3장. 3절. 훈련 최적화.ipynb

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  {
   "cell_type": "markdown",
   "id": "5ee650ad",
   "metadata": {},
   "source": [
    "# 훈련 최적화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4158e9b",
   "metadata": {},
   "source": [
    "## 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3bbbf213",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "redwine = pd.read_csv(\"winequality-red.csv\", sep=\";\")\n",
    "redwine_X = redwine.iloc[:, :-1]\n",
    "redwine_y = redwine.iloc[:, -1] - 3\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(redwine_X, redwine_y, \n",
    "                                                    test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83400c3",
   "metadata": {},
   "source": [
    "## 패키지 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc06f977",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f19942c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T12:07:15.192754Z",
     "start_time": "2021-06-11T12:06:41.665375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 2ms/step - loss: 1.1529 - accuracy: 0.4938\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_shape=(11,), activation='sigmoid'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(50, activation='tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_X, train_y, batch_size=200, epochs=300, verbose=0)\n",
    "test_loss, test_acc = model.evaluate(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8be514c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 1ms/step - loss: 1.1663 - accuracy: 0.4979\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_shape=(11,), activation='sigmoid'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(50, activation='tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adagrad',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_X, train_y, batch_size=200, epochs=300, verbose=0)\n",
    "test_loss, test_acc = model.evaluate(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0e29eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 1ms/step - loss: 1.1754 - accuracy: 0.4854\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.optimizers import SGD\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(100, input_shape=(11,), activation='sigmoid'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(50, activation='tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer=SGD(0.1, momentum=0.9),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_X, train_y, batch_size=200, epochs=300, verbose=0)\n",
    "test_loss, test_acc = model.evaluate(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4200f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 1ms/step - loss: 1.0189 - accuracy: 0.5646\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_shape=(11,), activation='sigmoid'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(50, activation='tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_X, train_y, batch_size=200, epochs=300, verbose=0)\n",
    "test_loss, test_acc = model.evaluate(test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4bb249c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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