ML-Kurs-SS2023/notebooks/03_ml_basics_display_HorseOrHuman.ipynb

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  {
   "cell_type": "markdown",
   "id": "2eaba66b",
   "metadata": {},
   "source": [
    "Read and Display Horse or Human machine learning dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1e48ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow.keras import regularizers\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feda024e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the horse or human dataset\n",
    "#(300, 300, 3) unint8\n",
    "dataset, label = tfds.load('horses_or_humans', with_info=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35991dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the horse/human class\n",
    "horse_ds = dataset['train'].filter(lambda x: x['label'] == 0)\n",
    "human_ds = dataset['train'].filter(lambda x: x['label'] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab03aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take a few examples < 16\n",
    "n_examples = 5\n",
    "horse_examples = horse_ds.take(n_examples)\n",
    "human_examples = human_ds.take(n_examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33f1acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the examples\n",
    "fig, axes = plt.subplots(1, n_examples, figsize=(15, 15))\n",
    "for i, example in enumerate(human_examples):\n",
    "    image = example['image']\n",
    "    axes[i].imshow(image)\n",
    "    axes[i].set_title(f\"humans {i+1}\")\n",
    "plt.show()\n",
    "\n",
    "fig, axes = plt.subplots(1, n_examples, figsize=(15, 15))\n",
    "for i, example in enumerate(horse_examples):\n",
    "    image = example['image']\n",
    "    axes[i].imshow(image)\n",
    "    axes[i].set_title(f\"horses {i+1}\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25f3eeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and validation sets\n",
    "# as_supervised: Specifies whether to return the dataset as a tuple\n",
    "# of (input, label) pairs.\n",
    "train_dataset, valid_dataset = tfds.load('horses_or_humans', split=['train','test'], as_supervised=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29dc0e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the number of elements in the training and validation dataset\n",
    "train_size = tf.data.experimental.cardinality(train_dataset).numpy()\n",
    "valid_size = tf.data.experimental.cardinality(valid_dataset).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8aaf91",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 300\n",
    "NUM_CLASSES = 2\n",
    "\n",
    "def preprocess(image, label):\n",
    "    image = tf.cast(image, tf.float32)\n",
    "#    # Resize the images to a fixed size\n",
    "    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
    "#    # Rescale the pixel values to be between 0 and 1\n",
    "    image = image / 255.0\n",
    "    label = tf.one_hot(label, NUM_CLASSES)\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d59661c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the preprocessing function to the datasets\n",
    "train_dataset = train_dataset.map(preprocess)\n",
    "valid_dataset = valid_dataset.map(preprocess)\n",
    "\n",
    "# Batch and shuffle the datasets\n",
    "train_dataset = train_dataset.shuffle(2000).batch(80)\n",
    "valid_dataset = valid_dataset.batch(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9399bc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the number of elements in the trainingand validation dataset\n",
    "train_size = tf.data.experimental.cardinality(train_dataset).numpy()\n",
    "valid_size = tf.data.experimental.cardinality(valid_dataset).numpy()\n",
    "print(\"Training dataset size:\", train_size)\n",
    "print(\"Validation dataset size:\", valid_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13af7d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store images and labels of the validation data for predictions\n",
    "for images, labels in valid_dataset:\n",
    "    x_val = images\n",
    "    y_val = labels\n",
    "    \n",
    "print(x_val.shape, y_val.shape)"
   ]
  }
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