a2-1 10-16.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VtqfC-BqUWK4"
   },
   "source": [
    "# A2: Image classification in the brower\n",
    "\n",
    "In this assignment, you will gain real-world experience with image classification on small datasets - a common scenario in practice. There are three parts.\n",
    "\n",
    "1. First, you will train a model on a small existing dataset.\n",
    "\n",
    "2. Next,  you will collect a small dataset yourself. Of course, in practice there isn’t often a dataset available for tasks you care about, so it’s valuable to get a feel for this process. You will train a small model from scratch, then use data augmentation to improve accuracy.\n",
    "\n",
    "3. Finally, you will deploy your model from part two in a webpage using TensorFlow.js. A user will be able to upload a photo, and your model will classify it.\n",
    "\n",
    "## Instructions\n",
    "\n",
    "Please complete this notebook by searching for **\"TODO\"**.\n",
    "\n",
    "## Submission instructions\n",
    "\n",
    "Please submit this assignment on CourseWorks by uploading a Jupyter notebook that includes saved output. If you are working in Colab, you can prepare your notebook for submission by ensuring that runs end-to-end, then saving and downloading it:\n",
    "\n",
    "1. ```Runtime -> Restart and run all```\n",
    "1. ```File -> Save```\n",
    "1. ```File -> Download.ipynb```\n",
    "\n",
    "Note: you will need to include a URL to your webpage for part 3, see the final TODO at the bottom of this notebook.\n",
    "\n",
    "## Setup instructions\n",
    "1. If you are running this notebook in Colab, make sure a GPU is enabled (```Edit -> Notebook settings -> Hardware accelerator```).\n",
    "\n",
    "2. Create a GitHub Pages repo by following the instructions at https://pages.github.com/. You may use this to host your webpage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3TmzbQJkCeXC"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  %tensorflow_version 2.x # enable TF 2.x in Colab\n",
    "except Exception:\n",
    "  pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2oLmcZORRh7_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A5UDOaU1AzVr"
   },
   "source": [
    "## 1a) Flowers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HQ3Hv7NGRTdy"
   },
   "source": [
    "In this part of the assignment, you will train a model on a small existing dataset (flowers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ks38bmgRA-xO"
   },
   "outputs": [],
   "source": [
    "import IPython.display as display\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import time\n",
    "\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "BATCH_SIZE = 32\n",
    "IMG_SIZE = 192\n",
    "SHUFFLE_SIZE = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Rc1LdwCVFb4"
   },
   "source": [
    "### Download the flowers dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GRYep9qMqG15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
      "228818944/228813984 [==============================] - 48s 0us/step\n",
      "C:\\Users\\XBBNWBL\\.keras\\datasets\\flower_photos\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "data_root_orig = tf.keras.utils.get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\n",
    "                                         fname='flower_photos', untar=True)\n",
    "data_root = pathlib.Path(data_root_orig)\n",
    "print(data_root)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WziL5x5QJUYk"
   },
   "source": [
    "### Write an input pipeline from scratch\n",
    "There are several ways to load images in TensorFlow. Later in this assignment, you'll use the [Keras preprocessing utilities](https://keras.io/preprocessing/image/). For starters, though, you'll write your own using ```tf.data```, based on this [tutorial](https://www.tensorflow.org/beta/tutorials/load_data/images). This is valuable to do once (just so you can see how the nuts and bolts work) before using the higher level utils. A bunch of the code is written for you, there are only a couple of TODOs. Follow along, though, and try to understand each piece.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MsLuAd_9BOpr"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3670"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_paths = list(data_root.glob('*/*'))\n",
    "all_image_paths = [str(path) for path in all_image_paths]\n",
    "random.shuffle(all_image_paths)\n",
    "\n",
    "image_count = len(all_image_paths)\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ViQ3n2MLBO49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\tulips\\\\14836105101_1d07520932_m.jpg',\n",
       " 'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\tulips\\\\3213012716_b4c0f7db88.jpg',\n",
       " 'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\dandelion\\\\10043234166_e6dd915111_n.jpg',\n",
       " 'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\dandelion\\\\4552591312_02fe1dcc04_n.jpg',\n",
       " 'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\roses\\\\16903172207_2cd7aca66a.jpg']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_paths[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KPxnpUSEVrZb"
   },
   "source": [
    "### Get to know your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B6UzHIm9CJvM"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(3):\n",
    "  image_path = random.choice(all_image_paths)\n",
    "  display.display(display.Image(image_path))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8GN3o_6PWRol"
   },
   "source": [
    "### Classes are given by directory names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WcHJPTI2BUFj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "prA5LRsL-kAp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index = dict((name, index) for index, name in enumerate(label_names))\n",
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CLedPtLeBo3P"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 labels indices:  [4, 4, 1, 1, 2, 0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "all_labels = [label_to_index[pathlib.Path(path).parent.name]\n",
    "                    for path in all_image_paths]\n",
    "\n",
    "print(\"First 10 labels indices: \", all_labels[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "liCVsOmMAg0h"
   },
   "source": [
    "### Create a train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VUWoRvziAjNR"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_paths, test_paths, train_labels, test_labels = train_test_split(all_image_paths, all_labels)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UWclr76cWY6d"
   },
   "source": [
    "### Display a few images and their labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H88pb_WZCQ2V"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dandelion\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dandelion\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dandelion\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(3):\n",
    "  i = random.randint(0,len(train_paths))\n",
    "  image_path = train_paths[i]\n",
    "  print(label_names[train_labels[i]])\n",
    "  display.display(display.Image(image_path))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6X-BkNVMWhFS"
   },
   "source": [
    "### Begin using TensorFlow ops to read and decode the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XC1hfUW8Bt__"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Tensor: id=1, shape=(), dtype=string, numpy=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01\\x00H\\...\n"
     ]
    }
   ],
   "source": [
    "img_raw = tf.io.read_file(train_paths[0])\n",
    "print(repr(img_raw)[:100]+\"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nNhUSCKPBxo3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(333, 500, 3)\n",
      "<dtype: 'uint8'>\n"
     ]
    }
   ],
   "source": [
    "img_tensor = tf.image.decode_image(img_raw)\n",
    "print(img_tensor.shape)\n",
    "print(img_tensor.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y2c_Cg3xB0sc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 192, 3)\n",
      "0.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "img_final = tf.image.resize(img_tensor, [IMG_SIZE, IMG_SIZE])\n",
    "img_final = img_final / 255.0 # normalize pixel values\n",
    "print(img_final.shape)\n",
    "print(img_final.numpy().min())\n",
    "print(img_final.numpy().max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RfRp-wOQR9-9"
   },
   "source": [
    "### Wrap those in a function\n",
    "Tip: pay careful attention to the preprocessing. When you deploy models in the browser, you will need to ensure that images are preprocessed identically in JavaScript as they are in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k5XfV-IoB33F"
   },
   "outputs": [],
   "source": [
    "def load_and_preprocess_image(path):\n",
    "  img = tf.io.read_file(path)\n",
    "  img = tf.image.decode_jpeg(img, channels=3)\n",
    "  img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE])\n",
    "  img /= 255.0  # normalize to [0,1] range\n",
    "  return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G0iUW3KZB66L"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show(img, label):\n",
    "  plt.imshow(img)\n",
    "  plt.title(label)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  print()\n",
    "\n",
    "img_path = train_paths[0]\n",
    "img = load_and_preprocess_image(img_path)\n",
    "label = label_names[train_labels[0]]\n",
    "show(img, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E5rBhm42YFC9"
   },
   "source": [
    "### Build an input pipeline to return images and labels\n",
    "I realize this is complicated. The problem we're trying to solve using tf.data is performance (we want our preprocessing to run in C, but to write our code in Python). There are a bunch of advanced tricks you can do with tf.data as well (e.g. prefetching images to the GPU).\n",
    "\n",
    "Note: although your *peak* performance can be higher, it's also very easy to make mistakes and end up with code that's super slow. Always benchmark your input pipelines before using them (shown in a bit). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G3vFPZ15B8vz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tf.Tensor(b'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\dandelion\\\\479115838_0771a6cdff.jpg', shape=(), dtype=string)\n",
      "1 tf.Tensor(b'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\daisy\\\\5087720485_c0914fb623.jpg', shape=(), dtype=string)\n",
      "2 tf.Tensor(b'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\sunflowers\\\\417251603_69f0ee57a9_m.jpg', shape=(), dtype=string)\n",
      "3 tf.Tensor(b'C:\\\\Users\\\\XBBNWBL\\\\.keras\\\\datasets\\\\flower_photos\\\\dandelion\\\\8989067485_aab399460b_n.jpg', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "# a dataset that returns image paths\n",
    "path_ds = tf.data.Dataset.from_tensor_slices(train_paths)\n",
    "for n, img_path in enumerate(path_ds.take(4)):\n",
    "  print(n, img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JBKMDNUaI1VW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Entity <function load_and_preprocess_image at 0x000001A41DA61F78> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <function load_and_preprocess_image at 0x000001A41DA61F78> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
      "0 (192, 192, 3)\n",
      "1 (192, 192, 3)\n",
      "2 (192, 192, 3)\n",
      "3 (192, 192, 3)\n"
     ]
    }
   ],
   "source": [
    "# a dataset that returns images (loaded off disk, decoded, and preprocessed)\n",
    "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\n",
    "for n, image in enumerate(image_ds.take(4)):\n",
    "  print(n, image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "73M1D4kcJKA5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dandelion\n",
      "daisy\n",
      "sunflowers\n",
      "dandelion\n"
     ]
    }
   ],
   "source": [
    "# a dataset that returns labels\n",
    "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(train_labels, tf.int64))\n",
    "for label in label_ds.take(4):\n",
    "  print(label_names[label.numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rz4G6ec5JN14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ZipDataset shapes: ((192, 192, 3), ()), types: (tf.float32, tf.int64)>\n"
     ]
    }
   ],
   "source": [
    "# a dataset that returns images and labels\n",
    "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))\n",
    "print(image_label_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZOxThxj5KtgY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 192, 3) dandelion\n",
      "(192, 192, 3) daisy\n"
     ]
    }
   ],
   "source": [
    "for img, label in image_label_ds.take(2):\n",
    "  print(img.shape, label_names[label.numpy()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2YQxyvQdYkwS"
   },
   "source": [
    "### Batch and shuffle\n",
    "Why do we need to specify a shuffle_size parameter? tf.data works with streams (it doesn't know their length). To shuffle items, we maintain an in-memory buffer of this size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MRC09hBCYmNH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 192, 192, 3) (32,)\n",
      "(32, 192, 192, 3) (32,)\n"
     ]
    }
   ],
   "source": [
    "ds = image_label_ds.shuffle(SHUFFLE_SIZE)\n",
    "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "for img, label in ds.take(2):\n",
    "  print(img.shape, label.shape) # notice it's returning batches of data now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gTUXFRceYvgB"
   },
   "source": [
    "At this point, you could use the dataset above to train a model with ```model.fit(ds)``` but first, let's improve performance. As written, the dataset will load each image off disk, one at a time (super slow). Instead, we want to cache them in memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "topeFvIZYKkM"
   },
   "source": [
    "### Improve performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JiRagWwcLNZW"
   },
   "outputs": [],
   "source": [
    "# A benchmark utility to time how long it takes\n",
    "# to iterate once over the entire dataset\n",
    "def time_one_epoch(ds):\n",
    "  start = time.time()\n",
    "  batches = 0\n",
    "  for i,(images,labels) in enumerate(ds):\n",
    "    batches += 1\n",
    "    if i % 10 == 0:\n",
    "      print('.', end='')\n",
    "  print()\n",
    "  end = time.time()\n",
    "  duration = end-start\n",
    "  print(\"Read {} batches\".format(batches))\n",
    "  print(\"{:0.2f} Batches/s\".format(batches/duration))\n",
    "  print(\"{:0.2f} Images/s\".format(BATCH_SIZE*batches/duration))\n",
    "  print(\"Total time: {}s\".format(duration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tl39fR2WNM4t"
   },
   "source": [
    "### Use in-memory caching\n",
    "This is a small dataset, so let's keep it in memory. The first time we iterate over this dataset, images will be loaded off disk, then cached. The first iteration will be quite slow, and subsequent ones will be faster. Let's show that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vzs0lpOxKf1x"
   },
   "outputs": [],
   "source": [
    "ds = image_label_ds.cache() # cache data in mempry\n",
    "ds = ds.shuffle(SHUFFLE_SIZE)\n",
    "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JAVRB2J6NjQ_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........\n",
      "Read 86 batches\n",
      "22.40 Batches/s\n",
      "716.74 Images/s\n",
      "Total time: 3.8396012783050537s\n"
     ]
    }
   ],
   "source": [
    "time_one_epoch(ds) # this will be slow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kIZjksy8NS_J"
   },
   "source": [
    "Now that the cache is built, iteration will be much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fBD-VrIELSdU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........\n",
      "Read 86 batches\n",
      "91.71 Batches/s\n",
      "2934.60 Images/s\n",
      "Total time: 0.9377753734588623s\n"
     ]
    }
   ],
   "source": [
    "time_one_epoch(ds) # this will be fast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SR511DHJN_8A"
   },
   "source": [
    "If the dataset did not fit into memory, you could use a cache file on disk, like this:\n",
    "\n",
    "```\n",
    "ds = image_label_ds.cache(filename='./cache.tf-data')\n",
    "ds = ds.shuffle(buffer_size=BUFFER_SIZE)\n",
    "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n",
    "```\n",
    "\n",
    "This can be useful to perform expensive preprocessing only once, and/or to improve file I/O (TF saves the cache file in an efficient format - it can be faster to read one large file than a bunch of small ones)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rp8BS9PKBs6S"
   },
   "outputs": [],
   "source": [
    "# here's our final training dataset\n",
    "train_ds = image_label_ds.cache()\n",
    "train_ds = train_ds.shuffle(SHUFFLE_SIZE)\n",
    "train_ds = train_ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "# let's make a test dataset as well\n",
    "path_ds = tf.data.Dataset.from_tensor_slices(test_paths)\n",
    "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\n",
    "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(test_labels, tf.int64))\n",
    "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))\n",
    "test_ds = image_label_ds.cache().batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4CWKa6YmaAYJ"
   },
   "source": [
    "### Finally, we have a fast pipeline ready to go, written from scratch\n",
    "Now, let's define a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3COLSNc89pRl"
   },
   "source": [
    "## 1b) Create a simple CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_GS41XUM98QO"
   },
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', \n",
    "                        input_shape=(IMG_SIZE, IMG_SIZE, 3)))\n",
    "model.add(layers.MaxPooling2D())\n",
    "### TODO: your code here\n",
    "# Add another pair of Conv2d and MaxPoolin2dD layers\n",
    "# As the depth of the network increases, the images\n",
    "# become smaller, and you can afford to use more \n",
    "# filters in each layer\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bUxfKSDE99Oz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 190, 190, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 95, 95, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 93, 93, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 46, 46, 64)        0         \n",
      "=================================================================\n",
      "Total params: 19,392\n",
      "Trainable params: 19,392\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iw7diGWD9q4Y"
   },
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(5, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qLm40HMqS_Wh"
   },
   "source": [
    "### Tip: pay attention to the size of the model\n",
    "Later, when you export a model to run in the webpage, you will want a small one (in terms of the number of parameters) that downloads quickly. Notice how much more efficient the convolutional layers are than the dense ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pX7TsjJZU8OL"
   },
   "source": [
    "### Tip: pay attention to exactly how your images are preprocessed\n",
    "Later, when you run your model in a browser, you'll need to preprocess images in Javascript in exactly the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rrYOXpLc90wt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 190, 190, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 95, 95, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 93, 93, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 135424)            0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 5)                 677125    \n",
      "=================================================================\n",
      "Total params: 696,517\n",
      "Trainable params: 696,517\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SV9mFvExCvZV"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zyJjOEfd94cu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A407497AF8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A407497AF8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "29/29 [==============================] - 64s 2s/step - loss: 1.6914 - accuracy: 0.3497 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/5\n",
      "29/29 [==============================] - 72s 2s/step - loss: 1.1484 - accuracy: 0.5098 - val_loss: 1.0132 - val_accuracy: 0.5828\n",
      "Epoch 3/5\n",
      "29/29 [==============================] - 71s 2s/step - loss: 0.9528 - accuracy: 0.6198 - val_loss: 0.7523 - val_accuracy: 0.7397\n",
      "Epoch 4/5\n",
      "29/29 [==============================] - 70s 2s/step - loss: 0.7279 - accuracy: 0.7320 - val_loss: 0.5117 - val_accuracy: 0.8573\n",
      "Epoch 5/5\n",
      "29/29 [==============================] - 70s 2s/step - loss: 0.5064 - accuracy: 0.8301 - val_loss: 0.3208 - val_accuracy: 0.9248\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1a41ef49ec8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I realize we're using the test dataset (that's fine for this assignment)\n",
    "model.fit(train_ds, validation_data=test_ds, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vqSkDOWiatnY"
   },
   "source": [
    "### Make predictions on a single image\n",
    "Tip: models are implemented to make predictions on batches of images for efficiency. This means that to make a prediction on a single image, you'll need to first wrap it in a batch. The syntax can feel a little unusual at first, but gets easier with time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GtiTdJm3-yrS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 192, 3)\n",
      "(1, 192, 192, 3)\n",
      "Prediction: sunflowers\n",
      "Actual: dandelion\n"
     ]
    }
   ],
   "source": [
    "# load an image off disk\n",
    "img_index = 0\n",
    "img = load_and_preprocess_image(train_paths[img_index])\n",
    "\n",
    "print(img.shape) # before\n",
    "\n",
    "#########\n",
    "## TODO: your code here\n",
    "## use tf.expand_dims to create an empty batch dimension\n",
    "## the starting image shape is (192, 192, 3)\n",
    "## you want it to be (1, 192, 192, 3)\n",
    "## that's read as \"a batch of 1 image, with 192 rows, 192 cols, \n",
    "## and 3 color channels\"\n",
    "#########\n",
    "im_batch = tf.expand_dims(img, 0) # YOUR CODE HERE, use tf.expand_dims\n",
    "\n",
    "print(im_batch.shape) # after\n",
    "\n",
    "# make predictions\n",
    "batch_pred = model.predict(im_batch) # returns a list of predictions\n",
    "pred = batch_pred[0] \n",
    "\n",
    "print(\"Prediction:\", label_names[tf.argmax(pred)])\n",
    "print(\"Actual:\", label_names[train_labels[img_index]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UDiBS04Q_rRL"
   },
   "source": [
    "### TODO: Improve accuracy\n",
    "Write a new model that's more accurate than the baseline above (the baseline has only one Conv2D and MaxPooling2D layer). Produce plots of loss / accuracy as a function of epochs. Train your model without overfitting. For this assignment, it is not necessary to build a super accurate model - just experiment a bit and try to improve over the baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AuuLJJszDAy1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_12 (Conv2D)           (None, 192, 192, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 96, 96, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 96, 96, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 48, 48, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 48, 48, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 24, 24, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 73728)             0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                4718656   \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 5)                 325       \n",
      "=================================================================\n",
      "Total params: 4,812,229\n",
      "Trainable params: 4,812,229\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# TODO: your code here\n",
    "# adding another conv2d layer with pooling and a dense layer to reduce immediate generalization to the dense 5 output\n",
    "model2 = models.Sequential()\n",
    "model2.add(layers.Conv2D(32, (3, 3), activation='relu', \n",
    "                        input_shape=(IMG_SIZE, IMG_SIZE, 3), padding=\"same\"))\n",
    "model2.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
    "model2.add(layers.Conv2D(64, (3, 3), activation='relu', padding=\"same\"))\n",
    "model2.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
    "model2.add(layers.Conv2D(128, (3, 3), activation='relu', padding=\"same\"))\n",
    "model2.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
    "model2.add(layers.Flatten())\n",
    "model2.add(layers.Dense(64, activation='relu'))\n",
    "model2.add(layers.Dense(5, activation='softmax'))\n",
    "\n",
    "model2.summary()\n",
    "\n",
    "#plotting accuracy and loss as a function of epochs to ensure no overfitting\n",
    "def plot(history):\n",
    "  \n",
    "  # The history object contains results on the training and test\n",
    "  # sets for each epoch\n",
    "  acc = history.history['accuracy']\n",
    "  val_acc = history.history['val_accuracy']\n",
    "  loss = history.history['loss']\n",
    "  val_loss = history.history['val_loss']\n",
    "\n",
    "  # Get the number of epochs\n",
    "  epochs = range(len(acc))\n",
    "\n",
    "  plt.title('Training and validation accuracy')\n",
    "  plt.plot(epochs, acc, color='blue', label='Train')\n",
    "  plt.plot(epochs, val_acc, color='orange', label='Val')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Accuracy')\n",
    "  plt.legend()\n",
    "\n",
    "  _ = plt.figure()\n",
    "  plt.title('Training and validation loss')\n",
    "  plt.plot(epochs, loss, color='blue', label='Train')\n",
    "  plt.plot(epochs, val_loss, color='orange', label='Val')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A43E50BC18> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A43E50BC18> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "29/29 [==============================] - 131s 5s/step - loss: 1.8059 - accuracy: 0.2745 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/20\n",
      "29/29 [==============================] - 137s 5s/step - loss: 1.2910 - accuracy: 0.4390 - val_loss: 1.1086 - val_accuracy: 0.5403\n",
      "Epoch 3/20\n",
      "29/29 [==============================] - 136s 5s/step - loss: 1.0950 - accuracy: 0.5338 - val_loss: 1.0312 - val_accuracy: 0.5686\n",
      "Epoch 4/20\n",
      "29/29 [==============================] - 137s 5s/step - loss: 0.9503 - accuracy: 0.6253 - val_loss: 0.8391 - val_accuracy: 0.6732\n",
      "Epoch 5/20\n",
      "29/29 [==============================] - 139s 5s/step - loss: 0.8170 - accuracy: 0.6830 - val_loss: 0.6987 - val_accuracy: 0.7516\n",
      "Epoch 6/20\n",
      "29/29 [==============================] - 137s 5s/step - loss: 0.5865 - accuracy: 0.7789 - val_loss: 0.4148 - val_accuracy: 0.8638\n",
      "Epoch 7/20\n",
      "29/29 [==============================] - 136s 5s/step - loss: 0.3858 - accuracy: 0.8725 - val_loss: 0.2827 - val_accuracy: 0.9183\n",
      "Epoch 8/20\n",
      "29/29 [==============================] - 148s 5s/step - loss: 0.2708 - accuracy: 0.9237 - val_loss: 0.1609 - val_accuracy: 0.9684\n",
      "Epoch 9/20\n",
      "29/29 [==============================] - 143s 5s/step - loss: 0.1750 - accuracy: 0.9423 - val_loss: 0.1477 - val_accuracy: 0.9597\n",
      "Epoch 10/20\n",
      "29/29 [==============================] - 138s 5s/step - loss: 0.1564 - accuracy: 0.9488 - val_loss: 0.0759 - val_accuracy: 0.9782\n",
      "Epoch 11/20\n",
      "29/29 [==============================] - 138s 5s/step - loss: 0.0865 - accuracy: 0.9749 - val_loss: 0.1057 - val_accuracy: 0.9717\n",
      "Epoch 12/20\n",
      "29/29 [==============================] - 134s 5s/step - loss: 0.1402 - accuracy: 0.9532 - val_loss: 0.0738 - val_accuracy: 0.9771\n",
      "Epoch 13/20\n",
      "29/29 [==============================] - 134s 5s/step - loss: 0.0526 - accuracy: 0.9858 - val_loss: 0.0190 - val_accuracy: 0.9978\n",
      "Epoch 14/20\n",
      "29/29 [==============================] - 130s 4s/step - loss: 0.0216 - accuracy: 0.9946 - val_loss: 0.0125 - val_accuracy: 0.9978\n",
      "Epoch 15/20\n",
      "29/29 [==============================] - 133s 5s/step - loss: 0.0114 - accuracy: 0.9978 - val_loss: 0.0065 - val_accuracy: 0.9978\n",
      "Epoch 16/20\n",
      "29/29 [==============================] - 130s 4s/step - loss: 0.0055 - accuracy: 0.9989 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
      "Epoch 17/20\n",
      "29/29 [==============================] - 131s 5s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 9.7468e-04 - val_accuracy: 1.0000\n",
      "Epoch 18/20\n",
      "29/29 [==============================] - 133s 5s/step - loss: 9.2433e-04 - accuracy: 1.0000 - val_loss: 7.2723e-04 - val_accuracy: 1.0000\n",
      "Epoch 19/20\n",
      "29/29 [==============================] - 143s 5s/step - loss: 6.8901e-04 - accuracy: 1.0000 - val_loss: 5.7682e-04 - val_accuracy: 1.0000\n",
      "Epoch 20/20\n",
      "29/29 [==============================] - 148s 5s/step - loss: 5.4716e-04 - accuracy: 1.0000 - val_loss: 4.6226e-04 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model2.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model2_history = model2.fit(train_ds, validation_data=test_ds, epochs=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots of accuracy and loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot(model2_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is no overfitting even out to 20 epochs since the validation (using test data) is reaching a 100% accuracy\n",
    "#### This is definitely better than baseline but lets check that same image prediction that failed above with the baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: dandelion\n",
      "Actual: dandelion\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "batch_pred2 = model2.predict(im_batch) # returns a list of predictions\n",
    "pred2 = batch_pred2[0] \n",
    "\n",
    "print(\"Prediction:\", label_names[tf.argmax(pred2)])\n",
    "print(\"Actual:\", label_names[train_labels[img_index]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Success.. much better than baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hc3yX-amDMFG"
   },
   "source": [
    "## 1c) Try to get your flowers model working in TensorFlow.js\n",
    "In the final part of the assignment, you'll export the model you build to recognize landmarks on Columbia's campus, and get it working in the browser. It's easiest to start by exporting your flowers model, and getting it working with TensorFlow.js (the starter code is build around your flowers model, so you can learn the mechanics before trying something more complex)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DLVgG2eBDmu6"
   },
   "source": [
    "### Save your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dM20AUr4Doqu"
   },
   "outputs": [],
   "source": [
    "model2.save(\"my_flower_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V4ALIsEbLs07"
   },
   "outputs": [],
   "source": [
    "#!ls -lha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eCSblyG1J42B"
   },
   "source": [
    "### Download it to your local machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hYwiPmqXJ5mf"
   },
   "outputs": [],
   "source": [
    "#from google.colab import files\n",
    "#files.download(\"/content/my_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Already downloaded to machine since not using Colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "itHCUxY3DytP"
   },
   "source": [
    "### Visit notebook #2 to convert it to TensorFlow.js format\n",
    "Follow the instructions there to prepare a webpage to run your flowers model in the browser. Once you have that working, continue with the rest of this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The flowers model is working in the browser. See below website:\n",
    "http://muftayebaly.com/apps/deepflowers/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tTU9vX0Kcawh"
   },
   "source": [
    "## 1d) Transfer learning\n",
    "\n",
    "In this part of the assignment, you'll use transfer learning to take advantage of a large pretrained model. It is not necesary to deploy this part in the browser.\n",
    "\n",
    "\n",
    "References:\n",
    "* https://www.tensorflow.org/beta/tutorials/images/transfer_learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kk4pYnFiXuKb"
   },
   "outputs": [],
   "source": [
    "## TODO: your code here\n",
    "# Choose a pretrained model, and import the application\n",
    "# See https://keras.io/applications/ for a few choices\n",
    "# When you import the model, you will want to remove the \n",
    "# final dense layer that performs classification (include_top=False)\n",
    "# you will also want to import weights from ImageNet,\n",
    "# and you will want to specify the input shape to match your images. \n",
    "\n",
    "base_model = tf.keras.applications.xception.Xception(include_top=False, weights='imagenet', input_tensor=None, input_shape=(IMG_SIZE, IMG_SIZE, 3)) # fix me. base_model = tf.keras.applications..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lW9kjNblDRBx"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 6, 6, 2048)\n"
     ]
    }
   ],
   "source": [
    "# A hack to show you the output shape of the model\n",
    "for image_batch, label_batch in train_ds.take(1):\n",
    "   pass\n",
    "  \n",
    "feature_batch = base_model(image_batch)\n",
    "print(feature_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vwxsas1j-JYY"
   },
   "outputs": [],
   "source": [
    "# do not update the pretrained weights during training\n",
    "# (we won't use finetuning here)\n",
    "base_model.trainable = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U7JdYOWG-Lsp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"xception\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 192, 192, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 95, 95, 32)   864         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_bn (BatchNormaliza (None, 95, 95, 32)   128         block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_act (Activation)   (None, 95, 95, 32)   0           block1_conv1_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 93, 93, 64)   18432       block1_conv1_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_bn (BatchNormaliza (None, 93, 93, 64)   256         block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_act (Activation)   (None, 93, 93, 64)   0           block1_conv2_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1 (SeparableConv2 (None, 93, 93, 128)  8768        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1_bn (BatchNormal (None, 93, 93, 128)  512         block2_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_act (Activation (None, 93, 93, 128)  0           block2_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2 (SeparableConv2 (None, 93, 93, 128)  17536       block2_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_bn (BatchNormal (None, 93, 93, 128)  512         block2_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 47, 47, 128)  8192        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 47, 47, 128)  0           block2_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 47, 47, 128)  512         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 47, 47, 128)  0           block2_pool[0][0]                \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_act (Activation (None, 47, 47, 128)  0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1 (SeparableConv2 (None, 47, 47, 256)  33920       block3_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_bn (BatchNormal (None, 47, 47, 256)  1024        block3_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_act (Activation (None, 47, 47, 256)  0           block3_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2 (SeparableConv2 (None, 47, 47, 256)  67840       block3_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_bn (BatchNormal (None, 47, 47, 256)  1024        block3_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 24, 24, 256)  32768       add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 24, 24, 256)  0           block3_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 24, 24, 256)  1024        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 24, 24, 256)  0           block3_pool[0][0]                \n",
      "                                                                 batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_act (Activation (None, 24, 24, 256)  0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1 (SeparableConv2 (None, 24, 24, 728)  188672      block4_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_bn (BatchNormal (None, 24, 24, 728)  2912        block4_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_act (Activation (None, 24, 24, 728)  0           block4_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2 (SeparableConv2 (None, 24, 24, 728)  536536      block4_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_bn (BatchNormal (None, 24, 24, 728)  2912        block4_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 12, 12, 728)  186368      add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 12, 12, 728)  0           block4_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 12, 12, 728)  2912        conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 12, 12, 728)  0           block4_pool[0][0]                \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_act (Activation (None, 12, 12, 728)  0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1 (SeparableConv2 (None, 12, 12, 728)  536536      block5_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_bn (BatchNormal (None, 12, 12, 728)  2912        block5_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_act (Activation (None, 12, 12, 728)  0           block5_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2 (SeparableConv2 (None, 12, 12, 728)  536536      block5_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_bn (BatchNormal (None, 12, 12, 728)  2912        block5_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_act (Activation (None, 12, 12, 728)  0           block5_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3 (SeparableConv2 (None, 12, 12, 728)  536536      block5_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_bn (BatchNormal (None, 12, 12, 728)  2912        block5_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 12, 12, 728)  0           block5_sepconv3_bn[0][0]         \n",
      "                                                                 add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_act (Activation (None, 12, 12, 728)  0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1 (SeparableConv2 (None, 12, 12, 728)  536536      block6_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_bn (BatchNormal (None, 12, 12, 728)  2912        block6_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_act (Activation (None, 12, 12, 728)  0           block6_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2 (SeparableConv2 (None, 12, 12, 728)  536536      block6_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_bn (BatchNormal (None, 12, 12, 728)  2912        block6_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_act (Activation (None, 12, 12, 728)  0           block6_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3 (SeparableConv2 (None, 12, 12, 728)  536536      block6_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_bn (BatchNormal (None, 12, 12, 728)  2912        block6_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 12, 12, 728)  0           block6_sepconv3_bn[0][0]         \n",
      "                                                                 add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_act (Activation (None, 12, 12, 728)  0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1 (SeparableConv2 (None, 12, 12, 728)  536536      block7_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_bn (BatchNormal (None, 12, 12, 728)  2912        block7_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_act (Activation (None, 12, 12, 728)  0           block7_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2 (SeparableConv2 (None, 12, 12, 728)  536536      block7_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_bn (BatchNormal (None, 12, 12, 728)  2912        block7_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_act (Activation (None, 12, 12, 728)  0           block7_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3 (SeparableConv2 (None, 12, 12, 728)  536536      block7_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_bn (BatchNormal (None, 12, 12, 728)  2912        block7_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 12, 12, 728)  0           block7_sepconv3_bn[0][0]         \n",
      "                                                                 add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_act (Activation (None, 12, 12, 728)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1 (SeparableConv2 (None, 12, 12, 728)  536536      block8_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_bn (BatchNormal (None, 12, 12, 728)  2912        block8_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_act (Activation (None, 12, 12, 728)  0           block8_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2 (SeparableConv2 (None, 12, 12, 728)  536536      block8_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_bn (BatchNormal (None, 12, 12, 728)  2912        block8_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_act (Activation (None, 12, 12, 728)  0           block8_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3 (SeparableConv2 (None, 12, 12, 728)  536536      block8_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_bn (BatchNormal (None, 12, 12, 728)  2912        block8_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 12, 12, 728)  0           block8_sepconv3_bn[0][0]         \n",
      "                                                                 add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_act (Activation (None, 12, 12, 728)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1 (SeparableConv2 (None, 12, 12, 728)  536536      block9_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_bn (BatchNormal (None, 12, 12, 728)  2912        block9_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_act (Activation (None, 12, 12, 728)  0           block9_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2 (SeparableConv2 (None, 12, 12, 728)  536536      block9_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_bn (BatchNormal (None, 12, 12, 728)  2912        block9_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_act (Activation (None, 12, 12, 728)  0           block9_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3 (SeparableConv2 (None, 12, 12, 728)  536536      block9_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_bn (BatchNormal (None, 12, 12, 728)  2912        block9_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 12, 12, 728)  0           block9_sepconv3_bn[0][0]         \n",
      "                                                                 add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_act (Activatio (None, 12, 12, 728)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1 (SeparableConv (None, 12, 12, 728)  536536      block10_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_bn (BatchNorma (None, 12, 12, 728)  2912        block10_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_act (Activatio (None, 12, 12, 728)  0           block10_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2 (SeparableConv (None, 12, 12, 728)  536536      block10_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_bn (BatchNorma (None, 12, 12, 728)  2912        block10_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_act (Activatio (None, 12, 12, 728)  0           block10_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3 (SeparableConv (None, 12, 12, 728)  536536      block10_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_bn (BatchNorma (None, 12, 12, 728)  2912        block10_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 12, 12, 728)  0           block10_sepconv3_bn[0][0]        \n",
      "                                                                 add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_act (Activatio (None, 12, 12, 728)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1 (SeparableConv (None, 12, 12, 728)  536536      block11_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_bn (BatchNorma (None, 12, 12, 728)  2912        block11_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_act (Activatio (None, 12, 12, 728)  0           block11_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2 (SeparableConv (None, 12, 12, 728)  536536      block11_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_bn (BatchNorma (None, 12, 12, 728)  2912        block11_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_act (Activatio (None, 12, 12, 728)  0           block11_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3 (SeparableConv (None, 12, 12, 728)  536536      block11_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_bn (BatchNorma (None, 12, 12, 728)  2912        block11_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 12, 12, 728)  0           block11_sepconv3_bn[0][0]        \n",
      "                                                                 add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_act (Activatio (None, 12, 12, 728)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1 (SeparableConv (None, 12, 12, 728)  536536      block12_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_bn (BatchNorma (None, 12, 12, 728)  2912        block12_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_act (Activatio (None, 12, 12, 728)  0           block12_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2 (SeparableConv (None, 12, 12, 728)  536536      block12_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_bn (BatchNorma (None, 12, 12, 728)  2912        block12_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_act (Activatio (None, 12, 12, 728)  0           block12_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3 (SeparableConv (None, 12, 12, 728)  536536      block12_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_bn (BatchNorma (None, 12, 12, 728)  2912        block12_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 12, 12, 728)  0           block12_sepconv3_bn[0][0]        \n",
      "                                                                 add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_act (Activatio (None, 12, 12, 728)  0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1 (SeparableConv (None, 12, 12, 728)  536536      block13_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_bn (BatchNorma (None, 12, 12, 728)  2912        block13_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_act (Activatio (None, 12, 12, 728)  0           block13_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2 (SeparableConv (None, 12, 12, 1024) 752024      block13_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_bn (BatchNorma (None, 12, 12, 1024) 4096        block13_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 6, 6, 1024)   745472      add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_pool (MaxPooling2D)     (None, 6, 6, 1024)   0           block13_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 6, 6, 1024)   4096        conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 6, 6, 1024)   0           block13_pool[0][0]               \n",
      "                                                                 batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1 (SeparableConv (None, 6, 6, 1536)   1582080     add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_bn (BatchNorma (None, 6, 6, 1536)   6144        block14_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_act (Activatio (None, 6, 6, 1536)   0           block14_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2 (SeparableConv (None, 6, 6, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_bn (BatchNorma (None, 6, 6, 2048)   8192        block14_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_act (Activatio (None, 6, 6, 2048)   0           block14_sepconv2_bn[0][0]        \n",
      "==================================================================================================\n",
      "Total params: 20,861,480\n",
      "Trainable params: 0\n",
      "Non-trainable params: 20,861,480\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7l0gBu3o-NLu"
   },
   "outputs": [],
   "source": [
    "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "prediction_layer = tf.keras.layers.Dense(5, activation='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yXQPRAsF-UWk"
   },
   "outputs": [],
   "source": [
    "# build a new model reusing the pretrained base\n",
    "model = tf.keras.Sequential([\n",
    "  base_model,\n",
    "  global_average_layer,\n",
    "  prediction_layer\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9k_CFl0d-vg2"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T3w5RPot-wMl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A4AD1E4798> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001A4AD1E4798> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "29/29 [==============================] - 531s 18s/step - loss: 1.3181 - accuracy: 0.5163 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/5\n",
      "29/29 [==============================] - 533s 18s/step - loss: 0.9014 - accuracy: 0.7636 - val_loss: 1.0244 - val_accuracy: 0.5926\n",
      "Epoch 3/5\n",
      "29/29 [==============================] - 577s 20s/step - loss: 0.7128 - accuracy: 0.8290 - val_loss: 0.8671 - val_accuracy: 0.6667\n",
      "Epoch 4/5\n",
      "29/29 [==============================] - 522s 18s/step - loss: 0.6265 - accuracy: 0.8410 - val_loss: 0.8973 - val_accuracy: 0.6699\n",
      "Epoch 5/5\n",
      "29/29 [==============================] - 529s 18s/step - loss: 0.5528 - accuracy: 0.8584 - val_loss: 0.9788 - val_accuracy: 0.6536\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1a4abae91c8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_ds, validation_data=test_ds, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bTHrHpf6da5d"
   },
   "source": [
    "## 2a) Recognize landmarks on Columbia's campus\n",
    "\n",
    "In this part of the assignment, you will train a model to recognize landmarks (famous places, like the [Alma Mater sculpture](https://en.wikipedia.org/wiki/Alma_Mater_(New_York_sculpture), or Butler library) on Columbia’s campus. Instead of tf.data, you will gain experience with the higher level Keras utilties. You will also experiment with data augmentation to increase the effective size of your dataset.\n",
    "\n",
    "Starter code is not provided for this part of the assignment. You should base your work off the following Keras notebook, which works identically in TF2 (the only thing you'll need to change is the imports, an example of correct imports is given below).\n",
    "\n",
    "* https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.2-using-convnets-with-small-datasets.ipynb\n",
    "\n",
    "Here are steps you should complete.\n",
    "\n",
    "1. Collect a dataset of at least three landmarks. Your dataset should include at least 50 images of each landmark in train, and 50 in validation (using more images is fine). Randomly shuffle your data to create these splits. You do not need to use a separate test set in this assignment.\n",
    "\n",
    " You will need to upload your dataset to Colab in order to train a model. To do so, you can either upload it using the file browser (slow and tedious, you'll need to repeat that if your runtime is reset), or (better) you can upload your dataset to Google Drive, then mount your drive as a filesystem in Colab (View -> Table of contents -> Code snippets -> search for \"drive\"). This will enable you to access the contents of your drive with commands like `!ls /gdrive`. As a another option, you could upload your dataset to a cloud provider or your Columbia account, then download it similarly to flowers at the top of this notebook.\n",
    " \n",
    "1. Write a model to classify your data. Try to train a small model in terms of the number of parameters (you do not need to use transfer learning). \n",
    "\n",
    "1. Show predictions on several images.\n",
    "\n",
    "1. Use data augmentation, see if this helps to improve accuracy.\n",
    "\n",
    "1. Produce plots of accuracy / loss as a function of epochs. Determine the right place to stop training without overfitting.\n",
    "\n",
    "When you have a reasonably accurate model, proceed to the next step. There are no guidelines for accuracy, try to build something you feel works well, given the small amount of data you have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hDfkcDbzHjsl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3670 images belonging to 5 classes.\n",
      "data batch shape: (32, 192, 192, 3)\n",
      "labels batch shape: (32, 5)\n"
     ]
    }
   ],
   "source": [
    "# Example code showing the correct imports\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Example code:\n",
    "# You should use the keras preprocessing utilities here instead of tf.data\n",
    "# Here's an example of how to use them with the flowers dataset\n",
    "# You will need to add data augmentation\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        data_root,\n",
    "        target_size=(IMG_SIZE, IMG_SIZE),\n",
    "        batch_size=32)\n",
    "\n",
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break\n",
    "\n",
    "###\n",
    "# TODO: your code for 2a here.\n",
    "###\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "BATCH_SIZE = 32\n",
    "IMG_SIZE = 300\n",
    "SHUFFLE_SIZE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://muftayebaly.com/dropbox/ColumbiaLandmarks.zip\n",
      "1179688960/1179680798 [==============================] - 118s 0us/step\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "data_root_orig = tf.keras.utils.get_file(origin='http://muftayebaly.com/dropbox/ColumbiaLandmarks.zip',\n",
    "                                         fname='ColumbiaLandmarks.zip', extract=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Muf/.keras/datasets/ColumbiaLandmarks\n"
     ]
    }
   ],
   "source": [
    "data_root = str(pathlib.Path(data_root_orig).parents[0])+'/ColumbiaLandmarks'\n",
    "print(data_root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 225 images belonging to 4 classes.\n",
      "Found 225 images belonging to 4 classes.\n",
      "data batch shape: (32, 300, 300, 3)\n",
      "labels batch shape: (32,)\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(rescale=1./255, validation_split=0.5)\n",
    "train_gen = datagen.flow_from_directory(\n",
    "        data_root,\n",
    "        target_size=(IMG_SIZE, IMG_SIZE),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=True,\n",
    "        subset=\"training\",\n",
    "        class_mode=\"binary\")\n",
    "\n",
    "validation_gen = datagen.flow_from_directory(\n",
    "        data_root,\n",
    "        target_size=(IMG_SIZE, IMG_SIZE),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=True,\n",
    "        subset=\"validation\",\n",
    "        class_mode=\"binary\")\n",
    "\n",
    "for data_batch, labels_batch in train_gen:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'butler_library', 1: 'le_marteleur', 2: 'the_curl', 3: 'whispering_bench'}\n"
     ]
    }
   ],
   "source": [
    "landmark_classes = {y:x for x,y in train_gen.class_indices.items()}\n",
    "print(landmark_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "le_marteleur\n"
     ]
    }
   ],
   "source": [
    "print(landmark_classes[labels_batch[6]])"
   ]
  },
  {
   "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": [
    "def split_dataset_into_test_and_train_sets(all_data_dir, training_data_dir, testing_data_dir, testing_data_pct):\n",
    "    # Recreate testing and training directories\n",
    "    if testing_data_dir.count('/') > 1:\n",
    "        shutil.rmtree(testing_data_dir, ignore_errors=False)\n",
    "        os.makedirs(testing_data_dir)\n",
    "        print(\"Successfully cleaned directory \" + testing_data_dir)\n",
    "    else:\n",
    "        print(\"Refusing to delete testing data directory \" + testing_data_dir + \" as we prevent you from doing stupid things!\")\n",
    "\n",
    "    if training_data_dir.count('/') > 1:\n",
    "        shutil.rmtree(training_data_dir, ignore_errors=False)\n",
    "        os.makedirs(training_data_dir)\n",
    "        print(\"Successfully cleaned directory \" + training_data_dir)\n",
    "    else:\n",
    "        print(\"Refusing to delete testing data directory \" + training_data_dir + \" as we prevent you from doing stupid things!\")\n",
    "\n",
    "    num_training_files = 0\n",
    "    num_testing_files = 0\n",
    "\n",
    "    for subdir, dirs, files in os.walk(all_data_dir):\n",
    "        category_name = os.path.basename(subdir)\n",
    "\n",
    "        # Don't create a subdirectory for the root directory\n",
    "        print(category_name + \" vs \" + os.path.basename(all_data_dir))\n",
    "        if category_name == os.path.basename(all_data_dir):\n",
    "            continue\n",
    "\n",
    "        training_data_category_dir = training_data_dir + '/' + category_name\n",
    "        testing_data_category_dir = testing_data_dir + '/' + category_name\n",
    "\n",
    "        if not os.path.exists(training_data_category_dir):\n",
    "            os.mkdir(training_data_category_dir)\n",
    "        \n",
    "        if not os.path.exists(testing_data_category_dir):\n",
    "            os.mkdir(testing_data_category_dir)\n",
    "\n",
    "        for file in files:\n",
    "            input_file = os.path.join(subdir, file)\n",
    "            if np.random.rand(1) < testing_data_pct:\n",
    "                shutil.copy(input_file, testing_data_dir + '/' + category_name + '/' + file)\n",
    "                num_testing_files += 1\n",
    "            else:\n",
    "                shutil.copy(input_file, training_data_dir + '/' + category_name + '/' + file)\n",
    "                num_training_files += 1\n",
    "\n",
    "    print(\"Processed \" + str(num_training_files) + \" training files.\")\n",
    "    print(\"Processed \" + str(num_testing_files) + \" testing files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LHvhvaVdJAu8"
   },
   "source": [
    "## 2b) Donate your dataset (optional)\n",
    "We will use the dataset collected by the class later in the semester.\n",
    "\n",
    "Requirements:\n",
    "\n",
    "* Please do not include any images with people in the foreground.\n",
    "* Only include images you have taken yourself and that you are comfortable sharing publicly under a [CCO](https://creativecommons.org/share-your-work/public-domain/cc0/) license (TLDR, public domain).\n",
    "\n",
    "Link to upload a zip is on CourseWorks. Your file should be in the same format as the flowers dataset (the directory names gives the label for each image).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset found at the url below:\n",
    "http://muftayebaly.com/dropbox/ColumbiaLandmarks.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aUf9FFXa80E4"
   },
   "source": [
    "## 3) Run your model in the browser\n",
    "Save and download your model, and head to notebook two again. After you've finished creating a webpage, upload it to GitHub pages or your Columbia account, list the URL here, and submit your assignment. You may not need to make many changes to the starter code, but pay attention to the preprocessing, number of output classes, image size, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gCAdQMFo86Gc"
   },
   "outputs": [],
   "source": [
    "# TODO: http://your_webpage"
   ]
  }
 ],
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