inception.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/tensorflow/models/tree/master/inception\n",
    "\n",
    "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "from show_graph import show_graph\n",
    "from nbutil import imshow_multi, to_pil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py\n",
    "\n",
    "BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'\n",
    "JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'\n",
    "RESIZED_INPUT_TENSOR_NAME = 'ResizeBilinear:0'\n",
    "SOFTMAX_TENSOR_NAME = 'softmax:0'\n",
    "PATH = '/Users/nateparrott/Documents/SW/models/inception-2015-12-05'\n",
    "\n",
    "def create_inception_graph(path):\n",
    "  \"\"\"\"Creates a graph from saved GraphDef file and returns a Graph object.\n",
    "  Returns:\n",
    "    Graph holding the trained Inception network, and various tensors we'll be\n",
    "    manipulating.\n",
    "  \"\"\"\n",
    "  with tf.Session() as sess:\n",
    "    model_filename = os.path.join(\n",
    "        path, 'classify_image_graph_def.pb')\n",
    "    with open(model_filename, 'rb') as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "        bottleneck_tensor, jpeg_data_tensor, resized_input_tensor, softmax_tensor = (\n",
    "          tf.import_graph_def(graph_def, name='', return_elements=[\n",
    "              BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,\n",
    "              RESIZED_INPUT_TENSOR_NAME, SOFTMAX_TENSOR_NAME]))\n",
    "    return graph_def, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor, softmax_tensor\n",
    "\n",
    "# graph_def, bottleneck, jpeg_input, resized_image_input, softmax_tensor = create_inception_graph(PATH)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def create_image_queue(filenames, batch_size=16, grayscale=False):\n",
    "    filename_tensor = tf.convert_to_tensor(filenames, dtype=tf.string)\n",
    "    filename_q = tf.train.slice_input_producer([filename_tensor], num_epochs=None, shuffle=True)[0]\n",
    "\n",
    "    image_255 = tf.image.decode_jpeg(tf.read_file(filename_q))\n",
    "    image = tf.cast(image_255, tf.float32) / 255.0\n",
    "    if grayscale:\n",
    "        image = tf.image.grayscale_to_rgb(image)\n",
    "\n",
    "    def resize_image(image):\n",
    "        pre_crop_size = int(299 + random.random() * 100)\n",
    "        image_as_batch_of_1 = tf.expand_dims(image, 0)\n",
    "        image = tf.image.resize_bilinear(image_as_batch_of_1, [pre_crop_size, pre_crop_size])[0]\n",
    "        image = tf.random_crop(image, [299, 299, 3])\n",
    "        return image\n",
    "    \n",
    "    image = resize_image(image)\n",
    "\n",
    "    def distort_image(image):\n",
    "        # noise_amt = tf.abs(tf.random_normal([], stddev=0.2))\n",
    "        # distorted_image = image + tf.random_uniform([299, 299, 3], maxval=noise_amt)\n",
    "        distorted_image = tf.image.random_flip_left_right(image)\n",
    "        distorted_image = tf.image.random_brightness(distorted_image, max_delta=0.3)\n",
    "        distorted_image = tf.image.random_contrast(distorted_image, lower=0.6, upper=1.6)\n",
    "        return distorted_image\n",
    "    \n",
    "    image = distort_image(image)\n",
    "    \n",
    "    images_batch, filenames_batch = tf.train.shuffle_batch([image, filename_q], batch_size=batch_size, capacity=128, min_after_dequeue=64)\n",
    "    return images_batch, filenames_batch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Classifier(object):\n",
    "    def __init__(self, inception_path, n_classes, filenames, filename_to_class_fn):\n",
    "        self.filenames = filenames\n",
    "        self.filename_to_class_fn = filename_to_class_fn\n",
    "        \n",
    "        self.graph = tf.Graph()\n",
    "                        \n",
    "        with self.graph.as_default():\n",
    "            graph_def, bottleneck, jpeg_input, resized_image_input, softmax_tensor = create_inception_graph(inception_path)\n",
    "            \n",
    "            with tf.variable_scope('additional'):\n",
    "                identity = lambda x: x\n",
    "                logits = tf.layers.dense(bottleneck, 256, tf.nn.relu, name='fc1')\n",
    "                logits = tf.layers.dense(logits, n_classes, identity, name='fc2')\n",
    "                labels = tf.placeholder(tf.int64, name='correct')\n",
    "\n",
    "                loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)\n",
    "\n",
    "                trainable = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='additional')\n",
    "                step = tf.contrib.framework.get_or_create_global_step()\n",
    "                train_step = tf.train.AdamOptimizer(1e-5).minimize(loss, var_list=trainable, global_step=step)\n",
    "\n",
    "                predictions = tf.argmax(logits, axis=1)\n",
    "                acc = tf.reduce_mean(tf.cast(tf.equal(predictions, labels), tf.float32))\n",
    "\n",
    "                self.logits = logits    \n",
    "                self.resized_image_input = resized_image_input\n",
    "                self.labels = labels\n",
    "                self.loss = loss\n",
    "                self.acc = acc\n",
    "                self.train_step = train_step\n",
    "                self.global_step = step\n",
    "                self.predictions = predictions\n",
    "                self.bottleneck = bottleneck\n",
    "\n",
    "                self.images_batch, self.filenames_batch = create_image_queue(self.filenames, 1, grayscale=True)\n",
    "                self.session = None\n",
    "    \n",
    "    def setup(self):\n",
    "        with self.graph.as_default():\n",
    "            self.session = tf.Session(graph=self.graph)\n",
    "            # uninitialized = list(self.session.run(tf.report_uninitialized_variables()))\n",
    "            # print uninitialized\n",
    "            # uninitialized_vars = [tf.get_variable(v) for v in uninitialized]\n",
    "            # self.session.run(tf.variables_initializer(uninitialized_vars))\n",
    "            init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "            self.session.run(init_op)\n",
    "            tf.train.start_queue_runners(sess=self.session)\n",
    "    \n",
    "    def train(self, batch_iterator):\n",
    "        for x, y in batch_iterator:\n",
    "            step = self.session.run(self.global_step)\n",
    "            feed = {\n",
    "                self.resized_image_input: x,\n",
    "                self.labels: y\n",
    "            }\n",
    "            _, loss, acc, predictions = self.session.run([\n",
    "                self.train_step, \n",
    "                self.loss, \n",
    "                self.acc, \n",
    "                self.predictions], feed_dict=feed)\n",
    "            print \"{}: {}, acc={}, pred={}, actual={}\".format(step, loss, acc, predictions, y)\n",
    "    \n",
    "    def train_loop(self):\n",
    "        if self.session is None:\n",
    "            self.setup()\n",
    "        \n",
    "        def batch_gen():\n",
    "            while True:\n",
    "                images, filenames = self.session.run([self.images_batch, self.filenames_batch])\n",
    "                x = images\n",
    "                y = [self.filename_to_class_fn(filename) for filename in filenames]\n",
    "                y = np.array(y, dtype=int)\n",
    "                yield x, y\n",
    "        \n",
    "        self.train(batch_gen())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "scenes_dir = '/Users/nateparrott/Documents/School/cv/proj4/data/train'\n",
    "class_names = [c for c in os.listdir(scenes_dir) if not c.startswith('.')]\n",
    "filename_to_class_map = {}\n",
    "for class_idx, class_name in enumerate(class_names):\n",
    "    class_path = os.path.join(scenes_dir, class_name)\n",
    "    for name in os.listdir(class_path):\n",
    "        if name.endswith('.jpg') or name.endswith('.png'):\n",
    "            filename = os.path.join(class_path, name)\n",
    "            filename_to_class_map[filename] = class_idx\n",
    "n_classes = len(set(filename_to_class_map.values()))\n",
    "print n_classes\n",
    "filenames = filename_to_class_map.keys()\n",
    "def filename_to_class(filename): return filename_to_class_map[filename]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n",
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      "32: [ 2.34651661], acc=0.0, pred=[4], actual=[12]\n",
      "33: [ 3.95073843], acc=0.0, pred=[4], actual=[6]\n",
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      "44: [ 2.4218719], acc=0.0, pred=[4], actual=[13]\n",
      "45: [ 3.15362024], acc=0.0, pred=[4], actual=[3]\n",
      "46: [ 3.31406116], acc=0.0, pred=[4], actual=[1]\n",
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      "53: [ 2.73130322], acc=0.0, pred=[4], actual=[2]\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-ee5888a2a08d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'ok'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-4-5c8360597717>\u001b[0m in \u001b[0;36mtrain_loop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-4-5c8360597717>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, batch_iterator)\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                 self.predictions], feed_dict=feed)\n\u001b[0m\u001b[1;32m     62\u001b[0m             \u001b[0;32mprint\u001b[0m \u001b[0;34m\"{}: {}, acc={}, pred={}, actual={}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Python/2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "x = Classifier(PATH, n_classes, filenames, filename_to_class)\n",
    "print 'ok'\n",
    "\n",
    "x.train_loop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# show_graph(graph_def)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    images_batch, filenames_batch = create_image_queue(['/Users/nateparrott/Desktop/lucy.jpg'])\n",
    "\n",
    "    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "    sess.run(init_op)\n",
    "    tf.train.start_queue_runners(sess=sess)\n",
    "\n",
    "    images, filenames = sess.run([images_batch, filenames_batch])\n",
    "    print filenames[0]\n",
    "    to_pil(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kit fox, Vulpes macrotis\n",
      "\n",
      "English setter\n",
      "\n",
      "Siberian husky\n",
      "\n",
      "Australian terrier\n",
      "\n",
      "English springer, English springer spaniel\n",
      "\n",
      "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus\n",
      "\n",
      "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens\n",
      "\n",
      "Egyptian cat\n",
      "\n",
      "ibex, Capra ibex\n",
      "\n",
      "Persian cat\n",
      "\n",
      "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor\n",
      "\n",
      "gazelle\n",
      "\n",
      "porcupine, hedgehog\n",
      "\n",
      "sea lion\n",
      "\n",
      "malamute, malemute, Alaskan malamute\n",
      "\n",
      "badger\n",
      "\n",
      "Great Dane\n",
      "\n",
      "Walker hound, Walker foxhound\n",
      "\n",
      "Welsh springer spaniel\n",
      "\n",
      "whippet\n",
      "\n",
      "Scottish deerhound, deerhound\n",
      "\n",
      "killer whale, killer, orca, grampus, sea wolf, Orcinus orca\n",
      "\n",
      "mink\n",
      "\n",
      "African elephant, Loxodonta africana\n",
      "\n",
      "Weimaraner\n",
      "\n",
      "soft-coated wheaten terrier\n",
      "\n",
      "Dandie Dinmont, Dandie Dinmont terrier\n",
      "\n",
      "red wolf, maned wolf, Canis rufus, Canis niger\n",
      "\n",
      "Old English sheepdog, bobtail\n",
      "\n",
      "jaguar, panther, Panthera onca, Felis onca\n",
      "\n",
      "otterhound, otter hound\n",
      "\n",
      "bloodhound, sleuthhound\n",
      "\n",
      "Airedale, Airedale terrier\n",
      "\n",
      "hyena, hyaena\n",
      "\n",
      "meerkat, mierkat\n",
      "\n",
      "giant schnauzer\n",
      "\n",
      "titi, titi monkey\n",
      "\n",
      "three-toed sloth, ai, Bradypus tridactylus\n",
      "\n",
      "sorrel\n",
      "\n",
      "black-footed ferret, ferret, Mustela nigripes\n",
      "\n",
      "dalmatian, coach dog, carriage dog\n",
      "\n",
      "black-and-tan coonhound\n",
      "\n",
      "papillon\n",
      "\n",
      "skunk, polecat, wood pussy\n",
      "\n",
      "Staffordshire bullterrier, Staffordshire bull terrier\n",
      "\n",
      "Mexican hairless\n",
      "\n",
      "Bouvier des Flandres, Bouviers des Flandres\n",
      "\n",
      "weasel\n",
      "\n",
      "miniature poodle\n",
      "\n",
      "Cardigan, Cardigan Welsh corgi\n",
      "\n",
      "malinois\n",
      "\n",
      "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis\n",
      "\n",
      "fox squirrel, eastern fox squirrel, Sciurus niger\n",
      "\n",
      "colobus, colobus monkey\n",
      "\n",
      "tiger cat\n",
      "\n",
      "Lhasa, Lhasa apso\n",
      "\n",
      "impala, Aepyceros melampus\n",
      "\n",
      "coyote, prairie wolf, brush wolf, Canis latrans\n",
      "\n",
      "Yorkshire terrier\n",
      "\n",
      "Newfoundland, Newfoundland dog\n",
      "\n",
      "brown bear, bruin, Ursus arctos\n",
      "\n",
      "red fox, Vulpes vulpes\n",
      "\n",
      "Norwegian elkhound, elkhound\n",
      "\n",
      "Rottweiler\n",
      "\n",
      "hartebeest\n",
      "\n",
      "Saluki, gazelle hound\n",
      "\n",
      "grey fox, gray fox, Urocyon cinereoargenteus\n",
      "\n",
      "schipperke\n",
      "\n",
      "Pekinese, Pekingese, Peke\n",
      "\n",
      "Brabancon griffon\n",
      "\n",
      "West Highland white terrier\n",
      "\n",
      "Sealyham terrier, Sealyham\n",
      "\n",
      "guenon, guenon monkey\n",
      "\n",
      "mongoose\n",
      "\n",
      "indri, indris, Indri indri, Indri brevicaudatus\n",
      "\n",
      "tiger, Panthera tigris\n",
      "\n",
      "Irish wolfhound\n",
      "\n",
      "wild boar, boar, Sus scrofa\n",
      "\n",
      "EntleBucher\n",
      "\n",
      "zebra\n",
      "\n",
      "ram, tup\n",
      "\n",
      "French bulldog\n",
      "\n",
      "orangutan, orang, orangutang, Pongo pygmaeus\n",
      "\n",
      "basenji\n",
      "\n",
      "leopard, Panthera pardus\n",
      "\n",
      "Bernese mountain dog\n",
      "\n",
      "Maltese dog, Maltese terrier, Maltese\n",
      "\n",
      "Norfolk terrier\n",
      "\n",
      "toy terrier\n",
      "\n",
      "vizsla, Hungarian pointer\n",
      "\n",
      "cairn, cairn terrier\n",
      "\n",
      "squirrel monkey, Saimiri sciureus\n",
      "\n",
      "groenendael\n",
      "\n",
      "clumber, clumber spaniel\n",
      "\n",
      "Siamese cat, Siamese\n",
      "\n",
      "chimpanzee, chimp, Pan troglodytes\n",
      "\n",
      "komondor\n",
      "\n",
      "Afghan hound, Afghan\n",
      "\n",
      "Japanese spaniel\n",
      "\n",
      "proboscis monkey, Nasalis larvatus\n",
      "\n",
      "guinea pig, Cavia cobaya\n",
      "\n",
      "white wolf, Arctic wolf, Canis lupus tundrarum\n",
      "\n",
      "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus\n",
      "\n",
      "gorilla, Gorilla gorilla\n",
      "\n",
      "borzoi, Russian wolfhound\n",
      "\n",
      "toy poodle\n",
      "\n",
      "Kerry blue terrier\n",
      "\n",
      "ox\n",
      "\n",
      "Scotch terrier, Scottish terrier, Scottie\n",
      "\n",
      "Tibetan mastiff\n",
      "\n",
      "spider monkey, Ateles geoffroyi\n",
      "\n",
      "Doberman, Doberman pinscher\n",
      "\n",
      "Boston bull, Boston terrier\n",
      "\n",
      "Greater Swiss Mountain dog\n",
      "\n",
      "Appenzeller\n",
      "\n",
      "Shih-Tzu\n",
      "\n",
      "Irish water spaniel\n",
      "\n",
      "Pomeranian\n",
      "\n",
      "Bedlington terrier\n",
      "\n",
      "warthog\n",
      "\n",
      "Arabian camel, dromedary, Camelus dromedarius\n",
      "\n",
      "siamang, Hylobates syndactylus, Symphalangus syndactylus\n",
      "\n",
      "miniature schnauzer\n",
      "\n",
      "collie\n",
      "\n",
      "golden retriever\n",
      "\n",
      "Irish terrier\n",
      "\n",
      "affenpinscher, monkey pinscher, monkey dog\n",
      "\n",
      "Border collie\n",
      "\n",
      "hare\n",
      "\n",
      "boxer\n",
      "\n",
      "silky terrier, Sydney silky\n",
      "\n",
      "beagle\n",
      "\n",
      "Leonberg\n",
      "\n",
      "German short-haired pointer\n",
      "\n",
      "patas, hussar monkey, Erythrocebus patas\n",
      "\n",
      "dhole, Cuon alpinus\n",
      "\n",
      "baboon\n",
      "\n",
      "macaque\n",
      "\n",
      "Chesapeake Bay retriever\n",
      "\n",
      "bull mastiff\n",
      "\n",
      "kuvasz\n",
      "\n",
      "capuchin, ringtail, Cebus capucinus\n",
      "\n",
      "pug, pug-dog\n",
      "\n",
      "curly-coated retriever\n",
      "\n",
      "Norwich terrier\n",
      "\n",
      "flat-coated retriever\n",
      "\n",
      "hog, pig, grunter, squealer, Sus scrofa\n",
      "\n",
      "keeshond\n",
      "\n",
      "Eskimo dog, husky\n",
      "\n",
      "Brittany spaniel\n",
      "\n",
      "standard poodle\n",
      "\n",
      "Lakeland terrier\n",
      "\n",
      "snow leopard, ounce, Panthera uncia\n",
      "\n",
      "Gordon setter\n",
      "\n",
      "dingo, warrigal, warragal, Canis dingo\n",
      "\n",
      "standard schnauzer\n",
      "\n",
      "hamster\n",
      "\n",
      "Tibetan terrier, chrysanthemum dog\n",
      "\n",
      "Arctic fox, white fox, Alopex lagopus\n",
      "\n",
      "wire-haired fox terrier\n",
      "\n",
      "basset, basset hound\n",
      "\n",
      "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis\n",
      "\n",
      "American black bear, black bear, Ursus americanus, Euarctos americanus\n",
      "\n",
      "Angora, Angora rabbit\n",
      "\n",
      "bison\n",
      "\n",
      "howler monkey, howler\n",
      "\n",
      "hippopotamus, hippo, river horse, Hippopotamus amphibius\n",
      "\n",
      "chow, chow chow\n",
      "\n",
      "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca\n",
      "\n",
      "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier\n",
      "\n",
      "Shetland sheepdog, Shetland sheep dog, Shetland\n",
      "\n",
      "Great Pyrenees\n",
      "\n",
      "Chihuahua\n",
      "\n",
      "tabby, tabby cat\n",
      "\n",
      "marmoset\n",
      "\n",
      "Labrador retriever\n",
      "\n",
      "Saint Bernard, St Bernard\n",
      "\n",
      "armadillo\n",
      "\n",
      "Samoyed, Samoyede\n",
      "\n",
      "bluetick\n",
      "\n",
      "redbone\n",
      "\n",
      "polecat, fitch, foulmart, foumart, Mustela putorius\n",
      "\n",
      "marmot\n",
      "\n",
      "kelpie\n",
      "\n",
      "gibbon, Hylobates lar\n",
      "\n",
      "llama\n",
      "\n",
      "miniature pinscher\n",
      "\n",
      "wood rabbit, cottontail, cottontail rabbit\n",
      "\n",
      "Italian greyhound\n",
      "\n",
      "lion, king of beasts, Panthera leo\n",
      "\n",
      "cocker spaniel, English cocker spaniel, cocker\n",
      "\n",
      "Irish setter, red setter\n",
      "\n",
      "dugong, Dugong dugon\n",
      "\n",
      "Indian elephant, Elephas maximus\n",
      "\n",
      "beaver\n",
      "\n",
      "Sussex spaniel\n",
      "\n",
      "Pembroke, Pembroke Welsh corgi\n",
      "\n",
      "Blenheim spaniel\n",
      "\n",
      "Madagascar cat, ring-tailed lemur, Lemur catta\n",
      "\n",
      "Rhodesian ridgeback\n",
      "\n",
      "lynx, catamount\n",
      "\n",
      "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus\n",
      "\n",
      "langur\n",
      "\n",
      "Ibizan hound, Ibizan Podenco\n",
      "\n",
      "timber wolf, grey wolf, gray wolf, Canis lupus\n",
      "\n",
      "cheetah, chetah, Acinonyx jubatus\n",
      "\n",
      "English foxhound\n",
      "\n",
      "briard\n",
      "\n",
      "sloth bear, Melursus ursinus, Ursus ursinus\n",
      "\n",
      "Border terrier\n",
      "\n",
      "German shepherd, German shepherd dog, German police dog, alsatian\n",
      "\n",
      "otter\n",
      "\n",
      "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\n",
      "\n",
      "tusker\n",
      "\n",
      "echidna, spiny anteater, anteater\n",
      "\n",
      "wallaby, brush kangaroo\n",
      "\n",
      "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus\n",
      "\n",
      "wombat\n",
      "\n",
      "revolver, six-gun, six-shooter\n",
      "\n",
      "umbrella\n",
      "\n",
      "schooner\n",
      "\n",
      "soccer ball\n",
      "\n",
      "accordion, piano accordion, squeeze box\n",
      "\n",
      "ant, emmet, pismire\n",
      "\n",
      "starfish, sea star\n",
      "\n",
      "chambered nautilus, pearly nautilus, nautilus\n",
      "\n",
      "grand piano, grand\n",
      "\n",
      "laptop, laptop computer\n",
      "\n",
      "strawberry\n",
      "\n",
      "airliner\n",
      "\n",
      "warplane, military plane\n",
      "\n",
      "airship, dirigible\n",
      "\n",
      "balloon\n",
      "\n",
      "space shuttle\n",
      "\n",
      "fireboat\n",
      "\n",
      "gondola\n",
      "\n",
      "speedboat\n",
      "\n",
      "lifeboat\n",
      "\n",
      "canoe\n",
      "\n",
      "yawl\n",
      "\n",
      "catamaran\n",
      "\n",
      "trimaran\n",
      "\n",
      "container ship, containership, container vessel\n",
      "\n",
      "liner, ocean liner\n",
      "\n",
      "pirate, pirate ship\n",
      "\n",
      "aircraft carrier, carrier, flattop, attack aircraft carrier\n",
      "\n",
      "submarine, pigboat, sub, U-boat\n",
      "\n",
      "wreck\n",
      "\n",
      "half track\n",
      "\n",
      "tank, army tank, armored combat vehicle, armoured combat vehicle\n",
      "\n",
      "missile\n",
      "\n",
      "bobsled, bobsleigh, bob\n",
      "\n",
      "dogsled, dog sled, dog sleigh\n",
      "\n",
      "bicycle-built-for-two, tandem bicycle, tandem\n",
      "\n",
      "mountain bike, all-terrain bike, off-roader\n",
      "\n",
      "freight car\n",
      "\n",
      "passenger car, coach, carriage\n",
      "\n",
      "barrow, garden cart, lawn cart, wheelbarrow\n",
      "\n",
      "shopping cart\n",
      "\n",
      "motor scooter, scooter\n",
      "\n",
      "forklift\n",
      "\n",
      "electric locomotive\n",
      "\n",
      "steam locomotive\n",
      "\n",
      "amphibian, amphibious vehicle\n",
      "\n",
      "ambulance\n",
      "\n",
      "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon\n",
      "\n",
      "cab, hack, taxi, taxicab\n",
      "\n",
      "convertible\n",
      "\n",
      "jeep, landrover\n",
      "\n",
      "limousine, limo\n",
      "\n",
      "minivan\n",
      "\n",
      "Model T\n",
      "\n",
      "racer, race car, racing car\n",
      "\n",
      "sports car, sport car\n",
      "\n",
      "go-kart\n",
      "\n",
      "golfcart, golf cart\n",
      "\n",
      "moped\n",
      "\n",
      "snowplow, snowplough\n",
      "\n",
      "fire engine, fire truck\n",
      "\n",
      "garbage truck, dustcart\n",
      "\n",
      "pickup, pickup truck\n",
      "\n",
      "tow truck, tow car, wrecker\n",
      "\n",
      "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi\n",
      "\n",
      "moving van\n",
      "\n",
      "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria\n",
      "\n",
      "recreational vehicle, RV, R.V.\n",
      "\n",
      "streetcar, tram, tramcar, trolley, trolley car\n",
      "\n",
      "snowmobile\n",
      "\n",
      "tractor\n",
      "\n",
      "mobile home, manufactured home\n",
      "\n",
      "tricycle, trike, velocipede\n",
      "\n",
      "unicycle, monocycle\n",
      "\n",
      "horse cart, horse-cart\n",
      "\n",
      "jinrikisha, ricksha, rickshaw\n",
      "\n",
      "oxcart\n",
      "\n",
      "bassinet\n",
      "\n",
      "cradle\n",
      "\n",
      "crib, cot\n",
      "\n",
      "four-poster\n",
      "\n",
      "bookcase\n",
      "\n",
      "china cabinet, china closet\n",
      "\n",
      "medicine chest, medicine cabinet\n",
      "\n",
      "chiffonier, commode\n",
      "\n",
      "table lamp\n",
      "\n",
      "file, file cabinet, filing cabinet\n",
      "\n",
      "park bench\n",
      "\n",
      "barber chair\n",
      "\n",
      "throne\n",
      "\n",
      "folding chair\n",
      "\n",
      "rocking chair, rocker\n",
      "\n",
      "studio couch, day bed\n",
      "\n",
      "toilet seat\n",
      "\n",
      "desk\n",
      "\n",
      "pool table, billiard table, snooker table\n",
      "\n",
      "dining table, board\n",
      "\n",
      "entertainment center\n",
      "\n",
      "wardrobe, closet, press\n",
      "\n",
      "Granny Smith\n",
      "\n",
      "orange\n",
      "\n",
      "lemon\n",
      "\n",
      "fig\n",
      "\n",
      "pineapple, ananas\n",
      "\n",
      "banana\n",
      "\n",
      "jackfruit, jak, jack\n",
      "\n",
      "custard apple\n",
      "\n",
      "pomegranate\n",
      "\n",
      "acorn\n",
      "\n",
      "hip, rose hip, rosehip\n",
      "\n",
      "ear, spike, capitulum\n",
      "\n",
      "rapeseed\n",
      "\n",
      "corn\n",
      "\n",
      "buckeye, horse chestnut, conker\n",
      "\n",
      "organ, pipe organ\n",
      "\n",
      "upright, upright piano\n",
      "\n",
      "chime, bell, gong\n",
      "\n",
      "drum, membranophone, tympan\n",
      "\n",
      "gong, tam-tam\n",
      "\n",
      "maraca\n",
      "\n",
      "marimba, xylophone\n",
      "\n",
      "steel drum\n",
      "\n",
      "banjo\n",
      "\n",
      "cello, violoncello\n",
      "\n",
      "violin, fiddle\n",
      "\n",
      "harp\n",
      "\n",
      "acoustic guitar\n",
      "\n",
      "electric guitar\n",
      "\n",
      "cornet, horn, trumpet, trump\n",
      "\n",
      "French horn, horn\n",
      "\n",
      "trombone\n",
      "\n",
      "harmonica, mouth organ, harp, mouth harp\n",
      "\n",
      "ocarina, sweet potato\n",
      "\n",
      "panpipe, pandean pipe, syrinx\n",
      "\n",
      "bassoon\n",
      "\n",
      "oboe, hautboy, hautbois\n",
      "\n",
      "sax, saxophone\n",
      "\n",
      "flute, transverse flute\n",
      "\n",
      "daisy\n",
      "\n",
      "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\n",
      "\n",
      "cliff, drop, drop-off\n",
      "\n",
      "valley, vale\n",
      "\n",
      "alp\n",
      "\n",
      "volcano\n",
      "\n",
      "promontory, headland, head, foreland\n",
      "\n",
      "sandbar, sand bar\n",
      "\n",
      "coral reef\n",
      "\n",
      "lakeside, lakeshore\n",
      "\n",
      "seashore, coast, seacoast, sea-coast\n",
      "\n",
      "geyser\n",
      "\n",
      "hatchet\n",
      "\n",
      "cleaver, meat cleaver, chopper\n",
      "\n",
      "letter opener, paper knife, paperknife\n",
      "\n",
      "plane, carpenter's plane, woodworking plane\n",
      "\n",
      "power drill\n",
      "\n",
      "lawn mower, mower\n",
      "\n",
      "hammer\n",
      "\n",
      "corkscrew, bottle screw\n",
      "\n",
      "can opener, tin opener\n",
      "\n",
      "plunger, plumber's helper\n",
      "\n",
      "screwdriver\n",
      "\n",
      "shovel\n",
      "\n",
      "plow, plough\n",
      "\n",
      "chain saw, chainsaw\n",
      "\n",
      "cock\n",
      "\n",
      "hen\n",
      "\n",
      "ostrich, Struthio camelus\n",
      "\n",
      "brambling, Fringilla montifringilla\n",
      "\n",
      "goldfinch, Carduelis carduelis\n",
      "\n",
      "house finch, linnet, Carpodacus mexicanus\n",
      "\n",
      "junco, snowbird\n",
      "\n",
      "indigo bunting, indigo finch, indigo bird, Passerina cyanea\n",
      "\n",
      "robin, American robin, Turdus migratorius\n",
      "\n",
      "bulbul\n",
      "\n",
      "jay\n",
      "\n",
      "magpie\n",
      "\n",
      "chickadee\n",
      "\n",
      "water ouzel, dipper\n",
      "\n",
      "kite\n",
      "\n",
      "bald eagle, American eagle, Haliaeetus leucocephalus\n",
      "\n",
      "vulture\n",
      "\n",
      "great grey owl, great gray owl, Strix nebulosa\n",
      "\n",
      "black grouse\n",
      "\n",
      "ptarmigan\n",
      "\n",
      "ruffed grouse, partridge, Bonasa umbellus\n",
      "\n",
      "prairie chicken, prairie grouse, prairie fowl\n",
      "\n",
      "peacock\n",
      "\n",
      "quail\n",
      "\n",
      "partridge\n",
      "\n",
      "African grey, African gray, Psittacus erithacus\n",
      "\n",
      "macaw\n",
      "\n",
      "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita\n",
      "\n",
      "lorikeet\n",
      "\n",
      "coucal\n",
      "\n",
      "bee eater\n",
      "\n",
      "hornbill\n",
      "\n",
      "hummingbird\n",
      "\n",
      "jacamar\n",
      "\n",
      "toucan\n",
      "\n",
      "drake\n",
      "\n",
      "red-breasted merganser, Mergus serrator\n",
      "\n",
      "goose\n",
      "\n",
      "black swan, Cygnus atratus\n",
      "\n",
      "white stork, Ciconia ciconia\n",
      "\n",
      "black stork, Ciconia nigra\n",
      "\n",
      "spoonbill\n",
      "\n",
      "flamingo\n",
      "\n",
      "American egret, great white heron, Egretta albus\n",
      "\n",
      "little blue heron, Egretta caerulea\n",
      "\n",
      "bittern\n",
      "\n",
      "crane\n",
      "\n",
      "limpkin, Aramus pictus\n",
      "\n",
      "American coot, marsh hen, mud hen, water hen, Fulica americana\n",
      "\n",
      "bustard\n",
      "\n",
      "ruddy turnstone, Arenaria interpres\n",
      "\n",
      "red-backed sandpiper, dunlin, Erolia alpina\n",
      "\n",
      "redshank, Tringa totanus\n",
      "\n",
      "dowitcher\n",
      "\n",
      "oystercatcher, oyster catcher\n",
      "\n",
      "European gallinule, Porphyrio porphyrio\n",
      "\n",
      "pelican\n",
      "\n",
      "king penguin, Aptenodytes patagonica\n",
      "\n",
      "albatross, mollymawk\n",
      "\n",
      "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
      "\n",
      "tiger shark, Galeocerdo cuvieri\n",
      "\n",
      "hammerhead, hammerhead shark\n",
      "\n",
      "electric ray, crampfish, numbfish, torpedo\n",
      "\n",
      "stingray\n",
      "\n",
      "barracouta, snoek\n",
      "\n",
      "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch\n",
      "\n",
      "tench, Tinca tinca\n",
      "\n",
      "goldfish, Carassius auratus\n",
      "\n",
      "eel\n",
      "\n",
      "rock beauty, Holocanthus tricolor\n",
      "\n",
      "anemone fish\n",
      "\n",
      "lionfish\n",
      "\n",
      "puffer, pufferfish, blowfish, globefish\n",
      "\n",
      "sturgeon\n",
      "\n",
      "gar, garfish, garpike, billfish, Lepisosteus osseus\n",
      "\n",
      "loggerhead, loggerhead turtle, Caretta caretta\n",
      "\n",
      "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea\n",
      "\n",
      "mud turtle\n",
      "\n",
      "terrapin\n",
      "\n",
      "box turtle, box tortoise\n",
      "\n",
      "banded gecko\n",
      "\n",
      "common iguana, iguana, Iguana iguana\n",
      "\n",
      "American chameleon, anole, Anolis carolinensis\n",
      "\n",
      "whiptail, whiptail lizard\n",
      "\n",
      "agama\n",
      "\n",
      "frilled lizard, Chlamydosaurus kingi\n",
      "\n",
      "alligator lizard\n",
      "\n",
      "Gila monster, Heloderma suspectum\n",
      "\n",
      "green lizard, Lacerta viridis\n",
      "\n",
      "African chameleon, Chamaeleo chamaeleon\n",
      "\n",
      "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis\n",
      "\n",
      "triceratops\n",
      "\n",
      "African crocodile, Nile crocodile, Crocodylus niloticus\n",
      "\n",
      "American alligator, Alligator mississipiensis\n",
      "\n",
      "thunder snake, worm snake, Carphophis amoenus\n",
      "\n",
      "ringneck snake, ring-necked snake, ring snake\n",
      "\n",
      "hognose snake, puff adder, sand viper\n",
      "\n",
      "green snake, grass snake\n",
      "\n",
      "king snake, kingsnake\n",
      "\n",
      "garter snake, grass snake\n",
      "\n",
      "water snake\n",
      "\n",
      "vine snake\n",
      "\n",
      "night snake, Hypsiglena torquata\n",
      "\n",
      "boa constrictor, Constrictor constrictor\n",
      "\n",
      "rock python, rock snake, Python sebae\n",
      "\n",
      "Indian cobra, Naja naja\n",
      "\n",
      "green mamba\n",
      "\n",
      "sea snake\n",
      "\n",
      "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus\n",
      "\n",
      "diamondback, diamondback rattlesnake, Crotalus adamanteus\n",
      "\n",
      "sidewinder, horned rattlesnake, Crotalus cerastes\n",
      "\n",
      "European fire salamander, Salamandra salamandra\n",
      "\n",
      "common newt, Triturus vulgaris\n",
      "\n",
      "eft\n",
      "\n",
      "spotted salamander, Ambystoma maculatum\n",
      "\n",
      "axolotl, mud puppy, Ambystoma mexicanum\n",
      "\n",
      "bullfrog, Rana catesbeiana\n",
      "\n",
      "tree frog, tree-frog\n",
      "\n",
      "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui\n",
      "\n",
      "whistle\n",
      "\n",
      "wing\n",
      "\n",
      "paintbrush\n",
      "\n",
      "hand blower, blow dryer, blow drier, hair dryer, hair drier\n",
      "\n",
      "oxygen mask\n",
      "\n",
      "snorkel\n",
      "\n",
      "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system\n",
      "\n",
      "microphone, mike\n",
      "\n",
      "screen, CRT screen\n",
      "\n",
      "mouse, computer mouse\n",
      "\n",
      "electric fan, blower\n",
      "\n",
      "oil filter\n",
      "\n",
      "strainer\n",
      "\n",
      "space heater\n",
      "\n",
      "stove\n",
      "\n",
      "guillotine\n",
      "\n",
      "barometer\n",
      "\n",
      "rule, ruler\n",
      "\n",
      "odometer, hodometer, mileometer, milometer\n",
      "\n",
      "scale, weighing machine\n",
      "\n",
      "analog clock\n",
      "\n",
      "digital clock\n",
      "\n",
      "wall clock\n",
      "\n",
      "hourglass\n",
      "\n",
      "sundial\n",
      "\n",
      "parking meter\n",
      "\n",
      "stopwatch, stop watch\n",
      "\n",
      "digital watch\n",
      "\n",
      "stethoscope\n",
      "\n",
      "syringe\n",
      "\n",
      "magnetic compass\n",
      "\n",
      "binoculars, field glasses, opera glasses\n",
      "\n",
      "projector\n",
      "\n",
      "sunglasses, dark glasses, shades\n",
      "\n",
      "loupe, jeweler's loupe\n",
      "\n",
      "radio telescope, radio reflector\n",
      "\n",
      "bow\n",
      "\n",
      "cannon\n",
      "\n",
      "assault rifle, assault gun\n",
      "\n",
      "rifle\n",
      "\n",
      "projectile, missile\n",
      "\n",
      "computer keyboard, keypad\n",
      "\n",
      "typewriter keyboard\n",
      "\n",
      "crane\n",
      "\n",
      "lighter, light, igniter, ignitor\n",
      "\n",
      "abacus\n",
      "\n",
      "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM\n",
      "\n",
      "slide rule, slipstick\n",
      "\n",
      "desktop computer\n",
      "\n",
      "hand-held computer, hand-held microcomputer\n",
      "\n",
      "notebook, notebook computer\n",
      "\n",
      "web site, website, internet site, site\n",
      "\n",
      "harvester, reaper\n",
      "\n",
      "thresher, thrasher, threshing machine\n",
      "\n",
      "printer\n",
      "\n",
      "slot, one-armed bandit\n",
      "\n",
      "vending machine\n",
      "\n",
      "sewing machine\n",
      "\n",
      "joystick\n",
      "\n",
      "switch, electric switch, electrical switch\n",
      "\n",
      "hook, claw\n",
      "\n",
      "car wheel\n",
      "\n",
      "paddlewheel, paddle wheel\n",
      "\n",
      "pinwheel\n",
      "\n",
      "potter's wheel\n",
      "\n",
      "gas pump, gasoline pump, petrol pump, island dispenser\n",
      "\n",
      "carousel, carrousel, merry-go-round, roundabout, whirligig\n",
      "\n",
      "swing\n",
      "\n",
      "reel\n",
      "\n",
      "radiator\n",
      "\n",
      "puck, hockey puck\n",
      "\n",
      "hard disc, hard disk, fixed disk\n",
      "\n",
      "sunglass\n",
      "\n",
      "pick, plectrum, plectron\n",
      "\n",
      "car mirror\n",
      "\n",
      "solar dish, solar collector, solar furnace\n",
      "\n",
      "remote control, remote\n",
      "\n",
      "disk brake, disc brake\n",
      "\n",
      "buckle\n",
      "\n",
      "hair slide\n",
      "\n",
      "knot\n",
      "\n",
      "combination lock\n",
      "\n",
      "padlock\n",
      "\n",
      "nail\n",
      "\n",
      "safety pin\n",
      "\n",
      "screw\n",
      "\n",
      "muzzle\n",
      "\n",
      "seat belt, seatbelt\n",
      "\n",
      "ski\n",
      "\n",
      "candle, taper, wax light\n",
      "\n",
      "jack-o'-lantern\n",
      "\n",
      "spotlight, spot\n",
      "\n",
      "torch\n",
      "\n",
      "neck brace\n",
      "\n",
      "pier\n",
      "\n",
      "tripod\n",
      "\n",
      "maypole\n",
      "\n",
      "mousetrap\n",
      "\n",
      "spider web, spider's web\n",
      "\n",
      "trilobite\n",
      "\n",
      "harvestman, daddy longlegs, Phalangium opilio\n",
      "\n",
      "scorpion\n",
      "\n",
      "black and gold garden spider, Argiope aurantia\n",
      "\n",
      "barn spider, Araneus cavaticus\n",
      "\n",
      "garden spider, Aranea diademata\n",
      "\n",
      "black widow, Latrodectus mactans\n",
      "\n",
      "tarantula\n",
      "\n",
      "wolf spider, hunting spider\n",
      "\n",
      "tick\n",
      "\n",
      "centipede\n",
      "\n",
      "isopod\n",
      "\n",
      "Dungeness crab, Cancer magister\n",
      "\n",
      "rock crab, Cancer irroratus\n",
      "\n",
      "fiddler crab\n",
      "\n",
      "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica\n",
      "\n",
      "American lobster, Northern lobster, Maine lobster, Homarus americanus\n",
      "\n",
      "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish\n",
      "\n",
      "crayfish, crawfish, crawdad, crawdaddy\n",
      "\n",
      "hermit crab\n",
      "\n",
      "tiger beetle\n",
      "\n",
      "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle\n",
      "\n",
      "ground beetle, carabid beetle\n",
      "\n",
      "long-horned beetle, longicorn, longicorn beetle\n",
      "\n",
      "leaf beetle, chrysomelid\n",
      "\n",
      "dung beetle\n",
      "\n",
      "rhinoceros beetle\n",
      "\n",
      "weevil\n",
      "\n",
      "fly\n",
      "\n",
      "bee\n",
      "\n",
      "grasshopper, hopper\n",
      "\n",
      "cricket\n",
      "\n",
      "walking stick, walkingstick, stick insect\n",
      "\n",
      "cockroach, roach\n",
      "\n",
      "mantis, mantid\n",
      "\n",
      "cicada, cicala\n",
      "\n",
      "leafhopper\n",
      "\n",
      "lacewing, lacewing fly\n",
      "\n",
      "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\n",
      "\n",
      "damselfly\n",
      "\n",
      "admiral\n",
      "\n",
      "ringlet, ringlet butterfly\n",
      "\n",
      "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus\n",
      "\n",
      "cabbage butterfly\n",
      "\n",
      "sulphur butterfly, sulfur butterfly\n",
      "\n",
      "lycaenid, lycaenid butterfly\n",
      "\n",
      "jellyfish\n",
      "\n",
      "sea anemone, anemone\n",
      "\n",
      "brain coral\n",
      "\n",
      "flatworm, platyhelminth\n",
      "\n",
      "nematode, nematode worm, roundworm\n",
      "\n",
      "conch\n",
      "\n",
      "snail\n",
      "\n",
      "slug\n",
      "\n",
      "sea slug, nudibranch\n",
      "\n",
      "chiton, coat-of-mail shell, sea cradle, polyplacophore\n",
      "\n",
      "sea urchin\n",
      "\n",
      "sea cucumber, holothurian\n",
      "\n",
      "iron, smoothing iron\n",
      "\n",
      "espresso maker\n",
      "\n",
      "microwave, microwave oven\n",
      "\n",
      "Dutch oven\n",
      "\n",
      "rotisserie\n",
      "\n",
      "toaster\n",
      "\n",
      "waffle iron\n",
      "\n",
      "vacuum, vacuum cleaner\n",
      "\n",
      "dishwasher, dish washer, dishwashing machine\n",
      "\n",
      "refrigerator, icebox\n",
      "\n",
      "washer, automatic washer, washing machine\n",
      "\n",
      "Crock Pot\n",
      "\n",
      "frying pan, frypan, skillet\n",
      "\n",
      "wok\n",
      "\n",
      "caldron, cauldron\n",
      "\n",
      "coffeepot\n",
      "\n",
      "teapot\n",
      "\n",
      "spatula\n",
      "\n",
      "altar\n",
      "\n",
      "triumphal arch\n",
      "\n",
      "patio, terrace\n",
      "\n",
      "steel arch bridge\n",
      "\n",
      "suspension bridge\n",
      "\n",
      "viaduct\n",
      "\n",
      "barn\n",
      "\n",
      "greenhouse, nursery, glasshouse\n",
      "\n",
      "palace\n",
      "\n",
      "monastery\n",
      "\n",
      "library\n",
      "\n",
      "apiary, bee house\n",
      "\n",
      "boathouse\n",
      "\n",
      "church, church building\n",
      "\n",
      "mosque\n",
      "\n",
      "stupa, tope\n",
      "\n",
      "planetarium\n",
      "\n",
      "restaurant, eating house, eating place, eatery\n",
      "\n",
      "cinema, movie theater, movie theatre, movie house, picture palace\n",
      "\n",
      "home theater, home theatre\n",
      "\n",
      "lumbermill, sawmill\n",
      "\n",
      "coil, spiral, volute, whorl, helix\n",
      "\n",
      "obelisk\n",
      "\n",
      "totem pole\n",
      "\n",
      "castle\n",
      "\n",
      "prison, prison house\n",
      "\n",
      "grocery store, grocery, food market, market\n",
      "\n",
      "bakery, bakeshop, bakehouse\n",
      "\n",
      "barbershop\n",
      "\n",
      "bookshop, bookstore, bookstall\n",
      "\n",
      "butcher shop, meat market\n",
      "\n",
      "confectionery, confectionary, candy store\n",
      "\n",
      "shoe shop, shoe-shop, shoe store\n",
      "\n",
      "tobacco shop, tobacconist shop, tobacconist\n",
      "\n",
      "toyshop\n",
      "\n",
      "fountain\n",
      "\n",
      "cliff dwelling\n",
      "\n",
      "yurt\n",
      "\n",
      "dock, dockage, docking facility\n",
      "\n",
      "brass, memorial tablet, plaque\n",
      "\n",
      "megalith, megalithic structure\n",
      "\n",
      "bannister, banister, balustrade, balusters, handrail\n",
      "\n",
      "breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
      "\n",
      "dam, dike, dyke\n",
      "\n",
      "chainlink fence\n",
      "\n",
      "picket fence, paling\n",
      "\n",
      "worm fence, snake fence, snake-rail fence, Virginia fence\n",
      "\n",
      "stone wall\n",
      "\n",
      "grille, radiator grille\n",
      "\n",
      "sliding door\n",
      "\n",
      "turnstile\n",
      "\n",
      "mountain tent\n",
      "\n",
      "scoreboard\n",
      "\n",
      "honeycomb\n",
      "\n",
      "plate rack\n",
      "\n",
      "pedestal, plinth, footstall\n",
      "\n",
      "beacon, lighthouse, beacon light, pharos\n",
      "\n",
      "mashed potato\n",
      "\n",
      "bell pepper\n",
      "\n",
      "head cabbage\n",
      "\n",
      "broccoli\n",
      "\n",
      "cauliflower\n",
      "\n",
      "zucchini, courgette\n",
      "\n",
      "spaghetti squash\n",
      "\n",
      "acorn squash\n",
      "\n",
      "butternut squash\n",
      "\n",
      "cucumber, cuke\n",
      "\n",
      "artichoke, globe artichoke\n",
      "\n",
      "cardoon\n",
      "\n",
      "mushroom\n",
      "\n",
      "shower curtain\n",
      "\n",
      "jean, blue jean, denim\n",
      "\n",
      "carton\n",
      "\n",
      "handkerchief, hankie, hanky, hankey\n",
      "\n",
      "sandal\n",
      "\n",
      "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin\n",
      "\n",
      "safe\n",
      "\n",
      "plate\n",
      "\n",
      "necklace\n",
      "\n",
      "croquet ball\n",
      "\n",
      "fur coat\n",
      "\n",
      "thimble\n",
      "\n",
      "pajama, pyjama, pj's, jammies\n",
      "\n",
      "running shoe\n",
      "\n",
      "cocktail shaker\n",
      "\n",
      "chest\n",
      "\n",
      "manhole cover\n",
      "\n",
      "modem\n",
      "\n",
      "tub, vat\n",
      "\n",
      "tray\n",
      "\n",
      "balance beam, beam\n",
      "\n",
      "bagel, beigel\n",
      "\n",
      "prayer rug, prayer mat\n",
      "\n",
      "kimono\n",
      "\n",
      "hot pot, hotpot\n",
      "\n",
      "whiskey jug\n",
      "\n",
      "knee pad\n",
      "\n",
      "book jacket, dust cover, dust jacket, dust wrapper\n",
      "\n",
      "spindle\n",
      "\n",
      "ski mask\n",
      "\n",
      "beer bottle\n",
      "\n",
      "crash helmet\n",
      "\n",
      "bottlecap\n",
      "\n",
      "tile roof\n",
      "\n",
      "mask\n",
      "\n",
      "maillot\n",
      "\n",
      "Petri dish\n",
      "\n",
      "football helmet\n",
      "\n",
      "bathing cap, swimming cap\n",
      "\n",
      "teddy, teddy bear\n",
      "\n",
      "holster\n",
      "\n",
      "pop bottle, soda bottle\n",
      "\n",
      "photocopier\n",
      "\n",
      "vestment\n",
      "\n",
      "crossword puzzle, crossword\n",
      "\n",
      "golf ball\n",
      "\n",
      "trifle\n",
      "\n",
      "suit, suit of clothes\n",
      "\n",
      "water tower\n",
      "\n",
      "feather boa, boa\n",
      "\n",
      "cloak\n",
      "\n",
      "red wine\n",
      "\n",
      "drumstick\n",
      "\n",
      "shield, buckler\n",
      "\n",
      "Christmas stocking\n",
      "\n",
      "hoopskirt, crinoline\n",
      "\n",
      "menu\n",
      "\n",
      "stage\n",
      "\n",
      "bonnet, poke bonnet\n",
      "\n",
      "meat loaf, meatloaf\n",
      "\n",
      "baseball\n",
      "\n",
      "face powder\n",
      "\n",
      "scabbard\n",
      "\n",
      "sunscreen, sunblock, sun blocker\n",
      "\n",
      "beer glass\n",
      "\n",
      "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa\n",
      "\n",
      "guacamole\n",
      "\n",
      "lampshade, lamp shade\n",
      "\n",
      "wool, woolen, woollen\n",
      "\n",
      "hay\n",
      "\n",
      "bow tie, bow-tie, bowtie\n",
      "\n",
      "mailbag, postbag\n",
      "\n",
      "water jug\n",
      "\n",
      "bucket, pail\n",
      "\n",
      "dishrag, dishcloth\n",
      "\n",
      "soup bowl\n",
      "\n",
      "eggnog\n",
      "\n",
      "mortar\n",
      "\n",
      "trench coat\n",
      "\n",
      "paddle, boat paddle\n",
      "\n",
      "chain\n",
      "\n",
      "swab, swob, mop\n",
      "\n",
      "mixing bowl\n",
      "\n",
      "potpie\n",
      "\n",
      "wine bottle\n",
      "\n",
      "shoji\n",
      "\n",
      "bulletproof vest\n",
      "\n",
      "drilling platform, offshore rig\n",
      "\n",
      "binder, ring-binder\n",
      "\n",
      "cardigan\n",
      "\n",
      "sweatshirt\n",
      "\n",
      "pot, flowerpot\n",
      "\n",
      "birdhouse\n",
      "\n",
      "hamper\n",
      "\n",
      "ping-pong ball\n",
      "\n",
      "pencil box, pencil case\n",
      "\n",
      "pay-phone, pay-station\n",
      "\n",
      "consomme\n",
      "\n",
      "apron\n",
      "\n",
      "punching bag, punch bag, punching ball, punchball\n",
      "\n",
      "backpack, back pack, knapsack, packsack, rucksack, haversack\n",
      "\n",
      "groom, bridegroom\n",
      "\n",
      "bearskin, busby, shako\n",
      "\n",
      "pencil sharpener\n",
      "\n",
      "broom\n",
      "\n",
      "mosquito net\n",
      "\n",
      "abaya\n",
      "\n",
      "mortarboard\n",
      "\n",
      "poncho\n",
      "\n",
      "crutch\n",
      "\n",
      "Polaroid camera, Polaroid Land camera\n",
      "\n",
      "space bar\n",
      "\n",
      "cup\n",
      "\n",
      "racket, racquet\n",
      "\n",
      "traffic light, traffic signal, stoplight\n",
      "\n",
      "quill, quill pen\n",
      "\n",
      "radio, wireless\n",
      "\n",
      "dough\n",
      "\n",
      "cuirass\n",
      "\n",
      "military uniform\n",
      "\n",
      "lipstick, lip rouge\n",
      "\n",
      "shower cap\n",
      "\n",
      "monitor\n",
      "\n",
      "oscilloscope, scope, cathode-ray oscilloscope, CRO\n",
      "\n",
      "mitten\n",
      "\n",
      "brassiere, bra, bandeau\n",
      "\n",
      "French loaf\n",
      "\n",
      "vase\n",
      "\n",
      "milk can\n",
      "\n",
      "rugby ball\n",
      "\n",
      "paper towel\n",
      "\n",
      "earthstar\n",
      "\n",
      "envelope\n",
      "\n",
      "miniskirt, mini\n",
      "\n",
      "cowboy hat, ten-gallon hat\n",
      "\n",
      "trolleybus, trolley coach, trackless trolley\n",
      "\n",
      "perfume, essence\n",
      "\n",
      "bathtub, bathing tub, bath, tub\n",
      "\n",
      "hotdog, hot dog, red hot\n",
      "\n",
      "coral fungus\n",
      "\n",
      "bullet train, bullet\n",
      "\n",
      "pillow\n",
      "\n",
      "toilet tissue, toilet paper, bathroom tissue\n",
      "\n",
      "cassette\n",
      "\n",
      "carpenter's kit, tool kit\n",
      "\n",
      "ladle\n",
      "\n",
      "stinkhorn, carrion fungus\n",
      "\n",
      "lotion\n",
      "\n",
      "hair spray\n",
      "\n",
      "academic gown, academic robe, judge's robe\n",
      "\n",
      "dome\n",
      "\n",
      "crate\n",
      "\n",
      "wig\n",
      "\n",
      "burrito\n",
      "\n",
      "pill bottle\n",
      "\n",
      "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour\n",
      "\n",
      "theater curtain, theatre curtain\n",
      "\n",
      "window shade\n",
      "\n",
      "barrel, cask\n",
      "\n",
      "washbasin, handbasin, washbowl, lavabo, wash-hand basin\n",
      "\n",
      "ballpoint, ballpoint pen, ballpen, Biro\n",
      "\n",
      "basketball\n",
      "\n",
      "bath towel\n",
      "\n",
      "cowboy boot\n",
      "\n",
      "gown\n",
      "\n",
      "window screen\n",
      "\n",
      "agaric\n",
      "\n",
      "cellular telephone, cellular phone, cellphone, cell, mobile phone\n",
      "\n",
      "nipple\n",
      "\n",
      "barbell\n",
      "\n",
      "mailbox, letter box\n",
      "\n",
      "lab coat, laboratory coat\n",
      "\n",
      "fire screen, fireguard\n",
      "\n",
      "minibus\n",
      "\n",
      "packet\n",
      "\n",
      "maze, labyrinth\n",
      "\n",
      "pole\n",
      "\n",
      "horizontal bar, high bar\n",
      "\n",
      "sombrero\n",
      "\n",
      "pickelhaube\n",
      "\n",
      "rain barrel\n",
      "\n",
      "wallet, billfold, notecase, pocketbook\n",
      "\n",
      "cassette player\n",
      "\n",
      "comic book\n",
      "\n",
      "piggy bank, penny bank\n",
      "\n",
      "street sign\n",
      "\n",
      "bell cote, bell cot\n",
      "\n",
      "fountain pen\n",
      "\n",
      "Windsor tie\n",
      "\n",
      "volleyball\n",
      "\n",
      "overskirt\n",
      "\n",
      "sarong\n",
      "\n",
      "purse\n",
      "\n",
      "bolo tie, bolo, bola tie, bola\n",
      "\n",
      "bib\n",
      "\n",
      "parachute, chute\n",
      "\n",
      "sleeping bag\n",
      "\n",
      "television, television system\n",
      "\n",
      "swimming trunks, bathing trunks\n",
      "\n",
      "measuring cup\n",
      "\n",
      "espresso\n",
      "\n",
      "pizza, pizza pie\n",
      "\n",
      "breastplate, aegis, egis\n",
      "\n",
      "shopping basket\n",
      "\n",
      "wooden spoon\n",
      "\n",
      "saltshaker, salt shaker\n",
      "\n",
      "chocolate sauce, chocolate syrup\n",
      "\n",
      "ballplayer, baseball player\n",
      "\n",
      "goblet\n",
      "\n",
      "gyromitra\n",
      "\n",
      "stretcher\n",
      "\n",
      "water bottle\n",
      "\n",
      "dial telephone, dial phone\n",
      "\n",
      "soap dispenser\n",
      "\n",
      "jersey, T-shirt, tee shirt\n",
      "\n",
      "school bus\n",
      "\n",
      "jigsaw puzzle\n",
      "\n",
      "plastic bag\n",
      "\n",
      "reflex camera\n",
      "\n",
      "diaper, nappy, napkin\n",
      "\n",
      "Band Aid\n",
      "\n",
      "ice lolly, lolly, lollipop, popsicle\n",
      "\n",
      "velvet\n",
      "\n",
      "tennis ball\n",
      "\n",
      "gasmask, respirator, gas helmet\n",
      "\n",
      "doormat, welcome mat\n",
      "\n",
      "Loafer\n",
      "\n",
      "ice cream, icecream\n",
      "\n",
      "pretzel\n",
      "\n",
      "quilt, comforter, comfort, puff\n",
      "\n",
      "maillot, tank suit\n",
      "\n",
      "tape player\n",
      "\n",
      "clog, geta, patten, sabot\n",
      "\n",
      "iPod\n",
      "\n",
      "bolete\n",
      "\n",
      "scuba diver\n",
      "\n",
      "pitcher, ewer\n",
      "\n",
      "matchstick\n",
      "\n",
      "bikini, two-piece\n",
      "\n",
      "sock\n",
      "\n",
      "CD player\n",
      "\n",
      "lens cap, lens cover\n",
      "\n",
      "thatch, thatched roof\n",
      "\n",
      "vault\n",
      "\n",
      "beaker\n",
      "\n",
      "bubble\n",
      "\n",
      "cheeseburger\n",
      "\n",
      "parallel bars, bars\n",
      "\n",
      "flagpole, flagstaff\n",
      "\n",
      "coffee mug\n",
      "\n",
      "rubber eraser, rubber, pencil eraser\n",
      "\n",
      "stole\n",
      "\n",
      "carbonara\n",
      "\n",
      "dumbbell\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "idx_to_class_id = {}\n",
    "idx = None\n",
    "for line in open(os.path.join(PATH, 'imagenet_2012_challenge_label_map_proto.pbtxt')):\n",
    "    if 'target_class:' in line:\n",
    "        idx = int(line.strip().split()[-1])\n",
    "    elif 'target_class_string:' in line:\n",
    "        class_id = line.split()[-1][1:-1]\n",
    "        idx_to_class_id[idx] = class_id\n",
    "        \n",
    "class_id_to_name = {}\n",
    "for line in open(os.path.join(PATH, 'imagenet_synset_to_human_label_map.txt')):\n",
    "    if len(line.strip()):\n",
    "        id, name = line.split(None, 1)\n",
    "        class_id_to_name[id] = name\n",
    "\n",
    "def idx_to_label(idx):\n",
    "    return class_id_to_name[idx_to_class_id[idx]]\n",
    "\n",
    "for idx in idx_to_class_id.keys():\n",
    "    print idx_to_label(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    test = '/Users/nateparrott/Desktop/lucy.jpg'\n",
    "    jpeg_data = open(test).read()\n",
    "    resp = sess.run(softmax_tensor, feed_dict={jpeg_input: jpeg_data})[0]\n",
    "    top_indices = sorted(range(len(resp)), key=lambda idx: resp[idx], reverse=True)[:5]\n",
    "    print map(idx_to_label, top_indices)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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