icnet_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import datetime\n",
    "from model.icnet import icnet\n",
    "from data_loader.data_loader import convert_class, train_images\n",
    "from IPython.display import clear_output\n",
    "from data_loader.display import create_mask\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "\n",
    "IMG_WIDTH = 288\n",
    "IMG_HEIGHT = 480\n",
    "\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  # 텐서플로가 첫 번째 GPU에 1GB 메모리만 할당하도록 제한\n",
    "    try:\n",
    "        tf.config.experimental.set_virtual_device_configuration(\n",
    "       gpus[0],\n",
    "        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=7000)])\n",
    "    except RuntimeError as e:\n",
    "    # 프로그램 시작시에 가장 장치가 설정되어야만 합니다\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, test_dataset = data_load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(1e-4)\n",
    "\n",
    "train_loss = tf.keras.metrics.Mean()\n",
    "train_accuracy1 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "train_accuracy2 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "train_accuracy3 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "\n",
    "test_loss = tf.keras.metrics.Mean()\n",
    "test_accuracy1 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "test_accuracy2 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "test_accuracy3 = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "\n",
    "train_mIoU =tf.keras.metrics.MeanIoU(num_classes = n_classes, name = \"train_mIoU\")\n",
    "test_mIoU =tf.keras.metrics.MeanIoU(num_classes = n_classes, name = \"test_mIoU\")\n",
    "\n",
    "model = icnet((IMG_WIDTH, IMG_HEIGHT, 3), 7, train_mode = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "train_log_dir = 'logs/icnet/gradient_tape/' + current_time + '/train'\n",
    "test_log_dir = 'logs/icnet/gradient_tape/' + current_time + '/test'\n",
    "train_summary_writer = tf.summary.create_file_writer(train_log_dir)\n",
    "test_summary_writer = tf.summary.create_file_writer(test_log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display(image, label):\n",
    "    pred = model.predict(image)\n",
    "    sub_4 = tf.image.resize(label, (label.shape[0]//16, label.shape[1]//16))\n",
    "    sub_24 = tf.image.resize(label, (label.shape[0]//8, label.shape[1]//8))\n",
    "    sub_124 = tf.image.resize(label, (label.shape[0]//4, label.shape[1]//4))\n",
    "\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.subplot(2,3,1)\n",
    "    plt.imshow(tf.keras.preprocessing.image.array_to_img(sub_4))\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,3,2)\n",
    "    plt.imshow(tf.keras.preprocessing.image.array_to_img(sub_24))\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,3,3)\n",
    "    plt.imshow(tf.keras.preprocessing.image.array_to_img(sub_124))\n",
    "    plt.axis(\"off\")\n",
    "                                       \n",
    "    for i in range(3):\n",
    "        pred_mask = tf.argmax(pred[i], axis=-1)\n",
    "        pred_mask = pred_mask[..., tf.newaxis][0]\n",
    "        plt.subplot(2,3,4+i)\n",
    "        plt.imshow(tf.keras.preprocessing.image.array_to_img(pred_mask))   \n",
    "        plt.axis(\"off\")\n",
    "\n",
    "#show_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sub_loss(output, label, size):\n",
    "    label = tf.image.resize(label, (label.shape[1] // size, label.shape[2] // size))\n",
    "    output = tf.dtypes.cast(output,tf.float32)\n",
    "    label = tf.dtypes.cast(label,tf.float32)\n",
    "    loss = tf.keras.losses.sparse_categorical_crossentropy(label, output, from_logits= True)\n",
    "    loss = tf.reduce_mean(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(images, label):\n",
    "    label_shape = label.shape\n",
    "    with tf.GradientTape() as tape:\n",
    "        pred_img = model(images, training=True)\n",
    "        \n",
    "        loss_sub4 =  sub_loss(pred_img[2], label, 4)\n",
    "        loss_sub24 =  sub_loss(pred_img[1], label, 8)\n",
    "        loss_sub124 =  sub_loss(pred_img[0], label, 16)\n",
    "        \n",
    "        loss = loss_sub4 + loss_sub24 +loss_sub124\n",
    "\n",
    "    gradients_of_model = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients_of_model, model.trainable_variables))\n",
    "    \n",
    "    \n",
    "    \n",
    "    train_mIoU(label, pred_mask)\n",
    "\n",
    "    train_loss(loss)\n",
    "    train_accuracy1(tf.image.resize(label, (label_shape[1] // 4, label_shape[2] // 4)), pred_img[2])\n",
    "    train_accuracy2(tf.image.resize(label, (label_shape[1] // 8, label_shape[2] // 8)), pred_img[1])\n",
    "    train_accuracy3(tf.image.resize(label, (label_shape[1] // 16, label_shape[2] // 16)), pred_img[0])\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def test_step(images, label):\n",
    "    label_shape = label.shape\n",
    "    pred_img = model(images)\n",
    "        \n",
    "    loss_sub4 =  sub_loss(pred_img[2], label, 4)\n",
    "    loss_sub24 =  sub_loss(pred_img[1], label, 8)\n",
    "    loss_sub124 =  sub_loss(pred_img[0], label, 16)\n",
    "    \n",
    "    loss = loss_sub4 + loss_sub24 +loss_sub124\n",
    "    \n",
    "    test_loss(loss)\n",
    "    test_accuracy1(tf.image.resize(label, (label_shape[1] // 4, label_shape[2] // 4)), pred_img[2])\n",
    "    test_accuracy2(tf.image.resize(label, (label_shape[1] // 8, label_shape[2] // 8)), pred_img[1])\n",
    "    test_accuracy3(tf.image.resize(label, (label_shape[1] // 16, label_shape[2] // 16)), pred_img[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(data_set, epochs, batch_size):\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        start = time.time()\n",
    "        \n",
    "        for batch_load in range(DATA_LENGTH//1000):\n",
    "            \n",
    "            for image_batch, label_batch in data_set.take(100).batch(batch_size).prefetch(10):\n",
    "\n",
    "                if tf.random.uniform(()) > 0.5:\n",
    "                    image_batch = tf.image.flip_left_right(image_batch)\n",
    "                    label_batch = tf.image.flip_left_right(label_batch)\n",
    "                    \n",
    "                label_batch = convert_class(label_batch.numpy())\n",
    "                image_batch = tf.image.random_brightness(image_batch, 0.3)\n",
    "                train_step(image_batch, label_batch)\n",
    "                \n",
    "            data_set = data_set.skip(100)\n",
    "\n",
    "            for image_batch, label_batch in data_set.take(25).batch(batch_size).prefetch(10):\n",
    "                label_batch = convert_class(label_batch.numpy())\n",
    "                test_step(image_batch, label_batch)\n",
    "\n",
    "            data_set = data_set.skip(25)\n",
    "\n",
    "            clear_output(wait=True)\n",
    "            display(image_batch[0][tf.newaxis, ...], label_batch[0])     \n",
    "            \n",
    "            print ('Time for epoch {} , batch load {} is {} sec'.format(epoch + 1, batch_load +1 ,round(time.time()-start),3))\n",
    "            train_acc =(train_accuracy1.result()+ train_accuracy2.result()+ train_accuracy3.result())/3\n",
    "            test_acc =(test_accuracy1.result()+ test_accuracy2.result()+ test_accuracy3.result())/3\n",
    "\n",
    "            print ('train_acc {}, loss {} , test_acc {}, loss {}'.format(train_acc,\n",
    "                                                                             train_loss.result(),\n",
    "                                                                             test_acc,\n",
    "                                                                             test_loss.result()))\n",
    "            \n",
    "        with train_summary_writer.as_default():\n",
    "                tf.summary.scalar('loss', train_loss.result(), step=epoch)\n",
    "                tf.summary.scalar('accuracy_sub124', train_accuracy1.result(), step=epoch)\n",
    "                tf.summary.scalar('accuracy_sub24', train_accuracy2.result(), step=epoch)\n",
    "                tf.summary.scalar('accuracy_sub4', train_accuracy3.result(), step=epoch)\n",
    "                \n",
    "                \n",
    "        with test_summary_writer.as_default():\n",
    "                tf.summary.scalar('loss', test_loss.result(), step=epoch)\n",
    "                tf.summary.scalar('accuracy_sub124', test_accuracy1.result(), step=epoch)                \n",
    "                tf.summary.scalar('accuracy_sub24', test_accuracy2.result(), step=epoch)    \n",
    "                tf.summary.scalar('accuracy_sub4', test_accuracy3.result(), step=epoch)    \n",
    "                \n",
    "        path = \"icnet/icnet_\" + str(test_loss.result())+\".h5\"\n",
    "        \n",
    "        model.save(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for epoch 1 , batch load 8 is 144 sec\n",
      "train_acc 0.449314147233963, loss 4.837527275085449 , test_acc 0.21174021065235138, loss 5.775640487670898\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-5cd340b6da43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-77-75140f989eff>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(data_set, epochs, batch_size)\u001b[0m\n\u001b[1;32m     12\u001b[0m                     \u001b[0mlabel_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflip_left_right\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m                 \u001b[0mlabel_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m                 \u001b[0mimage_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_brightness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/road_segmetation/data_loader/data_loader.py\u001b[0m in \u001b[0;36mconvert_class\u001b[0;34m(label_img)\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mlabel_rgb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabel_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mlabel_rgb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabel_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_img\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mlabel_rgb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_all\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_count_reduce_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(data_set, 30, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can't convert non-rectangular Python sequence to Tensor.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31m_FallbackException\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36marg_max\u001b[0;34m(input, dimension, output_type, name)\u001b[0m\n\u001b[1;32m    942\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_execution_callbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"output_type\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 943\u001b[0;31m         output_type)\n\u001b[0m\u001b[1;32m    944\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31m_FallbackException\u001b[0m: This function does not handle the case of the path where all inputs are not already EagerTensors.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-b99225c889c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mlabel_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mshow_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-23-f4361277e9f4>\u001b[0m in \u001b[0;36mshow_predictions\u001b[0;34m(image, label)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mpred_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mdisplay_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_mask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisplay_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32melse\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-f4361277e9f4>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mpred_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mdisplay_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_mask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisplay_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32melse\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/road_segmetation/data_loader/display.py\u001b[0m in \u001b[0;36mcreate_mask\u001b[0;34m(pred_mask)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mpred_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0mpred_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpred_mask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpred_mask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py\u001b[0m in \u001b[0;36margmax_v2\u001b[0;34m(input, axis, output_type, name)\u001b[0m\n\u001b[1;32m    182\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marg_max\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36marg_max\u001b[0;34m(input, dimension, output_type, name)\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m         return arg_max_eager_fallback(\n\u001b[0;32m--> 948\u001b[0;31m             input, dimension, output_type=output_type, name=name, ctx=_ctx)\n\u001b[0m\u001b[1;32m    949\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m         \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36marg_max_eager_fallback\u001b[0;34m(input, dimension, output_type, name, ctx)\u001b[0m\n\u001b[1;32m    986\u001b[0m     \u001b[0moutput_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m   \u001b[0moutput_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"output_type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 988\u001b[0;31m   \u001b[0m_attr_T\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs_to_matching_eager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    989\u001b[0m   \u001b[0m_attr_Tidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdimension\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs_to_matching_eager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdimension\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36margs_to_matching_eager\u001b[0;34m(l, ctx, default_dtype)\u001b[0m\n\u001b[1;32m    255\u001b[0m       ret.append(\n\u001b[1;32m    256\u001b[0m           internal_convert_to_tensor(\n\u001b[0;32m--> 257\u001b[0;31m               t, dtype, preferred_dtype=default_dtype, ctx=ctx))\n\u001b[0m\u001b[1;32m    258\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, ctx, accept_composite_tensors)\u001b[0m\n\u001b[1;32m   1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1295\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m    284\u001b[0m                                          as_ref=False):\n\u001b[1;32m    285\u001b[0m   \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    225\u001b[0m   \"\"\"\n\u001b[1;32m    226\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 227\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    233\u001b[0m   \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m     94\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Can't convert non-rectangular Python sequence to Tensor."
     ]
    }
   ],
   "source": [
    "for image_batch, label_batch in data_set.take(3).batch(3):\n",
    "    label_batch = convert_class(label_batch.numpy())\n",
    "    show_predictions(image_batch, label_batch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGyY85/U4PcbTFTlNB+WPZHk9CnoYPjoirlZ8Mkj5D61Ww/psr0fpEaJuTPVuRGsqB+vApjypNOD1SOhh2FIBUsii6mz93y2X69ixMLu64AmigrgK+Bvly8tRNMgNDOMp4kZVFavX+uqxKt+ZLe4XCa8QFcTVI6Of1dOvTNVNWc96PUpK+0NNpr73/ldVWxkkKPAUUUFcTQo6mtRvlddjxF1ptFoNrqt8J11+y0n4+g829lbt3kxZ0YSvGmiGA/VAF5XHavWFTX+vWe/M0+ZIxOtxAE8RFSTUkWi1NjbUqixa4/UoRjVEHUUijmIuh8fRs7H7Cwn1o9IL9dKGcbqlZLnuytnq9ThGZNnpenH0QtW5lgb6gl6PA3iKqCChjtRnyD7mU1ljyOtRjOrvy/B6BCApsPsLAGAMUUFCDQ2XKq2oWsXBI16PAiAO2P2FhPq3nA26o8+7CllpkhJ/6i2A+GJLBQkVsPzKsr25liOVFfsPK2fIUVln13k9Cno4ogKkgEvS67V83LOaO+od7m8PT7H7CzCkJtagZyqLdSSSKUnK95frhsw9CtlpcV+3Y9lyZCtgNcZ9XUBriApgyJFYg36+bpbc47ebt/PrNOuChxISFSBZEBWgi+rdiBYcG6oNlUWKVvtkH7+hI/d1RE9EVIAuqolF9OjGaYrsDXOQEj0evwMAAGOICgDgjCZM2qpYBx+wR1SALgrZft02apkunvqBYhk80ASp41/7/lFuWqxDP0NUgC4KWH59K/tj/Z+CJXLCjZ9dJ+JaikmKuh37pQS6M6ICGJJjp+l7n/uDrpz2rmKhmKIVfn3hg69r/t5pqok1eD0ekBBEBTiu3o0o4nZ+91XITtPNWQc1L2eZlBaTXWvr6NY++usnAxURu8XQvdwy888akdbxRBAVQNKquqgu3HCD5n9yYZfCAqSKktAuBSx/h3+OqACSSqOZOrQvW1uO5SkmQ8dALMkNuAr4E3frFL/dqFgwJtfHpZfomm+8cbM2NtR2+Oe4+BGQNC14RAsuelq5TqUClpnbqrjZEd098X81KrBPGVbAyDLbckOvDRp52T4t2HuRNq8bkJB1IjU51bYibse3O4gKICnbCenyUL2krgfFlit/ekSBYESzwztUkMBHDRf6MlToq9MbmYe1WUQFnXf/NU9rXKDjH4aICmDYIL9fL056TLblKsdJ93ocoMN+eNVvdU24plM/yzEVwLCA5de4QEBj04I8jAzdkv+kRyh8dOXjiobaf5yRqAAAjGH3F5BCjkSrtTWSrr01vb0eBd2U60hOFx7cQFSAFPJMxSg9sOIyWRGbpwqjU74z8xVdn1HR6Z8nKkA3Vhat0cr6sxRxm36VVx0bJLvG4Qlh8AxRAbqx5XU5uv2tubIixw+PRiWLoKADYgFXsiS7zlI0FFOmU9el5REVoBsqi9boler+Wl5+jqx6W1aEnV3onLFjdmtoZqkWfXievluyRHMzj3ZpeUQF6IY+iIT0/eWfl1XjyOJWZeiCw7Vh3Ve8XL+49D0jyyMq6PbW1Ef0p8rRujBjs6YGbb1e49famkFGlj0xvEOXpLf+VzviRvVcZb72NvRp9vV8f7luyNyjkN36VfrL6qQVVeec8Xt+u1E39NqgwuNX5ZfHavVMxTCtrSiW1WATFHTZoY15+rp1o54a/v802N/1uz8QFXR7L3w6UYuXTdDOybma2n+FHtp3iTauKzay7DWfK9YlQ5e0+pqqWL1++sEVatgbbvZ1O79Osy54qM2oPHFouv66auQZvxcLxjTysn0q9DXt597bKN27eqbsY36OncCY/R/m65Gc6bqvYN0Zv1885JD2fFggqx3XQBIVJMSyOul3ZSWanbVBM0ORE19/uiJH66qLJUkBu1Hzz1qhXMenh8vG6EBDVruWvXz/IMmV1h4s0h3+Em0vzTV29tO2w7m6I6PkxL+vy16r6cGm/466Mf1XRaHerRygusqA7FPW2Vjt1/f3X65xmZ/o1t47TtxGfE19RC98OlHR49cerz/Ur8V5rYilBXsv0huZhyVJpXWZUh1nd8GwgjpNzNzZ4rffGvWSBm2ZJ6uh7WN3RAUJ8buyEr3ylxKVT0nXzP7LJTX9UX5s93Qd2JQnqelT+fSZWzTGOqLHNlwglXbsZnZVu7L0yq6Stl/YAfV7MvTKns+W6Z8R1fSzm/Y917uNWrBtuip29j7jrSnsSkdLV47W2/2L9dVJW05E5U+Vo7V42YR2hcFqtLR53YBmN4fkNhg9i5tXrzH992vTgXzF9oXiso5pg3bqSxnlRpZFVJBQq/cO0N/HPrsf1oEjn22NWI2W7v1olrLS6hSr8iflH88le4brQF3TzI2urfKycJsXGdaWB3XL7quUZjcdANl8NC/OUyKV9D/7Uy0eukSLCnrpe+7nFdt/5puUfm7idoV9TY+tXrZyVLt3jzqFNbq2T/sO0k+avEV+u/UDeUQFCVW/J0Nv7xl+xu9ZjZb2fFAgKXk/jVfvytLbu04KYTt+xi736d01Q+M3FOLq2hlr2nzNytKBOrwpt1PLjriOXvvL+DZfe31GhZySRVpeMUyLN50r6/iW/IRJW1UQLNd/5K86cfzuTn91s589FgnpL38dfcbljjj7kOaEq9o171MD/tzmTVKJCgC04GuXLdU9OVvafN37fVbq5X7jTvv6k+9NkX3k9BM15l66XH4rqntytijqxpRxcb1efHNKm+uZE67SnPA6TczcqW11TR/Abstep2wnpJOfBXTqAfeaWIPuv+yIPqrJaRYXf/9qfavwT22utyOICuIu6sYUc7k4D8khd+Rh3TRglf5z00Vq+PizU2g/f9FqDQ6WNnvt/N772rXMsWlBjT1DfMZO2aP9kWxJ0s/+cqWcmqZt8Hty3j/xid+xbH0nZ5WeD07Wv170qiTpvar++vPycyVJuz/O1Y/yhzeL25cyyqUTx0DaPs4SstN0T84Wlcdq9dwVH2ll+WCtXDlSZ/euaPOU+Y4iKoirl6tDunfXLB042r4zudCzRLMa9bPpv9XS8hF6fdnpn/RNsArq9OOS3+v5gxP0/juDNTy7VPN779P545/UzrGf7bKaHTqkDDtodN1ND7pqethV0WVP61t/vEl2ffMPWBE3qslvz5PruCciVpa5XXdPd/X6snHqlVOta3qtl9T12bLsdM3vvU/XZW7T0qs3aLD/sEw87fRkRAVx9WFtkfZtyucUWJwmmhHVC5cs0ISAXxF3u15X16MSS3P1+Ownmn0t16nW2LSgphX/Xpv6ZWmov1xShsYH0jQ+cPIZT2aDcqorQ3XKnP1rRVyn2XEJv+Xo4fHPKmhF9LejidlOSD8++01dP+cdFfkqNMwfbmGpnZPnhI+f7dX+oCy88tfteugcUQHgDZ+rCYGm06yvDe/TOzPW6tWlTadvDx//sX40YHGHF2lbrsamnRqHpn8X+DJU4ItK6vpV453VdI3T6bubmnZBNT89JdsJHf+62aB01vR2NpeoIC4iblRVsXpVRTt2rQl6DrvCp1Fvz9XGyf+tDDuon+Sv1Le+sFSSdJZtHz/4jGRyxeVfkVUX0R9bOXeBqCAu/lCTqe+9/1XVVga5nQjOyIpJdXsyNbD0VklNtwJ5a9RLHk+FM7n8yrlySssU29f2mXBEBXFxsLG3avdmcsNDtMqK6cQZUdUNZg8YwxyntEyN+/a367VEBYCn7L612jj9STU9ADlZL3tNbYNfmK+h/9byVfVuffuCIhEVAB6K5TZox4W/kdT2WUWIj4H/e4uGfXu1XNfMfmrL1IIAAGBbEwBgDFEBABhDVAAAxhAVAIAxRAUAYAxRAQAY8/8BUaECCKK0QWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(image_batch[0][tf.newaxis, ...], label_batch[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow2",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}