiU/wejau9awjSnzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 1 and actual label: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKBK/EIki8QuRKBK/EIki8QuRKP8Ha0TfxfUPuloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 8 and actual label: 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 9 and actual label: 9\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAI1r/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 3 and actual label: 3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkUQp+UUSpeQXSdT/AX8SgzgCC4UMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 8 and actual label: 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAF41JREFUeJzt3WuMnOV1B/D/mdvOXu292Ov1HYwL\nckhw6MpqBKpSpYkIigRRJRRURVRCcT4EKZHyoYh+KOonWjWJkFpFcgoKRAmkUkDwgRKoWwlQW2BB\nri+A8WLW4LW9u/ba3tt4dy6nH3YcFrPPObM7szNDn/9Psrw7Z573febdOTuzc56LqCqIKD6JRneA\niBqDyU8UKSY/UaSY/ESRYvITRYrJTxQpJj9RpJj8RJFi8hNFKlXPk7W2tmvnuu4qjmCNRhS7aXVh\n+x7usascRel2LnwHv6n/yFd35koa262r65l//EaSavpmjMq9OHkBszPTFR28quQXkTsAPAogCeBf\nVPUR6/6d67rxF3/5Q+OABeeM4QftXcxkMmnGvfaJZPhNUiJhJ7dI0Y4777/EuUMiYfVt9W0r4bW3\nrmu1fXN/ZkZ7N/mceMn5hV7N89HLXC2WgrF/+vu/c1p/YtU/eRFJAvhnAN8EsAfAvSKyZ7XHI6L6\nqubX/j4Aw6p6UlUXADwN4K7adIuI1lo1yb8FwMdLvj9dvu1TRGS/iAyJyFBubraK0xFRLa35p/2q\nekBVB1V1sLWtfa1PR0QVqib5RwFsW/L91vJtRPQ5UE3yvwlgt4hcJyIZAN8B8HxtukVEa23VpT5V\nLYjIAwB+j8VS3+OqesxuAxRL4TKFVzKz6/zV8VY0KhnlFa9f4jwu92E7x7f6XjKudy14162aclu1\nq0xV1b7aFa7Ebl80nk+5KzmzbSadCcZW8pirqvOr6gsAXqjmGETUGBzeSxQpJj9RpJj8RJFi8hNF\nislPFCkmP1Gk6jqfX1VRLBjTW1NeTTpcw/Smf3r1bn9+tVVLd2rdsM9dciZxeuMfSkZt1/vtvtY7\nNlnH987dyDq/+3xwjr046TUsX1gIxj48+aHZ9vrrrze6Vflj5is/UaSY/ESRYvITRYrJTxQpJj9R\npJj8RJGqa6kPANQouZWK3vTQcNwsIQJQtctt7kqy1uq93nRgb9qrU53xypQJqwzpLQ1c5TTphDct\n14qteXk2zF212Dl3qWQ/3xIp+7qmEuG+77npRrNt0Xg+reSa8JWfKFJMfqJIMfmJIsXkJ4oUk58o\nUkx+okgx+YkiVfc6v1g1SndGr7Xjq1MT9mrGTns1pt06wxPgHBre7+Ckswy01Tdvz9eScU0Bfzqx\nOn3XknF8KwZAnZcmdcYoWJNq2zItZtuO9lYzns/by2u3ZO3jt2bDx9++Y4fZ9q3Dh4MxazzKZ+5b\n8T2J6P8VJj9RpJj8RJFi8hNFislPFCkmP1GkmPxEkaqqzi8iIwCmARQBFFR10GzgLN3t1XUTxhxo\nf3lsZ76/Mw1a3HnxBm/uuNvcWSa6FD6+VwsXZxwAvPn6zrLl5s/U/pG4fU8knTnzEn56t2ezZtvN\n/RvNeLbNTp2Ojk4znsmkg7FUJrwFNwBs377VOK7d9lPnqfieYX+mqudrcBwiqiO+7SeKVLXJrwBe\nEpG3RGR/LTpERPVR7dv+21V1VEQ2AnhZRN5T1VeW3qH8S2E/ALR3rKvydERUK1W98qvqaPn/cQDP\nAti3zH0OqOqgqg5ms23VnI6IamjVyS8i7SLSefVrAN8AcLRWHSOitVXN2/5+AM+WlwpOAfiNqr5Y\nk14R0ZpbdfKr6kkAt6yoDfy12O1zGkFnzrtXam9rtedvF4x12vP5vNm2pE5B26mVl5zHVsGCAUHq\njV8o2U+RVNKOJ41xBN4K8yl7l2u0O3Puu9rag7Et/f1m297ebjPe0WWfO9Ni19ut8S6zc3Nm240b\neoOxtHfRlmCpjyhSTH6iSDH5iSLF5CeKFJOfKFJMfqJI1XfpblUUi4VguOQs5WyW67y6kRP3Slbm\nlsxOqc7bDlqd7Z5hTGVePIAdtogzXdidjVzFkuhJsR93e9Yul93yBXsr680bN4WDzs+skF8w4+u7\n7Cm7nc6U3smLk8HYxMSM2TbplKUrxVd+okgx+YkixeQnihSTnyhSTH6iSDH5iSLF5CeKVN236LZY\nS3MvxsOxpNfWmRabTNpTIWXeOLZTZ8+knDEERbveLc6UYOuRu4/LWZrbWxK9JW0ff2BzXzC2ebNR\nhwcwM3PZjI+NfmzGb9gWXuJ6y8Bms22haE/TLjjjG+Zm7Vr9e+8cC8ZOj46abTft2B6MFQvhcTTX\n4is/UaSY/ESRYvITRYrJTxQpJj9RpJj8RJFi8hNFqqnq/G7NORn+XeWtQO1t5+xtVW1FW1tazLZZ\nZzvo2Rm7JqxFu6bsPTJLytgqGgA6O+y54x2dzlbXA+Et2vYN7jHbLizYNevcbM6Md7SG1wOYn5s2\n2358+rQZPzMxZsYvXLhgxicmJoKx/oEBs+2V2dlgbCVL4/OVnyhSTH6iSDH5iSLF5CeKFJOfKFJM\nfqJIMfmJIuXW+UXkcQDfAjCuqjeXb+sB8FsAOwGMALhHVS96x/K26PZqlIlS+HdVttWuN3d3d5nx\nbNaud6NwJRjq7ekxm27ZusWMf3DyAzM+cfacGZ+8GL7069aF6+wAkErZ4wDaWu3rcvFiuF4NALOz\n4fXpBwbCc/0BYPeuPzLjxTljkQUAc5engrETZ8+YbY+fPGnG12/YaMYLal/X/s3GWgNb7OdL3piz\nn3LWjliqklf+XwK445rbHgRwUFV3AzhY/p6IPkfc5FfVVwBc++v7LgBPlL9+AsDdNe4XEa2x1f7N\n36+qZ8tfnwPQX6P+EFGdVP2Bn6oqjOHlIrJfRIZEZGj+yly1pyOiGllt8o+JyAAAlP8fD91RVQ+o\n6qCqDrZk21Z5OiKqtdUm//MA7it/fR+A52rTHSKqFzf5ReQpAP8N4EYROS0i9wN4BMDXReQEgD8v\nf09EnyNuUVBV7w2EvrbSkyUEyGTCp+zosOfFb9sa/lxxh1NLH+i3a8qZjL3+vLWG/JWc/VnGwBZ7\nffpU2p63fum8Xefv6gzPue/rtev8npzz2IoFe2zGdC5ciz/+7odm2/lZ+9hTE/ac+e1GLX38gj0s\npa3Hfr4M7LzBjCetTSYAtLSEx6VcvmT3bexc+PlQyHPdfiJyMPmJIsXkJ4oUk58oUkx+okgx+Yki\nVdelu5OpFHp7e4PxG2+6zmy/67pw6QbO1sSTF86b8c2bNpjxP957SzA2n18w2yacbaw3DtilwNKC\nXfLqMaYUn3OmA58etZeo7uy0R2Xu2LHDjM8ay0xfvmQvnz0Ce9rttk32NtudPeFpt/3OWu/SZi9Z\n3t5mTxH3t0YPnz+XCw6YBQAMnzgRjF2ZD089vxZf+YkixeQnihSTnyhSTH6iSDH5iSLF5CeKFJOf\nKFL1rfMnkujo6AjGN/U79e5SeDPqtLF9NwDzvACQmwnXowHgo+Hw8tot6+ya7/o+e3poX4+9BOJX\nvnKbGbeWay44U27TGXsadW/vejPu7KqOU6dOBWMzzjXv7rO3qs522df1SiJ8XZKtnWbblHNd0kk7\nvjBvj/1IpYzna8m+qKOjo8FYfiFvtl2Kr/xEkWLyE0WKyU8UKSY/UaSY/ESRYvITRYrJTxSputb5\nPd4caKuenU3bD6V7nV3XPfrG62b8nIbXC1i3fZt97jm75rtjs/24E6mMGV8oFoOxnbt2m23PT9hb\nbI+cHDbjExP23POuzvB1v+VLt5pt+wfs65pps3+mBWP57MmpGbNta8b+mWW77a3Lk874h0sXwsuO\nv3P0iNl2ZGQkGJtfsLctX4qv/ESRYvITRYrJTxQpJj9RpJj8RJFi8hNFislPFCm3zi8ijwP4FoBx\nVb25fNvDAL4H4GqR+CFVfcE/ll2r9ySMum3BWbf/wmx4i20AOHLokBnvNtZx37vT3m8gm2034wnY\ndXxN2HPyS8XwOgfeGIHTZ86a8alpux7+pS+G9zMAgO3btwdj6bRdK7900V7XP+HU0q8Uw7X67vX2\n1uVtzvN06pI9PuL9998346+++mowNj4+ZrYVhB+4c0k+pZJX/l8CuGOZ23+mqnvL/9zEJ6Lm4ia/\nqr4CYLIOfSGiOqrmb/4HROSwiDwuIt016xER1cVqk//nAHYB2AvgLICfhO4oIvtFZEhEhmZn7b8f\niah+VpX8qjqmqkVVLQH4BYB9xn0PqOqgqg62t9uLaBJR/awq+UVk6bKq3wZwtDbdIaJ6qaTU9xSA\nrwLoE5HTAP4WwFdFZC8ABTAC4Ptr2EciWgNu8qvqvcvc/NhqTiaSQEtLuO7sfSaQmwvPW087Bc7O\njqwZH+i3185v0XAt3R27IHZc1Z7PX4Rd508Y5086ld8bdt9oxrtvtefcd3XYYxjUuG6TkxfNth8c\nP27Gb7rli2a8lAyfOzdjj/s4PnzCjL/xxn+Z8WPvHDPjubm5YCybDY8pAYC0MXbD2tviWhzhRxQp\nJj9RpJj8RJFi8hNFislPFCkmP1Gk6rp0d2trC76w54ZgvFiyl0uemQ5v6VxyymEi9tbFW6/bacY7\nMkap0NgKGgAKJbtvufwVMw5nSq9VTvOuS19/rxnvdkZllpyp1Lkr4cc2Z8QAIO88H3JzdrnuneHw\ntNq3hobMtsNOmXH8vD2lt621zYz39G0IxtxynYRLw+LNc16Cr/xEkWLyE0WKyU8UKSY/UaSY/ESR\nYvITRYrJTxSputb5F+bn8dGp8JbP2bYWs/3UpXBdt6vdnrKbznSZcTGmGgPA5GwuGJs59ZHZtv2y\nPVV5l7P0d1+/vURiUcNTnUtq1/kzSfspUIJdc84b24O7cackPTMfHtcBAL95+ldm/D2jVj81NWW2\nzWbt8Q19GzabcW+7eYs6P7OEtR29VP56zld+okgx+YkixeQnihSTnyhSTH6iSDH5iSLF5CeKVF3r\n/JenLuPFf3sxGO/qtmvxrS3hWv7G3vVm2w5nDIHO2/P921rDdd/ZpP07NGGtBQAgnbbHGAjsmvHC\n/Hww5m1dnkrY22TDXkUaCwvhcwPAsLEE9sH/OGi2PXjw92a8aGzBDQBdneHnU6cRA1DBcuv2+IeV\nLKG9UqWiMQ5gBaflKz9RpJj8RJFi8hNFislPFCkmP1GkmPxEkWLyE0XKrfOLyDYATwLox2IV8YCq\nPioiPQB+C2AngBEA96iqvecyBGr8vkkn7Xp3yqiHp1qcOn7eLoBePn/JjHfv7gvGNm7dYrbt6x8w\n494W3/krdq1+IRceoyBiT5qfn7Nr5SfGwmvfA8Brr71ixl966aVgbGRkxGybStvjGzZsCP9MACCd\nCT8n8iV7HYJS0R73kUx66+NXvn7+SllrNOgKCv2VvPIXAPxYVfcA+BMAPxCRPQAeBHBQVXcDOFj+\nnog+J9zkV9Wzqvp2+etpAO8C2ALgLgBPlO/2BIC716qTRFR7K/qbX0R2AvgygNcB9Kvq2XLoHBb/\nLCCiz4mKk19EOgD8DsCPVPVTC6Dp4kDnZf/YEJH9IjIkIkML886edERUNxUlv4iksZj4v1bVZ8o3\nj4nIQDk+AGB8ubaqekBVB1V1MGNMzCGi+nKTXxY/Ln4MwLuq+tMloecB3Ff++j4Az9W+e0S0ViqZ\n0nsbgO8COCIih8q3PQTgEQD/KiL3AzgF4J5KTpgwpkr2dYe3LQaAAsLlmXzeXu64ODNnxk+d+MCM\n92wM923TDbvMtiVn+ue8s1V1KW9Pmy0Y02rHxsbMtsedrajffvt/zPjwsN1+YSFcMuvsbDfbJtL2\ndGN1tkbPm9fdft1LWstjA0g554ZTYi0ZS5p7z5eVLM9tcZNfVV9DuGj5tZr0gojqjiP8iCLF5CeK\nFJOfKFJMfqJIMfmJIsXkJ4pUXZfuFohZ5+/qtJffXtBwzTiRtGujkyNnzPjMRXvLZotXl83n7emh\n09P2uS+MnzbjR48eCcaOHAnHAGB01D52sWT3PZOxa/HZbHharbv8tVdLT9hTfhPG9uPeub0puV57\nLdnjTqz2VR3bfVyf4Cs/UaSY/ESRYvITRYrJTxQpJj9RpJj8RJFi8hNFqr51fhFzO+renl6zfa5o\nzGsXu67autleXjuTt5fH7u7pCcbOnrHHEExOzZjxUydPmvGR4cNm/MyZcK3eWxa8vd2eU5826vQA\nkHC2Jzfr2c4y04mE89rkzGu3dsn2ttB2a+3G8tmVtLeGEYjzuOxrWjm+8hNFislPFCkmP1GkmPxE\nkWLyE0WKyU8UKSY/UaTqWucHACmF6+n5eXtt/YsXJ4KxS1P2Ftu5iUkzPjF21owfeubDcNvL9rkn\nL1024/O5nBlPpZwxCOvD6yC0dXSabb26cL5gb2XtLKNgbhHu17PtY2vRrrVbtXzvcZfcHbidcQDO\nGaxavThtvetWKb7yE0WKyU8UKSY/UaSY/ESRYvITRYrJTxQpJj9RpNw6v4hsA/AkgH4slkcPqOqj\nIvIwgO8BuFp8f0hVX7COlZubw9HDh4LxQ85e8NOz4fXt56/Ye9h7a+eXnNpqQcO1dquWDQCZTHgN\nAwBYv36dGe/sDK8lAADJVHj9+oI9RMCtd6ec+fqlolcQD/PL1V4t3X5w3s/F4mwJACScYzvhkrUe\ngDPAIWEPb6hYJYN8CgB+rKpvi0gngLdE5OVy7Geq+o+16QoR1ZOb/Kp6FsDZ8tfTIvIuAHtZHCJq\neiv6m19EdgL4MoDXyzc9ICKHReRxEekOtNkvIkMiMlQs2m+9iah+Kk5+EekA8DsAP1LVKQA/B7AL\nwF4svjP4yXLtVPWAqg6q6mAyae/rRkT1U1Hyi0gai4n/a1V9BgBUdUxVi7q4kuEvAOxbu24SUa25\nyS+LH5k+BuBdVf3pktsHltzt2wCO1r57RLRWKvm0/zYA3wVwRESu1ukeAnCviOzFYj1mBMD3vQMV\nigVcuHAhGM/lps32ai3P7dSsSs6WyZK0azOt2dZgLJ32tqnOVhX3lse2lrj2lr/24uLEvetqHtur\nh7kHqLK9QZ2lvb3SsMe6bt51KRpxf+vxT1Tyaf9rWL5qadb0iai5cYQfUaSY/ESRYvITRYrJTxQp\nJj9RpJj8RJGq79LdatdPrampACASjnv16oIzt9VbajmZDJ/bilUSTzjzR/324ce+krrvspx6t1eT\nFmPqq7sFtzdGwanz28uG221Lai9Z7m3R7alqm+0qz30VX/mJIsXkJ4oUk58oUkx+okgx+YkixeQn\nihSTnyhSUnUdeCUnE5kAcGrJTX0AztetAyvTrH1r1n4B7Ntq1bJvO1R1QyV3rGvyf+bkIkOqOtiw\nDhiatW/N2i+AfVutRvWNb/uJIsXkJ4pUo5P/QIPPb2nWvjVrvwD2bbUa0reG/s1PRI3T6Fd+ImqQ\nhiS/iNwhIsdFZFhEHmxEH0JEZEREjojIIREZanBfHheRcRE5uuS2HhF5WUROlP9fdpu0BvXtYREZ\nLV+7QyJyZ4P6tk1E/lNE3hGRYyLyw/LtDb12Rr8act3q/rZfFiflvw/g6wBOA3gTwL2q+k5dOxIg\nIiMABlW14TVhEflTADMAnlTVm8u3/QOASVV9pPyLs1tV/7pJ+vYwgJlG79xc3lBmYOnO0gDuBvBX\naOC1M/p1Dxpw3Rrxyr8PwLCqnlTVBQBPA7irAf1oeqr6CoDJa26+C8AT5a+fwOKTp+4CfWsKqnpW\nVd8ufz0N4OrO0g29dka/GqIRyb8FwMdLvj+N5tryWwG8JCJvicj+RndmGf3lbdMB4ByA/kZ2Zhnu\nzs31dM3O0k1z7Vaz43Wt8QO/z7pdVW8F8E0APyi/vW1Kuvg3WzOVayraubleltlZ+g8aee1Wu+N1\nrTUi+UcBbFvy/dbybU1BVUfL/48DeBbNt/vw2NVNUsv/jze4P3/QTDs3L7ezNJrg2jXTjteNSP43\nAewWketEJAPgOwCeb0A/PkNE2ssfxEBE2gF8A823+/DzAO4rf30fgOca2JdPaZadm0M7S6PB167p\ndrxW1br/A3AnFj/x/wDA3zSiD4F+XQ/gf8v/jjW6bwCewuLbwDwWPxu5H0AvgIMATgD4dwA9TdS3\nXwE4AuAwFhNtoEF9ux2Lb+kPAzhU/ndno6+d0a+GXDeO8COKFD/wI4oUk58oUkx+okgx+YkixeQn\nihSTnyhSTH6iSDH5iSL1f4JoiDLqu5CLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 9 and actual label: 9\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ4sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 0 and actual label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJj9RpJj8RJFi8hNF6v8AIwJx3kK2IeIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 1 and actual label: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGk9JREFUeJztnVmMZGd5ht+v9t732XfbONgje2ya\nEQkmckRABqHYXMTCUogjIYYLkILERZBzEV9aUQBxESENwcJEBBwpIFvCYTNEltnktmPGnhnPymw9\nPdM9vVdvtX256DJp2/O/p6dnpqqt/32k0XTX1/85f51z3jpV9f7f95m7QwgRH6lmT0AI0RwkfiEi\nReIXIlIkfiEiReIXIlIkfiEiReIXIlIkfiEiReIXIlIyjdxZX3fBd2zqCP9BrUbHO8LxapWvVJya\nWqLxhTIfX62F4+UKH9uaNxof6CvwfSdsP4VwvOZ83/nWFho3PhyW4nNzhDdQq1Tp2Mr8PI2XF8s0\nXlwkYxOul1TC804n3DbTxrdv5FJvaeE7z5CdX5ypYGqhljD7+nZW80chzOwBAF8HkAbwb+7+BPv7\nHZs68IuDD4b/YHGB7q9cC18MczP8Qnjm2VM0/tpFPn6aXGgXx/iJvuc2fpg/9zfvpfHiFf7C1YJK\nMLZQ41fprn17aTybTbgQ8/wFu5ZKB2MLEzN07JWXXqHx4WPDNP6r4+HY5ZmEF+wcf2HqShBoeyp8\nTgAgMx/e/t17+c2gpyv8gv23T4/SsStZ89t+M0sD+FcAHwNwB4BHzOyOtW5PCNFYrucz/34AJ939\ntLuXAHwfALmtCyHWE9cj/q0Azq/4/UL9sbdgZgfMbMjMhq5M8bf1QojGcdO/7Xf3g+4+6O6D/d38\nyyUhROO4HvEPA9i+4vdt9ceEEO8Crkf8LwG4zcx2m1kOwKcAPHtjpiWEuNms2epz94qZfQHAT7Bs\n9T3p7ofZmNJiGRdOXg7Gc0tFvk+bC8a6+tro2C25Et92V57GJ3f3BGOzxYt82wnFkvLZbhrPdY7R\n+IXXzwZjvz/J7bTsXPiYAkBbltuMFZ+m8Y4dtwRjxw+N0LGjR/i2x5f4gc1Z2J7dt51blPkUt/oc\n/Lj05PhH3JZUazDW3cZlWc3mwvNKWpixguvy+d39OQDPXc82hBDNQct7hYgUiV+ISJH4hYgUiV+I\nSJH4hYgUiV+ISGloPn+lWsPEZDgtt7uFvxa1t4X98HQb91Xb5rknfOdiOPUUACb2vCNt4Y/kizwd\neN/gbhrv2xj2fAEg55tovHIhvHbiYoqneE5fDo8FACdeOQBUFhJSSKvh9NTsIj/f3dUEL34LP25b\nc+G595P0cAAozfHnPceXjaA2ya+33r1h6RXa+bU4Uwx7+dfSgEt3fiEiReIXIlIkfiEiReIXIlIk\nfiEiReIXIlIaavUhZUB7NhieL/GKp8Mnwyme9/STkuAAWsNZkACApQmePppeCtsrO9t5GuWH7grb\nhABQLnBbqTbHX6PT+bDN2ZrltlFx7AKNW0IN69aOhEtoJpy229LTR4duf+h+vu29f0rDudkfB2Ol\nV16lY6+c5jZjdi4h7bbCL7gNuzcGY+k8Txce+Z+pYKxW5vNeie78QkSKxC9EpEj8QkSKxC9EpEj8\nQkSKxC9EpEj8QkRKY31+M9TS4RTPxQov3X35cjgNs1rlfrYP8M6n2VQ7jS/WwqWcK5M8v/PK0XM0\n3nL3BhpPz/PjslAl+zf++p5PKDFtxlusZROuoJqH126kOvng3J08FTp/K/f57fUXg7F0UpfdDn5c\nlua4F2+d/LhPzIWvN59OuCcXJsOxa7id684vRKRI/EJEisQvRKRI/EJEisQvRKRI/EJEisQvRKRc\nl89vZmcAzAKoAqi4+yD7+1rNsbAYzl1vT3GvvloJ+9m1hFoAmYEuGi9tGqDxi4fPB2N3LfB8/LFf\nvULj23Z/hMZLCSna06SUc3Ex3FocAGZmeIvu7g5+XPMZ7pcPl8PrK3p4Z3IszF3ifzAfzmsHgOp0\neI1CNuFay7fzlu9pHkahna8TGD5xJhjLFPkagj27wuc0l+HH5C37WfVfhvkLd79yA7YjhGggetsv\nRKRcr/gdwE/N7GUzO3AjJiSEaAzX+7b/PncfNrMNAH5mZm+4+wsr/6D+onAAAAZ6+Pp6IUTjuK47\nv7sP1/8fBfBDAPuv8jcH3X3Q3Qe72hOqaAohGsaaxW9mbWbW8ebPAD4K4PUbNTEhxM3let72bwTw\nQzN7czv/4e7hWslCiHXFmsXv7qcB3H0tY3K5NHbu6AzGp85N0PE7toe9eEtxPzqX5mb5Qj6hHkAl\n/CYpU+E1/0uzfNtW5XMv85R6XBwLrzP48dFZOrY6G65TAAC3b87T+PYt/M1j3y07grFchvvZmQSf\nPz91mMaXyuHzkgb/CJqt8XOWy/Jzlp7h6x+y1XAv7ZxzWZanwutdnGz37cjqEyJSJH4hIkXiFyJS\nJH4hIkXiFyJSJH4hIqWhpbsL+SxuuSXcmvj1y9zqK3SGWzq3pXiL7u2beNotKjwFs5YOW0PtHTw3\ndfYit9vmh8do/OI4H3/+dLi0945ebln99yjf9rEj/P6wc5Rbge8nFmtnlaefZtu51ZfrHaXxifGw\n7ZV5kSeiTh/m1vBUjltqtsAt1PwHtgRjre38mI8fOROMVUjK/NvRnV+ISJH4hYgUiV+ISJH4hYgU\niV+ISJH4hYgUiV+ISGmoz19aLGHkyNlgvLsznO4LABPFcIrm3GxCiWludyeWcl6shn3fasJhXBjn\nLbYnT3Ofv9DDy44vkbTc/jRvH/7Rvbw1+dAb3DceTmgnPfPrE8HYe+7jabX5Ab7uo/jS8zRem7oQ\njLWc5+ekepg/r9YuHi8npJDXPHxe2rta6djcfPhaT9eU0iuESEDiFyJSJH4hIkXiFyJSJH4hIkXi\nFyJSJH4hIqWxPv9SBedOjgfjhS6ek19pC+fcLxUSWoFNJbQuTsjPrqXCh6rq3AvPVXlu9+ixcPtv\nAJjp5SWuW1vCr+EdLbzWwLYOvrZi4Q/hdRkA8IcSP257esK9rHPgayumx/jzro6F1xAsEx6fAi+t\nnUnIqS9k+POe56ccng5fT/lslg9Oh+dmSOjnvgLd+YWIFIlfiEiR+IWIFIlfiEiR+IWIFIlfiEiR\n+IWIlESf38yeBPAJAKPuvrf+WC+ApwHsAnAGwMPuPpm0LUcKSxb26ouXuBe/1NobjG3cspOOnZsZ\n4ZPr4a+DLE26Ul6kY9tS3PRNyvdvqXHv9t7924KxzhQ/LZUaz/f/7Rvcz84V+NzuvCvcVr0wxZ93\nx3E+N+vl9QDGJ8PHPTPJ6z9U8nwdQIrvGim+RAGZVHh9xcw8XzdSIqqt8mm/hdXc+b8N4IG3PfZl\nAM+7+20Anq//LoR4F5Eofnd/AcDbS6o8COCp+s9PAXjoBs9LCHGTWetn/o3u/ub76EsAwj24hBDr\nkuv+ws/dHUDwg6GZHTCzITMbmp7jn+GEEI1jreK/bGabAaD+f7BjorsfdPdBdx/sakv4lkQI0TDW\nKv5nATxa//lRAM/cmOkIIRpFovjN7HsAfgPgdjO7YGafAfAEgI+Y2QkAf1n/XQjxLiLR53f3RwKh\nD1/rzqqVCqauhGuxd2e5Hz5zKezVH13kfeZbZnleekt+E41XSe55JcN71Gede+H5VHjtAwD0DvA6\nB1cWF4Kxo5PhXgcAsKmbG9L33sXrAexJhfP1AWBzbzg++we+BmHTVv4xsauPH5fWc+Hnbll+6c/0\n8HNanpyj8UpCPv/lYfLcd/LvxgY+EF7v4qd4r4OVaIWfEJEi8QsRKRK/EJEi8QsRKRK/EJEi8QsR\nKQ0t3Z3LpLG9rycY7+DuCvor4XzF0vwMHTtXTvBeSvN8333hFMzZngSLMp1Qotp4vH+Ap07MZsNW\nYv7W99CxrRvD5wMABhPag7e28BbfLLd1fu89dOjSEretJqcSbK3Dw8FQx23coiykN9D4oR8cpfE0\nKa8NAFcmwlbhpm3c+p3rCM+9lubW7kp05xciUiR+ISJF4hciUiR+ISJF4hciUiR+ISJF4hciUhrq\n86eQQquFzfylBZ7KWKuFX6vSV/jYvjKPtxZ4Kee/uv+2YCy1tIOOrSQ8r8lyQnrpfMJzS4VLPXe2\n8tblWfDnnZ7nqas2z9dHZFrD56yti6fsdrfwcuxTNT5+zMPrQvIZPrbUzdc3TOb52oxCgrLOzYVL\noh8/xO/JlZ+PBWPT4/x8rkR3fiEiReIXIlIkfiEiReIXIlIkfiEiReIXIlIkfiEipaE+P8yQKYR9\nfuMVrlGthr3VWon3Jq6Oc1+2u5eXgc50kLbJzn34UiVcWhsA2rI8f3usM9zmGgBefvl4MFZYOE/H\nbmvjr/8tee6HpzN8fDZL1mZk+Dlp726l8a6BcAlrAJi/b38w9vL/nqBjT13kLd237+T1ALqMn/MB\nC19Pv53jLbrTi+HrpeJ8XcZKdOcXIlIkfiEiReIXIlIkfiEiReIXIlIkfiEiReIXIlISfX4zexLA\nJwCMuvve+mOPA/gsgDcTix9z9+eStpVKGXKt4V3WLJzjDAClSvi1qriBe76FrdwrL/TzeLUWzluv\nJbQWL+cScqznrtBwr2VpfM/ddwVjP/nRj+jYUpF7ylsL3GtfSlibkcqGvfzWhFoDF0d52/WLcxdp\n/Nxk+Lj/5hj3w0fGeR+IB+/ltQZ237mLxvMnL4RjY7xGgveE17TYBF/vspLV3Pm/DeCBqzz+NXff\nV/+XKHwhxPoiUfzu/gKAhNYoQoh3G9fzmf8LZnbIzJ40M97zSQix7lir+L8B4BYA+wCMAPhK6A/N\n7ICZDZnZ0EQx3LdNCNFY1iR+d7/s7lV3rwH4JoBgBoW7H3T3QXcf7G1P6MQphGgYaxK/mW1e8esn\nAbx+Y6YjhGgUq7H6vgfgfgD9ZnYBwD8BuN/M9gFwAGcAfO4mzlEIcRNIFL+7P3KVh7+1lp2VKlWc\nH5sMxru28Frpe24Pe6vTOxfp2ItTRRqfrvJDkSuF45V5bnZXFvi2q0vcm12YDtdpB4CduzcHYztu\nfS8de+HYMRrv7mun8fbeThpv6QjXA1is8uc9Mc2/I5ou8vURi6SOwl3v4z79/gKv79DXyj/ClrZ2\n0/jU5FQw1g9+XDotvAYhz8s3vAWt8BMiUiR+ISJF4hciUiR+ISJF4hciUiR+ISKloaW7iwtl/Or3\no8H40tFLdPz9k+EUz23cWcGmhJbMNsHTKGupcGpqtcRTelHlVmC5xNNqy4s8Pn3stWBsO6+Ojf5N\n3Mpr6+FWXvf2sM0IAL2k/Ha2xo/L5ARP6e1u5anOHa3hcz49z23EmnG7banEr5dfD3F7tqUYfm57\nWhLahxsph34Nt3Pd+YWIFIlfiEiR+IWIFIlfiEiR+IWIFIlfiEiR+IWIlIb6/JWqY7IYbmd9bp6X\n7v7lS0PBWEuC57uxi8cH2rm3un1DOL6th7fY7i7wbXfl+WkoZBLipbBn3Vfjr+8LvHo2Zi/x8thn\nR3g80xpuZd2d522u3zhxjsYvkmsJAAqd4bTcPQnrE3bs5KXcz50Jl94GgKFXztL4fbvCpea72ni6\n8OQi0YnxNOeV6M4vRKRI/EJEisQvRKRI/EJEisQvRKRI/EJEisQvRKQ01OfPZlPYuDHs7VZrvB30\n2cvhksXnR8JlmgHg8DmeG16r8DUGmVQ4TrpQAwDacjw3vJ+UtwaALX28jPTmzrBZv6EtYX0DaZkO\nAIUaf3KLFZ6TP7sYPu5jNX5cysa3vTVh7ca+9+8KxvZ/6P10bGuWz+0X89M0Xkhoy35sLtwivDjF\nr9Xbu8PnpOb8Ol6J7vxCRIrEL0SkSPxCRIrEL0SkSPxCRIrEL0SkSPxCREqiz29m2wF8B8BGAA7g\noLt/3cx6ATwNYBeAMwAedvdw/20A+WwGuzeG85iLY7yW+qbu8GtVW47Xl5+b5y2855Z4bfzZhbBv\nO7vI532lyLd9/HLY8wWAynF6WAHS0jlr3Pfty3EfvyuhlkB7Qi2Cwd3hvPj+PL/3dG/vp/HO1oQ1\nCOPhNtjDx07QsQuTEzR++RyvY7BxA69VYKnw2oyJGs/nn1kKP6/qDfb5KwC+5O53APgAgM+b2R0A\nvgzgeXe/DcDz9d+FEO8SEsXv7iPu/kr951kARwFsBfAggKfqf/YUgIdu1iSFEDeea/rMb2a7ANwD\n4HcANrr7SD10CcsfC4QQ7xJWLX4zawfwXwC+6O5vWWTv7o7l7wOuNu6AmQ2Z2dDMPK+5JoRoHKsS\nv5llsSz877r7D+oPXzazzfX4ZgBX7cDp7gfdfdDdBztJ40QhRGNJFL+ZGYBvATjq7l9dEXoWwKP1\nnx8F8MyNn54Q4maxmpTeDwL4NIDXzOzV+mOPAXgCwH+a2WcAnAXwcOKWvAZUw7bYvXffToePTxWD\nsdNnuDXDyjgDQFuFt9luK4TtuuICtxEXE1pwLybYjPNL/OPSYiVsQ1acp6ZeKnNr6BIpCw4AmEuw\nSDPh1Nc/u52Xxx6+xMtQz5/i+3YPpwT3/eYUHVtJuB6K5QSbkewbAIqz4eeW0C0e/beGU99rq3f6\nksXv7i8ibCR/ePW7EkKsJ7TCT4hIkfiFiBSJX4hIkfiFiBSJX4hIkfiFiJSGlu4GgFSNeNIJfna+\nrTsYm5w5T8e2JNTXria8DmYy4fGtBZ6CmU7zfde4JYy+Tl7SfFN3eP/nR8bp2AszvMR00oJsr/Dj\ndmE6vDbjSimc3g0AXW18RejlWT67JVKO/ehYOC0WAObL/KSUy3yNwUAHLyvO2rrfuYX3Td/VF76e\n8hm+rmMluvMLESkSvxCRIvELESkSvxCRIvELESkSvxCRIvELESkN9fkzKUN/a9j/fOnVITr+zKlw\nCes0aaENAKWE/OylBN+2RNpJV69ewez/4wnllEtV7rVv3crLI/71h/8kGHvjGF//8PTPj9D4lSI/\nbm587kuV8HE7PcJbUfd0hr1wADg7yscvlMPrAIoJNRZKZf68d3RzL/6+O7fS+Oau8NqM9oRjaiS+\nepdfd34hokXiFyJSJH4hIkXiFyJSJH4hIkXiFyJSJH4hIqWhPn/KgDaS5ryH5KUDgN/SF4ydHl+g\nY8dnuW87OcNr708Xw57xTEIdgoUEz7hU5r7uVEKbs/7NW4KxHbPcCy/k+Ou/86kvn9Q1cmp4jMbT\nvAs20ll+veRy4cs7k+LHPE/GAsDOAd4HIl3h/Q6WSMv3bIHvO5tmceXzCyESkPiFiBSJX4hIkfiF\niBSJX4hIkfiFiBSJX4hISfT5zWw7gO8A2AjAARx096+b2eMAPgvgTbP2MXd/jm/NAQvXQ28nuf4A\nsKESHjvDbX60pnkd9oF2XiN+oRSe2/QCzzsfT6gvPzrJJz87yWvMn74Qrs3f0cNrARTJMQWAEjlf\nAJBOuH0YKWXgxgenM/x6KKS5p50hdRTSCT5+d1sbjacS1jeMTHGfv4pwPYAq+DFvIddyLaF2xEpW\ns8inAuBL7v6KmXUAeNnMflaPfc3d/2XVexNCrBsSxe/uIwBG6j/PmtlRALxMiRBi3XNNn/nNbBeA\newD8rv7QF8zskJk9aWY9gTEHzGzIzIam5njpJCFE41i1+M2sHcB/Afiiu88A+AaAWwDsw/I7g69c\nbZy7H3T3QXcf7GYL+4UQDWVV4jezLJaF/113/wEAuPtld6+6ew3ANwHsv3nTFELcaBLFb2YG4FsA\njrr7V1c8vnnFn30SwOs3fnpCiJvFar7t/yCATwN4zcxerT/2GIBHzGwflu2/MwA+l7gld1QqYdur\nVOKWWIa8VHUX+EeKrjx/qrUEh6RUDdsr80s8PXS2k+97IiGVebLI042PHHojGMtkuWU1l/A9TD6h\ntXmBtC4HgFwqHE9qXZ7P8ntTW8Lc2KfMFu7soiPpemrhc+su8DgdnlBmfm4pfM6qSRfyClbzbf+L\nuHqScIKnL4RYz2iFnxCRIvELESkSvxCRIvELESkSvxCRIvELESkNLd0NM4CUHU6leBpkhtSRLiSk\nWC6WuP/Zkk9IH82E950z7pW3ZPi+e1r4aVjo4l79wuJMMHbp/Agd+75d3TRuKT63VEJaLjsr2YSr\nryXPt92asIG2fHgdQAs5n8vxhPULuYQ1Bgkl0VMk17mWkNJbrhANXUMldd35hYgUiV+ISJH4hYgU\niV+ISJH4hYgUiV+ISJH4hYgU82so9XvdOzMbA3B2xUP9AK40bALXxnqd23qdF6C5rZUbObed7j6w\nmj9sqPjfsXOzIXcfbNoECOt1but1XoDmtlaaNTe97RciUiR+ISKl2eI/2OT9M9br3NbrvADNba00\nZW5N/cwvhGgezb7zCyGaRFPEb2YPmNkxMztpZl9uxhxCmNkZM3vNzF41s6Emz+VJMxs1s9dXPNZr\nZj8zsxP1/6/aJq1Jc3vczIbrx+5VM/t4k+a23cx+aWZHzOywmf19/fGmHjsyr6Yct4a/7TezNIDj\nAD4C4AKAlwA84u5HGjqRAGZ2BsCguzfdEzazPwdQBPAdd99bf+yfAUy4+xP1F84ed/+HdTK3xwEU\nm925ud5QZvPKztIAHgLwd2jisSPzehhNOG7NuPPvB3DS3U+7ewnA9wE82IR5rHvc/QUAE297+EEA\nT9V/fgrLF0/DCcxtXeDuI+7+Sv3nWQBvdpZu6rEj82oKzRD/VgDnV/x+Aeur5bcD+KmZvWxmB5o9\nmauwsd42HQAuAdjYzMlchcTOzY3kbZ2l182xW0vH6xuNvvB7J/e5+70APgbg8/W3t+sSX/7Mtp7s\nmlV1bm4UV+ks/UeaeezW2vH6RtMM8Q8D2L7i9231x9YF7j5c/38UwA+x/roPX36zSWr9/9Emz+eP\nrKfOzVfrLI11cOzWU8frZoj/JQC3mdluM8sB+BSAZ5swj3dgZm31L2JgZm0APor11334WQCP1n9+\nFMAzTZzLW1gvnZtDnaXR5GO37jpeu3vD/wH4OJa/8T8F4B+bMYfAvPYA+H393+Fmzw3A97D8NrCM\n5e9GPgOgD8DzAE4A+DmA3nU0t38H8BqAQ1gW2uYmze0+LL+lPwTg1fq/jzf72JF5NeW4aYWfEJGi\nL/yEiBSJX4hIkfiFiBSJX4hIkfiFiBSJX4hIkfiFiBSJX4hI+T9whEIv1sp2MAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 1 and actual label: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRJH6P0ND+5q05pP8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 3 and actual label: 3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGXxJREFUeJztnWuMXGd5x//PmfvsxetdX2M7thOc\nVG5EEtgaaAKlpUQhRUpQ1ZR8QKmEMB9AKhIfilKpzYdWjaoC4kOFZEpEqCgXCSLyIWoJaSFEVDRO\nFHIhIRfHCXZ89673vjNn5umHnaC14/f/bnbXMxve/09a7e48857zzpnznzMz//d5HnN3CCHSI+v1\nBIQQvUHiFyJRJH4hEkXiFyJRJH4hEkXiFyJRJH4hEkXiFyJRJH4hEqXYzZ3VygUfqId3We1bR8dX\n+gaDsckzx/jOvUXDZsbHr4AMBb7v9gpfg7Pw3AvlMh1aKPFTICvwubXbNAzPSuF9Gx+84qeELF6N\nrWu95AtfVzA3IHxgTp05g8nJqSUduRWJ38xuBvAVAAUA/+bu97D7D9SL+Mv3bwvGr3rvR+j+rnz3\nTcHYT7/1D3SsNyZpvBQRATsZjIgPAPqMv6iVpio0njvffrE/PH7w8q107LrNm2i8PthH45MNfqq2\nq5uDsYHCLB1bDr9uAAA88qLqefjFJY+ou9XicXe+byZQgL9okml3xob3/Xf/+E988CKWfckxswKA\nfwXwEQB7AdxhZnuXuz0hRHdZyfvNfQBecvdD7t4A8B0At67OtIQQl5qViH8bgN8s+v9I57bzMLP9\nZnbQzA7ONvjnbiFE97jk3/a7+wF3H3X30Vo59jlJCNEtViL+owB2LPp/e+c2IcTbgJWI/zEAe8xs\nt5mVAXwcwAOrMy0hxKVm2Vafu+dm9lkA/4UFq+9ed3+WjamUi9h5+cZgfPfu3XSfO3fuDI/dOULH\n+hx/ncsKkUNBTOeY1Ze1qjQ+d4TvujHJvyspF8Ne/sjIBjp2/WVbaLxW4zbkpj5+3Dftvi4Ymzr5\nEh2LfIaGY354u5mHN93mx7TVCI8FgMZ8k8bnZ+dpfGbyXHjf01N0bHuuEYx5zve7mBX5/O7+IIAH\nV7INIURv0PJeIRJF4hciUSR+IRJF4hciUSR+IRJF4hciUbqaz2+WoVQJe961vgE6PiNee6XC8z+z\nAvfasyIfXyiE951FfH5v8203Jviy53yK+74Z8bOR8ae4WKvTeKHC6wFUIjUY+shzOl+p0bEtW1lS\nvWVhL99bsVoCkTyUjPv8hWLkfMvIc278mtwohNc/ZNnSr+e68guRKBK/EIki8QuRKBK/EIki8QuR\nKBK/EInSVavP4Wh72JYqFLitBA/bMwaeglkqcTuuVOF2W4FYKBGXEHmTP65z5yZo/PRpHt+xKZy2\nW10fLncOAJV6P40XS3zupTK3tIqF8HEtxg5cxCKFRar3ImzHeZOnvnrkumjMqgOQFbhNWaqGbc46\nOc8BcB3I6hNCxJD4hUgUiV+IRJH4hUgUiV+IRJH4hUgUiV+IROmqz5+3HCfG5oLxFmnnDAB5M+zb\njkXKW9eL/HWuFumEWyqH4+1ICmYspTef4XMfn+IlrN+xMVw+e90GXrq7GClZTlNPAVikhXerGX6+\nLdIpt1jiKb8eKd7dahK/PGalt/i6Eea1A4BF5kYyxFEs8fOlTNLiY2XkF6MrvxCJIvELkSgSvxCJ\nIvELkSgSvxCJIvELkSgSvxCJsiKf38wOA5gE0AKQu/sovz9QI3n19Rr3N1t52A+fmOW+as7tauQt\nPr5SCe+7WOSeb6nEd75p6yYabzf5OoDhzWGfvz7AS2sDkfbiEZ+/WODPmZHtW2RsochPz1bOj7sT\nM9/bkfUL7YhfHjlf2i3+nDlpER5b/1Auh2ssZJE1J4tZjUU+f+zup1dhO0KILqK3/UIkykrF7wB+\nZGaPm9n+1ZiQEKI7rPRt/43uftTMNgF4yMyed/dHFt+h86KwHwDW9Udq9AkhusaKrvzufrTz+ySA\n+wHsu8h9Drj7qLuP1qtdzSMSQhCWLX4z6zOzgTf+BnATgGdWa2JCiEvLSi7FmwHcbwudc4sA/sPd\n/3NVZiWEuOQsW/zufgjAtW9tlCF3sstIW+O5+UYwNj7OWyZPk3bNADDAU8cxPBT2pCO2LHLjNeKr\nl22m8V3D62l8y45twZhF8vXbrcibP4u0+I7U3i9Xwy3Ai+U+OrYVSbr3LLK2ozUbjM03+dgsVref\ntIsH4nn1bVJrgK0BWE1k9QmRKBK/EIki8QuRKBK/EIki8QuRKBK/EInS3SV3ZijUSGviwWE6/Nz4\ndDA2NTnJdw1un1QKFRr3dtiGjNlCWcS6KQ7wlN6h3VfTeH04XJ7bI6mp85F0YY+0wUYshTQjp1ik\nJXu7FbZ2ASCPpM02ScovKwMPANbm9uy5MydpfG6al1uv9oXPt1LEJmRRb0d850Xoyi9Eokj8QiSK\nxC9Eokj8QiSKxC9Eokj8QiSKxC9EonTV53cAeR5ufZxFWnSXi2EP86qd/KGwcscAMFDn43NSNry/\nzl9DixW+b+vjqczFCl+DcOjXr4SDzuc2NMJbeNeHeenvVqSENSslbZGy4Ih0yW7HymeT9RVTZ3jB\n6UPPP0/jT/7qKRqfng6vSQGAy0i59it27qRjt23ZEoy125He44vQlV+IRJH4hUgUiV+IRJH4hUgU\niV+IRJH4hUgUiV+IROmuz992zM6FfcgGKWcMAO08nIO9ro+PHRrkXjtZfrCwb3Kk6hGfvx1Zv1Cs\nDdL4iaPHaPzHD/0kGJuc5Tnv77r2ehofvfFNTZjOo7mF58UzLFIWHK1I+euIz9+YCnvtLz7Dffyf\nPfEojT9/9BSNF6t87cZ4czwYO3v6DB078IcfCMZYG/sL0ZVfiESR+IVIFIlfiESR+IVIFIlfiESR\n+IVIFIlfiESJ+vxmdi+AjwI46e7XdG4bBvBdALsAHAZwu7uPRbeVFVHu2xi+Q4F7o6wO+8mzvMb7\noWPcyD8zxsfv3R1uJz3Yz+eNjL/GliK1BsZPH6bxY2PHg7FXTkV8+OoADW/beRmN79jDewpQJz7S\nU6A5x5+zfJ7X1p8+dTYYe+GlF+jYuQrfduy4TTd4rYLacLhGgzf442b1ISxyri1mKff8BoCbL7jt\nCwAedvc9AB7u/C+EeBsRFb+7PwLgwpfQWwHc1/n7PgC3rfK8hBCXmOV+5t/s7m+sOT0OYPMqzUcI\n0SVW/IWfuzvIRzsz229mB83s4Mws/1wthOgeyxX/CTPbCgCd38Guhe5+wN1H3X20XuNfbAkhusdy\nxf8AgDs7f98J4IerMx0hRLeIit/Mvg3gfwFcbWZHzOyTAO4B8GEzexHAn3b+F0K8jYj6/O5+RyD0\nobe6M0eGphN/M5L3npN8/nNT/HVspraVxn955BCN95N6ARuG6VBU+/nHncFKP40Xyvy4ZGSdwGyk\nTsGZKV6//vXXj9B4u8V3sPCV0MXJG3wNwtzUJI03Zmdo/NzrwU+jGBsP59MDwOAW3iuhcJLPLZ+d\novEsC9ft37AhXJcfAAaGw2sMssLq+vxCiN9BJH4hEkXiFyJRJH4hEkXiFyJRJH4hEqWrpbtbrRbG\nx84F4+68/PbJsbB9ciyS0rvr97id9s69l9P44EC4JPLsPE9NzSItuAulcLowAMB5eqhZ2E4bHuTl\nrYsZbyU9PRV+vgCg3eLPGauu3Zyb49tu8ue03eDxfCZsBbYb/DnzGb7tKy7n0pkmJeoBoEpaae/c\nfiUdWyPnk6w+IUQUiV+IRJH4hUgUiV+IRJH4hUgUiV+IRJH4hUiUrvr8mWWoV2vheIF7r3OkpHEb\n4e0CwKmj4fROAChm3Jdl2aMnnB/GDRXutaPAffxyia8TKHl4DcKuLREffo7PrWr8uHqkTXZOWkY3\n53hKbuzKZCRdGADqtfDctw/zspPPn+Q+f6l/gsbXG5/9rm17grEr3rGdjs1Y6W7jGjpvO0u+pxDi\ndwqJX4hEkfiFSBSJX4hEkfiFSBSJX4hEkfiFSJSu+vwwQ7EU3mXM9/VW2DOu9vGc+Kxa53NrRcpI\nM7u8yX36SiS3O4+0qt6z9xoefzZcdvyZl1+kY7dv4H737u285HmxyB97geSXF40fFy9FWniDx+sD\ng8HY+/ZdS8dePrGTxk+P8ZLnGzeN0PhVvx/2+QdH1tOxbuGy4pYtXdK68guRKBK/EIki8QuRKBK/\nEIki8QuRKBK/EIki8QuRKFFT0MzuBfBRACfd/ZrObXcD+BSAU5273eXuD8a2lRUM9f5wbvrs2Ct0\nfGM2XEO+PsDr8tfr3Oefn5ml8VZzPhyL+PSz83wNwcQEb+e8bSv32v/stpuCsT84fQMdW3Q+t3qF\nXx+yIj+FWmT9RKz1eD7P8/XbbZ5zX6yH5z48GG6RDQCbB3nt/BI5jwGgXIu0+CbrXVqR+hBNcj5Z\ntrp1+78B4OaL3P5ld7+u8xMVvhBibREVv7s/AuBsF+YihOgiK/nM/1kze8rM7jUzvh5RCLHmWK74\nvwrgSgDXATgG4IuhO5rZfjM7aGYHZ2bCn5uFEN1lWeJ39xPu3vKFzppfA7CP3PeAu4+6+2i9zr8E\nEUJ0j2WJ38wWf/38MQDPrM50hBDdYilW37cBfBDABjM7AuDvAXzQzK4D4AAOA/j0JZyjEOISEBW/\nu99xkZu/vpyd9fcP4j1/9KFgvK/Op3PVlRuDsZ1bb6Fjixn3lOdzHmc14j3mN5f549py2RU0PrR+\nmMY3b78qGLsKPN9+YvwMjRt4zn3/EJ+b52FPusZLMKCURWoFlPi+q+R88oyvzegb4XUOKv18XQk/\nasDc3FwwNhupa5E3wsc00iHiPLTCT4hEkfiFSBSJX4hEkfiFSBSJX4hEkfiFSJSulu7Om02cOR5u\nlT1zlls7WWsyGCtVBujYdoWvLqzU1vF4JXyozLjVZxEDptngxtDEJE+7LZbDLZuzLFzuHACszC2r\ncjG8bQBozITbpgPA9OSpYCxrhmMAkFnYDgOA0gBPuy30hc+JZosvNS9WI1ZeJO12tsmfs2aL2ZD8\nXC7VwjqxiD26GF35hUgUiV+IRJH4hUgUiV+IRJH4hUgUiV+IRJH4hUiUrvr883MzOPTswWB8+1Wj\ndPyZY8fDsdd+Rsdaq0bj5Wqk1HI57MVbgXvpxUir6WqV+7r9I7xd9MYdYb+7r4+XLG83+RqF/gIv\nUT3X4msYplvhx96PiNdufNvlda/z8ZWw591yvj4hy/jc2pHHfW6al2OfJym9rXakdXkeHtsg270Q\nXfmFSBSJX4hEkfiFSBSJX4hEkfiFSBSJX4hEkfiFSJSu+vzl6gB27g2X7t677z10/OtHw2WmX8H9\ndOz4qy/ReNaMeMpF8jpZ4mNZLQAAqJS4N1tsh1uTA0ClGN7/0Hq+hgAz3I8uTXK/u7ouUlZ8w/Zg\nrP3KITq20eBt09eN8BaR1cFwTv58gx/zZot77TPTvLx2YYaPnxo7EY5NTtCxrO15k7SSvxBd+YVI\nFIlfiESR+IVIFIlfiESR+IVIFIlfiESR+IVIlKjPb2Y7AHwTwGYsdAA+4O5fMbNhAN8FsAvAYQC3\nu/sY3xZQKIc96YHBETqXwuvjwVixj+edt9s8f7sxy/3RdZsuC8Zq63ht+1qNx4tZJH/beb2AqbPh\nvPaBoSE6ttTg25547mUav+w9vJX10MiGYOzQTx+hY5vOa9/XrrmWxvvXh/ddmuLrG+Ya/HzwSA/u\n+Rpfo1AqhVvCl4pclhnpA2HgtSPO306cHMDn3X0vgPcC+IyZ7QXwBQAPu/seAA93/hdCvE2Iit/d\nj7n7E52/JwE8B2AbgFsB3Ne5230AbrtUkxRCrD5v6TO/me0CcD2AXwDY7O7HOqHjWPhYIIR4m7Bk\n8ZtZP4DvA/icu5+3+NjdHbj4BxEz229mB83s4GTkc5YQonssSfxmVsKC8L/l7j/o3HzCzLZ24lsB\nXLQDp7sfcPdRdx8d6OfND4UQ3SMqfjMzAF8H8Jy7f2lR6AEAd3b+vhPAD1d/ekKIS8VSUnpvAPAJ\nAE+b2ZOd2+4CcA+A75nZJwG8CuD22IasUEJ1YFMw7m1u7XgznNr6vg//OR37WM4f6suP/5zGK4Ph\n0t99/bzsdy2S0hvzjXLuxqE5Ez4ujVn+UasyyFNyxyMpv+tPn6XxKrHMTkTGXn79XhqvDXMbs0Xc\n3Vabt7L2SGnuYoGPLxT4dbVI7DxmAwIAnFh9tnSrLyp+d38UCJqH4eR8IcSaRiv8hEgUiV+IRJH4\nhUgUiV+IRJH4hUgUiV+IROlq6W7LiihUNgbj05Ph0twA0D8YTh/Y+859dOzY0SM0fuI3z9F4sRRO\ny3WSYgkA7Vj+p/PX4FiaZqkUHt/OeSqzlfgahcqeXTQ+NjNN4+uzsGe9+/030LEjV/N9tyKetrXY\nAgk+No+U7s6bfE2KRZ5z5sfnkeesTXx+fiaej678QiSKxC9Eokj8QiSKxC9Eokj8QiSKxC9Eokj8\nQiRKV33+VnMaE8cPBuONTe+l44+8/H/B2H+fCMcAYD7nDujIJl42vE183bzAD2NeiORYRzzl5jxP\n6C9Ww6/hs7O8lTQmePvvwnqe798q8OpMM3n4seX1cI0EAJiY5GsISo1Izj1ZY9BocJ9+bp638G7M\n8eM6O8Pjc3Nk+xGzPsvC59PSs/l15RciWSR+IRJF4hciUSR+IRJF4hciUSR+IRJF4hciUbqbz28F\nlCrrgvG8xV3KsfHJYOzVpx6lY4dGwv0CAGAm4hm/eDhcD2Dn5Vvp2I0jgzQ+Oc797ONneO38gf6w\nX76jMkDHNi3S2jzn14cN28KtywGg0WgEY6+9cpiOrfbVaXxo4xYaL5XDtQoakRbc8w1+zGem+PqI\nqTFem2KO1EFo5uFjBvBlAE5y/S9EV34hEkXiFyJRJH4hEkXiFyJRJH4hEkXiFyJRJH4hEiXq85vZ\nDgDfBLAZCxbjAXf/ipndDeBTAE517nqXuz/ItuXeQnM+7I+2YrXU504HYyM73kHH1qo87/y1156h\n8Z/8/Olg7MY2n/f6Qb7vE6cmaPzZl4/T+NaN4XUE27fz9Q35LF9jcG6S++HVgXAfBgDoW7chGDty\n6DAdm0V63A9v5V58pRZew1Ao8m232txrn5kap/HpCR5nXr47r99gWXjusR4Si1nKIp8cwOfd/Qkz\nGwDwuJk91Il92d3/Zcl7E0KsGaLid/djAI51/p40s+cAbLvUExNCXFre0md+M9sF4HoAv+jc9Fkz\ne8rM7jWz9YEx+83soJkdnIyUZRJCdI8li9/M+gF8H8Dn3H0CwFcBXAngOiy8M/jixca5+wF3H3X3\n0YGBvlWYshBiNViS+M2shAXhf8vdfwAA7n7C3Vvu3gbwNQC8U6YQYk0RFb8ttBP9OoDn3P1Li25f\nnMr2MQD863IhxJpiKd/23wDgEwCeNrMnO7fdBeAOM7sOC/bfYQCfjm3IHWiQ7sNZFm6DDQBtLwRj\no3/yF3Ts1BS30w698AqNbyOlvZusDDOA+RleJrocsbRGBniJ600bhoKxEmktDgDFIm8HXS7xsuLt\nNrcCC8VwWm2hFC6tDQBzU+EUbgA4e+oYjbeJ61Wq8lPfjNttzSZ/3HOR0t6tPHxOWMbtOvacvpWU\n3qV82/8oLl4OnHr6Qoi1jVb4CZEoEr8QiSLxC5EoEr8QiSLxC5EoEr8QidLV0t2A0XTEnLTBBoCc\nlIGenQin+wJAI+LFV/t4Cet97746GLMm98pbkXbPQ0M85Xdow0XTJn5LpRYucT0bKUk+CO7jDw/x\n8tnVGr9+NPOwH94/El6fAADlGl8H4MZTqVut8POS5/w5mZ3hawxi5bVbOV8nwApwE4nEhsLZ4oYL\n97PkewohfqeQ+IVIFIlfiESR+IVIFIlfiESR+IVIFIlfiESxt5L/u+KdmZ0C8OqimzYA4AZ971ir\nc1ur8wI0t+WymnPb6e68nnqHror/TTs3O+juoz2bAGGtzm2tzgvQ3JZLr+amt/1CJIrEL0Si9Fr8\nB3q8f8ZandtanReguS2Xnsytp5/5hRC9o9dXfiFEj+iJ+M3sZjP7tZm9ZGZf6MUcQpjZYTN72sye\nNLODPZ7LvWZ20syeWXTbsJk9ZGYvdn7zfN/uzu1uMzvaOXZPmtktPZrbDjP7HzP7lZk9a2Z/3bm9\np8eOzKsnx63rb/vNrADgBQAfBnAEwGMA7nD3X3V1IgHM7DCAUXfvuSdsZh8AMAXgm+5+Tee2fwZw\n1t3v6bxwrnf3v1kjc7sbwFSvOzd3GspsXdxZGsBtAP4KPTx2ZF63owfHrRdX/n0AXnL3Q+7eAPAd\nALf2YB5rHnd/BMDZC26+FcB9nb/vw8LJ03UCc1sTuPsxd3+i8/ckgDc6S/f02JF59YReiH8bgN8s\n+v8I1lbLbwfwIzN73Mz293oyF2Fzp206ABwHsLmXk7kI0c7N3eSCztJr5tgtp+P1aqMv/N7Mje7+\nLgAfAfCZztvbNYkvfGZbS3bNkjo3d4uLdJb+Lb08dsvteL3a9EL8RwHsWPT/9s5tawJ3P9r5fRLA\n/Vh73YdPvNEktfP7ZI/n81vWUufmi3WWxho4dmup43UvxP8YgD1mttvMygA+DuCBHszjTZhZX+eL\nGJhZH4CbsPa6Dz8A4M7O33cC+GEP53Iea6Vzc6izNHp87NZcx2t37/oPgFuw8I3/ywD+thdzCMzr\nCgC/7Pw82+u5Afg2Ft4GNrHw3cgnAYwAeBjAiwB+DGB4Dc3t3wE8DeApLAhta4/mdiMW3tI/BeDJ\nzs8tvT52ZF49OW5a4SdEougLPyESReIXIlEkfiESReIXIlEkfiESReIXIlEkfiESReIXIlH+H1CI\n72utHqJOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5hciUf4PoTL7CyfGiwIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 9 and actual label: 9\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5BdJlJJfJFFKfpFE/R/8UDMvE63Y8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 2 and actual label: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 8 and actual label: 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 8 and actual label: 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRJH4hUgUiV+IRJH4hUiU/wfQ/vDLwj2djgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 3 and actual label: 3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niV+IRJH4hUiU/wMYJju0rO9x0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 8 and actual label: 8\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 4 and actual label: 4\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 0 and actual label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP1GkmPxEkWLyE0WKyU8UKSY/UaT+H4P6fBlUegsbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 2 and actual label: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 7 and actual label: 7\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 6 and actual label: 6\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQkSKzC9EpMj8QkTK/wJEM7W77fabTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 2 and actual label: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMfmJIsXkJ4oUk58oUkx+okgx+Yki9f+wq96g7rlnFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 4 and actual label: 4\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEr8QiSLxC5EoEr8QiSLxC5Eo/wfXHC8F3gBbrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nokj8QiSKxC9EovwfITNk+91viG8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 5 and actual label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkl8kUUp+kUQp+UUSpeQXSdT/A73fpAnPuFepAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 0 and actual label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyS+SKSW/SKaU/CKZUvKLZOp/AVa8Y+JDWTSNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "#\n",
    "# DISPLAY\n",
    "#\n",
    "################################################################################\n",
    "\n",
    "# extract a batch from the testing dataset\n",
    "# then extract images and labels for this batch\n",
    "dataset_display                = dataset_test.take(1)\n",
    "it                             = iter(dataset_display)\n",
    "display_images, display_labels = next(it)\n",
    "\n",
    "# predict pmf and labels for this dataset\n",
    "predict_labels_pmf = model.predict(x=dataset_display)\n",
    "predict_labels     = np.argmax(predict_labels_pmf, axis=1)\n",
    "\n",
    "# for display normalize images to [0, 1]\n",
    "display_images = ((display_images*DATA_STD_DEV.reshape((1, 1, 1, 3))) + DATA_MEAN.reshape((1, 1, 1, 3)))/255.0;\n",
    "\n",
    "# cycle through the images in the batch\n",
    "for image_index in range(predict_labels.size):\n",
    "        \n",
    "    # display the predicted label, actual label and image\n",
    "    print('Predicted label: {0:1d} and actual label: {1:1d}'.format(predict_labels[image_index], display_labels[image_index]))\n",
    "    plt.imshow(display_images[image_index, :, :, :])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_-fxCtC5diBR"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "CIFAR.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}