notebooks/20200330 기본저녁반 필기자료.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#딥러닝-프레임워크-이야기\" data-toc-modified-id=\"딥러닝-프레임워크-이야기-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>딥러닝 프레임워크 이야기</a></span></li><li><span><a href=\"#데이터-가져오기\" data-toc-modified-id=\"데이터-가져오기-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>데이터 가져오기</a></span></li><li><span><a href=\"#scikit-learn\" data-toc-modified-id=\"scikit-learn-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>scikit-learn</a></span></li><li><span><a href=\"#keras\" data-toc-modified-id=\"keras-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>keras</a></span><ul class=\"toc-item\"><li><span><a href=\"#Sequential\" data-toc-modified-id=\"Sequential-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Sequential</a></span></li><li><span><a href=\"#activation\" data-toc-modified-id=\"activation-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>activation</a></span></li><li><span><a href=\"#compile\" data-toc-modified-id=\"compile-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>compile</a></span><ul class=\"toc-item\"><li><span><a href=\"#optimizer\" data-toc-modified-id=\"optimizer-4.3.1\"><span class=\"toc-item-num\">4.3.1&nbsp;&nbsp;</span>optimizer</a></span></li><li><span><a href=\"#loss\" data-toc-modified-id=\"loss-4.3.2\"><span class=\"toc-item-num\">4.3.2&nbsp;&nbsp;</span>loss</a></span></li><li><span><a href=\"#metrics\" data-toc-modified-id=\"metrics-4.3.3\"><span class=\"toc-item-num\">4.3.3&nbsp;&nbsp;</span>metrics</a></span></li></ul></li><li><span><a href=\"#fit\" data-toc-modified-id=\"fit-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>fit</a></span><ul class=\"toc-item\"><li><span><a href=\"#epoch\" data-toc-modified-id=\"epoch-4.4.1\"><span class=\"toc-item-num\">4.4.1&nbsp;&nbsp;</span>epoch</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [AI 오픈 이노베이션] 기본 저녁반<a class=\"tocSkip\">\n",
    "GitHub link: [https://github.com/realblack0/ai_lecture_fundamental](https://github.com/realblack0/ai_lecture_fundamental)  \n",
    "E-Mail: realblack0@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 딥러닝 프레임워크 이야기  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "theano : 처음에 왕이었다.  \n",
    "→ keras : 이론과 코드의 간극을 줄였다. 쓰기 쉽다. 인기를 끌었다.  \n",
    "→ tensorflow : 구글에서 만들었다. 대학에서 만든 것(theano)보다 기업에서 만든게 더 좋다. 딥러닝 프레임워크의 대세가 된다.  \n",
    "→ cntk : 마이크로소프트에서도 만들었다.   \n",
    "→ keras : wrapper 방식으로 만들어졌기 때문에 keras 문법을 쓰면서 내부적으로 theano, tensorflow, cntk를 백엔드로 쓸 수 있다.  \n",
    "→ torch : 루아 언어 기반, 페이스북에서 만들었다.  \n",
    "→ pytorch : torch를 python으로 만들었다. 사용하기도 쉬워서 tensorflow의 점유율을 뺏기 시작한다.  \n",
    "→ keras + tensorflow : 위기를 느낀 구글이 keras를 영입한다. keras가 tensorflow에 흡수된다.  \n",
    "→ tensorflow 2.0 : tensorflow가 업데이트하면서 사용하기 쉬운 keras를 전면에 내세운다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow로 모델 만드는 방법은 여러가지가 있다. \n",
    "- tensor\n",
    "- estimator\n",
    "- keras\n",
    "    - functional\n",
    "    - sequential\n",
    "    - class\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우리는 tensorflow에 내장된 keras부터 배울 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※ 이번 강의는 [TensorFlow 공식 튜토리얼](https://www.tensorflow.org/tutorials/quickstart/beginner?hl=ko)을 기준으로 진행되었습니다.  \n",
    "※ tensorflow는 2.x 버전을 이용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tensorflow는 기능이 많다. 다 배우는데 6개월 정도 걸린다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AggregationMethod',\n",
       " 'Assert',\n",
       " 'CriticalSection',\n",
       " 'DType',\n",
       " 'DeviceSpec',\n",
       " 'GradientTape',\n",
       " 'Graph',\n",
       " 'IndexedSlices',\n",
       " 'IndexedSlicesSpec',\n",
       " 'Module',\n",
       " 'Operation',\n",
       " 'OptionalSpec',\n",
       " 'RaggedTensor',\n",
       " 'RaggedTensorSpec',\n",
       " 'RegisterGradient',\n",
       " 'SparseTensor',\n",
       " 'SparseTensorSpec',\n",
       " 'Tensor',\n",
       " 'TensorArray',\n",
       " 'TensorArraySpec',\n",
       " 'TensorShape',\n",
       " 'TensorSpec',\n",
       " 'TypeSpec',\n",
       " 'UnconnectedGradients',\n",
       " 'Variable',\n",
       " 'VariableAggregation',\n",
       " 'VariableSynchronization',\n",
       " '_LazyLoader',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__compiler_version__',\n",
       " '__cxx11_abi_flag__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__git_version__',\n",
       " '__loader__',\n",
       " '__monolithic_build__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_absolute_import',\n",
       " '_api',\n",
       " '_division',\n",
       " '_forward_module',\n",
       " '_importlib',\n",
       " '_m',\n",
       " '_print_function',\n",
       " '_root_estimator',\n",
       " '_sys',\n",
       " '_top_level_modules',\n",
       " '_types',\n",
       " 'abs',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'add_n',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argsort',\n",
       " 'as_dtype',\n",
       " 'as_string',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'assert_equal',\n",
       " 'assert_greater',\n",
       " 'assert_less',\n",
       " 'assert_rank',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'audio',\n",
       " 'autograph',\n",
       " 'batch_to_space',\n",
       " 'bfloat16',\n",
       " 'bitcast',\n",
       " 'bitwise',\n",
       " 'bool',\n",
       " 'boolean_mask',\n",
       " 'broadcast_dynamic_shape',\n",
       " 'broadcast_static_shape',\n",
       " 'broadcast_to',\n",
       " 'case',\n",
       " 'cast',\n",
       " 'clip_by_global_norm',\n",
       " 'clip_by_norm',\n",
       " 'clip_by_value',\n",
       " 'compat',\n",
       " 'complex',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'concat',\n",
       " 'cond',\n",
       " 'config',\n",
       " 'constant',\n",
       " 'constant_initializer',\n",
       " 'control_dependencies',\n",
       " 'convert_to_tensor',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'cumsum',\n",
       " 'custom_gradient',\n",
       " 'data',\n",
       " 'debugging',\n",
       " 'device',\n",
       " 'distribute',\n",
       " 'divide',\n",
       " 'double',\n",
       " 'dtypes',\n",
       " 'dynamic_partition',\n",
       " 'dynamic_stitch',\n",
       " 'edit_distance',\n",
       " 'einsum',\n",
       " 'ensure_shape',\n",
       " 'equal',\n",
       " 'errors',\n",
       " 'estimator',\n",
       " 'executing_eagerly',\n",
       " 'exp',\n",
       " 'expand_dims',\n",
       " 'experimental',\n",
       " 'extract_volume_patches',\n",
       " 'eye',\n",
       " 'feature_column',\n",
       " 'fill',\n",
       " 'fingerprint',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'floor',\n",
       " 'foldl',\n",
       " 'foldr',\n",
       " 'function',\n",
       " 'gather',\n",
       " 'gather_nd',\n",
       " 'get_logger',\n",
       " 'get_static_value',\n",
       " 'grad_pass_through',\n",
       " 'gradients',\n",
       " 'graph_util',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'group',\n",
       " 'guarantee_const',\n",
       " 'half',\n",
       " 'hessians',\n",
       " 'histogram_fixed_width',\n",
       " 'histogram_fixed_width_bins',\n",
       " 'identity',\n",
       " 'identity_n',\n",
       " 'image',\n",
       " 'import_graph_def',\n",
       " 'init_scope',\n",
       " 'initializers',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'io',\n",
       " 'is_tensor',\n",
       " 'keras',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'lite',\n",
       " 'load_library',\n",
       " 'load_op_library',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'lookup',\n",
       " 'losses',\n",
       " 'make_ndarray',\n",
       " 'make_tensor_proto',\n",
       " 'map_fn',\n",
       " 'math',\n",
       " 'matmul',\n",
       " 'matrix_square_root',\n",
       " 'maximum',\n",
       " 'meshgrid',\n",
       " 'metrics',\n",
       " 'minimum',\n",
       " 'multiply',\n",
       " 'name_scope',\n",
       " 'negative',\n",
       " 'nest',\n",
       " 'newaxis',\n",
       " 'nn',\n",
       " 'no_gradient',\n",
       " 'no_op',\n",
       " 'nondifferentiable_batch_function',\n",
       " 'norm',\n",
       " 'not_equal',\n",
       " 'numpy_function',\n",
       " 'one_hot',\n",
       " 'ones',\n",
       " 'ones_initializer',\n",
       " 'ones_like',\n",
       " 'optimizers',\n",
       " 'pad',\n",
       " 'parallel_stack',\n",
       " 'pow',\n",
       " 'print',\n",
       " 'py_function',\n",
       " 'qint16',\n",
       " 'qint32',\n",
       " 'qint8',\n",
       " 'quantization',\n",
       " 'queue',\n",
       " 'quint16',\n",
       " 'quint8',\n",
       " 'ragged',\n",
       " 'random',\n",
       " 'random_normal_initializer',\n",
       " 'random_uniform_initializer',\n",
       " 'range',\n",
       " 'rank',\n",
       " 'raw_ops',\n",
       " 'realdiv',\n",
       " 'recompute_grad',\n",
       " 'reduce_all',\n",
       " 'reduce_any',\n",
       " 'reduce_logsumexp',\n",
       " 'reduce_max',\n",
       " 'reduce_mean',\n",
       " 'reduce_min',\n",
       " 'reduce_prod',\n",
       " 'reduce_sum',\n",
       " 'register_tensor_conversion_function',\n",
       " 'required_space_to_batch_paddings',\n",
       " 'reshape',\n",
       " 'resource',\n",
       " 'reverse',\n",
       " 'reverse_sequence',\n",
       " 'roll',\n",
       " 'round',\n",
       " 'saturate_cast',\n",
       " 'saved_model',\n",
       " 'scalar_mul',\n",
       " 'scan',\n",
       " 'scatter_nd',\n",
       " 'searchsorted',\n",
       " 'sequence_mask',\n",
       " 'sets',\n",
       " 'shape',\n",
       " 'shape_n',\n",
       " 'sigmoid',\n",
       " 'sign',\n",
       " 'signal',\n",
       " 'sin',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'slice',\n",
       " 'sort',\n",
       " 'space_to_batch',\n",
       " 'space_to_batch_nd',\n",
       " 'sparse',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'stop_gradient',\n",
       " 'strided_slice',\n",
       " 'string',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'summary',\n",
       " 'switch_case',\n",
       " 'sysconfig',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensor_scatter_nd_add',\n",
       " 'tensor_scatter_nd_sub',\n",
       " 'tensor_scatter_nd_update',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'tile',\n",
       " 'timestamp',\n",
       " 'tpu',\n",
       " 'train',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncatediv',\n",
       " 'truncatemod',\n",
       " 'tuple',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'unique',\n",
       " 'unique_with_counts',\n",
       " 'unravel_index',\n",
       " 'unstack',\n",
       " 'variable_creator_scope',\n",
       " 'variant',\n",
       " 'vectorized_map',\n",
       " 'version',\n",
       " 'where',\n",
       " 'while_loop',\n",
       " 'xla',\n",
       " 'zeros',\n",
       " 'zeros_initializer',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터는 2-way hold out되어 있다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unpacking\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터는 numpy array로 되어 있고, 총 6만개이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터는 28 x 28 크기의 이미지들이다. 이미지 데이터이므로 `imshow`로 볼 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fd891dc518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Snqvd9nRRb/8u6SlJT2okWPM61NtpGvlo+KSkTbW/czv93hX6asv7xumyQBKcQQckQdiBJAg7kARhB5Ig7EAShB1IgrADSfwfs4RxaLJFjqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 손글씨 5 이미지 데이터\n",
    "plt.imshow(X_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이미지 데이터는 기계학습에 바로 사용할 수 없다.  \n",
    "2차원을 1차원으로 바꾸면 기계학습에 사용 가능하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.reshape(60000, -1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit에서도 이미지 데이터를 쓸 수 있다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실행 X\n",
    "# 데이터 6만개가 많아서 오래 걸림\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train.reshape(60000, -1), y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn에서도 딥러닝 쓸 수 있다.  \n",
    "단, scikit-learn의 딥러닝은 복잡한 건 못한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시간상 실행은 생략\n",
    "mlp.fit(X_train.reshape(60000, -1), y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "keras에서 모델을 만드는 방법은 3가지가 있는데, 그 중 `Sequential`을 이용하는 방법을 알아보자.  \n",
    "`Sequential`은 단순한 모델만 만들 수 있다. \n",
    "    - 일방향만으로만 foward 가능하다. (양방향 불가)  \n",
    "    - output도 1개만 출력 가능하다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 대문자는 클래스 -> 인스턴스\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Sequential`에는 layer를 넣어줘야 한다.  \n",
    "keras는 기존에 유명한 모델 그대로 가져다 쓸 수 있다.  \n",
    "`import` 뒤에서 tab 눌러보면 종류가 많다. 계속 발전 중이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 그림을 보면 노드가 서로 완전히 연결(fully connected)되어 있다.  \n",
    "줄여서 fc layer라고 부르기도 한다.  \n",
    "촘촘하게(Dense) 연결되어 있다고 해서 `Dense`라고 이름 지어졌다. "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인스턴스해서 사용한다. 인자가 반드시 필요하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer1 = Dense(4) # 노드 개수 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "output layer의 노드 개수는 분류할 target 개수와 같게 한다.   \n",
    "손글씨 데이터는 숫자 0 ~ 10을 분류하므로 10종이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Dense(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`add` 메소드로 sequential 인스턴스에 layer를 추가한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layer1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(output) # 순차적으로 add"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또는 `Sequetial`을 인스턴스할 때 layer를 한번에 넣을 수도 있다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential(layers=[layer1, output]) # layers => 복수형 => 리스트로 묶어서"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사용법이 간단해서 할당 안하고 바로 추가하기도 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(4))\n",
    "model.add(Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 일반적으로 레이어 많이 써야 성능 좋다.   \n",
    "    - 그러나 항상 그렇지는 않다. 무조건 데이터의 특성에 따라 정해진다.  \n",
    "- 딥러닝은 오비피팅이 잘 일어난다.  \n",
    "- 오캄의 면도칼 법칙에 따라서 간단한 모델 만드는게 좋다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(16))\n",
    "model.add(Dense(12))\n",
    "model.add(Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`summary`는 모델의 구조를 보여주는 메서드이다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Error\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Sequential`은 첫번째 레이어에 input 모양을 알려줘야 한다.  \n",
    "앞서 만든 모델은 input_shape을 정해주지 않아서 에러가 발생한 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`input_shape`은 데이터 하나의 모양을 의미한다.  \n",
    "mnist 데이터를 reshape한 모양을 알려준다.  \n",
    "반드시 tuple로 넣어줘야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(16, input_shape=(784, ))) # 값이 1개인 tuple은 콤마\n",
    "model.add(Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델에 레이어가 어떻게 쌓여있는지 정리해준다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 16)                12560     \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                170       \n",
      "=================================================================\n",
      "Total params: 12,730\n",
      "Trainable params: 12,730\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※ 주의할 점: 만약 실수로 같은 셀을 또 실행하면 model의 구조가 바뀐다. (mutable은 실수하기 쉽다)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(16, input_shape=(784, ))) \n",
    "model.add(Dense(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 16)                12560     \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                170       \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                176       \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 10)                170       \n",
      "=================================================================\n",
      "Total params: 13,076\n",
      "Trainable params: 13,076\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "나중에 add하지 않고 처음부터 집어 넣으면 실수를 줄일 수 있다.  \n",
    "구글은 초보자들의 이런 실수를 캐치하고 초보자용 튜토리얼에 넣어두었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(16, input_shape=(784, )),\n",
    "    Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Param은 업데이트해야하는 요소이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_11 (Dense)             (None, 16)                12560     \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 10)                170       \n",
      "=================================================================\n",
      "Total params: 12,730\n",
      "Trainable params: 12,730\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "activation은 공간을 왜곡하는 역할을 한다.  \n",
    "복잡한 문제도 해결할 수 있게 된다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(16, input_shape=(784, ), activation=\"relu\")) \n",
    "model.add(Dense(10)) # output layer도 activation이 있어야하지만, 아직은 그냥 둔다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 처음에 활성함수는 계단함수였다.\n",
    "# 미분이 불가능해서 학습이 안되는 문제가 있었다.\n",
    "\n",
    "plt.plot([-1, 0, 0, 1], [0, 0, 1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigmoid는 미분 가능하다.\n",
    "# layer를 쌓아도 학습이 가능하다. \n",
    "\n",
    "import numpy as np\n",
    "sigmoid = lambda x:1/(1 + np.exp(-x)) \n",
    "\n",
    "x = np.linspace(-10, 10)\n",
    "y = sigmoid(x)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tanh(하이퍼볼릭 탄젠트)를 쓰면 값이 -1 ~ 1 사이가 된다. \n",
    "# 학습 속도가 좀 더 빠른 경향이 있다.\n",
    "# sigmoid는 값이 0 ~ 1 사이이다.\n",
    "\n",
    "x = np.linspace(-10, 10)\n",
    "y = np.tanh(x)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.yticks([-1, 0, 1])\n",
    "plt.axhline(color=\"gray\"); plt.axvline(color=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigmoid, tanh는 레이어가 깊게 쌓이면 기울기가 소실되는 문제가 있었다. (gradient vanishing)\n",
    "# 즉, 학습이 안된다. \n",
    "# hinton 교수가 ReLU로 해결한다. \n",
    "# 딥러닝에서 활성함수는 relu 거의 기본이다.\n",
    "\n",
    "x = np.linspace(-10, 10)\n",
    "y = np.clip(x, 0, 100)\n",
    "\n",
    "plt.title(\"ReLU\")\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iris 데이터를 학습할 keras 모델을 만들어 보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_iris(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(12, input_shape=(4,), activation=\"relu\"), # 첫번째 레이어: input shape을 써줘야 한다.\n",
    "                                                    # iris는 feature가 4개\n",
    "    Dense(12, activation=\"relu\"),\n",
    "    Dense(3, activation=\"softmax\") # 마지막 레이어: classification, regression에 따라서 다르다.\n",
    "                                   # iris는 target이 3개\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막 output 레이어의 활성함수는 classification, regression에 따라서 다르다.\n",
    "- classification \n",
    "     - 0과 1로 binary(이진) 분류 : activation에 sigmoid 쓴다.\n",
    "     - multi class 분류: activation에 softmax 쓴다.\n",
    " - regression\n",
    "     - activation 안 쓴다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_15 (Dense)             (None, 12)                60        \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 12)                156       \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 3)                 39        \n",
      "=================================================================\n",
      "Total params: 255\n",
      "Trainable params: 255\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wine 데이터를 학습할 모델을 만들어 보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_wine()\n",
    "data_pd = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "data_ta = pd.DataFrame(data.target, columns=[\"target\"])\n",
    "wine = pd.concat([data_pd, data_ta], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(12, input_shape=(13,), activation=\"relu\"), # wine 데이터의 feature는 13개\n",
    "    Dense(13, activation=\"relu\"), # 복잡할수록 성능 좋다. (unit 많거나, layer가 깊음)\n",
    "    Dense(16, activation=\"relu\"), # 복잡할수록 오버피팅 걸릴 확률이 높다.\n",
    "    Dense(1) # regression 문제이므로 1, 활성함수 없음.\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "딥러닝은 모델 구조를 어떻게 만드느냐가 관건이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_18 (Dense)             (None, 12)                168       \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 13)                169       \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 16)                224       \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 578\n",
      "Trainable params: 578\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제까지는 모델의 뼈대만 만들었다.  \n",
    "\n",
    "학습을 하기 위해서는 전략이 필요하다.  \n",
    "이 전략을 모델에 주입하는 것이 `compile`이다.  \n",
    "\n",
    "`compile`의 3요소는 다음 세가지가 있다.\n",
    "```python\n",
    "model.compile(optimizer=, loss=, metrics=)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "optimizer는 모델의 loss(오차)를 줄여가는 방식이다.  \n",
    "activation function으로 ReLU를 쓰듯이, optimizer에는 Adam을 쓰는 것이 거의 기본적이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "실제값과 모델의 예측값 간의 오차(loss)를 어떻게 계산할지 정한다.  \n",
    "딥러닝 이론에서 Loss Function(오차함수 또는 손실함수)에 해당한다.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "classification 문제에는 loss function으로 categorical_crossentropy를 사용한다.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "keras에서 target은 숫자여야한다.  \n",
    "기계학습에서 배웠던 것처럼 One Hot Encoding과 Label Encoding으로 전처러가 필요하다.  \n",
    "어떤 방식으로 인코딩했느냐에 따라서 loss function이 달라진다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "metric은 성능 평가 방법이다.  \n",
    "기계학습에서 배웠던 Accuracy, Precision, Recall, F1-score 를 모두 쓸 수 있다.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iris를 예측할 모델을 만들고 학습 전략까지 세워 보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(12, input_shape=(4,), activation=\"relu\"), # 첫번째 레이어: input shape을 써줘야 한다.\n",
    "                                                    # iris는 feature가 4개\n",
    "    Dense(12, activation=\"relu\"),\n",
    "    Dense(3, activation=\"softmax\") # 마지막 레이어: classification, regression에 따라서 다르다.\n",
    "                                # iris는 target이 3개\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습 전략\n",
    "model.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=[\"acc\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "학습은 `fit`메서드를 이용한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Error\n",
    "model.fit(data.data, data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "에러 메시지를 보면 `to_categorical`을 사용하거나 `sparse_categorical_crossentropy`를 사용하라고 한다.  \n",
    "\n",
    "> ValueError: You are passing a target array of shape (150, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via:\n",
    "> ```\n",
    "> from keras.utils import to_categorical\n",
    "> y_binary = to_categorical(y_int)\n",
    "> ```\n",
    ">\n",
    "> Alternatively, you can use the loss function `sparse_categorical_crossentropy` instead, which does expect integer targets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`to_categorical`은 One Hot Encoding하는 tensorflow의 함수이다.  \n",
    "scikit-learn의 `OneHotEncoder`와 같다.  \n",
    "\n",
    "앞서 인코딩 방식에 따라서 loss function이 달라진다고 언급했다.  \n",
    "target이 one hot enconding일 때만 `categorical_crossentropy`를 쓸 수 있다.  \n",
    "label encoding이면 `sparse_categorical_crossentropy` 써야 한다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss='sparse_categorical_crossentropy', metrics=[\"acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 150 samples\n",
      "150/150 [==============================] - 1s 4ms/sample - loss: 1.4945 - acc: 0.3333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fd8b7ea748>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data.data, data.target) # iris 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "딥러닝은 loss가 줄어드는 방향으로 parameter를 조금씩 업데이트해나가는 학습 방식이다.  \n",
    "모든 데이터를 사용해서 한번 학습하는 것을 epoch라고 한다.  \n",
    "epoch을 거듭할수록 loss가 줄어드는게 보인다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 150 samples\n",
      "Epoch 1/10\n",
      "150/150 [==============================] - 0s 106us/sample - loss: 1.3516 - acc: 0.3333\n",
      "Epoch 2/10\n",
      "150/150 [==============================] - 0s 93us/sample - loss: 1.2294 - acc: 0.3333\n",
      "Epoch 3/10\n",
      "150/150 [==============================] - 0s 53us/sample - loss: 1.1321 - acc: 0.3333\n",
      "Epoch 4/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 1.0541 - acc: 0.3333\n",
      "Epoch 5/10\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.9962 - acc: 0.3333\n",
      "Epoch 6/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.9500 - acc: 0.3333\n",
      "Epoch 7/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.9110 - acc: 0.3533\n",
      "Epoch 8/10\n",
      "150/150 [==============================] - 0s 53us/sample - loss: 0.8786 - acc: 0.5600\n",
      "Epoch 9/10\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.8493 - acc: 0.6533\n",
      "Epoch 10/10\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.8251 - acc: 0.6533\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fd8bd7bc50>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data.data, data.target, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "구조를 더 복잡하게 하면 성능이 더 좋아진다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(12, input_shape=(4,), activation=\"relu\"),\n",
    "    Dense(12, activation=\"relu\"),\n",
    "    Dense(12, activation=\"relu\"),\n",
    "    Dense(12, activation=\"relu\"),\n",
    "    Dense(3, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimize=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 150 samples\n",
      "Epoch 1/10\n",
      "150/150 [==============================] - 1s 5ms/sample - loss: 1.0439 - acc: 0.3333\n",
      "Epoch 2/10\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 1.0166 - acc: 0.3333\n",
      "Epoch 3/10\n",
      "150/150 [==============================] - 0s 80us/sample - loss: 0.9948 - acc: 0.3333\n",
      "Epoch 4/10\n",
      "150/150 [==============================] - 0s 80us/sample - loss: 0.9763 - acc: 0.3333\n",
      "Epoch 5/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.9559 - acc: 0.3333\n",
      "Epoch 6/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.9355 - acc: 0.3333\n",
      "Epoch 7/10\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 0.9174 - acc: 0.3333\n",
      "Epoch 8/10\n",
      "150/150 [==============================] - 0s 80us/sample - loss: 0.8987 - acc: 0.3333\n",
      "Epoch 9/10\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.8821 - acc: 0.4133\n",
      "Epoch 10/10\n",
      "150/150 [==============================] - 0s 80us/sample - loss: 0.8633 - acc: 0.4933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fd8bdab0f0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data.data, data.target, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow tutorial에 있는 코드를 찬찬히 살펴보자.  \n",
    "거진 다 배웠던 내용이다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0 # 전처리\n",
    "                                                  # sciki-learn의 MinMaxScaler와 같음\n",
    "                                                  # 이미지는 0 ~ 255 사이의 값으로 구성되어 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)), # Flatten은 데이터를 1차원으로 바꿔준다. \n",
    "                                                 # 우리는 Flatten 안쓰고 reshape으로 바꿀 수 있다.\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dropout(0.2), # 오버피팅을 막는 딥러닝만의 기법 (기계학습에 없음)\n",
    "  tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_30 (Dense)             (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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