Chapter12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41b74139",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9ac9110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]], dtype=float32)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.constant([[1.,2.,3.], [4.,5.,6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "74974496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=42>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.constant(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c81238a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = tf.constant([[1.,2.,3.], [4.,5.,6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f65350c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([2, 3]), tf.float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape, t.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b403c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[2., 3.],\n",
       "       [5., 6.]], dtype=float32)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6158ac7b",
   "metadata": {},
   "source": [
    "### ...은 ellipsis라고 하는데 생략된 모든 차원원소를 모두 포함한다고 생각하면 된다\n",
    "### tf.newaxis 차원을 추가해준다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7bdd0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 1), dtype=float32, numpy=\n",
       "array([[2.],\n",
       "       [5.]], dtype=float32)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[..., 1, tf.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbcaddf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[11., 12., 13.],\n",
       "       [14., 15., 16.]], dtype=float32)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4e00b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.]], dtype=float32)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.square(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433bc600",
   "metadata": {},
   "source": [
    "### @는 행렬 곱 연산이다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aadb1fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[14., 32.],\n",
       "       [32., 77.]], dtype=float32)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t @ tf.transpose(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6748ec55",
   "metadata": {},
   "source": [
    "# Tensorflow and numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e65bb2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([2., 4., 5.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d392705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=float64, numpy=array([2., 4., 5.])>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.constant(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "92ca4630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eb2a3429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=float64, numpy=array([ 4., 16., 25.])>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.square(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7a13776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3892d39c",
   "metadata": {},
   "source": [
    "### 텐서플로는 32bit numpy는 64bit 정밀도를 사용하므로 tf의 속도가 더 빠르다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7770d061",
   "metadata": {},
   "source": [
    "# 타입변환 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee264111",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    tf.constant(2.) + tf.constant(40)\n",
    "except:\n",
    "    print(\"conversion error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "754d21ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32bit and 64bit calculation error\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    tf.constant(2.) + tf.constant(40., dtype=tf.float64)\n",
    "except:\n",
    "    print(\"32bit and 64bit calculation error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f3ab4977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=42.0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = tf.constant(40., tf.float64)\n",
    "tf.constant(2.0) + tf.cast(t2, tf.float32)\n",
    "### 당연히 int형에도 cast적용 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d863fc7",
   "metadata": {},
   "source": [
    "# 변수 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b21aa285",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = tf.Variable([[1., 2., 3.], [4., 5., 6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a02b59c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]], dtype=float32)>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b4646a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[ 2.,  4.,  6.],\n",
       "       [ 8., 10., 12.]], dtype=float32)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.assign(2 * v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a40457a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[ 2., 42.,  6.],\n",
       "       [ 8., 10., 12.]], dtype=float32)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[0, 1].assign(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ebb4184f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[ 2., 42.,  0.],\n",
       "       [ 8., 10.,  1.]], dtype=float32)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[:, 2].assign([0., 1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c1c3d8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[100.,  42.,   0.],\n",
       "       [  8.,  10., 200.]], dtype=float32)>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.scatter_nd_update(indices=[[0, 0], [1, 2]], updates=[100., 200.])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e30acba",
   "metadata": {},
   "source": [
    "### 변수연산은 값을 기본적으로 바꾼다 ex)default = inplace=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fb2a43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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