receptor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prooooot.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "738wIpAhARCt"
      },
      "source": [
        "import gensim\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import STOPWORDS\n",
        "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
        "from nltk.stem.porter import *\n",
        "import numpy as np\n",
        "import json\n",
        "import joblib\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from nltk.corpus import wordnet\n",
        "from sklearn.model_selection import train_test_split, cross_validate,StratifiedKFold\n",
        "from pickle import load\n",
        "from numpy import array\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras.models import Model\n",
        "from keras.layers import Input\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Embedding\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D\n",
        "from keras.layers.merge import concatenate\n",
        "#from sklearn.pipeline import Pipel\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ast\n",
        "import string\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "#nltk.download('wordnet')\n",
        "import seaborn as sns\n",
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix,roc_auc_score\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
        "from tensorflow.keras.losses import sparse_categorical_crossentropy\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5ldrrCOAY9f",
        "outputId": "dcd75ef1-4881-4438-f6a8-2a9bc95b42ce"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcH9mHnQAveo"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/gdrive/My Drive/prot')  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUi0QqvHA3bU"
      },
      "source": [
        "with open(\"input.json\") as fp:\n",
        "  input=json.load(fp)\n",
        "with open(\"output.json\") as fp:\n",
        "  output=json.load(fp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJnRyS66HWyx"
      },
      "source": [
        "with open(\"row_label.json\") as fp:\n",
        "  row_labels=json.load(fp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRnlSzUN_3YZ"
      },
      "source": [
        "input[:20]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyJOa3ZgBGLT"
      },
      "source": [
        "temp=[]\n",
        "op=[]\n",
        "for n,i in enumerate(input):\n",
        "  if n!=156:\n",
        "    temp.append(np.sum(np.array(i),axis=0))\n",
        "    #temp.append(np.sum(np.array(i),axis=0).reshape((100,1)))\n",
        "    #temp.append((np.array(i)))\n",
        "    #print(i)\n",
        "    #print(temp)\n",
        "    op.append(output[n])\n",
        "inp=np.array(temp)\n",
        "output=np.array(op)#.reshape((558,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGEkePYoCrO9",
        "outputId": "04320504-e3f0-4853-a904-5e8fb565c349"
      },
      "source": [
        "print(np.sum(output==0))\n",
        "print(np.sum(output==1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "156\n",
            "395\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehPVt1S6ClV7"
      },
      "source": [
        "inp.reshape((558,100,1))\n",
        "output.reshape((558,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aryBOOG9BL1P"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "inp=np.asarray(inp)\n",
        "output=np.asarray(output)\n",
        "X_train, X_test, y_train, y_test = train_test_split(inp, output, test_size=0.35,random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "COEKt-Ii_zWY",
        "outputId": "c4c834d2-91ff-494c-cefb-2e5338bc3b57"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "# fit model no training data\n",
        "model = XGBClassifier()\n",
        "print(\"f1\",np.mean(cross_val_score(model, inp, output,scoring=\"f1_macro\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"precision\",np.mean(cross_val_score(model, inp, output,scoring=\"precision\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"accuracy\",np.mean(cross_val_score(model, inp, output,scoring=\"accuracy\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"roc_auc\",np.mean(cross_val_score(model, inp, output,scoring=\"roc_auc\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "predictions = [round(value) for value in y_pred]\n",
        "print(confusion_matrix(y_test, predictions))\n",
        "print(classification_report(y_test, predictions))\n",
        "metrics.plot_roc_curve(model, X_test, y_test)  # doctest: +SKIP\n",
        "plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1 0.7441414502239743\n",
            "precision 0.8324174854166962\n",
            "accuracy 0.813116883116883\n",
            "roc_auc 0.8428493589743591\n",
            "[[ 28  25]\n",
            " [ 11 129]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.53      0.61        53\n",
            "           1       0.84      0.92      0.88       140\n",
            "\n",
            "    accuracy                           0.81       193\n",
            "   macro avg       0.78      0.72      0.74       193\n",
            "weighted avg       0.80      0.81      0.80       193\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GAq_SNo85zN"
      },
      "source": [
        "_ = joblib.dump(model, \"xgb_w2v.pkl\", compress=9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7u2iO1qOOqmC"
      },
      "source": [
        "from xgboost import cv\n",
        "import xgboost as xgb\n",
        "params = {\"objective\":\"binary:logistic\",'colsample_bytree': 1,'learning_rate': 0.1,\n",
        "                'max_depth': 3, 'alpha': 0}\n",
        "data_dmatrix = xgb.DMatrix(data=inp,label=output)\n",
        "xgb_cv = cv(dtrain=data_dmatrix, params=params, nfold=10,stratified=True,\n",
        "                    num_boost_round=500, metrics={\"map\",\"auc\",\"error\"}, seed=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "PVFE8mnEOxH3",
        "outputId": "b1609bd9-2c6c-4dd5-89ab-70edec207ada"
      },
      "source": [
        "xgb_cv.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>train-auc-mean</th>\n",
              "      <th>train-auc-std</th>\n",
              "      <th>train-error-mean</th>\n",
              "      <th>train-error-std</th>\n",
              "      <th>train-map-mean</th>\n",
              "      <th>train-map-std</th>\n",
              "      <th>test-auc-mean</th>\n",
              "      <th>test-auc-std</th>\n",
              "      <th>test-error-mean</th>\n",
              "      <th>test-error-std</th>\n",
              "      <th>test-map-mean</th>\n",
              "      <th>test-map-std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>0.997328</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.026369</td>\n",
              "      <td>0.002619</td>\n",
              "      <td>0.998936</td>\n",
              "      <td>0.000173</td>\n",
              "      <td>0.828524</td>\n",
              "      <td>0.051180</td>\n",
              "      <td>0.188442</td>\n",
              "      <td>0.043281</td>\n",
              "      <td>0.909762</td>\n",
              "      <td>0.034335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0.997328</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.026369</td>\n",
              "      <td>0.002619</td>\n",
              "      <td>0.998918</td>\n",
              "      <td>0.000181</td>\n",
              "      <td>0.828691</td>\n",
              "      <td>0.051198</td>\n",
              "      <td>0.188442</td>\n",
              "      <td>0.043281</td>\n",
              "      <td>0.909797</td>\n",
              "      <td>0.034356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>0.997328</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.026369</td>\n",
              "      <td>0.002619</td>\n",
              "      <td>0.998933</td>\n",
              "      <td>0.000168</td>\n",
              "      <td>0.828047</td>\n",
              "      <td>0.051118</td>\n",
              "      <td>0.190260</td>\n",
              "      <td>0.040180</td>\n",
              "      <td>0.909333</td>\n",
              "      <td>0.034381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>0.997328</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.026369</td>\n",
              "      <td>0.002619</td>\n",
              "      <td>0.998919</td>\n",
              "      <td>0.000192</td>\n",
              "      <td>0.827893</td>\n",
              "      <td>0.051242</td>\n",
              "      <td>0.190260</td>\n",
              "      <td>0.040180</td>\n",
              "      <td>0.909241</td>\n",
              "      <td>0.034641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>0.997328</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.026369</td>\n",
              "      <td>0.002619</td>\n",
              "      <td>0.998953</td>\n",
              "      <td>0.000163</td>\n",
              "      <td>0.828534</td>\n",
              "      <td>0.051630</td>\n",
              "      <td>0.193896</td>\n",
              "      <td>0.042477</td>\n",
              "      <td>0.909535</td>\n",
              "      <td>0.034538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     train-auc-mean  train-auc-std  ...  test-map-mean  test-map-std\n",
              "495        0.997328       0.000425  ...       0.909762      0.034335\n",
              "496        0.997328       0.000425  ...       0.909797      0.034356\n",
              "497        0.997328       0.000425  ...       0.909333      0.034381\n",
              "498        0.997328       0.000425  ...       0.909241      0.034641\n",
              "499        0.997328       0.000425  ...       0.909535      0.034538\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "ZuoWhHWiBOnT",
        "outputId": "0e4b0837-8ada-4255-885d-4c9e0cde3620"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "model = AdaBoostClassifier()\n",
        "# evaluate the model\n",
        "print(\"f1\",np.mean(cross_val_score(model, inp, output,scoring=\"f1_macro\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"precision\",np.mean(cross_val_score(model, inp, output,scoring=\"precision\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"accuracy\",np.mean(cross_val_score(model, inp, output,scoring=\"accuracy\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"roc_auc\",np.mean(cross_val_score(model, inp, output,scoring=\"roc_auc\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "predictions = [round(value) for value in y_pred]\n",
        "print(confusion_matrix(y_test, predictions))\n",
        "print(classification_report(y_test, predictions))\n",
        "metrics.plot_roc_curve(model, X_test, y_test)  # doctest: +SKIP\n",
        "plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1 0.727575401177076\n",
            "precision 0.8299035958739035\n",
            "accuracy 0.795\n",
            "roc_auc 0.7693697916666667\n",
            "[[ 29  24]\n",
            " [ 14 126]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.55      0.60        53\n",
            "           1       0.84      0.90      0.87       140\n",
            "\n",
            "    accuracy                           0.80       193\n",
            "   macro avg       0.76      0.72      0.74       193\n",
            "weighted avg       0.79      0.80      0.80       193\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtNp3EbeJSjG"
      },
      "source": [
        "_ = joblib.dump(model, \"adaboost_w2v.pkl\", compress=9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "CAvTa_jWMCGs",
        "outputId": "bfcf88cb-691d-4408-f170-e1227f897e88"
      },
      "source": [
        "output.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(558, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "_88k_G22y6pX",
        "outputId": "50f23c41-9ae7-4138-e367-1f2dd385e835"
      },
      "source": [
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix,roc_auc_score\n",
        "#Create a svm Classifier\n",
        "clf = svm.SVC()# Linear Kernel\n",
        "\n",
        "#Train the model using the training sets\n",
        "print(\"f1\",np.mean(cross_val_score(clf, inp, output,scoring=\"f1_macro\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"precision\",np.mean(cross_val_score(clf, inp, output,scoring=\"precision\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"accuracy\",np.mean(cross_val_score(clf, inp, output,scoring=\"accuracy\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"roc_auc\",np.mean(cross_val_score(clf, inp, output,scoring=\"roc_auc\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "clf.fit(X_train, y_train)\n",
        "#Predict the response for test dataset\n",
        "y_pred = clf.predict(X_test)\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "metrics.plot_roc_curve(clf, X_test, y_test)  # doctest: +SKIP\n",
        "plt.show()  \n",
        "roc_auc_score(y_test, y_pred)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1 0.7022629368545382\n",
            "precision 0.8118713707268462\n",
            "accuracy 0.789512987012987\n",
            "roc_auc 0.8200993589743589\n",
            "[[ 23  30]\n",
            " [ 10 130]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.43      0.53        53\n",
            "           1       0.81      0.93      0.87       140\n",
            "\n",
            "    accuracy                           0.79       193\n",
            "   macro avg       0.75      0.68      0.70       193\n",
            "weighted avg       0.78      0.79      0.78       193\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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46l5p+6qtezilm969E5HjW133+h8FngOuIBiB9L8benIzG21ma8ys1Mxur6PcFWbmZlbS0GscD15es52X39nOvoNH+Djh0ye/LRcP7p7u8ERE6lTXraH27v5IuLzGzJY25MRmlgM8RDDVZRmwyMxmufuqKuXaAzcDCxty/uPRzOtH0DxH/egiklnqSgS5ZnYmf5+HoHXiurvXlxiGAaXuvhbAzGYQjFe0qkq5HwA/Am5rYOwiItIE6koEW4GfJKxvS1h34Px6zt0D2JSwXgYMTyxgZkOAXu7+vJnVmgjMbCIwEaCgQCNgi4g0pbompvlclBcOB6/7CTChvrLu/jDwMEBJSYnXU1xERBogyhvam4FeCes9w20V2gODgHlmth44C5iVqR3GIiKZKspEsAjoZ2ZFZtYSGAfMqtjp7rvdPc/dC929EHgdGOPuiyOMSUREqogsEbj7EeAmYA6wGnjS3Vea2d1mNiaq64qISMMkM/qoAV8Berv73eF8BCe6+xv1Hevus6kyHIW7f7+WsqOSilhERJpUMi2C/wVGAOPD9b0E7weIiEgWSGbQueHuPsTM3gRw913hPX8REckCybQIDodvCTscm4/gaKRRiYhIyiSTCB4Efg90NbN7gb8C/xlpVCIikjLJDEM9zcyWABcQDC9xmbuvjjwyERFJiWSeGioA9gN/SNzm7hujDExERFIjmc7i5wn6BwzIBYqANcDACOMSEZEUSebW0OmJ6+FAcTdGFpGIiKRUg98sDoefHl5vwZg4Un6UnfsPpTsMEZFPLZk+gkkJq82AIcCWyCI6zh06cpS3Nn/E62t3snDdTpas38nHh8rp2LoFzczqP4GIyHEmmT6CxEl3jxD0GTwdTTjHnwOHy1m26SMWrt3JwnU7WLpxFwcOB69R9O/Wjn8c0pPhvTszoncXmjVTIhCRzFNnIghfJGvv7v+WoniOG4+/voE/LN/Csk0fcejIUczg1BM7MO4zBZzVuzOfKexMl3at0h2miEij1ZoIzKy5ux8xs5GpDOh48YPnVpHfrhVfO+tkhvfuwrDCznRs0yLdYYmINLm6WgRvEPQHLDOzWcBM4OOKne7+u4hjSy+HMcUn8e+jT013JCIikUqmjyAX2EEwR3HF+wQOZHciEBGJiboSQdfwiaG3+XsCqJA18wa/v+cAyzZ9VG17uWdNFUVE6lRXIsgB2lE5AVTImt+Sdzy7khdXbqtxX4dc9QmISParKxFsdfe7UxZJmnxyuJz+3drxwNjiStubmdG/W/tajhIRyR51JYLYPBTfumVzBp7UMd1hiIikRV1DTFyQsihERCRtak0E7r4zlYGIiEh6NHjQORERyS7JvEeQFQ4dOcryso84Ul75gaeP9h8CDRYnIjEWm0Tw5OJNfO+Zt2vcN7JvlxRHIyJy/IhNIvj44BEAfnXtZ8htnlNpX79u7dIRkojIcSE2iaDC8KLOtGkZu2qLiNRKncUiIjGnRCAiEnNKBCIiMadEICISc5EmAjMbbWZrzKzUzG6vYf8kM1tlZivM7M9mdnKU8YiISHWRJYJwvuOHgC8CA4DxZjagSrE3gRJ3Hww8BfxXVPGIiEjNomwRDANK3X2tux8CZgCXJhZw97nuvj9cfR3oGWE8IiJSgygTQQ9gU8J6WbitNtcBL9S0w8wmmtliM1u8ffv2JgxRRESOi85iM/sqUAJMrmm/uz/s7iXuXpKfn5/a4EREslyUr9huBnolrPcMt1ViZp8Hvgt81t0PRhiPiIjUIMoWwSKgn5kVmVlLYBwwK7GAmZ0J/AIY4+4fRBiLiIjUIrJE4O5HgJuAOcBq4El3X2lmd5vZmLDYZKAdMNPMlpnZrFpOJyIiEYl09DV3nw3MrrLt+wnLn4/y+iIiUr/jorNYRETSR4lARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGKueboDEJFoHT58mLKyMg4cOJDuUCQFcnNz6dmzJy1atEj6GCUCkSxXVlZG+/btKSwsxMzSHY5EyN3ZsWMHZWVlFBUVJX2cbg2JZLkDBw7QpUsXJYEYMDO6dOnS4NafEoFIDCgJxMen+VkrEYiIxJwSgYhE7t5772XgwIEMHjyY4uJiFi5cyF133cV3vvOdSuWWLVvGaaedBsC+ffu4/vrr6dOnD0OHDmXUqFEsXLiw2rndnfPPP589e/Yc2/bMM89gZvztb387tm3evHlccskllY6dMGECTz31FBB0qt9+++3069ePIUOGMGLECF544YVG1/2HP/whffv25ZRTTmHOnDk1lvnzn//MkCFDKC4u5pxzzqG0tBSAV155hSFDhtC8efNjcQJs376d0aNHNzq2CkoEIhKp1157jeeee46lS5eyYsUK/vSnP9GrVy/Gjx/PE088UansjBkzGD9+PADf+MY36Ny5M++++y5LlizhV7/6FR9++GG188+ePZszzjiDDh06HNs2ffp0zjnnHKZPn550nP/xH//B1q1befvtt1m6dCnPPPMMe/fu/ZS1DqxatYoZM2awcuVKXnzxRW688UbKy8urlbvhhhuYNm0ay5Yt4+qrr+aee+4BoKCggKlTp3L11VdXKp+fn0/37t1ZsGBBo+KroKeGRGLkrj+sZNWWPfUXbIABJ3Xgji8NrHX/1q1bycvLo1WrVgDk5eUd29epUycWLlzI8OHDAXjyySeZM2cO7733HgsXLmTatGk0axZ8Xy0qKqrxSZhp06YxceLEY+v79u3jr3/9K3PnzuVLX/oSd911V7112L9/P4888gjr1q07Fme3bt246qqrkvgbqN2zzz7LuHHjaNWqFUVFRfTt25c33niDESNGVCpnZsdaNLt37+akk04CoLCwEODY30Giyy67jGnTpjFy5MhGxQhqEYhIxC688EI2bdpE//79ufHGG3n55ZeP7Rs/fjwzZswA4PXXX6dz587069ePlStXUlxcTE5OTr3nX7BgAUOHDj22/uyzzzJ69Gj69+9Ply5dWLJkSb3nKC0tpaCgoFKroja33HILxcXF1T733XdftbKbN2+mV69ex9Z79uzJ5s2bq5WbMmUKF110ET179uTxxx/n9ttvrzeOkpIS5s+fX2+5ZKhFIBIjdX1zj0q7du1YsmQJ8+fPZ+7cuYwdO5b77ruPCRMmMHbsWM4++2zuv//+SreFGmLnzp20b9/+2Pr06dO5+eabARg3bhzTp09n6NChtT5N09CnbB544IEGx5jMOWfPns3w4cOZPHkykyZNYsqUKXUe07VrV7Zs2dIk1480EZjZaOCnQA4wxd3vq7K/FfBrYCiwAxjr7uujjElEUi8nJ4dRo0YxatQoTj/9dB577DEmTJhAr169KCoq4uWXX+bpp5/mtddeA2DgwIEsX76c8vLyelsFzZs35+jRozRr1oydO3fyl7/8hbfeegszo7y8HDNj8uTJdOnShV27dlU6dufOneTl5dG3b182btzInj176m0V3HLLLcydO7fa9nHjxlX7Jt+jRw82bdp0bL2srIwePXpUKrN9+3aWL19+7PbY2LFjk+oIPnDgAK1bt663XDIiuzVkZjnAQ8AXgQHAeDMbUKXYdcAud+8LPAD8KKp4RCQ91qxZw7vvvntsfdmyZZx88snH1sePH88tt9xC79696dmzJwB9+vShpKSEO+64A3cHYP369Tz//PPVzn/KKaewdu1aAJ566imuueYaNmzYwPr169m0aRNFRUXMnz+ffv36sWXLFlavXg3Ahg0bWL58OcXFxbRp04brrruOm2++mUOHDgHBL+iZM2dWu94DDzzAsmXLqn1qup0zZswYZsyYwcGDB1m3bh3vvvsuw4YNq1SmU6dO7N69m3feeQeAl1566diTU3V55513GDRoUL3lkhFlH8EwoNTd17r7IWAGcGmVMpcCj4XLTwEXmN58Eckq+/bt4+tf/zoDBgxg8ODBrFq1ijvvvPPY/i9/+cusXLmy2m2hKVOm8P7779O3b18GDRrEhAkT6Nq1a7XzX3zxxcybNw8IbgtdfvnllfZfccUVTJ8+nVatWvGb3/yGa6+9luLiYq688kqmTJlCx44dAbjnnnvIz89nwIABDBo0iEsuuSSpPoO6DBw4kKuuuooBAwYwevRoHnrooWMtnIsuuogtW7bQvHlzHnnkEa644grOOOMMHn/8cSZPngzAokWL6NmzJzNnzuT6669n4MC/39qbO3cuF198caPiq2AV2bapmdmVwGh3/0a4fg0w3N1vSijzdlimLFx/LyzzYZVzTQQmAhQUFAzdsGFDg+P548ptPLNsMz+5qpjcFvV3QIlki9WrVyf1DTNTbd26la997Wu89NJL6Q4lpc477zyeffZZOnXqVG1fTT9zM1vi7iU1nSsjnhpy94fdvcTdS/Lz8z/VOS4ceCL/+5WhSgIiWaZ79+5885vfrPRCWbbbvn07kyZNqjEJfBpRdhZvBnolrPcMt9VUpszMmgMdCTqNRUSS1tjn/TNNfn4+l112WZOdL8oWwSKgn5kVmVlLYBwwq0qZWcDXw+Urgb94VPeqRGJM/63i49P8rCNLBO5+BLgJmAOsBp5095VmdreZjQmL/RLoYmalwCSg/rcoRKRBcnNz2bFjh5JBDFTMR5Cbm9ug4yLrLI5KSUmJL168ON1hiGQMzVAWL7XNUFZXZ7HeLBbJci1atGjQbFUSPxnx1JCIiERHiUBEJOaUCEREYi7jOovNbDvQ8FeLA3lA9ZktspvqHA+qczw0ps4nu3uNb+RmXCJoDDNbXFuvebZSneNBdY6HqOqsW0MiIjGnRCAiEnNxSwQPpzuANFCd40F1jodI6hyrPgIREakubi0CERGpQolARCTmsjIRmNloM1tjZqVmVm1EUzNrZWZPhPsXmllh6qNsWknUeZKZrTKzFWb2ZzM7uabzZJL66pxQ7gozczPL+EcNk6mzmV0V/qxXmtlvUx1jU0vi33aBmc01szfDf98XpSPOpmJmj5rZB+EMjjXtNzN7MPz7WGFmQxp9UXfPqg+QA7wH9AZaAsuBAVXK3Aj8PFweBzyR7rhTUOfPAW3C5RviUOewXHvgFeB1oCTdcafg59wPeBPoFK53TXfcKajzw8AN4fIAYH26425knc8DhgBv17L/IuAFwICzgIWNvWY2tgiGAaXuvtbdDwEzgEurlLkUeCxcfgq4wMwshTE2tXrr7O5z3X1/uPo6wYxxmSyZnzPAD4AfAdkwBnMydf4m8JC77wJw9w9SHGNTS6bODlTMMt8R2JLC+Jqcu78C7KyjyKXArz3wOnCCmXVvzDWzMRH0ADYlrJeF22os48EEOruBLimJLhrJ1DnRdQTfKDJZvXUOm8y93P35VAYWoWR+zv2B/ma2wMxeN7PRKYsuGsnU+U7gq2ZWBswGvp2a0NKmof/f66X5CGLGzL4KlACfTXcsUTKzZsBPgAlpDiXVmhPcHhpF0Op7xcxOd/eP0hpVtMYDU939fjMbATxuZoPc/Wi6A8sU2dgi2Az0SljvGW6rsYyZNSdoTu5ISXTRSKbOmNnnge8CY9z9YIpii0p9dW4PDALmmdl6gnupszK8wziZn3MZMMvdD7v7OuAdgsSQqZKp83XAkwDu/hqQSzA4W7ZK6v97Q2RjIlgE9DOzIjNrSdAZPKtKmVnA18PlK4G/eNgLk6HqrbOZnQn8giAJZPp9Y6inzu6+293z3L3Q3QsJ+kXGuHsmz3OazL/tZwhaA5hZHsGtorWpDLKJJVPnjcAFAGZ2GkEi2J7SKFNrFvC18Omhs4Dd7r61MSfMultD7n7EzG4C5hA8cfCou680s7uBxe4+C/glQfOxlKBTZlz6Im68JOs8GWgHzAz7xTe6+5i0Bd1ISdY5qyRZ5znAhWa2CigHbnP3jG3tJlnnW4FHzOwWgo7jCZn8xc7MphMk87yw3+MOoAWAu/+coB/kIqAU2A9c2+hrZvDfl4iINIFsvDUkIiINoEQgIhJzSgQiIjGnRCAiEnNKBCIiMadEIMclMys3s2UJn8I6yhqlLmcAAAPzSURBVO5rgutNNbN14bWWhm+oNvQcU8xsQLj8f6vse7WxMYbnqfh7edvM/mBmJ9RTvjjTR+OU6OnxUTkumdk+d2/X1GXrOMdU4Dl3f8rMLgR+7O6DG3G+RsdU33nN7DHgHXe/t47yEwhGXb2pqWOR7KEWgWQEM2sXzqOw1MzeMrNqI42aWXczeyXhG/O54fYLzey18NiZZlbfL+hXgL7hsZPCc71tZv8abmtrZs+b2fJw+9hw+zwzKzGz+4DWYRzTwn37wj9nmNnFCTFPNbMrzSzHzCab2aJwjPnrk/hreY1wsDEzGxbW8U0ze9XMTgnfxL0bGBvGMjaM/VEzeyMsW9OIrRI36R57Wx99avoQvBW7LPz8nuAt+A7hvjyCtyorWrT7wj9vBb4bLucQjDeUR/CLvW24/d+B79dwvanAleHyl4GFwFDgLaAtwVvZK4EzgSuARxKO7Rj+OY9wzoOKmBLKVMR4OfBYuNySYBTJ1sBE4Hvh9lbAYqCohjj3JdRvJjA6XO8ANA+XPw88HS5PAP4n4fj/BL4aLp9AMBZR23T/vPVJ7yfrhpiQrPGJuxdXrJhZC+A/zew84CjBN+FuwLaEYxYBj4Zln3H3ZWb2WYLJShaEQ2u0JPgmXZPJZvY9gnFqriMYv+b37v5xGMPvgHOBF4H7zexHBLeT5jegXi8APzWzVsBo4BV3/yS8HTXYzK4My3UkGCxuXZXjW5vZsrD+q4GXEso/Zmb9CIZZaFHL9S8ExpjZv4XruUBBeC6JKSUCyRRfAfKBoe5+2IIRRXMTC7j7K2GiuBiYamY/AXYBL7n7+CSucZu7P1WxYmYX1FTI3d+xYK6Di4B7zOzP7n53MpVw9wNmNg/4B2AswUQrEMw29W13n1PPKT5x92Iza0Mw/s63gAcJJuCZ6+6Xhx3r82o53oAr3H1NMvFKPKiPQDJFR+CDMAl8Dqg257IF8zC/7+6PAFMIpvt7HRhpZhX3/NuaWf8krzkfuMzM2phZW4LbOvPN7CRgv7v/hmAwv5rmjD0ctkxq8gTBQGEVrQsIfqnfUHGMmfUPr1kjD2ab+xfgVvv7UOoVQxFPSCi6l+AWWYU5wLctbB5ZMCqtxJwSgWSKaUCJmb0FfA34Ww1lRgHLzexNgm/bP3X37QS/GKeb2QqC20KnJnNBd19K0HfwBkGfwRR3fxM4HXgjvEVzB3BPDYc/DKyo6Cyu4o8EEwP9yYPpFyFIXKuApRZMWv4L6mmxh7GsIJiY5b+AH4Z1TzxuLjCgorOYoOXQIoxtZbguMafHR0VEYk4tAhGRmFMiEBGJOSUCEZGYUyIQEYk5JQIRkZhTIhARiTklAhGRmPv/9R+ASCVKyDUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.681266846361186"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsfolBc6JYIa"
      },
      "source": [
        "_ = joblib.dump(clf, \"svm_w2v.pkl\", compress=9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        },
        "id": "4EO9zb89Rh44",
        "outputId": "8c1d5500-22e5-430c-cfae-4e5790376eb4"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV \n",
        "param_grid = {'C': [0.01,0.001,0.1, 1,5, 10, 100, 1000],  \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001,10,20,\"scale\"], \n",
        "              'kernel': ['rbf','linear']}  \n",
        "  \n",
        "grid = GridSearchCV(svm.SVC(), param_grid, refit = True, verbose = 0,scoring=\"f1\",cv=10) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(X_train, y_train) \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "y_pred = grid.predict(X_test)\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "metrics.plot_roc_curve(grid, X_test, y_test)  # doctest: +SKIP\n",
        "plt.savefig(\"svm_grid.png\")\n",
        "plt.show()\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, annot_kws={\"size\": 14}, fmt='g')\n",
        "plt.savefig(\"svm_heat.png\")\n",
        "\n",
        "plt.show()\n",
        "roc_auc_score(y_test, y_pred)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 5, 'gamma': 10, 'kernel': 'rbf'}\n",
            "SVC(C=5, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=10, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n",
            "[[ 24  28]\n",
            " [  9 133]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.46      0.56        52\n",
            "           1       0.83      0.94      0.88       142\n",
            "\n",
            "    accuracy                           0.81       194\n",
            "   macro avg       0.78      0.70      0.72       194\n",
            "weighted avg       0.80      0.81      0.79       194\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6990790899241603"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "ihIw6CGr2b9s",
        "outputId": "e29d6ee0-f94a-4fd7-92fc-85c22b526efd"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest = RandomForestClassifier()\n",
        "#print(X_test\n",
        "print(\"f1\",np.mean(cross_val_score(forest, inp, output,scoring=\"f1_macro\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"precision\",np.mean(cross_val_score(forest, inp, output,scoring=\"precision\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"accuracy\",np.mean(cross_val_score(forest, inp, output,scoring=\"accuracy\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "print(\"roc_auc\",np.mean(cross_val_score(forest, inp, output,scoring=\"roc_auc\",cv = StratifiedKFold(n_splits=10,shuffle=True, random_state=0))))\n",
        "forest.fit(X_train, y_train)\n",
        "y_pred = forest.predict(X_test)\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "metrics.plot_roc_curve(forest, X_test, y_test)  # doctest: +SKIP\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f1 0.7255430489352144\n",
            "precision 0.8191493039597185\n",
            "accuracy 0.7985714285714285\n",
            "roc_auc 0.8395889423076923\n",
            "[[ 22  31]\n",
            " [  9 131]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.42      0.52        53\n",
            "           1       0.81      0.94      0.87       140\n",
            "\n",
            "    accuracy                           0.79       193\n",
            "   macro avg       0.76      0.68      0.70       193\n",
            "weighted avg       0.78      0.79      0.77       193\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kOgwRkZjmLB5rZucC3w82Penu/ww3LBERiZfy5iNIB+4DjgYWADe6+9p4BSYiIvFRXl//s8CrwI+JjED6SGUrN7NTzWypmS03s5vLKfdjM3Mzy6zsMUREpGrK6xpq4u5PBctLzeyzylRsZinAY0SmuswBZplZlrsvLlGuCXAt8Gll6hcRkepRXiJINbO+/G8egrTodXevKDEcCyx395UAZjaZyHhFi0uU+wNwDzC2krGLiEg1KC8RrAceiFr/OmrdgR9WUHc7YE3Ueg4wILqAmfUDOrj7a2ZWZiIws8uAywA6dtQI2CIi1am8iWmGhnngYPC6B4AxFZV19yeBJwEyMzM9zLhERJJNLM8RHKy1QIeo9fbBtkJNgJ7Ae2YGcASQZWbD3X12iHHF3Te79zPzy618+uVWZn4Zmeq5rp4gFpFDRJiJYBaQbmadiSSAnwAXFO509+1Ay8J1M3uPyC2qtSYJvJK9lif+u5LPv/4Wd6hftw59OzTnmhPTGTVAXVwicmgILRG4e56ZXQW8BaQAz7r7IjMbB8x296ywjn2omDxzDRu/3ct1PzqGAZ0Pp0+H5qTWS0l0WCIixVSYCCzSb/NT4Ch3HxfMV3yEu8+s6L3u/jolhqNw99vLKDskpohrmKNbNeaaE9MTHYaISJliGTzucWAgMCpY30Hk+QAREakFYukaGuDu/cxsLoC7bzOz+iHHJSIicRLLGUFu8JSwQ9F8BAWhRiUiInETSyJ4GPgn0NrM/gh8CPwp1KhERCRuYhmGeqKZzQFOJDK8xNnuviT0yEREJC5iuWuoI7Ab+Hf0Nnf/KszAREQkPmK5WPwakesDBqQCnYGlQI8Q4xIRkTiJpWuoV/R6MFDcFaFFJCIicVXpSeiD4acHVFhQRERqhFiuEVwftVoH6AesCy2iWsDdeebDL5m9eisn9zgi0eGIiJQrlmsETaKW84hcM/hHOOHUfFt37efGafN49/ONnNy9DX88u2eiQxIRKVe5iSB4kKyJu98Yp3hqtE9XbuHaydls3bWf3w/vwcUDjyQYYltE5JBVZiIws7rBCKKD4xlQTZRf4Dz67nIeemcZR7ZoxMujB9GzXbNEhyUiEpPyzghmErkekG1mWcA0YFfhTnd/OeTYaoxps9fw4NvLOLdvO8ad3ZPGDcKc5kFEpHrF8omVCmwhMkdx4fMEDigRBDbt2AfAn8/rTd2USt+IJSKSUOUlgtbBHUML+V8CKKR5g0VEaonyEkEK0JjiCaCQEoGISC1RXiJY7+7j4haJiIgkRHkd2rrvUUQkCZSXCE6MWxQiIpIwZSYCd98az0BERCQxdK+jiEiSUyIQEUlySgQiIklOiUBEJMkpEYiIJDklAhGRJKdEICKS5JQIRESSnBKBiEiSCzURmNmpZrbUzJab2c2l7L/ezBab2Xwze8fMjgwzHhEROVBoiSCY7/gx4DSgOzDKzLqXKDYXyHT33sBLwJ/DikdEREoX5hnBscByd1/p7vuBycBZ0QXcfbq77w5WPwHahxiPiIiUIsxE0A5YE7WeE2wry6XAG6XtMLPLzGy2mc3etGlTNYYoIiKHxMViM7sQyATuLW2/uz/p7pnuntmqVav4BiciUsvFMnn9wVoLdIhabx9sK8bMfgTcCvzA3feFGI+IiJQizDOCWUC6mXU2s/rAT4Cs6AJm1hf4GzDc3TeGGIuIiJQhtETg7nnAVcBbwBJgqrsvMrNxZjY8KHYv0BiYZmbZZpZVRnUiIhKSMLuGcPfXgddLbLs9avlHYR5fREQqdkhcLBYRkcRRIhARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0RQDfbnFyQ6BBGRg6ZEUEUffrGZpz5YSa92zUipY4kOR0Sk0pQIquC9pRu55LlZdGrRiAk/+x5mSgQiUvOE+mRxbTb984388oU5dGndmL//fACHN6qf6JBERA6KEsFBeHvxBi6fOIeuRzTlhUuPpXlDJQERqbmUCCrpzYVfc9WLn9GjXTOev+RYmqXVS3RIIiJVokRQCR9+sZmrXvyMXu2b8dwlx9I0VUlARGo+JYIYrdq8iytf/IyjWzXm+UuOpYmSgIjUErprKAY79+Xxi+dnYwZPXZypJCAitYrOCCpQUOBcNyWblZt38cIlx9KxRcNEhyQiUq10RlCBv7y9jP8s3sBtZ3RjUJeWiQ5HRKTaKRGU4/UF63n43eWMyGzP6EGdEh2OiEgolAjKsGT9t9wwdR59OzbnD2f31FPDIlJrKRGUYuuu/fzi+dk0S6vH3y7sT4O6KYkOSUQkNLpYXEJufgFXTJzDxh37mPbLgbRumprokEREQqVEUMJdry7mk5VbeWBEH/p0aJ7ocGq03NxccnJy2Lt3b6JDEUkaqamptG/fnnr1Yr/NXYkgyuSZX/Hcx6v5xfGdObdf+0SHU+Pl5OTQpEkTOnXqpGssInHg7mzZsoWcnBw6d+4c8/t0jSAwe9VWbntlIcent+Q3p3ZNdDi1wt69e2nRooWSgEicmBktWrSo9Fm4EgGw7ps9/Orvn9GueRqPjupH3RT9WqqLkoBIfB3M/7mk7xram5vPL1+Yw97cfCb9YgDNGmr4CBFJLkn91dfd+c0/5rNw3Xb+MjKD9DZNEh2SVLOUlBQyMjLo2bMnw4YN45tvvqmWeidMmMBVV11VLXV16tSJXr16kZGRQUZGBjNmzKiWekvKzs7m9ddfL7btjTfeIDMzk+7du9O3b19uuOEGAO68807uu+++ajv2oEGDipbHjh1Ljx49GDt2LE888QTPP/98leqeO3cul156abFtZ599Nscdd1yxbWPGjOGll14qtq1x48ZFy8uWLeP0008nPT2dfv36MWLECDZs2FCl2LZu3cpJJ51Eeno6J510Etu2bTugzPTp04v+9hkZGaSmpvKvf/0LgHfffZd+/frRs2dPRo8eTV5eHgCvvvoqt99+e5ViK8bda9Srf//+Xl2eeG+5H/mbV/2Rd5ZVW53yP4sXL050CN6oUaOi5Ysvvtjvuuuuaql3/PjxfuWVV1ZLXUceeaRv2rSp0u/Lzc2tVPmSMS9YsMCPOuooX7Jkibu75+Xl+eOPP+7u7nfccYffe++9lY4pFk2bNvW8vLyDem9pbT7vvPM8Ozu7aH3btm3evn1779q1q69YsaJo++jRo33atGnF3lv472PPnj3epUsXz8rKKto3ffp0X7BgwUHFWWjs2LF+9913u7v73Xff7TfddFO55bds2eKHHXaY79q1y/Pz8719+/a+dOlSd3e/7bbb/Omnn3Z394KCAs/IyPBdu3aVWk9p//eA2V7G52rSdg29t3Qj/+/NzzmjV1uuHNol0eHUer//9yIWr/u2Wuvs/p2m3DGsR8zlBw4cyPz58wGYOXMm1157LXv37iUtLY3x48fz3e9+lwkTJpCVlcXu3btZsWIF55xzDn/+858BGD9+PHfffTfNmzenT58+NGjQAIBVq1ZxySWXsHnzZlq1asX48ePp2LEjY8aMIS0tjblz57Jx40aeffZZnn/+eT7++GMGDBjAhAkTyoy1vDpTU1OZO3cugwcP5sorr+TKK69k06ZNNGzYkKeeeoquXbsybdo0fv/735OSkkKzZs14++23uf3229mzZw8ffvght9xyC6+99hq33norXbtGbo5ISUnh8ssvPyCWp556iieffJL9+/fTpUsXXnjhBRo2bHjAMd5//30WLVrEz372M/bv309BQQH/+Mc/SE9Pp3HjxuzcuZPhw4ezc+dO+vfvzy233MKSJUto3LgxN954IytWrCi1LSXb/MADDxTFtmPHDubPn0+fPn2Ktr388ssMGzaMNm3aMHnyZH77299W+G/jxRdfZODAgQwbNqxo25AhQyp8X0VeeeUV3nvvPQBGjx7NkCFDuOeee8os/9JLL3HaaafRsGFDNm3aRP369TnmmGMAOOmkk7j77ru59NJLMTOGDBnCq6++yogRI6ocZ1J2Da3ctJOrJ82l6xFNuff83rqgmQTy8/N55513GD58OABdu3blgw8+YO7cuYwbN67Yh0V2djZTpkxhwYIFTJkyhTVr1rB+/XruuOMOPvroIz788EMWL15cVP7qq69m9OjRzJ8/n5/+9Kdcc801Rfu2bdvGxx9/zIMPPsjw4cO57rrrWLRoEQsWLCA7O7uo3NChQ8nIyGDAgAEV1pmTk8OMGTN44IEHuOyyy3jkkUeYM2cO9913H1dccQUA48aN46233mLevHlkZWVRv359xo0bx8iRI8nOzmbkyJEsXLiQ/v37V/i7O/fcc5k1axbz5s2jW7duPPPMM6UeA+CJJ57g2muvJTs7m9mzZ9O+ffHbsLOyskhLSyuKIVpZbSnZ5mizZ8+mZ8+exbZNmjSJUaNGMWrUKCZNmlRh+4CYfxc7duwo1o0T/Yr+N1Fow4YNtG3bFoAjjjiiwq6myZMnM2rUKABatmxJXl4es2fPBiJJYs2aNUVlMzMz+eCDD2JqX0WS7ozg2725/Pz52dRLqcOTF/WnYf2k+xUkRGW+uVenPXv2kJGRwdq1a+nWrRsnnXQSANu3b2f06NF88cUXmBm5ublF7znxxBNp1qwZAN27d2f16tVs3ryZIUOG0KpVKwBGjhzJsmXLAPj44495+eWXAbjooou46aabiuoaNmwYZkavXr1o06YNvXr1AqBHjx6sWrWKjIwMINJP3LLl/0a3La/O888/n5SUFHbu3MmMGTM4//zzi/bt27cPgMGDBzNmzBhGjBjBueeeW6Xf4cKFC/nd737HN998w86dOznllFPKPMbAgQP54x//SE5ODueeey7p6ekxHaO8tkS3uaT169cX/U0g8sH7xRdf8P3vfx8zo169eixcuJCePUsfL6yyXwKbNGlSLIFXhpmVe7z169ezYMGCot+vmTF58mSuu+469u3bx8knn1zsd9C6dWvWrVt3ULGUFOoZgZmdamZLzWy5md1cyv4GZjYl2P+pmXUKM578AufXk7P5astuHv9pPzocrrkFarvCb5+rV6/G3XnssccAuO222xg6dCgLFy7k3//+d7H7rgu7fCDSXVJ4ge5gFNZVp06dYvXWqVPnoOtt1KgRAAUFBTRv3pzs7Oyi15IlS4DIN/O77rqLNWvW0L9/f7Zs2XJAPT169GDOnDkVHm/MmDE8+uijLFiwgDvuuKPod1XaMS644IKib/2nn3467777bkxtKq8t0W0uKS0trdjfburUqWzbto3OnTvTqVMnVq1aVXRW0KJFi2IXa7du3VqUfGP9XVT2jKBNmzasX78eiHzQt27dusy6p06dyjnnnFPsieCBAwfywQcfMHPmTE444YSibiKgqFuzOoSWCMwsBXgMOA3oDowys+4lil0KbHP3LsCDQNmdZ9Xg/v9byrufb+SOYd057qgWYR5KDjENGzbk4Ycf5v777ycvL4/t27fTrl07gHL76gsNGDCA//73v2zZsoXc3FymTZtWtG/QoEFMnjwZgIkTJ3L88cdXOd5Y6mzatCmdO3cuisXdmTdvHgArVqxgwIABjBs3jlatWrFmzRqaNGnCjh07it4/duxY/vSnPxWd2RQUFPDEE08ccJwdO3bQtm1bcnNzmThxYtH20o6xcuVKjjrqKK655hrOOuusomsyFSmvLeXp1q0by5cvL1qfNGkSb775JqtWrWLVqlXMmTOn6Pc4ZMgQpkyZwv79+4HI333o0KEAXHDBBcyYMYPXXnutqK7333+fhQsXFjte4RlBaa/u3Ut+vMHw4cN57rnnAHjuuec466yzymxLYZdWtI0bNwKRs6N77rmHX/3qV0X7li1bdkC32MEK84zgWGC5u6909/3AZKDkb+Es4Llg+SXgRAupwz5r3joef28Fo47tyIXHHRnGIeQQ17dvX3r37s2kSZO46aabuOWWW+jbt29M38zbtm3LnXfeycCBAxk8eDDdunUr2vfII48wfvx4evfuzQsvvMBDDz1U5VhjrXPixIk888wz9OnThx49evDKK68AkQ/5Xr160bNnTwYNGkSfPn0YOnQoixcvJiMjgylTptC7d2/+8pe/MGrUKLp160bPnj1ZuXLlAcf4wx/+wIABAxg8eHDRheWyjjF16lR69sbTunYAAAnpSURBVOxJRkYGCxcu5OKLL465zWW1pTxdu3Zl+/bt7Nixg1WrVrF69epit4127tyZZs2a8emnn3LmmWdy/PHH079/fzIyMvjoo4+KLtympaXx6quv8sgjj5Cenk737t15/PHHi3U7HYybb76Z//znP6Snp/P2229z882RjpHZs2fz85//vKjcqlWrWLNmDT/4wQ+Kvf/ee++lW7du9O7dm2HDhvHDH/6waN/06dM544wzqhRfIYvcVVT9zOw84FR3/3mwfhEwwN2viiqzMCiTE6yvCMpsLlHXZcBlAB07duy/evXqSsczY/lmxs9YxWMX9KN+3aS8Rh53S5YsKfaBKRKGBx98kCZNmhT7YK3tNmzYwAUXXMA777xT6v7S/u+Z2Rx3zyytfI34RHT3J909090zDzZDD+rSkqcuzlQSEKllLr/88mLXX5LBV199xf33319t9YV5y8xaoEPUevtgW2llcsysLtAMOPCqlohIGVJTU7nooosSHUZcfe9736vW+sL8ejwLSDezzmZWH/gJkFWiTBYwOlg+D3jXw+qrkoTQn1Mkvg7m/1xoicDd84CrgLeAJcBUd19kZuPMbHhQ7BmghZktB64HDrjFVGqu1NRUtmzZomQgEicezEeQmlq5mRVDu1gclszMTC980k4ObZqhTCT+ypqhrLyLxXqsVkJTr169Ss2SJCKJoVtoRESSnBKBiEiSUyIQEUlyNe5isZltAir/aHFES2BzhaVqF7U5OajNyaEqbT7S3Ut9IrfGJYKqMLPZZV01r63U5uSgNieHsNqsriERkSSnRCAikuSSLRE8megAEkBtTg5qc3IIpc1JdY1AREQOlGxnBCIiUoISgYhIkquVicDMTjWzpWa23MwOGNHUzBqY2ZRg/6dm1in+UVavGNp8vZktNrP5ZvaOmdX4+ToranNUuR+bmZtZjb/VMJY2m9mI4G+9yMxejHeM1S2Gf9sdzWy6mc0N/n2fnog4q4uZPWtmG4MZHEvbb2b2cPD7mG9m/ap8UHevVS8gBVgBHAXUB+YB3UuUuQJ4Ilj+CTAl0XHHoc1DgYbB8uXJ0OagXBPgfeATIDPRccfh75wOzAUOC9ZbJzruOLT5SeDyYLk7sCrRcVexzScA/YCFZew/HXgDMOA44NOqHrM2nhEcCyx395Xuvh+YDJxVosxZwHPB8kvAiWZmcYyxulXYZnef7u67g9VPiMwYV5PF8ncG+ANwD1AbxsKOpc2/AB5z920A7r4xzjFWt1ja7EDTYLkZsC6O8VU7d38f2FpOkbOA5z3iE6C5mbWtyjFrYyJoB6yJWs8JtpVaxiMT6GwHWsQlunDE0uZolxL5RlGTVdjm4JS5g7u/Fs/AQhTL3/kY4Bgz+8jMPjGzU+MWXThiafOdwIVmlgO8Dlwdn9ASprL/3yuk+QiSjJldCGQCP0h0LGEyszrAA8CYBIcSb3WJdA8NIXLW976Z9XL3bxIaVbhGARPc/X4zGwi8YGY93b0g0YHVFLXxjGAt0CFqvX2wrdQyZlaXyOnklrhEF45Y2oyZ/Qi4FRju7vviFFtYKmpzE6An8J6ZrSLSl5pVwy8Yx/J3zgGy3D3X3b8ElhFJDDVVLG2+FJgK4O4fA6lEBmerrWL6/14ZtTERzALSzayzmdUncjE4q0SZLGB0sHwe8K4HV2FqqArbbGZ9gb8RSQI1vd8YKmizu29395bu3sndOxG5LjLc3WvyPKex/Nv+F5GzAcysJZGuopXxDLKaxdLmr4ATAcysG5FEsCmuUcZXFnBxcPfQccB2d19flQprXdeQu+eZ2VXAW0TuOHjW3ReZ2ThgtrtnAc8QOX1cTuSizE8SF3HVxdjme4HGwLTguvhX7j48YUFXUYxtrlVibPNbwMlmthjIB8a6e409242xzTcAT5nZdUQuHI+pyV/szGwSkWTeMrjucQdQD8DdnyByHeR0YDmwG/hZlY9Zg39fIiJSDWpj15CIiFSCEoGISJJTIhARSXJKBCIiSU6JQEQkySkRyCHJzPLNLDvq1amcsjur4XgTzOzL4FifBU+oVraOp82se7D82xL7ZlQ1xqCewt/LQjP7t5k1r6B8Rk0fjVPCp9tH5ZBkZjvdvXF1ly2njgnAq+7+kpmdDNzn7r2rUF+VY6qoXjN7Dljm7n8sp/wYIqOuXlXdsUjtoTMCqRHMrHEwj8JnZrbAzA4YadTM2prZ+1HfmI8Ptp9sZh8H751mZhV9QL8PdAnee31Q10Iz+3WwrZGZvWZm84LtI4Pt75lZppn9PyAtiGNisG9n8HOymZ0RFfMEMzvPzFLM7F4zmxWMMf/LGH4tHxMMNmZmxwZtnGtmM8zsu8GTuOOAkUEsI4PYnzWzmUHZ0kZslWST6LG39dKrtBeRp2Kzg9c/iTwF3zTY15LIU5WFZ7Q7g583ALcGyylExhtqSeSDvVGw/TfA7aUcbwJwXrB8PvAp0B9YADQi8lT2IqAv8GPgqaj3Ngt+vkcw50FhTFFlCmM8B3guWK5PZBTJNOAy4HfB9gbAbKBzKXHujGrfNODUYL0pUDdY/hHwj2B5DPBo1Pv/BFwYLDcnMhZRo0T/vfVK7KvWDTEhtcYed88oXDGzesCfzOwEoIDIN+E2wNdR75kFPBuU/Ze7Z5vZD4hMVvJRMLRGfSLfpEtzr5n9jsg4NZcSGb/mn+6+K4jhZeB44E3gfjO7h0h30geVaNcbwENm1gA4FXjf3fcE3VG9zey8oFwzIoPFfVni/Wlmlh20fwnwn6jyz5lZOpFhFuqVcfyTgeFmdmOwngp0DOqSJKVEIDXFT4FWQH93z7XIiKKp0QXc/f0gUZwBTDCzB4BtwH/cfVQMxxjr7i8VrpjZiaUVcvdlFpnr4HTgLjN7x93HxdIId99rZu8BpwAjiUy0ApHZpq5297cqqGKPu2eYWUMi4+9cCTxMZAKe6e5+TnBh/b0y3m/Aj919aSzxSnLQNQKpKZoBG4MkMBQ4YM5li8zDvMHdnwKeJjLd3yfAYDMr7PNvZGbHxHjMD4CzzayhmTUi0q3zgZl9B9jt7n8nMphfaXPG5gZnJqWZQmSgsMKzC4h8qF9e+B4zOyY4Zqk8MtvcNcAN9r+h1AuHIh4TVXQHkS6yQm8BV1twemSRUWklySkRSE0xEcg0swXAxcDnpZQZAswzs7lEvm0/5O6biHwwTjKz+US6hbrGckB3/4zItYOZRK4ZPO3uc4FewMygi+YO4K5S3v4kML/wYnEJ/0dkYqC3PTL9IkQS12LgM4tMWv43KjhjD2KZT2Rilj8Ddwdtj37fdKB74cViImcO9YLYFgXrkuR0+6iISJLTGYGISJJTIhARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0QgIpLk/j+Dm4hTJxu6cQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRlPeL9-Je5Y"
      },
      "source": [
        "_ = joblib.dump(forest, \"rf_w2v.pkl\", compress=9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "hwoXn7vVJsCI",
        "outputId": "081efde6-0756-4e20-9e70-67473ca0f2fb"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "param_grid = [\n",
        "{'n_estimators': [10, 25,50], 'max_features': [5, 10,15,20,100], \n",
        " 'max_depth': [10,None], 'bootstrap': [True, False]}\n",
        "]\n",
        "forest = RandomForestClassifier()\n",
        "h_forest = GridSearchCV(forest, param_grid, cv=10, scoring='f1',verbose=0)\n",
        "h_forest.fit(X_train, y_train)\n",
        "\n",
        "print(h_forest.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(h_forest.best_estimator_) \n",
        "y_pred = h_forest.predict(X_test)\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "metrics.plot_roc_curve(h_forest, X_test, y_test)  # doctest: +SKIP\n",
        "plt.savefig(\"rf_grid.png\")\n",
        "plt.show()\n",
        "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, annot_kws={\"size\": 14}, fmt='g')\n",
        "plt.savefig(\"rf_heat.png\")\n",
        "\n",
        "plt.show()\n",
        "roc_auc_score(y_test, y_pred) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': True, 'max_depth': 10, 'max_features': 5, 'n_estimators': 25}\n",
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=10, max_features=5,\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=25,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n",
            "[[ 23  29]\n",
            " [ 17 125]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.44      0.50        52\n",
            "           1       0.81      0.88      0.84       142\n",
            "\n",
            "    accuracy                           0.76       194\n",
            "   macro avg       0.69      0.66      0.67       194\n",
            "weighted avg       0.75      0.76      0.75       194\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD4CAYAAADSIzzWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVOUlEQVR4nO3deZhU5ZXH8e9hlwbZGlsEVBQUNRmiIbglDuMKLoDGBUQFwXR8XFDJBEGTMe4oRiUSTVpA0DEYxCSQjDAaxG0MKBojAgINpKURAUVcaKJU1Zk/usRubLqqi+p6rdu/D8996HrvrVsH5fn5eu57b5m7IyIiudcodAEiIg2VAlhEJBAFsIhIIApgEZFAFMAiIoE0qe8PKGrTU8ss5GsKW7QJXYJ8Ay3duMj29Bw7PliTduY0LTxojz9vT2gGLCISSL3PgEVEcioRD11B2hTAIhIt8VjoCtKmABaRSHFPhC4hbQpgEYmWhAJYRCSMPJoBaxWEiERLIp7+loKZTTWzTWb2dpWxCWb2jpm9ZWZ/NLO2VfaNM7NSM1thZqelOr8CWESixRPpb6lNA/rtMvYs8C13/zdgJTAOwMwOBwYDRyTf86CZNa7t5ApgEYkUj8fS3lKey/1FYMsuY8+4+5dvXgh0Sf48EHjC3T9397VAKdCntvMrgEUkWhKJtDczKzazxVW24jp+2ghgbvLnzsC6KvvKk2O7pYtwIhItdbgI5+4lQEkmH2NmNwIx4PFM3g8KYBGJmhzcCWdmw4EzgZP8q68VWg90rXJYl+TYbqkFISLRkt2LcF9jZv2AMcAAd6+osmsOMNjMmptZN6AH8Gpt59IMWESiJYu3IpvZDKAvUGhm5cBNVK56aA48a2YAC939cndfamYzgWVUtiaudPdap+MKYBGJlizeCefuQ2oYnlLL8bcDt6d7fgWwiERKiknnN4oCWESiJY9uRVYAi0i06GE8IiKBaAYsIhJIfEfoCtKmABaRaFELQkQkELUgREQC0QxYRCQQBbCISBiui3AiIoGoBywiEohaECIigWgGLCISiGbAIiKBaAYsIhJILHsPZK9vCmARiRbNgEVEAlEPWEQkEM2ARUQC0QxYRCQQzYBFRALRKggRkUDcQ1eQNgWwiESLesAiIoEogEVEAtFFOBGRQOLx0BWkTQEsItGiFoSISCAKYBGRQNQDFhEJwxNaBywiEkYetSAahS5ARCSr4vH0txTMbKqZbTKzt6uMtTezZ81sVfL3dslxM7NfmVmpmb1lZkelOr8CWESiJZFIf0ttGtBvl7GxwHx37wHMT74G6A/0SG7FwEOpTq4AzqJRo4uZt+BJStctZunqV3jsiYfoeViPasdcf+MoXn7tada+9wYryhYxa84j9O5zZKCKJRcuGzWM3897hEWlz/HS0nn8+rF76N7zoGrHdOjYntsn/pwF//gLi9e+wG9n3M/+3boGqjjPZTGA3f1FYMsuwwOB6cmfpwODqow/6pUWAm3NrFNt51cAZ9Fx3+/DtMkzOPPUIZx71nBisRhPzp5K23Ztdh5Tumot4/7zFvoeN4ABpw3l3bJynnjqYTp27BCwcqlPfY47ihnTnmLomZcx4twricXiTHlyEm3a7r3zmF9Nu5sDDurKqOFjOPfki3mv/H2mPPkAe7VsEbDyPOWe9mZmxWa2uMpWnMYnFLn7huTP7wNFyZ87A+uqHFeeHNst83p+clBRm575c0kyy1oWtKR03WsMv/Aqnpm3oMZjWrUuYHX561xwzmU8P//lHFcYTmGLNqkPiqiWLfdiYel8Rg0fw/PPvMwBB3Xl6b/N4pz/uIgVy1YBYGa88PbTTLzjIZ56fE7ginNn6cZFtqfnqLj3R2lnTsvRD6f8PDM7EPiLu38r+Xqru7etsv8jd29nZn8Bxrv7y8nx+cD17r54d+dOuQrCzHpSObX+MsnXA3PcfXmq9zZ0rVoV0LhxY7Zu/bjG/U2bNuXi4RfwycefsvQt/eNsKFq2aknjxo35ZOunADRr3gyAzz//fOcx7s4Xn+/gqD69GlQAZ0X9L0PbaGad3H1DssWwKTm+HqjaN+qSHNutWlsQZnY98ARgwKvJzYAZZja2tvcK3HbXDSx5axmLX32z2vgpp/VlzfrXeXfTP/jxFcM4f9AINm/+MFCVkmvjbhvN8iUreHPxEgDWrvon763bwLU3XEGbtnvTtGkTRl51MZ06F9GxqDBwtXkoi6sgdmMOMCz58zBgdpXxS5KrIY4BPq7SqqhRqhnwSOAId99RddDM7gWWAuNrelOyj1IM0LpFEXs1a1vTYZF28+1jOfqY7zKg34Ukdmn2/99LizjxB2fToX07Lhp+HiXT7ueMUwazaePmQNVKroy5+RqOOroXFw8o3vn3IhaLc82Isdx63428suJZYrEYC198jRf/+gq2x/9D3vB4FtcBm9kMoC9QaGblwE1U5t5MMxsJlAHnJw9/GjgdKAUqgEtTnr+2HrCZvQOc5u5lu4wfADzj7oem+oCG2AO+5Y6xDPrh6Zxz5jBKV61Nefzf3pjHzBmzuW9CylUrkdEQe8DX33It/QedwqXnXMHa0rIaj2nVuoCmzZry0YdbmTF3CkvffIfbxk3IcaXhZKMHvO32S9LOnIIbHw36n7hUM+Brgflmtoqvru7tD3QHrqrPwvLVbeNvYOA5/dMOX4BGjRrRPNkHlGgae9to+g88udbwBfjs020A7N+tK0f0OowHxpfkqsToiMqzINx9npkdAvSh+kW419w9fx66mSN33vNzzrtgIMOHXsnWrZ/QcZ/K/t22bRVUbKugVesCrrrmMp6Zu4CNGzfTobA9Iy67kE777cucP84NXL3Ul5/d+VPOOq8/o4b/lE+2fkJhx/YAVGzbTkXFdgBOPetEtm75mPfKN9DjsO6Mu/U6npv7Iq+8sChk6fkpSs+CcPcEsDAHteS9ET8aCsBTf55ebXzCnZO4Z/wk4rE4h/bszpCLfki79m35aMtW3nxjCYP6X8SypStDlCw5MGTEuQBMferBauO/nvAwD94zGYCORYWMuflaCju2Z/PGD5jz5Fx+c++UnNcaCbH8mRtqHbAE0RB7wJJaVnrAPz8//R7wrTO/0T1gEZH8EqUWhIhIPsnmMrT6pgAWkWjRDFhEJBAFsIhIIPpaehGRMPSdcCIioSiARUQC0SoIEZFANAMWEQlEASwiEobH1YIQEQlDM2ARkTC0DE1EJBQFsIhIIPnTAlYAi0i0eCx/ElgBLCLRkj/5qwAWkWjRRTgRkVA0AxYRCUMzYBGRUDQDFhEJw2OhK0ifAlhEIsU1AxYRCUQBLCIShmbAIiKBKIBFRALxuIUuIW2NQhcgIpJNnkh/S8XMrjOzpWb2tpnNMLMWZtbNzBaZWamZ/d7MmmVaqwJYRCLFE5b2Vhsz6wyMAnq7+7eAxsBg4C7gPnfvDnwEjMy0VgWwiERKNmfAVLZp9zKzJkBLYANwIjAruX86MCjTWhXAIhIp7pb2ZmbFZra4ylb81Xl8PXAP8C6Vwfsx8Dqw1X3n7R7lQOdMa9VFOBGJlLqsgnD3EqCkpn1m1g4YCHQDtgJPAv32vMKvKIBFJFIS2VsFcTKw1t03A5jZH4DjgbZm1iQ5C+4CrM/0A9SCEJFIydZFOCpbD8eYWUszM+AkYBmwADg3ecwwYHamtSqARSRSshXA7r6IyottbwBLqMzLEuB6YLSZlQIdgCmZ1qoWhIhEimfxccDufhNw0y7Da4A+2Ti/AlhEIiWN1sI3hgJYRCLFXQEsIhJEPI+eBaEAFpFI0QxYRCQQ9YBFRALJ5iqI+qYAFpFI0QxYRCSQeCJ/7i9TAItIpKgFISISSEKrIEREwtAyNBGRQNSCqOLD7Z/W90dIHipf/XToEiSi1IIQEQlEqyBERALJow6EAlhEokUtCBGRQLQKQkQkkDp8KXJwCmARiRRHM2ARkSBiakGIiIShGbCISCDqAYuIBKIZsIhIIJoBi4gEEtcMWEQkjDz6RiIFsIhES0IzYBGRMPQwHhGRQHQRTkQkkISpBSEiEkQ8dAF1kD+PjhcRSUPC0t9SMbO2ZjbLzN4xs+VmdqyZtTezZ81sVfL3dpnWqgAWkUhJYGlvaZgIzHP3nkAvYDkwFpjv7j2A+cnXGVEAi0ikeB222phZG+AEYAqAu3/h7luBgcD05GHTgUGZ1qoAFpFIqUsLwsyKzWxxla24yqm6AZuBR8zs72Y22cwKgCJ335A85n2gKNNadRFORCKlLsvQ3L0EKNnN7ibAUcDV7r7IzCayS7vB3d3MMl56rBmwiERK3NLfUigHyt19UfL1LCoDeaOZdQJI/r4p01oVwCISKYk6bLVx9/eBdWZ2aHLoJGAZMAcYlhwbBszOtFa1IEQkUrJ8J9zVwONm1gxYA1xK5cR1ppmNBMqA8zM9uQJYRCIlm18J5+5vAr1r2HVSNs6vABaRSNGzIEREAsmnW5EVwCISKXogu4hIIGpBiIgEogAWEQlE34ghIhKIesAiIoFoFYSISCCJPGpCKIBFJFJ0EU5EJJD8mf8qgEUkYjQDFhEJJJb589FzTgEsIpGSP/GrABaRiFELQkQkEC1DExEJJH/iVwEsIhGjFoSISCDxPJoDK4BFJFI0AxYRCcQ1A264fvD9oxk9+nKOOvLbdO7ciREjr+PRx2bu3B/7Yn2N73vwoWmMuubGXJUp9Wjxm0uY9runWLailE0ffMhtN4xm0BmnALAjFuOBkum8tHAx5es3UFDQkj5H9eK6yy+l07777DzH8KvGsPjvS6qdt99JJ3DPLeNy+mfJR5oBN2CtWhWwdOkKHvvvWUybOvFr+zt3/U61172/24vZf5rOrFl/zlWJUs8qKrbT/aADGdD/JG649ZfV9v3rX5+zbMVqii8ZTM8eB/PZtm1MeOBhfvyTn/GH6Q/RpEnjnccOOuMUrv3x8J2vmzdvnqs/Ql7TMrQGbO6855g77zkApk6+72v7N27cXO31WWedyoqVq3nxpYU5qU/q3wnH9eGE4/oAcOPt91bb17pVAZMn3lFt7KYxVzPwostZU/Yuhxzcbef4Xs1bUNihff0XHDH5E78K4KAKClpywfkDufW2e1MfLJH12bYKAPZu3ara+Nz5LzB3/gt0aNeW7x/bmysuHUpBQcsQJeaVWB5FsAI4oCGDz6ZZs6Y8+tiToUuRQHbs2MGESZPpe/zR7LtPx53jZ5zSl/32LaJjYXtK15Yx8TfTWFm6lofvv6OWswk0kItwZnapuz+ym33FQDGANW5Do0YFmX5MpI0ceSFz/vwMH3ywJXQpEkAsFmfsLRP49NPPmHTXTdX2nTfw9J0/H3JwN7ru14khP7qWZStKOfzQ7rkuNa/k00W4Rnvw3pt3t8PdS9y9t7v3VvjWrFevI/he7+8wZcrjoUuRAGKxOGN+MZ6VpWuZ8qs7adtm71qPP6JnDxo3bkTZuppX0chXvA6/Qqt1Bmxmb+1uF1CU/XIajstGDmXNmjL+Ov+l0KVIju2Ixfjpf42ndM0/eWTS3WldaFu5+p/E4wk6FuqiXCr5NANO1YIoAk4DPtpl3IBX6qWiPFdQ0JLu3SuvZDdq1Ij999+PXr2OYMuWj1i37j0A9tqrBRcOOZt7fvlQyFKlnlRUbOfd8sp/155wNmzcxDsrV9Nm79Z0LOzAT352B28vX8mku3+BGXzwYWULqlWrAlo0b8675e/xP88s4AfHfo92bduwem0ZEyZN5rBDDubIbx8e8o+WF+IefmabLvNaijWzKcAj7v5yDft+5+4XpvqAJs06588/jSz49xOOZf5fZ31tfPqjMxl52XUADLvkfH77mwl0O7gPGzZszHWJ3wjb34vuzP/VN95ixNXXf218YP+TuWLkRZx27vAa3/flDRsbNm5m3C13s2pNGRXbt7PvPh054bg+XDFiKG32bl3P1YfVtPAg29NzXHjA2Wlnzu/K/rjHn7cnag3gbGhoASzpiXIAS+ayEcBDDhiUdubMKPtTys8zs8bAYmC9u59pZt2AJ4AOwOvAxe7+RSa17slFOBGRb5xEHbY0XQMsr/L6LuA+d+9OZXt2ZKa1KoBFJFISeNpbKmbWBTgDmJx8bcCJwJd9xunAoExrVQCLSKTUZRmamRWb2eIqW/Eup7sfGMNXE+YOwFZ3jyVflwOdM61Vd8KJSKTUZRWEu5cAJTXtM7MzgU3u/rqZ9c1OddUpgEUkUrL4NLTjgQFmdjrQAtgbmAi0NbMmyVlwFyDju2PUghCRSMnWRTh3H+fuXdz9QGAw8Jy7DwUWAOcmDxsGzM60VgWwiERKDm5Fvh4YbWalVPaEp2R6IrUgRCRS6uOB7O7+PPB88uc1QJ9snFcBLCKRUt83l2WTAlhEIkVfSy8iEoi+E05EJBC1IEREAtEMWEQkkG/CN12kSwEsIpGSTw9kVwCLSKSoBSEiEogCWEQkEK2CEBEJRDNgEZFAtApCRCSQuNfh294CUwCLSKSoBywiEoh6wCIigagHLCISSEItCBGRMDQDFhEJRKsgREQCUQtCRCQQtSBERALRDFhEJBDNgEVEAol7PHQJaVMAi0ik6FZkEZFAdCuyiEggmgGLiASiVRAiIoFoFYSISCC6FVlEJBD1gEVEAsmnHnCj0AWIiGSTu6e91cbMuprZAjNbZmZLzeya5Hh7M3vWzFYlf2+Xaa0KYBGJlASe9pZCDPiJux8OHANcaWaHA2OB+e7eA5iffJ0RBbCIREq2ZsDuvsHd30j+/CmwHOgMDASmJw+bDgzKtFb1gEUkUuqyCsLMioHiKkMl7l5Sw3EHAkcCi4Aid9+Q3PU+UJRprQpgEYmUulyES4bt1wK3KjNrBTwFXOvun5hZ1fe7mWV81U8BLCKRks1laGbWlMrwfdzd/5Ac3mhmndx9g5l1AjZlen71gEUkUrwOv2pjlVPdKcByd7+3yq45wLDkz8OA2ZnWqhmwiERKFmfAxwMXA0vM7M3k2A3AeGCmmY0EyoDzM/0ABbCIREq2bsRw95cB283uk7LxGZZPt+3lOzMrrukKqzRs+nvRcKkHnFvFqQ+RBkh/LxooBbCISCAKYBGRQBTAuaU+n9REfy8aKF2EExEJRDNgEZFAFMAiIoEogHPEzPqZ2QozKzWzjJ8fKtFhZlPNbJOZvR26FglDAZwDZtYY+DXQHzgcGJJ8sLM0bNOAfqGLkHAUwLnRByh19zXu/gXwBJUPdZYGzN1fBLaErkPCUQDnRmdgXZXX5ckxEWnAFMAiIoEogHNjPdC1yusuyTERacAUwLnxGtDDzLqZWTNgMJUPdRaRBkwBnAPuHgOuAv6Xym9WnenuS8NWJaGZ2Qzgb8ChZlaefMC3NCC6FVlEJBDNgEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUD+H5JO719ckckjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6612946912242686"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "Z3iSkg5QhPBD",
        "outputId": "77ef5ee2-88cc-4756-83ca-c0ea54c58799"
      },
      "source": [
        "inp.reshape((558,100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.11448825, -0.06767737,  0.07417489, ...,  0.02459422,\n",
              "         0.03619924,  0.01279044],\n",
              "       [ 0.03131074, -0.0800982 , -0.0890515 , ..., -0.04288026,\n",
              "        -0.03894006, -0.00636407],\n",
              "       [ 0.11354048, -0.07318777,  0.07378678, ...,  0.00200416,\n",
              "         0.05123808, -0.01472694],\n",
              "       ...,\n",
              "       [ 0.057279  ,  0.01416489,  0.01486477, ..., -0.02366633,\n",
              "         0.06776406,  0.01028517],\n",
              "       [-0.16485774,  0.09376617, -0.08762894, ..., -0.04578647,\n",
              "        -0.04865688, -0.05080542],\n",
              "       [ 0.057279  ,  0.01416489,  0.01486477, ..., -0.02366633,\n",
              "         0.06776406,  0.01028517]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "jZYahbDpjev0",
        "outputId": "f259dd6c-c9ec-41a8-c5fb-02aaf41e2626"
      },
      "source": [
        "inp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(558, 100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "id": "6xyf3TdPgEPi",
        "outputId": "0028c117-df48-4883-8f78-6896d33624c6"
      },
      "source": [
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "d2=pd.DataFrame(data=inp)\n",
        "#d2.insert(100, \"100\", output, True) \n",
        "#print(d2)\n",
        "df_subset=pd.DataFrame()\n",
        "\n",
        "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
        "tsne_results = tsne.fit_transform(d2)\n",
        "df_subset['tsne-2d-one'] = tsne_results[:,0]\n",
        "df_subset['tsne-2d-two'] = tsne_results[:,1]\n",
        "df_subset[\"y\"]=output\n",
        "plt.figure(figsize=(16,10))\n",
        "sns.scatterplot(\n",
        "    x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n",
        "    hue=\"y\",\n",
        "    data=df_subset,\n",
        "    legend=\"full\",\n",
        "    alpha=0.3\n",
        ")\n",
        "#plt.savefig(\"plot1.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[t-SNE] Computing 121 nearest neighbors...\n",
            "[t-SNE] Indexed 551 samples in 0.006s...\n",
            "[t-SNE] Computed neighbors for 551 samples in 0.087s...\n",
            "[t-SNE] Computed conditional probabilities for sample 551 / 551\n",
            "[t-SNE] Mean sigma: 0.200379\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 70.269516\n",
            "[t-SNE] KL divergence after 300 iterations: 0.775220\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3e16c6198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "Gxoc-8ya8lUk",
        "outputId": "588c9823-c326-44ab-8217-2012868ccee1"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import seaborn as sns\n",
        "dfx = pd.DataFrame()\n",
        "pca = PCA(n_components=3)\n",
        "\n",
        "pca_result = pca.fit_transform(d2)\n",
        "print(pca_result.shape)\n",
        "dfx['pca-one'] = pca_result[:,0]\n",
        "dfx['pca-two'] = pca_result[:,1] \n",
        "dfx['pca-three'] = pca_result[:,2]\n",
        "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(552, 3)\n",
            "Explained variation per principal component: [0.12844652 0.08090727 0.06018487]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "GEhd6HIL8vLs",
        "outputId": "08458f2e-03d9-4591-ad60-1f1d2e4eb501"
      },
      "source": [
        "np.random.seed(42)\n",
        "rndperm = np.random.permutation(dfx.shape[0])\n",
        "plt.figure(figsize=(16,10))\n",
        "dfx[\"y\"]=output\n",
        "sns.scatterplot(\n",
        "    x='pca-one', y='pca-two',\n",
        "    hue=\"y\",\n",
        "    data=dfx.loc[rndperm,:],\n",
        "    legend=\"full\",\n",
        "    alpha=0.3\n",
        ")\n",
        "#plt.savefig(\"plot2.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08789a1898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0cz8Odu951o"
      },
      "source": [
        "ax = plt.figure(figsize=(16,10)).gca(projection='3d')\n",
        "ax.scatter(\n",
        "    xs=dfx.loc[rndperm,:][\"pca-one\"], \n",
        "    ys=dfx.loc[rndperm,:][\"pca-two\"], \n",
        "    zs=dfx.loc[rndperm,:][\"pca-three\"]\n",
        ")\n",
        "ax.set_xlabel('pca-one')\n",
        "ax.set_ylabel('pca-two')\n",
        "ax.set_zlabel('pca-three')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "SSlohYQ6cBEk",
        "outputId": "944e69ff-1fd4-48f2-e4c4-4180792318bf"
      },
      "source": [
        "d2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>0</th>\n",
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              "      <th>6</th>\n",
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              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
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              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
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              "      <th>23</th>\n",
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              "      <th>27</th>\n",
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              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "      <th>72</th>\n",
              "      <th>73</th>\n",
              "      <th>74</th>\n",
              "      <th>75</th>\n",
              "      <th>76</th>\n",
              "      <th>77</th>\n",
              "      <th>78</th>\n",
              "      <th>79</th>\n",
              "      <th>80</th>\n",
              "      <th>81</th>\n",
              "      <th>82</th>\n",
              "      <th>83</th>\n",
              "      <th>84</th>\n",
              "      <th>85</th>\n",
              "      <th>86</th>\n",
              "      <th>87</th>\n",
              "      <th>88</th>\n",
              "      <th>89</th>\n",
              "      <th>90</th>\n",
              "      <th>91</th>\n",
              "      <th>92</th>\n",
              "      <th>93</th>\n",
              "      <th>94</th>\n",
              "      <th>95</th>\n",
              "      <th>96</th>\n",
              "      <th>97</th>\n",
              "      <th>98</th>\n",
              "      <th>99</th>\n",
              "      <th>100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1ERR</th>\n",
              "      <td>0.114488</td>\n",
              "      <td>-0.067677</td>\n",
              "      <td>0.074175</td>\n",
              "      <td>0.008188</td>\n",
              "      <td>-0.015419</td>\n",
              "      <td>0.035176</td>\n",
              "      <td>0.019838</td>\n",
              "      <td>0.021438</td>\n",
              "      <td>-0.021309</td>\n",
              "      <td>-0.004414</td>\n",
              "      <td>-0.011633</td>\n",
              "      <td>0.042724</td>\n",
              "      <td>-0.043248</td>\n",
              "      <td>0.043986</td>\n",
              "      <td>0.043440</td>\n",
              "      <td>0.005377</td>\n",
              "      <td>0.080785</td>\n",
              "      <td>-0.036527</td>\n",
              "      <td>0.017522</td>\n",
              "      <td>-0.029131</td>\n",
              "      <td>0.053889</td>\n",
              "      <td>0.063394</td>\n",
              "      <td>-0.010131</td>\n",
              "      <td>-0.025053</td>\n",
              "      <td>0.018540</td>\n",
              "      <td>0.009884</td>\n",
              "      <td>-0.032545</td>\n",
              "      <td>-0.054697</td>\n",
              "      <td>-0.016547</td>\n",
              "      <td>0.001511</td>\n",
              "      <td>-0.051861</td>\n",
              "      <td>0.045040</td>\n",
              "      <td>-0.009677</td>\n",
              "      <td>-0.007352</td>\n",
              "      <td>-0.037499</td>\n",
              "      <td>-0.050220</td>\n",
              "      <td>0.024081</td>\n",
              "      <td>-0.023015</td>\n",
              "      <td>0.048981</td>\n",
              "      <td>-0.026001</td>\n",
              "      <td>...</td>\n",
              "      <td>0.060211</td>\n",
              "      <td>-0.020724</td>\n",
              "      <td>-0.032858</td>\n",
              "      <td>0.035808</td>\n",
              "      <td>-0.027678</td>\n",
              "      <td>-0.081321</td>\n",
              "      <td>-0.064323</td>\n",
              "      <td>-0.081415</td>\n",
              "      <td>0.089226</td>\n",
              "      <td>0.016528</td>\n",
              "      <td>-0.021993</td>\n",
              "      <td>0.041144</td>\n",
              "      <td>-0.087281</td>\n",
              "      <td>-0.000648</td>\n",
              "      <td>-0.003453</td>\n",
              "      <td>-0.006864</td>\n",
              "      <td>0.053127</td>\n",
              "      <td>-0.024128</td>\n",
              "      <td>0.010631</td>\n",
              "      <td>-0.012285</td>\n",
              "      <td>0.006483</td>\n",
              "      <td>0.049565</td>\n",
              "      <td>0.066146</td>\n",
              "      <td>-0.006196</td>\n",
              "      <td>0.037061</td>\n",
              "      <td>-0.059050</td>\n",
              "      <td>0.132639</td>\n",
              "      <td>-0.024286</td>\n",
              "      <td>-0.006539</td>\n",
              "      <td>0.005639</td>\n",
              "      <td>-0.087751</td>\n",
              "      <td>-0.011105</td>\n",
              "      <td>-0.021300</td>\n",
              "      <td>-0.077248</td>\n",
              "      <td>0.093578</td>\n",
              "      <td>0.002187</td>\n",
              "      <td>0.024594</td>\n",
              "      <td>0.036199</td>\n",
              "      <td>0.012790</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1FTL</th>\n",
              "      <td>0.031311</td>\n",
              "      <td>-0.080098</td>\n",
              "      <td>-0.089052</td>\n",
              "      <td>0.014268</td>\n",
              "      <td>0.064415</td>\n",
              "      <td>0.075148</td>\n",
              "      <td>-0.028559</td>\n",
              "      <td>0.031288</td>\n",
              "      <td>-0.001948</td>\n",
              "      <td>-0.010877</td>\n",
              "      <td>0.047970</td>\n",
              "      <td>-0.095987</td>\n",
              "      <td>0.017819</td>\n",
              "      <td>-0.053143</td>\n",
              "      <td>-0.049800</td>\n",
              "      <td>0.063240</td>\n",
              "      <td>0.037501</td>\n",
              "      <td>0.014960</td>\n",
              "      <td>-0.140583</td>\n",
              "      <td>-0.096620</td>\n",
              "      <td>-0.021676</td>\n",
              "      <td>-0.009074</td>\n",
              "      <td>0.010504</td>\n",
              "      <td>-0.016222</td>\n",
              "      <td>-0.008440</td>\n",
              "      <td>0.031167</td>\n",
              "      <td>0.025315</td>\n",
              "      <td>-0.055243</td>\n",
              "      <td>0.049544</td>\n",
              "      <td>0.037906</td>\n",
              "      <td>-0.002999</td>\n",
              "      <td>0.065024</td>\n",
              "      <td>-0.063577</td>\n",
              "      <td>-0.066291</td>\n",
              "      <td>-0.041878</td>\n",
              "      <td>-0.015602</td>\n",
              "      <td>-0.006686</td>\n",
              "      <td>-0.010404</td>\n",
              "      <td>-0.085886</td>\n",
              "      <td>-0.008884</td>\n",
              "      <td>...</td>\n",
              "      <td>0.017359</td>\n",
              "      <td>0.003396</td>\n",
              "      <td>-0.082489</td>\n",
              "      <td>0.094961</td>\n",
              "      <td>0.079555</td>\n",
              "      <td>0.011955</td>\n",
              "      <td>-0.063329</td>\n",
              "      <td>-0.037825</td>\n",
              "      <td>-0.013795</td>\n",
              "      <td>-0.006828</td>\n",
              "      <td>-0.050789</td>\n",
              "      <td>0.003951</td>\n",
              "      <td>-0.023788</td>\n",
              "      <td>0.011982</td>\n",
              "      <td>-0.011511</td>\n",
              "      <td>-0.000344</td>\n",
              "      <td>-0.038216</td>\n",
              "      <td>-0.021729</td>\n",
              "      <td>0.089686</td>\n",
              "      <td>0.036471</td>\n",
              "      <td>0.076457</td>\n",
              "      <td>0.018439</td>\n",
              "      <td>0.069402</td>\n",
              "      <td>-0.022761</td>\n",
              "      <td>-0.025327</td>\n",
              "      <td>-0.000189</td>\n",
              "      <td>-0.019926</td>\n",
              "      <td>0.060641</td>\n",
              "      <td>0.048020</td>\n",
              "      <td>0.059391</td>\n",
              "      <td>-0.003729</td>\n",
              "      <td>-0.059561</td>\n",
              "      <td>-0.024916</td>\n",
              "      <td>-0.001789</td>\n",
              "      <td>-0.083276</td>\n",
              "      <td>-0.023521</td>\n",
              "      <td>-0.042880</td>\n",
              "      <td>-0.038940</td>\n",
              "      <td>-0.006364</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1LK2</th>\n",
              "      <td>0.046392</td>\n",
              "      <td>-0.001892</td>\n",
              "      <td>0.057446</td>\n",
              "      <td>-0.036598</td>\n",
              "      <td>0.038909</td>\n",
              "      <td>0.002165</td>\n",
              "      <td>0.062266</td>\n",
              "      <td>-0.007311</td>\n",
              "      <td>-0.033605</td>\n",
              "      <td>0.022630</td>\n",
              "      <td>0.010970</td>\n",
              "      <td>-0.046406</td>\n",
              "      <td>0.073361</td>\n",
              "      <td>-0.048945</td>\n",
              "      <td>-0.096388</td>\n",
              "      <td>-0.065353</td>\n",
              "      <td>0.054235</td>\n",
              "      <td>0.072471</td>\n",
              "      <td>0.007437</td>\n",
              "      <td>0.008918</td>\n",
              "      <td>-0.028573</td>\n",
              "      <td>0.035827</td>\n",
              "      <td>-0.018635</td>\n",
              "      <td>0.002286</td>\n",
              "      <td>-0.049865</td>\n",
              "      <td>-0.059017</td>\n",
              "      <td>0.002135</td>\n",
              "      <td>0.102353</td>\n",
              "      <td>-0.094404</td>\n",
              "      <td>0.017788</td>\n",
              "      <td>-0.038698</td>\n",
              "      <td>0.057676</td>\n",
              "      <td>-0.002578</td>\n",
              "      <td>0.104546</td>\n",
              "      <td>-0.041400</td>\n",
              "      <td>0.006325</td>\n",
              "      <td>-0.001717</td>\n",
              "      <td>0.035302</td>\n",
              "      <td>0.060713</td>\n",
              "      <td>0.023696</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018517</td>\n",
              "      <td>-0.059261</td>\n",
              "      <td>0.093720</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.108461</td>\n",
              "      <td>-0.046503</td>\n",
              "      <td>-0.038499</td>\n",
              "      <td>0.111403</td>\n",
              "      <td>-0.042911</td>\n",
              "      <td>0.040618</td>\n",
              "      <td>-0.032121</td>\n",
              "      <td>0.021260</td>\n",
              "      <td>0.052598</td>\n",
              "      <td>0.021980</td>\n",
              "      <td>0.025729</td>\n",
              "      <td>0.035554</td>\n",
              "      <td>-0.056716</td>\n",
              "      <td>0.021723</td>\n",
              "      <td>-0.005700</td>\n",
              "      <td>-0.005896</td>\n",
              "      <td>-0.023164</td>\n",
              "      <td>0.010455</td>\n",
              "      <td>0.086244</td>\n",
              "      <td>-0.014327</td>\n",
              "      <td>-0.018559</td>\n",
              "      <td>0.020853</td>\n",
              "      <td>0.006803</td>\n",
              "      <td>0.089252</td>\n",
              "      <td>0.046666</td>\n",
              "      <td>0.091635</td>\n",
              "      <td>-0.064885</td>\n",
              "      <td>-0.056825</td>\n",
              "      <td>-0.014010</td>\n",
              "      <td>-0.013170</td>\n",
              "      <td>-0.032540</td>\n",
              "      <td>-0.068385</td>\n",
              "      <td>0.023507</td>\n",
              "      <td>-0.090147</td>\n",
              "      <td>-0.040490</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1NHZ</th>\n",
              "      <td>0.004482</td>\n",
              "      <td>-0.024705</td>\n",
              "      <td>0.039548</td>\n",
              "      <td>-0.003737</td>\n",
              "      <td>0.092521</td>\n",
              "      <td>0.011880</td>\n",
              "      <td>-0.040954</td>\n",
              "      <td>-0.039757</td>\n",
              "      <td>-0.006686</td>\n",
              "      <td>-0.046732</td>\n",
              "      <td>0.077879</td>\n",
              "      <td>-0.090718</td>\n",
              "      <td>-0.020280</td>\n",
              "      <td>-0.061331</td>\n",
              "      <td>-0.019321</td>\n",
              "      <td>-0.012679</td>\n",
              "      <td>-0.106398</td>\n",
              "      <td>0.035937</td>\n",
              "      <td>0.009704</td>\n",
              "      <td>-0.024868</td>\n",
              "      <td>-0.000618</td>\n",
              "      <td>0.041637</td>\n",
              "      <td>-0.009815</td>\n",
              "      <td>-0.037375</td>\n",
              "      <td>-0.076493</td>\n",
              "      <td>0.047085</td>\n",
              "      <td>-0.017826</td>\n",
              "      <td>0.004970</td>\n",
              "      <td>-0.020108</td>\n",
              "      <td>-0.009096</td>\n",
              "      <td>0.019433</td>\n",
              "      <td>0.128987</td>\n",
              "      <td>0.041934</td>\n",
              "      <td>0.033675</td>\n",
              "      <td>0.002078</td>\n",
              "      <td>0.069860</td>\n",
              "      <td>0.012405</td>\n",
              "      <td>-0.013023</td>\n",
              "      <td>0.082290</td>\n",
              "      <td>-0.047176</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.019217</td>\n",
              "      <td>0.032557</td>\n",
              "      <td>0.020232</td>\n",
              "      <td>0.055365</td>\n",
              "      <td>-0.010633</td>\n",
              "      <td>-0.082446</td>\n",
              "      <td>-0.043578</td>\n",
              "      <td>-0.059566</td>\n",
              "      <td>-0.007319</td>\n",
              "      <td>0.058687</td>\n",
              "      <td>0.012920</td>\n",
              "      <td>-0.085950</td>\n",
              "      <td>-0.037030</td>\n",
              "      <td>0.013677</td>\n",
              "      <td>0.068075</td>\n",
              "      <td>-0.001675</td>\n",
              "      <td>0.068789</td>\n",
              "      <td>-0.081749</td>\n",
              "      <td>-0.016347</td>\n",
              "      <td>-0.072323</td>\n",
              "      <td>0.032149</td>\n",
              "      <td>0.022134</td>\n",
              "      <td>0.034430</td>\n",
              "      <td>0.045229</td>\n",
              "      <td>0.024161</td>\n",
              "      <td>-0.029787</td>\n",
              "      <td>-0.039765</td>\n",
              "      <td>-0.040700</td>\n",
              "      <td>0.010035</td>\n",
              "      <td>0.003174</td>\n",
              "      <td>0.006724</td>\n",
              "      <td>0.000423</td>\n",
              "      <td>-0.020422</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>-0.098196</td>\n",
              "      <td>-0.003772</td>\n",
              "      <td>-0.004666</td>\n",
              "      <td>-0.030469</td>\n",
              "      <td>-0.019508</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1PBQ</th>\n",
              "      <td>-0.014348</td>\n",
              "      <td>0.062804</td>\n",
              "      <td>-0.010717</td>\n",
              "      <td>-0.008924</td>\n",
              "      <td>0.054095</td>\n",
              "      <td>0.017412</td>\n",
              "      <td>0.100746</td>\n",
              "      <td>-0.039664</td>\n",
              "      <td>-0.075522</td>\n",
              "      <td>0.004005</td>\n",
              "      <td>0.005986</td>\n",
              "      <td>-0.074940</td>\n",
              "      <td>-0.017732</td>\n",
              "      <td>-0.018714</td>\n",
              "      <td>0.043795</td>\n",
              "      <td>-0.016669</td>\n",
              "      <td>-0.086378</td>\n",
              "      <td>0.038470</td>\n",
              "      <td>0.122078</td>\n",
              "      <td>-0.015073</td>\n",
              "      <td>-0.006005</td>\n",
              "      <td>0.007436</td>\n",
              "      <td>-0.023012</td>\n",
              "      <td>-0.008805</td>\n",
              "      <td>-0.004331</td>\n",
              "      <td>-0.012174</td>\n",
              "      <td>-0.013237</td>\n",
              "      <td>0.008724</td>\n",
              "      <td>0.025828</td>\n",
              "      <td>-0.038898</td>\n",
              "      <td>0.030279</td>\n",
              "      <td>0.041870</td>\n",
              "      <td>-0.114335</td>\n",
              "      <td>0.040141</td>\n",
              "      <td>-0.062045</td>\n",
              "      <td>-0.014126</td>\n",
              "      <td>-0.001966</td>\n",
              "      <td>0.011282</td>\n",
              "      <td>-0.070098</td>\n",
              "      <td>-0.130098</td>\n",
              "      <td>...</td>\n",
              "      <td>0.022635</td>\n",
              "      <td>-0.012237</td>\n",
              "      <td>-0.060020</td>\n",
              "      <td>0.007409</td>\n",
              "      <td>-0.104875</td>\n",
              "      <td>-0.008295</td>\n",
              "      <td>-0.036496</td>\n",
              "      <td>-0.105953</td>\n",
              "      <td>-0.101224</td>\n",
              "      <td>0.004254</td>\n",
              "      <td>-0.029624</td>\n",
              "      <td>0.008548</td>\n",
              "      <td>-0.026390</td>\n",
              "      <td>0.088975</td>\n",
              "      <td>0.082512</td>\n",
              "      <td>0.031702</td>\n",
              "      <td>-0.076733</td>\n",
              "      <td>-0.004900</td>\n",
              "      <td>0.088244</td>\n",
              "      <td>-0.058311</td>\n",
              "      <td>0.022887</td>\n",
              "      <td>0.038968</td>\n",
              "      <td>0.052685</td>\n",
              "      <td>0.008953</td>\n",
              "      <td>-0.037170</td>\n",
              "      <td>0.048288</td>\n",
              "      <td>-0.039379</td>\n",
              "      <td>-0.012852</td>\n",
              "      <td>0.044102</td>\n",
              "      <td>-0.003510</td>\n",
              "      <td>0.026071</td>\n",
              "      <td>0.039252</td>\n",
              "      <td>0.040641</td>\n",
              "      <td>-0.044288</td>\n",
              "      <td>-0.028253</td>\n",
              "      <td>0.009416</td>\n",
              "      <td>-0.048084</td>\n",
              "      <td>0.002770</td>\n",
              "      <td>-0.044453</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6VIF</th>\n",
              "      <td>-0.056973</td>\n",
              "      <td>-0.106001</td>\n",
              "      <td>0.057178</td>\n",
              "      <td>-0.015267</td>\n",
              "      <td>0.047276</td>\n",
              "      <td>-0.054155</td>\n",
              "      <td>0.052410</td>\n",
              "      <td>0.079906</td>\n",
              "      <td>-0.028828</td>\n",
              "      <td>-0.011291</td>\n",
              "      <td>0.046655</td>\n",
              "      <td>-0.000140</td>\n",
              "      <td>-0.007243</td>\n",
              "      <td>0.007241</td>\n",
              "      <td>0.037347</td>\n",
              "      <td>-0.083086</td>\n",
              "      <td>-0.023759</td>\n",
              "      <td>0.029494</td>\n",
              "      <td>0.010133</td>\n",
              "      <td>0.036700</td>\n",
              "      <td>-0.006238</td>\n",
              "      <td>0.125341</td>\n",
              "      <td>0.062849</td>\n",
              "      <td>0.007609</td>\n",
              "      <td>0.016768</td>\n",
              "      <td>0.056868</td>\n",
              "      <td>-0.046525</td>\n",
              "      <td>0.077087</td>\n",
              "      <td>0.030025</td>\n",
              "      <td>0.055947</td>\n",
              "      <td>0.020931</td>\n",
              "      <td>0.060239</td>\n",
              "      <td>0.004840</td>\n",
              "      <td>0.022709</td>\n",
              "      <td>-0.057307</td>\n",
              "      <td>0.058584</td>\n",
              "      <td>-0.034958</td>\n",
              "      <td>-0.116127</td>\n",
              "      <td>-0.119020</td>\n",
              "      <td>-0.095833</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001371</td>\n",
              "      <td>-0.035879</td>\n",
              "      <td>0.013808</td>\n",
              "      <td>-0.084259</td>\n",
              "      <td>-0.037672</td>\n",
              "      <td>-0.065007</td>\n",
              "      <td>0.031830</td>\n",
              "      <td>0.046763</td>\n",
              "      <td>0.004149</td>\n",
              "      <td>-0.101629</td>\n",
              "      <td>-0.039409</td>\n",
              "      <td>0.031477</td>\n",
              "      <td>0.073722</td>\n",
              "      <td>0.000716</td>\n",
              "      <td>0.017485</td>\n",
              "      <td>0.008235</td>\n",
              "      <td>-0.020917</td>\n",
              "      <td>-0.056294</td>\n",
              "      <td>-0.063236</td>\n",
              "      <td>-0.040602</td>\n",
              "      <td>-0.060849</td>\n",
              "      <td>-0.002314</td>\n",
              "      <td>0.030642</td>\n",
              "      <td>-0.138180</td>\n",
              "      <td>0.023032</td>\n",
              "      <td>-0.072090</td>\n",
              "      <td>-0.018326</td>\n",
              "      <td>0.015715</td>\n",
              "      <td>0.000354</td>\n",
              "      <td>-0.060172</td>\n",
              "      <td>-0.067600</td>\n",
              "      <td>-0.028773</td>\n",
              "      <td>0.011924</td>\n",
              "      <td>0.085786</td>\n",
              "      <td>0.013450</td>\n",
              "      <td>-0.077960</td>\n",
              "      <td>-0.023025</td>\n",
              "      <td>-0.020168</td>\n",
              "      <td>0.062807</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6W9H</th>\n",
              "      <td>0.057279</td>\n",
              "      <td>0.014165</td>\n",
              "      <td>0.014865</td>\n",
              "      <td>-0.056097</td>\n",
              "      <td>0.022375</td>\n",
              "      <td>-0.002606</td>\n",
              "      <td>-0.000651</td>\n",
              "      <td>0.035594</td>\n",
              "      <td>-0.076766</td>\n",
              "      <td>-0.024073</td>\n",
              "      <td>-0.023961</td>\n",
              "      <td>-0.039036</td>\n",
              "      <td>-0.028259</td>\n",
              "      <td>-0.039442</td>\n",
              "      <td>-0.082987</td>\n",
              "      <td>0.018511</td>\n",
              "      <td>0.086624</td>\n",
              "      <td>-0.024612</td>\n",
              "      <td>-0.053241</td>\n",
              "      <td>-0.010259</td>\n",
              "      <td>0.018931</td>\n",
              "      <td>0.078122</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>-0.068499</td>\n",
              "      <td>0.069178</td>\n",
              "      <td>0.028348</td>\n",
              "      <td>-0.037600</td>\n",
              "      <td>0.051515</td>\n",
              "      <td>0.031220</td>\n",
              "      <td>0.026036</td>\n",
              "      <td>-0.022138</td>\n",
              "      <td>0.067423</td>\n",
              "      <td>-0.055297</td>\n",
              "      <td>-0.014000</td>\n",
              "      <td>0.102621</td>\n",
              "      <td>0.057053</td>\n",
              "      <td>0.042842</td>\n",
              "      <td>0.013249</td>\n",
              "      <td>0.035250</td>\n",
              "      <td>-0.015710</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.035981</td>\n",
              "      <td>0.031889</td>\n",
              "      <td>-0.018539</td>\n",
              "      <td>0.075544</td>\n",
              "      <td>-0.052518</td>\n",
              "      <td>0.020822</td>\n",
              "      <td>-0.107076</td>\n",
              "      <td>0.029207</td>\n",
              "      <td>-0.024844</td>\n",
              "      <td>-0.023067</td>\n",
              "      <td>-0.015968</td>\n",
              "      <td>-0.025857</td>\n",
              "      <td>0.028352</td>\n",
              "      <td>-0.028423</td>\n",
              "      <td>0.012542</td>\n",
              "      <td>0.044388</td>\n",
              "      <td>-0.009177</td>\n",
              "      <td>0.053936</td>\n",
              "      <td>0.002139</td>\n",
              "      <td>-0.029852</td>\n",
              "      <td>-0.062243</td>\n",
              "      <td>0.079484</td>\n",
              "      <td>-0.002308</td>\n",
              "      <td>0.063949</td>\n",
              "      <td>0.070013</td>\n",
              "      <td>-0.032769</td>\n",
              "      <td>-0.006471</td>\n",
              "      <td>0.002467</td>\n",
              "      <td>0.010400</td>\n",
              "      <td>0.014553</td>\n",
              "      <td>0.017641</td>\n",
              "      <td>0.005941</td>\n",
              "      <td>0.019001</td>\n",
              "      <td>0.057026</td>\n",
              "      <td>0.012162</td>\n",
              "      <td>0.006526</td>\n",
              "      <td>-0.023666</td>\n",
              "      <td>0.067764</td>\n",
              "      <td>0.010285</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6W9I</th>\n",
              "      <td>0.057279</td>\n",
              "      <td>0.014165</td>\n",
              "      <td>0.014865</td>\n",
              "      <td>-0.056097</td>\n",
              "      <td>0.022375</td>\n",
              "      <td>-0.002606</td>\n",
              "      <td>-0.000651</td>\n",
              "      <td>0.035594</td>\n",
              "      <td>-0.076766</td>\n",
              "      <td>-0.024073</td>\n",
              "      <td>-0.023961</td>\n",
              "      <td>-0.039036</td>\n",
              "      <td>-0.028259</td>\n",
              "      <td>-0.039442</td>\n",
              "      <td>-0.082987</td>\n",
              "      <td>0.018511</td>\n",
              "      <td>0.086624</td>\n",
              "      <td>-0.024612</td>\n",
              "      <td>-0.053241</td>\n",
              "      <td>-0.010259</td>\n",
              "      <td>0.018931</td>\n",
              "      <td>0.078122</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>-0.068499</td>\n",
              "      <td>0.069178</td>\n",
              "      <td>0.028348</td>\n",
              "      <td>-0.037600</td>\n",
              "      <td>0.051515</td>\n",
              "      <td>0.031220</td>\n",
              "      <td>0.026036</td>\n",
              "      <td>-0.022138</td>\n",
              "      <td>0.067423</td>\n",
              "      <td>-0.055297</td>\n",
              "      <td>-0.014000</td>\n",
              "      <td>0.102621</td>\n",
              "      <td>0.057053</td>\n",
              "      <td>0.042842</td>\n",
              "      <td>0.013249</td>\n",
              "      <td>0.035250</td>\n",
              "      <td>-0.015710</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.035981</td>\n",
              "      <td>0.031889</td>\n",
              "      <td>-0.018539</td>\n",
              "      <td>0.075544</td>\n",
              "      <td>-0.052518</td>\n",
              "      <td>0.020822</td>\n",
              "      <td>-0.107076</td>\n",
              "      <td>0.029207</td>\n",
              "      <td>-0.024844</td>\n",
              "      <td>-0.023067</td>\n",
              "      <td>-0.015968</td>\n",
              "      <td>-0.025857</td>\n",
              "      <td>0.028352</td>\n",
              "      <td>-0.028423</td>\n",
              "      <td>0.012542</td>\n",
              "      <td>0.044388</td>\n",
              "      <td>-0.009177</td>\n",
              "      <td>0.053936</td>\n",
              "      <td>0.002139</td>\n",
              "      <td>-0.029852</td>\n",
              "      <td>-0.062243</td>\n",
              "      <td>0.079484</td>\n",
              "      <td>-0.002308</td>\n",
              "      <td>0.063949</td>\n",
              "      <td>0.070013</td>\n",
              "      <td>-0.032769</td>\n",
              "      <td>-0.006471</td>\n",
              "      <td>0.002467</td>\n",
              "      <td>0.010400</td>\n",
              "      <td>0.014553</td>\n",
              "      <td>0.017641</td>\n",
              "      <td>0.005941</td>\n",
              "      <td>0.019001</td>\n",
              "      <td>0.057026</td>\n",
              "      <td>0.012162</td>\n",
              "      <td>0.006526</td>\n",
              "      <td>-0.023666</td>\n",
              "      <td>0.067764</td>\n",
              "      <td>0.010285</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6WWZ</th>\n",
              "      <td>-0.164858</td>\n",
              "      <td>0.093766</td>\n",
              "      <td>-0.087629</td>\n",
              "      <td>-0.008175</td>\n",
              "      <td>0.043789</td>\n",
              "      <td>0.158547</td>\n",
              "      <td>0.206052</td>\n",
              "      <td>0.188948</td>\n",
              "      <td>0.053996</td>\n",
              "      <td>0.008175</td>\n",
              "      <td>-0.177308</td>\n",
              "      <td>0.047783</td>\n",
              "      <td>-0.019827</td>\n",
              "      <td>-0.001993</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>-0.034169</td>\n",
              "      <td>0.055590</td>\n",
              "      <td>0.111515</td>\n",
              "      <td>0.058006</td>\n",
              "      <td>-0.200569</td>\n",
              "      <td>0.048446</td>\n",
              "      <td>0.449035</td>\n",
              "      <td>0.059953</td>\n",
              "      <td>0.072009</td>\n",
              "      <td>0.065567</td>\n",
              "      <td>0.172628</td>\n",
              "      <td>-0.017097</td>\n",
              "      <td>-0.175138</td>\n",
              "      <td>-0.300513</td>\n",
              "      <td>0.055081</td>\n",
              "      <td>-0.165816</td>\n",
              "      <td>0.297215</td>\n",
              "      <td>-0.046115</td>\n",
              "      <td>0.052260</td>\n",
              "      <td>0.097573</td>\n",
              "      <td>-0.025014</td>\n",
              "      <td>0.018360</td>\n",
              "      <td>-0.045302</td>\n",
              "      <td>0.079945</td>\n",
              "      <td>0.076831</td>\n",
              "      <td>...</td>\n",
              "      <td>0.017351</td>\n",
              "      <td>0.042203</td>\n",
              "      <td>0.105930</td>\n",
              "      <td>-0.019416</td>\n",
              "      <td>0.024012</td>\n",
              "      <td>-0.188241</td>\n",
              "      <td>-0.246646</td>\n",
              "      <td>-0.064713</td>\n",
              "      <td>0.096966</td>\n",
              "      <td>0.089166</td>\n",
              "      <td>-0.035140</td>\n",
              "      <td>-0.079810</td>\n",
              "      <td>0.031257</td>\n",
              "      <td>0.091354</td>\n",
              "      <td>0.279628</td>\n",
              "      <td>-0.333845</td>\n",
              "      <td>-0.007852</td>\n",
              "      <td>-0.051265</td>\n",
              "      <td>0.028697</td>\n",
              "      <td>-0.149323</td>\n",
              "      <td>0.202444</td>\n",
              "      <td>0.205749</td>\n",
              "      <td>-0.000704</td>\n",
              "      <td>-0.078078</td>\n",
              "      <td>-0.159696</td>\n",
              "      <td>-0.087268</td>\n",
              "      <td>0.007889</td>\n",
              "      <td>-0.024314</td>\n",
              "      <td>0.113386</td>\n",
              "      <td>-0.068093</td>\n",
              "      <td>-0.098106</td>\n",
              "      <td>-0.021759</td>\n",
              "      <td>-0.004644</td>\n",
              "      <td>-0.017650</td>\n",
              "      <td>-0.147657</td>\n",
              "      <td>-0.079998</td>\n",
              "      <td>-0.045786</td>\n",
              "      <td>-0.048657</td>\n",
              "      <td>-0.050805</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6XAE</th>\n",
              "      <td>0.057279</td>\n",
              "      <td>0.014165</td>\n",
              "      <td>0.014865</td>\n",
              "      <td>-0.056097</td>\n",
              "      <td>0.022375</td>\n",
              "      <td>-0.002606</td>\n",
              "      <td>-0.000651</td>\n",
              "      <td>0.035594</td>\n",
              "      <td>-0.076766</td>\n",
              "      <td>-0.024073</td>\n",
              "      <td>-0.023961</td>\n",
              "      <td>-0.039036</td>\n",
              "      <td>-0.028259</td>\n",
              "      <td>-0.039442</td>\n",
              "      <td>-0.082987</td>\n",
              "      <td>0.018511</td>\n",
              "      <td>0.086624</td>\n",
              "      <td>-0.024612</td>\n",
              "      <td>-0.053241</td>\n",
              "      <td>-0.010259</td>\n",
              "      <td>0.018931</td>\n",
              "      <td>0.078122</td>\n",
              "      <td>0.000296</td>\n",
              "      <td>-0.068499</td>\n",
              "      <td>0.069178</td>\n",
              "      <td>0.028348</td>\n",
              "      <td>-0.037600</td>\n",
              "      <td>0.051515</td>\n",
              "      <td>0.031220</td>\n",
              "      <td>0.026036</td>\n",
              "      <td>-0.022138</td>\n",
              "      <td>0.067423</td>\n",
              "      <td>-0.055297</td>\n",
              "      <td>-0.014000</td>\n",
              "      <td>0.102621</td>\n",
              "      <td>0.057053</td>\n",
              "      <td>0.042842</td>\n",
              "      <td>0.013249</td>\n",
              "      <td>0.035250</td>\n",
              "      <td>-0.015710</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.035981</td>\n",
              "      <td>0.031889</td>\n",
              "      <td>-0.018539</td>\n",
              "      <td>0.075544</td>\n",
              "      <td>-0.052518</td>\n",
              "      <td>0.020822</td>\n",
              "      <td>-0.107076</td>\n",
              "      <td>0.029207</td>\n",
              "      <td>-0.024844</td>\n",
              "      <td>-0.023067</td>\n",
              "      <td>-0.015968</td>\n",
              "      <td>-0.025857</td>\n",
              "      <td>0.028352</td>\n",
              "      <td>-0.028423</td>\n",
              "      <td>0.012542</td>\n",
              "      <td>0.044388</td>\n",
              "      <td>-0.009177</td>\n",
              "      <td>0.053936</td>\n",
              "      <td>0.002139</td>\n",
              "      <td>-0.029852</td>\n",
              "      <td>-0.062243</td>\n",
              "      <td>0.079484</td>\n",
              "      <td>-0.002308</td>\n",
              "      <td>0.063949</td>\n",
              "      <td>0.070013</td>\n",
              "      <td>-0.032769</td>\n",
              "      <td>-0.006471</td>\n",
              "      <td>0.002467</td>\n",
              "      <td>0.010400</td>\n",
              "      <td>0.014553</td>\n",
              "      <td>0.017641</td>\n",
              "      <td>0.005941</td>\n",
              "      <td>0.019001</td>\n",
              "      <td>0.057026</td>\n",
              "      <td>0.012162</td>\n",
              "      <td>0.006526</td>\n",
              "      <td>-0.023666</td>\n",
              "      <td>0.067764</td>\n",
              "      <td>0.010285</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>552 rows × 101 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             0         1         2         3  ...        97        98        99  100\n",
              "1ERR  0.114488 -0.067677  0.074175  0.008188  ...  0.024594  0.036199  0.012790    0\n",
              "1FTL  0.031311 -0.080098 -0.089052  0.014268  ... -0.042880 -0.038940 -0.006364    0\n",
              "1LK2  0.046392 -0.001892  0.057446 -0.036598  ...  0.023507 -0.090147 -0.040490    0\n",
              "1NHZ  0.004482 -0.024705  0.039548 -0.003737  ... -0.004666 -0.030469 -0.019508    0\n",
              "1PBQ -0.014348  0.062804 -0.010717 -0.008924  ... -0.048084  0.002770 -0.044453    0\n",
              "...        ...       ...       ...       ...  ...       ...       ...       ...  ...\n",
              "6VIF -0.056973 -0.106001  0.057178 -0.015267  ... -0.023025 -0.020168  0.062807    1\n",
              "6W9H  0.057279  0.014165  0.014865 -0.056097  ... -0.023666  0.067764  0.010285    1\n",
              "6W9I  0.057279  0.014165  0.014865 -0.056097  ... -0.023666  0.067764  0.010285    1\n",
              "6WWZ -0.164858  0.093766 -0.087629 -0.008175  ... -0.045786 -0.048657 -0.050805    1\n",
              "6XAE  0.057279  0.014165  0.014865 -0.056097  ... -0.023666  0.067764  0.010285    1\n",
              "\n",
              "[552 rows x 101 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BfpO8vr-RTR"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "principalComponents = pca.fit_transform(d2)\n",
        "principalDf = pd.DataFrame(data = principalComponents\n",
        "             , columns = ['principal component 1', 'principal component 2'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWe4LX1q-Y51"
      },
      "source": [
        "op = pd.Series(output)\n",
        "finalDf = pd.concat([principalDf, op], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "bsnHODQb-fJ0",
        "outputId": "42f16f9f-b86d-454c-d3c7-b74070021cf2"
      },
      "source": [
        "fig = plt.figure(figsize = (8,8))\n",
        "ax = fig.add_subplot(1,1,1) \n",
        "#ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "#ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('PCA of Word2Vec Features', fontsize = 20)\n",
        "targets = [0,1]\n",
        "colors = ['darkorchid', 'seagreen', 'b']\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf.iloc[:,2] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
        "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend([\"Antagonist\",\"Agonist\"])\n",
        "ax.grid()\n",
        "plt.savefig(\"plot3.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVd0_D4N6fgu"
      },
      "source": [
        "new_num_arr = np.load('dat2a.npy',allow_pickle=True)\n",
        "#new_num_arr = np.load('data.npy',allow_pickle=True)\n",
        "extra_data = np.load('dat3a.npy',allow_pickle=True)\n",
        "\n",
        "with open(\"asarsa.json\") as fp:\n",
        "  extra_ss_data=json.load(fp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zM2OcCokjE4z"
      },
      "source": [
        "binding site descriptors (useless tbh)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlzrvSq7sC49"
      },
      "source": [
        "with open(\"bind_site_feats.json\") as fp:\n",
        "  input=json.load(fp)\n",
        "with open(\"bind_site_labels.json\") as fp:\n",
        "  output=json.load(fp)\n",
        "extra_data=input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tUHwcCVviM3"
      },
      "source": [
        "extra_ss_data[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75jYTR2cvZ1X"
      },
      "source": [
        "for i in extra_data:\n",
        "  print(len(i))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_mZOqkvH1xu"
      },
      "source": [
        "te=[]\n",
        "for num,i in enumerate(output):\n",
        "  if num!=156:\n",
        "    te.append(i)\n",
        "output=te"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmCQAJ3ZXJih",
        "outputId": "ab64911e-90e3-49a4-8fd2-6d2ee010b798"
      },
      "source": [
        "temp=[]\n",
        "\n",
        "for i in new_num_arr:\n",
        "  temp2=[]\n",
        "  #temp2 = np.asarray(temp2)\n",
        "  for j in i:\n",
        "    for k in j:\n",
        "      z=k[1:].astype(np.float32)\n",
        "      #print(z)\n",
        "      #temp2=np.array(np.append(temp2,np.asarray(z),axis=0))#.reshape((len(z),1)\n",
        "      temp2.append(np.array(z))\n",
        "  #print(temp2.shape)\n",
        "  temp.append(np.array(temp2))\n",
        "print(len(temp))\n",
        "temp = np.asarray(temp)\n",
        "print(temp.shape)\n",
        "#output= output.astype(np.float32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "552\n",
            "(552,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "meWgoyiNdPvb",
        "outputId": "25b84f98-9c65-4924-f8d1-7a714d067541"
      },
      "source": [
        "temp=[]\n",
        "for num,i in enumerate(new_num_arr):\n",
        "  temp2=[]\n",
        "  #temp2 = np.asarray(temp2)\n",
        "  if num>=156:\n",
        "    num2=num-1\n",
        "  else:\n",
        "    num2=num\n",
        "  if num!=156:\n",
        "    for j in i:\n",
        "      for k in j:\n",
        "        z=k[1:].astype(np.float32)\n",
        "        temp2.append(np.array(z))\n",
        "    temp.append(np.array(temp2))\n",
        "print(len(temp))\n",
        "temp = np.asarray(temp)\n",
        "print(temp.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "551\n",
            "(551,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFqFyYXmddq4",
        "outputId": "3ac4aa74-7473-4669-d3fe-ab831b3263c6"
      },
      "source": [
        "len(output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "551"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwj7uSYL7nLE"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(temp, np.asarray(output), test_size=0.35)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_xC_HUwBHO1"
      },
      "source": [
        "ragged_x_train = tf.ragged.constant(X_train)\n",
        "ragged_x_test = tf.ragged.constant(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cw1IhULoGdu0",
        "outputId": "496924d4-e62f-4a32-9b2e-9a4fa5a383c8"
      },
      "source": [
        "ragged_x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([358, None, None])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heXdD4duG8g4",
        "outputId": "bbec51e0-56ef-4060-c61b-5f824b296732"
      },
      "source": [
        "ragged_x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([193, None, None])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zd3o7au9Svrk"
      },
      "source": [
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from keras.layers import LSTM\n",
        "tf.random.set_seed(1337)\n",
        "np.random.seed(1337)\n",
        "def model():\n",
        "  keras_model = tf.keras.Sequential([\n",
        "      tf.keras.layers.Input(shape=[None,8000],ragged=True),\n",
        "      #tf.keras.layers.Embedding(1454409, 27),\n",
        "      tf.keras.layers.LSTM(16, return_sequences=True,dropout=0.2),#64\n",
        "      tf.keras.layers.LSTM(16,dropout=0.2),#32\n",
        "      tf.keras.layers.Dense(64),#32\n",
        "      tf.keras.layers.Activation(tf.nn.relu),\n",
        "      tf.keras.layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  keras_model.compile(loss='binary_crossentropy', optimizer='adam',metrics=[\"accuracy\"])#(loss='binary_crossentropy', optimizer='adam',metrics=[\"accuracy\"])\n",
        "  keras_model.summary()\n",
        "  return keras_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s21nu3lm910n"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "scores=[]\n",
        "scs=[]\n",
        "kfold = StratifiedKFold(n_splits=2, shuffle=True, random_state=1337)\n",
        "for train, test in kfold.split(temp, output):\n",
        "  print(train)\n",
        "  ragged_x_train = tf.ragged.constant(temp[train])\n",
        "  ragged_x_test = tf.ragged.constant(temp[test])\n",
        "  keras_model = model()\n",
        "  keras_model.fit(ragged_x_train,output[train],batch_size=100,epochs=300)\n",
        "  scor = keras_model.evaluate(ragged_x_test, output[test], verbose=0)\n",
        "  scores.append(scor[1] * 100)\n",
        "  pds=keras_model.predict(ragged_x_test)\n",
        "  pds=rounder(pds)\n",
        "  count=0\n",
        "  for n,(i,j) in enumerate(zip(pds,output[test])):\n",
        "    if i==j:\n",
        "      count+=1\n",
        "  print(count/len(output[test]))\n",
        "  scs.append(count/len(output[test]))\n",
        "(np.mean(scores), np.std(scores))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDHZNKtndGYK"
      },
      "source": [
        "import numpy as np\n",
        "(np.mean(scores), np.std(scores))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3Mem4LTS8ju"
      },
      "source": [
        "keras_model=model()\n",
        "keras_model.fit(ragged_x_train,y_train,batch_size=100,epochs=150)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmF-CohWnayo",
        "outputId": "357bfcc7-33c0-4e87-c76b-cda00626a51f"
      },
      "source": [
        "count=0\n",
        "for n,(i,j) in enumerate(zip(rounder(pds),y_test)):\n",
        "  if i==j:\n",
        "    count+=1\n",
        "count/len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7823834196891192"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npRDegF7CCMr"
      },
      "source": [
        "def rounder(arr):\n",
        "  rounded=[]\n",
        "  for i in arr:\n",
        "    if i<0.5:\n",
        "      rounded.append(0)\n",
        "    else:\n",
        "      rounded.append(1)\n",
        "  return rounded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "UK9b8Ap3DVlG",
        "outputId": "ce174b17-6707-4e13-b319-80767f79337b"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "np.round(pds)\n",
        "matrix = confusion_matrix(y_test, rounder(pds))\n",
        "sns.heatmap(matrix, annot=True, annot_kws={\"size\": 14}, fmt='g')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "O8WLy4q4A0NW",
        "outputId": "5a29f168-136a-4ca6-de81-a6fe1ad1808a"
      },
      "source": [
        "data_with_seq=[]\n",
        "for n,i in enumerate(temp):\n",
        "  t=[]\n",
        "  for j in i:\n",
        "    t.append(j)\n",
        "  t.append(inp[n])\n",
        "  t=np.array(t)\n",
        "  data_with_seq.append(t)\n",
        "data_with_seq=np.array(data_with_seq)\n",
        "print(data_with_seq.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(552,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQ5soEc2mraP",
        "outputId": "0d5053d2-5fef-4bac-fb5f-9385444c104e"
      },
      "source": [
        "%load_ext tensorboard\n",
        "from datetime import datetime\n",
        "import tensorboard\n",
        "tensorboard.__version__\n",
        "logdir=\"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQBPK2U7nteT"
      },
      "source": [
        "%tensorboard --logdir logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "tXDKHhyQxnv6",
        "outputId": "41cb510f-123c-4353-8ae4-6859d991e09c"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(extra_data, np.asarray(output), test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-fae299df89bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'extra_data' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5y2YyeZt7_B"
      },
      "source": [
        "ragged_x_train = tf.ragged.constant(X_train)\n",
        "ragged_x_test = tf.ragged.constant(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6UNYd3RxuRO",
        "outputId": "236ec865-65f6-4c2f-c5ee-0c7f5ec4cd5d"
      },
      "source": [
        "keras_model = model()\n",
        "keras_model.fit(ragged_x_train,y_train,batch_size=100,epochs=1)\n",
        "scor = keras_model.evaluate(ragged_x_test, y_test, batch_size=100,callbacks=[tensorboard_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_14 (LSTM)               (None, None, 16)          513088    \n",
            "_________________________________________________________________\n",
            "lstm_15 (LSTM)               (None, 16)                2112      \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 64)                1088      \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 516,353\n",
            "Trainable params: 516,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "4/4 [==============================] - 4s 1s/step - loss: 2.6308 - accuracy: 0.2849\n",
            "2/2 [==============================] - 0s 220ms/step - loss: 0.8613 - accuracy: 0.2798\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e12bsAuR7q0u"
      },
      "source": [
        "scor = keras_model.predict(ragged_x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "aUu7Kw910XUG",
        "outputId": "536bd699-ef6c-460e-d53b-9a6230c3be45"
      },
      "source": [
        "from keras_self_attention import SeqSelfAttention\n",
        "from keras.layers import Bidirectional,LSTM,Dense,Activation\n",
        "inps = Input(shape=[None,8000],ragged=True)\n",
        "\n",
        "lstm = Bidirectional(LSTM(64,dropout=0.2, return_sequences=1))(inps)\n",
        "attention_probs = Dense( 1800, activation='sigmoid', name='attention_probs')(lstm)\n",
        "attention_mul = multiply([ lstm, attention_probs], name='attention_mul')\n",
        "lstm2 = LSTM(32,dropout=0.2)(attention_mul)\n",
        "dene = Dense(32)(lstm2)\n",
        "acc=Activation(tf.nn.relu)(dene)\n",
        "dene=Dense(1)(acc)\n",
        "keras_model = Model(inputs=[inps], outputs=dene)\n",
        "keras_model.compile(loss='binary_crossentropy', optimizer='adam',metrics=[\"accuracy\"])\n",
        "keras_model.compile(loss='binary_crossentropy', optimizer=Adam(0.001), metrics=['accuracy'])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py\u001b[0m in \u001b[0;36mmake_tensor_proto\u001b[0;34m(values, dtype, shape, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 548\u001b[0;31m       \u001b[0mstr_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mproto_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 548\u001b[0;31m       \u001b[0mstr_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mproto_values\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/compat.py\u001b[0m in \u001b[0;36mas_bytes\u001b[0;34m(bytes_or_text, encoding)\u001b[0m\n\u001b[1;32m     86\u001b[0m     raise TypeError('Expected binary or unicode string, got %r' %\n\u001b[0;32m---> 87\u001b[0;31m                     (bytes_or_text,))\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Expected binary or unicode string, got tf.RaggedTensor(values=Tensor(\"bidirectional_1/RaggedConcat/concat:0\", shape=(None, 128), dtype=float32), row_splits=Tensor(\"bidirectional_1/forward_lstm_29/RaggedFromTensor/concat:0\", shape=(None,), dtype=int64))",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-b8db39a8c912>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlstm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBidirectional\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mattention_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m1800\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sigmoid'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'attention_probs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mattention_mul\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mlstm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_probs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'attention_mul'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mlstm2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattention_mul\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    924\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0;32m--> 926\u001b[0;31m                                                 input_list)\n\u001b[0m\u001b[1;32m    927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m     \u001b[0;31m# Maintains info about the `Layer.call` stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m   1115\u001b[0m           \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_auto_cast_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1117\u001b[0;31m               \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOperatorNotAllowedInGraphError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1196\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1198\u001b[0;31m         dtype=self._compute_dtype_object)\n\u001b[0m\u001b[1;32m   1199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1200\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/ops/core.py\u001b[0m in \u001b[0;36mdense\u001b[0;34m(inputs, kernel, bias, activation, dtype)\u001b[0m\n\u001b[1;32m     54\u001b[0m   \u001b[0;31m# Broadcast kernel to inputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandard_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensordot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrank\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m     \u001b[0;31m# Reshape the output back to the original ndim of the input.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mtensordot\u001b[0;34m(a, b, axes, name)\u001b[0m\n\u001b[1;32m   4511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4512\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Tensordot\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4513\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4514\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4515\u001b[0m     \u001b[0ma_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tensordot_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m   1497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1498\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1499\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1501\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m    336\u001b[0m                                          as_ref=False):\n\u001b[1;32m    337\u001b[0m   \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    262\u001b[0m   \"\"\"\n\u001b[1;32m    263\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 264\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    280\u001b[0m       tensor_util.make_tensor_proto(\n\u001b[1;32m    281\u001b[0m           \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m           allow_broadcast=allow_broadcast))\n\u001b[0m\u001b[1;32m    283\u001b[0m   \u001b[0mdtype_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAttrValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensor_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m   \u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"value\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtensor_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdtype_value\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py\u001b[0m in \u001b[0;36mmake_tensor_proto\u001b[0;34m(values, dtype, shape, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    550\u001b[0m       raise TypeError(\"Failed to convert object of type %s to Tensor. \"\n\u001b[1;32m    551\u001b[0m                       \u001b[0;34m\"Contents: %s. Consider casting elements to a \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                       \"supported type.\" % (type(values), values))\n\u001b[0m\u001b[1;32m    553\u001b[0m     \u001b[0mtensor_proto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtensor_proto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Failed to convert object of type <class 'tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor'> to Tensor. Contents: tf.RaggedTensor(values=Tensor(\"bidirectional_1/RaggedConcat/concat:0\", shape=(None, 128), dtype=float32), row_splits=Tensor(\"bidirectional_1/forward_lstm_29/RaggedFromTensor/concat:0\", shape=(None,), dtype=int64)). Consider casting elements to a supported type."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CY1y7INigQ66",
        "outputId": "3d424b47-68e8-4dab-dec0-7a0d94a7f1cc"
      },
      "source": [
        "len(inp[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQFfGAZ-gRy-"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "class LSTM(nn.Module):\n",
        "  def __init__(self, input_size=100, hidden_layer_size=100, output_size=1):\n",
        "      super().__init__()\n",
        "      self.hidden_layer_size = hidden_layer_size\n",
        "      self.lstm = nn.LSTM(input_size, hidden_layer_size,batch_first=True)\n",
        "      self.linear = nn.Linear(hidden_layer_size, output_size)\n",
        "      self.hidden_cell = (torch.zeros(1,self.hidden_layer_size,self.hidden_layer_size),\n",
        "                            torch.zeros(1,self.hidden_layer_size,self.hidden_layer_size))\n",
        "      self.ReLU = nn.ReLU(output_size)\n",
        "      self.Sigmoid = nn.Sigmoid()\n",
        "\n",
        "  def forward(self, input_seq):\n",
        "    lstm_out1, self.hidden_cell1 = self.lstm(input_seq, self.hidden_cell)\n",
        "    prediction1 = self.linear(lstm_out1)\n",
        "    x = self.Sigmoid(prediction1)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDfFcEqG2pvs",
        "outputId": "06a5756f-bcb0-4062-8222-d8f02f69f55b"
      },
      "source": [
        "for num,i in enumerate(new_num_arr):\n",
        "  temp2=[]\n",
        "  #temp2 = np.asarray(temp2)\n",
        "  if num>=156:\n",
        "    num2=num-1\n",
        "  else:\n",
        "    num2=num\n",
        "  if num!=156:\n",
        "    for j in i:\n",
        "      for k in j:\n",
        "        z=k[1:].astype(np.float32)\n",
        "        #print(z)\n",
        "        #temp2=np.array(np.append(temp2,np.asarray(z),axis=0))#.reshape((len(z),1)\n",
        "        temp2.append(np.array(z))\n",
        "    #print(temp2.shape)\n",
        "    #for j in extra_data[num]:\n",
        "    #  temp2.append(np.array(j))\n",
        "    #temp2.append(extra_ss_data[2*num2])\n",
        "    #temp2.append(extra_ss_data[(2*num2)+1])\n",
        "    temp.append(np.array(temp2))\n",
        "#output= output.astype(np.float32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za6khaweSXN4",
        "outputId": "46ec9cc0-88d8-4725-98d1-d44e1e918d97"
      },
      "source": [
        "!pip install optuna"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting optuna\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/88/9c53460b97c61bce926dfe9dce51e4887c283416ff89ed30af0b73f44efa/optuna-2.5.0-py3-none-any.whl (287kB)\n",
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            "Collecting colorlog\n",
            "  Downloading https://files.pythonhosted.org/packages/5e/39/0230290df0519d528d8d0ffdfd900150ed24e0076d13b1f19e279444aab1/colorlog-4.7.2-py2.py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/01/1f/43b01223a0366171f474320c6e966c39a11587287f098a5f09809b45e05f/cmaes-0.8.2-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/d6/7d9acb68a77acd140be7fececb7f2701b2a29d2da9c54184cb8f93509590/cliff-3.7.0-py3-none-any.whl (80kB)\n",
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            "\u001b[?25hCollecting alembic\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/07/799a76aca0acd406e3259cc6c558ca1cdadf88250953b6c8105b421a9e33/alembic-1.5.5.tar.gz (1.2MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/48/69046506f6ac61c1eaa9a0d42d22d54673b69e176d30ca98e3f61513e980/pbr-5.5.1-py2.py3-none-any.whl (106kB)\n",
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            "\u001b[?25hCollecting stevedore>=2.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/49/b602307aeac3df3384ff1fcd05da9c0376c622a6c48bb5325f28ab165b57/stevedore-3.3.0-py3-none-any.whl (49kB)\n",
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            "\u001b[?25hCollecting cmd2>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/8b/15061b32332bb35ea2a2f6263d0f616779d576e82739ec8e7fcf3c94abf5/cmd2-1.5.0-py3-none-any.whl (133kB)\n",
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            "Collecting Mako\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/db/2d2d88b924aa4674a080aae83b59ea19d593250bfe5ed789947c21736785/Mako-1.1.4.tar.gz (479kB)\n",
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            "\u001b[?25hCollecting python-editor>=0.3\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/a7/2c/4c64579f847bd5d539803c8b909e54ba087a79d01bb3aba433a95879a6c5/pyperclip-1.8.2.tar.gz\n",
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            "Building wheels for collected packages: alembic, Mako, pyperclip\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alembic: filename=alembic-1.5.5-py2.py3-none-any.whl size=156597 sha256=2067e89c0d37b5864b4b686736e8fb75c7220c8d0f4c347ff823309f24c3a703\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/3f/61/7de6e3cef766d2680a5d81b1a388286e640f6a681eb589d643\n",
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            "  Created wheel for Mako: filename=Mako-1.1.4-py2.py3-none-any.whl size=75675 sha256=ee92f1e2982ae3ba568441b35e0604eb1f5c47048b1de8b0da23b8bcacf15f3b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/10/d3/aeb26e20d19045e2a68e5d3cbb57432e11b5d9c92c99f98d47\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-cp37-none-any.whl size=11107 sha256=b914e20dc7d9875e4c61667442fb18353eb2805940404d5a25edf637e284a6c3\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/af/b8/3407109267803f4015e1ee2ff23be0c8c19ce4008665931ee1\n",
            "Successfully built alembic Mako pyperclip\n",
            "Installing collected packages: colorlog, cmaes, pbr, stevedore, colorama, pyperclip, cmd2, cliff, Mako, python-editor, alembic, optuna\n",
            "Successfully installed Mako-1.1.4 alembic-1.5.5 cliff-3.7.0 cmaes-0.8.2 cmd2-1.5.0 colorama-0.4.4 colorlog-4.7.2 optuna-2.5.0 pbr-5.5.1 pyperclip-1.8.2 python-editor-1.0.4 stevedore-3.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7uIYcDoZq4y",
        "outputId": "36d750e2-0703-4474-93d2-ab1493fafef8"
      },
      "source": [
        "type(ragged_x_train),type(ragged_x_test),type(y_train),type(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor,\n",
              " tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor,\n",
              " numpy.ndarray,\n",
              " numpy.ndarray)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "of6u0-z12qo-",
        "outputId": "8ee690dc-7371-44fc-be31-87cead8d3a8d"
      },
      "source": [
        "import keras\n",
        "\n",
        "import optuna\n",
        "\n",
        "# 1. Define an objective function to be maximized.\n",
        "def objective(trial):\n",
        "    model = Sequential()\n",
        "    # 2. Suggest values of the hyperparameters using a trial object.\n",
        "    model.add(tf.keras.layers.Input(shape=[None,8000],ragged=True))\n",
        "    model.add(tf.keras.layers.LSTM(dropout=trial.suggest_float(\"dropout\", 0.1, 0.5),units=trial.suggest_int(\"units\", 2,128), return_sequences=True))\n",
        "    model.add(tf.keras.layers.LSTM(dropout=trial.suggest_float(\"dropout\", 0.1, 0.5),units=trial.suggest_int(\"units\", 2,128)))\n",
        "    model.add(tf.keras.layers.Dense(1, activation='softmax'))\n",
        "\n",
        "    # We compile our model with a sampled learning rate.\n",
        "    lr = trial.suggest_float('lr', 1e-5, 1e-1,log=True)\n",
        "    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.RMSprop(lr=lr), metrics=['accuracy'])\n",
        "    model.fit(ragged_x_train,y_train,shuffle=True,batch_size=50,epochs=100)\n",
        "    print(model.summary())\n",
        "    score = model.evaluate(ragged_x_test, y_test, verbose=0)\n",
        "    return score[1]\n",
        "\n",
        "# 3. Create a study object and optimize the objective function.\n",
        "study = optuna.create_study(direction='maximize')\n",
        "study.optimize(objective, n_trials=100)\n",
        "print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
        "\n",
        "print(\"Best trial:\")\n",
        "trial = study.best_trial\n",
        "print(\"  Value: {}\".format(trial.value))\n",
        "print(\"  Params: \")\n",
        "for key, value in trial.params.items():\n",
        "    print(\"    {}: {}\".format(key, value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 10:15:09,586]\u001b[0m A new study created in memory with name: no-name-02c7ba2a-11d6-43c6-b675-37ce67227652\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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            "Epoch 56/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1506 - accuracy: 0.7129\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1932 - accuracy: 0.7240\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2111 - accuracy: 0.7287\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1425 - accuracy: 0.7196\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1496 - accuracy: 0.7205\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1879 - accuracy: 0.7134\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1791 - accuracy: 0.7280\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1760 - accuracy: 0.7309\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1654 - accuracy: 0.7122\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2692 - accuracy: 0.7059\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1984 - accuracy: 0.7148\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1562 - accuracy: 0.6990\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1261 - accuracy: 0.7310\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1312 - accuracy: 0.7145\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1644 - accuracy: 0.6992\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1486 - accuracy: 0.7365\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1747 - accuracy: 0.7303\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1809 - accuracy: 0.7012\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1602 - accuracy: 0.7294\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1385 - accuracy: 0.7459\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1214 - accuracy: 0.7329\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1284 - accuracy: 0.7317\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1454 - accuracy: 0.6991\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1695 - accuracy: 0.7359\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1663 - accuracy: 0.7347\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1452 - accuracy: 0.7140\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1116 - accuracy: 0.7055\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1415 - accuracy: 0.7058\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1593 - accuracy: 0.6880\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1732 - accuracy: 0.7290\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1460 - accuracy: 0.7077\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1293 - accuracy: 0.7027\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1544 - accuracy: 0.7066\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1560 - accuracy: 0.7137\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1724 - accuracy: 0.7436\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1537 - accuracy: 0.7343\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1420 - accuracy: 0.7028\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1491 - accuracy: 0.7142\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1585 - accuracy: 0.7029\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1372 - accuracy: 0.7270\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1381 - accuracy: 0.7305\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1088 - accuracy: 0.7297\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1319 - accuracy: 0.7165\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1719 - accuracy: 0.7121\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1068 - accuracy: 0.7097\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_13 (LSTM)               (None, None, 99)          3207600   \n",
            "_________________________________________________________________\n",
            "lstm_14 (LSTM)               (None, 99)                78804     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 100       \n",
            "=================================================================\n",
            "Total params: 3,286,504\n",
            "Trainable params: 3,286,504\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 10:38:46,210]\u001b[0m Trial 0 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.4348938291475265, 'units': 99, 'lr': 0.0065858323348970405}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 8s 589ms/step - loss: 0.6761 - accuracy: 0.7280\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 5s 579ms/step - loss: 0.6726 - accuracy: 0.7188\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 5s 595ms/step - loss: 0.6749 - accuracy: 0.7135\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 5s 602ms/step - loss: 0.6706 - accuracy: 0.7038\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 5s 587ms/step - loss: 0.6707 - accuracy: 0.7227\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 5s 618ms/step - loss: 0.6703 - accuracy: 0.7325\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 4s 539ms/step - loss: 0.6675 - accuracy: 0.7216\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 5s 644ms/step - loss: 0.6660 - accuracy: 0.7175\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 5s 578ms/step - loss: 0.6637 - accuracy: 0.7432\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 5s 578ms/step - loss: 0.6703 - accuracy: 0.7086\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 5s 574ms/step - loss: 0.6685 - accuracy: 0.7265\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 5s 580ms/step - loss: 0.6705 - accuracy: 0.7054\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 5s 580ms/step - loss: 0.6683 - accuracy: 0.7108\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 5s 606ms/step - loss: 0.6651 - accuracy: 0.6997\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 5s 621ms/step - loss: 0.6652 - accuracy: 0.7190\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 5s 548ms/step - loss: 0.6625 - accuracy: 0.7071\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 5s 616ms/step - loss: 0.6629 - accuracy: 0.7121\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 5s 618ms/step - loss: 0.6561 - accuracy: 0.7463\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 5s 588ms/step - loss: 0.6580 - accuracy: 0.7444\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 5s 584ms/step - loss: 0.6646 - accuracy: 0.7029\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6584 - accuracy: 0.7230\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 5s 581ms/step - loss: 0.6595 - accuracy: 0.7055\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 5s 536ms/step - loss: 0.6567 - accuracy: 0.7422\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 5s 567ms/step - loss: 0.6641 - accuracy: 0.6997\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 5s 573ms/step - loss: 0.6585 - accuracy: 0.7116\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 5s 601ms/step - loss: 0.6568 - accuracy: 0.7360\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 5s 578ms/step - loss: 0.6553 - accuracy: 0.7309\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 5s 624ms/step - loss: 0.6585 - accuracy: 0.7179\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 5s 587ms/step - loss: 0.6546 - accuracy: 0.7189\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 5s 561ms/step - loss: 0.6567 - accuracy: 0.7219\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 5s 583ms/step - loss: 0.6514 - accuracy: 0.7327\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 4s 557ms/step - loss: 0.6491 - accuracy: 0.7232\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 5s 554ms/step - loss: 0.6565 - accuracy: 0.7091\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 5s 599ms/step - loss: 0.6628 - accuracy: 0.6859\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6485 - accuracy: 0.7333\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 5s 578ms/step - loss: 0.6537 - accuracy: 0.6985\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 5s 554ms/step - loss: 0.6525 - accuracy: 0.7265\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 5s 557ms/step - loss: 0.6557 - accuracy: 0.7238\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 5s 600ms/step - loss: 0.6505 - accuracy: 0.7201\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 5s 597ms/step - loss: 0.6495 - accuracy: 0.7184\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 4s 565ms/step - loss: 0.6490 - accuracy: 0.7414\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 5s 594ms/step - loss: 0.6643 - accuracy: 0.6904\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 5s 575ms/step - loss: 0.6451 - accuracy: 0.7387\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 5s 578ms/step - loss: 0.6515 - accuracy: 0.7126\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 5s 612ms/step - loss: 0.6558 - accuracy: 0.6909\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 5s 619ms/step - loss: 0.6471 - accuracy: 0.7451\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 5s 550ms/step - loss: 0.6507 - accuracy: 0.7092\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 4s 519ms/step - loss: 0.6528 - accuracy: 0.7117\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 5s 602ms/step - loss: 0.6508 - accuracy: 0.7197\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 5s 556ms/step - loss: 0.6503 - accuracy: 0.7299\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 5s 615ms/step - loss: 0.6464 - accuracy: 0.7215\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6547 - accuracy: 0.6908\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 5s 596ms/step - loss: 0.6470 - accuracy: 0.7333\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 5s 595ms/step - loss: 0.6508 - accuracy: 0.7001\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 5s 600ms/step - loss: 0.6622 - accuracy: 0.6810\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 5s 547ms/step - loss: 0.6476 - accuracy: 0.7253\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 5s 595ms/step - loss: 0.6424 - accuracy: 0.7366\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 5s 612ms/step - loss: 0.6462 - accuracy: 0.7140\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 5s 593ms/step - loss: 0.6453 - accuracy: 0.7317\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 5s 561ms/step - loss: 0.6622 - accuracy: 0.6658\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 5s 582ms/step - loss: 0.6472 - accuracy: 0.7052\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 5s 613ms/step - loss: 0.6505 - accuracy: 0.7075\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 5s 563ms/step - loss: 0.6467 - accuracy: 0.7169\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6450 - accuracy: 0.7228\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 5s 593ms/step - loss: 0.6463 - accuracy: 0.7077\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 5s 575ms/step - loss: 0.6454 - accuracy: 0.7129\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 5s 567ms/step - loss: 0.6472 - accuracy: 0.7124\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 5s 618ms/step - loss: 0.6452 - accuracy: 0.7126\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 5s 588ms/step - loss: 0.6378 - accuracy: 0.7373\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 5s 559ms/step - loss: 0.6434 - accuracy: 0.7153\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 4s 563ms/step - loss: 0.6489 - accuracy: 0.7054\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 5s 564ms/step - loss: 0.6451 - accuracy: 0.7099\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 5s 588ms/step - loss: 0.6432 - accuracy: 0.7102\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 5s 619ms/step - loss: 0.6511 - accuracy: 0.6980\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 5s 575ms/step - loss: 0.6362 - accuracy: 0.7315\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 5s 564ms/step - loss: 0.6437 - accuracy: 0.7161\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 5s 610ms/step - loss: 0.6504 - accuracy: 0.6959\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 5s 603ms/step - loss: 0.6464 - accuracy: 0.7154\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 5s 581ms/step - loss: 0.6427 - accuracy: 0.7195\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6409 - accuracy: 0.7214\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 5s 585ms/step - loss: 0.6469 - accuracy: 0.7003\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 5s 613ms/step - loss: 0.6410 - accuracy: 0.7158\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 4s 577ms/step - loss: 0.6445 - accuracy: 0.6978\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 5s 621ms/step - loss: 0.6386 - accuracy: 0.7347\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 5s 582ms/step - loss: 0.6369 - accuracy: 0.7492\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 5s 598ms/step - loss: 0.6375 - accuracy: 0.7258\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 5s 547ms/step - loss: 0.6382 - accuracy: 0.7240\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 4s 526ms/step - loss: 0.6450 - accuracy: 0.7021\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 5s 601ms/step - loss: 0.6431 - accuracy: 0.7103\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 5s 597ms/step - loss: 0.6388 - accuracy: 0.7173\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 4s 561ms/step - loss: 0.6267 - accuracy: 0.7542\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 5s 588ms/step - loss: 0.6281 - accuracy: 0.7530\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 5s 602ms/step - loss: 0.6367 - accuracy: 0.7291\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 5s 567ms/step - loss: 0.6410 - accuracy: 0.7270\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 5s 547ms/step - loss: 0.6385 - accuracy: 0.7367\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 5s 604ms/step - loss: 0.6429 - accuracy: 0.6999\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 5s 602ms/step - loss: 0.6355 - accuracy: 0.7303\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 5s 572ms/step - loss: 0.6433 - accuracy: 0.7053\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 5s 587ms/step - loss: 0.6331 - accuracy: 0.7404\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 5s 588ms/step - loss: 0.6338 - accuracy: 0.7332\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_15 (LSTM)               (None, None, 3)           96048     \n",
            "_________________________________________________________________\n",
            "lstm_16 (LSTM)               (None, 3)                 84        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 4         \n",
            "=================================================================\n",
            "Total params: 96,136\n",
            "Trainable params: 96,136\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 10:46:46,364]\u001b[0m Trial 1 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.2210649859910904, 'units': 3, 'lr': 1.051388889485286e-05}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.7045 - accuracy: 0.7162\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.6201 - accuracy: 0.7119\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5419 - accuracy: 0.7154\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5468 - accuracy: 0.7073\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5058 - accuracy: 0.6782\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3980 - accuracy: 0.7272\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.4061 - accuracy: 0.7150\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3478 - accuracy: 0.7363\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2998 - accuracy: 0.7314\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3757 - accuracy: 0.7131\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3429 - accuracy: 0.7204\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3568 - accuracy: 0.7264\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3449 - accuracy: 0.7316\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3538 - accuracy: 0.7218\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2821 - accuracy: 0.7081\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2926 - accuracy: 0.7187\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2613 - accuracy: 0.7032\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3118 - accuracy: 0.7214\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2753 - accuracy: 0.7132\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2587 - accuracy: 0.7050\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2596 - accuracy: 0.7107\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2846 - accuracy: 0.7085\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2703 - accuracy: 0.6952\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2908 - accuracy: 0.7061\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2162 - accuracy: 0.7271\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2493 - accuracy: 0.7488\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2359 - accuracy: 0.7011\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2403 - accuracy: 0.7079\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1754 - accuracy: 0.7198\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1826 - accuracy: 0.7385\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2059 - accuracy: 0.7112\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2902 - accuracy: 0.7105\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1601 - accuracy: 0.7162\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1551 - accuracy: 0.7345\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1689 - accuracy: 0.7070\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2071 - accuracy: 0.7236\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1625 - accuracy: 0.7127\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1868 - accuracy: 0.7096\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1838 - accuracy: 0.6976\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1887 - accuracy: 0.7209\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1657 - accuracy: 0.7206\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1616 - accuracy: 0.7337\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1795 - accuracy: 0.7325\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1861 - accuracy: 0.7053\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1428 - accuracy: 0.7133\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1675 - accuracy: 0.7325\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2170 - accuracy: 0.7213\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1649 - accuracy: 0.7363\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1798 - accuracy: 0.7223\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1489 - accuracy: 0.7269\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1546 - accuracy: 0.6871\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1918 - accuracy: 0.7087\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2237 - accuracy: 0.7323\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1948 - accuracy: 0.7187\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1803 - accuracy: 0.6919\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1380 - accuracy: 0.7468\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1826 - accuracy: 0.7162\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1526 - accuracy: 0.7138\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1413 - accuracy: 0.7344\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1829 - accuracy: 0.7066\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1568 - accuracy: 0.7266\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1797 - accuracy: 0.7196\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1742 - accuracy: 0.6999\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1060 - accuracy: 0.7140\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1734 - accuracy: 0.7231\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1760 - accuracy: 0.7080\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1229 - accuracy: 0.7187\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1764 - accuracy: 0.7141\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1816 - accuracy: 0.6586\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1330 - accuracy: 0.7109\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1183 - accuracy: 0.7263\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1614 - accuracy: 0.7116\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1239 - accuracy: 0.7129\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1373 - accuracy: 0.7140\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1568 - accuracy: 0.6947\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1472 - accuracy: 0.6838\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.0899 - accuracy: 0.6973\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1176 - accuracy: 0.7251\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1510 - accuracy: 0.7143\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1479 - accuracy: 0.7079\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1818 - accuracy: 0.7672\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1642 - accuracy: 0.7172\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1295 - accuracy: 0.7220\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1274 - accuracy: 0.7124\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1312 - accuracy: 0.7332\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1238 - accuracy: 0.7311\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.0914 - accuracy: 0.6946\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1542 - accuracy: 0.7245\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1654 - accuracy: 0.6954\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1532 - accuracy: 0.7092\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1190 - accuracy: 0.6811\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1590 - accuracy: 0.7065\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1037 - accuracy: 0.6926\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1091 - accuracy: 0.7510\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1533 - accuracy: 0.7172\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1111 - accuracy: 0.7039\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1550 - accuracy: 0.7247\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1158 - accuracy: 0.7283\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1235 - accuracy: 0.6996\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1534 - accuracy: 0.7348\n",
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_17 (LSTM)               (None, None, 105)         3404520   \n",
            "_________________________________________________________________\n",
            "lstm_18 (LSTM)               (None, 105)               88620     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 106       \n",
            "=================================================================\n",
            "Total params: 3,493,246\n",
            "Trainable params: 3,493,246\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 11:11:44,356]\u001b[0m Trial 2 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.3536172502031061, 'units': 105, 'lr': 0.0023596186923326096}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 19s 2s/step - loss: 1.4188 - accuracy: 0.7111\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6450 - accuracy: 0.7148\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5990 - accuracy: 0.7042\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6236 - accuracy: 0.7170\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6309 - accuracy: 0.7077\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6140 - accuracy: 0.7250\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5961 - accuracy: 0.7037\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.6135 - accuracy: 0.6921\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5959 - accuracy: 0.6879\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.5996 - accuracy: 0.7135\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5490 - accuracy: 0.7404\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5653 - accuracy: 0.7072\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6153 - accuracy: 0.7081\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5763 - accuracy: 0.7131\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5218 - accuracy: 0.7268\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5890 - accuracy: 0.7173\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5537 - accuracy: 0.7241\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5904 - accuracy: 0.7330\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5298 - accuracy: 0.7284\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.5507 - accuracy: 0.6796\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5528 - accuracy: 0.7116\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5382 - accuracy: 0.7081\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4967 - accuracy: 0.7276\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5098 - accuracy: 0.7431\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5242 - accuracy: 0.7162\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5105 - accuracy: 0.7313\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5179 - accuracy: 0.6890\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4710 - accuracy: 0.7488\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4378 - accuracy: 0.7215\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.4541 - accuracy: 0.7312\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5052 - accuracy: 0.6837\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3843 - accuracy: 0.7347\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3609 - accuracy: 0.7441\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3582 - accuracy: 0.7082\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3523 - accuracy: 0.7295\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5155 - accuracy: 0.6899\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3460 - accuracy: 0.7123\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3627 - accuracy: 0.7068\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3240 - accuracy: 0.7136\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3092 - accuracy: 0.7319\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3628 - accuracy: 0.7106\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2911 - accuracy: 0.7090\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2975 - accuracy: 0.6997\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2374 - accuracy: 0.7206\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3577 - accuracy: 0.7192\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 25s 3s/step - loss: 0.2606 - accuracy: 0.7352\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2665 - accuracy: 0.7212\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2539 - accuracy: 0.7360\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2894 - accuracy: 0.7250\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2198 - accuracy: 0.7131\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2085 - accuracy: 0.7239\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2836 - accuracy: 0.7068\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2891 - accuracy: 0.7310\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2169 - accuracy: 0.7166\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2204 - accuracy: 0.6925\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2021 - accuracy: 0.7209\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2029 - accuracy: 0.6972\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2716 - accuracy: 0.7066\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2076 - accuracy: 0.7052\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2383 - accuracy: 0.7036\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2367 - accuracy: 0.6951\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2009 - accuracy: 0.7284\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2008 - accuracy: 0.6902\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2331 - accuracy: 0.7065\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2053 - accuracy: 0.7207\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1709 - accuracy: 0.6967\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1893 - accuracy: 0.7476\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2421 - accuracy: 0.6802\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1918 - accuracy: 0.7088\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1836 - accuracy: 0.7086\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2166 - accuracy: 0.7021\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2240 - accuracy: 0.7127\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1645 - accuracy: 0.7289\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1932 - accuracy: 0.7182\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2275 - accuracy: 0.7127\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2241 - accuracy: 0.7066\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1774 - accuracy: 0.7270\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1810 - accuracy: 0.7185\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1834 - accuracy: 0.7185\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2140 - accuracy: 0.7150\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2340 - accuracy: 0.7152\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1661 - accuracy: 0.7163\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2636 - accuracy: 0.7112\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2460 - accuracy: 0.7333\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2170 - accuracy: 0.7086\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2530 - accuracy: 0.7140\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2053 - accuracy: 0.7138\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2395 - accuracy: 0.7513\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2100 - accuracy: 0.6924\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2454 - accuracy: 0.7384\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2508 - accuracy: 0.7191\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1769 - accuracy: 0.6948\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1638 - accuracy: 0.7228\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2270 - accuracy: 0.7154\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1611 - accuracy: 0.7229\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1805 - accuracy: 0.7398\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.1781 - accuracy: 0.7252\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2174 - accuracy: 0.7322\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2203 - accuracy: 0.7366\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2308 - accuracy: 0.7294\n",
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_19 (LSTM)               (None, None, 116)         3766288   \n",
            "_________________________________________________________________\n",
            "lstm_20 (LSTM)               (None, 116)               108112    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 117       \n",
            "=================================================================\n",
            "Total params: 3,874,517\n",
            "Trainable params: 3,874,517\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 11:38:31,191]\u001b[0m Trial 3 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.39464712931251167, 'units': 116, 'lr': 0.023733300126942455}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 14s 1s/step - loss: 1.1025 - accuracy: 0.6928\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5997 - accuracy: 0.7191\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.6042 - accuracy: 0.7332\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.6264 - accuracy: 0.7089\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5805 - accuracy: 0.7401\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.6011 - accuracy: 0.6924\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5730 - accuracy: 0.7367\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.5732 - accuracy: 0.7105\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5775 - accuracy: 0.7056\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.7156 - accuracy: 0.6903\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5271 - accuracy: 0.7295\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5518 - accuracy: 0.6965\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5692 - accuracy: 0.7113\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5246 - accuracy: 0.7072\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.5195 - accuracy: 0.7383\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.6869 - accuracy: 0.7128\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4967 - accuracy: 0.7317\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.5337 - accuracy: 0.7122\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4388 - accuracy: 0.7235\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3832 - accuracy: 0.7116\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3766 - accuracy: 0.6909\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.4626 - accuracy: 0.6860\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4588 - accuracy: 0.7275\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4922 - accuracy: 0.7024\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4195 - accuracy: 0.6972\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4086 - accuracy: 0.7043\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4172 - accuracy: 0.7174\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3836 - accuracy: 0.7141\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3764 - accuracy: 0.7162\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3969 - accuracy: 0.7311\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3798 - accuracy: 0.7501\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4808 - accuracy: 0.7196\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3921 - accuracy: 0.7099\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3877 - accuracy: 0.6768\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4105 - accuracy: 0.7107\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4392 - accuracy: 0.6829\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3939 - accuracy: 0.6998\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3927 - accuracy: 0.7256\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3637 - accuracy: 0.7243\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3543 - accuracy: 0.7045\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3932 - accuracy: 0.7134\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3855 - accuracy: 0.7293\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3614 - accuracy: 0.7161\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3449 - accuracy: 0.6866\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3525 - accuracy: 0.7121\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4012 - accuracy: 0.7223\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2546 - accuracy: 0.6984\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3481 - accuracy: 0.7189\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3177 - accuracy: 0.7172\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4149 - accuracy: 0.7241\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4345 - accuracy: 0.6988\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3205 - accuracy: 0.7151\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3253 - accuracy: 0.6995\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2809 - accuracy: 0.7159\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2839 - accuracy: 0.7017\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3088 - accuracy: 0.7073\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2755 - accuracy: 0.6827\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2760 - accuracy: 0.7296\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3204 - accuracy: 0.6962\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2944 - accuracy: 0.7355\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3140 - accuracy: 0.7209\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3153 - accuracy: 0.7032\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3189 - accuracy: 0.7248\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3068 - accuracy: 0.7134\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2941 - accuracy: 0.7027\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3176 - accuracy: 0.7025\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2927 - accuracy: 0.6939\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4216 - accuracy: 0.7039\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2660 - accuracy: 0.7230\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3087 - accuracy: 0.6852\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3191 - accuracy: 0.7275\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3485 - accuracy: 0.7217\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3497 - accuracy: 0.7138\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3442 - accuracy: 0.7274\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3804 - accuracy: 0.7278\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3666 - accuracy: 0.7297\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3432 - accuracy: 0.7119\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3355 - accuracy: 0.7096\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2973 - accuracy: 0.7456\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3541 - accuracy: 0.7317\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3983 - accuracy: 0.7240\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3110 - accuracy: 0.7143\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2953 - accuracy: 0.7252\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2994 - accuracy: 0.6993\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2708 - accuracy: 0.7205\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2788 - accuracy: 0.7109\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2699 - accuracy: 0.7009\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3242 - accuracy: 0.6918\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2787 - accuracy: 0.6940\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2975 - accuracy: 0.7234\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3330 - accuracy: 0.7146\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2870 - accuracy: 0.7027\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2916 - accuracy: 0.7461\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.3166 - accuracy: 0.7182\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3034 - accuracy: 0.7152\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2877 - accuracy: 0.7262\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.2812 - accuracy: 0.7098\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2872 - accuracy: 0.7250\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2830 - accuracy: 0.7121\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2469 - accuracy: 0.7222\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_21 (LSTM)               (None, None, 64)          2064640   \n",
            "_________________________________________________________________\n",
            "lstm_22 (LSTM)               (None, 64)                33024     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 2,097,729\n",
            "Trainable params: 2,097,729\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 11:55:39,085]\u001b[0m Trial 4 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.4798866795178178, 'units': 64, 'lr': 0.04523726082583492}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 21s 2s/step - loss: 1.8674 - accuracy: 0.7032\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6985 - accuracy: 0.7271\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6036 - accuracy: 0.7248\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6251 - accuracy: 0.7228\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6903 - accuracy: 0.6931\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6218 - accuracy: 0.7084\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6476 - accuracy: 0.7104\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6862 - accuracy: 0.7336\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5759 - accuracy: 0.7294\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5874 - accuracy: 0.7244\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5758 - accuracy: 0.7151\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5496 - accuracy: 0.7506\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6840 - accuracy: 0.7041\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5775 - accuracy: 0.7034\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6333 - accuracy: 0.6923\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6243 - accuracy: 0.7093\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6480 - accuracy: 0.6981\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.7041 - accuracy: 0.7024\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6630 - accuracy: 0.7156\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6015 - accuracy: 0.7288\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6387 - accuracy: 0.7246\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6538 - accuracy: 0.6965\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5573 - accuracy: 0.7308\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5734 - accuracy: 0.7320\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5587 - accuracy: 0.7103\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5881 - accuracy: 0.7261\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5225 - accuracy: 0.7182\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5702 - accuracy: 0.6970\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5558 - accuracy: 0.7297\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5282 - accuracy: 0.7049\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5772 - accuracy: 0.6962\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5453 - accuracy: 0.7191\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4906 - accuracy: 0.7402\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4118 - accuracy: 0.7519\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4110 - accuracy: 0.7373\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3559 - accuracy: 0.7275\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4208 - accuracy: 0.7161\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3819 - accuracy: 0.7178\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4943 - accuracy: 0.7100\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3725 - accuracy: 0.6981\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2636 - accuracy: 0.7398\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3879 - accuracy: 0.7174\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3448 - accuracy: 0.6983\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3344 - accuracy: 0.7105\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2613 - accuracy: 0.7421\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2556 - accuracy: 0.7441\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4413 - accuracy: 0.7061\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3855 - accuracy: 0.6952\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2718 - accuracy: 0.7170\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2470 - accuracy: 0.7337\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2805 - accuracy: 0.7366\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3263 - accuracy: 0.7192\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3133 - accuracy: 0.6906\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2801 - accuracy: 0.6945\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3205 - accuracy: 0.7394\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3152 - accuracy: 0.7100\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2993 - accuracy: 0.7044\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2962 - accuracy: 0.7037\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2780 - accuracy: 0.7298\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2066 - accuracy: 0.7077\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2555 - accuracy: 0.7320\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2474 - accuracy: 0.7346\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3237 - accuracy: 0.7123\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2696 - accuracy: 0.7109\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2359 - accuracy: 0.7076\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3270 - accuracy: 0.7288\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2624 - accuracy: 0.7410\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3492 - accuracy: 0.7190\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2894 - accuracy: 0.6940\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2893 - accuracy: 0.7214\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2575 - accuracy: 0.7261\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2809 - accuracy: 0.7142\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2341 - accuracy: 0.7034\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2836 - accuracy: 0.7175\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3148 - accuracy: 0.7340\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2314 - accuracy: 0.7101\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2982 - accuracy: 0.7312\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2160 - accuracy: 0.7348\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2477 - accuracy: 0.6975\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2033 - accuracy: 0.7150\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2243 - accuracy: 0.7311\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2499 - accuracy: 0.7127\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3201 - accuracy: 0.6982\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3068 - accuracy: 0.7118\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2219 - accuracy: 0.7152\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3011 - accuracy: 0.7153\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2546 - accuracy: 0.7172\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2506 - accuracy: 0.7025\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2488 - accuracy: 0.7341\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3482 - accuracy: 0.7055\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3039 - accuracy: 0.7236\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2327 - accuracy: 0.7407\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2383 - accuracy: 0.7254\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3005 - accuracy: 0.7146\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4304 - accuracy: 0.6825\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2814 - accuracy: 0.7147\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2415 - accuracy: 0.7238\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3066 - accuracy: 0.7229\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3702 - accuracy: 0.7136\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4036 - accuracy: 0.7085\n",
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_23 (LSTM)               (None, None, 121)         3931048   \n",
            "_________________________________________________________________\n",
            "lstm_24 (LSTM)               (None, 121)               117612    \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 122       \n",
            "=================================================================\n",
            "Total params: 4,048,782\n",
            "Trainable params: 4,048,782\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 12:23:50,132]\u001b[0m Trial 5 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.48816617562673337, 'units': 121, 'lr': 0.04125027128411712}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 22s 2s/step - loss: 0.6537 - accuracy: 0.7234\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6178 - accuracy: 0.7207\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 19s 2s/step - loss: 0.5969 - accuracy: 0.7227\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5980 - accuracy: 0.7138\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5890 - accuracy: 0.7228\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.6139 - accuracy: 0.6906\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5769 - accuracy: 0.7242\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5866 - accuracy: 0.7169\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5940 - accuracy: 0.6933\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5650 - accuracy: 0.7403\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5522 - accuracy: 0.7478\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5795 - accuracy: 0.7084\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5726 - accuracy: 0.7239\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5786 - accuracy: 0.7096\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 19s 2s/step - loss: 0.5736 - accuracy: 0.7205\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5874 - accuracy: 0.7021\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5579 - accuracy: 0.7354\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5538 - accuracy: 0.7349\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5539 - accuracy: 0.7202\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5600 - accuracy: 0.7216\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5557 - accuracy: 0.7144\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5449 - accuracy: 0.7281\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5408 - accuracy: 0.7132\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5438 - accuracy: 0.7094\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5303 - accuracy: 0.7213\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5241 - accuracy: 0.7003\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.5221 - accuracy: 0.7181\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4755 - accuracy: 0.7367\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4931 - accuracy: 0.7134\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4478 - accuracy: 0.7255\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4481 - accuracy: 0.7230\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4508 - accuracy: 0.7212\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4368 - accuracy: 0.7184\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4484 - accuracy: 0.7071\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4321 - accuracy: 0.7371\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4465 - accuracy: 0.7215\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4070 - accuracy: 0.7244\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4225 - accuracy: 0.7096\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4154 - accuracy: 0.7295\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3806 - accuracy: 0.7080\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4449 - accuracy: 0.7166\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4177 - accuracy: 0.7045\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3829 - accuracy: 0.7263\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4088 - accuracy: 0.6878\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3573 - accuracy: 0.7266\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3964 - accuracy: 0.7074\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3567 - accuracy: 0.6946\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3490 - accuracy: 0.7284\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3604 - accuracy: 0.7142\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3928 - accuracy: 0.7260\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3517 - accuracy: 0.7040\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3233 - accuracy: 0.7178\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3124 - accuracy: 0.7385\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.4116 - accuracy: 0.7153\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3204 - accuracy: 0.7252\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3634 - accuracy: 0.6962\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3405 - accuracy: 0.7413\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3366 - accuracy: 0.7031\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3224 - accuracy: 0.7413\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3362 - accuracy: 0.7232\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3450 - accuracy: 0.7329\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3272 - accuracy: 0.7148\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3433 - accuracy: 0.7024\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3360 - accuracy: 0.7198\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3056 - accuracy: 0.7130\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3227 - accuracy: 0.7342\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2800 - accuracy: 0.7387\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3319 - accuracy: 0.7094\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2757 - accuracy: 0.7055\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3114 - accuracy: 0.7130\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3097 - accuracy: 0.6855\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2633 - accuracy: 0.7098\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.6902\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3309 - accuracy: 0.6873\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2740 - accuracy: 0.7045\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3039 - accuracy: 0.7155\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2626 - accuracy: 0.7219\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3052 - accuracy: 0.7229\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2967 - accuracy: 0.7068\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2611 - accuracy: 0.7303\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2710 - accuracy: 0.7034\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2879 - accuracy: 0.7226\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.3173 - accuracy: 0.7114\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2247 - accuracy: 0.7217\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2721 - accuracy: 0.7243\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.2853 - accuracy: 0.7378\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3029 - accuracy: 0.7430\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.3106 - accuracy: 0.7120\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 19s 2s/step - loss: 0.2769 - accuracy: 0.7098\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2410 - accuracy: 0.7125\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2602 - accuracy: 0.7351\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2503 - accuracy: 0.6952\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2370 - accuracy: 0.6942\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2675 - accuracy: 0.7004\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2557 - accuracy: 0.7162\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.3030 - accuracy: 0.7020\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2421 - accuracy: 0.7154\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.7065\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2410 - accuracy: 0.7314\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.2622 - accuracy: 0.7006\n",
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_25 (LSTM)               (None, None, 128)         4162048   \n",
            "_________________________________________________________________\n",
            "lstm_26 (LSTM)               (None, 128)               131584    \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 4,293,761\n",
            "Trainable params: 4,293,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 12:52:47,797]\u001b[0m Trial 6 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.46175097242435115, 'units': 128, 'lr': 5.503268206118775e-05}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 12s 1s/step - loss: 0.6882 - accuracy: 0.7116\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.6384 - accuracy: 0.6768\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6054 - accuracy: 0.7184\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5842 - accuracy: 0.7242\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 8s 984ms/step - loss: 0.5868 - accuracy: 0.7127\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 8s 994ms/step - loss: 0.6081 - accuracy: 0.6941\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 8s 1000ms/step - loss: 0.5792 - accuracy: 0.7257\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5791 - accuracy: 0.7153\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.6085 - accuracy: 0.6900\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5602 - accuracy: 0.7343\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 8s 995ms/step - loss: 0.5636 - accuracy: 0.7284\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5691 - accuracy: 0.7200\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 8s 956ms/step - loss: 0.5703 - accuracy: 0.7162\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5717 - accuracy: 0.7170\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 8s 993ms/step - loss: 0.5874 - accuracy: 0.6944\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5448 - accuracy: 0.7277\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 8s 953ms/step - loss: 0.5299 - accuracy: 0.7466\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5464 - accuracy: 0.7134\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.5203 - accuracy: 0.7414\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5213 - accuracy: 0.7261\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 7s 919ms/step - loss: 0.5203 - accuracy: 0.7175\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5005 - accuracy: 0.7153\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5218 - accuracy: 0.7051\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4753 - accuracy: 0.7278\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4812 - accuracy: 0.7066\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.4843 - accuracy: 0.6951\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.4833 - accuracy: 0.7099\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 8s 993ms/step - loss: 0.4569 - accuracy: 0.7021\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4488 - accuracy: 0.7216\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.4357 - accuracy: 0.7133\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.4293 - accuracy: 0.7255\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4342 - accuracy: 0.7126\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.4021 - accuracy: 0.7286\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.4054 - accuracy: 0.7149\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 8s 974ms/step - loss: 0.3877 - accuracy: 0.7031\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3690 - accuracy: 0.7192\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3923 - accuracy: 0.7177\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3154 - accuracy: 0.7273\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3400 - accuracy: 0.7148\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3353 - accuracy: 0.7506\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3400 - accuracy: 0.7093\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3143 - accuracy: 0.7404\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3448 - accuracy: 0.6966\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3045 - accuracy: 0.7106\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3238 - accuracy: 0.7172\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 8s 990ms/step - loss: 0.3216 - accuracy: 0.7056\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3160 - accuracy: 0.7206\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.3280 - accuracy: 0.7209\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2970 - accuracy: 0.7325\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 8s 972ms/step - loss: 0.3024 - accuracy: 0.7141\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 8s 970ms/step - loss: 0.3013 - accuracy: 0.7168\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 8s 974ms/step - loss: 0.2792 - accuracy: 0.7414\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2632 - accuracy: 0.7120\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 8s 999ms/step - loss: 0.2796 - accuracy: 0.7230\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 8s 994ms/step - loss: 0.3216 - accuracy: 0.6946\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2704 - accuracy: 0.7089\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 8s 917ms/step - loss: 0.2772 - accuracy: 0.7381\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2962 - accuracy: 0.7293\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2860 - accuracy: 0.7155\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2683 - accuracy: 0.7045\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2413 - accuracy: 0.7214\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 8s 882ms/step - loss: 0.2223 - accuracy: 0.7066\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2915 - accuracy: 0.6949\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 8s 964ms/step - loss: 0.2283 - accuracy: 0.7269\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 8s 969ms/step - loss: 0.2077 - accuracy: 0.7450\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 8s 954ms/step - loss: 0.2414 - accuracy: 0.7038\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2341 - accuracy: 0.7260\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 8s 974ms/step - loss: 0.2121 - accuracy: 0.7157\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 8s 928ms/step - loss: 0.2643 - accuracy: 0.7130\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2641 - accuracy: 0.7372\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2169 - accuracy: 0.7188\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2195 - accuracy: 0.7178\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2278 - accuracy: 0.7058\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1976 - accuracy: 0.7239\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 8s 982ms/step - loss: 0.1701 - accuracy: 0.7337\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2449 - accuracy: 0.7124\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1904 - accuracy: 0.7117\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2198 - accuracy: 0.7329\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2415 - accuracy: 0.7092\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2080 - accuracy: 0.7187\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2213 - accuracy: 0.6993\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2006 - accuracy: 0.7216\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2051 - accuracy: 0.7142\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2067 - accuracy: 0.7090\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 8s 946ms/step - loss: 0.1910 - accuracy: 0.7203\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 7s 878ms/step - loss: 0.2336 - accuracy: 0.6929\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 8s 953ms/step - loss: 0.2286 - accuracy: 0.7085\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 8s 953ms/step - loss: 0.2190 - accuracy: 0.7407\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 8s 999ms/step - loss: 0.2333 - accuracy: 0.7256\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 8s 959ms/step - loss: 0.2267 - accuracy: 0.7226\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 8s 948ms/step - loss: 0.2076 - accuracy: 0.6903\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 8s 981ms/step - loss: 0.1883 - accuracy: 0.7085\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2079 - accuracy: 0.7109\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1816 - accuracy: 0.7036\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.2250 - accuracy: 0.7171\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2018 - accuracy: 0.6887\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 8s 949ms/step - loss: 0.1834 - accuracy: 0.7268\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1584 - accuracy: 0.7086\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1894 - accuracy: 0.7162\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 8s 975ms/step - loss: 0.1939 - accuracy: 0.7282\n",
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_27 (LSTM)               (None, None, 42)          1351224   \n",
            "_________________________________________________________________\n",
            "lstm_28 (LSTM)               (None, 42)                14280     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 43        \n",
            "=================================================================\n",
            "Total params: 1,365,547\n",
            "Trainable params: 1,365,547\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 13:06:41,119]\u001b[0m Trial 7 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.25114432073438797, 'units': 42, 'lr': 0.00010930017665486733}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6557 - accuracy: 0.7062\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.5833 - accuracy: 0.7180\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.4678 - accuracy: 0.7268\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3910 - accuracy: 0.7430\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3725 - accuracy: 0.7007\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3193 - accuracy: 0.7408\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3353 - accuracy: 0.6917\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3085 - accuracy: 0.7241\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.3181 - accuracy: 0.6942\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3230 - accuracy: 0.7000\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3367 - accuracy: 0.7274\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.3260 - accuracy: 0.7008\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2730 - accuracy: 0.7099\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2621 - accuracy: 0.7449\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2195 - accuracy: 0.7323\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2490 - accuracy: 0.7307\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2961 - accuracy: 0.7391\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2340 - accuracy: 0.6933\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2418 - accuracy: 0.7192\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2106 - accuracy: 0.7086\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2921 - accuracy: 0.7074\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2200 - accuracy: 0.7153\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2336 - accuracy: 0.7270\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2944 - accuracy: 0.7475\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1918 - accuracy: 0.7134\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1898 - accuracy: 0.7367\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2399 - accuracy: 0.6975\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1849 - accuracy: 0.7486\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1855 - accuracy: 0.7159\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2107 - accuracy: 0.7130\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2317 - accuracy: 0.7147\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2819 - accuracy: 0.7019\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1790 - accuracy: 0.7308\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1826 - accuracy: 0.7246\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1594 - accuracy: 0.6921\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1954 - accuracy: 0.7258\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1719 - accuracy: 0.7135\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1709 - accuracy: 0.6997\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2187 - accuracy: 0.7151\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1840 - accuracy: 0.7018\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2127 - accuracy: 0.7206\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1871 - accuracy: 0.7325\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2154 - accuracy: 0.7196\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2683 - accuracy: 0.6931\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1808 - accuracy: 0.7233\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1611 - accuracy: 0.7221\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1736 - accuracy: 0.7313\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1920 - accuracy: 0.6844\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1696 - accuracy: 0.7162\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1530 - accuracy: 0.6806\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1642 - accuracy: 0.7259\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1613 - accuracy: 0.7322\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1520 - accuracy: 0.7469\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1511 - accuracy: 0.7115\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1419 - accuracy: 0.7383\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1734 - accuracy: 0.7102\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2015 - accuracy: 0.7116\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1948 - accuracy: 0.7139\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.2007 - accuracy: 0.7268\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.1770 - accuracy: 0.7534\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2164 - accuracy: 0.7021\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1368 - accuracy: 0.7257\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1570 - accuracy: 0.7130\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1524 - accuracy: 0.6858\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1550 - accuracy: 0.7424\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1565 - accuracy: 0.6919\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1362 - accuracy: 0.7071\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2175 - accuracy: 0.7055\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1660 - accuracy: 0.7218\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1664 - accuracy: 0.7110\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1651 - accuracy: 0.7292\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1980 - accuracy: 0.7120\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1773 - accuracy: 0.7132\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1449 - accuracy: 0.7149\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1740 - accuracy: 0.7127\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1630 - accuracy: 0.7111\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1469 - accuracy: 0.7019\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1546 - accuracy: 0.7255\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2948 - accuracy: 0.7323\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1818 - accuracy: 0.6992\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.1482 - accuracy: 0.7162\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1742 - accuracy: 0.7053\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1545 - accuracy: 0.7008\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1540 - accuracy: 0.7234\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1412 - accuracy: 0.6910\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1420 - accuracy: 0.7429\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1416 - accuracy: 0.7100\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1502 - accuracy: 0.7521\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1389 - accuracy: 0.7182\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1624 - accuracy: 0.7053\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1530 - accuracy: 0.7478\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1224 - accuracy: 0.6994\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1469 - accuracy: 0.7123\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1403 - accuracy: 0.7022\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1310 - accuracy: 0.7060\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.1423 - accuracy: 0.7326\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1559 - accuracy: 0.7311\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1778 - accuracy: 0.7178\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1386 - accuracy: 0.7137\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.1745 - accuracy: 0.7147\n",
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_29 (LSTM)               (None, None, 102)         3306024   \n",
            "_________________________________________________________________\n",
            "lstm_30 (LSTM)               (None, 102)               83640     \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 1)                 103       \n",
            "=================================================================\n",
            "Total params: 3,389,767\n",
            "Trainable params: 3,389,767\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 13:30:40,886]\u001b[0m Trial 8 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.31419366989971953, 'units': 102, 'lr': 0.0009648158706090487}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 15s 1s/step - loss: 0.6413 - accuracy: 0.7135\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5848 - accuracy: 0.7229\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5747 - accuracy: 0.7212\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.5716 - accuracy: 0.7054\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4571 - accuracy: 0.7422\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.4373 - accuracy: 0.7142\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3858 - accuracy: 0.7041\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3403 - accuracy: 0.7310\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.3262 - accuracy: 0.7312\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2972 - accuracy: 0.7068\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2757 - accuracy: 0.7426\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2501 - accuracy: 0.6941\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 9s 991ms/step - loss: 0.3028 - accuracy: 0.7060\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3348 - accuracy: 0.7180\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2787 - accuracy: 0.7328\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2641 - accuracy: 0.7382\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2680 - accuracy: 0.7236\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2706 - accuracy: 0.7204\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2683 - accuracy: 0.7031\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2383 - accuracy: 0.7517\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.3010 - accuracy: 0.7289\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2525 - accuracy: 0.7090\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2467 - accuracy: 0.7104\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2643 - accuracy: 0.7309\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2177 - accuracy: 0.7140\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2553 - accuracy: 0.7154\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2604 - accuracy: 0.7177\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2058 - accuracy: 0.7440\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1705 - accuracy: 0.7217\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2276 - accuracy: 0.7255\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2143 - accuracy: 0.7199\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1983 - accuracy: 0.7066\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1954 - accuracy: 0.7095\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 9s 998ms/step - loss: 0.2230 - accuracy: 0.6952\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2146 - accuracy: 0.7073\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2515 - accuracy: 0.7048\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1704 - accuracy: 0.7169\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2036 - accuracy: 0.6986\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1695 - accuracy: 0.7060\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1651 - accuracy: 0.7308\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1582 - accuracy: 0.7345\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1837 - accuracy: 0.7100\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1521 - accuracy: 0.7401\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1757 - accuracy: 0.7128\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1854 - accuracy: 0.7203\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2075 - accuracy: 0.7017\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1887 - accuracy: 0.7159\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1788 - accuracy: 0.7150\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.2054 - accuracy: 0.7118\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1445 - accuracy: 0.7385\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1906 - accuracy: 0.7039\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1313 - accuracy: 0.7063\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1661 - accuracy: 0.7368\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1713 - accuracy: 0.7168\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.2056 - accuracy: 0.7199\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1758 - accuracy: 0.7003\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1791 - accuracy: 0.7323\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1797 - accuracy: 0.7004\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1322 - accuracy: 0.7358\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1708 - accuracy: 0.7119\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1615 - accuracy: 0.7194\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1838 - accuracy: 0.7063\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1458 - accuracy: 0.7290\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1643 - accuracy: 0.7314\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1657 - accuracy: 0.7300\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1748 - accuracy: 0.7070\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1765 - accuracy: 0.6980\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1330 - accuracy: 0.7390\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1679 - accuracy: 0.7220\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1776 - accuracy: 0.7105\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1804 - accuracy: 0.7213\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1359 - accuracy: 0.7431\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1345 - accuracy: 0.7153\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1537 - accuracy: 0.7172\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1499 - accuracy: 0.7153\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1363 - accuracy: 0.7335\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1462 - accuracy: 0.7236\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1236 - accuracy: 0.7256\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1594 - accuracy: 0.7098\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1826 - accuracy: 0.7168\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1399 - accuracy: 0.6996\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1746 - accuracy: 0.7049\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1657 - accuracy: 0.7335\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1622 - accuracy: 0.6992\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1736 - accuracy: 0.7188\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1483 - accuracy: 0.6861\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1664 - accuracy: 0.6966\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1600 - accuracy: 0.6960\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 10s 1s/step - loss: 0.1328 - accuracy: 0.7492\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 8s 961ms/step - loss: 0.1490 - accuracy: 0.7131\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1282 - accuracy: 0.7004\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1314 - accuracy: 0.7222\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1471 - accuracy: 0.7346\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1306 - accuracy: 0.7145\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1435 - accuracy: 0.7228\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 8s 1s/step - loss: 0.1139 - accuracy: 0.7075\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1489 - accuracy: 0.7113\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1302 - accuracy: 0.7147\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1274 - accuracy: 0.7152\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 9s 1s/step - loss: 0.1290 - accuracy: 0.7252\n",
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_31 (LSTM)               (None, None, 53)          1707448   \n",
            "_________________________________________________________________\n",
            "lstm_32 (LSTM)               (None, 53)                22684     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 54        \n",
            "=================================================================\n",
            "Total params: 1,730,186\n",
            "Trainable params: 1,730,186\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-05 13:46:01,133]\u001b[0m Trial 9 finished with value: 0.7150259017944336 and parameters: {'dropout': 0.2722218597770443, 'units': 53, 'lr': 0.0004802884165322298}. Best is trial 0 with value: 0.7150259017944336.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.7654 - accuracy: 0.7049\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.6005 - accuracy: 0.7242\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.5852 - accuracy: 0.7317\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 12s 2s/step - loss: 0.5116 - accuracy: 0.7469\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 11s 1s/step - loss: 0.4706 - accuracy: 0.7299\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.4391 - accuracy: 0.7268\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 12s 1s/step - loss: 0.4047 - accuracy: 0.7325\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.4202 - accuracy: 0.7132\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.3927 - accuracy: 0.7224\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 12s 1s/step - loss: 0.3868 - accuracy: 0.7083\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 12s 2s/step - loss: 0.3983 - accuracy: 0.7312\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.3885 - accuracy: 0.7211\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 12s 2s/step - loss: 0.3353 - accuracy: 0.7080\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.2724 - accuracy: 0.7388\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 12s 2s/step - loss: 0.2775 - accuracy: 0.7200\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 14s 2s/step - loss: 0.2945 - accuracy: 0.6997\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 13s 2s/step - loss: 0.3169 - accuracy: 0.6799\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 12s 1s/step - loss: 0.2452 - accuracy: 0.7194\n",
            "Epoch 19/100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-b61c90f577de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# 3. Create a study object and optimize the objective function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'maximize'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of finished trials: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgc_after_trial\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m             \u001b[0mshow_progress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_progress_bar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m         )\n\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     71\u001b[0m                 \u001b[0mreseed_sampler_rng\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0mtime_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m                 \u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprogress_bar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             )\n\u001b[1;32m     75\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mvalue_or_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;31m# TODO(mamu): Handle multi-objective cases.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-69-b61c90f577de>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_float\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRMSprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mragged_x_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mragged_x_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqLRG25xUDyz"
      },
      "source": [
        "pip install -U keras-tuner"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a94dZwdSwCAa"
      },
      "source": [
        "def build_model(hp):\n",
        "    model = keras.Sequential()\n",
        "    for i in range(hp.Int('num_layers', 2, 10)):\n",
        "        model.add(tf.keras.layers.LSTM(units=hp.Int('units_' + str(i), 2, 128, 4),dropout=0.2))\n",
        "    model.add(tf.keras.layers.Dense(2, activation='softmax'))\n",
        "    model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),loss='binary_crossentropy',metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49BZwYDaw6LN"
      },
      "source": [
        "from kerastuner.tuners import RandomSearch\n",
        "tuner = RandomSearch(build_model,objective='val_accuracy',max_trials=5,executions_per_trial=3,directory='my_dir',project_name='helloworld')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWYenp6Iw9gk",
        "outputId": "5ece630a-3759-4c74-86e2-84b446e581b7"
      },
      "source": [
        "tuner.search_space_summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Search space summary\n",
            "Default search space size: 4\n",
            "num_layers (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 10, 'step': 1, 'sampling': None}\n",
            "units_0 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 128, 'step': 4, 'sampling': None}\n",
            "units_1 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 128, 'step': 4, 'sampling': None}\n",
            "learning_rate (Choice)\n",
            "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FzgtJqbaxOnl",
        "outputId": "7ac0af69-e391-42f8-d823-57d0f2bd7871"
      },
      "source": [
        "tuner.search(ragged_x_train, y_train,epochs=5,validation_data=(ragged_x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Search: Running Trial #1\n",
            "\n",
            "Hyperparameter    |Value             |Best Value So Far \n",
            "num_layers        |9                 |?                 \n",
            "units_0           |82                |?                 \n",
            "units_1           |30                |?                 \n",
            "learning_rate     |0.0001            |?                 \n",
            "\n",
            "Epoch 1/5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-66-2be6c47c9f3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtuner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mragged_x_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mragged_x_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/kerastuner/engine/base_tuner.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_trial_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_trial_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_search_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/kerastuner/engine/multi_execution_tuner.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(self, trial, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0mcopied_fit_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'callbacks'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_and_fit_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopied_fit_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_values\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moracle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirection\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'min'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    869\u001b[0m       \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    872\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m       \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2967\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2968\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2969\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2970\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m           \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3204\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3205\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3206\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3208\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m           \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    975\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: in user code:\n\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *\n        return step_function(self, iterator)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **\n        outputs = model.train_step(data)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step\n        y_pred = self(x, training=True)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__\n        outputs = call_fn(inputs, *args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:389 call\n        outputs = layer(inputs, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/recurrent.py:660 __call__\n        return super(RNN, self).__call__(inputs, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1008 __call__\n        self._maybe_build(inputs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:2710 _maybe_build\n        self.build(input_shapes)  # pylint:disable=not-callable\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/recurrent.py:578 build\n        self.cell.build(step_input_shape)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/tf_utils.py:272 wrapper\n        output_shape = fn(instance, input_shape)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/recurrent.py:2350 build\n        caching_device=default_caching_device)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:639 add_weight\n        caching_device=caching_device)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py:810 _add_variable_with_custom_getter\n        **kwargs_for_getter)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py:142 make_variable\n        shape=variable_shape if variable_shape else None)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:260 __call__\n        return cls._variable_v1_call(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:221 _variable_v1_call\n        shape=shape)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:67 getter\n        return captured_getter(captured_previous, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator\n        return next_creator(**kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:67 getter\n        return captured_getter(captured_previous, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator\n        return next_creator(**kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:67 getter\n        return captured_getter(captured_previous, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator\n        return next_creator(**kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:67 getter\n        return captured_getter(captured_previous, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:714 variable_capturing_scope\n        lifted_initializer_graph=lifted_initializer_graph, **kwds)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variables.py:264 __call__\n        return super(VariableMetaclass, cls).__call__(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:227 __init__\n        initial_value = initial_value()\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/initializers/initializers_v2.py:410 __call__\n        shape, dtype=_get_dtype(dtype), **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/init_ops_v2.py:582 __call__\n        fan_in, fan_out = _compute_fans(shape)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/init_ops.py:1425 _compute_fans\n        return int(fan_in), int(fan_out)\n\n    TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzx_28S6axBa"
      },
      "source": [
        "visualize\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgtEh4-PxYC_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "297a0c79-40e4-4f71-aba7-4491e3d5f787"
      },
      "source": [
        "!pip install conx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: conx in /usr/local/lib/python3.7/dist-packages (3.7.10)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from conx) (2.10.0)\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.7/dist-packages (from conx) (1.3.0)\n",
            "Requirement already satisfied: cairosvg in /usr/local/lib/python3.7/dist-packages (from conx) (2.5.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from conx) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from conx) (1.19.5)\n",
            "Requirement already satisfied: keras>=2.1.3 in /usr/local/lib/python3.7/dist-packages (from conx) (2.4.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from conx) (4.41.1)\n",
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from conx) (0.0)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.7/dist-packages (from conx) (5.5.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from conx) (7.0.0)\n",
            "Requirement already satisfied: svgwrite in /usr/local/lib/python3.7/dist-packages (from conx) (1.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from conx) (3.2.2)\n",
            "Requirement already satisfied: ipywidgets>=7.0 in /usr/local/lib/python3.7/dist-packages (from conx) (7.6.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py->conx) (1.15.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.7/dist-packages (from pydot->conx) (2.4.7)\n",
            "Requirement already satisfied: cairocffi in /usr/local/lib/python3.7/dist-packages (from cairosvg->conx) (1.2.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from cairosvg->conx) (0.7.0)\n",
            "Requirement already satisfied: cssselect2 in /usr/local/lib/python3.7/dist-packages (from cairosvg->conx) (0.4.1)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.7/dist-packages (from cairosvg->conx) (1.1.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->conx) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->conx) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->conx) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->conx) (1.24.3)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras>=2.1.3->conx) (3.13)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras>=2.1.3->conx) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn->conx) (0.22.2.post1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from IPython->conx) (0.8.1)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from IPython->conx) (5.0.5)\n",
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            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (5.3.1)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0->conx) (2.6.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0->conx) (4.7.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.1.0->cairocffi->cairosvg->conx) (2.20)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0->conx) (22.0.3)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (1.5.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (2.11.3)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (5.6.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (0.9.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (1.1.1)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (0.4.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (3.3.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (0.3)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (0.8.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (1.4.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0->conx) (20.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iYBNo2Pkbkco",
        "outputId": "703f01cc-02ea-4a20-eab9-e2025ba3c78e"
      },
      "source": [
        "!pip install tensorflow==2.2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/1a/0d79814736cfecc825ab8094b39648cc9c46af7af1bae839928acb73b4dd/tensorflow-2.2.0-cp37-cp37m-manylinux2010_x86_64.whl (516.2MB)\n",
            "\u001b[K     |████████████████████████████████| 516.2MB 22kB/s \n",
            "\u001b[?25hCollecting tensorflow-estimator<2.3.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/f5/926ae53d6a226ec0fda5208e0e581cffed895ccc89e36ba76a8e60895b78/tensorflow_estimator-2.2.0-py2.py3-none-any.whl (454kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 47.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.15.0)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (2.10.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (0.2.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (0.10.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.19.5)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.12.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.32.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (0.36.2)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.6.3)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (3.12.4)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.1.0)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (3.3.0)\n",
            "Collecting gast==0.3.3\n",
            "  Downloading https://files.pythonhosted.org/packages/d6/84/759f5dd23fec8ba71952d97bcc7e2c9d7d63bdc582421f3cd4be845f0c98/gast-0.3.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.2) (1.1.2)\n",
            "Collecting tensorboard<2.3.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/74/0a6fcb206dcc72a6da9a62dd81784bfdbff5fedb099982861dc2219014fb/tensorboard-2.2.2-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 43.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorflow==2.2) (54.0.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.3.4)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.4.3)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.27.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2.23.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.0.1)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.7.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (4.7.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (4.2.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.24.3)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.4.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.4.8)\n",
            "Installing collected packages: tensorflow-estimator, gast, tensorboard, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "  Found existing installation: gast 0.2.2\n",
            "    Uninstalling gast-0.2.2:\n",
            "      Successfully uninstalled gast-0.2.2\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed gast-0.3.3 tensorboard-2.2.2 tensorflow-2.2.0 tensorflow-estimator-2.2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gast",
                  "tensorboard",
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5q6amldau1C",
        "outputId": "14cd4a1c-cc4d-4a33-afe0-a995b1d98a3b"
      },
      "source": [
        "import conx as cx\n",
        "tf.keras.layers.Input(shape=[None,8000],ragged=True),\n",
        "      #tf.keras.layers.Embedding(1454409, 27),\n",
        "      tf.keras.layers.LSTM(16, return_sequences=True,dropout=0.2),#64\n",
        "      tf.keras.layers.LSTM(16,dropout=0.2),#32\n",
        "      tf.keras.layers.Dense(64),#32\n",
        "      tf.keras.layers.Activation(tf.nn.relu),\n",
        "      tf.keras.layers.Dense(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ConX, version 3.7.10\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GgZ6I-Va3Qt"
      },
      "source": [
        "import numpy as np\n",
        "net4 = cx.Network(\"Model Architecture\")\n",
        "net4.add(cx.Layer(\"input\", shape=[None,8000],ragged=True))\n",
        "net4.add(cx.LSTMLayer(\"lstm-1\", 95, return_sequences=True)) \n",
        "net4.add(cx.LSTMLayer(\"lstm-2\", 20)) \n",
        "net4.add(cx.Layer(\"Dense\", 1))\n",
        "net4.add(cx.Layer(\"Threshold\", 1))\n",
        "net4.connect()\n",
        "net4.compile(error=\"mse\", optimizer=\"adam\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "VvxHNmOsgeU6",
        "outputId": "5e6ab4a6-c1c0-48dd-c9af-039bd69abdca"
      },
      "source": [
        "net4.picture(format=\"image\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=333x400 at 0x7FB5A4837DD0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOrGk8iGjp78"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}