modified_vgg_keras.ipynb

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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.python.keras import models, layers, losses, optimizers, metrics, activations\n",
    "from tensorflow.python.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.python.keras.preprocessing import image\n",
    "from tensorflow.python.keras.applications.vgg16 import preprocess_input, decode_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_VGG = VGG16(weights='imagenet')\n",
    "model_VGG.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model_VGG.layers:\n",
    "    model.add(layer)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers.pop()\n",
    "model.layers.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Reshape((19, 14, 16), input_shape=(4096,)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Conv2D(64, 1, 1,\n",
    "                 padding='same',\n",
    "                 input_shape=(19,14,16)))\n",
    "model.add(layers.UpSampling2D(size=(4, 4), data_format=None))\n",
    "model.summary()"
   ]
  }
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