Week 3 - Class 3 LAB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bd64156",
   "metadata": {},
   "source": [
    "# Week 3 - Class 3\n",
    "\n",
    "This notebook have the most important exercises data wrangling operations for the data used in this course\n",
    "\n",
    "- Text\n",
    "- Numbers\n",
    "- Images Segmentation\n",
    "- Images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5512175",
   "metadata": {},
   "source": [
    "# Text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da184482",
   "metadata": {},
   "source": [
    "Your dataset information:\n",
    "\n",
    "https://keras.io/api/datasets/imdb/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "04a32efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "17464789/17464789 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import imdb\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "    num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "074c5036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of samples in training and testing dataset\n",
    "\n",
    "<your-answer>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0b60a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the size of training and testing dataset for one sample\n",
    "\n",
    "<your-answer>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8e7ed85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the maximum length in the data\n",
    "\n",
    "<your-answer>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc92d03a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
      "1641221/1641221 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Retrieves a dict mapping words to their index in the IMDB dataset.\n",
    "\n",
    "word_index = imdb.get_word_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "988ad89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 1)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the word with index 1\n",
    "\n",
    "<your-answer>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ebb541",
   "metadata": {},
   "source": [
    "Note that the indices are offset by 3 because 0, 1, and 2 are reserved indices for “padding,” “start of sequence,” and “unknown.”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c415bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a reverse dictionary (first the index and then the str value)\n",
    "reverse_word_index = <your-answer>\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9671598",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the string from the train sample (first one). Use the method .get(k, pass_value) to avoid errors\n",
    "decoded_review = <your-answer>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7675e544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_review\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3a5b3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorize the data: create the dataset to be ingested by the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0452a33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "744763e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        for j in sequence:\n",
    "            results[i, j] = 1.\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "50e90040",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c858a331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d66543ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.asarray(train_labels).astype(\"float32\")\n",
    "y_test = np.asarray(test_labels).astype(\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7072f1b4",
   "metadata": {},
   "source": [
    "# Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dadcad",
   "metadata": {},
   "source": [
    "Here is the info on the data:\n",
    "https://keras.io/api/datasets/boston_housing/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4fdf5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import boston_housing\n",
    "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3983c186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "688c024a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0b6c3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
       "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
       "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
       "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
       "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
       "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
       "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
       "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
       "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
       "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
       "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
       "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
       "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
       "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
       "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
       "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
       "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
       "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
       "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
       "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
       "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
       "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
       "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
       "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
       "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
       "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
       "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
       "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
       "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
       "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
       "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
       "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
       "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
       "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
       "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
       "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
       "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4556c164",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = train_data.mean(axis=0)\n",
    "train_data -= mean\n",
    "std = train_data.std(axis=0)\n",
    "train_data /= std\n",
    "test_data -= mean\n",
    "test_data /= std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c534c0e9",
   "metadata": {},
   "source": [
    "# Images Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4c6e85",
   "metadata": {},
   "source": [
    "Get the images from these two links and extract them:\n",
    "http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
    "http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e639b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
    "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
    "!tar -xf images.tar.gz\n",
    "!tar -xf annotations.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a448c22",
   "metadata": {},
   "source": [
    "Do a list of images paths and of target paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f421752b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "input_dir = \"images/\"\n",
    "target_dir = \"annotations/trimaps/\"\n",
    "\n",
    "input_img_paths = sorted(\n",
    "    [os.path.join(input_dir, fname)\n",
    "     for fname in os.listdir(input_dir)\n",
    "     if fname.endswith(\".jpg\")])\n",
    "target_paths = sorted(\n",
    "    [os.path.join(target_dir, fname)\n",
    "     for fname in os.listdir(target_dir)\n",
    "     if fname.endswith(\".png\") and not fname.startswith(\".\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17695ba",
   "metadata": {},
   "source": [
    "Plot the image 10th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c42bb269",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.utils import load_img, img_to_array\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(load_img(input_img_paths[9]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806d9a75",
   "metadata": {},
   "source": [
    "Plot its target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9d2d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_target(target_array):\n",
    "    normalized_array = (target_array.astype(\"uint8\") - 1) * 127\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(normalized_array[:, :, 0])\n",
    "\n",
    "img = img_to_array(load_img(target_paths[9], color_mode=\"grayscale\"))\n",
    "display_target(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc92f073",
   "metadata": {},
   "source": [
    "How to load an image to a numeric tensor!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5a56eb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "img_size = (200, 200)\n",
    "num_imgs = len(input_img_paths)\n",
    "\n",
    "random.Random(1337).shuffle(input_img_paths)\n",
    "random.Random(1337).shuffle(target_paths)\n",
    "\n",
    "def path_to_input_image(path):\n",
    "    return img_to_array(load_img(path, target_size=img_size))\n",
    "\n",
    "def path_to_target(path):\n",
    "    img = img_to_array(\n",
    "        load_img(path, target_size=img_size, color_mode=\"grayscale\"))\n",
    "    img = img.astype(\"uint8\") - 1\n",
    "    return img\n",
    "\n",
    "input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\"float32\")\n",
    "targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\"uint8\")\n",
    "for i in range(num_imgs):\n",
    "    input_imgs[i] = path_to_input_image(input_img_paths[i])\n",
    "    targets[i] = path_to_target(target_paths[i])\n",
    "\n",
    "num_val_samples = 1000\n",
    "train_input_imgs = input_imgs[:-num_val_samples]\n",
    "train_targets = targets[:-num_val_samples]\n",
    "val_input_imgs = input_imgs[-num_val_samples:]\n",
    "val_targets = targets[-num_val_samples:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7da3c7",
   "metadata": {},
   "source": [
    "# Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dd6ec56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "mkdir: cannot create directory ‘/home/ubuntu/.kaggle’: File exists\n",
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "cp: cannot stat 'kaggle.json': No such file or directory\n",
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "Downloading dogs-vs-cats.zip to /home/ubuntu/deeplearning4socialsciences\n",
      " 99%|████████████████████████████████████████▊| 808M/812M [00:03<00:00, 239MB/s]\n",
      "100%|█████████████████████████████████████████| 812M/812M [00:03<00:00, 268MB/s]\n",
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
      "/bin/bash: /home/ubuntu/miniconda3/envs/tf-py38/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n"
     ]
    }
   ],
   "source": [
    "!mkdir ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "!chmod 600 ~/.kaggle/kaggle.json\n",
    "!kaggle competitions download -c dogs-vs-cats\n",
    "!unzip -qq dogs-vs-cats.zip\n",
    "!unzip -qq train.zip\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf18c27",
   "metadata": {},
   "source": [
    "Create directories and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02ca3fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 files belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-15 10:17:08.864339: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-02-15 10:17:09.469260: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\n",
      "2023-02-15 10:17:09.486625: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 203.62M (213516288 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 files belonging to 2 classes.\n",
      "Found 2000 files belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "import os, shutil, pathlib\n",
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "\n",
    "original_dir = pathlib.Path(\"train\")\n",
    "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")\n",
    "\n",
    "def make_subset(subset_name, start_index, end_index):\n",
    "    for category in (\"cat\", \"dog\"):\n",
    "        dir = new_base_dir / subset_name / category\n",
    "        os.makedirs(dir)\n",
    "        fnames = [f\"{category}.{i}.jpg\" for i in range(start_index, end_index)]\n",
    "        for fname in fnames:\n",
    "            shutil.copyfile(src=original_dir / fname,\n",
    "                            dst=dir / fname)\n",
    "\n",
    "make_subset(\"train\", start_index=0, end_index=1000)\n",
    "make_subset(\"validation\", start_index=1000, end_index=1500)\n",
    "make_subset(\"test\", start_index=1500, end_index=2500)\n",
    "\n",
    "train_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"train\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)\n",
    "validation_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"validation\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)\n",
    "test_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"test\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0de2f9fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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