ResNet50_TF-TRT_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path = 'elephant.jpg'\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(np.squeeze(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(x)\n",
    "# decode the results into a list of tuples (class, description, probability)\n",
    "# (one such list for each sample in the batch)\n",
    "print('Predicted:', decode_predictions(preds, top=3)[0])\n",
    "# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf; print('TensorFlow Version:', tf.__version__)\n",
    "import keras; print('Keras Version:', keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import keras.backend as K\n",
    "from tensorflow.python.framework import graph_io\n",
    "from tensorflow.python.tools import freeze_graph\n",
    "from tensorflow.core.protobuf import saver_pb2\n",
    "from tensorflow.python.training import saver as saver_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.set_learning_phase(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = K.get_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_path = saver.save(sess, 'saved_ckpt', global_step=0, latest_filename='checkpoint_state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_io.write_graph(sess.graph, '.', 'tmp.pb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_names = model.input.op.name\n",
    "print(in_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_names = model.output.op.name\n",
    "print(out_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "freeze_graph.freeze_graph('./tmp.pb', '',\n",
    "                          False, checkpoint_path, out_names,\n",
    "                          \"save/restore_all\", \"save/Const:0\",\n",
    "                          './frozen_graph.pb', False, \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uff_model = uff.from_tensorflow_frozen_model('frozen_graph.pb', [out_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!convert-to-uff frozen_graph.pb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorrt as trt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "TRT_LOGGER = trt.Logger(trt.Logger.WARNING)\n",
    "model_file = \"frozen_graph.uff\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:\n",
    "    # Foo\n",
    "    parser.register_input(in_names, trt.DimsCHW(3, 224, 224), trt.UffInputOrder.NCHW)\n",
    "    parser.register_output(out_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parser.parse(model_file, network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.max_workspace_size = 2**30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = builder.build_cuda_engine(network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import TensorFlow and TensorRT\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.tensorrt as trt\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inference with TF-TRT frozen graph workflow:\n",
    "graph = tf.Graph()\n",
    "\n",
    "session = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running against TensorRT version 0.0.0\n"
     ]
    }
   ],
   "source": [
    "with graph.as_default():\n",
    "    with session:\n",
    "        # First deserialize your frozen graph:\n",
    "        with tf.gfile.GFile('frozen_graph.pb', 'rb') as f:\n",
    "            graph_def = tf.GraphDef()\n",
    "            graph_def.ParseFromString(f.read())\n",
    "\n",
    "        # Now you can create a TensorRT inference graph from your\n",
    "        # frozen graph:\n",
    "        trt_graph = trt.create_inference_graph(\n",
    "            input_graph_def=graph_def,\n",
    "            outputs=['fc1000/Softmax'])\n",
    "        \n",
    "        # Import the TensorRT graph into a new graph and run:\n",
    "        output_node = tf.import_graph_def(\n",
    "            trt_graph,\n",
    "            return_elements=['fc1000/Softmax'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Operation 'import/fc1000/Softmax' type=Softmax>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'import/input_1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'import/input_1' with dtype float and shape [?,224,224,3]\n\t [[node import/input_1 (defined at <ipython-input-4-ea759e1b4134>:17) ]]\n\nCaused by op 'import/input_1', defined at:\n  File \"/usr/lib/python3.5/runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/platform/asyncio.py\", line 148, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 345, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 1312, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 781, in inner\n    self.run()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 742, in run\n    yielded = self.gen.send(value)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 272, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 542, in execute_request\n    user_expressions, allow_stdin,\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2848, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2874, in _run_cell\n    return runner(coro)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3049, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3214, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3296, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-4-ea759e1b4134>\", line 17, in <module>\n    return_elements=['fc1000/Softmax'])\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py\", line 442, in import_graph_def\n    _ProcessNewOps(graph)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py\", line 235, in _ProcessNewOps\n    for new_op in graph._add_new_tf_operations(compute_devices=False):  # pylint: disable=protected-access\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3433, in _add_new_tf_operations\n    for c_op in c_api_util.new_tf_operations(self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3433, in <listcomp>\n    for c_op in c_api_util.new_tf_operations(self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3325, in _create_op_from_tf_operation\n    ret = Operation(c_op, self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 1801, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'import/input_1' with dtype float and shape [?,224,224,3]\n\t [[node import/input_1 (defined at <ipython-input-4-ea759e1b4134>:17) ]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1333\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\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   1335\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[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1319\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\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[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'import/input_1' with dtype float and shape [?,224,224,3]\n\t [[{{node import/input_1}}]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-b51b944ef8ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    927\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 929\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    930\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    931\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/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1150\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1152\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1153\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\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/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\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   1326\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1328\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1346\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\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[0m\u001b[1;32m   1349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'import/input_1' with dtype float and shape [?,224,224,3]\n\t [[node import/input_1 (defined at <ipython-input-4-ea759e1b4134>:17) ]]\n\nCaused by op 'import/input_1', defined at:\n  File \"/usr/lib/python3.5/runpy.py\", line 184, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.5/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/platform/asyncio.py\", line 148, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 345, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.5/asyncio/base_events.py\", line 1312, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.5/asyncio/events.py\", line 125, in _run\n    self._callback(*self._args)\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 781, in inner\n    self.run()\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 742, in run\n    yielded = self.gen.send(value)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 272, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py\", line 542, in execute_request\n    user_expressions, allow_stdin,\n  File \"/usr/local/lib/python3.5/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2848, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2874, in _run_cell\n    return runner(coro)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3049, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3214, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 3296, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-4-ea759e1b4134>\", line 17, in <module>\n    return_elements=['fc1000/Softmax'])\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py\", line 442, in import_graph_def\n    _ProcessNewOps(graph)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py\", line 235, in _ProcessNewOps\n    for new_op in graph._add_new_tf_operations(compute_devices=False):  # pylint: disable=protected-access\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3433, in _add_new_tf_operations\n    for c_op in c_api_util.new_tf_operations(self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3433, in <listcomp>\n    for c_op in c_api_util.new_tf_operations(self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 3325, in _create_op_from_tf_operation\n    ret = Operation(c_op, self)\n  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py\", line 1801, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'import/input_1' with dtype float and shape [?,224,224,3]\n\t [[node import/input_1 (defined at <ipython-input-4-ea759e1b4134>:17) ]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(output_node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1048576"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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