FinanceData/Tutorial_01_Basic_Classification.ipynb

## Tutorial_01_Basic_Classification.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tutorial 01 Basic Classification",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37T46as44Vk5",
        "colab_type": "text"
      },
      "source": [
        "# Keras Tutorial - 01 Basic Classification\n",
        "\n",
        "<img src=\"https://i.imgur.com/fUl86EA.jpg\" >\n",
        "\n",
        "https://www.tensorflow.org/tutorials/keras/basic_classification\n",
        "\n",
        "#### 2019 [FinanceData.KR]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DpA_dSo4Hvw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Setup TensorFlow 2.0\n",
        "!pip install -q tensorflow-gpu==2.0.0-beta1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URS_QKGuGWFs",
        "colab_type": "code",
        "outputId": "113155f6-3c84-4502-8b00-ad1b23497ebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-beta1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "# fashion mnist dataset\n",
        "\n",
        "* 28x28 픽셀(저해상도)\n",
        "* 70,000 흑백 이미지 (학습용 6만개, 테스트용 1만개)\n",
        "* 10개 category 의류 이미지\n",
        "\n",
        "https://github.com/zalandoresearch/fashion-mnist\n",
        "\n",
        "\n",
        "<img width=\"600\" src=\"https://i.imgur.com/C5jZGQP.png\" >\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7MqDQO0KCaWS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "a38e1c33-f6d5-4010-f69c-aafc66dd64e3"
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuixJMKX6F9e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "03160382-6af0-4158-83b4-e018c95de735"
      },
      "source": [
        "train_images.shape # 60000개, 28x28 이미지"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRhfYFz_6JY2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e8f9da90-97c4-4069-aa94-290c229217ee"
      },
      "source": [
        "train_images.dtype # 0~255"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('uint8')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1NHWeP66W5m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f233b93d-525c-4153-8521-2e6a9efb6ff6"
      },
      "source": [
        "train_labels # 60000개 레이블, 값은 0~9(10가지)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t9FDsUlxCaWW"
      },
      "source": [
        "<table>\n",
        "  <tr>\n",
        "    <th>레이블</th>\n",
        "    <th>클래스</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>0</td>\n",
        "    <td>T-shirt/top</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>1</td>\n",
        "    <td>Trouser</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>2</td>\n",
        "    <td>Pullover</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>3</td>\n",
        "    <td>Dress</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>4</td>\n",
        "    <td>Coat</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>5</td>\n",
        "    <td>Sandal</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>6</td>\n",
        "    <td>Shirt</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>7</td>\n",
        "    <td>Sneaker</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>8</td>\n",
        "    <td>Bag</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>9</td>\n",
        "    <td>Ankle boot</td>\n",
        "  </tr>\n",
        "</table>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IjnLH5S2CaWx",
        "colab": {}
      },
      "source": [
        "# 클래스 이름\n",
        "\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ES6uQoLKCaWr"
      },
      "source": [
        "# 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "m4VEw8Ud9Quh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4ed54244-be86-40a4-cbf7-64fd416c9079"
      },
      "source": [
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JyMCjkZiIJCu108l0eioiLdOou5NZMfxuM9tQtW2smT1jZluyP8dk283M/t3MtmaF9IUe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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NsN-sYn7qL-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 데이터의 값을 0~1 사이로 조정 (Scaling)\n",
        "\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oZTImqg_CaW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "8a62bb5c-0f47-4fc8-e8c5-4ec23786eb2a"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(train_images[i], cmap='Greys')\n",
        "  plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2or9YJpjZRqWmDcuNd+k66F+yl5nZ5Wb2CoDzUPPgOcLMRpTaWwNYB8BWAA4G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1+yjfucfxfHA3k4dZ7jToA0rZpT/YE0To2vV58uzzE1tQr3A8fS+WtjwIAB0fbxqzyf8j78GOB7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\nAxBji/9JwtrXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "59veuiEZCaW4"
      },
      "source": [
        "# 모델 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Gxg1XGm0eOBy"
      },
      "source": [
        "### 1) 레이어 구성하기\n",
        "\n",
        "1. 28x28(2D)을 784(1D)로 변환\n",
        "1. 128 출력하는 FC층 통과\n",
        "1. 10 출력하는 FC층, softmax로 10개에 속할 각각의 확률을 반환 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ODch-OFCaW4",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QV4qGW_v-1xY",
        "colab_type": "text"
      },
      "source": [
        "### 2) 모델 컴파일\n",
        "loss function(비용함수), optimizer(옵티마이저), metrics(지표) 등을 지정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lhan11blCaW7",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy', \n",
        "    metrics=['accuracy']) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "### 3) 모델 훈련"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xvwvpA64CaW_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "68e72362-e6d4-4341-9f51-f2de371065a1"
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0713 23:27:36.327693 140669747373952 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 6s 95us/sample - loss: 0.4965 - accuracy: 0.8251\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.3749 - accuracy: 0.8645\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 6s 94us/sample - loss: 0.3387 - accuracy: 0.8771\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 6s 98us/sample - loss: 0.3136 - accuracy: 0.8852\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 6s 101us/sample - loss: 0.2975 - accuracy: 0.8908\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fefccddb438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oEw4bZgGCaXB"
      },
      "source": [
        "### 모델 훈련"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VflXLEeECaXC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "90dce0ee-ef44-4027-ace8-0ab52b2ed106"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "\n",
        "print('테스트 정확도:', test_acc)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 43us/sample - loss: 0.3568 - accuracy: 0.8737\n",
            "테스트 정확도: 0.8737\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xsoS7CPDCaXH"
      },
      "source": [
        "### 모델 예측"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Gl91RPhdCaXI",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3DmJEUinCaXK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "55fe753e-7022-4881-dc3d-c3f395454cbe"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.4430734e-05, 3.8634687e-07, 5.5460591e-06, 1.0840963e-05,\n",
              "       2.3232524e-05, 1.7616193e-01, 2.6957333e-04, 5.1324058e-02,\n",
              "       2.6742282e-05, 7.7214324e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qsqenuPnCaXO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "53e8a1e3-0961-4054-93f5-b5569146579f"
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sd7Pgsu6CaXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1445bd11-6b02-4a33-822e-0a331b126a64"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIaDXh-BGqww",
        "colab_type": "text"
      },
      "source": [
        "9 = Ankle boot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DvYmmrpIy6Y1",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "#@title 예측 시각화\n",
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H89yRRaTNz59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "3a98df6d-1df5-4d82-a5dd-88f3b3449b5e"
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions,  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADFCAYAAABjLIjfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqoQJWZIq8f9EXi+v8Z+K2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BmT6HnVwNzo2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "f7e297d9-c415-4a81-db80-19c31858a2ec"
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions,  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Th2+1RfuxwM3A/Zl5wkj12zbLgMdo/phvyGYgvZd2e9KcmBxG+ykuMxeN0GYecOmQogOA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\nSaqEgSxJlTCQJakSBrIkVeL/AJSBm0tTm49XAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hQlnbqaw2Qu_",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "f500a641-ea27-4042-8bdc-bd33f33fb6dc"
      },
      "source": [
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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deTue7D/9bSGwAngA+IcK80tZSTFlJb+iFxaGEPwVvSiE8CDwcpn1NZZZFkKY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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MK_vqV78cfN3",
        "colab_type": "text"
      },
      "source": [
        "#### 2019 [FinanceData.KR]()"
      ]
    }
  ]
}