220623_ml_trader.ipynb

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            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/siruku6/fx_notebooks/blob/master/220623_ml_trader.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. モジュールの準備"
      ],
      "metadata": {
        "id": "Svhk6UVa9ljo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q pytorch_lightning torchinfo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owp7Z_taNRqP",
        "outputId": "da752028-3832-4bdc-af1c-80ce6532814c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 585 kB 11.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 62.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 140 kB 65.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 419 kB 58.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 28.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 54.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 55.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 94 kB 1.3 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RuE5KNyy9eJr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from glob import glob\n",
        "from typing import List\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "\n",
        "# Pytorch Lightning\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.loggers import CSVLogger\n",
        "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data.dataset import Subset\n",
        "import torchmetrics\n",
        "from torchmetrics.functional import accuracy\n",
        "import torchvision\n",
        "from torchinfo import summary\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. データの準備\n"
      ],
      "metadata": {
        "id": "MMvJufv5-4uz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2-1. アップロード"
      ],
      "metadata": {
        "id": "31ZrBJeWBETo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload `chart_pngs.zip`\n",
        "uploaded = files.upload()\n",
        "!unzip chart_pngs.zip"
      ],
      "metadata": {
        "id": "9-mH5M3o-KH7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload `fx_train_test_data.cav`\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "id": "mqZ5jWIzAnVj",
        "outputId": "b3bd44bc-aa8b-4b9c-933d-50c58df7e31a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-728a1dd1-78c4-4130-a7c1-58e9bb87bb4f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-728a1dd1-78c4-4130-a7c1-58e9bb87bb4f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving fx_train_test_data.csv to fx_train_test_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_image: str = './content/chart_pngs/sample_2511.png'\n",
        "with open(sample_image, 'rb') as f:\n",
        "    image = Image.open(f)\n",
        "    image = image.convert('RGB')\n",
        "\n",
        "plt.imshow(image)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "eFyk_ytT_F18",
        "outputId": "37a331af-4141-4ee8-c216-6a00c48b2242"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filenames: List[str] = glob('./content/chart_pngs/*')\n",
        "len(filenames)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxYRjClHEW-2",
        "outputId": "f0164ab0-22d4-41e7-c7e9-3448224c8866"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3833"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('./fx_train_test_data.csv')\n",
        "print(\"Size of df\", len(df))\n",
        "df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "A4HUkfemBBaZ",
        "outputId": "d724047e-7ff5-460c-da1d-beb7127b15f4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of df 3863\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       low     open     high    close                 time  close:lag+5  class\n",
              "0  156.053  156.087  156.193  156.136  2021-10-31 21:00:00      156.134      0\n",
              "1  156.088  156.117  156.240  156.226  2021-10-31 22:00:00      156.225      0\n",
              "2  156.118  156.224  156.334  156.311  2021-10-31 23:00:00      156.238      0\n",
              "3  155.922  156.314  156.338  155.992  2021-11-01 00:00:00      156.351      1\n",
              "4  155.952  155.990  156.204  156.185  2021-11-01 01:00:00      156.274      0\n",
              "5  156.042  156.183  156.183  156.134  2021-11-01 02:00:00      156.197      0\n",
              "6  156.129  156.136  156.298  156.225  2021-11-01 03:00:00      156.016      0\n",
              "7  156.204  156.222  156.322  156.238  2021-11-01 04:00:00      156.024      0\n",
              "8  156.236  156.240  156.448  156.351  2021-11-01 05:00:00      155.991      2\n",
              "9  156.210  156.353  156.389  156.274  2021-11-01 06:00:00      156.235      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9b227f8-fdeb-4232-99b8-8eb6b6f58d97\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>close</th>\n",
              "      <th>time</th>\n",
              "      <th>close:lag+5</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>156.053</td>\n",
              "      <td>156.087</td>\n",
              "      <td>156.193</td>\n",
              "      <td>156.136</td>\n",
              "      <td>2021-10-31 21:00:00</td>\n",
              "      <td>156.134</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>156.088</td>\n",
              "      <td>156.117</td>\n",
              "      <td>156.240</td>\n",
              "      <td>156.226</td>\n",
              "      <td>2021-10-31 22:00:00</td>\n",
              "      <td>156.225</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>156.118</td>\n",
              "      <td>156.224</td>\n",
              "      <td>156.334</td>\n",
              "      <td>156.311</td>\n",
              "      <td>2021-10-31 23:00:00</td>\n",
              "      <td>156.238</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>155.922</td>\n",
              "      <td>156.314</td>\n",
              "      <td>156.338</td>\n",
              "      <td>155.992</td>\n",
              "      <td>2021-11-01 00:00:00</td>\n",
              "      <td>156.351</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>155.952</td>\n",
              "      <td>155.990</td>\n",
              "      <td>156.204</td>\n",
              "      <td>156.185</td>\n",
              "      <td>2021-11-01 01:00:00</td>\n",
              "      <td>156.274</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>156.042</td>\n",
              "      <td>156.183</td>\n",
              "      <td>156.183</td>\n",
              "      <td>156.134</td>\n",
              "      <td>2021-11-01 02:00:00</td>\n",
              "      <td>156.197</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>156.129</td>\n",
              "      <td>156.136</td>\n",
              "      <td>156.298</td>\n",
              "      <td>156.225</td>\n",
              "      <td>2021-11-01 03:00:00</td>\n",
              "      <td>156.016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>156.204</td>\n",
              "      <td>156.222</td>\n",
              "      <td>156.322</td>\n",
              "      <td>156.238</td>\n",
              "      <td>2021-11-01 04:00:00</td>\n",
              "      <td>156.024</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>156.236</td>\n",
              "      <td>156.240</td>\n",
              "      <td>156.448</td>\n",
              "      <td>156.351</td>\n",
              "      <td>2021-11-01 05:00:00</td>\n",
              "      <td>155.991</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>156.210</td>\n",
              "      <td>156.353</td>\n",
              "      <td>156.389</td>\n",
              "      <td>156.274</td>\n",
              "      <td>2021-11-01 06:00:00</td>\n",
              "      <td>156.235</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9b227f8-fdeb-4232-99b8-8eb6b6f58d97')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9b227f8-fdeb-4232-99b8-8eb6b6f58d97 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9b227f8-fdeb-4232-99b8-8eb6b6f58d97');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 末尾30行を削除\n",
        "print(\"Size of images:\", len(filenames))\n",
        "print(\"Size of df: \", len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hkXHIexUUwW",
        "outputId": "d8e1966d-b34c-45a3-9f6b-e35d10fecd2b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of images: 3833\n",
            "Size of df:  3863\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1枚の画像を作るのに30行必要なので、画像の方が、データ30行分少ない\n",
        "\n",
        "そのため、後で学習データを作成する時は、csvの末尾30行を無視してデータを作成する"
      ],
      "metadata": {
        "id": "UIL6H5oeE1O7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# こんな感じでOK\n",
        "df[['class']][:-30].to_numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-hrR-BtG_TR",
        "outputId": "fc4bfdea-a45a-4869-bcde-68ebb630a28a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       ...,\n",
              "       [0],\n",
              "       [1],\n",
              "       [1]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Size of df: \", len(df[['class']][:-30]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FXCP4hQUo_1",
        "outputId": "8378e441-5901-4ea7-fbde-d4c1929cfd58"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of df:  3833\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2-2. DataLoaderの作成"
      ],
      "metadata": {
        "id": "hviVzGuKD0vg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os.listdir('./content/chart_pngs')[:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1s__w1QnSY8x",
        "outputId": "64729eaf-b773-4671-df78-d0cc45b53398"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sample_3242.png', 'sample_3313.png', 'sample_796.png', 'sample_3001.png']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filenames = sorted(os.listdir('./content/chart_pngs'))\n",
        "filenames[:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcLwKCBYhwbS",
        "outputId": "b2846959-6428-4ff7-efd1-d28ed180b905"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sample_0.png', 'sample_1.png', 'sample_10.png', 'sample_100.png']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 元の並び順も、sorted実行後も画像の順番が時系列順にならないので、自分でファイル名配列を作る\n",
        "filenames = [f'sample_{index}.png' for index in range(0, 3634)]\n",
        "filenames[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3xX6_V5ifTZ",
        "outputId": "f4ba49d1-0489-4e28-9a02-302971c74ee9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sample_0.png',\n",
              " 'sample_1.png',\n",
              " 'sample_2.png',\n",
              " 'sample_3.png',\n",
              " 'sample_4.png',\n",
              " 'sample_5.png',\n",
              " 'sample_6.png',\n",
              " 'sample_7.png',\n",
              " 'sample_8.png',\n",
              " 'sample_9.png']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset作成\n",
        "class ChartDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, transform=None):\n",
        "        self.transform = transform\n",
        "        self.images: List[str] = [f'sample_{index}.png' for index in range(0, 3634)]\n",
        "\n",
        "        # 末尾30行を無視\n",
        "        df_chart: pd.DataFrame = pd.read_csv('./fx_train_test_data.csv')[:-30]\n",
        "        self.labels = df_chart['class'].to_numpy()\n",
        "\n",
        "    def __getitem__(self, index: int):\n",
        "        image: str = self.images[index]\n",
        "        label: np.ndarray = self.labels[index]\n",
        "\n",
        "        with open(f'./content/chart_pngs/{image}', 'rb') as f:\n",
        "            image = Image.open(f).convert('RGB')\n",
        "        \n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "        return image, label\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n"
      ],
      "metadata": {
        "id": "_eF_FdeID36J"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# seed 固定\n",
        "pl.seed_everything(0)\n",
        "\n",
        "# Dataset初期化\n",
        "transforms = torchvision.transforms.Compose([\n",
        "    torchvision.transforms.ToTensor(),\n",
        "])\n",
        "chart_dataset: ChartDataset = ChartDataset(transform=transforms)\n",
        "\n",
        "n_train: int = int(len(chart_dataset) * 0.6)\n",
        "n_val: int = int(len(chart_dataset) * 0.2)\n",
        "n_test: int = len(chart_dataset) - n_train - n_val\n",
        "\n",
        "train_indices: List[int] = list(range(0, n_train))\n",
        "val_indices: List[int] = list(range(n_train, n_train + n_val))\n",
        "test_indices: List[int] = list(range(n_train + n_val, len(chart_dataset)))\n",
        "\n",
        "# NOTE: Subset をつかうと randomではない分割が可能\n",
        "# https://qiita.com/takurooo/items/ba8c509eaab080e2752c#%E8%A7%A3%E6%B1%BA%E7%AD%961-torchutilsdatasubset\n",
        "# FXは過去のデータから未来の予測をしたいので、学習データは過去データ。\n",
        "# validation, testデータは未来のデータ、という分け方にしたい。\n",
        "train = Subset(chart_dataset, train_indices)\n",
        "val = Subset(chart_dataset, val_indices)\n",
        "test = Subset(chart_dataset, test_indices)\n",
        "\n",
        "# DataLoader作成\n",
        "batch_size = 50\n",
        "train_loader = torch.utils.data.DataLoader(train, batch_size, shuffle=True, drop_last=True)\n",
        "val_loader = torch.utils.data.DataLoader(val, batch_size)\n",
        "test_loader = torch.utils.data.DataLoader(test, batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu9nh1kaNLvT",
        "outputId": "e9a128da-4c0f-40d9-fa0c-8363a8eb27c6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Global seed set to 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DataLoaderの動作確認\n",
        "def show_sample_images(img):\n",
        "    plt.imshow(np.transpose(img.numpy(), (1, 2 ,0)), interpolation='nearest')\n",
        "\n",
        "for _, (images, labels) in enumerate(train_loader):\n",
        "    print(labels.numpy())\n",
        "    show_sample_images(torchvision.utils.make_grid(images, padding=1))\n",
        "    plt.axis('off')\n",
        "    break\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "qf4MrmB9QKAe",
        "outputId": "4b2eabd8-7a91-4c20-e328-985b4221134c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 1 0 1 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 1 2 0 0 0 1 2 1 0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 学習"
      ],
      "metadata": {
        "id": "j03GipRfcFi9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3-1. 学習ネットワーク実装"
      ],
      "metadata": {
        "id": "VLQQg0AncOMf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 画像入力サイズの確認\n",
        "image, label = next(iter(train_loader))\n",
        "image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xeYYwDc2UG9I",
        "outputId": "e53b2654-ee09-48d9-ec05-5efc742b5f66"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([30, 3, 192, 192])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1 layerずつ動作確認\n",
        "\n",
        "conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
        "conv2 = nn.Conv2d(32, 48, kernel_size=3, padding=1)\n",
        "dropout1 = nn.Dropout2d(p=0.25)\n",
        "conv3 = nn.Conv2d(48, 64, kernel_size=3, padding=1)\n",
        "conv4 = nn.Conv2d(64, 96, kernel_size=3, padding=1)\n",
        "dropout2 = nn.Dropout2d(p=0.25)\n",
        "\n",
        "fc1 = nn.Linear(13824, 256)\n",
        "dropout3 = nn.Dropout(p=0.5)\n",
        "fc2 = nn.Linear(256, 3)\n",
        "\n",
        "\n",
        "h = conv1(image)\n",
        "h = F.relu(h)\n",
        "h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "\n",
        "h = conv2(h)\n",
        "h = F.relu(h)\n",
        "h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "h = dropout1(h)\n",
        "\n",
        "h = conv3(h)\n",
        "h = F.relu(h)\n",
        "h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "\n",
        "h = conv4(h)\n",
        "h = F.relu(h)\n",
        "h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "h = dropout2(h)\n",
        "# h.shape -> torch.Size([30, 96, 12, 12])\n",
        "# 96 * 12 * 12 -> 13824\n",
        "\n",
        "h = h.view(-1, 13824)  # ベクトル化\n",
        "\n",
        "h = fc1(h)\n",
        "h = F.relu(h)\n",
        "h = dropout3(h)\n",
        "y = fc2(h)\n",
        "y.shape\n",
        "\n",
        "# 動いた"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7INxk0Ukg4v",
        "outputId": "a574d613-5bed-442a-d559-730394866028"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([30, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCwmigVonJ6R",
        "outputId": "d249bcf6-18d9-421f-fa4c-e0f121e5e0ab"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 2, 0, 0, 1, 0,\n",
              "        0, 0, 0, 1, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F.cross_entropy(y, label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1eL0F7CnDyF",
        "outputId": "69cc7bd9-8f4a-4581-d4c8-2b30627459e5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.0943, grad_fn=<NllLossBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Network(pl.LightningModule):\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(32, 48, kernel_size=3, padding=1)\n",
        "        self.dropout1 = nn.Dropout2d(p=0.25)\n",
        "        self.conv3 = nn.Conv2d(48, 64, kernel_size=3, padding=1)\n",
        "        self.conv4 = nn.Conv2d(64, 96, kernel_size=3, padding=1)\n",
        "        self.dropout2 = nn.Dropout2d(p=0.25)\n",
        "\n",
        "        self.fc1 = nn.Linear(13824, 256)\n",
        "        self.dropout3 = nn.Dropout(p=0.5)\n",
        "        self.fc2 = nn.Linear(256, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # NOTE: 株価予測の論文に大体倣う実装にした\n",
        "        # https://github.com/rosdyana/Going-Deeper-with-Convolutional-Neural-Network-for-Stock-Market-Prediction/blob/master/myDeepCNN.py\n",
        "        h = self.conv1(x)\n",
        "        h = F.relu(h)\n",
        "        h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "\n",
        "        h = self.conv2(h)\n",
        "        h = F.relu(h)\n",
        "        h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "        h = self.dropout1(h)\n",
        "\n",
        "        h = self.conv3(h)\n",
        "        h = F.relu(h)\n",
        "        h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "\n",
        "        h = self.conv4(h)\n",
        "        h = F.relu(h)\n",
        "        h = F.max_pool2d(h, kernel_size=2, stride=2)\n",
        "        h = self.dropout2(h)\n",
        "\n",
        "        h = h.view(-1, 13824)  # テンソルからベクトルへ変換\n",
        "\n",
        "        h = self.fc1(h)\n",
        "        h = F.relu(h)\n",
        "        h = self.dropout3(h)\n",
        "        h = self.fc2(h)\n",
        "        return h\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x, t = batch\n",
        "        y = self(x)\n",
        "        loss = F.cross_entropy(y, t)\n",
        "        self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)\n",
        "        self.log('train_acc', accuracy(y.softmax(dim=-1), t), on_step=True, on_epoch=True, prog_bar=True)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        x, t = batch\n",
        "        y = self(x)\n",
        "        loss = F.cross_entropy(y, t)\n",
        "        self.log('val_loss', loss, on_step=False, on_epoch=True)\n",
        "        self.log('val_acc', accuracy(y.softmax(dim=-1), t), on_step=False, on_epoch=True)\n",
        "        return loss\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        x, t = batch\n",
        "        y = self(x)\n",
        "        loss = F.cross_entropy(y, t)\n",
        "        self.log('test_loss', loss, on_step=False, on_epoch=True)\n",
        "        self.log('test_acc', accuracy(y.softmax(dim=-1), t), on_step=False, on_epoch=True)\n",
        "        return loss\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # optimizer = torch.optim.SGD(params=self.parameters(), lr=0.001)\n",
        "        # return optimizer\n",
        "        optimizer = torch.optim.SGD(params=self.parameters(), lr=0.1, weight_decay=0.001)\n",
        "        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
        "        return [optimizer, ], [scheduler, ]"
      ],
      "metadata": {
        "id": "YAVN-Z4Ycdhd"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# アーキテクチャの確認\n",
        "net = Network()  # .cuda()\n",
        "summary(net, input_size=(3, 192, 192))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_v-DaYMqgoXS",
        "outputId": "fb7eb23a-d053-4917-aebf-2177ea8b9ad0"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "Network                                  [1, 3]                    --\n",
              "├─Conv2d: 1-1                            [32, 192, 192]            896\n",
              "├─Conv2d: 1-2                            [48, 96, 96]              13,872\n",
              "├─Dropout2d: 1-3                         [48, 48, 48]              --\n",
              "├─Conv2d: 1-4                            [64, 48, 48]              27,712\n",
              "├─Conv2d: 1-5                            [96, 24, 24]              55,392\n",
              "├─Dropout2d: 1-6                         [96, 12, 12]              --\n",
              "├─Linear: 1-7                            [1, 256]                  3,539,200\n",
              "├─Dropout: 1-8                           [1, 256]                  --\n",
              "├─Linear: 1-9                            [1, 3]                    771\n",
              "==========================================================================================\n",
              "Total params: 3,637,843\n",
              "Trainable params: 3,637,843\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (M): 285.72\n",
              "==========================================================================================\n",
              "Input size (MB): 0.44\n",
              "Forward/backward pass size (MB): 14.60\n",
              "Params size (MB): 14.55\n",
              "Estimated Total Size (MB): 29.59\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "結構ネットワーク大きい\n",
        "\n",
        "画像サイズ減らしても精度落ちないのであれば減らしたい"
      ],
      "metadata": {
        "id": "iCmoLO-Xk01_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3-2. training / validation"
      ],
      "metadata": {
        "id": "hJkoXZiOmAJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# gpu有効なら実行しておく\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    import psutil\n",
        "    import humanize\n",
        "    import os\n",
        "    import GPUtil as GPU\n",
        "\n",
        "    GPUs = GPU.getGPUs()\n",
        "    # XXX: only one GPU on Colab and isn’t guaranteed\n",
        "    gpu = GPUs[0]\n",
        "    def printm():\n",
        "        process = psutil.Process(os.getpid())\n",
        "        print(\"Gen RAM Free: \" + humanize.naturalsize(psutil.virtual_memory().available), \" |     Proc size: \" + humanize.naturalsize(process.memory_info().rss))\n",
        "        print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total     {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "    printm()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jfnchvxpr_F0",
        "outputId": "566373c9-883b-47a4-e7c6-9e650169978b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gputil\n",
            "  Downloading GPUtil-1.4.0.tar.gz (5.5 kB)\n",
            "Building wheels for collected packages: gputil\n",
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gputil: filename=GPUtil-1.4.0-py3-none-any.whl size=7411 sha256=787c5521580014cc863ef1d834f57ee4e5f3de0b4d7fce6bbade68f6a907950e\n",
            "  Stored in directory: /root/.cache/pip/wheels/6e/f8/83/534c52482d6da64622ddbf72cd93c35d2ef2881b78fd08ff0c\n",
            "Successfully built gputil\n",
            "Installing collected packages: gputil\n",
            "Successfully installed gputil-1.4.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (5.4.8)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.7/dist-packages (0.5.1)\n",
            "Gen RAM Free: 12.1 GB  |     Proc size: 904.8 MB\n",
            "GPU RAM Free: 15106MB | Used: 3MB | Util   0% | Total     15109MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 学習\n",
        "pl.seed_everything(0)\n",
        "net = Network()\n",
        "\n",
        "log_dir: str = 'fx_trader'\n",
        "logger = CSVLogger(save_dir='logs', name=log_dir)\n",
        "trainer = pl.Trainer(max_epochs=50, gpus=1, deterministic=True, logger=logger)\n",
        "trainer.fit(net, train_loader, val_loader)\n",
        "trainer.callback_metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702,
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      "outputs": [
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          "text": [
            "Global seed set to 0\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name     | Type      | Params\n",
            "---------------------------------------\n",
            "0 | conv1    | Conv2d    | 896   \n",
            "1 | conv2    | Conv2d    | 13.9 K\n",
            "2 | dropout1 | Dropout2d | 0     \n",
            "3 | conv3    | Conv2d    | 27.7 K\n",
            "4 | conv4    | Conv2d    | 55.4 K\n",
            "5 | dropout2 | Dropout2d | 0     \n",
            "6 | fc1      | Linear    | 3.5 M \n",
            "7 | dropout3 | Dropout   | 0     \n",
            "8 | fc2      | Linear    | 771   \n",
            "---------------------------------------\n",
            "3.6 M     Trainable params\n",
            "0         Non-trainable params\n",
            "3.6 M     Total params\n",
            "14.551    Total estimated model params size (MB)\n"
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              "model_id": "a3399dee28c54e0f9d6e05ec1694ad94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52ee9ca6feb84de691e38d0c24d7601d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72769e9058bf4774b29eef67f72af32e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd0a0e972f6141a6a4582f33536a4a2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91ad0ce86ec84587bf6b3b9306e74f94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c43580665954130b9dad31618e75f7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be203d5c7211487cbeb652dec6f61158"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1de5b6aafb574bc09b4f05f1df036298"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8944bd198041436d94affec3932d22fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4554e8ea5c5c4bcf8fb88c13b1e15dd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b3ca7cafb20439d9dfef477e54369f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67134a7d33ca4f8791237f0a22610a18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f67e2983ce34def9f0c04972dab8fc6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94650085f2f54b0495a8a2b9d3d74c82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e789fdf32d3d4271978759d6147335df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8438cce5165645ed9c597fd8f27309b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2de701894e81465abb64fc6866eb98cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea9c83d831b246e795df92f684d246a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train_acc': tensor(0.9265),\n",
              " 'train_acc_epoch': tensor(0.9265),\n",
              " 'train_acc_step': tensor(0.8800),\n",
              " 'train_loss': tensor(0.1694),\n",
              " 'train_loss_epoch': tensor(0.1694),\n",
              " 'train_loss_step': tensor(0.2670),\n",
              " 'val_acc': tensor(0.7961),\n",
              " 'val_loss': tensor(0.6927)}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3-3. test"
      ],
      "metadata": {
        "id": "KwJm6oMoqgmB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = trainer.test(dataloaders=test_loader)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277,
          "referenced_widgets": [
            "3b6058447df94f199f454c9b039ea48b",
            "32c93c29b50149ca969e11bf3e0976ce",
            "766d3ae8d85846209e40947d84c87c10",
            "f99573b1ed594c75b7730910db3ffcba",
            "b4f46c61df3f4c5da3004d5874d0fd6f",
            "612471ceb0c848f3946a6f9db9c8414a",
            "e61e23acfe9a420285b1eee632b7af9f",
            "9e0b0794902a4bd3a3e45d9a03ae8709",
            "5d94fa4059fc4276a341cf0b4c3827c5",
            "7453a7528a9a4210846c5b11fd40cefe",
            "1d6acb4e20384c0d8c7ca999e1720218"
          ]
        },
        "id": "dSHHybV8qfwN",
        "outputId": "493adc63-dc99-4bae-b522-5eddf76060b6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:1447: UserWarning: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `test(ckpt_path='best')` to use and best model checkpoint and avoid this warning or `ckpt_path=trainer.checkpoint_callback.last_model_path` to use the last model.\n",
            "  f\"`.{fn}(ckpt_path=None)` was called without a model.\"\n",
            "Restoring states from the checkpoint path at logs/fx_trader/version_3/checkpoints/epoch=49-step=2150.ckpt\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "Loaded model weights from checkpoint at logs/fx_trader/version_3/checkpoints/epoch=49-step=2150.ckpt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b6058447df94f199f454c9b039ea48b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "       Test metric             DataLoader 0\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "        test_acc            0.7403846383094788\n",
            "        test_loss           1.0602726936340332\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_acc': 0.7403846383094788, 'test_loss': 1.0602726936340332}]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3-4. Display result"
      ],
      "metadata": {
        "id": "oSRCqKTdqo5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 結果確認\n",
        "log = pd.read_csv(f'logs/{log_dir}/version_3/metrics.csv')\n",
        "\n",
        "log[['train_loss_epoch', 'epoch']].dropna(how='any', axis=0).reset_index()['train_loss_epoch'].plot()\n",
        "log[['val_loss', 'epoch']].dropna(how='any', axis=0).reset_index()['val_loss'].plot()\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-kDeSP00qslA",
        "outputId": "eb9a327d-1d7d-4196-9ecb-b89bd5076a7c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd6c1d36d90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log[['train_acc_epoch', 'epoch']].dropna(how='any', axis=0).reset_index()['train_acc_epoch'].plot()\n",
        "log[['val_acc', 'epoch']].dropna(how='any', axis=0).reset_index()['val_acc'].plot()\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cjEHZmINqunw",
        "outputId": "583b667d-5d1e-4dd8-9be5-7741e3fdb4ca"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd6c1ce6bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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q7SZ6VW0SkXtxkrYLeFpVs0XkISBLVVfj1Np/JCKK03Rzj3fdMhH5Ps7JAuCh5guzJnDWflrM11dsRYHffymTyyfayI/GmJPsCVMhrry2gZk/fIdzU/ry29tmMHKANcUY0xud6QlTft0wZXquv2wvoKHJw/8tmWxJ3hjTKkv0IW7l5jwmDO1H+rCOPQzaGNN7WKIPYXuKqtieV8EXZ6S2X9gY02tZog9hK7fkERkhLJw6LNihGGN6MEv0IarJ7eG1LfnMPW8QA23cd2PMGViiD1Hrc49SXHXcmm2MMe2yRB+i/rw5j6S4KC4dPyjYoRhjejhL9CGooraRf+QUsXDqcKIj7VdojDkzyxIh6C87nL7z1mxjjPGHJfoQtHJzHuOHJJA+rF+wQzHGhABL9CEmt7iabYfLWTw91YYTNsb4xRJ9iPnzljxcEcLCadZ33hjjH0v0IcTtUV7dksfccSkMSogJdjjGmBBhiT6EvJ97lKLK4yy2i7DGmA6wRB9C/rw5j8TYKC6bYH3njTH+s0QfIo7VNPBmdiELpw6jT6Qr2OEYY0KIJfoQoKp8Z9VO3B7l1lmjgh2OMSbEWKIPAa9uyeeNT46wdP44zhuSEOxwjDEhxq9ELyILRGS3iOSKyLJWlo8UkbUislVEdojIVd75o0WkTkS2eV+/DfQBhLvDZbU8uDqbmWnJ/NtFY4IdjjEmBLX7cHARcQGPAfOAPGCTiKxW1RyfYt8BXlbVx0VkIrAGGO1dtldVpwY27N7B7VHue2kbAvzihim4IuwGKWNMx/lTo58J5KrqPlVtAFYAC1uUUaD5fvxEoCBwIfZej6/LJevgMb6/aBKpSfY8WGNM5/iT6IcDh32m87zzfC0HbhORPJza/Nd8lqV5m3T+KSIXtrYDEblbRLJEJKukpMT/6MPY9sPl/OrtPVw7ZRiLprX8cRtjjP8CdTH2ZuBZVU0FrgL+KCIRwBFgpKpOA5YCL4jIaSNxqeoTqpqpqpkpKSkBCil01TY0cd9L2xiU0IfvL5wU7HCMMSHOn0SfD4zwmU71zvP1r8DLAKr6ERADDFTV46pa6p2/GdgLjOtq0OHu4Td2sb+0hp/fMJXEuKhgh2OMCXH+JPpNwFgRSRORaOAmYHWLMoeAywBEZAJOoi8RkRTvxVxE5BxgLLAvUMGHm4OlNTzw+k5e+PgQd190DrPHDAh2SMaYMNBurxtVbRKRe4E3ARfwtKpmi8hDQJaqrga+ATwpIvfhXJi9U1VVRC4CHhKRRsAD/LuqlnXb0YQgVSXr4DF+v34f/8gpIjJCuOn8ESydZ198jDGBIaoa7BhOkZmZqVlZWcEOo9s1uT2s2VnIU+v3sT2vgv5xUdw2axRfmj2KQf1sZEpjTMeIyGZVzWxtWbs1etM9/vvPO3h1Sz7nDIzn4UWTWDw9ldhoG8PGGBN4luiD4KO9pby6JZ9/u+gc/mfBeCLsRihjTDeysW7Oska3hwde38mI5FjumzfOkrwxpttZjf4se/aDA+wpruapOzKJibKmGmNM97Ma/VlUWFHPr97+jMsnDOKyCYODHY4xppewRH8W/WDNLpo8yoPXpAc7FGNML2KJ/iz5MPcof9lewFfnnsuIZBugzBhz9liiPwsamjw8sDqbkclx/NvF5wQ7HGNML2MXY8+CZz7YT25xNU/faRdgjTFnn9Xou9mRijoeeWcP8yYO5tLxdgHWGHP2WaLvZg//dRduj/LA1RODHYoxppeyRN+N3tlVxBufHOFrl9oFWGNM8Fii7yY1x5v47qqdjBvcl7vtod7GmCCyi7Hd5Of/+IwjlfWsvOVzREfa+dQYEzyWgbrB9sPlPPvhfm6bNYoZo5KCHY4xppezRB9gjW4Py179hIF9+/CtBecFOxxjjLGmm0B7+v397DpSyW9vm06/mLP0vNf8zVBfAYkjITEVouzBJcaYkyzRB9Ch0lp++fZnzJs4mCvSh3T/DlVh3Y/hnz8+dX78ICfh9x8BIz8H02+H6Pjuj8cY0yP51XQjIgtEZLeI5IrIslaWjxSRtSKyVUR2iMhVPsu+7V1vt4hcEcjgexJV5f5Vn+AS4aGF6Yh08zjzjXWw8l+cJD/1VrjzDVj0W7jkfhh3BcQkwpEd8Pf/gV9lwHv/B3Xl3RuTMaZHardGLyIu4DFgHpAHbBKR1aqa41PsO8DLqvq4iEwE1gCjvZ9vAtKBYcDbIjJOVd2BPpBge31bAev3HOV716YzNDG2e3dWVQgrboH8LXD592DO16GtE8uhDbD+5/Duw/DBozDzLrjgqxA/8GSZpuNQkee8jldB2kUQ0697j8EYc9b403QzE8hV1X0AIrICWAj4JnoFmjNDIlDg/bwQWKGqx4H9IpLr3d5HAYi9xyivbeD7f81h6oj+3HbBqO7d2ZEd8OJNUHcMbvwTTLj6zOVHXgC3vgJHtjsJf/0vYMPjcM5cqC52knt14anrRMbAeVfC5BthzGUQGX36dqtL4NBHkLcREkdA+vXQNyVQRxl8lQVw8EPIy4LIPt6msJHOsSam2onQhBR/Ev1w4LDPdB4wq0WZ5cA/RORrQDxwuc+6G1qsO7zlDkTkbuBugJEjR/oTd4/yk79/SnldI3+6PgNXdz4a8NM18OevQGx/+Je/w9Ap/q87dArc8ByUfAbv/xLyNkG/YTD2cucibn9vAhMX5KyCnX+G7NcgNhkmXQ8TF0LlETj0oZMAj37mbDciCjyN8Pdvw7mXOSeH866CaD/vBFZ1vpnseAkOfwzpiyDzX7snkXo8UJkP6jl9WWOd8zM59BEc/ACOHXDmR8WBu9E5Rl8xic4rEMQFQyY511NGfQ6GZEBEJwa/O14NtaWBielMouMhbkDb3yJNjyOqeuYCIl8EFqjqV7zTtwOzVPVenzJLvdv6uYjMBp4CJgGPAhtU9U/eck8Bf1PVlW3tLzMzU7Oysrp4WGfP5oNlLH78I+6+6Bz+96oJpxco3gVRsZA0ums7KtwJv/08DJsGN78ICd18sdfdCHvfhe0rYPcaaKp35vdJhJGznIQ0ag4MnQple51EveMVqMyD6L4w4VrnJNJ/lFML7jvo1MRQth8+ecVZrzQXXH1g0Hjnm0dMIsz6d+cVl9z1YynZfTK+ikNnLhub7D22z8HI2TBkMkgEVBd5m7cOQflhqDgMDTVdjw2cn23+Fig/6ExHJzg/45GzW/89qzq9rCoOe2PxxlR/Fq/BRMaevOCfOMJ57zvY+VmZzosbCOct6NSqIrJZVTNbXeZHop8NLFfVK7zT3wZQ1R/5lMnGORkc9k7vAy4A/tW3rIi86d1Wm003oZToG90ervn1+1TWNfLW0ouJ79PiC1LlEXhsJiBw20oYMbPzO/vrUtj2PCzdFZjk1xH1lbD/PafpYnB627VNj8ep8e94CbJfh+MVJ5e5vM0fianQWOvUngFGXwiTb3C+McQkOl1F1/8CPv0rRMXD+f8Cs+/t+ImtqtD5VrLjJefkIREw5lIYt8CppbcUEel86xk4DiKClKwq8r3fKLzfmkp2nbl8dMLJb2LNyTZuYPcn2+OVzkmv/JD35HcYakq6d5+9xfBMuOudTq3a1UQfCXwGXAbkA5uAW1Q126fM34CXVPVZEZkAvIPTRDMReAGnXX6Yd/7YM12MDaVE/8R7e/nhmk/53e0zWu9OueJWyH3bSVLVJXDLS5B2Ycd31FADPx/vNIlc/7uuB342NNY7Nf3m2q9vUvA0OYl90hed5NSaohx4/xdOso6IdBKY39SpgavH+QY0+UbnGkJCiA0TXXfMuTjemj4JENO/5zSfNNZBzVGcy3Wm01zRnf62fqZE324bvao2ici9wJuAC3haVbNF5CEgS1VXA98AnhSR+3B+03eqcwbJFpGXcS7cNgH3hEuPm/zyOn751h4unzCI+RNbSSA5q51a6eXfcxLNcwvh+S/CTc/DuZefXv5Msl9zalEz7ghM8GdDVIxT+x/cyefjDp4Ii38Pc78Nm5/peNfQfsNg0mJICeG7k2OTnFcoiIpt+6Rtgq7dGv3ZFio1+ruey+L9PUd5a+lFpCa1aAqoK3eabPoOhrvWgivSqe38cZHTXrzkWRj/Bf939vt5TvvrPRt7Tg3OGNOjnKlGb1dOOuGtnCLeyini65ePPT3JA7z1XSexX/trJ8mD02/9jr84PSpeut1pkvBHUY7ThXH6HZbkjTGdYom+g2obmli+Optxg/vyr59PO73A/vdgy3Mw+x4YNvXUZbFJcPsqGDHL6Sa57YX2d7jlD0673ZSbA3MAxphexxJ9Bz3yzh7yy+v4wXUZRLla/Pga6+AvX3e6Us79dusbiOkHt/3Zuft09degYGvbO2usg+0vwoRrIH5AwI7BGNO7WKLvgEOltTy1fj83Zo7g/NGtdHH850+gbB9c88iZbxiKjoMvPgPxKfDq3U5Cb03Oaqe/9Iw7AxK/MaZ3skTfARv2l9LkUe66qJUmmyM7nLFkpt7mDC/QnrhkWPQb5w7Tt5e3Xmbzs5B8jtPX3BhjOskSfQfkFFQSG+UibWDfUxe4G51mmLhkmP99/zc45lKY+W/w8W9h79pTl5Xsdm4+souwxpguskTfAdkFFUwYmnD6eDbrfw5HtsFV/9fxu1YvXw4DxsKqrzo3yDTb8pwzjszUW7satjGml7NE7yePR8kpqCR9WIuBrPI3wz9/Chk3QPp1Hd9wdBxc/wTUFMMb33TmNR13euSMvyq8RoQ0xgSFPWHKTwfLaqlpcJM+zGdUxYZa52JqwhCnNt9Zw6fDxf8Da3/gDA8MUFdmF2GNMQFhid5P2QXOAF2n1OjfesAZefFLrztDB3fF55fCZ2/CG0ud7pn9R0Ha3K5t0xhjsKYbv2UXVBIZIYwb4r0Qm/s2bHrSeVrTOXO7vgNXJFz3O+fC7pHtMP1LwRtF0RgTViyT+Cm7oJJzB/WlT6QLastg1T2QMh4ueyBwOxl4Llz1M6dGP+22wG3XGNOrWdONn3IKKrl4XIrz0Ic3ljpP8rn1ZWfUvkCaditMvcW6VBpjAsZq9H4orqznaPVx50LsJyudYYPnLuvYo/w6wpK8MSaALNH7IbugEoApyY3wxjecQcnm/FeQozLGGP9YovdDc4+bCZrrPB7vsgdPDj9sjDE9nCV6P2QXVDJqQBxxx486M+xJOsaYEGKJ3g/ZBZVO+3xVkTOjb4g9e9QY06v5lehFZIGI7BaRXBFZ1sryX4rINu/rMxEp91nm9lm2OpDBnw2V9Y0cKqt1bpSqLnQeHhLZJ9hhGWOM39ptaBYRF/AYMA/IAzaJyGpVzWkuo6r3+ZT/GjDNZxN1qtriUUuhI8d7IXbisH6wtRD6du4J7cYYEyz+1OhnArmquk9VG4AVwMIzlL8ZeDEQwfUEzT1unKabQkiwZhtjTGjxJ9EPBw77TOd5551GREYBacC7PrNjRCRLRDaIyKI21rvbWyarpKTEz9DPjuyCClIS+jAoIQaqi6xGb4wJOYG+GHsTsFJV3T7zRqlqJnAL8CsRGdNyJVV9QlUzVTUzJaVnDcub03whVtVJ9FajN8aEGH8SfT7g258w1TuvNTfRotlGVfO97/uAdZzaft+j1Te62VNc7ST6umPgbrAavTEm5PiT6DcBY0UkTUSicZL5ab1nRGQ8kAR85DMvSUT6eD8PBOYAOS3X7ak+K6rC7VGnx01VoTPTavTGmBDTbq8bVW0SkXuBNwEX8LSqZovIQ0CWqjYn/ZuAFaqqPqtPAH4nIh6ck8qPfXvr9HSnXIgt3+XMTBgaxIiMMabj/LqPX1XXAGtazHugxfTyVtb7EMjoQnxBlV1QQUKfSEYkxcFhu1nKGBOa7M7YM8guqGTCsH5ERIhzsxQ4jw00xpgQYom+DW6P8umRqpPPiK0qgugEiI4PbmDGGNNBlujbsP9oNXWN7pPPiK22m6WMMaHJEn0bTrkQC06vG+taaYwJQZbo25BdUEl0ZATnDvI+DNyGPzDGhChL9G3ILqjgvMEJRLkiTt4VazV6Y0wIskTfClU9OQY9wPEqaKy1Gr0xJiRZom9FQUU95bWNJxN9dXMfeqvRG2NCjyX6VmTnO8+Indjc48aGPzDGhDBL9K34cG8p0a4IJgxNcHHdPzoAABTTSURBVGY01+ht+ANjTAiyRN9CfaOb17bmMz99MHHR3hEimmv0NvyBMSYEWaJv4R85RVTUNXLj+T4jM1cXQmQMxCQGLzBjjOkkS/QtvJJ1mOH9Y5kzZuDJmVVFTm1eJHiBGWNMJ1mi95F3rJb3c4/yxRmpzkBmzaqO2GBmxpiQZYnexytZeQAsyUw9dUF1kbXPG2NCliV6L49HWbk5j8+fO5DUpLhTF1YVWY3eGBOyLNF7fbD3KPnldSzJHHHqgsY6OF5hNXpjTMiyRO/10qbDJMZGMX9ii4ReZQ8cMcaENr8SvYgsEJHdIpIrIstaWf5LEdnmfX0mIuU+y+4QkT3e1x2BDD5QjtU08I/sIq6bNpyYKNepC234A2NMiGv3mbEi4gIeA+YBecAmEVnt+5BvVb3Pp/zXgGnez8nAg0AmoMBm77rHAnoUXfT6tnwa3J7TL8KCDX9gjAl5/tToZwK5qrpPVRuAFcDCM5S/GXjR+/kK4C1VLfMm97eABV0JONBUlZey8pg0vN/Jp0n5suEPjDEhzp9EPxw47DOd5513GhEZBaQB73ZkXRG5W0SyRCSrpKTEn7gDZmd+JbuOVHJjy4uwzaoKISISYpPPalzGGBMogb4YexOwUlXdHVlJVZ9Q1UxVzUxJSQlwSGf2ctZhoiMjuHZKq+euk33oI+y6tTEmNPmTvfIB3+puqndea27iZLNNR9c96+ob3azals+Vk4aQGBfVeqGqI9a10hgT0vxJ9JuAsSKSJiLROMl8dctCIjIeSAI+8pn9JjBfRJJEJAmY753XI/x9ZyFV9U1tN9uA3SxljAl57SZ6VW0C7sVJ0LuAl1U1W0QeEpFrfYreBKxQVfVZtwz4Ps7JYhPwkHdej/DixkOMSI7lgnMGtF2outBq9MaYkNZu90oAVV0DrGkx74EW08vbWPdp4OlOxtdt9hRV8fH+Mv57wXmnDmDmq6kBakutRm+MCWm99grj8x8fItoVwQ1narapKXberUZvjAlhvTLR1zY08efNeVyZMYSBffu0XbCquQ+91eiNMaGrVyb617cVUHW8idsuGHXmgtX2CEFjTOjrdYleVfnThoOcNziBzFFJZy58YvgDuyvWGBO6el2i33a4nOyCSm6bPQpp79GA1UWAQPzZvYnLGGMCqdcl+j9uOEh8tIvrprVxJ6yvqkInybv86pxkjDE9Uq9K9MdqGvjrjiMsmjacvn38SN5VhTZqpTEm5PWqRL9ycx4NTZ72L8I2qy60ceiNMSGv1yR6j0d5/uODZI5KYsLQfv6tVFVkNXpjTMjrNYn+/dyjHCit9b8273E7N0xZjd4YE+J6TaL/04aDJMdHc2WGn4m75iiox26WMsaEvF6R6I9U1PH2riJuyBxBn0hX+yuA3SxljAkbvSLRv/jxIRS4ddZI/1ey4Q+MMWEirDuI7yup5jfr9vLa1nwuOW8QI5Lj/F/ZavTGmDARlol+d2EVj63N5a87CohyRfCl2aP42qVjO7YRq9EbY8JEWCX6nfkV/PrdPbyZXURctIu7LjqHr3z+HFISzjBCZVuqCyE2CSI7sa4xxvQgYZPo9x+t4epfv09CTCT/eem5fHlOGknx0Z3fYJXdLGWMCQ9hk+jTBsbzqxuncumEQfSLaeNB3x1hwx8YY8KEX71uRGSBiOwWkVwRWdZGmRtEJEdEskXkBZ/5bhHZ5n2d9lDxQFo0bXhgkjw4I1dajd4YEwbardGLiAt4DJgH5AGbRGS1qub4lBkLfBuYo6rHRGSQzybqVHVqgOPuXqpOorcavTEmDPhTo58J5KrqPlVtAFYAC1uUuQt4TFWPAahqcWDDPMvqjoG7wWr0xpiw4E+iHw4c9pnO887zNQ4YJyIfiMgGEVngsyxGRLK88xe1tgMRudtbJqukpKRDB9AtTjxZymr0xpjQF6iLsZHAWGAukAq8JyIZqloOjFLVfBE5B3hXRD5R1b2+K6vqE8ATAJmZmRqgmDrvxM1SVqM3xoQ+f2r0+cAIn+lU7zxfecBqVW1U1f3AZziJH1XN977vA9YB07oYc/ezm6WMMWHEn0S/CRgrImkiEg3cBLTsPbMKpzaPiAzEacrZJyJJItLHZ/4cIIeeriLPebdEb4wJA+023ahqk4jcC7wJuICnVTVbRB4CslR1tXfZfBHJAdzAt1S1VEQ+B/xORDw4J5Uf+/bW6bFKdkHiSIiOD3YkxhjTZX610avqGmBNi3kP+HxWYKn35VvmQyCj62GeZcWfwqAJwY7CGGMColcMU9wh7kY4+hkMGh/sSIwxJiAs0bdUtg88jTBoYrAjMcaYgLBE31Kx9xKCNd0YY8KEJfqWij8FiYCB44IdiTHGBETYjF4ZMMU5kJQGUbHBjsSYgGlsbCQvL4/6+vpgh2K6KCYmhtTUVKKi/B/A0RJ9SyXW48aEn7y8PBISEhg9ejQiEuxwTCepKqWlpeTl5ZGWlub3etZ046vpOJTutURvwk59fT0DBgywJB/iRIQBAwZ0+JuZJXpfR/eAuiHFulaa8GNJPjx05vdoid5X8S7n3bpWGmPCiCV6XyW7ICISBpwb7EiMMSZgLNH7Kt7lJPnILjxU3BhzmvLycn7zm990eL2rrrqK8vLyboioZ5o7dy5ZWVkB3671uvFVnANDQ+uph8Z01Pf+kk1OQWVAtzlxWD8evCa9zeXNif6rX/3qKfObmpqIjGw7Da1Zs6bNZcZ/VqNv1lADxw5a+7wx3WDZsmXs3buXqVOncv7553PhhRdy7bXXMnGi8/+2aNEiZsyYQXp6Ok888cSJ9UaPHs3Ro0c5cOAAEyZM4K677iI9PZ358+dTV1fX5v6efPJJzj//fKZMmcLixYupra0FoKioiOuuu44pU6YwZcoUPvzwQwCee+45Jk+ezJQpU7j99tvb3G5JSQmLFy/m/PPP5/zzz+eDDz4AYPny5dx+++3Mnj2bsWPH8uSTTwJOd8hvfetbTJo0iYyMDF566aUT2/rJT35CRkYGU6ZMYdmyZSfmv/LKK8ycOZNx48axfv36jv6oW6eqPeo1Y8YMDYq8zaoP9lPNXhWc/RvTjXJycoK6//3792t6erqqqq5du1bj4uJ03759J5aXlpaqqmptba2mp6fr0aNHVVV11KhRWlJSovv371eXy6Vbt25VVdUlS5boH//4xzb317y+qur999+vjz76qKqq3nDDDfrLX/5SVVWbmpq0vLxcd+7cqWPHjtWSkpJTYmnNzTffrOvXr1dV1YMHD+r48eNVVfXBBx/UyZMna21trZaUlGhqaqrm5+frypUr9fLLL9empiYtLCzUESNGaEFBga5Zs0Znz56tNTU1p+zz4osv1qVLl6qq6htvvKGXXXZZq3G09vvEGTa+1bxqTTfNSj513q1Gb0y3mzlz5ik3/Dz66KO89tprABw+fJg9e/YwYMCAU9ZJS0tj6lSnaXXGjBkcOHCgze3v3LmT73znO5SXl1NdXc0VV1wBwLvvvstzzz0HgMvlIjExkeeee44lS5YwcOBAAJKTk9vc7ttvv01OzslHalRWVlJdXQ3AwoULiY2NJTY2lksuuYSNGzfy/vvvc/PNN+NyuRg8eDAXX3wxmzZt4p///Cdf/vKXiYuLO22f119/vV/H2BGW6JsV54Ar2hn+wBjTreLjTz7UZ926dbz99tt89NFHxMXFMXfu3FZvCOrTp8+Jzy6X64xNN3feeSerVq1iypQpPPvss6xbty4gcXs8HjZs2EBMTMxpy1r2b+/sfQvNx+lyuWhqaurUNlqyNvpmxZ/CwPPAZec+YwItISGBqqqqVpdVVFSQlJREXFwcn376KRs2bOjy/qqqqhg6dCiNjY08//zzJ+ZfdtllPP744wC43W4qKiq49NJLeeWVVygtLQWgrKysze3Onz+fX//61yemt23bduLz66+/Tn19PaWlpaxbt+7EtYiXXnoJt9tNSUkJ7733HjNnzmTevHk888wzJ64dnGmfgWCJvlnxLnvYiDHdZMCAAcyZM4dJkybxrW9965RlCxYsoKmpiQkTJrBs2TIuuOCCLu/v+9//PrNmzWLOnDmMH3/y//qRRx5h7dq1ZGRkMGPGDHJyckhPT+f+++/n4osvZsqUKSxdurTN7T766KNkZWUxefJkJk6cyG9/+9sTyyZPnswll1zCBRdcwHe/+12GDRvGddddd+Ii76WXXspPf/pThgwZwoIFC7j22mvJzMxk6tSp/OxnP+vyMZ+JOG347RQSWQA8gvPM2N+r6o9bKXMDsBxQYLuq3uKdfwfwHW+xh1X1D2faV2ZmpnZHP9Izqq+EH4+Ayx6AC79xdvdtzFmwa9cuJkywMZy6y/Lly+nbty/f/OY3z8r+Wvt9ishmVc1srXy77RQi4gIeA+YBecAmEVmtPg/5FpGxwLeBOap6TEQGeecnAw8CmTgngM3edY916ui6S/OF2BT7RzDGhB9/GqRnArmqug9ARFYAC4EcnzJ3AY81J3BVLfbOvwJ4S1XLvOu+BSwAXgxM+AFyYowbS/TGhJJ77rnnRF/2Zl//+tf58pe/3KXt/uAHP+CVV145Zd6SJUu4//77Wy2/fPnyLu2vu/mT6IcDh32m84BZLcqMAxCRD3Cad5ar6t/bWHd4p6PtLsW7ICoO+o8KdiTGmA547LHHumW7999/f5tJPRQFqotJJDAWmAukAu+JSIa/K4vI3cDdACNHjgxQSB1QsgtSzoMIuzZtjAk//mS2fGCEz3Sqd56vPGC1qjaq6n7gM5zE78+6qOoTqpqpqpkpKSkdiT8windZ+7wxJmz5k+g3AWNFJE1EooGbgNUtyqzCqc0jIgNxmnL2AW8C80UkSUSSgPneeT1HbRlUF1n7vDEmbLXbdKOqTSJyL06CdgFPq2q2iDyEM7bCak4m9BzADXxLVUsBROT7OCcLgIeaL8z2GHYh1hgT5vxqo1fVNcCaFvMe8PmswFLvq+W6TwNPdy3MblRiid6YnqZv374nxpAxXWf3+xfvgj79oF/P6wxkTLf42zIo/CSw2xySAVeedh+l6SGsm0nxLudh4PbgZGO6zbJly07pCrl8+XIefvhhLrvsMqZPn05GRgavv/66X9uqrq5uc73WxpVvawz6XqWt8YuD9Tqr49F7PKo/Hq36+tfO3j6NCYJgj0e/ZcsWveiii05MT5gwQQ8dOqQVFRWqqlpSUqJjxoxRj8ejqqrx8fFtbquxsbHV9doaV761MehDnY1H3xHVxVBXZu3zxnSzadOmUVxcTEFBASUlJSQlJTFkyBDuu+8+3nvvPSIiIsjPz6eoqIghQ4accVuqyv/+7/+ett67777b6rjyrY1B39v07kRvF2KNOWuWLFnCypUrKSws5MYbb+T555+npKSEzZs3ExUVxejRo1sdh76lzq7Xm/XuNvrmrpV2s5Qx3e7GG29kxYoVrFy5kiVLllBRUcGgQYOIiopi7dq1HDx40K/ttLVeW+PKtzYGfW8TPjX62jJ45sqOrVNdDLHJ0HdQ98RkjDkhPT2dqqoqhg8fztChQ7n11lu55ppryMjIIDMz85Rx48+krfV8x5V3uVxMmzaNZ599lkceeYS7776bp556CpfLxeOPP87s2bO781B7HL/Goz+bOj0efX0FrP5ax9dLuwjO/0rH1zMmhNh49OEl4OPRh4yYRLjhuWBHYYwxPU74JHpjTFj55JNPTvSFb9anTx8+/vjjIEUUuizRG9NLqCoSQjcGZmRknPLwbePoTHN77+51Y0wvERMTQ2lpaaeShOk5VJXS0lJiYmI6tJ7V6I3pBVJTU8nLy6OkpCTYoZguiomJITU1tUPrWKI3pheIiooiLS0t2GGYILGmG2OMCXOW6I0xJsxZojfGmDDX4+6MFZESwL9BL1o3EDgaoHBCiR1372LH3bv4c9yjVDWltQU9LtF3lYhktXUbcDiz4+5d7Lh7l64etzXdGGNMmLNEb4wxYS4cE/0TwQ4gSOy4exc77t6lS8cddm30xhhjThWONXpjjDE+LNEbY0yYC5tELyILRGS3iOSKyLJgx9OdRORpESkWkZ0+85JF5C0R2eN9TwpmjIEmIiNEZK2I5IhItoh83Ts/3I87RkQ2ish273F/zzs/TUQ+9v69vyQi0cGOtTuIiEtEtorIX73TveW4D4jIJyKyTUSyvPM6/bceFoleRFzAY8CVwETgZhGZGNyoutWzwIIW85YB76jqWOAd73Q4aQK+oaoTgQuAe7y/43A/7uPApao6BZgKLBCRC4CfAL9U1XOBY8C/BjHG7vR1YJfPdG85boBLVHWqT//5Tv+th0WiB2YCuaq6T1UbgBXAwiDH1G1U9T2grMXshcAfvJ//ACw6q0F1M1U9oqpbvJ+rcP75hxP+x62qWu2djPK+FLgUWOmdH3bHDSAiqcAXgN97p4VecNxn0Om/9XBJ9MOBwz7Ted55vclgVT3i/VwIDA5mMN1JREYD04CP6QXH7W2+2AYUA28Be4FyVW3yFgnXv/dfAf8NeLzTA+gdxw3OyfwfIrJZRO72zuv037qNRx+GVFVFJCz7zYpIX+DPwH+paqXvo/HC9bhV1Q1MFZH+wGvA+CCH1O1E5GqgWFU3i8jcYMcTBJ9X1XwRGQS8JSKf+i7s6N96uNTo84ERPtOp3nm9SZGIDAXwvhcHOZ6AE5EonCT/vKq+6p0d9sfdTFXLgbXAbKC/iDRX1MLx730OcK2IHMBpir0UeITwP24AVDXf+16Mc3KfSRf+1sMl0W8CxnqvyEcDNwGrgxzT2bYauMP7+Q7g9SDGEnDe9tmngF2q+gufReF+3CnemjwiEgvMw7k+sRb4ordY2B23qn5bVVNVdTTO//O7qnorYX7cACISLyIJzZ+B+cBOuvC3HjZ3xorIVThtei7gaVX9QZBD6jYi8iIwF2fo0iLgQWAV8DIwEmeY5xtUteUF25AlIp8H1gOfcLLN9n9x2unD+bgn41x4c+FUzF5W1YdE5Bycmm4ysBW4TVWPBy/S7uNtuvmmql7dG47be4yveScjgRdU9QciMoBO/q2HTaI3xhjTunBpujHGGNMGS/TGGBPmLNEbY0yYs0RvjDFhzhK9McaEOUv0xhgT5izRG2NMmPv/S399GUNqvZ0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "30epochで十分そうだ...\n",
        "\n",
        "しかも10epoch過ぎた時点で既に過学習の兆候が見えている\n"
      ],
      "metadata": {
        "id": "DMNb7b-Gwj6A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. その他 / 考察"
      ],
      "metadata": {
        "id": "5WBbwI-kw6zd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_chart = pd.read_csv('./fx_train_test_data.csv')[:-30]\n",
        "df_chart.groupby(['class'])['class'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YL2etEmww86x",
        "outputId": "a65c27b9-fa5f-4785-f6a9-f60f19408b9a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "0    2287\n",
              "1     848\n",
              "2     698\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- classごとのデータ数はそこまで大幅に偏っているわけではない\n",
        "- 今回は足5本先の時点で20pips変動があるかどうかを判定したが、もう少し厳しい条件にした場合などで、精度がどう変わるか見てみてもよいかもしれない\n",
        "- たとえば、一時的なドローダウンが間に挟まっている場合はトレードしない方が良いので、class: 0（変動なし）扱いにした場合、精度がどう変わるかは気になる"
      ],
      "metadata": {
        "id": "bQq7LXXhxeVI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-3Gik1rCxd7C"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}