08_Mini_Batch.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "import math\n",
    "import numpy as np\n",
    "import helper\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
    "tf.disable_v2_behavior()\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67f53436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Extracting mnist/train-images-idx3-ubyte.gz\n",
      "Extracting mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "n_input = 784  # MNIST data input (img shape: 28*28)\n",
    "n_classes = 10  # MNIST total classes (0-9 digits)\n",
    "\n",
    "# Import MNIST data\n",
    "mnist = helper.read_data_sets('mnist', one_hot=True)\n",
    "\n",
    "# The features are already scaled and the data is shuffled\n",
    "train_features = mnist.train.images\n",
    "test_features = mnist.test.images\n",
    "\n",
    "train_labels = mnist.train.labels.astype(np.float32)\n",
    "test_labels = mnist.test.labels.astype(np.float32)\n",
    "\n",
    "# Weights & bias\n",
    "weights = tf.Variable(tf.random_normal([n_input, n_classes]))\n",
    "bias = tf.Variable(tf.random_normal([n_classes]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "656a3d63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets shapes:\n",
      "train features: (55000, 784)\n",
      "test features: (10000, 784)\n",
      "train labels: (55000, 10)\n",
      "test labels: (55000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"Datasets shapes:\")\n",
    "print(f\"train features: {train_features.shape}\")\n",
    "print(f\"test features: {test_features.shape}\")\n",
    "print(f\"train labels: {train_labels.shape}\")\n",
    "print(f\"test labels: {train_labels.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb69729",
   "metadata": {},
   "source": [
    "## Quiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab90cf91",
   "metadata": {},
   "outputs": [],
   "source": [
    "def batches(batch_size, features, labels):\n",
    "    \"\"\"\n",
    "    Create batches of features and labels\n",
    "    :param batch_size: The batch size\n",
    "    :param features: List of features\n",
    "    :param labels: List of labels\n",
    "    :return: Batches of (Features, Labels)\n",
    "    \"\"\"\n",
    "    assert len(features) == len(labels)\n",
    "    # TODO: Implement batching\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd49ad3",
   "metadata": {},
   "source": [
    "## Run this cell to test your `batches` implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2ac8cd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[['F11', 'F12', 'F13', 'F14'],\n",
      "   ['F21', 'F22', 'F23', 'F24'],\n",
      "   ['F31', 'F32', 'F33', 'F34']],\n",
      "  [['L11', 'L12'], ['L21', 'L22'], ['L31', 'L32']]],\n",
      " [[['F41', 'F42', 'F43', 'F44']], [['L41', 'L42']]]]\n"
     ]
    }
   ],
   "source": [
    "# 4 Samples of features\n",
    "example_features = [\n",
    "    ['F11','F12','F13','F14'],\n",
    "    ['F21','F22','F23','F24'],\n",
    "    ['F31','F32','F33','F34'],\n",
    "    ['F41','F42','F43','F44']]\n",
    "# 4 Samples of labels\n",
    "example_labels = [\n",
    "    ['L11','L12'],\n",
    "    ['L21','L22'],\n",
    "    ['L31','L32'],\n",
    "    ['L41','L42']]\n",
    "\n",
    "# PPrint prints data structures like 2d arrays, so they are easier to read\n",
    "pprint(batches(3, example_features, example_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae152d63",
   "metadata": {},
   "source": [
    "## Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5573b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def batches(batch_size, features, labels):\n",
    "    \"\"\"\n",
    "    Create batches of features and labels\n",
    "    :param batch_size: The batch size\n",
    "    :param features: List of features\n",
    "    :param labels: List of labels\n",
    "    :return: Batches of (Features, Labels)\n",
    "    \"\"\"\n",
    "    assert len(features) == len(labels)\n",
    "    # TODO: Implement batching\n",
    "    output_batches = []\n",
    "    \n",
    "    sample_size = len(features)\n",
    "    for start_i in range(0, sample_size, batch_size):\n",
    "        end_i = start_i + batch_size\n",
    "        batch = [features[start_i:end_i], labels[start_i:end_i]]\n",
    "        output_batches.append(batch)\n",
    "        \n",
    "    return output_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac20fcad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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