07_TF_Developer_Certificate_Course.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "96dbRWBv9kVu"
   },
   "source": [
    "# Milestone Project 1 : Food Vision Big(TM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2438,
     "status": "ok",
     "timestamp": 1688437868868,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "mNTGzEJPLtqw",
    "outputId": "ab0fa1a5-2257-4647-b897-6e6cd54048b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 'helper_functions.py' already exists, skipping download.\n",
      "2.13.0\n",
      "1 Physical GPUs, 1 Logical GPUs\n",
      "INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n",
      "Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: NVIDIA GeForce RTX 3050 Laptop GPU, compute capability 8.6\n",
      "<Policy \"mixed_float16\">\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "import tensorflow as tf\n",
    "if not os.path.exists(\"helper_functions.py\"):\n",
    "  !wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
    "else:\n",
    "  print(\"[INFO] 'helper_functions.py' already exists, skipping download.\")\n",
    "from helper_functions import *\n",
    "# from tensorflow.keras import mixed_precision\n",
    "# mixed_precision.set_global_policy('mixed_float16')\n",
    "importTensorflow(memory=4090, precision=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1688437874936,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "ezS8tSlPAE9y",
    "outputId": "3d36a09c-7378-4729-ead9-6a5c02d7cf24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jan  1 00:56:20 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 545.23.08              Driver Version: 545.23.08    CUDA Version: 12.3     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce RTX 3050 ...    On  | 00000000:01:00.0 Off |                  N/A |\n",
      "| N/A   69C    P0              18W /  60W |     81MiB /  4096MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A      1126      G   /usr/lib/xorg/Xorg                            4MiB |\n",
      "|    0   N/A  N/A    390596      C   ...l/miniconda3/envs/tf2.13/bin/python       68MiB |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1688437875749,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "2gwyNh_PPYBU",
    "outputId": "a475b856-c763-40a8-8cd1-2b97f9327e56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU 0: NVIDIA GeForce RTX 3050 Laptop GPU (UUID: GPU-dfaf2dbb-aec7-1a43-9749-df5dc8fdb316)\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi -L"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X0_klKKlzuZT"
   },
   "source": [
    "# Use Tensorflow Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4910,
     "status": "ok",
     "timestamp": 1688437881813,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "a71VAGzHrkkl",
    "outputId": "93d7f9f2-48e1-4741-8a40-f531ad9b8b36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tdfs\n",
    "dataset_list = tdfs.list_builders()\n",
    "print(\"food101\" in dataset_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zmPiUuoiz9zG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset 4.65 GiB (download: 4.65 GiB, generated: Unknown size, total: 4.65 GiB) to /home/ujjwal/tensorflow_datasets/food101/2.0.0...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87d3fe091aaa4e7f8b28fb911c907f35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80dc0284364e415d8d73805cd4e3f196",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Size...: 0 MiB [00:00, ? MiB/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00d51b3fb10b4d059755b8f2606e2a19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extraction completed...: 0 file [00:00, ? file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the data\n",
    "(train_data,test_data), ds_info = tdfs.load(name=\"food101\",\n",
    "                                            split=[\"train\",\"validation\"],\n",
    "                                            shuffle_files=True,\n",
    "                                            as_supervised=True,\n",
    "                                            with_info=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1688438489877,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "m_-9Ud4KZGOy",
    "outputId": "7c38052e-c6f1-48b5-802d-4a604db39db9"
   },
   "outputs": [],
   "source": [
    "class_names = ds_info.features[\"label\"].names\n",
    "class_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1688438489877,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "ruXri_MFY_iU",
    "outputId": "d237fe26-7e08-4f07-fab9-2462b7df855a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FeaturesDict({\n",
       "    'image': Image(shape=(None, None, 3), dtype=uint8),\n",
       "    'label': ClassLabel(shape=(), dtype=int64, num_classes=101),\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Features in Food101 from tfds\n",
    "ds_info.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "V4lrUU7aahHK"
   },
   "outputs": [],
   "source": [
    "# take one sample of train_data\n",
    "train_one_sample = train_data.take(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hdr7AH1ga9xc"
   },
   "source": [
    "# Exploring the Food101 data from TensorFlow Datasets\n",
    "Now we've downloaded the Food101 dataset from TensorFlow Datasets, how about we do what any good data explorer should?\n",
    "\n",
    "In other words, \"visualize, visualize, visualize\".\n",
    "\n",
    "Let's find out a few details about our dataset:\n",
    "\n",
    "* The shape of our input data (image tensors)\n",
    "* The datatype of our input data\n",
    "* What the labels of our input data look like (e.g. one-hot encoded versus label-encoded)\n",
    "* Do the labels match up with the class names?\n",
    "\n",
    "To do, let's take one sample off the training data (using the .take() method) and explore it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1688438489878,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "YJ_D-JM-bO1i",
    "outputId": "77e1a8c9-2d4e-42d6-9f11-14abb6992adc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_TakeDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does one sample of training data look like\n",
    "train_one_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1688438489878,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "IyjCTcUDbUjn",
    "outputId": "87e55a8e-89c0-4553-805e-7448bba38fd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Image shape: (512, 512, 3),\n",
      "  Image datatype: <dtype: 'uint8'>,\n",
      "  Target class(tensor form): 64,\n",
      "  Class name(str form): miso_soup\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Output info about training sample\n",
    "for image,label in train_one_sample:\n",
    "  print(f\"\"\"\n",
    "  Image shape: {image.shape},\n",
    "  Image datatype: {image.dtype},\n",
    "  Target class(tensor form): {label},\n",
    "  Class name(str form): {class_names[label.numpy()]}\n",
    "  \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1688438489879,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "PdiXP_Audr5T",
    "outputId": "dfb24687-c19e-4e20-93c1-ebeef3576559"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(512, 512, 3), dtype=uint8, numpy=\n",
       "array([[[ 43,  89, 125],\n",
       "        [ 52,  96, 131],\n",
       "        [ 85, 128, 162],\n",
       "        ...,\n",
       "        [251, 254, 223],\n",
       "        [250, 253, 222],\n",
       "        [250, 253, 222]],\n",
       "\n",
       "       [[ 42,  88, 124],\n",
       "        [ 53,  97, 132],\n",
       "        [ 92, 135, 169],\n",
       "        ...,\n",
       "        [251, 254, 223],\n",
       "        [250, 253, 222],\n",
       "        [250, 253, 222]],\n",
       "\n",
       "       [[ 45,  89, 124],\n",
       "        [ 52,  96, 131],\n",
       "        [ 92, 135, 169],\n",
       "        ...,\n",
       "        [251, 254, 223],\n",
       "        [250, 253, 222],\n",
       "        [250, 253, 222]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 91,  99,  86],\n",
       "        [ 89,  97,  84],\n",
       "        [ 88,  94,  82],\n",
       "        ...,\n",
       "        [ 37,  44,  50],\n",
       "        [ 34,  41,  47],\n",
       "        [ 31,  38,  44]],\n",
       "\n",
       "       [[ 91,  99,  86],\n",
       "        [ 90,  98,  85],\n",
       "        [ 88,  96,  83],\n",
       "        ...,\n",
       "        [ 38,  43,  47],\n",
       "        [ 35,  40,  44],\n",
       "        [ 33,  38,  42]],\n",
       "\n",
       "       [[ 93, 101,  88],\n",
       "        [ 93, 101,  88],\n",
       "        [ 89,  97,  84],\n",
       "        ...,\n",
       "        [ 37,  42,  46],\n",
       "        [ 35,  40,  44],\n",
       "        [ 35,  40,  44]]], dtype=uint8)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How does image look like:\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1688438489879,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "NVY3-tqdeQfk",
    "outputId": "3d3d25f0-de5f-4f9f-8b2b-7fb28043eb5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=uint8, numpy=0>,\n",
       " <tf.Tensor: shape=(), dtype=uint8, numpy=255>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_min(image),tf.reduce_max(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1688438489879,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "PKZGeH26eXPp",
    "outputId": "ec5be9f3-e5de-4253-f966-bc46e4ba70e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 511.5, 511.5, -0.5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot an image from tensorflow datasets\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(image)\n",
    "plt.title(class_names[label.numpy()]) # add title to image by indexing on class_names list\n",
    "plt.axis(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LPNIJT9Qfb5F"
   },
   "source": [
    "## Create preprocessing functions for our data\n",
    "\n",
    "What we know about the data:\n",
    "* It is in `uint8` datatype\n",
    "* Image is not scaled\n",
    "* Different sized images/tensors\n",
    "\n",
    "What we know model prefers:\n",
    "* Data in `float32` dtype\n",
    "* For batches, tf likes all of tensors within a batch to be of same size\n",
    "* Scaled (values between 0 and 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1688438489879,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "c8GFMZIi9vFd",
    "outputId": "4399c271-9230-4458-c968-f506a1187152"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(512, 512, 3), dtype=uint8, numpy=\n",
       " array([[[ 43,  89, 125],\n",
       "         [ 52,  96, 131],\n",
       "         [ 85, 128, 162],\n",
       "         ...,\n",
       "         [251, 254, 223],\n",
       "         [250, 253, 222],\n",
       "         [250, 253, 222]],\n",
       " \n",
       "        [[ 42,  88, 124],\n",
       "         [ 53,  97, 132],\n",
       "         [ 92, 135, 169],\n",
       "         ...,\n",
       "         [251, 254, 223],\n",
       "         [250, 253, 222],\n",
       "         [250, 253, 222]],\n",
       " \n",
       "        [[ 45,  89, 124],\n",
       "         [ 52,  96, 131],\n",
       "         [ 92, 135, 169],\n",
       "         ...,\n",
       "         [251, 254, 223],\n",
       "         [250, 253, 222],\n",
       "         [250, 253, 222]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 91,  99,  86],\n",
       "         [ 89,  97,  84],\n",
       "         [ 88,  94,  82],\n",
       "         ...,\n",
       "         [ 37,  44,  50],\n",
       "         [ 34,  41,  47],\n",
       "         [ 31,  38,  44]],\n",
       " \n",
       "        [[ 91,  99,  86],\n",
       "         [ 90,  98,  85],\n",
       "         [ 88,  96,  83],\n",
       "         ...,\n",
       "         [ 38,  43,  47],\n",
       "         [ 35,  40,  44],\n",
       "         [ 33,  38,  42]],\n",
       " \n",
       "        [[ 93, 101,  88],\n",
       "         [ 93, 101,  88],\n",
       "         [ 89,  97,  84],\n",
       "         ...,\n",
       "         [ 37,  42,  46],\n",
       "         [ 35,  40,  44],\n",
       "         [ 35,  40,  44]]], dtype=uint8)>,\n",
       " <tf.Tensor: shape=(), dtype=int64, numpy=64>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image,label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "vto2DheTemNd"
   },
   "outputs": [],
   "source": [
    "# Use efficient net , no need to rescale, only need to reshape and cast the dtype\n",
    "def preprocess_img(image,label,image_shape=48):\n",
    "  image = tf.image.resize(image,[image_shape,image_shape])\n",
    "  # image=image/255.\n",
    "  return tf.cast(image,dtype=tf.float32), label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1688438489880,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "r2P4w3Ym86mt",
    "outputId": "d3c4e4d5-2789-4043-966f-9bfeed9bbbab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image before preprocessing:\n",
      " [[[ 43  89 125]\n",
      "  [ 52  96 131]\n",
      "  [ 85 128 162]\n",
      "  ...\n",
      "  [251 254 223]\n",
      "  [250 253 222]\n",
      "  [250 253 222]]\n",
      "\n",
      " [[ 42  88 124]\n",
      "  [ 53  97 132]\n",
      "  [ 92 135 169]\n",
      "  ...\n",
      "  [251 254 223]\n",
      "  [250 253 222]\n",
      "  [250 253 222]]]...,\n",
      "Shape: (512, 512, 3)...,\n",
      "Dtype: <dtype: 'uint8'>\n",
      "\n",
      "\n",
      "Image after preprocessing:\n",
      " [[[ 79.77777  116.77777  144.25    ]\n",
      "  [ 87.916664 131.41666  161.91666 ]\n",
      "  [ 44.47223   43.611115  38.638893]\n",
      "  [150.72223  148.69446  135.94444 ]\n",
      "  [152.66667  155.83333  131.25    ]\n",
      "  [175.       174.       145.66667 ]\n",
      "  [224.27783  218.27783  184.88895 ]\n",
      "  [246.83333  240.83333  210.33333 ]\n",
      "  [246.8611   241.8611   209.8611  ]\n",
      "  [248.3889   242.3889   210.3889  ]\n",
      "  [247.58333  241.58333  207.58333 ]\n",
      "  [250.16667  244.16667  212.16667 ]\n",
      "  [248.83333  243.83333  213.83333 ]\n",
      "  [247.25     242.25     212.25    ]\n",
      "  [249.       244.       212.      ]\n",
      "  [247.83336  242.83336  210.83336 ]\n",
      "  [251.       242.       211.      ]\n",
      "  [248.       242.       210.      ]\n",
      "  [249.13889  241.13889  205.13889 ]\n",
      "  [247.       239.       203.      ]\n",
      "  [248.44444  240.44444  204.44444 ]\n",
      "  [252.69446  244.69446  208.69446 ]\n",
      "  [249.83333  241.83333  205.33333 ]\n",
      "  [247.33334  239.33334  202.33334 ]\n",
      "  [247.80557  241.80557  205.80557 ]\n",
      "  [248.58333  242.58333  206.58333 ]\n",
      "  [249.99998  243.99998  211.99998 ]\n",
      "  [251.02777  245.02777  209.02777 ]\n",
      "  [250.08333  245.08333  210.58333 ]\n",
      "  [248.19447  244.19447  209.19447 ]\n",
      "  [252.       248.       213.      ]\n",
      "  [252.       249.       216.      ]\n",
      "  [251.83331  248.83331  215.83331 ]\n",
      "  [253.69446  250.69446  219.69446 ]\n",
      "  [253.       251.5      217.      ]\n",
      "  [250.8611   250.8611   216.8611  ]\n",
      "  [250.16667  250.16667  216.16667 ]\n",
      "  [252.91667  251.91667  221.41667 ]\n",
      "  [252.16669  251.16669  220.16669 ]\n",
      "  [252.       253.       221.      ]\n",
      "  [252.5      253.5      222.      ]\n",
      "  [252.16669  253.16669  222.16669 ]\n",
      "  [253.16666  252.16666  221.16666 ]\n",
      "  [254.       253.       222.5     ]\n",
      "  [253.83331  253.83331  219.83331 ]\n",
      "  [253.       254.       222.      ]\n",
      "  [252.41667  254.41667  222.91667 ]\n",
      "  [252.       255.       224.      ]]\n",
      "\n",
      " [[117.49999  160.       186.41666 ]\n",
      "  [ 55.5       57.25      58.      ]\n",
      "  [140.66666  141.66666  129.91667 ]\n",
      "  [142.33333  147.25     121.16666 ]\n",
      "  [157.       158.75     134.5     ]\n",
      "  [180.58334  179.08334  149.25    ]\n",
      "  [242.41669  236.83336  200.16669 ]\n",
      "  [244.75     237.5      202.25    ]\n",
      "  [247.08334  238.08334  207.08334 ]\n",
      "  [245.       237.5      205.      ]\n",
      "  [248.5      241.       207.5     ]\n",
      "  [246.83333  239.83333  204.83333 ]\n",
      "  [247.       239.5      206.      ]\n",
      "  [247.5      239.25     206.5     ]\n",
      "  [249.5      241.       206.5     ]\n",
      "  [247.       239.5      205.33331 ]\n",
      "  [249.75     242.       206.75    ]\n",
      "  [248.       240.       203.5     ]\n",
      "  [249.91667  239.41667  205.91667 ]\n",
      "  [250.25     239.5      204.75    ]\n",
      "  [248.16667  237.16667  200.16667 ]\n",
      "  [246.33334  237.33334  200.83334 ]\n",
      "  [245.75     236.       203.25    ]\n",
      "  [245.16666  237.66666  209.16666 ]\n",
      "  [240.99997  236.49997  207.99997 ]\n",
      "  [211.       206.5      181.75    ]\n",
      "  [185.1665   180.6665   159.1665  ]\n",
      "  [171.99997  166.99997  148.99997 ]\n",
      "  [188.5      184.5      163.25    ]\n",
      "  [219.66669  216.25003  193.83331 ]\n",
      "  [250.00009  248.50009  221.00009 ]\n",
      "  [248.25     243.       213.75    ]\n",
      "  [248.00006  244.00006  210.50006 ]\n",
      "  [252.50006  250.75008  216.75008 ]\n",
      "  [252.5      248.       215.      ]\n",
      "  [252.       248.41672  215.75003 ]\n",
      "  [252.08333  250.25002  214.83334 ]\n",
      "  [250.25     249.25     218.5     ]\n",
      "  [252.41666  250.5      218.08328 ]\n",
      "  [253.       250.       218.      ]\n",
      "  [253.5      251.75     219.5     ]\n",
      "  [252.41666  251.5      220.66663 ]\n",
      "  [251.33334  251.33334  218.83334 ]\n",
      "  [252.25     251.75     220.25    ]\n",
      "  [252.       251.5      219.      ]\n",
      "  [252.5      253.       220.      ]\n",
      "  [252.       253.       220.      ]\n",
      "  [252.83331  253.16669  222.08334 ]]]...,\n",
      "Shape: (48, 48, 3)...,\n",
      "Dtype: <dtype: 'float32'>\n"
     ]
    }
   ],
   "source": [
    "# Preprocess single sample\n",
    "preprocessed_img1 = preprocess_img(image,label)[0]\n",
    "print(f\"Image before preprocessing:\\n {image[:2]}...,\\nShape: {image.shape}...,\\nDtype: {image.dtype}\\n\\n\")\n",
    "print(f\"Image after preprocessing:\\n {preprocessed_img1[:2]}...,\\nShape: {preprocessed_img1.shape}...,\\nDtype: {preprocessed_img1.dtype}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ur5UL3by-6ai"
   },
   "source": [
    "Batch input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "zA3NK5P7-6Jp"
   },
   "outputs": [],
   "source": [
    "# Map preprocessing function to training data (and paralellize)\n",
    "train_data = train_data.map(map_func=preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "# Shuffle train_data and turn it into batches and prefetch it (load it faster)\n",
    "train_data = train_data.shuffle(buffer_size=1000).batch(batch_size=16).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
    "\n",
    "# Map prepreprocessing function to test data\n",
    "test_data = test_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "# Turn test data into batches (don't need to shuffle)\n",
    "test_data = test_data.batch(16).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1688440056331,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "LYDQ_HQP-BHF",
    "outputId": "7f16fc52-9cd7-44a1-ab2f-5f486a0dac06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 48, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>,\n",
       " <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 48, 48, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "945jyBtTD3vz"
   },
   "source": [
    "### Creating model callbacks\n",
    "\n",
    "1. Create Tensorboard callback\n",
    "2. ModelCheckpoint callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Q0_sumEkCpUO"
   },
   "outputs": [],
   "source": [
    "from helper_functions import create_tensorboard_callback\n",
    "\n",
    "checkpoint_path = 'model_checkpoint/cp.ckpt'\n",
    "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
    "                                                      monitor='val_accuracy',\n",
    "                                                      save_best_only=True,\n",
    "                                                      save_weights_only=True,\n",
    "                                                      verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7MewO8vFCRg"
   },
   "source": [
    "### Set up mixed precission training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "0pO1U4Q8FAf7"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import mixed_precision\n",
    "mixed_precision.set_global_policy(\"mixed_float16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1688438489880,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "1NjahTx8GiNN",
    "outputId": "d2dae1eb-de13-43f2-e502-a3461fbcbfce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Policy \"mixed_float16\">"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_precision.global_policy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4064,
     "status": "ok",
     "timestamp": 1688438493935,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "GKWfJxCVFxHo",
    "outputId": "76023150-9de5-4ebb-d023-62e3e1b36fda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
      "16705208/16705208 [==============================] - 2s 0us/step\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 48, 48, 3)]       0         \n",
      "                                                                 \n",
      " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
      " )                           )                                   \n",
      "                                                                 \n",
      " global_average_pooling2d (  (None, 1280)              0         \n",
      " GlobalAveragePooling2D)                                         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 101)               129381    \n",
      "                                                                 \n",
      " activation (Activation)     (None, 101)               0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4178952 (15.94 MB)\n",
      "Trainable params: 129381 (505.39 KB)\n",
      "Non-trainable params: 4049571 (15.45 MB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build feature extraction model\n",
    "# mixed_precision.set_global_policy(\"float32\")  # Set it to mixed_float16 in tf.__version__==2.4.1\n",
    "input_shape=(48,48,3)\n",
    "base_model=tf.keras.applications.EfficientNetB0(include_top=False)\n",
    "base_model.trainable=False\n",
    "\n",
    "inputs = tf.keras.layers.Input(shape=input_shape)\n",
    "x = base_model(inputs,training=False)\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(len(class_names))(x)\n",
    "\n",
    "outputs = tf.keras.layers.Activation(tf.keras.activations.softmax,dtype=tf.float32)(x)\n",
    "\n",
    "model=tf.keras.Model(inputs,outputs)\n",
    "\n",
    "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              optimizer=tf.keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZLMaNR_9IJvW"
   },
   "source": [
    "# Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 279621,
     "status": "ok",
     "timestamp": 1688438773533,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "wmabuL9dBoVu",
    "outputId": "d3db05b6-378d-4229-9f69-87e571a25913"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "   9/4735 [..............................] - ETA: 1:15 - loss: 4.8095 - accuracy: 0.0069     "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1703334763.379753    1588 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 531/4735 [==>...........................] - ETA: 1:05 - loss: 4.3687 - accuracy: 0.0679"
     ]
    }
   ],
   "source": [
    "history_101_feature_extract = model.fit(train_data,\n",
    "                                        epochs=5,\n",
    "                                        steps_per_epoch=len(train_data),\n",
    "                                        validation_data=test_data,\n",
    "                                        validation_steps=int(0.15*len(test_data)),\n",
    "                                        callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14193,
     "status": "ok",
     "timestamp": 1688438787701,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "Kf63KfRCK1Al",
    "outputId": "bbf50072-e0f7-47ec-b0af-09fc81fc4f8a"
   },
   "outputs": [],
   "source": [
    "#Evaluate\n",
    "result = model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1688438787702,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "9HcuJ59ZNsal",
    "outputId": "c253548f-1d55-4ae5-cd3c-b22b8b37f038"
   },
   "outputs": [],
   "source": [
    "for i,layers in enumerate(model.layers):\n",
    "  print(i,layers.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1688438787702,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "aH9YHa_qZTrc",
    "outputId": "f2b7abd9-2634-4d9f-9ea2-b5a8a8b6fa65"
   },
   "outputs": [],
   "source": [
    "for i,layer in enumerate(model.layers[1].layers):\n",
    "  print(i,layer.name,layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1688438787702,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "_QvKC_FYZcZw",
    "outputId": "7c401fe0-f3e2-4732-b910-8f2ab945c23b"
   },
   "outputs": [],
   "source": [
    "model.layers[1].trainable=True\n",
    "for layer in model.layers[1].layers[:-15]:\n",
    "  layer.trainable=False\n",
    "\n",
    "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 359642,
     "status": "ok",
     "timestamp": 1688439147323,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "o1HyQcb0ZtzS",
    "outputId": "00220a55-c424-4de9-aab0-c81a91888404"
   },
   "outputs": [],
   "source": [
    "history_101_fine_tune = model.fit(train_data,\n",
    "                                  epochs=10,\n",
    "                                  initial_epoch=history_101_feature_extract.epoch[-1],\n",
    "                                  validation_data=test_data,\n",
    "                                  validation_steps=int(0.15*len(test_data)),\n",
    "                                  steps_per_epoch=len(train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13964,
     "status": "ok",
     "timestamp": 1688439161270,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "z-dHFE1qae9q",
    "outputId": "e2adf798-0596-4e15-e65a-9bd5a3540c35"
   },
   "outputs": [],
   "source": [
    "result_fine_tune = model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1bGLiqJDei6-"
   },
   "source": [
    "# Challenge - Milestone Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2787,
     "status": "ok",
     "timestamp": 1688439164039,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "0o5gcPNhelNx",
    "outputId": "369ad63c-0b5a-4597-f83c-55c53ea738c1"
   },
   "outputs": [],
   "source": [
    "## Load in a gs model\n",
    "# Download the saved model from Google Storage\n",
    "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip\n",
    "!unzip 07_efficientnetb0_feature_extract_model_mixed_precision.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11400,
     "status": "ok",
     "timestamp": 1688439175435,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "hCfDoTYJfepz",
    "outputId": "aef9a0e2-5d8d-4077-b4cf-0c3a449efd18"
   },
   "outputs": [],
   "source": [
    "model_challenge = tf.keras.models.load_model(\"07_efficientnetb0_feature_extract_model_mixed_precision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1688439175435,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "LuZbKfxxf_4H",
    "outputId": "a13a46e3-2457-4082-f08b-0c27371c2129"
   },
   "outputs": [],
   "source": [
    "model_challenge.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1688439175435,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "L9NOgxfAgI44",
    "outputId": "c56292ab-0c40-40d2-b849-b083eb979e0a"
   },
   "outputs": [],
   "source": [
    "for i,layer in enumerate(model_challenge.layers):\n",
    "  print(i,layer.name,layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14423,
     "status": "ok",
     "timestamp": 1688439189843,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "N6BZy7_SgP40",
    "outputId": "f1f0549a-f9bb-4fb0-d18b-5813e4f5d0e9"
   },
   "outputs": [],
   "source": [
    "result1_challenge = model_challenge.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2MU51FZ4hAN8"
   },
   "outputs": [],
   "source": [
    "#Set up EarlyStopping callback\n",
    "earlyStop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                             patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X75UrSuqWc8Y"
   },
   "outputs": [],
   "source": [
    "model_checkpoint_challenge=tf.keras.callbacks.ModelCheckpoint('challenge/',\n",
    "                                                    save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1688439189844,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "6NBHVWN6ga4N",
    "outputId": "04ee6cda-6e83-47c2-ce50-baf673ce3229"
   },
   "outputs": [],
   "source": [
    "# Setting all layers to rainable\n",
    "for i,layer in enumerate(model_challenge.layers):\n",
    "  layer.trainable=True\n",
    "for i,layer in enumerate(model_challenge.layers):\n",
    "  print(i,layer.name,layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 760845,
     "status": "ok",
     "timestamp": 1688439950672,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "DqJHmXAthRss",
    "outputId": "41db7f89-6aea-4fdf-bb2c-b2e18c583fc4"
   },
   "outputs": [],
   "source": [
    "model_challenge.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                        metrics=['accuracy'])\n",
    "history_101_feature_extract_challenge = model_challenge.fit(train_data,\n",
    "                                                        steps_per_epoch=len(train_data),\n",
    "                                                        epochs=5,\n",
    "                                                        initial_epoch=history_101_feature_extract.epoch[-1],\n",
    "                                                        validation_data=test_data,\n",
    "                                                        validation_steps=int(0.15*len(test_data)),\n",
    "                                                        callbacks=[model_checkpoint_challenge,earlyStop],\n",
    "                                                        verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12500,
     "status": "ok",
     "timestamp": 1688439963154,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "4uK5Px43h2zF",
    "outputId": "51bcd84e-5456-4e3e-f2e3-d3bea6fb4161"
   },
   "outputs": [],
   "source": [
    "result1_finetune_challenge = model_challenge.evaluate(test_data)\n",
    "result1_finetune_challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "El5GReiWt9Qk"
   },
   "source": [
    "# EXERCISE\n",
    "1. Use the same evaluation techniques on the large-scale Food Vision model as you did in the previous notebook (Transfer Learning Part 3: Scaling up). More specifically, it would be good to see:\n",
    "  * A confusion matrix between all of the model's predictions and true labels.\n",
    "  * A graph showing the f1-scores of each class.\n",
    "  * A visualization of the model making predictions on various images and comparing the predictions to the ground truth.\n",
    "  \n",
    "  For example, plot a sample image from the test dataset and have the title of the plot show the prediction, the prediction probability and the ground truth label.\n",
    "  \n",
    "  **Note**: To compare predicted labels to test labels, it might be a good idea when loading the test data to set shuffle=False (so the ordering of test data is preserved alongside the order of predicted labels).\n",
    "2. Take 3 of your own photos of food and use the Food Vision model to make predictions on them. How does it go? Share your images/predictions with the other students.\n",
    "3. Retrain the model (feature extraction and fine-tuning) we trained in this notebook, except this time use EfficientNetB4 as the base model instead of EfficientNetB0. Do you notice an improvement in performance? Does it take longer to train? Are there any tradeoffs to consider?\n",
    "4. Name one important benefit of mixed precision training, how does this benefit take place?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12346,
     "status": "ok",
     "timestamp": 1688441662865,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "yQoyvFAMoRkB",
    "outputId": "d31b9ff5-92c5-4888-f8fa-f287883ce36c"
   },
   "outputs": [],
   "source": [
    "#1\n",
    "pred_probs=model_challenge.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1688441663607,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "RJXCKg7V39RI",
    "outputId": "b4574ced-b9d4-4f2f-a239-3b8ae78b3613"
   },
   "outputs": [],
   "source": [
    "pred_probs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1688441663607,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "kBOhctwP4A4p",
    "outputId": "556b182f-9f0c-44e6-eeb6-1880f17ce2e5"
   },
   "outputs": [],
   "source": [
    "len(pred_probs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "csYH_304wAXD"
   },
   "outputs": [],
   "source": [
    "class_names = ds_info.features[\"label\"].names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1688441663608,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "V-9SyzhRKwje",
    "outputId": "6ee7e15e-e790-4bac-a17a-9e3777b3d62a"
   },
   "outputs": [],
   "source": [
    "len(pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1688441663608,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "3Y-yniCjus-Z",
    "outputId": "4badcaa3-cf1a-4c7c-8b43-3f7fd2919942"
   },
   "outputs": [],
   "source": [
    "pred_probs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1688441663608,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "OMy-QYJrxGK8",
    "outputId": "2c0adc96-7212-405d-f204-769f2c0a8217"
   },
   "outputs": [],
   "source": [
    "len(train_data),len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1688441663608,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "Cqg1l2PFJ7eo",
    "outputId": "b54117d9-8f8b-4672-ded3-0fa9e122dbfc"
   },
   "outputs": [],
   "source": [
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1688441663608,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "CLtv-q4TMwZn",
    "outputId": "dfd91f08-8468-4a85-d022-8e791c4a8223"
   },
   "outputs": [],
   "source": [
    "pred_probs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Alu-82X7MsIc"
   },
   "outputs": [],
   "source": [
    "X_test = [label for img,label in test_data.unbatch()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1688441672383,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "k1fCukHI4Pud",
    "outputId": "59ff6893-97fa-4bd9-9949-6716803c271c"
   },
   "outputs": [],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1688441672383,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "ioFNYVSU6nSt",
    "outputId": "8b517bb2-9562-4b8d-9363-9ef808170724"
   },
   "outputs": [],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1688441672383,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "-BMxXk6i7LQJ",
    "outputId": "37894273-b7d2-44c2-9e91-80525e84173b"
   },
   "outputs": [],
   "source": [
    "pred_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1688441672383,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "5uvaaX9z7IGU",
    "outputId": "9b270ff4-37d1-4d27-8c84-71f2fb69212c"
   },
   "outputs": [],
   "source": [
    "pred_probs.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 612,
     "status": "ok",
     "timestamp": 1688441672975,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "9u_TxoRfJd20",
    "outputId": "0cc82449-0126-4f59-9766-6f0c24ea4d4e"
   },
   "outputs": [],
   "source": [
    "# correct_pred=0\n",
    "# index=0\n",
    "# for img,label in test_data:\n",
    "#   #if class_names[label.numpy()]==class_names[pred_probs[i].argmax()]:\n",
    "#   #  correct_pred+=1\n",
    "#   # x=label.numpy()\n",
    "#   for i in label.numpy():\n",
    "#     if i==pred_probs[index].argmax():\n",
    "#       correct_pred+=1\n",
    "#       print(i)\n",
    "#   # print(len(x))\n",
    "#   # break\n",
    "#   index+=1\n",
    "# print(correct_pred)\n",
    "from sklearn.metrics import classification_report\n",
    "X_pred=pred_probs.argmax(axis=1)\n",
    "# X_pred=X_pred.numpy()\n",
    "output = classification_report(X_test,X_pred)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4968,
     "status": "ok",
     "timestamp": 1688445645013,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "VhM7gdzr-CTV",
    "outputId": "ebefcf6e-ff3c-40a7-a698-355d091a7b27"
   },
   "outputs": [],
   "source": [
    "#3\n",
    "input_shape=(48,48,3)\n",
    "base_model=tf.keras.applications.EfficientNetB4(include_top=False)\n",
    "base_model.trainable=False\n",
    "\n",
    "inputs = tf.keras.layers.Input(shape=input_shape)\n",
    "x = base_model(inputs,training=False)\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(len(class_names))(x)\n",
    "\n",
    "outputs = tf.keras.layers.Activation(tf.keras.activations.softmax,dtype=tf.float32)(x)\n",
    "\n",
    "model2=tf.keras.Model(inputs,outputs)\n",
    "\n",
    "model2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              optimizer=tf.keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 510113,
     "status": "ok",
     "timestamp": 1688446155121,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "_m5WR7ij-N5Y",
    "outputId": "a9614b80-67ab-4cea-81b7-4c5776afcb95"
   },
   "outputs": [],
   "source": [
    "history_101_feature_extract2 = model2.fit(train_data,\n",
    "                                        epochs=5,\n",
    "                                        steps_per_epoch=len(train_data),\n",
    "                                        validation_data=test_data,\n",
    "                                        validation_steps=int(0.15*len(test_data)),\n",
    "                                        callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1688446155121,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "3dDw5wTJBgjw",
    "outputId": "4aff10f7-0386-4a91-b063-2b40a5d27ad6"
   },
   "outputs": [],
   "source": [
    "# Setting all layers to rainable\n",
    "for i,layer in enumerate(model2.layers):\n",
    "  layer.trainable=True\n",
    "for i,layer in enumerate(model2.layers):\n",
    "  print(i,layer.name,layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3147407,
     "status": "ok",
     "timestamp": 1688449302524,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "sFy1u_ooBiut",
    "outputId": "af6131e0-091b-4c75-a32a-e4f290e6f592"
   },
   "outputs": [],
   "source": [
    "model2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                        metrics=['accuracy'])\n",
    "history_101_b4 = model2.fit(train_data,\n",
    "                            steps_per_epoch=len(train_data),\n",
    "                            epochs=8,\n",
    "                            initial_epoch=history_101_feature_extract2.epoch[-1],\n",
    "                            validation_data=test_data,\n",
    "                            validation_steps=int(0.15*len(test_data)),\n",
    "                            callbacks=[model_checkpoint_challenge,earlyStop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 28344,
     "status": "ok",
     "timestamp": 1688449360436,
     "user": {
      "displayName": "ML TF",
      "userId": "03314444562734337696"
     },
     "user_tz": -330
    },
    "id": "qYKwj8Oea-Kv",
    "outputId": "714c748e-18ff-4a93-d43a-b9919da70c00"
   },
   "outputs": [],
   "source": [
    "result = model2.evaluate(test_data)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9SU-KgdvbLi4"
   },
   "source": [
    "Performance with efficientNetB4 has improved a lot with lesser training time(i.e. lesser epochs) but the time taken for the model to learn the patterns is very long."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tBK-Yz-ybZ_H"
   },
   "source": [
    "Benefit of mixed precision -> Train faster with lesser number of bits being used for computation"
   ]
  }
 ],
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