SoundNetworkTesting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sound Classification Testing\n",
    "\n",
    "This notebook restores the sound network trained for grasp testset1 stored in `grasp_60Freq_50TimePerSec_testset1_logs` and test it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "\n",
    "`Python 3.5.4` is used for development and following packages are required to run the code provided in the notebook:\n",
    "\n",
    "`pip install googledrivedownloader`<br>\n",
    "`pip install matplotlib`<br>\n",
    "`pip install tensorflow-gpu`<br>\n",
    "`pip install numpy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle, os, csv, time, shutil\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from google_drive_downloader import GoogleDriveDownloader as gdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_image(image, title):\n",
    "    \"\"\"Print the image\n",
    "\n",
    "    :param image: image pixels in list\n",
    "    :param title: title as string to be printed on top of the image\n",
    "    \"\"\"\n",
    "    plt.imshow(image)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "\n",
    "def time_taken(start, end):\n",
    "    \"\"\"Human readable time between `start` and `end`\n",
    "\n",
    "    :param start: time.time()\n",
    "    :param end: time.time()\n",
    "    :returns: day:hour:minute:second\n",
    "    \"\"\"\n",
    "    time = end-start\n",
    "    day = time // (24 * 3600)\n",
    "    time = time % (24 * 3600)\n",
    "    hour = time // 3600\n",
    "    time %= 3600\n",
    "    minutes = time // 60\n",
    "    time %= 60\n",
    "    seconds = time\n",
    "    day_hour_min_sec = str('%02d' % int(day))+\":\"+str('%02d' % int(hour))+\":\"+str('%02d' % int(minutes))+\":\"+str('%02d' % int(seconds))\n",
    "    \n",
    "    return day_hour_min_sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading `category_object_labels.bin`.\n",
    "This contains two dictionaries that stores category labels and object labels\n",
    "\"\"\"\n",
    "\n",
    "gdd.download_file_from_google_drive(file_id='15xPAbso4-uLk8PcRi9cb70r1r3FaU6ux',\n",
    "                                    dest_path='./dataset/category_object_labels.bin',\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"./dataset/category_object_labels.bin\", \"rb\")\n",
    "\n",
    "category_labels = pickle.load(bin_file)\n",
    "object_labels = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sound Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading testset1 for sound of grasp interaction and reading it.\n",
    "\"\"\"\n",
    "\n",
    "db_file_name = \"grasp_60Freq_50TimePerSec_testset1.bin\"\n",
    "gdd.download_file_from_google_drive(file_id='1WChpWIY53Ww1Z7NWjJ1YfQOim5goMD9N',\n",
    "                                    dest_path='./dataset/'+db_file_name,\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"./dataset/\"+db_file_name, \"rb\")\n",
    "\n",
    "audio_frames_2d_train = pickle.load(bin_file)\n",
    "category_label_train = pickle.load(bin_file)\n",
    "object_label_train = pickle.load(bin_file)\n",
    "category_label_train_one_hot = pickle.load(bin_file)\n",
    "object_label_train_one_hot = pickle.load(bin_file)\n",
    "\n",
    "audio_frames_2d_test = pickle.load(bin_file)\n",
    "category_label_test = pickle.load(bin_file)\n",
    "object_label_test = pickle.load(bin_file)\n",
    "category_label_test_one_hot = pickle.load(bin_file)\n",
    "object_label_test_one_hot = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch, frames, freq, channel: (400, 100, 60, 2)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  egg\n",
      "Object:  egg_rough_styrofoam\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Display a frame of the sound dataset\n",
    "\"\"\"\n",
    "\n",
    "batch_no = 188\n",
    "\n",
    "print(\"batch, frames, freq, channel:\", audio_frames_2d_train.shape)\n",
    "\n",
    "print_image(audio_frames_2d_train[batch_no, :, :, 0], db_file_name+\" Spec\")\n",
    "print_image(audio_frames_2d_train[batch_no, :, :, 1], db_file_name+\" Delta\")\n",
    "\n",
    "print(\"Category: \", list(category_labels.keys())[list(category_labels.values()).index(category_label_train[batch_no])])\n",
    "print(\"Object: \", list(object_labels.keys())[list(object_labels.values()).index(object_label_train[batch_no])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Sound Network Architecture\n",
    "\n",
    "![Sound Network Architecture](pics/Sound_CNN.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sound Network Hyper-parameters\n",
    "\n",
    "We applied dropout with 0.5 probability for fully connected layers and the first convolutional layer.\n",
    "We trained our network using Adam optimization, with a learning rate 1 x $10^{-4}$ and stopped training after 300 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network hyper-parameters\n",
    "batch = 1\n",
    "training_epochs = 300\n",
    "display_step = 1\n",
    "\n",
    "frames = time_step_size = audio_frames_2d_train.shape[1]\n",
    "freq_height = audio_frames_2d_train.shape[2]\n",
    "channel = audio_frames_2d_train.shape[3]\n",
    "\n",
    "num_classes = category_label_train_one_not.shape[1]\n",
    "\n",
    "model_path = db_file_name.split(\".\")[0]+\"_logs/model.ckpt\"\n",
    "logs_path = db_file_name.split(\".\")[0]+\"_logs/\"\n",
    "\n",
    "Y = tf.placeholder('float', [None, num_classes], name='LabelData')\n",
    "keep_prob = tf.placeholder_with_default(1.0, shape=(), name = 'keep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Functions used to define models\n",
    "\"\"\"\n",
    "\n",
    "def model():\n",
    "    with tf.name_scope(\"Model\"):\n",
    "        data_placeholder = tf.placeholder('float', [None, frames, freq_height, channel], name='InputData')\n",
    "        \n",
    "        net = tf.layers.conv2d(inputs=data_placeholder, filters=20, kernel_size=[57, 6], strides=[1, 1], padding=\"same\", activation=tf.nn.relu)\n",
    "        net = tf.layers.max_pooling2d(inputs=net, pool_size=[4, 4], strides=[4, 4])\n",
    "        net = tf.layers.dropout(inputs=net, rate=keep_prob)\n",
    "        net = tf.layers.conv2d(inputs=net, filters=40, kernel_size=[1, 3], strides=[1, 1], padding=\"same\", activation=tf.nn.relu)\n",
    "        if (db_file_name.split(\"_\")[0]) == \"hold\":\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[1, 4], strides=[1, 4])\n",
    "        else:\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[4, 4], strides=[4, 4])\n",
    "        net = tf.layers.flatten(net)\n",
    "        # Dense Layer\n",
    "        net = tf.layers.dense(inputs=net, units=256, activation=tf.nn.relu)\n",
    "        net = tf.layers.dropout(inputs=net, rate=keep_prob)\n",
    "        net = tf.layers.dense(inputs=net, units=256, activation=tf.nn.relu)\n",
    "        net = tf.layers.dropout(inputs=net, rate=keep_prob)\n",
    "        logits = tf.layers.dense(inputs=net, units=num_classes)\n",
    "        \n",
    "    return logits\n",
    "\n",
    "def loss(prediction, label_placeholder):\n",
    "    with tf.name_scope('Loss'):\n",
    "        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=label_placeholder))\n",
    "        # Create a summary to monitor cost tensor\n",
    "        cost_scalar = tf.summary.scalar(\"loss\", cost)\n",
    "    return cost, cost_scalar\n",
    "\n",
    "def training(prediction, label_placeholder):\n",
    "    with tf.name_scope('Optimizer'):\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)\n",
    "        train_op = optimizer.minimize(cost)\n",
    "    return train_op\n",
    "\n",
    "def evaluate(prediction, Y):\n",
    "    with tf.name_scope('Accuracy'):\n",
    "        # Test model\n",
    "        correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n",
    "        # Calculate accuracy\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))\n",
    "        # Create a summary to monitor accuracy tensor\n",
    "        accuracy_scalar = tf.summary.scalar(\"accuracy\", accuracy)\n",
    "    return accuracy, accuracy_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_dict:  {'Model': <tf.Tensor 'Model/dense_2/BiasAdd:0' shape=(?, 20) dtype=float32>, 'Loss': <tf.Tensor 'Loss/Mean:0' shape=() dtype=float32>, 'Loss_scalar': <tf.Tensor 'Loss/loss:0' shape=() dtype=string>, 'Optimizer': <tf.Operation 'Optimizer/Adam' type=NoOp>, 'Accuracy': <tf.Tensor 'Accuracy/Mean:0' shape=() dtype=float32>, 'Accuracy_scalar': <tf.Tensor 'Accuracy/accuracy:0' shape=() dtype=string>}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Creating the Neural Network\n",
    "\"\"\"\n",
    "\n",
    "model_dict = {}\n",
    "\n",
    "prediction = model()\n",
    "model_dict[\"Model\"] = prediction\n",
    "\n",
    "cost, cost_scalar = loss(prediction, Y)\n",
    "model_dict[\"Loss\"] = cost\n",
    "model_dict[\"Loss_scalar\"] = cost_scalar\n",
    "\n",
    "train_op = training(prediction, Y)\n",
    "model_dict[\"Optimizer\"] = train_op\n",
    "\n",
    "eval_op, accuracy_scalar = evaluate(prediction, Y)\n",
    "model_dict[\"Accuracy\"] = eval_op\n",
    "model_dict[\"Accuracy_scalar\"] = accuracy_scalar\n",
    "\n",
    "print(\"model_dict: \", model_dict)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "# 'Saver' op to save and restore all the variables\n",
    "saver = tf.train.Saver(max_to_keep=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing for the whole sound clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from grasp_60Freq_50TimePerSec_testset1_logs/model.ckpt-299\n",
      "Overall Accuracy:  0.47000000000000025\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test set\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    saver.restore(sess, tf.train.latest_checkpoint(logs_path))\n",
    "    \n",
    "    avg_accuracy_list = 0.0\n",
    "    total_batch = int(len(audio_frames_2d_test)/batch)\n",
    "    \n",
    "    for start, end in zip(range(0, len(audio_frames_2d_test), batch), range(batch, len(audio_frames_2d_test)+1, batch)):\n",
    "        X = tf.get_default_graph().get_tensor_by_name(\"Model/InputData:0\")\n",
    "        input_data, label_data = audio_frames_2d_test[start:end], category_label_test_one_not[start:end]\n",
    "        accuracy = sess.run(model_dict[\"Accuracy\"], feed_dict={X: input_data, Y: label_data})\n",
    "        avg_accuracy_list += accuracy/total_batch\n",
    "    \n",
    "    print(\"Overall Accuracy: \", avg_accuracy_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing for every 5th frame in the sound clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from grasp_60Freq_50TimePerSec_testset1_logs/model.ckpt-299\n",
      "Example Number:  1\n",
      "True Category:  ball\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  bottle\n",
      "Frame No.:  45 Prediction:  bottle\n",
      "Frame No.:  50 Prediction:  bottle\n",
      "Frame No.:  55 Prediction:  bottle\n",
      "Frame No.:  60 Prediction:  bottle\n",
      "Frame No.:  65 Prediction:  bottle\n",
      "Frame No.:  70 Prediction:  bottle\n",
      "Frame No.:  75 Prediction:  bottle\n",
      "Frame No.:  80 Prediction:  bottle\n",
      "Frame No.:  85 Prediction:  bottle\n",
      "Frame No.:  90 Prediction:  bottle\n",
      "Frame No.:  95 Prediction:  bottle\n",
      "Frame No.:  100 Prediction:  bottle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  2\n",
      "True Category:  ball\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  cone\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  tupperware\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  bottle\n",
      "Frame No.:  50 Prediction:  bottle\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  bottle\n",
      "Frame No.:  65 Prediction:  bottle\n",
      "Frame No.:  70 Prediction:  bottle\n",
      "Frame No.:  75 Prediction:  bottle\n",
      "Frame No.:  80 Prediction:  bottle\n",
      "Frame No.:  85 Prediction:  bottle\n",
      "Frame No.:  90 Prediction:  bottle\n",
      "Frame No.:  95 Prediction:  bottle\n",
      "Frame No.:  100 Prediction:  bottle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  3\n",
      "True Category:  ball\n",
      "Frame No.:  5 Prediction:  basket\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  cone\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  cone\n",
      "Frame No.:  35 Prediction:  weight\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  tupperware\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  weight\n",
      "Frame No.:  75 Prediction:  tupperware\n",
      "Frame No.:  80 Prediction:  weight\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  tupperware\n",
      "Frame No.:  95 Prediction:  weight\n",
      "Frame No.:  100 Prediction:  weight\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  4\n",
      "True Category:  ball\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  basket\n",
      "Frame No.:  50 Prediction:  bottle\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  5\n",
      "True Category:  ball\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  metal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  eggcoloringcup\n",
      "Frame No.:  40 Prediction:  tupperware\n",
      "Frame No.:  45 Prediction:  bottle\n",
      "Frame No.:  50 Prediction:  tupperware\n",
      "Frame No.:  55 Prediction:  bottle\n",
      "Frame No.:  60 Prediction:  bottle\n",
      "Frame No.:  65 Prediction:  eggcoloringcup\n",
      "Frame No.:  70 Prediction:  tupperware\n",
      "Frame No.:  75 Prediction:  bottle\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  tupperware\n",
      "Frame No.:  90 Prediction:  bottle\n",
      "Frame No.:  95 Prediction:  bottle\n",
      "Frame No.:  100 Prediction:  bottle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  6\n",
      "True Category:  basket\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  tupperware\n",
      "Frame No.:  15 Prediction:  timber\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  basket\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  basket\n",
      "Frame No.:  45 Prediction:  smallstuffedanimal\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  basket\n",
      "Frame No.:  65 Prediction:  basket\n",
      "Frame No.:  70 Prediction:  basket\n",
      "Frame No.:  75 Prediction:  basket\n",
      "Frame No.:  80 Prediction:  basket\n",
      "Frame No.:  85 Prediction:  basket\n",
      "Frame No.:  90 Prediction:  basket\n",
      "Frame No.:  95 Prediction:  basket\n",
      "Frame No.:  100 Prediction:  basket\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  7\n",
      "True Category:  basket\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  basket\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  cone\n",
      "Frame No.:  25 Prediction:  cone\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  basket\n",
      "Frame No.:  45 Prediction:  basket\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  basket\n",
      "Frame No.:  65 Prediction:  basket\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  basket\n",
      "Frame No.:  80 Prediction:  basket\n",
      "Frame No.:  85 Prediction:  bottle\n",
      "Frame No.:  90 Prediction:  basket\n",
      "Frame No.:  95 Prediction:  basket\n",
      "Frame No.:  100 Prediction:  basket\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  8\n",
      "True Category:  basket\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  basket\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  basket\n",
      "Frame No.:  50 Prediction:  ball\n",
      "Frame No.:  55 Prediction:  ball\n",
      "Frame No.:  60 Prediction:  ball\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  smallstuffedanimal\n",
      "Frame No.:  75 Prediction:  ball\n",
      "Frame No.:  80 Prediction:  basket\n",
      "Frame No.:  85 Prediction:  basket\n",
      "Frame No.:  90 Prediction:  basket\n",
      "Frame No.:  95 Prediction:  basket\n",
      "Frame No.:  100 Prediction:  basket\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  9\n",
      "True Category:  basket\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  basket\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  basket\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  basket\n",
      "Frame No.:  70 Prediction:  basket\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  basket\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  smallstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  10\n",
      "True Category:  basket\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  basket\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  basket\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  basket\n",
      "Frame No.:  70 Prediction:  basket\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  basket\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  smallstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  11\n",
      "True Category:  bigstuffedanimal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  bigstuffedanimal\n",
      "Frame No.:  45 Prediction:  bigstuffedanimal\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  bigstuffedanimal\n",
      "Frame No.:  65 Prediction:  bigstuffedanimal\n",
      "Frame No.:  70 Prediction:  smallstuffedanimal\n",
      "Frame No.:  75 Prediction:  bigstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  12\n",
      "True Category:  bigstuffedanimal\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  bigstuffedanimal\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  smallstuffedanimal\n",
      "Frame No.:  65 Prediction:  bigstuffedanimal\n",
      "Frame No.:  70 Prediction:  smallstuffedanimal\n",
      "Frame No.:  75 Prediction:  bigstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  13\n",
      "True Category:  bigstuffedanimal\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  noodle\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  noodle\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  noodle\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  noodle\n",
      "Frame No.:  80 Prediction:  noodle\n",
      "Frame No.:  85 Prediction:  noodle\n",
      "Frame No.:  90 Prediction:  noodle\n",
      "Frame No.:  95 Prediction:  noodle\n",
      "Frame No.:  100 Prediction:  noodle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  14\n",
      "True Category:  bigstuffedanimal\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  bigstuffedanimal\n",
      "Frame No.:  40 Prediction:  bigstuffedanimal\n",
      "Frame No.:  45 Prediction:  ball\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  bigstuffedanimal\n",
      "Frame No.:  60 Prediction:  bigstuffedanimal\n",
      "Frame No.:  65 Prediction:  bigstuffedanimal\n",
      "Frame No.:  70 Prediction:  bigstuffedanimal\n",
      "Frame No.:  75 Prediction:  bigstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  15\n",
      "True Category:  bigstuffedanimal\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  bigstuffedanimal\n",
      "Frame No.:  40 Prediction:  bigstuffedanimal\n",
      "Frame No.:  45 Prediction:  ball\n",
      "Frame No.:  50 Prediction:  bottle\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  bigstuffedanimal\n",
      "Frame No.:  65 Prediction:  bigstuffedanimal\n",
      "Frame No.:  70 Prediction:  bigstuffedanimal\n",
      "Frame No.:  75 Prediction:  bigstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  16\n",
      "True Category:  bottle\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  eggcoloringcup\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  cone\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  cone\n",
      "Frame No.:  100 Prediction:  cone\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  17\n",
      "True Category:  bottle\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  cone\n",
      "Frame No.:  40 Prediction:  weight\n",
      "Frame No.:  45 Prediction:  weight\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  weight\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  18\n",
      "True Category:  bottle\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  pvc\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  tupperware\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  cone\n",
      "Frame No.:  80 Prediction:  cone\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  cone\n",
      "Frame No.:  100 Prediction:  cone\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  19\n",
      "True Category:  bottle\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  pvc\n",
      "Frame No.:  30 Prediction:  cone\n",
      "Frame No.:  35 Prediction:  cone\n",
      "Frame No.:  40 Prediction:  weight\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  cone\n",
      "Frame No.:  80 Prediction:  weight\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  cone\n",
      "Frame No.:  100 Prediction:  cone\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  20\n",
      "True Category:  bottle\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  smallstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  cup\n",
      "Frame No.:  45 Prediction:  medicine\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  cannedfood\n",
      "Frame No.:  65 Prediction:  cannedfood\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  21\n",
      "True Category:  can\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  egg\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  22\n",
      "True Category:  can\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  eggcoloringcup\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  23\n",
      "True Category:  can\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  metal\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  bottle\n",
      "Frame No.:  80 Prediction:  bottle\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  24\n",
      "True Category:  can\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  noodle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  tin\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  25\n",
      "True Category:  can\n",
      "Frame No.:  5 Prediction:  cone\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  eggcoloringcup\n",
      "Frame No.:  30 Prediction:  cone\n",
      "Frame No.:  35 Prediction:  metal\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  metal\n",
      "Frame No.:  50 Prediction:  metal\n",
      "Frame No.:  55 Prediction:  metal\n",
      "Frame No.:  60 Prediction:  metal\n",
      "Frame No.:  65 Prediction:  metal\n",
      "Frame No.:  70 Prediction:  metal\n",
      "Frame No.:  75 Prediction:  metal\n",
      "Frame No.:  80 Prediction:  metal\n",
      "Frame No.:  85 Prediction:  metal\n",
      "Frame No.:  90 Prediction:  metal\n",
      "Frame No.:  95 Prediction:  metal\n",
      "Frame No.:  100 Prediction:  metal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  26\n",
      "True Category:  cannedfood\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  cone\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  27\n",
      "True Category:  cannedfood\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  28\n",
      "True Category:  cannedfood\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  29\n",
      "True Category:  cannedfood\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  cone\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  weight\n",
      "Frame No.:  40 Prediction:  weight\n",
      "Frame No.:  45 Prediction:  weight\n",
      "Frame No.:  50 Prediction:  basket\n",
      "Frame No.:  55 Prediction:  weight\n",
      "Frame No.:  60 Prediction:  cannedfood\n",
      "Frame No.:  65 Prediction:  cannedfood\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  weight\n",
      "Frame No.:  80 Prediction:  cannedfood\n",
      "Frame No.:  85 Prediction:  weight\n",
      "Frame No.:  90 Prediction:  weight\n",
      "Frame No.:  95 Prediction:  weight\n",
      "Frame No.:  100 Prediction:  weight\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  30\n",
      "True Category:  cannedfood\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  cannedfood\n",
      "Frame No.:  50 Prediction:  cannedfood\n",
      "Frame No.:  55 Prediction:  cannedfood\n",
      "Frame No.:  60 Prediction:  cannedfood\n",
      "Frame No.:  65 Prediction:  cannedfood\n",
      "Frame No.:  70 Prediction:  cannedfood\n",
      "Frame No.:  75 Prediction:  cannedfood\n",
      "Frame No.:  80 Prediction:  cannedfood\n",
      "Frame No.:  85 Prediction:  cannedfood\n",
      "Frame No.:  90 Prediction:  cannedfood\n",
      "Frame No.:  95 Prediction:  cannedfood\n",
      "Frame No.:  100 Prediction:  cannedfood\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  31\n",
      "True Category:  cone\n",
      "Frame No.:  5 Prediction:  timber\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  bottle\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  32\n",
      "True Category:  cone\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  ball\n",
      "Frame No.:  50 Prediction:  ball\n",
      "Frame No.:  55 Prediction:  ball\n",
      "Frame No.:  60 Prediction:  bigstuffedanimal\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  bigstuffedanimal\n",
      "Frame No.:  75 Prediction:  bigstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  33\n",
      "True Category:  cone\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  medicine\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  34\n",
      "True Category:  cone\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  metal\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  smallstuffedanimal\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  smallstuffedanimal\n",
      "Frame No.:  45 Prediction:  smallstuffedanimal\n",
      "Frame No.:  50 Prediction:  smallstuffedanimal\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  smallstuffedanimal\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  ball\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  smallstuffedanimal\n",
      "Frame No.:  85 Prediction:  smallstuffedanimal\n",
      "Frame No.:  90 Prediction:  smallstuffedanimal\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  smallstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  35\n",
      "True Category:  cone\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  cup\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  36\n",
      "True Category:  cup\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  cup\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  37\n",
      "True Category:  cup\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  noodle\n",
      "Frame No.:  25 Prediction:  eggcoloringcup\n",
      "Frame No.:  30 Prediction:  weight\n",
      "Frame No.:  35 Prediction:  can\n",
      "Frame No.:  40 Prediction:  can\n",
      "Frame No.:  45 Prediction:  can\n",
      "Frame No.:  50 Prediction:  can\n",
      "Frame No.:  55 Prediction:  can\n",
      "Frame No.:  60 Prediction:  can\n",
      "Frame No.:  65 Prediction:  can\n",
      "Frame No.:  70 Prediction:  can\n",
      "Frame No.:  75 Prediction:  can\n",
      "Frame No.:  80 Prediction:  can\n",
      "Frame No.:  85 Prediction:  can\n",
      "Frame No.:  90 Prediction:  can\n",
      "Frame No.:  95 Prediction:  can\n",
      "Frame No.:  100 Prediction:  can\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  38\n",
      "True Category:  cup\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  cone\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  can\n",
      "Frame No.:  40 Prediction:  can\n",
      "Frame No.:  45 Prediction:  can\n",
      "Frame No.:  50 Prediction:  can\n",
      "Frame No.:  55 Prediction:  can\n",
      "Frame No.:  60 Prediction:  can\n",
      "Frame No.:  65 Prediction:  can\n",
      "Frame No.:  70 Prediction:  can\n",
      "Frame No.:  75 Prediction:  can\n",
      "Frame No.:  80 Prediction:  can\n",
      "Frame No.:  85 Prediction:  can\n",
      "Frame No.:  90 Prediction:  can\n",
      "Frame No.:  95 Prediction:  can\n",
      "Frame No.:  100 Prediction:  can\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  39\n",
      "True Category:  cup\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  cone\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  can\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  can\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  40\n",
      "True Category:  cup\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  41\n",
      "True Category:  eggcoloringcup\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  cannedfood\n",
      "Frame No.:  45 Prediction:  cannedfood\n",
      "Frame No.:  50 Prediction:  cannedfood\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  cannedfood\n",
      "Frame No.:  65 Prediction:  cannedfood\n",
      "Frame No.:  70 Prediction:  cannedfood\n",
      "Frame No.:  75 Prediction:  cannedfood\n",
      "Frame No.:  80 Prediction:  cannedfood\n",
      "Frame No.:  85 Prediction:  cannedfood\n",
      "Frame No.:  90 Prediction:  cannedfood\n",
      "Frame No.:  95 Prediction:  cannedfood\n",
      "Frame No.:  100 Prediction:  cannedfood\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  42\n",
      "True Category:  eggcoloringcup\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  noodle\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  eggcoloringcup\n",
      "Frame No.:  55 Prediction:  eggcoloringcup\n",
      "Frame No.:  60 Prediction:  eggcoloringcup\n",
      "Frame No.:  65 Prediction:  eggcoloringcup\n",
      "Frame No.:  70 Prediction:  eggcoloringcup\n",
      "Frame No.:  75 Prediction:  eggcoloringcup\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  eggcoloringcup\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  43\n",
      "True Category:  eggcoloringcup\n",
      "Frame No.:  5 Prediction:  cone\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  cone\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  can\n",
      "Frame No.:  40 Prediction:  can\n",
      "Frame No.:  45 Prediction:  can\n",
      "Frame No.:  50 Prediction:  eggcoloringcup\n",
      "Frame No.:  55 Prediction:  can\n",
      "Frame No.:  60 Prediction:  eggcoloringcup\n",
      "Frame No.:  65 Prediction:  eggcoloringcup\n",
      "Frame No.:  70 Prediction:  can\n",
      "Frame No.:  75 Prediction:  eggcoloringcup\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  can\n",
      "Frame No.:  90 Prediction:  can\n",
      "Frame No.:  95 Prediction:  eggcoloringcup\n",
      "Frame No.:  100 Prediction:  can\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  44\n",
      "True Category:  eggcoloringcup\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  45\n",
      "True Category:  eggcoloringcup\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  can\n",
      "Frame No.:  35 Prediction:  eggcoloringcup\n",
      "Frame No.:  40 Prediction:  bottle\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  eggcoloringcup\n",
      "Frame No.:  55 Prediction:  eggcoloringcup\n",
      "Frame No.:  60 Prediction:  eggcoloringcup\n",
      "Frame No.:  65 Prediction:  eggcoloringcup\n",
      "Frame No.:  70 Prediction:  eggcoloringcup\n",
      "Frame No.:  75 Prediction:  eggcoloringcup\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  eggcoloringcup\n",
      "Frame No.:  90 Prediction:  eggcoloringcup\n",
      "Frame No.:  95 Prediction:  eggcoloringcup\n",
      "Frame No.:  100 Prediction:  eggcoloringcup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  46\n",
      "True Category:  egg\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  47\n",
      "True Category:  egg\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  pvc\n",
      "Frame No.:  35 Prediction:  eggcoloringcup\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  eggcoloringcup\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  48\n",
      "True Category:  egg\n",
      "Frame No.:  5 Prediction:  basket\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  pvc\n",
      "Frame No.:  20 Prediction:  metal\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  basket\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  pvc\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  basket\n",
      "Frame No.:  55 Prediction:  pvc\n",
      "Frame No.:  60 Prediction:  pvc\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  pvc\n",
      "Frame No.:  75 Prediction:  pvc\n",
      "Frame No.:  80 Prediction:  pvc\n",
      "Frame No.:  85 Prediction:  pvc\n",
      "Frame No.:  90 Prediction:  pvc\n",
      "Frame No.:  95 Prediction:  pvc\n",
      "Frame No.:  100 Prediction:  pvc\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  49\n",
      "True Category:  egg\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  egg\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  egg\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  50\n",
      "True Category:  egg\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  weight\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  51\n",
      "True Category:  medicine\n",
      "Frame No.:  5 Prediction:  basket\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  pvc\n",
      "Frame No.:  35 Prediction:  can\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  medicine\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  52\n",
      "True Category:  medicine\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  pvc\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  pvc\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  medicine\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  53\n",
      "True Category:  medicine\n",
      "Frame No.:  5 Prediction:  egg\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  medicine\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  54\n",
      "True Category:  medicine\n",
      "Frame No.:  5 Prediction:  egg\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  smallstuffedanimal\n",
      "Frame No.:  35 Prediction:  bigstuffedanimal\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  bigstuffedanimal\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  medicine\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  55\n",
      "True Category:  medicine\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  can\n",
      "Frame No.:  35 Prediction:  pvc\n",
      "Frame No.:  40 Prediction:  cup\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  56\n",
      "True Category:  metal\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  timber\n",
      "Frame No.:  15 Prediction:  timber\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  can\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  metal\n",
      "Frame No.:  45 Prediction:  metal\n",
      "Frame No.:  50 Prediction:  metal\n",
      "Frame No.:  55 Prediction:  can\n",
      "Frame No.:  60 Prediction:  metal\n",
      "Frame No.:  65 Prediction:  metal\n",
      "Frame No.:  70 Prediction:  can\n",
      "Frame No.:  75 Prediction:  metal\n",
      "Frame No.:  80 Prediction:  metal\n",
      "Frame No.:  85 Prediction:  can\n",
      "Frame No.:  90 Prediction:  can\n",
      "Frame No.:  95 Prediction:  can\n",
      "Frame No.:  100 Prediction:  can\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  57\n",
      "True Category:  metal\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  cone\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  metal\n",
      "Frame No.:  45 Prediction:  metal\n",
      "Frame No.:  50 Prediction:  metal\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  metal\n",
      "Frame No.:  65 Prediction:  metal\n",
      "Frame No.:  70 Prediction:  metal\n",
      "Frame No.:  75 Prediction:  metal\n",
      "Frame No.:  80 Prediction:  metal\n",
      "Frame No.:  85 Prediction:  metal\n",
      "Frame No.:  90 Prediction:  metal\n",
      "Frame No.:  95 Prediction:  metal\n",
      "Frame No.:  100 Prediction:  metal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  58\n",
      "True Category:  metal\n",
      "Frame No.:  5 Prediction:  cone\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  eggcoloringcup\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  metal\n",
      "Frame No.:  40 Prediction:  metal\n",
      "Frame No.:  45 Prediction:  metal\n",
      "Frame No.:  50 Prediction:  metal\n",
      "Frame No.:  55 Prediction:  metal\n",
      "Frame No.:  60 Prediction:  metal\n",
      "Frame No.:  65 Prediction:  metal\n",
      "Frame No.:  70 Prediction:  metal\n",
      "Frame No.:  75 Prediction:  metal\n",
      "Frame No.:  80 Prediction:  metal\n",
      "Frame No.:  85 Prediction:  metal\n",
      "Frame No.:  90 Prediction:  metal\n",
      "Frame No.:  95 Prediction:  metal\n",
      "Frame No.:  100 Prediction:  metal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  59\n",
      "True Category:  metal\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  egg\n",
      "Frame No.:  50 Prediction:  pvc\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  pvc\n",
      "Frame No.:  65 Prediction:  eggcoloringcup\n",
      "Frame No.:  70 Prediction:  pvc\n",
      "Frame No.:  75 Prediction:  eggcoloringcup\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  pvc\n",
      "Frame No.:  90 Prediction:  pvc\n",
      "Frame No.:  95 Prediction:  pvc\n",
      "Frame No.:  100 Prediction:  pvc\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  60\n",
      "True Category:  metal\n",
      "Frame No.:  5 Prediction:  cone\n",
      "Frame No.:  10 Prediction:  cone\n",
      "Frame No.:  15 Prediction:  cone\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  eggcoloringcup\n",
      "Frame No.:  30 Prediction:  eggcoloringcup\n",
      "Frame No.:  35 Prediction:  metal\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  61\n",
      "True Category:  noodle\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  smallstuffedanimal\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  basket\n",
      "Frame No.:  50 Prediction:  egg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  ball\n",
      "Frame No.:  65 Prediction:  noodle\n",
      "Frame No.:  70 Prediction:  eggcoloringcup\n",
      "Frame No.:  75 Prediction:  eggcoloringcup\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  eggcoloringcup\n",
      "Frame No.:  90 Prediction:  eggcoloringcup\n",
      "Frame No.:  95 Prediction:  eggcoloringcup\n",
      "Frame No.:  100 Prediction:  eggcoloringcup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  62\n",
      "True Category:  noodle\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  noodle\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  noodle\n",
      "Frame No.:  60 Prediction:  noodle\n",
      "Frame No.:  65 Prediction:  noodle\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  noodle\n",
      "Frame No.:  80 Prediction:  noodle\n",
      "Frame No.:  85 Prediction:  noodle\n",
      "Frame No.:  90 Prediction:  noodle\n",
      "Frame No.:  95 Prediction:  noodle\n",
      "Frame No.:  100 Prediction:  noodle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  63\n",
      "True Category:  noodle\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  noodle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  noodle\n",
      "Frame No.:  40 Prediction:  noodle\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  noodle\n",
      "Frame No.:  55 Prediction:  noodle\n",
      "Frame No.:  60 Prediction:  noodle\n",
      "Frame No.:  65 Prediction:  noodle\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  noodle\n",
      "Frame No.:  80 Prediction:  noodle\n",
      "Frame No.:  85 Prediction:  noodle\n",
      "Frame No.:  90 Prediction:  noodle\n",
      "Frame No.:  95 Prediction:  noodle\n",
      "Frame No.:  100 Prediction:  noodle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  64\n",
      "True Category:  noodle\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  ball\n",
      "Frame No.:  50 Prediction:  ball\n",
      "Frame No.:  55 Prediction:  ball\n",
      "Frame No.:  60 Prediction:  smallstuffedanimal\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  ball\n",
      "Frame No.:  75 Prediction:  ball\n",
      "Frame No.:  80 Prediction:  ball\n",
      "Frame No.:  85 Prediction:  ball\n",
      "Frame No.:  90 Prediction:  ball\n",
      "Frame No.:  95 Prediction:  ball\n",
      "Frame No.:  100 Prediction:  ball\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  65\n",
      "True Category:  noodle\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  cone\n",
      "Frame No.:  15 Prediction:  cone\n",
      "Frame No.:  20 Prediction:  pvc\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  noodle\n",
      "Frame No.:  40 Prediction:  bottle\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  noodle\n",
      "Frame No.:  55 Prediction:  noodle\n",
      "Frame No.:  60 Prediction:  noodle\n",
      "Frame No.:  65 Prediction:  noodle\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  noodle\n",
      "Frame No.:  80 Prediction:  noodle\n",
      "Frame No.:  85 Prediction:  noodle\n",
      "Frame No.:  90 Prediction:  noodle\n",
      "Frame No.:  95 Prediction:  noodle\n",
      "Frame No.:  100 Prediction:  noodle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  66\n",
      "True Category:  pasta\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  pasta\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  metal\n",
      "Frame No.:  30 Prediction:  eggcoloringcup\n",
      "Frame No.:  35 Prediction:  eggcoloringcup\n",
      "Frame No.:  40 Prediction:  pasta\n",
      "Frame No.:  45 Prediction:  pasta\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  67\n",
      "True Category:  pasta\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  pvc\n",
      "Frame No.:  35 Prediction:  pvc\n",
      "Frame No.:  40 Prediction:  medicine\n",
      "Frame No.:  45 Prediction:  pasta\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  medicine\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  68\n",
      "True Category:  pasta\n",
      "Frame No.:  5 Prediction:  medicine\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  pvc\n",
      "Frame No.:  25 Prediction:  metal\n",
      "Frame No.:  30 Prediction:  eggcoloringcup\n",
      "Frame No.:  35 Prediction:  eggcoloringcup\n",
      "Frame No.:  40 Prediction:  pasta\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  69\n",
      "True Category:  pasta\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  smallstuffedanimal\n",
      "Frame No.:  45 Prediction:  cup\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  pasta\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  pasta\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  70\n",
      "True Category:  pasta\n",
      "Frame No.:  5 Prediction:  egg\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  metal\n",
      "Frame No.:  30 Prediction:  pvc\n",
      "Frame No.:  35 Prediction:  metal\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  pasta\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  egg\n",
      "Frame No.:  60 Prediction:  pasta\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  pasta\n",
      "Frame No.:  85 Prediction:  pasta\n",
      "Frame No.:  90 Prediction:  pasta\n",
      "Frame No.:  95 Prediction:  pasta\n",
      "Frame No.:  100 Prediction:  pasta\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  71\n",
      "True Category:  pvc\n",
      "Frame No.:  5 Prediction:  eggcoloringcup\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  cup\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  72\n",
      "True Category:  pvc\n",
      "Frame No.:  5 Prediction:  cone\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  pvc\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  cannedfood\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cannedfood\n",
      "Frame No.:  65 Prediction:  cannedfood\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  cannedfood\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  pvc\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  pvc\n",
      "Frame No.:  100 Prediction:  pvc\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  73\n",
      "True Category:  pvc\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  pvc\n",
      "Frame No.:  30 Prediction:  pvc\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  pvc\n",
      "Frame No.:  60 Prediction:  pvc\n",
      "Frame No.:  65 Prediction:  cup\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  pvc\n",
      "Frame No.:  80 Prediction:  cup\n",
      "Frame No.:  85 Prediction:  pvc\n",
      "Frame No.:  90 Prediction:  pvc\n",
      "Frame No.:  95 Prediction:  pvc\n",
      "Frame No.:  100 Prediction:  pvc\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  74\n",
      "True Category:  pvc\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  cup\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  pvc\n",
      "Frame No.:  70 Prediction:  pvc\n",
      "Frame No.:  75 Prediction:  cup\n",
      "Frame No.:  80 Prediction:  pvc\n",
      "Frame No.:  85 Prediction:  cup\n",
      "Frame No.:  90 Prediction:  cup\n",
      "Frame No.:  95 Prediction:  cup\n",
      "Frame No.:  100 Prediction:  cup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  75\n",
      "True Category:  pvc\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  weight\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  pvc\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  pvc\n",
      "Frame No.:  45 Prediction:  pvc\n",
      "Frame No.:  50 Prediction:  cup\n",
      "Frame No.:  55 Prediction:  pvc\n",
      "Frame No.:  60 Prediction:  cup\n",
      "Frame No.:  65 Prediction:  pvc\n",
      "Frame No.:  70 Prediction:  cup\n",
      "Frame No.:  75 Prediction:  pvc\n",
      "Frame No.:  80 Prediction:  pvc\n",
      "Frame No.:  85 Prediction:  pvc\n",
      "Frame No.:  90 Prediction:  pvc\n",
      "Frame No.:  95 Prediction:  pvc\n",
      "Frame No.:  100 Prediction:  pvc\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  76\n",
      "True Category:  smallstuffedanimal\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  noodle\n",
      "Frame No.:  30 Prediction:  smallstuffedanimal\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  noodle\n",
      "Frame No.:  45 Prediction:  ball\n",
      "Frame No.:  50 Prediction:  smallstuffedanimal\n",
      "Frame No.:  55 Prediction:  noodle\n",
      "Frame No.:  60 Prediction:  bottle\n",
      "Frame No.:  65 Prediction:  bottle\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  bottle\n",
      "Frame No.:  80 Prediction:  bottle\n",
      "Frame No.:  85 Prediction:  bottle\n",
      "Frame No.:  90 Prediction:  bottle\n",
      "Frame No.:  95 Prediction:  bottle\n",
      "Frame No.:  100 Prediction:  bottle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  77\n",
      "True Category:  smallstuffedanimal\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  smallstuffedanimal\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  noodle\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  cone\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  noodle\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  noodle\n",
      "Frame No.:  55 Prediction:  noodle\n",
      "Frame No.:  60 Prediction:  ball\n",
      "Frame No.:  65 Prediction:  ball\n",
      "Frame No.:  70 Prediction:  noodle\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  noodle\n",
      "Frame No.:  85 Prediction:  smallstuffedanimal\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  noodle\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  78\n",
      "True Category:  smallstuffedanimal\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  bigstuffedanimal\n",
      "Frame No.:  40 Prediction:  smallstuffedanimal\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  weight\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  medicine\n",
      "Frame No.:  70 Prediction:  medicine\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  bottle\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  weight\n",
      "Frame No.:  95 Prediction:  weight\n",
      "Frame No.:  100 Prediction:  weight\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  79\n",
      "True Category:  smallstuffedanimal\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  smallstuffedanimal\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  smallstuffedanimal\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  smallstuffedanimal\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  egg\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  smallstuffedanimal\n",
      "Frame No.:  90 Prediction:  smallstuffedanimal\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  smallstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  80\n",
      "True Category:  smallstuffedanimal\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  bigstuffedanimal\n",
      "Frame No.:  30 Prediction:  ball\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  smallstuffedanimal\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  smallstuffedanimal\n",
      "Frame No.:  60 Prediction:  bigstuffedanimal\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  bigstuffedanimal\n",
      "Frame No.:  75 Prediction:  ball\n",
      "Frame No.:  80 Prediction:  bigstuffedanimal\n",
      "Frame No.:  85 Prediction:  bigstuffedanimal\n",
      "Frame No.:  90 Prediction:  bigstuffedanimal\n",
      "Frame No.:  95 Prediction:  bigstuffedanimal\n",
      "Frame No.:  100 Prediction:  bigstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  81\n",
      "True Category:  timber\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  eggcoloringcup\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  82\n",
      "True Category:  timber\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  metal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  noodle\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  noodle\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  83\n",
      "True Category:  timber\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  timber\n",
      "Frame No.:  25 Prediction:  cone\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  84\n",
      "True Category:  timber\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  85\n",
      "True Category:  timber\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  noodle\n",
      "Frame No.:  15 Prediction:  weight\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  cone\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  86\n",
      "True Category:  tin\n",
      "Frame No.:  5 Prediction:  weight\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  noodle\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  tin\n",
      "Frame No.:  30 Prediction:  tin\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  87\n",
      "True Category:  tin\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  tin\n",
      "Frame No.:  25 Prediction:  tin\n",
      "Frame No.:  30 Prediction:  tin\n",
      "Frame No.:  35 Prediction:  tin\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  88\n",
      "True Category:  tin\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  egg\n",
      "Frame No.:  15 Prediction:  bottle\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  basket\n",
      "Frame No.:  30 Prediction:  bottle\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  bottle\n",
      "Frame No.:  45 Prediction:  bottle\n",
      "Frame No.:  50 Prediction:  bottle\n",
      "Frame No.:  55 Prediction:  bottle\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  89\n",
      "True Category:  tin\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  cone\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  tin\n",
      "Frame No.:  30 Prediction:  tin\n",
      "Frame No.:  35 Prediction:  tin\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  90\n",
      "True Category:  tin\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  bigstuffedanimal\n",
      "Frame No.:  20 Prediction:  bottle\n",
      "Frame No.:  25 Prediction:  tin\n",
      "Frame No.:  30 Prediction:  tin\n",
      "Frame No.:  35 Prediction:  tin\n",
      "Frame No.:  40 Prediction:  tin\n",
      "Frame No.:  45 Prediction:  tin\n",
      "Frame No.:  50 Prediction:  tin\n",
      "Frame No.:  55 Prediction:  tin\n",
      "Frame No.:  60 Prediction:  tin\n",
      "Frame No.:  65 Prediction:  tin\n",
      "Frame No.:  70 Prediction:  tin\n",
      "Frame No.:  75 Prediction:  tin\n",
      "Frame No.:  80 Prediction:  tin\n",
      "Frame No.:  85 Prediction:  tin\n",
      "Frame No.:  90 Prediction:  tin\n",
      "Frame No.:  95 Prediction:  tin\n",
      "Frame No.:  100 Prediction:  tin\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  91\n",
      "True Category:  tupperware\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  tupperware\n",
      "Frame No.:  40 Prediction:  metal\n",
      "Frame No.:  45 Prediction:  noodle\n",
      "Frame No.:  50 Prediction:  tupperware\n",
      "Frame No.:  55 Prediction:  tupperware\n",
      "Frame No.:  60 Prediction:  tupperware\n",
      "Frame No.:  65 Prediction:  tupperware\n",
      "Frame No.:  70 Prediction:  tupperware\n",
      "Frame No.:  75 Prediction:  tupperware\n",
      "Frame No.:  80 Prediction:  tupperware\n",
      "Frame No.:  85 Prediction:  tupperware\n",
      "Frame No.:  90 Prediction:  tupperware\n",
      "Frame No.:  95 Prediction:  tupperware\n",
      "Frame No.:  100 Prediction:  tupperware\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  92\n",
      "True Category:  tupperware\n",
      "Frame No.:  5 Prediction:  ball\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  bottle\n",
      "Frame No.:  30 Prediction:  eggcoloringcup\n",
      "Frame No.:  35 Prediction:  bottle\n",
      "Frame No.:  40 Prediction:  eggcoloringcup\n",
      "Frame No.:  45 Prediction:  eggcoloringcup\n",
      "Frame No.:  50 Prediction:  tupperware\n",
      "Frame No.:  55 Prediction:  tupperware\n",
      "Frame No.:  60 Prediction:  eggcoloringcup\n",
      "Frame No.:  65 Prediction:  tupperware\n",
      "Frame No.:  70 Prediction:  eggcoloringcup\n",
      "Frame No.:  75 Prediction:  tupperware\n",
      "Frame No.:  80 Prediction:  eggcoloringcup\n",
      "Frame No.:  85 Prediction:  eggcoloringcup\n",
      "Frame No.:  90 Prediction:  eggcoloringcup\n",
      "Frame No.:  95 Prediction:  eggcoloringcup\n",
      "Frame No.:  100 Prediction:  eggcoloringcup\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  93\n",
      "True Category:  tupperware\n",
      "Frame No.:  5 Prediction:  ball\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  tupperware\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  tupperware\n",
      "Frame No.:  40 Prediction:  tupperware\n",
      "Frame No.:  45 Prediction:  tupperware\n",
      "Frame No.:  50 Prediction:  tupperware\n",
      "Frame No.:  55 Prediction:  tupperware\n",
      "Frame No.:  60 Prediction:  tupperware\n",
      "Frame No.:  65 Prediction:  tupperware\n",
      "Frame No.:  70 Prediction:  tupperware\n",
      "Frame No.:  75 Prediction:  tupperware\n",
      "Frame No.:  80 Prediction:  tupperware\n",
      "Frame No.:  85 Prediction:  tupperware\n",
      "Frame No.:  90 Prediction:  tupperware\n",
      "Frame No.:  95 Prediction:  tupperware\n",
      "Frame No.:  100 Prediction:  tupperware\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  94\n",
      "True Category:  tupperware\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  metal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  smallstuffedanimal\n",
      "Frame No.:  35 Prediction:  ball\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  cone\n",
      "Frame No.:  50 Prediction:  medicine\n",
      "Frame No.:  55 Prediction:  bottle\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  pasta\n",
      "Frame No.:  75 Prediction:  medicine\n",
      "Frame No.:  80 Prediction:  medicine\n",
      "Frame No.:  85 Prediction:  medicine\n",
      "Frame No.:  90 Prediction:  medicine\n",
      "Frame No.:  95 Prediction:  medicine\n",
      "Frame No.:  100 Prediction:  medicine\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  95\n",
      "True Category:  tupperware\n",
      "Frame No.:  5 Prediction:  eggcoloringcup\n",
      "Frame No.:  10 Prediction:  ball\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  pvc\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  egg\n",
      "Frame No.:  35 Prediction:  tupperware\n",
      "Frame No.:  40 Prediction:  tupperware\n",
      "Frame No.:  45 Prediction:  tupperware\n",
      "Frame No.:  50 Prediction:  pasta\n",
      "Frame No.:  55 Prediction:  tupperware\n",
      "Frame No.:  60 Prediction:  tupperware\n",
      "Frame No.:  65 Prediction:  pasta\n",
      "Frame No.:  70 Prediction:  tupperware\n",
      "Frame No.:  75 Prediction:  tupperware\n",
      "Frame No.:  80 Prediction:  tupperware\n",
      "Frame No.:  85 Prediction:  tupperware\n",
      "Frame No.:  90 Prediction:  tupperware\n",
      "Frame No.:  95 Prediction:  tupperware\n",
      "Frame No.:  100 Prediction:  tupperware\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  96\n",
      "True Category:  weight\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  egg\n",
      "Frame No.:  20 Prediction:  egg\n",
      "Frame No.:  25 Prediction:  metal\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  cone\n",
      "Frame No.:  40 Prediction:  egg\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  weight\n",
      "Frame No.:  55 Prediction:  cone\n",
      "Frame No.:  60 Prediction:  egg\n",
      "Frame No.:  65 Prediction:  egg\n",
      "Frame No.:  70 Prediction:  weight\n",
      "Frame No.:  75 Prediction:  egg\n",
      "Frame No.:  80 Prediction:  egg\n",
      "Frame No.:  85 Prediction:  egg\n",
      "Frame No.:  90 Prediction:  egg\n",
      "Frame No.:  95 Prediction:  egg\n",
      "Frame No.:  100 Prediction:  egg\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  97\n",
      "True Category:  weight\n",
      "Frame No.:  5 Prediction:  smallstuffedanimal\n",
      "Frame No.:  10 Prediction:  basket\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  bigstuffedanimal\n",
      "Frame No.:  25 Prediction:  egg\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  egg\n",
      "Frame No.:  40 Prediction:  cone\n",
      "Frame No.:  45 Prediction:  bottle\n",
      "Frame No.:  50 Prediction:  cone\n",
      "Frame No.:  55 Prediction:  bottle\n",
      "Frame No.:  60 Prediction:  cone\n",
      "Frame No.:  65 Prediction:  cone\n",
      "Frame No.:  70 Prediction:  cone\n",
      "Frame No.:  75 Prediction:  cone\n",
      "Frame No.:  80 Prediction:  cone\n",
      "Frame No.:  85 Prediction:  cone\n",
      "Frame No.:  90 Prediction:  cone\n",
      "Frame No.:  95 Prediction:  cone\n",
      "Frame No.:  100 Prediction:  cone\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  98\n",
      "True Category:  weight\n",
      "Frame No.:  5 Prediction:  bottle\n",
      "Frame No.:  10 Prediction:  bigstuffedanimal\n",
      "Frame No.:  15 Prediction:  ball\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  timber\n",
      "Frame No.:  35 Prediction:  timber\n",
      "Frame No.:  40 Prediction:  timber\n",
      "Frame No.:  45 Prediction:  timber\n",
      "Frame No.:  50 Prediction:  timber\n",
      "Frame No.:  55 Prediction:  timber\n",
      "Frame No.:  60 Prediction:  timber\n",
      "Frame No.:  65 Prediction:  timber\n",
      "Frame No.:  70 Prediction:  timber\n",
      "Frame No.:  75 Prediction:  timber\n",
      "Frame No.:  80 Prediction:  timber\n",
      "Frame No.:  85 Prediction:  timber\n",
      "Frame No.:  90 Prediction:  timber\n",
      "Frame No.:  95 Prediction:  timber\n",
      "Frame No.:  100 Prediction:  timber\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  99\n",
      "True Category:  weight\n",
      "Frame No.:  5 Prediction:  noodle\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  smallstuffedanimal\n",
      "Frame No.:  20 Prediction:  ball\n",
      "Frame No.:  25 Prediction:  timber\n",
      "Frame No.:  30 Prediction:  weight\n",
      "Frame No.:  35 Prediction:  basket\n",
      "Frame No.:  40 Prediction:  ball\n",
      "Frame No.:  45 Prediction:  basket\n",
      "Frame No.:  50 Prediction:  weight\n",
      "Frame No.:  55 Prediction:  basket\n",
      "Frame No.:  60 Prediction:  basket\n",
      "Frame No.:  65 Prediction:  basket\n",
      "Frame No.:  70 Prediction:  ball\n",
      "Frame No.:  75 Prediction:  basket\n",
      "Frame No.:  80 Prediction:  weight\n",
      "Frame No.:  85 Prediction:  weight\n",
      "Frame No.:  90 Prediction:  basket\n",
      "Frame No.:  95 Prediction:  basket\n",
      "Frame No.:  100 Prediction:  basket\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "Example Number:  100\n",
      "True Category:  weight\n",
      "Frame No.:  5 Prediction:  bigstuffedanimal\n",
      "Frame No.:  10 Prediction:  bottle\n",
      "Frame No.:  15 Prediction:  timber\n",
      "Frame No.:  20 Prediction:  smallstuffedanimal\n",
      "Frame No.:  25 Prediction:  ball\n",
      "Frame No.:  30 Prediction:  bigstuffedanimal\n",
      "Frame No.:  35 Prediction:  smallstuffedanimal\n",
      "Frame No.:  40 Prediction:  smallstuffedanimal\n",
      "Frame No.:  45 Prediction:  smallstuffedanimal\n",
      "Frame No.:  50 Prediction:  bigstuffedanimal\n",
      "Frame No.:  55 Prediction:  bigstuffedanimal\n",
      "Frame No.:  60 Prediction:  smallstuffedanimal\n",
      "Frame No.:  65 Prediction:  smallstuffedanimal\n",
      "Frame No.:  70 Prediction:  smallstuffedanimal\n",
      "Frame No.:  75 Prediction:  smallstuffedanimal\n",
      "Frame No.:  80 Prediction:  smallstuffedanimal\n",
      "Frame No.:  85 Prediction:  smallstuffedanimal\n",
      "Frame No.:  90 Prediction:  smallstuffedanimal\n",
      "Frame No.:  95 Prediction:  smallstuffedanimal\n",
      "Frame No.:  100 Prediction:  smallstuffedanimal\n",
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n"
     ]
    }
   ],
   "source": [
    "# Predict after every 5th frame of a audio\n",
    "\n",
    "all_frames_acc = {}\n",
    "for a_frame in range(5, frames+1, 5):\n",
    "    all_frames_acc[a_frame] = [0, 0, 0] # correct, wrong, percentage\n",
    "\n",
    "y_pred = {}\n",
    "y_score = {}\n",
    "for a_frame in range(5, frames+ 1, 5):\n",
    "    y_pred[a_frame] = []\n",
    "    y_score[a_frame] = []\n",
    "\n",
    "def fill_example(example, fill_frames, a_frame):\n",
    "    temp = []\n",
    "    temp.append(example[a_frame-1])\n",
    "    for _ in range(fill_frames):\n",
    "        example = np.concatenate((example, temp))\n",
    "    return example\n",
    "\n",
    "def give_me_complete_clip(num_of_frame, example):\n",
    "    fill_frames = frames - num_of_frame\n",
    "    if fill_frames != 0:\n",
    "        example = fill_example(example, fill_frames, num_of_frame)\n",
    "    return example\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    saver.restore(sess, tf.train.latest_checkpoint(logs_path))\n",
    "    \n",
    "    for i_example in range(len(audio_frames_2d_test)):\n",
    "        print(\"Example Number: \", i_example+1)\n",
    "        input_data, label_data = audio_frames_2d_test[i_example], category_label_test_one_not[i_example]\n",
    "        \n",
    "        truth = np.argmax(label_data, 0)+1\n",
    "        truth_cate = list(category_labels.keys())[list(category_labels.values()).index(truth)]\n",
    "        print(\"True Category: \", truth_cate)\n",
    "        \n",
    "        for a_frame in range(5, frames+1, 5):\n",
    "            \n",
    "            example = give_me_complete_clip(a_frame, input_data[0:a_frame])\n",
    "            \n",
    "            temp = []\n",
    "            temp.append(example)\n",
    "            \n",
    "            X = tf.get_default_graph().get_tensor_by_name(\"Model/InputData:0\")\n",
    "            predic = sess.run(model_dict[\"Model\"], feed_dict={X: temp, keep_prob: 1.0})\n",
    "            \n",
    "            argmax = sess.run(tf.argmax(predic, 1))+1\n",
    "            cate = list(category_labels.keys())[list(category_labels.values()).index(argmax)]\n",
    "            print(\"Frame No.: \", a_frame, \"Prediction: \", cate)\n",
    "            \n",
    "            y_pred[a_frame].extend(argmax)\n",
    "            y_score[a_frame].extend(predic)\n",
    "            \n",
    "            if truth_cate == cate:\n",
    "                all_frames_acc[a_frame][0] += 1\n",
    "            else:\n",
    "                all_frames_acc[a_frame][1] += 1\n",
    "            all_frames_acc[a_frame][2] = all_frames_acc[a_frame][0]/(all_frames_acc[a_frame][0]+all_frames_acc[a_frame][1])\n",
    "            \n",
    "        print(\"X\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Writing a CSV file that records accuracy for each frame\n",
    "\"\"\"\n",
    "\n",
    "with open(logs_path+db_file_name.split(\".\")[0]+\"_acc_every_frame.csv\",'w') as f:\n",
    "    writer = csv.writer(f, lineterminator=\"\\n\")\n",
    "    \n",
    "    first_row = [\"Frame_No\", \"Correct\", \"Wrong\", \"Accuracy\"]\n",
    "    writer.writerow(first_row)\n",
    "    for a_frame in range(5, frames+1, 5):\n",
    "        row = [a_frame]\n",
    "        row.append(all_frames_acc[a_frame][0])\n",
    "        row.append(all_frames_acc[a_frame][1])\n",
    "        row.append(all_frames_acc[a_frame][2])\n",
    "        writer.writerow(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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