Preprocessing Data With TF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.range(10)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from_tensor_slices() creates a tf.data.Dataset whose elements are slices of X along the first dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.int32>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.repeat(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.batch(7, drop_remainder=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int32)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.map(lambda x: x*2)\n",
    "for item in dataset:\n",
    "    print(item) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, each element of the dataset is a batch of 7 integers. Let's transform each item into a single integer tensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-10-4078b28dbb80>:1: unbatch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.unbatch()`.\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n",
      "tf.Tensor(16, shape=(), dtype=int32)\n",
      "tf.Tensor(18, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n",
      "tf.Tensor(16, shape=(), dtype=int32)\n",
      "tf.Tensor(18, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.apply(tf.data.experimental.unbatch())\n",
    "for item in dataset:\n",
    "    print(item) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.filter(lambda x: x < 10)\n",
    "for item in dataset:\n",
    "    print(item) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([2 3], shape=(2,), dtype=int64)\n",
      "tf.Tensor([6 7], shape=(2,), dtype=int64)\n",
      "tf.Tensor([0 1], shape=(2,), dtype=int64)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int64)\n",
      "tf.Tensor([4 5], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10).batch(2).shuffle(buffer_size=5)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([6 7], shape=(2,), dtype=int64)\n",
      "tf.Tensor([4 5], shape=(2,), dtype=int64)\n",
      "tf.Tensor([2 3], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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