time windows.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3695a7c8-4761-4920-8020-80f4918982a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fdc30c2-6451-44de-8415-d6ebee2504a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "for val in dataset:\n",
    "    print(val.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22609a17-06f0-472a-b861-1e3617763236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 \n",
      "1 2 3 4 5 \n",
      "2 3 4 5 6 \n",
      "3 4 5 6 7 \n",
      "4 5 6 7 8 \n",
      "5 6 7 8 9 \n",
      "6 7 8 9 \n",
      "7 8 9 \n",
      "8 9 \n",
      "9 \n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1)\n",
    "for window_dataset in dataset:\n",
    "    for val in window_dataset:\n",
    "        print(val.numpy(), end = \" \")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86817b88-6838-4cad-b9cc-2173c5ff1776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 \n",
      "1 2 3 4 5 \n",
      "2 3 4 5 6 \n",
      "3 4 5 6 7 \n",
      "4 5 6 7 8 \n",
      "5 6 7 8 9 \n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1, drop_remainder = True)\n",
    "for window_dataset in dataset:\n",
    "    for val in window_dataset:\n",
    "        print(val.numpy(), end = \" \")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "144af245-b0dc-44c2-92e6-da46ebd3cc36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n",
      "[1 2 3 4 5]\n",
      "[2 3 4 5 6]\n",
      "[3 4 5 6 7]\n",
      "[4 5 6 7 8]\n",
      "[5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1, drop_remainder = True)\n",
    "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
    "for window in dataset:\n",
    "    print(window.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4dde3e89-ddb7-4e54-a9a1-027dc9d4ff66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3] [4]\n",
      "[1 2 3 4] [5]\n",
      "[2 3 4 5] [6]\n",
      "[3 4 5 6] [7]\n",
      "[4 5 6 7] [8]\n",
      "[5 6 7 8] [9]\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1, drop_remainder = True)\n",
    "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
    "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
    "for x, y in dataset:\n",
    "    print(x.numpy(), y.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7aa7c0a-047d-4da6-9631-37dbe44e6710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 6 7 8] [9]\n",
      "[1 2 3 4] [5]\n",
      "[4 5 6 7] [8]\n",
      "[2 3 4 5] [6]\n",
      "[0 1 2 3] [4]\n",
      "[3 4 5 6] [7]\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1, drop_remainder = True)\n",
    "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
    "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
    "dataset = dataset.shuffle(buffer_size = 10)\n",
    "for x, y in dataset:\n",
    "    print(x.numpy(), y.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b68404bb-b866-48e4-a1ab-c38bf2d8bd73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  [[4 5 6 7]\n",
      " [3 4 5 6]] y =  [[8]\n",
      " [7]]\n",
      "x =  [[5 6 7 8]\n",
      " [2 3 4 5]] y =  [[9]\n",
      " [6]]\n",
      "x =  [[0 1 2 3]\n",
      " [1 2 3 4]] y =  [[4]\n",
      " [5]]\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10)\n",
    "dataset = dataset.window(5, shift = 1, drop_remainder = True)\n",
    "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
    "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
    "dataset = dataset.shuffle(buffer_size = 10)\n",
    "dataset = dataset.batch(2).prefetch(1)\n",
    "for x, y in dataset:\n",
    "    print(\"x = \", x.numpy(),\n",
    "          \"y = \", y.numpy())"
   ]
  }
 ],
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