7-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iNFOeMfl3tIu"
   },
   "source": [
    "# 심층 신경망"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zKfwb8gS3tI2"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/7-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dPE5XsFhcfVD"
   },
   "source": [
    "## 2개의 층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4sNOMcdaFVKa",
    "outputId": "b475af53-feaa-4b21-a5cd-166e654be897"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "aJJiRMa6FkWx"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_scaled = train_input / 255.0\n",
    "train_scaled = train_scaled.reshape(-1, 28*28)\n",
    "\n",
    "train_scaled, val_scaled, train_target, val_target = train_test_split(\n",
    "    train_scaled, train_target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "MCZWQiEwF6MD"
   },
   "outputs": [],
   "source": [
    "dense1 = keras.layers.Dense(100, activation='sigmoid', input_shape=(784,))\n",
    "dense2 = keras.layers.Dense(10, activation='softmax')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Agy5gCVhcrm-"
   },
   "source": [
    "## 심층 신경망 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "xmWL7kOoGB4P"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-08 04:41:26.907689: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([dense1, dense2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "em0xDl8qa12J",
    "outputId": "eed2e951-74cc-4dc4-c145-7492dc6fa173"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 100)               78500     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 79,510\n",
      "Trainable params: 79,510\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qAi41rBTdk7k"
   },
   "source": [
    "## 층을 추가하는 다른 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "0XeV6V4ha3I8"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(100, activation='sigmoid', input_shape=(784,), name='hidden'),\n",
    "    keras.layers.Dense(10, activation='softmax', name='output')\n",
    "], name='패션 MNIST 모델')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bwXDLSOWbm3L",
    "outputId": "7c05b767-9336-44e1-ff4b-7d0a575b9761"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"패션 MNIST 모델\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " hidden (Dense)              (None, 100)               78500     \n",
      "                                                                 \n",
      " output (Dense)              (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 79,510\n",
      "Trainable params: 79,510\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "yZSAxgZCbax7"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Dense(100, activation='sigmoid', input_shape=(784,)))\n",
    "model.add(keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bW2coaNQboe5",
    "outputId": "03f2bb46-3f7a-4c1a-da32-72e1a6e0f57c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_2 (Dense)             (None, 100)               78500     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 79,510\n",
      "Trainable params: 79,510\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kkYrPJembpYk",
    "outputId": "b4a3d887-9a7c-45f8-be15-90a30093728e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 4s 2ms/step - loss: 0.5602 - accuracy: 0.8091\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.4062 - accuracy: 0.8529\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3736 - accuracy: 0.8646\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3520 - accuracy: 0.8724\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3360 - accuracy: 0.8789\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f91c8695cf8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
    "\n",
    "model.fit(train_scaled, train_target, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_dfXJDhwcyAK"
   },
   "source": [
    "## 렐루 활성화 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "dG7yF8g6b062"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
    "model.add(keras.layers.Dense(100, activation='relu'))\n",
    "model.add(keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zHogWhu6g90a",
    "outputId": "85287775-38ef-4c90-b730-dd122653474f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 100)               78500     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 79,510\n",
      "Trainable params: 79,510\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "JfPe_ruQdhqA"
   },
   "outputs": [],
   "source": [
    "(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()\n",
    "\n",
    "train_scaled = train_input / 255.0\n",
    "\n",
    "train_scaled, val_scaled, train_target, val_target = train_test_split(\n",
    "    train_scaled, train_target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9PGejuuhdvvk",
    "outputId": "a90ef9cc-85f2-43b0-959a-1ec5401d3984"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 4s 2ms/step - loss: 0.5298 - accuracy: 0.8128\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3891 - accuracy: 0.8594\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3546 - accuracy: 0.8724\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3328 - accuracy: 0.8809\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3172 - accuracy: 0.8875\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f91c06e6128>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
    "\n",
    "model.fit(train_scaled, train_target, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lVYLpnjeep4y",
    "outputId": "b1fc705e-d4c5-42c1-db5f-9ecb0f588310"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "375/375 [==============================] - 1s 1ms/step - loss: 0.3531 - accuracy: 0.8821\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3530552089214325, 0.8820833563804626]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(val_scaled, val_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3YtLsmGAoavz"
   },
   "source": [
    "## 옵티마이저"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "NxVj04Haocwa"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "1426O4G8Hpfi"
   },
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.SGD()\n",
    "model.compile(optimizer=sgd, loss='sparse_categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Sh-HDiULlp18"
   },
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.SGD(learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "uF1XolBXsl3a"
   },
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.SGD(momentum=0.9, nesterov=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Hy2MENbL170j"
   },
   "outputs": [],
   "source": [
    "adagrad = keras.optimizers.Adagrad()\n",
    "model.compile(optimizer=adagrad, loss='sparse_categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "KkpbSMXWtakr"
   },
   "outputs": [],
   "source": [
    "rmsprop = keras.optimizers.RMSprop()\n",
    "model.compile(optimizer=rmsprop, loss='sparse_categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Gdu0hQIAz4JW"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
    "model.add(keras.layers.Dense(100, activation='relu'))\n",
    "model.add(keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2tcxIfILoi1t",
    "outputId": "e22f0731-1f72-46b8-d7d5-af1d0a12447c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.5192 - accuracy: 0.8183\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3887 - accuracy: 0.8608\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3512 - accuracy: 0.8728\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3256 - accuracy: 0.8806\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3039 - accuracy: 0.8890\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f91d52f54a8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')\n",
    "\n",
    "model.fit(train_scaled, train_target, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8gxAWehsv9Gi",
    "outputId": "6e5cd3a5-d16c-4fd5-bea3-03cd391f1580"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "375/375 [==============================] - 1s 1ms/step - loss: 0.3463 - accuracy: 0.8779\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3462972342967987, 0.877916693687439]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(val_scaled, val_target)"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "7-2 심층 신경망.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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