{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "# base_model = VGG19(weights='imagenet')\n",
    "# model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)\n",
    "\n",
    "img_path = 'images/Achanthidium delmontii-!-Achnanthidium delmontii.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)\n",
    "\n",
    "# block4_pool_features = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 1.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.min(), x.max()"
   ]
  }
 ],
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