chapter-08-serverless/chapter-08-model-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_image_helper import create_preprocessor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://bit.ly/mlbookcamp-pants:\n",
    "\n",
    "<img src=\"http://bit.ly/mlbookcamp-pants\" style=\"max-height: 300px\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = create_preprocessor('xception', target_size=(299, 299))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_url = 'http://bit.ly/mlbookcamp-pants'\n",
    "X = preprocessor.from_url(image_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 299, 299, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the TF-lite model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tensorflow.lite as tflite # this also works locally, but it won't work in Lambda\n",
    "import tflite_runtime.interpreter as tflite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tflite.Interpreter(model_path='clothing-model-v4.tflite')\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the input and the output to the model. We'll need them for putting in the array (input) and getting out the prediction (output):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_details = interpreter.get_input_details()\n",
    "input_index = input_details[0]['index']\n",
    "\n",
    "output_details = interpreter.get_output_details()\n",
    "output_index = output_details[0]['index']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.set_tensor(input_index, X)\n",
    "interpreter.invoke()\n",
    "\n",
    "preds = interpreter.get_tensor(output_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `preds` array contains the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.8682901, -4.7612457, -2.3169823, -1.0625706,  9.8871565,\n",
       "        -2.8124304, -3.6662836,  3.200361 , -2.6023388, -4.835045 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert these raw predictions to something more readable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\n",
    "    'dress',\n",
    "    'hat',\n",
    "    'longsleeve',\n",
    "    'outwear',\n",
    "    'pants',\n",
    "    'shirt',\n",
    "    'shoes',\n",
    "    'shorts',\n",
    "    'skirt',\n",
    "    't-shirt'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dress': -1.8682901,\n",
       " 'hat': -4.7612457,\n",
       " 'longsleeve': -2.3169823,\n",
       " 'outwear': -1.0625706,\n",
       " 'pants': 9.8871565,\n",
       " 'shirt': -2.8124304,\n",
       " 'shoes': -3.6662836,\n",
       " 'shorts': 3.200361,\n",
       " 'skirt': -2.6023388,\n",
       " 't-shirt': -4.835045}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = dict(zip(labels, preds[0]))\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}