AI_LSTM_NN.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ОПИСАНИЕ РЕШЕНИЯ"
      ],
      "metadata": {
        "id": "pRi3ikqjbSZC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**КЛЮЧЕВАЯ ПРОБЛЕМА АЛГОТРЕЙДЕРА**"
      ],
      "metadata": {
        "id": "bayt76L8bX1J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- наличие только одной реализации процесса колебаний цен финансового актива\n",
        "- при постоянно растущего числа фичей, оверфит \"стратегии\" превращается в дикую головную боль"
      ],
      "metadata": {
        "id": "F5oGade-bhNf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ИЛЛЮСТРАЦИЯ ПРОБЛЕМЫ: ТОРГОВАЯ СТРАТЕГИЯ**"
      ],
      "metadata": {
        "id": "hXTfoOPccAcR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Подойдем к этому цинично и честно расскажем, как мы зафитили стратегию:\n",
        "- использовали ограниченный набор известных показателей и [5] параметров из AlgoPak;\n",
        "- загрузили все параметры в RandomForestRegressor и безжалостно зафитили in-sample до откровенно стыдного уровня;\n",
        "- мы также ради любопытства зафитили LSTM нейронную сеть (число параметров перевалило за 5 миллионов, при наличии всего 900 торговых дней) с аналогичным результатом;\n",
        "- некоторые инструменты при этом, условно успешно переживают out-of-sample период: accuracy > 50%.\n",
        "- эту стратегия успешно запущена в нашем симуляторе, может демонстрировать сделки, и, возможно, даже останется в некотором \"плюсе\" [ИЛЛЮСТРАЦИЯ СТРАТЕГИИ]"
      ],
      "metadata": {
        "id": "9SBwkM0JcF72"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**РЕШЕНИЕ КЛЮЧЕВОЙ ПРОБЛЕМЫ: СЕРВИС ДЛЯ ИНВЕСТОРОВ**"
      ],
      "metadata": {
        "id": "4YqMSAhwdMfd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Предлагаемый инвестиционный сервис ориентирован на аудиторию алготрейдеров и состоит из трех главных элементов, каждый из которых доступен по API:\n",
        "\n",
        "1. Консолидатор рыночных данных и данных AlgoPak в объект, который мы назвали GigaCandles: [КОРОТКОЕ РЕЗЮМЕ]\n",
        "2. Стандартные технические индикаторы для удобства пользования\n",
        "3. И, ключевой элемент: синтетические рыночные данные"
      ],
      "metadata": {
        "id": "U2BAYPAQdkmV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**СИНТЕТИЧЕСКИЕ ДАННЫЕ: СУТЬ ПОДХОДА**"
      ],
      "metadata": {
        "id": "p7PGeFVjectC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Мы предлагаем воспользоваться нашим сервисом генерации синтетических данных по любому финансовому инструменту.\n",
        "\n",
        "Ключевые особенности нашего подхода:\n",
        "- **Сохранение зависимостей:** все темпоральные и кросс-темпоральные характеристики временных рядов полностью сохраняются, как линейные, так и любые иные;\n",
        "- **Корреляция с исходным рядом:** практически 100%, будет снижаться при роста числа блоков разбиения для бутстрэпа. Размер блока определяется пользователем и зависит от характеристик (памяти) стратегии;\n",
        "- **Эргодическая гипотеза:** сгенеренные сервисом альтернативные реализации цен, например SBER, обладают уникальным свойством - их средние показатели в точности соответствуют исходному ряду. По сути, речь идет о доступе к \"параллельным вселенным\", где торгуется выбранный финансовый инструмент;"
      ],
      "metadata": {
        "id": "3mNBXoOGehxx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**СИНТЕТИЧЕСКИЕ ДАННЫЕ: КАК ПОЛЬЗОВАТЬСЯ**"
      ],
      "metadata": {
        "id": "NvHaO1g9fqrf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Сервис рекомендуется использовать одним из двух способов:\n",
        "\n",
        "1. По итогам разработанной стратегии, сгенерить как минимум 10 синтетических траекторий (как аппроксимация 10 децилей), на которых как на дополнительном out of sample прогнать стратегию. При существенной разнице между исходным Шарпом и средним распределения из 10 Шарпов по синтетическим траекториям - красный флаг.\n",
        "\n",
        "2. Использовать синтетические данные изначально - несколько разных генераций при необходимости - для разработки стратегии. По итогам такого подхода использовать настоящую реализацию цены как out of sample период в полном объеме."
      ],
      "metadata": {
        "id": "AGG60taNf4cd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**СИНТЕТИЧЕСКИЕ ДАННЫЕ: ОГРАНИЧЕНИЯ**"
      ],
      "metadata": {
        "id": "uRp39yl6gmQg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- сервис лучше всего подходит для стратегий, построенных так или иначе из цен и их производных. Фундаментальные данные требуют отдельного сервиса и решения, куда более сложного, чем то, что предлагается;\n",
        "- требуется определенная осторожность при бутстрепе многомерных векторов, состоящих не только из цен закрытия: важно проверять полученные синтетические данные на сохранения внутри-векторных соотношений;\n",
        "- и главное: даже безграниченый объем синтетических данных не является гарантией от оверфита! не теряйте голову!"
      ],
      "metadata": {
        "id": "y-uoYxwNgo5_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s9IAMZVseb6s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eGouEDzN7Hcc"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time as tm\n",
        "import datetime as dt\n",
        "import tensorflow as tf\n",
        "\n",
        "# Data preparation\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from collections import deque\n",
        "\n",
        "# AI\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Dropout, Input, Conv2D, MaxPool2D, Flatten\n",
        "from tensorflow.keras import regularizers\n",
        "from keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDDHINyl8XuY",
        "outputId": "2efcf89a-084d-4c3a-9ac8-692263672fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Data/sber.csv'\n",
        "path2 = '/content/drive/MyDrive/Data/sber_price.csv'\n",
        "path3 = '/content/drive/MyDrive/Data/sber_large.csv'"
      ],
      "metadata": {
        "id": "gL-ndD5-7Yvx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path, parse_dates = ['date'], index_col = 'date')\n",
        "df.index.name = None\n",
        "df = df.drop(df.columns[[0, 1]], axis = 1)"
      ],
      "metadata": {
        "id": "UsE91Ow78diB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "price = pd.read_csv(path2, parse_dates = ['date'], index_col = 'date')\n",
        "price.index.name = None\n",
        "price = price.drop(price.columns[[0, 1]], axis = 1)"
      ],
      "metadata": {
        "id": "zaDbEQ5W8i1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frame = pd.concat([df, price], axis = 1)\n",
        "frame = frame.drop(columns = ['field'])"
      ],
      "metadata": {
        "id": "9elAyy0Q8jqI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LARGE DATAFRAME FOR LARGE LSTM NN"
      ],
      "metadata": {
        "id": "Ad0Wh_Vb3W3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path3, index_col = [0])"
      ],
      "metadata": {
        "id": "DR_44X_k3KeR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.copy()\n",
        "df.loc[:,'ret'] = df.close.pct_change(1)\n",
        "df.loc[:,'next_ret'] = df.ret.shift(-1)\n",
        "df = df.dropna()\n",
        "df.loc[:,'label'] = np.sign(df.next_ret)"
      ],
      "metadata": {
        "id": "OGq4ywesKPjW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_fields = df.columns.to_list()\n",
        "fields_need = ['RSI_2', 'EMA_RSI_14', 'RSI_50', 'adx_5', 'adx_10', 'macd_signal', 'macd_osc']"
      ],
      "metadata": {
        "id": "F7O8wFNIaPr3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fields_out = ['next_ret', 'label']\n",
        "total_fields = fields_need\n",
        "feats_cols = [x for x in total_fields if x not in fields_out]"
      ],
      "metadata": {
        "id": "mgLBuTHK4f8x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim_f = len(feats_cols)"
      ],
      "metadata": {
        "id": "RMiKaNVg7CrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feats = np.column_stack([x for y, x in df[feats_cols].items()])\n",
        "labels = np.column_stack([x for y, x in df['label'].items()]).ravel()"
      ],
      "metadata": {
        "id": "Fqok0eQMKPoh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(feats, labels, test_size = 200)"
      ],
      "metadata": {
        "id": "BK48GG2JLWqK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For n-size time steps in the model\n",
        "#steps = 5\n",
        "#dim1 = int(x_train.shape[0] / steps)\n",
        "#dim2 = int(x_train.shape[1])\n",
        "#x_train_prcs = x_train.reshape(dim1, steps, dim2)"
      ],
      "metadata": {
        "id": "X5V-vfDmPjxK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For single time step in the model\n",
        "x_train_prcs = x_train.reshape(-1,1,dim_f)"
      ],
      "metadata": {
        "id": "hlJ0ZQCVSEB-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def GetTrainedModel(x_train, y_train, dim = dim_f):\n",
        "\n",
        "  y_train = (y_train + 1)/2\n",
        "\n",
        "  model = Sequential()\n",
        "  model.add(LSTM(512, activation = 'tanh', return_sequences=True, input_shape = (1, dim)))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(LSTM(128, activation = 'tanh'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(32, activation = 'tanh'))\n",
        "  model.add(Dense(1, activation = 'sigmoid'))\n",
        "\n",
        "  BATCH_SIZE = 64\n",
        "  EPOCHS = 2250\n",
        "\n",
        "  model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "\n",
        "  model.fit(x_train, y_train,\n",
        "            batch_size=BATCH_SIZE,\n",
        "            epochs=EPOCHS,\n",
        "            verbose=1)\n",
        "\n",
        "  model.summary()\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "ljrT9QbA9K4S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GetTrainedModel(x_train_prcs, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qO5wcvHmLWst",
        "outputId": "8f6651c9-ad0a-4848-bdd1-3e1766613181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2250\n",
            "12/12 [==============================] - 6s 34ms/step - loss: 0.6994\n",
            "Epoch 2/2250\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.6923\n",
            "Epoch 3/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6897\n",
            "Epoch 4/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6894\n",
            "Epoch 5/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6922\n",
            "Epoch 6/2250\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.6967\n",
            "Epoch 7/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6908\n",
            "Epoch 8/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6870\n",
            "Epoch 9/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6855\n",
            "Epoch 10/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6865\n",
            "Epoch 11/2250\n",
            "12/12 [==============================] - 0s 32ms/step - loss: 0.6847\n",
            "Epoch 12/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6872\n",
            "Epoch 13/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6836\n",
            "Epoch 14/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6839\n",
            "Epoch 15/2250\n",
            "12/12 [==============================] - 0s 32ms/step - loss: 0.6778\n",
            "Epoch 16/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6837\n",
            "Epoch 17/2250\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.6844\n",
            "Epoch 18/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6795\n",
            "Epoch 19/2250\n",
            "12/12 [==============================] - 0s 32ms/step - loss: 0.6814\n",
            "Epoch 20/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6711\n",
            "Epoch 21/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6765\n",
            "Epoch 22/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.6798\n",
            "Epoch 23/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.6863\n",
            "Epoch 24/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.6928\n",
            "Epoch 25/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.6818\n",
            "Epoch 26/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.6860\n",
            "Epoch 27/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.6797\n",
            "Epoch 28/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.6806\n",
            "Epoch 29/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.6813\n",
            "Epoch 30/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.6766\n",
            "Epoch 31/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.6775\n",
            "Epoch 32/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.6810\n",
            "Epoch 33/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.6720\n",
            "Epoch 34/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6714\n",
            "Epoch 35/2250\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.6786\n",
            "Epoch 36/2250\n",
            "12/12 [==============================] - 0s 37ms/step - loss: 0.6841\n",
            "Epoch 37/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6773\n",
            "Epoch 38/2250\n",
            "12/12 [==============================] - 0s 33ms/step - loss: 0.6816\n",
            "Epoch 39/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.6749\n",
            "Epoch 40/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.6784\n",
            "Epoch 41/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.6825\n",
            "Epoch 42/2250\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.6796\n",
            "Epoch 43/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6734\n",
            "Epoch 44/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.6816\n",
            "Epoch 45/2250\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.6732\n",
            "Epoch 46/2250\n",
            "12/12 [==============================] - 0s 34ms/step - loss: 0.6807\n",
            "Epoch 47/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.6766\n",
            "Epoch 48/2250\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.6754\n",
            "Epoch 49/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.6822\n",
            "Epoch 50/2250\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.6763\n",
            "Epoch 51/2250\n",
            "12/12 [==============================] - 0s 37ms/step - loss: 0.6833\n",
            "Epoch 52/2250\n",
            "12/12 [==============================] - 0s 37ms/step - loss: 0.6755\n",
            "Epoch 53/2250\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.6773\n",
            "Epoch 54/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.6791\n",
            "Epoch 55/2250\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.6784\n",
            "Epoch 56/2250\n",
            "12/12 [==============================] - 0s 42ms/step - loss: 0.6758\n",
            "Epoch 57/2250\n",
            "12/12 [==============================] - 1s 54ms/step - loss: 0.6770\n",
            "Epoch 58/2250\n",
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            "Epoch 1987/2250\n",
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            "Epoch 1988/2250\n",
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            "Epoch 1990/2250\n",
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            "Epoch 1991/2250\n",
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            "Epoch 1992/2250\n",
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            "Epoch 2008/2250\n",
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            "Epoch 2009/2250\n",
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            "Epoch 2010/2250\n",
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            "Epoch 2011/2250\n",
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            "Epoch 2012/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3603\n",
            "Epoch 2013/2250\n",
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            "Epoch 2014/2250\n",
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            "Epoch 2015/2250\n",
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            "Epoch 2016/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3527\n",
            "Epoch 2017/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3651\n",
            "Epoch 2018/2250\n",
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            "Epoch 2019/2250\n",
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            "Epoch 2020/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3580\n",
            "Epoch 2021/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3492\n",
            "Epoch 2022/2250\n",
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            "Epoch 2023/2250\n",
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            "Epoch 2024/2250\n",
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            "Epoch 2025/2250\n",
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            "Epoch 2026/2250\n",
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            "Epoch 2027/2250\n",
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            "Epoch 2028/2250\n",
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            "Epoch 2029/2250\n",
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            "Epoch 2030/2250\n",
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            "Epoch 2031/2250\n",
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            "Epoch 2032/2250\n",
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            "Epoch 2033/2250\n",
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            "Epoch 2034/2250\n",
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            "Epoch 2035/2250\n",
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            "Epoch 2036/2250\n",
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            "Epoch 2037/2250\n",
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            "Epoch 2038/2250\n",
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            "Epoch 2039/2250\n",
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            "Epoch 2040/2250\n",
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            "Epoch 2042/2250\n",
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            "Epoch 2044/2250\n",
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            "Epoch 2045/2250\n",
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            "Epoch 2046/2250\n",
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            "Epoch 2047/2250\n",
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            "Epoch 2048/2250\n",
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            "Epoch 2049/2250\n",
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            "Epoch 2050/2250\n",
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            "Epoch 2051/2250\n",
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            "Epoch 2053/2250\n",
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            "Epoch 2054/2250\n",
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            "Epoch 2055/2250\n",
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            "Epoch 2056/2250\n",
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            "Epoch 2057/2250\n",
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            "Epoch 2058/2250\n",
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            "Epoch 2059/2250\n",
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            "Epoch 2060/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3581\n",
            "Epoch 2061/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3307\n",
            "Epoch 2062/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3204\n",
            "Epoch 2063/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3253\n",
            "Epoch 2064/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3564\n",
            "Epoch 2065/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3573\n",
            "Epoch 2066/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3817\n",
            "Epoch 2067/2250\n",
            "12/12 [==============================] - 1s 56ms/step - loss: 0.3562\n",
            "Epoch 2068/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3637\n",
            "Epoch 2069/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3753\n",
            "Epoch 2070/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3673\n",
            "Epoch 2071/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3681\n",
            "Epoch 2072/2250\n",
            "12/12 [==============================] - 1s 53ms/step - loss: 0.3558\n",
            "Epoch 2073/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3485\n",
            "Epoch 2074/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3198\n",
            "Epoch 2075/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3345\n",
            "Epoch 2076/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3648\n",
            "Epoch 2077/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3303\n",
            "Epoch 2078/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3554\n",
            "Epoch 2079/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3296\n",
            "Epoch 2080/2250\n",
            "12/12 [==============================] - 1s 41ms/step - loss: 0.3314\n",
            "Epoch 2081/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3564\n",
            "Epoch 2082/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3707\n",
            "Epoch 2083/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3410\n",
            "Epoch 2084/2250\n",
            "12/12 [==============================] - 1s 41ms/step - loss: 0.3369\n",
            "Epoch 2085/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3491\n",
            "Epoch 2086/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3736\n",
            "Epoch 2087/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3684\n",
            "Epoch 2088/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3438\n",
            "Epoch 2089/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3573\n",
            "Epoch 2090/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3452\n",
            "Epoch 2091/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3556\n",
            "Epoch 2092/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3398\n",
            "Epoch 2093/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3496\n",
            "Epoch 2094/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3241\n",
            "Epoch 2095/2250\n",
            "12/12 [==============================] - 1s 60ms/step - loss: 0.3230\n",
            "Epoch 2096/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3446\n",
            "Epoch 2097/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3808\n",
            "Epoch 2098/2250\n",
            "12/12 [==============================] - 1s 56ms/step - loss: 0.3498\n",
            "Epoch 2099/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3497\n",
            "Epoch 2100/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3732\n",
            "Epoch 2101/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3422\n",
            "Epoch 2102/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3342\n",
            "Epoch 2103/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3587\n",
            "Epoch 2104/2250\n",
            "12/12 [==============================] - 1s 54ms/step - loss: 0.3484\n",
            "Epoch 2105/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3364\n",
            "Epoch 2106/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3471\n",
            "Epoch 2107/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3588\n",
            "Epoch 2108/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3446\n",
            "Epoch 2109/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3388\n",
            "Epoch 2110/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3507\n",
            "Epoch 2111/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3326\n",
            "Epoch 2112/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3811\n",
            "Epoch 2113/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3533\n",
            "Epoch 2114/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3121\n",
            "Epoch 2115/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3534\n",
            "Epoch 2116/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3405\n",
            "Epoch 2117/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3490\n",
            "Epoch 2118/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3475\n",
            "Epoch 2119/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3612\n",
            "Epoch 2120/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3319\n",
            "Epoch 2121/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3234\n",
            "Epoch 2122/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3620\n",
            "Epoch 2123/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3684\n",
            "Epoch 2124/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3424\n",
            "Epoch 2125/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3506\n",
            "Epoch 2126/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3599\n",
            "Epoch 2127/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3351\n",
            "Epoch 2128/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3560\n",
            "Epoch 2129/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3474\n",
            "Epoch 2130/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3453\n",
            "Epoch 2131/2250\n",
            "12/12 [==============================] - 1s 56ms/step - loss: 0.3485\n",
            "Epoch 2132/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3361\n",
            "Epoch 2133/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3200\n",
            "Epoch 2134/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3367\n",
            "Epoch 2135/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3297\n",
            "Epoch 2136/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3539\n",
            "Epoch 2137/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3213\n",
            "Epoch 2138/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3657\n",
            "Epoch 2139/2250\n",
            "12/12 [==============================] - 1s 56ms/step - loss: 0.3265\n",
            "Epoch 2140/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3460\n",
            "Epoch 2141/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3212\n",
            "Epoch 2142/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3433\n",
            "Epoch 2143/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3242\n",
            "Epoch 2144/2250\n",
            "12/12 [==============================] - 1s 54ms/step - loss: 0.3253\n",
            "Epoch 2145/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3533\n",
            "Epoch 2146/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3319\n",
            "Epoch 2147/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3360\n",
            "Epoch 2148/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3497\n",
            "Epoch 2149/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3327\n",
            "Epoch 2150/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3270\n",
            "Epoch 2151/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3079\n",
            "Epoch 2152/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3376\n",
            "Epoch 2153/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3484\n",
            "Epoch 2154/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3459\n",
            "Epoch 2155/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3530\n",
            "Epoch 2156/2250\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.3522\n",
            "Epoch 2157/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3254\n",
            "Epoch 2158/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3383\n",
            "Epoch 2159/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3346\n",
            "Epoch 2160/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3433\n",
            "Epoch 2161/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3483\n",
            "Epoch 2162/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3023\n",
            "Epoch 2163/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3227\n",
            "Epoch 2164/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3568\n",
            "Epoch 2165/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3506\n",
            "Epoch 2166/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3501\n",
            "Epoch 2167/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3558\n",
            "Epoch 2168/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3204\n",
            "Epoch 2169/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3489\n",
            "Epoch 2170/2250\n",
            "12/12 [==============================] - 1s 60ms/step - loss: 0.3244\n",
            "Epoch 2171/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3583\n",
            "Epoch 2172/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3306\n",
            "Epoch 2173/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3249\n",
            "Epoch 2174/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3343\n",
            "Epoch 2175/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3314\n",
            "Epoch 2176/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3585\n",
            "Epoch 2177/2250\n",
            "12/12 [==============================] - 1s 52ms/step - loss: 0.3411\n",
            "Epoch 2178/2250\n",
            "12/12 [==============================] - 1s 53ms/step - loss: 0.3208\n",
            "Epoch 2179/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3495\n",
            "Epoch 2180/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3294\n",
            "Epoch 2181/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3350\n",
            "Epoch 2182/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3754\n",
            "Epoch 2183/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3539\n",
            "Epoch 2184/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3288\n",
            "Epoch 2185/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3375\n",
            "Epoch 2186/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3330\n",
            "Epoch 2187/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3534\n",
            "Epoch 2188/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3317\n",
            "Epoch 2189/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3457\n",
            "Epoch 2190/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3380\n",
            "Epoch 2191/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3180\n",
            "Epoch 2192/2250\n",
            "12/12 [==============================] - 1s 41ms/step - loss: 0.3439\n",
            "Epoch 2193/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3346\n",
            "Epoch 2194/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3198\n",
            "Epoch 2195/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3295\n",
            "Epoch 2196/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3424\n",
            "Epoch 2197/2250\n",
            "12/12 [==============================] - 0s 39ms/step - loss: 0.3039\n",
            "Epoch 2198/2250\n",
            "12/12 [==============================] - 1s 41ms/step - loss: 0.2966\n",
            "Epoch 2199/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3097\n",
            "Epoch 2200/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3252\n",
            "Epoch 2201/2250\n",
            "12/12 [==============================] - 1s 58ms/step - loss: 0.3013\n",
            "Epoch 2202/2250\n",
            "12/12 [==============================] - 1s 56ms/step - loss: 0.3448\n",
            "Epoch 2203/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3321\n",
            "Epoch 2204/2250\n",
            "12/12 [==============================] - 1s 52ms/step - loss: 0.3309\n",
            "Epoch 2205/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3098\n",
            "Epoch 2206/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3197\n",
            "Epoch 2207/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3092\n",
            "Epoch 2208/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3230\n",
            "Epoch 2209/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3337\n",
            "Epoch 2210/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3220\n",
            "Epoch 2211/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3053\n",
            "Epoch 2212/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3198\n",
            "Epoch 2213/2250\n",
            "12/12 [==============================] - 1s 63ms/step - loss: 0.3190\n",
            "Epoch 2214/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3145\n",
            "Epoch 2215/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3636\n",
            "Epoch 2216/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3214\n",
            "Epoch 2217/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3344\n",
            "Epoch 2218/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3280\n",
            "Epoch 2219/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3232\n",
            "Epoch 2220/2250\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.3338\n",
            "Epoch 2221/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3306\n",
            "Epoch 2222/2250\n",
            "12/12 [==============================] - 1s 43ms/step - loss: 0.3248\n",
            "Epoch 2223/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3315\n",
            "Epoch 2224/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3417\n",
            "Epoch 2225/2250\n",
            "12/12 [==============================] - 0s 40ms/step - loss: 0.3319\n",
            "Epoch 2226/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3474\n",
            "Epoch 2227/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3342\n",
            "Epoch 2228/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3571\n",
            "Epoch 2229/2250\n",
            "12/12 [==============================] - 0s 41ms/step - loss: 0.3119\n",
            "Epoch 2230/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3644\n",
            "Epoch 2231/2250\n",
            "12/12 [==============================] - 1s 42ms/step - loss: 0.3604\n",
            "Epoch 2232/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3312\n",
            "Epoch 2233/2250\n",
            "12/12 [==============================] - 1s 49ms/step - loss: 0.3268\n",
            "Epoch 2234/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3403\n",
            "Epoch 2235/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3401\n",
            "Epoch 2236/2250\n",
            "12/12 [==============================] - 1s 47ms/step - loss: 0.3260\n",
            "Epoch 2237/2250\n",
            "12/12 [==============================] - 1s 51ms/step - loss: 0.3263\n",
            "Epoch 2238/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3392\n",
            "Epoch 2239/2250\n",
            "12/12 [==============================] - 1s 50ms/step - loss: 0.3485\n",
            "Epoch 2240/2250\n",
            "12/12 [==============================] - 1s 59ms/step - loss: 0.3253\n",
            "Epoch 2241/2250\n",
            "12/12 [==============================] - 1s 53ms/step - loss: 0.3328\n",
            "Epoch 2242/2250\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.3352\n",
            "Epoch 2243/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3408\n",
            "Epoch 2244/2250\n",
            "12/12 [==============================] - 1s 54ms/step - loss: 0.3321\n",
            "Epoch 2245/2250\n",
            "12/12 [==============================] - 1s 57ms/step - loss: 0.3312\n",
            "Epoch 2246/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3102\n",
            "Epoch 2247/2250\n",
            "12/12 [==============================] - 1s 52ms/step - loss: 0.3242\n",
            "Epoch 2248/2250\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.3388\n",
            "Epoch 2249/2250\n",
            "12/12 [==============================] - 1s 46ms/step - loss: 0.3508\n",
            "Epoch 2250/2250\n",
            "12/12 [==============================] - 1s 45ms/step - loss: 0.3061\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 1, 512)            1064960   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 512)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 128)               328192    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1397313 (5.33 MB)\n",
            "Trainable params: 1397313 (5.33 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/content/drive/MyDrive/Data/MOEX_LSTM_Model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FpKQGMxdAB0",
        "outputId": "2ac3eb7d-04d6-4913-c5a0-399274319bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelAccuracy(model, x_train, x_test, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PToij9HHWJ90",
        "outputId": "0d7aa80d-0586-470e-9cb2-f34680e2acb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23/23 [==============================] - 3s 13ms/step\n",
            "7/7 [==============================] - 0s 10ms/step\n",
            "Shapes and types:\n",
            "y_train_binary: (726,) float64\n",
            "train_predictions: (726,) int64\n",
            "y_test_binary: (200,) float64\n",
            "test_predictions: (200,) int64\n",
            "Training Accuracy: 0.9256198347107438\n",
            "Test Accuracy: 0.53\n",
            "\n",
            "Classification Report for Training Data:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.91      0.92       345\n",
            "         1.0       0.92      0.94      0.93       381\n",
            "\n",
            "    accuracy                           0.93       726\n",
            "   macro avg       0.93      0.92      0.93       726\n",
            "weighted avg       0.93      0.93      0.93       726\n",
            "\n",
            "\n",
            "Classification Report for Test Data:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.53      0.48      0.51       100\n",
            "         1.0       0.53      0.58      0.55       100\n",
            "\n",
            "    accuracy                           0.53       200\n",
            "   macro avg       0.53      0.53      0.53       200\n",
            "weighted avg       0.53      0.53      0.53       200\n",
            "\n",
            "\n",
            "Confusion Matrix for Training Data:\n",
            "[[314  31]\n",
            " [ 23 358]]\n",
            "\n",
            "Confusion Matrix for Test Data:\n",
            "[[48 52]\n",
            " [42 58]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def modelAccuracy(model, x_train, x_test, y_train, y_test, dim = dim_f):\n",
        "\n",
        "  train_predictions = model.predict(x_train.reshape(-1,1,dim)).flatten()\n",
        "  test_predictions = model.predict(x_test.reshape(-1,1,dim)).flatten()\n",
        "\n",
        "  # Convert predictions to label format (0 or 1)\n",
        "  train_predictions = np.where(train_predictions < 0.5, 0, 1)\n",
        "  test_predictions = np.where(test_predictions < 0.5, 0, 1)\n",
        "\n",
        "  # Convert original y_train and y_test from -1 and 1 to 0 and 1\n",
        "  y_train_binary = (y_train + 1.0001) // 2\n",
        "  y_test_binary = (y_test + 1.0001) // 2\n",
        "\n",
        "  print(\"Shapes and types:\")\n",
        "  print(\"y_train_binary:\", y_train_binary.shape, y_train_binary.dtype)\n",
        "  print(\"train_predictions:\", train_predictions.shape, train_predictions.dtype)\n",
        "  print(\"y_test_binary:\", y_test_binary.shape, y_test_binary.dtype)\n",
        "  print(\"test_predictions:\", test_predictions.shape, test_predictions.dtype)\n",
        "\n",
        "  # Calculate accuracy\n",
        "  train_accuracy = accuracy_score(y_train_binary, train_predictions)\n",
        "  test_accuracy = accuracy_score(y_test_binary, test_predictions)\n",
        "\n",
        "  # Print accuracy\n",
        "  print(\"Training Accuracy:\", train_accuracy)\n",
        "  print(\"Test Accuracy:\", test_accuracy)\n",
        "\n",
        "  # Additional metrics\n",
        "  print(\"\\nClassification Report for Training Data:\")\n",
        "  print(classification_report(y_train_binary, train_predictions))\n",
        "\n",
        "  print(\"\\nClassification Report for Test Data:\")\n",
        "  print(classification_report(y_test_binary, test_predictions))\n",
        "\n",
        "  # Confusion Matrix\n",
        "  print(\"\\nConfusion Matrix for Training Data:\")\n",
        "  print(confusion_matrix(y_train_binary, train_predictions))\n",
        "\n",
        "  print(\"\\nConfusion Matrix for Test Data:\")\n",
        "  print(confusion_matrix(y_test_binary, test_predictions))"
      ],
      "metadata": {
        "id": "o6TQ-caCVnEO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def GetCNN_Model(seq_len=1, n_features=dim_f, n_filters=(2, 8, 8), droprate=0.2):\n",
        "    \"\"\"2D-CNNpred model according to the paper.\"\"\"\n",
        "    model = Sequential()\n",
        "    model.add(Input(shape=(1, n_features, 1)))\n",
        "    model.add(Conv2D(n_filters[0], kernel_size=(1, n_features), activation=\"relu\"))\n",
        "    model.add(Conv2D(n_filters[1], kernel_size=(1, 1), activation=\"tanh\"))\n",
        "    # Removed one Conv2D and MaxPool2D layers\n",
        "    model.add(Flatten())\n",
        "    model.add(Dropout(droprate))\n",
        "    model.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "j4BNDTQFvTGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_prcs_cnn = x_train.reshape(-1, 1, dim_f, 1)\n",
        "y_train_cnn = (y_train + 1.0001)//2\n",
        "y_test_cnn = (y_test + 1.0001) //2"
      ],
      "metadata": {
        "id": "2CLOfTjsfvB1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GetCNN_Model()\n",
        "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"acc\"])\n",
        "model.fit(x_train_prcs_cnn, y_train_cnn, epochs = 500, batch_size = 64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMC6QBYTfvmB",
        "outputId": "7da08147-f4a8-4816-d23d-cf8a67cf1e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "12/12 [==============================] - 3s 8ms/step - loss: 0.7493 - acc: 0.4807\n",
            "Epoch 2/500\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.7354 - acc: 0.5000\n",
            "Epoch 3/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.7251 - acc: 0.5207\n",
            "Epoch 4/500\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.7397 - acc: 0.5041\n",
            "Epoch 5/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.7300 - acc: 0.5014\n",
            "Epoch 6/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.7356 - acc: 0.5083\n",
            "Epoch 7/500\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.7450 - acc: 0.4862\n",
            "Epoch 8/500\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.7170 - acc: 0.5165\n",
            "Epoch 9/500\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.7207 - acc: 0.5289\n",
            "Epoch 10/500\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.7252 - acc: 0.5248\n",
            "Epoch 11/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.7333 - acc: 0.5041\n",
            "Epoch 12/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.7244 - acc: 0.5096\n",
            "Epoch 13/500\n",
            "12/12 [==============================] - 0s 9ms/step - loss: 0.7167 - acc: 0.5124\n",
            "Epoch 14/500\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.7205 - acc: 0.5220\n",
            "Epoch 15/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7119 - acc: 0.5152\n",
            "Epoch 16/500\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.7129 - acc: 0.5303\n",
            "Epoch 17/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7007 - acc: 0.5386\n",
            "Epoch 18/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7276 - acc: 0.4972\n",
            "Epoch 19/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7264 - acc: 0.5220\n",
            "Epoch 20/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7307 - acc: 0.4931\n",
            "Epoch 21/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7287 - acc: 0.4780\n",
            "Epoch 22/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7236 - acc: 0.4945\n",
            "Epoch 23/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7003 - acc: 0.5468\n",
            "Epoch 24/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7324 - acc: 0.5041\n",
            "Epoch 25/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7133 - acc: 0.5193\n",
            "Epoch 26/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7099 - acc: 0.5220\n",
            "Epoch 27/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7142 - acc: 0.4986\n",
            "Epoch 28/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7090 - acc: 0.5000\n",
            "Epoch 29/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7151 - acc: 0.5275\n",
            "Epoch 30/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7235 - acc: 0.4669\n",
            "Epoch 31/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7066 - acc: 0.5014\n",
            "Epoch 32/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7242 - acc: 0.4931\n",
            "Epoch 33/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7042 - acc: 0.5262\n",
            "Epoch 34/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7235 - acc: 0.5000\n",
            "Epoch 35/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7134 - acc: 0.5207\n",
            "Epoch 36/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7104 - acc: 0.5372\n",
            "Epoch 37/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7099 - acc: 0.5069\n",
            "Epoch 38/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7109 - acc: 0.4931\n",
            "Epoch 39/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6993 - acc: 0.5179\n",
            "Epoch 40/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7058 - acc: 0.5220\n",
            "Epoch 41/500\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.7046 - acc: 0.5041\n",
            "Epoch 42/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6944 - acc: 0.5510\n",
            "Epoch 43/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6955 - acc: 0.5358\n",
            "Epoch 44/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7087 - acc: 0.5220\n",
            "Epoch 45/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7155 - acc: 0.5000\n",
            "Epoch 46/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6946 - acc: 0.5317\n",
            "Epoch 47/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6965 - acc: 0.5262\n",
            "Epoch 48/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6963 - acc: 0.5358\n",
            "Epoch 49/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6889 - acc: 0.5358\n",
            "Epoch 50/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7002 - acc: 0.5289\n",
            "Epoch 51/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6962 - acc: 0.5399\n",
            "Epoch 52/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6904 - acc: 0.5317\n",
            "Epoch 53/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6896 - acc: 0.5317\n",
            "Epoch 54/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6868 - acc: 0.5413\n",
            "Epoch 55/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6900 - acc: 0.5482\n",
            "Epoch 56/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6828 - acc: 0.5523\n",
            "Epoch 57/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6895 - acc: 0.5455\n",
            "Epoch 58/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6885 - acc: 0.5510\n",
            "Epoch 59/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6817 - acc: 0.5689\n",
            "Epoch 60/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6857 - acc: 0.5523\n",
            "Epoch 61/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6912 - acc: 0.5441\n",
            "Epoch 62/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6840 - acc: 0.5482\n",
            "Epoch 63/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6872 - acc: 0.5413\n",
            "Epoch 64/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6923 - acc: 0.5344\n",
            "Epoch 65/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6869 - acc: 0.5386\n",
            "Epoch 66/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6922 - acc: 0.5413\n",
            "Epoch 67/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6853 - acc: 0.5441\n",
            "Epoch 68/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6804 - acc: 0.5551\n",
            "Epoch 69/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6842 - acc: 0.5510\n",
            "Epoch 70/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6860 - acc: 0.5551\n",
            "Epoch 71/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6811 - acc: 0.5579\n",
            "Epoch 72/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6875 - acc: 0.5510\n",
            "Epoch 73/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6833 - acc: 0.5455\n",
            "Epoch 74/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6911 - acc: 0.5344\n",
            "Epoch 75/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6838 - acc: 0.5537\n",
            "Epoch 76/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6738 - acc: 0.5675\n",
            "Epoch 77/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6947 - acc: 0.5386\n",
            "Epoch 78/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6860 - acc: 0.5634\n",
            "Epoch 79/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6888 - acc: 0.5399\n",
            "Epoch 80/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6854 - acc: 0.5468\n",
            "Epoch 81/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6931 - acc: 0.5234\n",
            "Epoch 82/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6928 - acc: 0.5372\n",
            "Epoch 83/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6830 - acc: 0.5592\n",
            "Epoch 84/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6871 - acc: 0.5551\n",
            "Epoch 85/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6818 - acc: 0.5565\n",
            "Epoch 86/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6801 - acc: 0.5523\n",
            "Epoch 87/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6806 - acc: 0.5634\n",
            "Epoch 88/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6897 - acc: 0.5551\n",
            "Epoch 89/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6849 - acc: 0.5510\n",
            "Epoch 90/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6801 - acc: 0.5606\n",
            "Epoch 91/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6802 - acc: 0.5592\n",
            "Epoch 92/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6807 - acc: 0.5634\n",
            "Epoch 93/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6840 - acc: 0.5510\n",
            "Epoch 94/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6865 - acc: 0.5537\n",
            "Epoch 95/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6849 - acc: 0.5537\n",
            "Epoch 96/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6836 - acc: 0.5620\n",
            "Epoch 97/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6755 - acc: 0.5689\n",
            "Epoch 98/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6848 - acc: 0.5606\n",
            "Epoch 99/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6848 - acc: 0.5565\n",
            "Epoch 100/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6852 - acc: 0.5468\n",
            "Epoch 101/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6770 - acc: 0.5689\n",
            "Epoch 102/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6849 - acc: 0.5468\n",
            "Epoch 103/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6777 - acc: 0.5661\n",
            "Epoch 104/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6904 - acc: 0.5413\n",
            "Epoch 105/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6821 - acc: 0.5537\n",
            "Epoch 106/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6804 - acc: 0.5620\n",
            "Epoch 107/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6841 - acc: 0.5620\n",
            "Epoch 108/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6800 - acc: 0.5523\n",
            "Epoch 109/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6836 - acc: 0.5331\n",
            "Epoch 110/500\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6808 - acc: 0.5592\n",
            "Epoch 111/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6758 - acc: 0.5785\n",
            "Epoch 112/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6753 - acc: 0.5702\n",
            "Epoch 113/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6810 - acc: 0.5661\n",
            "Epoch 114/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6809 - acc: 0.5579\n",
            "Epoch 115/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6880 - acc: 0.5482\n",
            "Epoch 116/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6871 - acc: 0.5289\n",
            "Epoch 117/500\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.6754 - acc: 0.5634\n",
            "Epoch 118/500\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.6838 - acc: 0.5565\n",
            "Epoch 119/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6791 - acc: 0.5647\n",
            "Epoch 120/500\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.6765 - acc: 0.5647\n",
            "Epoch 474/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6764 - acc: 0.5510\n",
            "Epoch 475/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6771 - acc: 0.5606\n",
            "Epoch 476/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6771 - acc: 0.5510\n",
            "Epoch 477/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6810 - acc: 0.5634\n",
            "Epoch 478/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6802 - acc: 0.5551\n",
            "Epoch 479/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6790 - acc: 0.5647\n",
            "Epoch 480/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6750 - acc: 0.5771\n",
            "Epoch 481/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6787 - acc: 0.5551\n",
            "Epoch 482/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6750 - acc: 0.5758\n",
            "Epoch 483/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6742 - acc: 0.5606\n",
            "Epoch 484/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6799 - acc: 0.5317\n",
            "Epoch 485/500\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.6789 - acc: 0.5661\n",
            "Epoch 486/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6747 - acc: 0.5785\n",
            "Epoch 487/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6786 - acc: 0.5592\n",
            "Epoch 488/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6745 - acc: 0.5702\n",
            "Epoch 489/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6783 - acc: 0.5702\n",
            "Epoch 490/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6748 - acc: 0.5592\n",
            "Epoch 491/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6745 - acc: 0.5702\n",
            "Epoch 492/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6721 - acc: 0.5758\n",
            "Epoch 493/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6771 - acc: 0.5620\n",
            "Epoch 494/500\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6784 - acc: 0.5620\n",
            "Epoch 495/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6755 - acc: 0.5592\n",
            "Epoch 496/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6745 - acc: 0.5661\n",
            "Epoch 497/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6721 - acc: 0.5771\n",
            "Epoch 498/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6741 - acc: 0.5689\n",
            "Epoch 499/500\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.6706 - acc: 0.5895\n",
            "Epoch 500/500\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.6770 - acc: 0.5523\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7cc3a148f670>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predictions_cnn = model.predict(x_train.reshape(-1,1,dim_f, 1)).flatten()\n",
        "test_predictions_cnn = model.predict(x_test.reshape(-1,1,dim_f, 1)).flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmQtmBkdf9EH",
        "outputId": "67ff49bd-2e4a-401e-a19b-d5451a0e21bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23/23 [==============================] - 0s 2ms/step\n",
            "7/7 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy = accuracy_score(y_train_cnn, np.where(train_predictions_cnn > 0.5, 1, 0))\n",
        "test_accuracy = accuracy_score(y_test_cnn, np.where(test_predictions_cnn > 0.5, 1, 0))"
      ],
      "metadata": {
        "id": "wCIfeAf6hXxw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy, test_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8BNbkDWhvi-",
        "outputId": "ff7a33ac-010d-40bc-b6ee-4c334fe66a24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.5743801652892562, 0.495)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a9a6pFOChxpp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}