01_Intro_to_TFlite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantized Neural Network \n",
    "## 01- Introduction to TFLite\n",
    "by [Soon Yau Cheong](http://www.linkedin.com/in/soonyau)\n",
    "\n",
    "For a starter, unlike Tensorflow(TF) that uses 32-bit floating point and run on desktop/server, Tensorflow Lite (TFlite) uses 8-bit integer and is designed to run on embedded devices and mobile phones with less powerful computational power. Quantization is a process to convert floating point into fixed-point integer and a converted network is therefore known as quantized neural network. \n",
    "\n",
    "In this tutorial, we will go through step-by-step on how to use TFLite to run inference and generate output from an image. We'll use a pre-trained MobileNet image trained on Imagenet dataset. Along the way, I'll introduce some fundamental concepts about TFLite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow 1.10.0\n",
      "Python 3.5.2 (default, Nov 23 2017, 16:37:01) \n",
      "[GCC 5.4.0 20160609]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from PIL import Image\n",
    "from glob import glob\n",
    "\n",
    "import utils\n",
    "print(\"Tensorflow\", tf.__version__)\n",
    "print(\"Python\", sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download pre-trained TFlite model\n",
    "Tensorflow lists some pre-trained quantized models on https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/models.md that we can download. Let's select a Mobilenet v1 model which can take a few minutes to download.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading mobilenet_v1_1.0_224_quant.tgz ...\n",
      "models/mobilenet_v1/mobilenet_v1_1.0_224_quant.tgz\n",
      "Download complete\n"
     ]
    }
   ],
   "source": [
    "url = \"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz\"\n",
    "dir_name = \"models/mobilenet_v1\"    \n",
    "utils.maybe_download(dir_name, url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files\n",
    "Now let's look at the unzip files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mobilenet_v1_1.0_224_quant_info.txt \t0.0 MB\n",
      "mobilenet_v1_1.0_224_quant.ckpt.index \t0.0 MB\n",
      "mobilenet_v1_1.0_224_quant_eval.pbtxt \t0.8 MB\n",
      "mobilenet_v1_1.0_224_quant.tflite \t4.1 MB\n",
      "mobilenet_v1_1.0_224_quant.ckpt.meta \t4.9 MB\n",
      "mobilenet_v1_1.0_224_quant.ckpt.data-00000-of-00001 \t16.2 MB\n",
      "mobilenet_v1_1.0_224_quant_frozen.pb \t16.4 MB\n",
      "mobilenet_v1_1.0_224_quant.tgz \t33.4 MB\n"
     ]
    }
   ],
   "source": [
    "fpaths = glob(dir_name+\"/*\")\n",
    "file_sizes = [os.path.getsize(f)/2**20 for f in fpaths]\n",
    "sort_indices = np.argsort(file_sizes)\n",
    "for i in sort_indices:\n",
    "    print(\"%s \\t%.1f MB\"%(fpaths[i].split('/')[-1], file_sizes[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from the compressed .tgz that we've just downloaded, there are only two files related to TFlite which we will go through now. The rests are for full precision Tensorflow graph which we will defer until later tutorials. The first file \\*_info.txt merely states basic information and name of input/output nodes. The TFlite model is stored in file extension .tflite and this is the file that we're interested in. We can already see that this file is much smaller about 4x smaller than the full precision frozen graph which is inline with reduction in memory going from 32-bit float to 8-bit integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: mobilenet_v1_1.0_224_quant\n",
      "\n",
      "Input: input\n",
      "\n",
      "Output: MobilenetV1/Predictions/Reshape_1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "utils.print_file(dir_name, 'mobilenet_v1_1.0_224_quant_info.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreter\n",
    "\n",
    "First, let's load the model. Unlike Tensorflow graph that requires you to create a session, load the graph and variables, TFlite uses a call called interpreter that load entire graph and we can use it almost straight away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'mobilenet_v1_1.0_224_quant.tflite'\n",
    "model_path = os.path.join(dir_name, model_name)\n",
    "interpreter = tf.contrib.lite.Interpreter(model_path=model_path)\n",
    "                                          \n",
    "interpreter.allocate_tensors()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All retrievable information are stored as \"tensor\" e.g. input, activations, weights and biases. The operation information e.g. the operation layer and its attributes are not available. We could deduce the layer type from the tensor name but other information will need to be retrieved from the full Tensorflow graph. Now let's try to read the input tensors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dtype': numpy.uint8,\n",
       " 'index': 88,\n",
       " 'name': 'input',\n",
       " 'quantization': (0.0078125, 128),\n",
       " 'shape': array([  1, 224, 224,   3], dtype=int32)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_details = interpreter.get_input_details()\n",
    "input_details[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the input details, we can get see that it expect 8-bit unsigned integer (uint8). The tensor index can be used to read the actual tensor which is all zeros because we haven't actually set the input yet.(I'll talk about the 'quantization' in detailed in next tutorial.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "input_index = input_details[0]['index']\n",
    "input_tensor = interpreter.get_tensor(input_index)\n",
    "\n",
    "# print part of tensor\n",
    "print(input_tensor[0,:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do the same for output by calling interpreter.get_output_details(). How about looking at the internal layers? Well, you can't ... at least not officially. I found out there is a internal function that can be used to get the tensor detail by index. However, there is no way to find out how many tensors are there, so I just started with some big numbers until the interpretor stop crashing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 MobilenetV1/Logits/AvgPool_1a/AvgPool\n",
      "1 MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd\n",
      "2 MobilenetV1/Logits/Conv2d_1c_1x1/Conv2D_bias\n",
      "3 MobilenetV1/Logits/Conv2d_1c_1x1/weights_quant/FakeQuantWithMinMaxVars\n",
      "4 MobilenetV1/Logits/SpatialSqueeze\n",
      "5 MobilenetV1/Logits/SpatialSqueeze_shape\n",
      "6 MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold_bias\n",
      "7 MobilenetV1/MobilenetV1/Conv2d_0/Relu6\n",
      "8 MobilenetV1/MobilenetV1/Conv2d_0/weights_quant/FakeQuantWithMinMaxVars\n",
      "9 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6\n",
      "10 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_Fold_bias\n",
      "11 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "12 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold_bias\n",
      "13 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6\n",
      "14 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "15 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6\n",
      "16 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_Fold_bias\n",
      "17 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "18 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold_bias\n",
      "19 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6\n",
      "20 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "21 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6\n",
      "22 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise_Fold_bias\n",
      "23 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "24 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Conv2D_Fold_bias\n",
      "25 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6\n",
      "26 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "27 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6\n",
      "28 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise_Fold_bias\n",
      "29 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "30 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_Fold_bias\n",
      "31 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6\n",
      "32 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "33 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6\n",
      "34 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold_bias\n",
      "35 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "36 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_Fold_bias\n",
      "37 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6\n",
      "38 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "39 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6\n",
      "40 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise_Fold_bias\n",
      "41 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "42 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_Fold_bias\n",
      "43 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6\n",
      "44 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "45 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6\n",
      "46 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold_bias\n",
      "47 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "48 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold_bias\n",
      "49 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6\n",
      "50 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "51 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6\n",
      "52 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise_Fold_bias\n",
      "53 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "54 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Conv2D_Fold_bias\n",
      "55 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6\n",
      "56 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "57 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6\n",
      "58 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise_Fold_bias\n",
      "59 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "60 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Conv2D_Fold_bias\n",
      "61 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6\n",
      "62 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "63 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6\n",
      "64 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise_Fold_bias\n",
      "65 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "66 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Conv2D_Fold_bias\n",
      "67 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6\n",
      "68 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "69 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6\n",
      "70 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise_Fold_bias\n",
      "71 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "72 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_Fold_bias\n",
      "73 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6\n",
      "74 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "75 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6\n",
      "76 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise_Fold_bias\n",
      "77 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "78 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_Fold_bias\n",
      "79 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6\n",
      "80 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "81 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6\n",
      "82 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise_Fold_bias\n",
      "83 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "84 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_Fold_bias\n",
      "85 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6\n",
      "86 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/weights_quant/FakeQuantWithMinMaxVars\n",
      "87 MobilenetV1/Predictions/Reshape_1\n",
      "88 input\n"
     ]
    }
   ],
   "source": [
    "num_layer = 89\n",
    "for i in range(num_layer):\n",
    "    detail = interpreter._get_tensor_details(i)\n",
    "    print(i, detail['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a long list!! No, the model doesn't actually have 89 layers. As mentioned earlier, the tensor includes everything like weight, bias etc. From the name, we can see that there are about 14 separable convolution layers. Also note that the layers are not arranged sequentially as the indices. Anyway, let's not worry about the details now. We'll now load an image and run it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "I have loaded a few sample images to try. Let's load one, reshape to the network input size, and display it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cat.jpg', 'red_fox.jpg', 'gold_fish.jpg']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"images\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f15d81ec9b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_image(img_path):\n",
    "    \n",
    "    img = Image.open(img_path).convert('RGB')\n",
    "    img = img.resize((224, 224))\n",
    "    return img\n",
    "\n",
    "img_path = \"images/red_fox.jpg\"\n",
    "img = load_image(img_path)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now format the input to match the network input dimension and double to check the shape and type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: (1, 224, 224, 3)\n",
      "image data type: uint8\n"
     ]
    }
   ],
   "source": [
    "input_image = np.array(img)\n",
    "input_image = np.expand_dims(input_image, axis=0)\n",
    "print(\"image shape:\", input_image.shape)\n",
    "print(\"image data type:\", input_image.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're good to go. Now feed the image into the network's input tensor and read the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inference time: 86.0 ms\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "input_details = interpreter.get_input_details()\n",
    "input_index = input_details[0]['index']\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "interpreter.set_tensor(input_index, input_image)\n",
    "interpreter.invoke()\n",
    "\n",
    "\n",
    "output_details = interpreter.get_output_details()\n",
    "output_index = output_details[0]['index']\n",
    "output_data = interpreter.get_tensor(output_index)\n",
    "\n",
    "elapsed = time.time()-start_time\n",
    "print(\"inference time: %.1f ms\"%(1000*elapsed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TFLite interpreter doens't use computer GPU and running on my laptop's CPU achieved 86ms inference time. The benchmark on Tensorflow page gives smaller number because those were tested on mobile phone. We should run testing on embedded devices later, perhaps on a Raspberry PI. \n",
    "\n",
    "If we nonw move our atention to the network output, the output is tensor with 1001 classes. Each value represent the likelihood of the image belong to that class. We can plot the value and pick the one with highest value as the predicted class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1001)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f15d82e44a8>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vxl3PEPv1Xjpv7R4BHmq3i+jMT24Dngb+G1jR9g86ZyB9G/gWnbMaxt6PPvt+LnBPWz4F+DowDfw7sLS1H9vWp9v2U8Zdd599PQ2YauP8H8Dy6mMM/A3wBLAD+DSwtNo4A7fT+Qzix3TeuV3Zz7gCf9T6Pg1cMUhNfjNWkopbzFM3kqR5MOglqTiDXpKKM+glqTiDXpKKM+glqTiDXpKKM+glqbj/B3+aLP4a+MIbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(output_data.shape)\n",
    "plt.plot(output_data[0,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's normalize the output so they sum to 1.0, and plot the top-3 predictions and confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id     confidence\t class name\n",
      "278 \t81.6%\t red fox, Vulpes vulpes\n",
      "279 \t12.9%\t kit fox, Vulpes macrotis\n",
      "281 \t2.7%\t grey fox, gray fox, Urocyon cinereoargenteus\n"
     ]
    }
   ],
   "source": [
    "confidence = output_data[0,:]/np.sum(output_data)\n",
    "\n",
    "top3_idx = np.flip(np.argsort(confidence))[:3]\n",
    "print(\"id\", \"    confidence\\t\", \"class name\")\n",
    "for i in top3_idx:\n",
    "    label_name = utils.imagenet_id_to_class(i)\n",
    "    print(i,\"\\t%.1f%%\\t\"%(100*confidence[i]), label_name,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's Next?\n",
    "Now you have learned how to run pre-trained TFlite model. In next tutorial we will talk about what quantization really is and how to quantize a floating point graph into integer graph. Now, feel free to play with the code and try different images. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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}