{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Event Embedding Test",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHrv_Q-8XG2N",
        "colab_type": "code",
        "outputId": "27f26626-7342-4860-f9b8-0311aac45faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gm4QWPj5Oqzy",
        "colab_type": "code",
        "outputId": "9b95e2e9-344e-4db2-98c3-9577ea444e8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQFl3HMVXYGR",
        "colab_type": "code",
        "outputId": "5b4688d7-bcbc-4e12-ad46-f51ac0ed585b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "%run ./gdrive/'My Drive'/'capstone 2'/event_embedding/Code/Model_EventEmbeding/EventEmbedding.py ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/IndexedEvents.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data 20"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cpu\n",
            "No Trained Model, create new\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/theano/tensor/basic.py:5281: UserWarning: flatten outdim parameter is deprecated, use ndim instead.\n",
            "  \"flatten outdim parameter is deprecated, use ndim instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "TRAIN:  [EmbeddingLayer_W, roleDependentLayer1_T, roleDependentLayer1_W1, roleDependentLayer1_W2, roleDependentLayer1_b, roleDependentLayer2_T, roleDependentLayer2_W1, roleDependentLayer2_W2, roleDependentLayer2_b, roleDependentLayer3_T, roleDependentLayer3_W1, roleDependentLayer3_W2, roleDependentLayer3_b, LogisticRegression_W, LogisticRegression_b]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING (theano.tensor.blas): We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n",
            "WARNING (theano.tensor.blas): We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "************************************************************************\n",
            "Begin Training process\n",
            "Begin new epoch: 0\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [2]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
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            "There are 12.394284 % left in this 0 epoch with average cost 0.899360\n",
            "{3, 6, 10, 13, 14, 39, 71, 83, 86, 105, 120, 126, 130, 145, 152, 158, 190, 203, 204, 212, 235, 242, 243, 261, 271, 280, 290, 291, 292, 297, 316, 352, 399, 400, 410, 429, 430, 473, 478, 500, 502, 507, 508, 525, 530, 554, 562, 567, 576, 579, 605, 610, 629, 631, 648, 652, 656, 668, 685, 690, 697, 699, 718, 719, 748, 766, 768, 769, 771, 774, 775, 783, 822, 831, 837, 852, 870, 881, 883, 903, 922, 929, 960, 966, 973, 978, 986, 990, 994, 995, 1005, 1006, 1010, 1022, 1027, 1032, 1064, 1082, 1088, 1089, 1095, 1103, 1110, 1111, 1113, 1117, 1118, 1119, 1136, 1162, 1168, 1169, 1172, 1174, 1183, 1185, 1186, 1203, 1219, 1227, 1251, 1262, 1269, 1276, 1279, 1295, 1302, 1306, 1322, 1325, 1326, 1335, 1347, 1352, 1354, 1357, 1372, 1413, 1414, 1415, 1416, 1421, 1422, 1432, 1455, 1457, 1471, 1488, 1497, 1509, 1516, 1518, 1543, 1550, 1570, 1573, 1592, 1607, 1619, 1626, 1633, 1649, 1651, 1655, 1656, 1662, 1673, 1705, 1706, 1708, 1713, 1727, 1734, 1738, 1740, 1741, 1743, 1750, 1755, 1766, 1776, 1777, 1784, 1791, 1792, 1794, 1797, 1812, 1814, 1824, 1831, 1840, 1850, 1877, 1889, 1893, 1894, 1919, 1922, 1927, 1944, 1951, 1980, 1987, 1997, 2001, 2003, 2017, 2025, 2027, 2028, 2033, 2046, 2054, 2055, 2061, 2065, 2066, 2083, 2085, 2100, 2102, 2104, 2109, 2115, 2121, 2132, 2134, 2144, 2145, 2163, 2175, 2183, 2185, 2197, 2210, 2213, 2218, 2246, 2255, 2257, 2262, 2265, 2268, 2271, 2273, 2279, 2284, 2288, 2293, 2298, 2301, 2324, 2334, 2343, 2348, 2349, 2357, 2390, 2398, 2400, 2404, 2425, 2427, 2444, 2449, 2464, 2469, 2485, 2489, 2499, 2501, 2508, 2509, 2514, 2517, 2526, 2538, 2556, 2575, 2584, 2605, 2613, 2638, 2661, 2663, 2664, 2671, 2677, 2678, 2700, 2701, 2704, 2705, 2706, 2708, 2721, 2750, 2754, 2761, 2779, 2786, 2808, 2821, 2825, 2829, 2838, 2851, 2869, 2903, 2912, 2916, 2991, 3021, 3028, 3038, 3065, 3072, 3077, 3078, 3079, 3101, 3105, 3109, 3115, 3125, 3126, 3133, 3139, 3166, 3182, 3188, 3202, 3203, 3228, 3235, 3237, 3245, 3252, 3254, 3276, 3281, 3284, 3287, 3296, 3312, 3322, 3333, 3334, 3336, 3340, 3347, 3348, 3350, 3355, 3364, 3367, 3380, 3401, 3411, 3424, 3431, 3433, 3438, 3456, 3459, 3463, 3465, 3470, 3472, 3487, 3547, 3557, 3559, 3564, 3565, 3574, 3581, 3603, 3607, 3612, 3624, 3633, 3634, 3636, 3644, 3647, 3648, 3655, 3671, 3672, 3675, 3678, 3695, 3700, 3701, 3705, 3727, 3734, 3736, 3740, 3742, 3768, 3769, 3770, 3771, 3773, 3788, 3791, 3793, 3798, 3809, 3823, 3825, 3826, 3832, 3845, 3848, 3861, 3906, 3907, 3921, 3924, 3925, 3931, 3935, 3937, 3941, 3945, 3946, 3948, 3954, 3965, 3972, 3982, 3985, 4000, 4002, 4005, 4008, 4024, 4025, 4034, 4047, 4054, 4057, 4077, 4078, 4082, 4087, 4111, 4116, 4132, 4140, 4141, 4155, 4162, 4167, 4168, 4176, 4182, 4185, 4187, 4189, 4202, 4228, 4230, 4231, 4239, 4240, 4247, 4251, 4265, 4275, 4288, 4300, 4310, 4313, 4338, 4339, 4340, 4341, 4342, 4344, 4345, 4348, 4357, 4371, 4373, 4385, 4393, 4402, 4403, 4406, 4416, 4430, 4457, 4465, 4486, 4501, 4502, 4506, 4516, 4520, 4557, 4559, 4563, 4589, 4590, 4613, 4619, 4621, 4624, 4625, 4637, 4650, 4685, 4686, 4690, 4708, 4709, 4716, 4730, 4731, 4733, 4737, 4745, 4770, 4774, 4812, 4813, 4824, 4838, 4839, 4843, 4849, 4855, 4869, 4873, 4876, 4905, 4906, 4908, 4924, 4937, 4940, 4954, 4956, 4962, 4977, 4988, 4992, 5000, 5007, 5012, 5013, 5019, 5020, 5022, 5023, 5033, 5038, 5049, 5066, 5079, 5083, 5102, 5104, 5107, 5120, 5138, 5143, 5146, 5147, 5152, 5153, 5157, 5160, 5171, 5181, 5186, 5190, 5193, 5195, 5204, 5205, 5206, 5207, 5208, 5233, 5235, 5249, 5255, 5263, 5264, 5274, 5279, 5285, 5297, 5299, 5325, 5350, 5352, 5371, 5374, 5406, 5424, 5437, 5453, 5475, 5483, 5488, 5489, 5495, 5518, 5519, 5526, 5527, 5528, 5540, 5541, 5547, 5548, 5554, 5556, 5559, 5564, 5568, 5571, 5572, 5578, 5586, 5606, 5609, 5612, 5617, 5619, 5623, 5637, 5644, 5645, 5652, 5655, 5667, 5673, 5676, 5678, 5680, 5681, 5697, 5705, 5713, 5714, 5716, 5717, 5719, 5720, 5723, 5733, 5738, 5741, 5743, 5748, 5749, 5754, 5772, 5779, 5790, 5801, 5806, 5810, 5821, 5823, 5826, 5828, 5831, 5840, 5843, 5876, 5888, 5897, 5904, 5907, 5916, 5918, 5922, 5925, 5927, 5935, 5936, 5937, 5952, 5964, 5965, 5970, 5973, 5974, 5975, 5979, 5985, 5992, 5993, 5995, 6004, 6029, 6050, 6063, 6069, 6073, 6103, 6106, 6116, 6123, 6125, 6127, 6129, 6130, 6142, 6158, 6164, 6170, 6178, 6189, 6194, 6195, 6208, 6210, 6211, 6215, 6217, 6223, 6226, 6242, 6245, 6264, 6276, 6279, 6280, 6298, 6309, 6312, 6315, 6316, 6339, 6353, 6362, 6372, 6383, 6389, 6394, 6397, 6398, 6414, 6425, 6433, 6434, 6447, 6460, 6461, 6472, 6475, 6476, 6477, 6484, 6488, 6489, 6500, 6502, 6523, 6527, 6535, 6553, 6565, 6568, 6579, 6581, 6582, 6601, 6606, 6608, 6610, 6615, 6616, 6619, 6621, 6622, 6623, 6628, 6630, 6631, 6635, 6636, 6642, 6644, 6649, 6660, 6661, 6687, 6690, 6693, 6694, 6698, 6706, 6722, 6723, 6733, 6735, 6736, 6743, 6744, 6758, 6760, 6774, 6782, 6784, 6785, 6789, 6793, 6794, 6800, 6801, 6802, 6827, 6830, 6833, 6850} [10] [534] [22, 0] [263, 739]\n",
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            "{3, 6, 10, 13, 14, 39, 71, 83, 86, 105, 120, 126, 130, 145, 152, 158, 190, 203, 204, 212, 235, 242, 243, 261, 271, 280, 290, 291, 292, 297, 316, 352, 399, 400, 410, 429, 430, 473, 478, 500, 502, 507, 508, 525, 530, 554, 562, 567, 576, 579, 605, 610, 629, 631, 648, 652, 656, 668, 685, 690, 697, 699, 718, 719, 748, 766, 768, 769, 771, 774, 775, 783, 822, 831, 837, 852, 870, 881, 883, 903, 922, 929, 960, 966, 973, 978, 986, 990, 994, 995, 1005, 1006, 1010, 1022, 1027, 1032, 1064, 1082, 1088, 1089, 1095, 1103, 1110, 1111, 1113, 1117, 1118, 1119, 1136, 1162, 1168, 1169, 1172, 1174, 1183, 1185, 1186, 1203, 1219, 1227, 1251, 1262, 1269, 1276, 1279, 1295, 1302, 1306, 1322, 1325, 1326, 1335, 1347, 1352, 1354, 1357, 1372, 1413, 1414, 1415, 1416, 1421, 1422, 1432, 1455, 1457, 1471, 1488, 1497, 1509, 1516, 1518, 1543, 1550, 1570, 1573, 1592, 1607, 1619, 1626, 1633, 1649, 1651, 1655, 1656, 1662, 1673, 1705, 1706, 1708, 1713, 1727, 1734, 1738, 1740, 1741, 1743, 1750, 1755, 1766, 1776, 1777, 1784, 1791, 1792, 1794, 1797, 1812, 1814, 1824, 1831, 1840, 1850, 1877, 1889, 1893, 1894, 1919, 1922, 1927, 1944, 1951, 1980, 1987, 1997, 2001, 2003, 2017, 2025, 2027, 2028, 2033, 2046, 2054, 2055, 2061, 2065, 2066, 2083, 2085, 2100, 2102, 2104, 2109, 2115, 2121, 2132, 2134, 2144, 2145, 2163, 2175, 2183, 2185, 2197, 2210, 2213, 2218, 2246, 2255, 2257, 2262, 2265, 2268, 2271, 2273, 2279, 2284, 2288, 2293, 2298, 2301, 2324, 2334, 2343, 2348, 2349, 2357, 2390, 2398, 2400, 2404, 2425, 2427, 2444, 2449, 2464, 2469, 2485, 2489, 2499, 2501, 2508, 2509, 2514, 2517, 2526, 2538, 2556, 2575, 2584, 2605, 2613, 2638, 2661, 2663, 2664, 2671, 2677, 2678, 2700, 2701, 2704, 2705, 2706, 2708, 2721, 2750, 2754, 2761, 2779, 2786, 2808, 2821, 2825, 2829, 2838, 2851, 2869, 2903, 2912, 2916, 2991, 3021, 3028, 3038, 3065, 3072, 3077, 3078, 3079, 3101, 3105, 3109, 3115, 3125, 3126, 3133, 3139, 3166, 3182, 3188, 3202, 3203, 3228, 3235, 3237, 3245, 3252, 3254, 3276, 3281, 3284, 3287, 3296, 3312, 3322, 3333, 3334, 3336, 3340, 3347, 3348, 3350, 3355, 3364, 3367, 3380, 3401, 3411, 3424, 3431, 3433, 3438, 3456, 3459, 3463, 3465, 3470, 3472, 3487, 3547, 3557, 3559, 3564, 3565, 3574, 3581, 3603, 3607, 3612, 3624, 3633, 3634, 3636, 3644, 3647, 3648, 3655, 3671, 3672, 3675, 3678, 3695, 3700, 3701, 3705, 3727, 3734, 3736, 3740, 3742, 3768, 3769, 3770, 3771, 3773, 3788, 3791, 3793, 3798, 3809, 3823, 3825, 3826, 3832, 3845, 3848, 3861, 3906, 3907, 3921, 3924, 3925, 3931, 3935, 3937, 3941, 3945, 3946, 3948, 3954, 3965, 3972, 3982, 3985, 4000, 4002, 4005, 4008, 4024, 4025, 4034, 4047, 4054, 4057, 4077, 4078, 4082, 4087, 4111, 4116, 4132, 4140, 4141, 4155, 4162, 4167, 4168, 4176, 4182, 4185, 4187, 4189, 4202, 4228, 4230, 4231, 4239, 4240, 4247, 4251, 4265, 4275, 4288, 4300, 4310, 4313, 4338, 4339, 4340, 4341, 4342, 4344, 4345, 4348, 4357, 4371, 4373, 4385, 4393, 4402, 4403, 4406, 4416, 4430, 4457, 4465, 4486, 4501, 4502, 4506, 4516, 4520, 4557, 4559, 4563, 4589, 4590, 4613, 4619, 4621, 4624, 4625, 4637, 4650, 4685, 4686, 4690, 4708, 4709, 4716, 4730, 4731, 4733, 4737, 4745, 4770, 4774, 4812, 4813, 4824, 4838, 4839, 4843, 4849, 4855, 4869, 4873, 4876, 4905, 4906, 4908, 4924, 4937, 4940, 4954, 4956, 4962, 4977, 4988, 4992, 5000, 5007, 5012, 5013, 5019, 5020, 5022, 5023, 5038, 5049, 5066, 5079, 5083, 5102, 5104, 5107, 5120, 5138, 5143, 5146, 5147, 5152, 5153, 5157, 5160, 5171, 5181, 5186, 5190, 5193, 5195, 5204, 5205, 5206, 5207, 5208, 5233, 5235, 5249, 5255, 5263, 5264, 5274, 5279, 5285, 5297, 5299, 5325, 5350, 5352, 5371, 5374, 5406, 5424, 5437, 5453, 5475, 5483, 5488, 5489, 5495, 5518, 5519, 5526, 5527, 5528, 5540, 5541, 5547, 5548, 5554, 5556, 5559, 5564, 5568, 5571, 5572, 5578, 5586, 5606, 5609, 5612, 5617, 5619, 5623, 5637, 5644, 5645, 5652, 5655, 5667, 5673, 5676, 5678, 5680, 5681, 5697, 5705, 5713, 5714, 5716, 5717, 5719, 5720, 5723, 5733, 5738, 5741, 5743, 5748, 5749, 5754, 5772, 5779, 5790, 5801, 5806, 5810, 5821, 5823, 5826, 5828, 5831, 5840, 5843, 5876, 5888, 5897, 5904, 5907, 5916, 5918, 5922, 5925, 5927, 5935, 5936, 5937, 5952, 5964, 5965, 5970, 5973, 5974, 5975, 5979, 5985, 5992, 5993, 5995, 6004, 6029, 6050, 6063, 6069, 6073, 6103, 6106, 6116, 6123, 6125, 6127, 6129, 6130, 6142, 6158, 6164, 6170, 6178, 6189, 6194, 6195, 6208, 6210, 6211, 6215, 6217, 6223, 6226, 6242, 6245, 6264, 6276, 6279, 6280, 6298, 6309, 6312, 6315, 6316, 6339, 6353, 6362, 6372, 6383, 6389, 6394, 6397, 6398, 6414, 6425, 6433, 6434, 6447, 6460, 6461, 6472, 6475, 6476, 6477, 6484, 6488, 6489, 6500, 6502, 6523, 6527, 6535, 6553, 6565, 6568, 6579, 6581, 6582, 6601, 6606, 6608, 6610, 6615, 6616, 6619, 6621, 6622, 6623, 6628, 6630, 6631, 6635, 6636, 6642, 6644, 6649, 6660, 6661, 6687, 6690, 6693, 6694, 6698, 6706, 6722, 6723, 6733, 6735, 6736, 6743, 6744, 6758, 6760, 6774, 6782, 6784, 6785, 6789, 6793, 6794, 6800, 6801, 6802, 6827, 6830, 6833, 6850} [180] [494, 360, 721] [37, 7] [628]\n",
            "Begin new epoch: 1\n",
            "WRONG faking object 1 [99]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [87]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [21]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [10]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [0]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 1 epoch with average cost 0.779383\n",
            "There are 99.154272 % left in this 1 epoch with average cost 0.627311\n",
            "There are 98.425197 % left in this 1 epoch with average cost 0.547163\n",
            "There are 97.696121 % left in this 1 epoch with average cost 0.558166\n",
            "There are 96.967046 % left in this 1 epoch with average cost 0.330530\n",
            "There are 96.237970 % left in this 1 epoch with average cost 0.564570\n",
            "There are 95.508895 % left in this 1 epoch with average cost 0.381350\n",
            "There are 94.779819 % left in this 1 epoch with average cost 0.358536\n",
            "There are 94.050744 % left in this 1 epoch with average cost 0.456956\n",
            "There are 93.321668 % left in this 1 epoch with average cost 0.563933\n",
            "There are 92.592593 % left in this 1 epoch with average cost 0.508731\n",
            "There are 91.863517 % left in this 1 epoch with average cost 0.546361\n",
            "There are 91.134442 % left in this 1 epoch with average cost 0.415850\n",
            "There are 90.405366 % left in this 1 epoch with average cost 0.511286\n",
            "There are 89.676290 % left in this 1 epoch with average cost 0.502941\n",
            "There are 88.947215 % left in this 1 epoch with average cost 0.522165\n",
            "There are 88.218139 % left in this 1 epoch with average cost 0.580842\n",
            "There are 87.489064 % left in this 1 epoch with average cost 0.507647\n",
            "There are 86.759988 % left in this 1 epoch with average cost 0.377990\n",
            "There are 86.030913 % left in this 1 epoch with average cost 0.415260\n",
            "There are 85.301837 % left in this 1 epoch with average cost 0.550513\n",
            "There are 84.572762 % left in this 1 epoch with average cost 0.446642\n",
            "There are 83.843686 % left in this 1 epoch with average cost 0.496983\n",
            "There are 83.114611 % left in this 1 epoch with average cost 0.461623\n",
            "There are 82.385535 % left in this 1 epoch with average cost 0.381044\n",
            "There are 81.656460 % left in this 1 epoch with average cost 0.549966\n",
            "There are 80.927384 % left in this 1 epoch with average cost 0.559430\n",
            "There are 80.198309 % left in this 1 epoch with average cost 0.421083\n",
            "There are 79.469233 % left in this 1 epoch with average cost 0.450967\n",
            "There are 78.740157 % left in this 1 epoch with average cost 0.572771\n",
            "There are 78.011082 % left in this 1 epoch with average cost 0.448094\n",
            "There are 77.282006 % left in this 1 epoch with average cost 0.493550\n",
            "There are 76.552931 % left in this 1 epoch with average cost 0.516648\n",
            "There are 75.823855 % left in this 1 epoch with average cost 0.364059\n",
            "There are 75.094780 % left in this 1 epoch with average cost 0.507418\n",
            "There are 74.365704 % left in this 1 epoch with average cost 0.562911\n",
            "There are 73.636629 % left in this 1 epoch with average cost 0.490707\n",
            "There are 72.907553 % left in this 1 epoch with average cost 0.568385\n",
            "There are 72.178478 % left in this 1 epoch with average cost 0.434923\n",
            "There are 71.449402 % left in this 1 epoch with average cost 0.588494\n",
            "There are 70.720327 % left in this 1 epoch with average cost 0.445552\n",
            "There are 69.991251 % left in this 1 epoch with average cost 0.421695\n",
            "There are 69.262176 % left in this 1 epoch with average cost 0.754782\n",
            "There are 68.533100 % left in this 1 epoch with average cost 0.711332\n",
            "There are 67.804024 % left in this 1 epoch with average cost 0.341544\n",
            "There are 67.074949 % left in this 1 epoch with average cost 0.495638\n",
            "There are 66.345873 % left in this 1 epoch with average cost 0.292614\n",
            "There are 65.616798 % left in this 1 epoch with average cost 0.559857\n",
            "There are 64.887722 % left in this 1 epoch with average cost 0.491637\n",
            "There are 64.158647 % left in this 1 epoch with average cost 0.769454\n",
            "There are 63.429571 % left in this 1 epoch with average cost 0.488764\n",
            "There are 62.700496 % left in this 1 epoch with average cost 0.602469\n",
            "There are 61.971420 % left in this 1 epoch with average cost 0.349243\n",
            "There are 61.242345 % left in this 1 epoch with average cost 0.489379\n",
            "There are 60.513269 % left in this 1 epoch with average cost 0.618049\n",
            "There are 59.784194 % left in this 1 epoch with average cost 0.464085\n",
            "There are 59.055118 % left in this 1 epoch with average cost 0.418444\n",
            "There are 58.326043 % left in this 1 epoch with average cost 0.346835\n",
            "There are 57.596967 % left in this 1 epoch with average cost 0.549706\n",
            "There are 56.867892 % left in this 1 epoch with average cost 0.564067\n",
            "There are 56.138816 % left in this 1 epoch with average cost 0.551032\n",
            "There are 55.409740 % left in this 1 epoch with average cost 0.411121\n",
            "There are 54.680665 % left in this 1 epoch with average cost 0.649417\n",
            "There are 53.951589 % left in this 1 epoch with average cost 0.509048\n",
            "There are 53.222514 % left in this 1 epoch with average cost 0.528170\n",
            "There are 52.493438 % left in this 1 epoch with average cost 0.476904\n",
            "There are 51.764363 % left in this 1 epoch with average cost 0.591944\n",
            "There are 51.035287 % left in this 1 epoch with average cost 0.504298\n",
            "There are 50.306212 % left in this 1 epoch with average cost 0.522907\n",
            "There are 49.577136 % left in this 1 epoch with average cost 0.687388\n",
            "There are 48.848061 % left in this 1 epoch with average cost 0.579726\n",
            "There are 48.118985 % left in this 1 epoch with average cost 0.466163\n",
            "There are 47.389910 % left in this 1 epoch with average cost 0.418202\n",
            "There are 46.660834 % left in this 1 epoch with average cost 0.337419\n",
            "There are 45.931759 % left in this 1 epoch with average cost 0.400456\n",
            "There are 45.202683 % left in this 1 epoch with average cost 0.330385\n",
            "There are 44.473607 % left in this 1 epoch with average cost 0.509582\n",
            "There are 43.744532 % left in this 1 epoch with average cost 0.475006\n",
            "There are 43.015456 % left in this 1 epoch with average cost 0.497811\n",
            "There are 42.286381 % left in this 1 epoch with average cost 0.406114\n",
            "There are 41.557305 % left in this 1 epoch with average cost 0.736516\n",
            "There are 40.828230 % left in this 1 epoch with average cost 0.899665\n",
            "There are 40.099154 % left in this 1 epoch with average cost 0.603835\n",
            "There are 39.370079 % left in this 1 epoch with average cost 0.742246\n",
            "There are 38.641003 % left in this 1 epoch with average cost 0.737517\n",
            "There are 37.911928 % left in this 1 epoch with average cost 0.973467\n",
            "There are 37.182852 % left in this 1 epoch with average cost 0.736722\n",
            "There are 36.453777 % left in this 1 epoch with average cost 0.950060\n",
            "There are 35.724701 % left in this 1 epoch with average cost 0.840846\n",
            "There are 34.995626 % left in this 1 epoch with average cost 0.792515\n",
            "There are 34.266550 % left in this 1 epoch with average cost 0.912986\n",
            "There are 33.537474 % left in this 1 epoch with average cost 0.583576\n",
            "There are 32.808399 % left in this 1 epoch with average cost 0.870855\n",
            "There are 32.079323 % left in this 1 epoch with average cost 0.866781\n",
            "There are 31.350248 % left in this 1 epoch with average cost 0.853834\n",
            "There are 30.621172 % left in this 1 epoch with average cost 0.818660\n",
            "There are 29.892097 % left in this 1 epoch with average cost 0.733043\n",
            "There are 29.163021 % left in this 1 epoch with average cost 0.814663\n",
            "There are 28.433946 % left in this 1 epoch with average cost 0.657754\n",
            "There are 27.704870 % left in this 1 epoch with average cost 0.644915\n",
            "There are 26.975795 % left in this 1 epoch with average cost 1.094552\n",
            "There are 26.246719 % left in this 1 epoch with average cost 0.967867\n",
            "There are 25.517644 % left in this 1 epoch with average cost 0.814492\n",
            "There are 24.788568 % left in this 1 epoch with average cost 0.874417\n",
            "There are 24.059493 % left in this 1 epoch with average cost 0.793276\n",
            "There are 23.330417 % left in this 1 epoch with average cost 0.997789\n",
            "There are 22.601341 % left in this 1 epoch with average cost 0.954530\n",
            "There are 21.872266 % left in this 1 epoch with average cost 0.879263\n",
            "There are 21.143190 % left in this 1 epoch with average cost 1.126592\n",
            "There are 20.414115 % left in this 1 epoch with average cost 0.903024\n",
            "There are 19.685039 % left in this 1 epoch with average cost 0.861482\n",
            "There are 18.955964 % left in this 1 epoch with average cost 1.075633\n",
            "There are 18.226888 % left in this 1 epoch with average cost 1.054320\n",
            "There are 17.497813 % left in this 1 epoch with average cost 0.979146\n",
            "There are 16.768737 % left in this 1 epoch with average cost 1.031131\n",
            "There are 16.039662 % left in this 1 epoch with average cost 1.102920\n",
            "There are 15.310586 % left in this 1 epoch with average cost 1.012519\n",
            "There are 14.581511 % left in this 1 epoch with average cost 0.843725\n",
            "{6, 18, 42, 55, 56, 59, 66, 76, 83, 86, 103, 122, 130, 145, 157, 162, 164, 168, 170, 190, 203, 206, 212, 213, 221, 225, 231, 242, 247, 254, 257, 271, 280, 290, 291, 292, 299, 309, 310, 314, 315, 316, 320, 321, 365, 378, 387, 390, 393, 400, 412, 421, 429, 430, 450, 451, 456, 503, 518, 519, 520, 525, 548, 551, 557, 562, 566, 567, 598, 609, 610, 612, 620, 627, 636, 643, 648, 652, 658, 690, 697, 700, 714, 718, 736, 738, 751, 753, 766, 783, 790, 803, 811, 813, 815, 819, 824, 837, 842, 846, 856, 859, 863, 880, 894, 914, 938, 958, 969, 973, 983, 994, 997, 1003, 1005, 1008, 1013, 1022, 1042, 1046, 1058, 1080, 1088, 1094, 1095, 1104, 1111, 1146, 1148, 1150, 1172, 1198, 1201, 1227, 1236, 1244, 1245, 1253, 1262, 1269, 1287, 1290, 1292, 1302, 1325, 1326, 1327, 1337, 1338, 1340, 1368, 1370, 1376, 1380, 1382, 1404, 1407, 1408, 1410, 1416, 1419, 1420, 1421, 1432, 1444, 1458, 1471, 1488, 1489, 1492, 1517, 1518, 1527, 1542, 1543, 1544, 1548, 1570, 1573, 1590, 1592, 1629, 1633, 1643, 1662, 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6287, 6296, 6298, 6299, 6313, 6321, 6324, 6325, 6336, 6338, 6340, 6353, 6364, 6367, 6368, 6372, 6389, 6392, 6397, 6405, 6408, 6411, 6417, 6425, 6426, 6437, 6446, 6447, 6452, 6454, 6455, 6461, 6472, 6475, 6476, 6477, 6479, 6482, 6488, 6489, 6502, 6510, 6517, 6531, 6558, 6563, 6564, 6566, 6573, 6578, 6580, 6581, 6591, 6601, 6607, 6608, 6609, 6620, 6628, 6629, 6630, 6631, 6639, 6640, 6652, 6663, 6664, 6666, 6672, 6675, 6687, 6690, 6692, 6693, 6694, 6698, 6700, 6705, 6707, 6710, 6721, 6722, 6723, 6736, 6743, 6756, 6757, 6758, 6762, 6784, 6785, 6789, 6800, 6801, 6802, 6808, 6809, 6814, 6815, 6816, 6825, 6834, 6843, 6849} [181] [115, 495, 120] [50, 69, 0, 11, 42, 0, 33] [16, 59, 3, 141]\n",
            "{6, 18, 42, 55, 56, 59, 66, 76, 83, 86, 103, 122, 130, 145, 157, 162, 164, 168, 170, 190, 203, 206, 212, 213, 221, 225, 231, 242, 247, 254, 257, 271, 280, 290, 291, 292, 299, 309, 310, 314, 315, 316, 320, 321, 365, 378, 387, 390, 393, 400, 412, 421, 429, 430, 450, 451, 456, 503, 518, 519, 520, 525, 548, 551, 557, 562, 566, 567, 598, 609, 610, 612, 620, 627, 636, 643, 648, 652, 658, 690, 697, 700, 714, 718, 736, 738, 751, 753, 766, 783, 790, 803, 811, 813, 815, 819, 824, 837, 842, 846, 856, 859, 863, 880, 894, 914, 938, 958, 969, 973, 983, 994, 997, 1003, 1005, 1008, 1013, 1022, 1042, 1046, 1058, 1080, 1088, 1094, 1095, 1104, 1111, 1146, 1148, 1150, 1172, 1198, 1201, 1227, 1236, 1244, 1245, 1253, 1262, 1269, 1287, 1290, 1292, 1302, 1325, 1326, 1327, 1337, 1338, 1340, 1368, 1370, 1376, 1380, 1382, 1404, 1407, 1408, 1410, 1416, 1419, 1420, 1421, 1432, 1444, 1458, 1471, 1488, 1489, 1492, 1517, 1518, 1527, 1542, 1543, 1544, 1548, 1570, 1573, 1590, 1592, 1629, 1633, 1643, 1662, 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6298, 6299, 6313, 6321, 6324, 6325, 6336, 6338, 6340, 6353, 6364, 6367, 6368, 6372, 6389, 6392, 6397, 6405, 6408, 6411, 6417, 6425, 6426, 6437, 6446, 6447, 6452, 6454, 6455, 6461, 6472, 6475, 6476, 6477, 6479, 6482, 6488, 6489, 6502, 6510, 6517, 6531, 6558, 6563, 6564, 6566, 6573, 6578, 6580, 6581, 6591, 6601, 6607, 6608, 6609, 6620, 6628, 6629, 6630, 6631, 6639, 6640, 6652, 6663, 6664, 6666, 6672, 6675, 6687, 6690, 6692, 6693, 6694, 6698, 6700, 6705, 6707, 6710, 6721, 6722, 6723, 6736, 6743, 6756, 6757, 6758, 6762, 6784, 6785, 6789, 6800, 6801, 6802, 6808, 6809, 6814, 6815, 6816, 6825, 6834, 6843, 6849} [705, 68] [434, 583] [51, 48, 0] [0, 445, 9, 40]\n",
            "{6, 18, 42, 55, 56, 59, 66, 76, 83, 86, 103, 122, 130, 145, 157, 162, 164, 168, 170, 190, 203, 206, 212, 213, 221, 225, 231, 242, 247, 254, 257, 271, 280, 290, 291, 292, 299, 309, 310, 314, 315, 316, 320, 321, 365, 378, 387, 390, 393, 400, 412, 421, 429, 430, 450, 451, 456, 503, 518, 519, 520, 525, 548, 551, 557, 562, 566, 567, 598, 609, 610, 612, 620, 627, 636, 643, 648, 652, 658, 690, 697, 700, 714, 718, 736, 738, 751, 753, 766, 783, 790, 803, 811, 813, 815, 819, 824, 837, 842, 846, 856, 859, 863, 880, 894, 914, 938, 958, 969, 973, 983, 994, 997, 1003, 1005, 1008, 1013, 1022, 1042, 1046, 1058, 1080, 1088, 1094, 1095, 1104, 1111, 1146, 1148, 1150, 1172, 1198, 1201, 1227, 1236, 1244, 1245, 1253, 1262, 1269, 1287, 1290, 1292, 1302, 1325, 1326, 1327, 1337, 1338, 1340, 1368, 1370, 1376, 1380, 1382, 1404, 1407, 1408, 1410, 1416, 1419, 1420, 1421, 1432, 1444, 1458, 1471, 1488, 1489, 1492, 1517, 1518, 1527, 1542, 1543, 1544, 1548, 1570, 1573, 1590, 1592, 1629, 1633, 1643, 1662, 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4263, 4268, 4275, 4288, 4293, 4310, 4311, 4313, 4339, 4340, 4341, 4342, 4344, 4350, 4355, 4368, 4374, 4393, 4397, 4403, 4410, 4416, 4430, 4439, 4441, 4454, 4455, 4461, 4462, 4464, 4479, 4486, 4496, 4502, 4505, 4520, 4521, 4523, 4544, 4559, 4563, 4574, 4577, 4580, 4589, 4598, 4604, 4605, 4606, 4625, 4633, 4642, 4647, 4648, 4655, 4656, 4661, 4663, 4667, 4669, 4683, 4687, 4688, 4689, 4690, 4692, 4693, 4696, 4698, 4699, 4706, 4708, 4709, 4713, 4719, 4737, 4754, 4759, 4765, 4775, 4777, 4780, 4782, 4788, 4789, 4794, 4813, 4817, 4824, 4826, 4831, 4835, 4844, 4846, 4850, 4852, 4871, 4879, 4893, 4900, 4904, 4908, 4910, 4912, 4922, 4937, 4939, 4949, 4956, 4977, 4982, 4994, 4998, 4999, 5022, 5023, 5046, 5048, 5049, 5054, 5055, 5066, 5067, 5070, 5075, 5083, 5091, 5092, 5094, 5099, 5104, 5109, 5112, 5120, 5128, 5129, 5134, 5138, 5143, 5154, 5156, 5157, 5181, 5183, 5185, 5186, 5193, 5195, 5197, 5200, 5211, 5216, 5231, 5233, 5234, 5240, 5249, 5262, 5263, 5264, 5274, 5279, 5282, 5286, 5287, 5295, 5300, 5302, 5308, 5310, 5311, 5314, 5332, 5337, 5342, 5346, 5349, 5355, 5357, 5363, 5370, 5375, 5377, 5380, 5384, 5395, 5401, 5404, 5406, 5411, 5426, 5428, 5432, 5440, 5452, 5475, 5476, 5477, 5478, 5480, 5482, 5485, 5488, 5503, 5523, 5526, 5527, 5528, 5532, 5540, 5541, 5544, 5547, 5548, 5559, 5579, 5580, 5581, 5584, 5603, 5606, 5607, 5609, 5612, 5614, 5619, 5624, 5634, 5644, 5652, 5670, 5678, 5679, 5689, 5691, 5697, 5699, 5705, 5713, 5716, 5717, 5719, 5729, 5733, 5742, 5751, 5753, 5761, 5763, 5786, 5787, 5788, 5797, 5798, 5805, 5806, 5807, 5812, 5822, 5823, 5825, 5826, 5827, 5830, 5837, 5843, 5844, 5872, 5876, 5881, 5900, 5904, 5914, 5917, 5920, 5921, 5922, 5924, 5934, 5935, 5936, 5937, 5938, 5939, 5954, 5961, 5964, 5973, 5992, 5994, 5995, 5996, 6001, 6003, 6004, 6017, 6018, 6022, 6029, 6030, 6038, 6055, 6063, 6068, 6071, 6073, 6100, 6102, 6110, 6116, 6122, 6125, 6129, 6139, 6143, 6161, 6162, 6164, 6171, 6211, 6222, 6223, 6227, 6231, 6253, 6256, 6260, 6269, 6279, 6283, 6287, 6296, 6298, 6299, 6313, 6321, 6324, 6325, 6336, 6338, 6340, 6353, 6364, 6367, 6368, 6372, 6389, 6392, 6397, 6405, 6408, 6411, 6417, 6425, 6426, 6437, 6446, 6447, 6452, 6454, 6455, 6461, 6472, 6475, 6476, 6477, 6479, 6482, 6488, 6489, 6502, 6510, 6517, 6531, 6558, 6563, 6564, 6566, 6573, 6578, 6580, 6581, 6591, 6601, 6607, 6608, 6609, 6620, 6628, 6629, 6630, 6631, 6639, 6640, 6652, 6663, 6664, 6666, 6672, 6675, 6687, 6690, 6692, 6693, 6694, 6698, 6700, 6705, 6707, 6710, 6721, 6722, 6723, 6736, 6743, 6756, 6757, 6758, 6762, 6784, 6785, 6789, 6800, 6801, 6802, 6808, 6809, 6814, 6815, 6816, 6825, 6834, 6843, 6849} [133] [718, 207] [32, 54, 4, 489] [0, 100]\n",
            "Begin new epoch: 2\n",
            "WRONG faking object 1 [21]\n",
            "WRONG faking object 1 [21]\n",
            "WRONG faking object 1 [0]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 2 epoch with average cost 1.007180\n",
            "There are 99.154272 % left in this 2 epoch with average cost 0.657729\n",
            "There are 98.425197 % left in this 2 epoch with average cost 0.618767\n",
            "There are 97.696121 % left in this 2 epoch with average cost 0.490352\n",
            "There are 96.967046 % left in this 2 epoch with average cost 0.496291\n",
            "There are 96.237970 % left in this 2 epoch with average cost 0.321370\n",
            "There are 95.508895 % left in this 2 epoch with average cost 0.655278\n",
            "There are 94.779819 % left in this 2 epoch with average cost 0.566182\n",
            "There are 94.050744 % left in this 2 epoch with average cost 0.613162\n",
            "There are 93.321668 % left in this 2 epoch with average cost 0.442478\n",
            "There are 92.592593 % left in this 2 epoch with average cost 0.595715\n",
            "There are 91.863517 % left in this 2 epoch with average cost 0.346007\n",
            "There are 91.134442 % left in this 2 epoch with average cost 0.365680\n",
            "There are 90.405366 % left in this 2 epoch with average cost 0.430638\n",
            "There are 89.676290 % left in this 2 epoch with average cost 0.529949\n",
            "There are 88.947215 % left in this 2 epoch with average cost 0.342429\n",
            "There are 88.218139 % left in this 2 epoch with average cost 0.536055\n",
            "There are 87.489064 % left in this 2 epoch with average cost 0.385679\n",
            "There are 86.759988 % left in this 2 epoch with average cost 0.375557\n",
            "There are 86.030913 % left in this 2 epoch with average cost 0.435461\n",
            "There are 85.301837 % left in this 2 epoch with average cost 0.327983\n",
            "There are 84.572762 % left in this 2 epoch with average cost 0.314940\n",
            "There are 83.843686 % left in this 2 epoch with average cost 0.494396\n",
            "There are 83.114611 % left in this 2 epoch with average cost 0.407369\n",
            "There are 82.385535 % left in this 2 epoch with average cost 0.528217\n",
            "There are 81.656460 % left in this 2 epoch with average cost 0.402740\n",
            "There are 80.927384 % left in this 2 epoch with average cost 0.607751\n",
            "There are 80.198309 % left in this 2 epoch with average cost 0.477278\n",
            "There are 79.469233 % left in this 2 epoch with average cost 0.501526\n",
            "There are 78.740157 % left in this 2 epoch with average cost 0.420491\n",
            "There are 78.011082 % left in this 2 epoch with average cost 0.425086\n",
            "There are 77.282006 % left in this 2 epoch with average cost 0.208554\n",
            "There are 76.552931 % left in this 2 epoch with average cost 0.428753\n",
            "There are 75.823855 % left in this 2 epoch with average cost 0.457540\n",
            "There are 75.094780 % left in this 2 epoch with average cost 0.722928\n",
            "There are 74.365704 % left in this 2 epoch with average cost 0.354230\n",
            "There are 73.636629 % left in this 2 epoch with average cost 0.472541\n",
            "There are 72.907553 % left in this 2 epoch with average cost 0.434782\n",
            "There are 72.178478 % left in this 2 epoch with average cost 0.457320\n",
            "There are 71.449402 % left in this 2 epoch with average cost 0.456084\n",
            "There are 70.720327 % left in this 2 epoch with average cost 0.258865\n",
            "There are 69.991251 % left in this 2 epoch with average cost 0.473344\n",
            "There are 69.262176 % left in this 2 epoch with average cost 0.552370\n",
            "There are 68.533100 % left in this 2 epoch with average cost 0.462600\n",
            "There are 67.804024 % left in this 2 epoch with average cost 0.634083\n",
            "There are 67.074949 % left in this 2 epoch with average cost 0.449903\n",
            "There are 66.345873 % left in this 2 epoch with average cost 0.459041\n",
            "There are 65.616798 % left in this 2 epoch with average cost 0.488410\n",
            "There are 64.887722 % left in this 2 epoch with average cost 0.436091\n",
            "There are 64.158647 % left in this 2 epoch with average cost 0.271666\n",
            "There are 63.429571 % left in this 2 epoch with average cost 0.425072\n",
            "There are 62.700496 % left in this 2 epoch with average cost 0.733086\n",
            "There are 61.971420 % left in this 2 epoch with average cost 0.644345\n",
            "There are 61.242345 % left in this 2 epoch with average cost 0.418187\n",
            "There are 60.513269 % left in this 2 epoch with average cost 0.388197\n",
            "There are 59.784194 % left in this 2 epoch with average cost 0.492079\n",
            "There are 59.055118 % left in this 2 epoch with average cost 0.460868\n",
            "There are 58.326043 % left in this 2 epoch with average cost 0.617086\n",
            "There are 57.596967 % left in this 2 epoch with average cost 0.544787\n",
            "There are 56.867892 % left in this 2 epoch with average cost 0.403601\n",
            "There are 56.138816 % left in this 2 epoch with average cost 0.556195\n",
            "There are 55.409740 % left in this 2 epoch with average cost 0.572849\n",
            "There are 54.680665 % left in this 2 epoch with average cost 0.512210\n",
            "There are 53.951589 % left in this 2 epoch with average cost 0.466861\n",
            "There are 53.222514 % left in this 2 epoch with average cost 0.312745\n",
            "There are 52.493438 % left in this 2 epoch with average cost 0.482515\n",
            "There are 51.764363 % left in this 2 epoch with average cost 0.574256\n",
            "There are 51.035287 % left in this 2 epoch with average cost 0.507559\n",
            "There are 50.306212 % left in this 2 epoch with average cost 0.563592\n",
            "There are 49.577136 % left in this 2 epoch with average cost 0.344967\n",
            "There are 48.848061 % left in this 2 epoch with average cost 0.469011\n",
            "There are 48.118985 % left in this 2 epoch with average cost 0.260727\n",
            "There are 47.389910 % left in this 2 epoch with average cost 0.463039\n",
            "There are 46.660834 % left in this 2 epoch with average cost 0.633785\n",
            "There are 45.931759 % left in this 2 epoch with average cost 0.415060\n",
            "There are 45.202683 % left in this 2 epoch with average cost 0.347850\n",
            "There are 44.473607 % left in this 2 epoch with average cost 0.728513\n",
            "There are 43.744532 % left in this 2 epoch with average cost 0.285605\n",
            "There are 43.015456 % left in this 2 epoch with average cost 0.551465\n",
            "There are 42.286381 % left in this 2 epoch with average cost 0.494370\n",
            "There are 41.557305 % left in this 2 epoch with average cost 0.411242\n",
            "There are 40.828230 % left in this 2 epoch with average cost 0.339451\n",
            "There are 40.099154 % left in this 2 epoch with average cost 0.479895\n",
            "There are 39.370079 % left in this 2 epoch with average cost 0.628874\n",
            "There are 38.641003 % left in this 2 epoch with average cost 0.909626\n",
            "There are 37.911928 % left in this 2 epoch with average cost 0.817618\n",
            "There are 37.182852 % left in this 2 epoch with average cost 0.977550\n",
            "There are 36.453777 % left in this 2 epoch with average cost 0.759079\n",
            "There are 35.724701 % left in this 2 epoch with average cost 0.866938\n",
            "There are 34.995626 % left in this 2 epoch with average cost 0.621022\n",
            "There are 34.266550 % left in this 2 epoch with average cost 0.704841\n",
            "There are 33.537474 % left in this 2 epoch with average cost 0.882819\n",
            "There are 32.808399 % left in this 2 epoch with average cost 0.791063\n",
            "There are 32.079323 % left in this 2 epoch with average cost 0.827936\n",
            "There are 31.350248 % left in this 2 epoch with average cost 0.977074\n",
            "There are 30.621172 % left in this 2 epoch with average cost 0.919379\n",
            "There are 29.892097 % left in this 2 epoch with average cost 0.855102\n",
            "There are 29.163021 % left in this 2 epoch with average cost 1.195381\n",
            "There are 28.433946 % left in this 2 epoch with average cost 0.887596\n",
            "There are 27.704870 % left in this 2 epoch with average cost 1.029636\n",
            "There are 26.975795 % left in this 2 epoch with average cost 1.057146\n",
            "There are 26.246719 % left in this 2 epoch with average cost 0.846908\n",
            "There are 25.517644 % left in this 2 epoch with average cost 1.112858\n",
            "There are 24.788568 % left in this 2 epoch with average cost 0.712355\n",
            "There are 24.059493 % left in this 2 epoch with average cost 1.021566\n",
            "There are 23.330417 % left in this 2 epoch with average cost 1.190872\n",
            "There are 22.601341 % left in this 2 epoch with average cost 1.087391\n",
            "There are 21.872266 % left in this 2 epoch with average cost 0.944279\n",
            "There are 21.143190 % left in this 2 epoch with average cost 1.041743\n",
            "There are 20.414115 % left in this 2 epoch with average cost 0.809559\n",
            "There are 19.685039 % left in this 2 epoch with average cost 0.966403\n",
            "There are 18.955964 % left in this 2 epoch with average cost 0.925746\n",
            "There are 18.226888 % left in this 2 epoch with average cost 1.055396\n",
            "There are 17.497813 % left in this 2 epoch with average cost 0.957415\n",
            "There are 16.768737 % left in this 2 epoch with average cost 0.861392\n",
            "There are 16.039662 % left in this 2 epoch with average cost 0.814749\n",
            "{0, 6, 19, 28, 43, 52, 63, 64, 68, 87, 100, 103, 104, 110, 113, 116, 127, 138, 139, 162, 163, 164, 168, 171, 185, 196, 197, 203, 216, 230, 233, 236, 246, 270, 280, 292, 302, 310, 317, 321, 330, 334, 352, 358, 364, 369, 392, 410, 416, 421, 428, 430, 447, 450, 451, 468, 476, 480, 486, 491, 496, 508, 519, 545, 551, 556, 567, 580, 584, 588, 602, 605, 609, 612, 620, 622, 627, 629, 639, 643, 648, 652, 655, 669, 670, 673, 694, 696, 698, 701, 704, 709, 716, 720, 730, 735, 769, 781, 794, 795, 822, 834, 837, 849, 864, 877, 910, 921, 923, 924, 936, 950, 973, 975, 978, 982, 983, 986, 991, 992, 995, 997, 1000, 1006, 1010, 1022, 1077, 1091, 1105, 1111, 1112, 1120, 1131, 1133, 1146, 1163, 1170, 1172, 1174, 1175, 1183, 1191, 1198, 1205, 1213, 1219, 1240, 1250, 1253, 1262, 1263, 1268, 1276, 1298, 1302, 1306, 1311, 1315, 1318, 1329, 1330, 1335, 1337, 1340, 1348, 1350, 1361, 1370, 1399, 1405, 1421, 1437, 1478, 1487, 1490, 1491, 1504, 1508, 1525, 1543, 1548, 1552, 1553, 1556, 1561, 1565, 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2853, 2862, 2873, 2889, 2892, 2893, 2896, 2907, 2908, 2910, 2912, 2914, 2917, 2918, 2933, 2941, 2954, 2976, 2990, 2998, 3013, 3016, 3021, 3049, 3059, 3060, 3065, 3070, 3072, 3076, 3077, 3078, 3079, 3084, 3095, 3098, 3099, 3102, 3105, 3108, 3116, 3119, 3125, 3129, 3130, 3149, 3152, 3166, 3168, 3173, 3182, 3184, 3199, 3203, 3214, 3222, 3236, 3242, 3254, 3261, 3267, 3268, 3269, 3281, 3284, 3287, 3289, 3291, 3302, 3303, 3305, 3314, 3316, 3320, 3324, 3329, 3333, 3340, 3345, 3347, 3349, 3350, 3352, 3354, 3360, 3364, 3366, 3367, 3368, 3370, 3375, 3378, 3397, 3418, 3433, 3438, 3448, 3459, 3465, 3472, 3483, 3496, 3501, 3508, 3509, 3513, 3523, 3528, 3529, 3531, 3536, 3537, 3553, 3558, 3567, 3573, 3581, 3582, 3587, 3616, 3622, 3630, 3631, 3632, 3635, 3653, 3656, 3657, 3671, 3672, 3675, 3680, 3681, 3685, 3692, 3693, 3701, 3705, 3706, 3710, 3711, 3732, 3734, 3741, 3745, 3752, 3756, 3762, 3765, 3766, 3767, 3768, 3769, 3770, 3771, 3775, 3788, 3793, 3810, 3814, 3822, 3823, 3824, 3825, 3843, 3848, 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5744, 5745, 5747, 5748, 5757, 5762, 5766, 5780, 5785, 5793, 5798, 5801, 5802, 5805, 5806, 5810, 5822, 5826, 5827, 5828, 5829, 5834, 5835, 5846, 5855, 5867, 5872, 5875, 5889, 5890, 5900, 5906, 5908, 5909, 5911, 5922, 5928, 5933, 5934, 5935, 5937, 5943, 5947, 5973, 5975, 5982, 5983, 5985, 5991, 5996, 5997, 6000, 6001, 6003, 6004, 6006, 6020, 6029, 6050, 6063, 6068, 6070, 6073, 6074, 6081, 6085, 6095, 6096, 6104, 6107, 6109, 6114, 6122, 6123, 6129, 6140, 6150, 6154, 6155, 6161, 6162, 6164, 6169, 6178, 6185, 6194, 6202, 6207, 6211, 6212, 6217, 6219, 6226, 6227, 6231, 6238, 6247, 6251, 6252, 6253, 6257, 6262, 6268, 6271, 6272, 6283, 6290, 6298, 6318, 6319, 6321, 6325, 6336, 6354, 6363, 6372, 6389, 6426, 6432, 6433, 6434, 6446, 6453, 6455, 6456, 6472, 6476, 6479, 6480, 6484, 6489, 6492, 6496, 6498, 6500, 6503, 6513, 6539, 6540, 6542, 6551, 6552, 6558, 6564, 6582, 6587, 6592, 6607, 6608, 6610, 6611, 6613, 6614, 6615, 6616, 6617, 6621, 6627, 6629, 6631, 6641, 6642, 6646, 6648, 6655, 6666, 6667, 6669, 6687, 6688, 6692, 6693, 6694, 6698, 6721, 6722, 6724, 6732, 6736, 6749, 6752, 6757, 6758, 6759, 6763, 6765, 6770, 6774, 6775, 6776, 6781, 6784, 6792, 6793, 6800, 6814, 6821, 6837, 6847, 6856, 6857} [392, 53] [125, 458] [50, 246, 33, 4] [0, 45, 483]\n",
            "{0, 6, 19, 28, 43, 52, 63, 64, 68, 87, 100, 103, 104, 110, 113, 116, 127, 138, 139, 162, 163, 164, 168, 171, 185, 196, 197, 203, 216, 230, 233, 236, 246, 270, 280, 292, 302, 310, 317, 321, 330, 334, 352, 358, 364, 369, 392, 410, 416, 421, 428, 430, 447, 450, 451, 468, 476, 480, 486, 491, 496, 508, 519, 545, 551, 556, 567, 580, 584, 588, 602, 605, 609, 612, 620, 622, 627, 629, 639, 643, 648, 652, 655, 669, 670, 673, 694, 696, 698, 701, 704, 709, 716, 720, 730, 735, 769, 781, 794, 795, 822, 834, 837, 849, 864, 877, 910, 921, 923, 924, 936, 950, 973, 975, 978, 982, 983, 986, 991, 992, 995, 997, 1000, 1006, 1010, 1022, 1077, 1091, 1105, 1111, 1112, 1120, 1131, 1133, 1146, 1163, 1170, 1172, 1174, 1175, 1183, 1191, 1198, 1205, 1213, 1219, 1240, 1250, 1253, 1262, 1263, 1268, 1276, 1298, 1302, 1306, 1311, 1315, 1318, 1329, 1330, 1335, 1337, 1340, 1348, 1350, 1361, 1370, 1399, 1405, 1421, 1437, 1478, 1487, 1490, 1491, 1504, 1508, 1525, 1543, 1548, 1552, 1553, 1556, 1561, 1565, 1573, 1580, 1582, 1592, 1614, 1619, 1622, 1623, 1655, 1662, 1663, 1675, 1709, 1711, 1727, 1738, 1741, 1753, 1755, 1771, 1773, 1776, 1793, 1798, 1812, 1831, 1846, 1863, 1875, 1879, 1880, 1900, 1904, 1929, 1930, 1934, 1937, 1940, 1944, 1945, 1950, 1966, 1974, 1976, 1980, 1987, 1988, 1989, 2000, 2002, 2003, 2004, 2015, 2028, 2032, 2039, 2061, 2063, 2078, 2081, 2085, 2087, 2094, 2106, 2115, 2119, 2126, 2134, 2169, 2173, 2197, 2207, 2209, 2214, 2229, 2232, 2236, 2239, 2243, 2246, 2257, 2262, 2264, 2267, 2268, 2270, 2274, 2279, 2288, 2293, 2294, 2309, 2325, 2338, 2346, 2347, 2348, 2354, 2357, 2373, 2385, 2389, 2390, 2393, 2398, 2408, 2410, 2424, 2427, 2432, 2454, 2464, 2471, 2473, 2475, 2484, 2491, 2493, 2505, 2517, 2549, 2551, 2553, 2575, 2584, 2586, 2590, 2597, 2604, 2605, 2616, 2622, 2623, 2631, 2640, 2664, 2679, 2686, 2693, 2700, 2701, 2704, 2710, 2716, 2721, 2731, 2734, 2746, 2749, 2755, 2769, 2772, 2779, 2780, 2787, 2795, 2798, 2803, 2808, 2815, 2821, 2829, 2830, 2831, 2842, 2849, 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6667, 6669, 6687, 6688, 6692, 6693, 6694, 6698, 6721, 6722, 6724, 6732, 6736, 6749, 6752, 6757, 6758, 6759, 6763, 6765, 6770, 6774, 6775, 6776, 6781, 6784, 6792, 6793, 6800, 6814, 6821, 6837, 6847, 6856, 6857} [392, 53] [619, 120] [50] [246, 33]\n",
            "There are 15.310586 % left in this 2 epoch with average cost 0.967481\n",
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            "{0, 6, 19, 28, 43, 52, 63, 64, 68, 87, 100, 103, 104, 110, 113, 116, 127, 138, 139, 162, 163, 164, 168, 171, 185, 196, 197, 203, 216, 230, 233, 236, 246, 270, 280, 292, 302, 310, 317, 321, 330, 334, 352, 358, 364, 369, 392, 410, 416, 421, 428, 430, 447, 450, 451, 468, 476, 480, 486, 491, 496, 508, 519, 545, 551, 556, 567, 580, 584, 588, 602, 605, 609, 612, 620, 622, 627, 629, 639, 643, 648, 652, 655, 669, 670, 673, 694, 696, 698, 701, 704, 709, 716, 720, 730, 735, 769, 781, 794, 795, 822, 834, 837, 849, 864, 877, 910, 921, 923, 924, 936, 950, 973, 975, 978, 982, 983, 986, 991, 992, 995, 997, 1000, 1006, 1010, 1022, 1077, 1091, 1105, 1111, 1112, 1120, 1131, 1133, 1146, 1163, 1170, 1172, 1174, 1175, 1183, 1191, 1198, 1205, 1213, 1219, 1240, 1250, 1253, 1262, 1263, 1268, 1276, 1298, 1302, 1306, 1311, 1315, 1318, 1329, 1330, 1335, 1337, 1340, 1348, 1350, 1361, 1370, 1399, 1405, 1421, 1437, 1478, 1487, 1490, 1491, 1504, 1508, 1525, 1543, 1548, 1552, 1553, 1556, 1561, 1565, 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6669, 6687, 6688, 6692, 6693, 6694, 6698, 6721, 6722, 6724, 6732, 6736, 6749, 6752, 6757, 6758, 6759, 6763, 6765, 6770, 6774, 6775, 6776, 6781, 6784, 6792, 6793, 6800, 6814, 6821, 6837, 6847, 6856, 6857} [401] [196] [54] [248, 0, 0]\n",
            "Begin new epoch: 3\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [2]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 3 epoch with average cost 1.199594\n",
            "There are 99.154272 % left in this 3 epoch with average cost 0.770985\n",
            "There are 98.425197 % left in this 3 epoch with average cost 0.390388\n",
            "There are 97.696121 % left in this 3 epoch with average cost 0.576663\n",
            "There are 96.967046 % left in this 3 epoch with average cost 0.727449\n",
            "There are 96.237970 % left in this 3 epoch with average cost 0.623076\n",
            "There are 95.508895 % left in this 3 epoch with average cost 0.540166\n",
            "There are 94.779819 % left in this 3 epoch with average cost 0.423651\n",
            "There are 94.050744 % left in this 3 epoch with average cost 0.408203\n",
            "There are 93.321668 % left in this 3 epoch with average cost 0.387298\n",
            "There are 92.592593 % left in this 3 epoch with average cost 0.443892\n",
            "There are 91.863517 % left in this 3 epoch with average cost 0.527567\n",
            "There are 91.134442 % left in this 3 epoch with average cost 0.334930\n",
            "There are 90.405366 % left in this 3 epoch with average cost 0.638114\n",
            "There are 89.676290 % left in this 3 epoch with average cost 0.440486\n",
            "There are 88.947215 % left in this 3 epoch with average cost 0.668632\n",
            "There are 88.218139 % left in this 3 epoch with average cost 0.612533\n",
            "There are 87.489064 % left in this 3 epoch with average cost 0.537214\n",
            "There are 86.759988 % left in this 3 epoch with average cost 0.459128\n",
            "There are 86.030913 % left in this 3 epoch with average cost 0.634134\n",
            "There are 85.301837 % left in this 3 epoch with average cost 0.477594\n",
            "There are 84.572762 % left in this 3 epoch with average cost 0.687812\n",
            "There are 83.843686 % left in this 3 epoch with average cost 0.589532\n",
            "There are 83.114611 % left in this 3 epoch with average cost 0.598799\n",
            "There are 82.385535 % left in this 3 epoch with average cost 0.583870\n",
            "There are 81.656460 % left in this 3 epoch with average cost 0.621280\n",
            "There are 80.927384 % left in this 3 epoch with average cost 0.530541\n",
            "There are 80.198309 % left in this 3 epoch with average cost 0.428234\n",
            "There are 79.469233 % left in this 3 epoch with average cost 0.532595\n",
            "There are 78.740157 % left in this 3 epoch with average cost 0.494936\n",
            "There are 78.011082 % left in this 3 epoch with average cost 0.440656\n",
            "There are 77.282006 % left in this 3 epoch with average cost 0.541586\n",
            "There are 76.552931 % left in this 3 epoch with average cost 0.507657\n",
            "There are 75.823855 % left in this 3 epoch with average cost 0.323567\n",
            "There are 75.094780 % left in this 3 epoch with average cost 0.608736\n",
            "There are 74.365704 % left in this 3 epoch with average cost 0.544049\n",
            "There are 73.636629 % left in this 3 epoch with average cost 0.392501\n",
            "There are 72.907553 % left in this 3 epoch with average cost 0.570445\n",
            "There are 72.178478 % left in this 3 epoch with average cost 0.553424\n",
            "There are 71.449402 % left in this 3 epoch with average cost 0.712597\n",
            "There are 70.720327 % left in this 3 epoch with average cost 0.623818\n",
            "There are 69.991251 % left in this 3 epoch with average cost 0.569298\n",
            "There are 69.262176 % left in this 3 epoch with average cost 0.546585\n",
            "There are 68.533100 % left in this 3 epoch with average cost 0.396669\n",
            "There are 67.804024 % left in this 3 epoch with average cost 0.562491\n",
            "There are 67.074949 % left in this 3 epoch with average cost 0.493884\n",
            "There are 66.345873 % left in this 3 epoch with average cost 0.491288\n",
            "There are 65.616798 % left in this 3 epoch with average cost 0.599577\n",
            "There are 64.887722 % left in this 3 epoch with average cost 0.614484\n",
            "There are 64.158647 % left in this 3 epoch with average cost 0.331478\n",
            "There are 63.429571 % left in this 3 epoch with average cost 0.371979\n",
            "There are 62.700496 % left in this 3 epoch with average cost 0.647367\n",
            "There are 61.971420 % left in this 3 epoch with average cost 0.539897\n",
            "There are 61.242345 % left in this 3 epoch with average cost 0.514251\n",
            "There are 60.513269 % left in this 3 epoch with average cost 0.510548\n",
            "There are 59.784194 % left in this 3 epoch with average cost 0.469396\n",
            "There are 59.055118 % left in this 3 epoch with average cost 0.429586\n",
            "There are 58.326043 % left in this 3 epoch with average cost 0.466840\n",
            "There are 57.596967 % left in this 3 epoch with average cost 0.599871\n",
            "There are 56.867892 % left in this 3 epoch with average cost 0.520430\n",
            "There are 56.138816 % left in this 3 epoch with average cost 0.440435\n",
            "There are 55.409740 % left in this 3 epoch with average cost 0.633049\n",
            "There are 54.680665 % left in this 3 epoch with average cost 0.496597\n",
            "There are 53.951589 % left in this 3 epoch with average cost 0.588164\n",
            "There are 53.222514 % left in this 3 epoch with average cost 0.382336\n",
            "There are 52.493438 % left in this 3 epoch with average cost 0.580500\n",
            "There are 51.764363 % left in this 3 epoch with average cost 0.410746\n",
            "There are 51.035287 % left in this 3 epoch with average cost 0.351603\n",
            "There are 50.306212 % left in this 3 epoch with average cost 0.179289\n",
            "There are 49.577136 % left in this 3 epoch with average cost 0.563806\n",
            "There are 48.848061 % left in this 3 epoch with average cost 0.579486\n",
            "There are 48.118985 % left in this 3 epoch with average cost 0.554330\n",
            "There are 47.389910 % left in this 3 epoch with average cost 0.509916\n",
            "There are 46.660834 % left in this 3 epoch with average cost 0.407862\n",
            "There are 45.931759 % left in this 3 epoch with average cost 0.380359\n",
            "There are 45.202683 % left in this 3 epoch with average cost 0.513901\n",
            "There are 44.473607 % left in this 3 epoch with average cost 0.489537\n",
            "There are 43.744532 % left in this 3 epoch with average cost 0.438102\n",
            "There are 43.015456 % left in this 3 epoch with average cost 0.741017\n",
            "There are 42.286381 % left in this 3 epoch with average cost 0.643984\n",
            "There are 41.557305 % left in this 3 epoch with average cost 0.728637\n",
            "There are 40.828230 % left in this 3 epoch with average cost 0.887586\n",
            "There are 40.099154 % left in this 3 epoch with average cost 0.801172\n",
            "There are 39.370079 % left in this 3 epoch with average cost 0.800703\n",
            "There are 38.641003 % left in this 3 epoch with average cost 0.989078\n",
            "There are 37.911928 % left in this 3 epoch with average cost 0.835854\n",
            "There are 37.182852 % left in this 3 epoch with average cost 0.853058\n",
            "There are 36.453777 % left in this 3 epoch with average cost 0.664122\n",
            "There are 35.724701 % left in this 3 epoch with average cost 0.743694\n",
            "There are 34.995626 % left in this 3 epoch with average cost 1.083104\n",
            "There are 34.266550 % left in this 3 epoch with average cost 0.712377\n",
            "There are 33.537474 % left in this 3 epoch with average cost 1.206488\n",
            "There are 32.808399 % left in this 3 epoch with average cost 0.812918\n",
            "There are 32.079323 % left in this 3 epoch with average cost 0.891710\n",
            "There are 31.350248 % left in this 3 epoch with average cost 0.989310\n",
            "There are 30.621172 % left in this 3 epoch with average cost 0.934187\n",
            "There are 29.892097 % left in this 3 epoch with average cost 0.950807\n",
            "There are 29.163021 % left in this 3 epoch with average cost 1.018048\n",
            "There are 28.433946 % left in this 3 epoch with average cost 1.031308\n",
            "There are 27.704870 % left in this 3 epoch with average cost 1.062259\n",
            "There are 26.975795 % left in this 3 epoch with average cost 0.984372\n",
            "There are 26.246719 % left in this 3 epoch with average cost 0.911258\n",
            "There are 25.517644 % left in this 3 epoch with average cost 1.008045\n",
            "There are 24.788568 % left in this 3 epoch with average cost 1.069304\n",
            "There are 24.059493 % left in this 3 epoch with average cost 0.874236\n",
            "There are 23.330417 % left in this 3 epoch with average cost 1.242305\n",
            "There are 22.601341 % left in this 3 epoch with average cost 0.681027\n",
            "There are 21.872266 % left in this 3 epoch with average cost 0.975613\n",
            "There are 21.143190 % left in this 3 epoch with average cost 1.188194\n",
            "There are 20.414115 % left in this 3 epoch with average cost 1.014003\n",
            "There are 19.685039 % left in this 3 epoch with average cost 1.076020\n",
            "{4, 7, 16, 22, 31, 42, 48, 49, 51, 56, 59, 64, 69, 74, 85, 98, 107, 109, 116, 118, 125, 129, 130, 145, 146, 156, 157, 164, 185, 194, 196, 198, 199, 202, 208, 218, 229, 237, 238, 241, 245, 246, 247, 253, 255, 260, 270, 271, 278, 280, 292, 314, 316, 320, 321, 328, 342, 349, 353, 365, 367, 385, 416, 424, 430, 431, 452, 457, 459, 475, 484, 489, 490, 520, 538, 539, 540, 543, 545, 546, 556, 562, 566, 580, 581, 588, 591, 603, 610, 612, 618, 619, 620, 627, 630, 648, 652, 655, 676, 677, 678, 685, 687, 696, 697, 698, 701, 705, 706, 708, 714, 717, 718, 720, 726, 760, 766, 774, 781, 789, 790, 804, 807, 812, 820, 828, 837, 840, 841, 842, 843, 847, 852, 860, 869, 870, 878, 886, 894, 897, 898, 904, 905, 917, 922, 925, 926, 928, 930, 933, 941, 948, 952, 958, 960, 967, 971, 973, 974, 983, 986, 992, 994, 997, 1000, 1003, 1006, 1007, 1008, 1009, 1010, 1012, 1013, 1014, 1021, 1022, 1023, 1033, 1042, 1065, 1077, 1080, 1082, 1088, 1094, 1095, 1112, 1115, 1118, 1119, 1120, 1124, 1128, 1132, 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6129, 6133, 6155, 6161, 6164, 6171, 6196, 6199, 6210, 6211, 6212, 6216, 6217, 6223, 6224, 6226, 6231, 6235, 6242, 6248, 6249, 6250, 6253, 6257, 6258, 6259, 6260, 6262, 6276, 6279, 6286, 6289, 6290, 6294, 6298, 6300, 6314, 6316, 6322, 6323, 6325, 6338, 6339, 6341, 6346, 6348, 6353, 6372, 6375, 6379, 6386, 6388, 6389, 6394, 6397, 6399, 6400, 6414, 6417, 6421, 6424, 6426, 6432, 6438, 6446, 6455, 6461, 6467, 6472, 6475, 6479, 6482, 6486, 6488, 6489, 6498, 6500, 6503, 6522, 6531, 6536, 6544, 6545, 6568, 6580, 6581, 6582, 6591, 6601, 6606, 6607, 6608, 6609, 6610, 6613, 6616, 6617, 6623, 6628, 6632, 6636, 6638, 6642, 6650, 6657, 6675, 6676, 6688, 6690, 6692, 6693, 6702, 6721, 6722, 6724, 6737, 6743, 6751, 6766, 6772, 6794, 6802, 6809, 6814, 6829, 6836, 6843, 6849} [1, 0, 8, 398, 0] [413, 737, 727, 518, 302] [14, 12] [1, 0, 0, 3, 1, 0, 0]\n",
            "Begin new epoch: 4\n",
            "WRONG faking object 1 [43]\n",
            "WRONG faking object 1 [43]\n",
            "WRONG faking object 1 [43]\n",
            "WRONG faking object 1 [102]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [586]\n",
            "WRONG faking object 1 [2]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 4 epoch with average cost 0.852933\n",
            "There are 99.154272 % left in this 4 epoch with average cost 0.464242\n",
            "There are 98.425197 % left in this 4 epoch with average cost 0.658087\n",
            "There are 97.696121 % left in this 4 epoch with average cost 0.424989\n",
            "There are 96.967046 % left in this 4 epoch with average cost 0.430849\n",
            "There are 96.237970 % left in this 4 epoch with average cost 0.540886\n",
            "There are 95.508895 % left in this 4 epoch with average cost 0.463061\n",
            "There are 94.779819 % left in this 4 epoch with average cost 0.633298\n",
            "There are 94.050744 % left in this 4 epoch with average cost 0.632948\n",
            "There are 93.321668 % left in this 4 epoch with average cost 0.446905\n",
            "There are 92.592593 % left in this 4 epoch with average cost 0.411114\n",
            "There are 91.863517 % left in this 4 epoch with average cost 0.398896\n",
            "There are 91.134442 % left in this 4 epoch with average cost 0.445498\n",
            "There are 90.405366 % left in this 4 epoch with average cost 0.276428\n",
            "There are 89.676290 % left in this 4 epoch with average cost 0.457356\n",
            "There are 88.947215 % left in this 4 epoch with average cost 0.473081\n",
            "There are 88.218139 % left in this 4 epoch with average cost 0.472505\n",
            "There are 87.489064 % left in this 4 epoch with average cost 0.467217\n",
            "There are 86.759988 % left in this 4 epoch with average cost 0.521041\n",
            "There are 86.030913 % left in this 4 epoch with average cost 0.347429\n",
            "There are 85.301837 % left in this 4 epoch with average cost 0.562698\n",
            "There are 84.572762 % left in this 4 epoch with average cost 0.539838\n",
            "There are 83.843686 % left in this 4 epoch with average cost 0.623874\n",
            "There are 83.114611 % left in this 4 epoch with average cost 0.337488\n",
            "There are 82.385535 % left in this 4 epoch with average cost 0.597414\n",
            "There are 81.656460 % left in this 4 epoch with average cost 0.534464\n",
            "There are 80.927384 % left in this 4 epoch with average cost 0.614026\n",
            "There are 80.198309 % left in this 4 epoch with average cost 0.575761\n",
            "There are 79.469233 % left in this 4 epoch with average cost 0.682901\n",
            "There are 78.740157 % left in this 4 epoch with average cost 0.374861\n",
            "There are 78.011082 % left in this 4 epoch with average cost 0.354631\n",
            "There are 77.282006 % left in this 4 epoch with average cost 0.482924\n",
            "There are 76.552931 % left in this 4 epoch with average cost 0.720024\n",
            "There are 75.823855 % left in this 4 epoch with average cost 0.312774\n",
            "There are 75.094780 % left in this 4 epoch with average cost 0.537628\n",
            "There are 74.365704 % left in this 4 epoch with average cost 0.552680\n",
            "There are 73.636629 % left in this 4 epoch with average cost 0.745545\n",
            "There are 72.907553 % left in this 4 epoch with average cost 0.381306\n",
            "There are 72.178478 % left in this 4 epoch with average cost 0.328479\n",
            "There are 71.449402 % left in this 4 epoch with average cost 0.558163\n",
            "There are 70.720327 % left in this 4 epoch with average cost 0.510731\n",
            "There are 69.991251 % left in this 4 epoch with average cost 0.643334\n",
            "There are 69.262176 % left in this 4 epoch with average cost 0.611102\n",
            "There are 68.533100 % left in this 4 epoch with average cost 0.422810\n",
            "There are 67.804024 % left in this 4 epoch with average cost 0.635141\n",
            "There are 67.074949 % left in this 4 epoch with average cost 0.392325\n",
            "There are 66.345873 % left in this 4 epoch with average cost 0.513873\n",
            "There are 65.616798 % left in this 4 epoch with average cost 0.378986\n",
            "There are 64.887722 % left in this 4 epoch with average cost 0.664641\n",
            "There are 64.158647 % left in this 4 epoch with average cost 0.610795\n",
            "There are 63.429571 % left in this 4 epoch with average cost 0.688682\n",
            "There are 62.700496 % left in this 4 epoch with average cost 0.527115\n",
            "There are 61.971420 % left in this 4 epoch with average cost 0.416870\n",
            "There are 61.242345 % left in this 4 epoch with average cost 0.416414\n",
            "There are 60.513269 % left in this 4 epoch with average cost 0.605697\n",
            "There are 59.784194 % left in this 4 epoch with average cost 0.621651\n",
            "There are 59.055118 % left in this 4 epoch with average cost 0.467092\n",
            "There are 58.326043 % left in this 4 epoch with average cost 0.546285\n",
            "There are 57.596967 % left in this 4 epoch with average cost 0.512365\n",
            "There are 56.867892 % left in this 4 epoch with average cost 0.319749\n",
            "There are 56.138816 % left in this 4 epoch with average cost 0.520510\n",
            "There are 55.409740 % left in this 4 epoch with average cost 0.549909\n",
            "There are 54.680665 % left in this 4 epoch with average cost 0.360806\n",
            "There are 53.951589 % left in this 4 epoch with average cost 0.219861\n",
            "There are 53.222514 % left in this 4 epoch with average cost 0.678385\n",
            "There are 52.493438 % left in this 4 epoch with average cost 0.536058\n",
            "There are 51.764363 % left in this 4 epoch with average cost 0.494901\n",
            "There are 51.035287 % left in this 4 epoch with average cost 0.474367\n",
            "There are 50.306212 % left in this 4 epoch with average cost 0.527287\n",
            "There are 49.577136 % left in this 4 epoch with average cost 0.640873\n",
            "There are 48.848061 % left in this 4 epoch with average cost 0.441230\n",
            "There are 48.118985 % left in this 4 epoch with average cost 0.511112\n",
            "There are 47.389910 % left in this 4 epoch with average cost 0.530768\n",
            "There are 46.660834 % left in this 4 epoch with average cost 0.647956\n",
            "There are 45.931759 % left in this 4 epoch with average cost 0.662562\n",
            "There are 45.202683 % left in this 4 epoch with average cost 0.500711\n",
            "There are 44.473607 % left in this 4 epoch with average cost 0.412785\n",
            "There are 43.744532 % left in this 4 epoch with average cost 0.462217\n",
            "There are 43.015456 % left in this 4 epoch with average cost 0.635281\n",
            "There are 42.286381 % left in this 4 epoch with average cost 0.411240\n",
            "There are 41.557305 % left in this 4 epoch with average cost 0.651829\n",
            "There are 40.828230 % left in this 4 epoch with average cost 0.744893\n",
            "There are 40.099154 % left in this 4 epoch with average cost 0.781491\n",
            "There are 39.370079 % left in this 4 epoch with average cost 0.857519\n",
            "There are 38.641003 % left in this 4 epoch with average cost 0.912009\n",
            "There are 37.911928 % left in this 4 epoch with average cost 0.857224\n",
            "There are 37.182852 % left in this 4 epoch with average cost 0.930512\n",
            "There are 36.453777 % left in this 4 epoch with average cost 0.751716\n",
            "There are 35.724701 % left in this 4 epoch with average cost 0.828637\n",
            "There are 34.995626 % left in this 4 epoch with average cost 1.057400\n",
            "There are 34.266550 % left in this 4 epoch with average cost 0.871551\n",
            "There are 33.537474 % left in this 4 epoch with average cost 0.951375\n",
            "There are 32.808399 % left in this 4 epoch with average cost 0.961505\n",
            "There are 32.079323 % left in this 4 epoch with average cost 0.753029\n",
            "There are 31.350248 % left in this 4 epoch with average cost 1.066521\n",
            "There are 30.621172 % left in this 4 epoch with average cost 0.804216\n",
            "There are 29.892097 % left in this 4 epoch with average cost 0.710749\n",
            "There are 29.163021 % left in this 4 epoch with average cost 0.960641\n",
            "There are 28.433946 % left in this 4 epoch with average cost 1.022954\n",
            "There are 27.704870 % left in this 4 epoch with average cost 1.033497\n",
            "There are 26.975795 % left in this 4 epoch with average cost 1.063747\n",
            "There are 26.246719 % left in this 4 epoch with average cost 1.024094\n",
            "There are 25.517644 % left in this 4 epoch with average cost 1.226916\n",
            "There are 24.788568 % left in this 4 epoch with average cost 1.119629\n",
            "There are 24.059493 % left in this 4 epoch with average cost 0.922969\n",
            "There are 23.330417 % left in this 4 epoch with average cost 0.773284\n",
            "There are 22.601341 % left in this 4 epoch with average cost 0.765739\n",
            "There are 21.872266 % left in this 4 epoch with average cost 1.071828\n",
            "There are 21.143190 % left in this 4 epoch with average cost 0.903184\n",
            "There are 20.414115 % left in this 4 epoch with average cost 1.076373\n",
            "There are 19.685039 % left in this 4 epoch with average cost 0.768781\n",
            "There are 18.955964 % left in this 4 epoch with average cost 0.824874\n",
            "{4, 6, 13, 20, 32, 43, 55, 60, 61, 68, 75, 83, 84, 86, 117, 121, 129, 145, 155, 157, 158, 164, 171, 199, 203, 204, 206, 218, 222, 230, 238, 241, 242, 250, 251, 256, 266, 274, 290, 291, 292, 299, 306, 316, 317, 320, 321, 340, 354, 378, 390, 398, 406, 414, 424, 439, 443, 450, 455, 457, 459, 489, 491, 497, 499, 520, 521, 522, 535, 573, 579, 581, 588, 602, 606, 612, 616, 620, 629, 631, 632, 637, 638, 639, 648, 656, 666, 668, 694, 696, 697, 698, 700, 701, 704, 708, 714, 719, 720, 721, 723, 741, 758, 761, 766, 777, 803, 819, 837, 847, 855, 857, 877, 878, 886, 904, 907, 917, 918, 919, 941, 942, 972, 973, 986, 994, 995, 1000, 1003, 1006, 1010, 1013, 1019, 1042, 1080, 1082, 1094, 1105, 1108, 1115, 1119, 1123, 1124, 1132, 1136, 1153, 1164, 1170, 1171, 1201, 1203, 1206, 1213, 1219, 1245, 1255, 1262, 1276, 1294, 1297, 1300, 1307, 1315, 1323, 1328, 1330, 1345, 1346, 1348, 1357, 1361, 1370, 1374, 1389, 1399, 1408, 1415, 1416, 1417, 1432, 1441, 1447, 1450, 1459, 1460, 1466, 1472, 1486, 1488, 1489, 1496, 1499, 1506, 1507, 1508, 1516, 1523, 1529, 1530, 1543, 1558, 1561, 1562, 1566, 1573, 1578, 1580, 1593, 1626, 1650, 1662, 1688, 1692, 1701, 1705, 1706, 1710, 1716, 1727, 1728, 1734, 1736, 1737, 1738, 1740, 1741, 1747, 1755, 1756, 1767, 1770, 1771, 1775, 1776, 1784, 1785, 1792, 1797, 1798, 1799, 1802, 1803, 1804, 1812, 1814, 1817, 1820, 1821, 1823, 1824, 1825, 1826, 1831, 1849, 1859, 1863, 1864, 1868, 1869, 1870, 1877, 1878, 1880, 1882, 1885, 1886, 1889, 1894, 1896, 1898, 1901, 1910, 1911, 1914, 1915, 1921, 1923, 1924, 1925, 1927, 1936, 1937, 1940, 1941, 1980, 1984, 1996, 1997, 1998, 2010, 2012, 2014, 2017, 2027, 2033, 2034, 2036, 2038, 2054, 2055, 2056, 2064, 2065, 2078, 2079, 2090, 2097, 2102, 2104, 2110, 2121, 2126, 2127, 2129, 2130, 2131, 2135, 2141, 2142, 2143, 2156, 2160, 2169, 2172, 2183, 2184, 2192, 2194, 2198, 2199, 2201, 2207, 2210, 2213, 2218, 2219, 2224, 2226, 2228, 2230, 2231, 2239, 2246, 2247, 2255, 2257, 2262, 2267, 2268, 2277, 2279, 2283, 2284, 2288, 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6443, 6446, 6449, 6451, 6472, 6476, 6477, 6484, 6486, 6487, 6488, 6489, 6500, 6503, 6509, 6513, 6520, 6522, 6531, 6537, 6541, 6563, 6578, 6580, 6582, 6591, 6592, 6599, 6606, 6607, 6609, 6610, 6613, 6628, 6629, 6636, 6640, 6642, 6648, 6660, 6661, 6665, 6667, 6668, 6671, 6683, 6692, 6698, 6703, 6718, 6722, 6741, 6743, 6746, 6749, 6758, 6760, 6761, 6763, 6764, 6765, 6766, 6767, 6774, 6777, 6784, 6801, 6803, 6808, 6813, 6814, 6816, 6822, 6825, 6826, 6828, 6831, 6838, 6843, 6857} [361, 236, 266] [443, 726, 183] [540] [0, 129, 11, 42, 0, 25]\n",
            "{4, 6, 13, 20, 32, 43, 55, 60, 61, 68, 75, 83, 84, 86, 117, 121, 129, 145, 155, 157, 158, 164, 171, 199, 203, 204, 206, 218, 222, 230, 238, 241, 242, 250, 251, 256, 266, 274, 290, 291, 292, 299, 306, 316, 317, 320, 321, 340, 354, 378, 390, 398, 406, 414, 424, 439, 443, 450, 455, 457, 459, 489, 491, 497, 499, 520, 521, 522, 535, 573, 579, 581, 588, 602, 606, 612, 616, 620, 629, 631, 632, 637, 638, 639, 648, 656, 666, 668, 694, 696, 697, 698, 700, 701, 704, 708, 714, 719, 720, 721, 723, 741, 758, 761, 766, 777, 803, 819, 837, 847, 855, 857, 877, 878, 886, 904, 907, 917, 918, 919, 941, 942, 972, 973, 986, 994, 995, 1000, 1003, 1006, 1010, 1013, 1019, 1042, 1080, 1082, 1094, 1105, 1108, 1115, 1119, 1123, 1124, 1132, 1136, 1153, 1164, 1170, 1171, 1201, 1203, 1206, 1213, 1219, 1245, 1255, 1262, 1276, 1294, 1297, 1300, 1307, 1315, 1323, 1328, 1330, 1345, 1346, 1348, 1357, 1361, 1370, 1374, 1389, 1399, 1408, 1415, 1416, 1417, 1432, 1441, 1447, 1450, 1459, 1460, 1466, 1472, 1486, 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5663, 5673, 5677, 5679, 5681, 5682, 5686, 5688, 5692, 5698, 5700, 5709, 5711, 5712, 5713, 5716, 5717, 5720, 5729, 5743, 5744, 5747, 5748, 5753, 5755, 5763, 5765, 5766, 5770, 5779, 5780, 5785, 5786, 5787, 5795, 5800, 5804, 5805, 5806, 5810, 5818, 5826, 5828, 5829, 5830, 5831, 5838, 5843, 5847, 5865, 5867, 5868, 5871, 5872, 5876, 5878, 5879, 5880, 5886, 5887, 5889, 5894, 5904, 5910, 5921, 5926, 5934, 5935, 5938, 5950, 5951, 5961, 5963, 5974, 5975, 5977, 5978, 5984, 5985, 5988, 5992, 5994, 5995, 6003, 6004, 6018, 6025, 6037, 6041, 6042, 6043, 6050, 6071, 6072, 6075, 6080, 6085, 6089, 6092, 6107, 6120, 6125, 6130, 6145, 6155, 6156, 6158, 6161, 6164, 6170, 6172, 6188, 6191, 6192, 6194, 6199, 6209, 6211, 6216, 6217, 6220, 6221, 6222, 6224, 6234, 6235, 6252, 6253, 6261, 6269, 6270, 6276, 6282, 6284, 6288, 6298, 6303, 6310, 6312, 6314, 6319, 6322, 6325, 6329, 6346, 6348, 6353, 6356, 6364, 6370, 6372, 6376, 6385, 6386, 6391, 6397, 6398, 6399, 6403, 6409, 6410, 6411, 6412, 6413, 6417, 6430, 6443, 6446, 6449, 6451, 6472, 6476, 6477, 6484, 6486, 6487, 6488, 6489, 6500, 6503, 6509, 6513, 6520, 6522, 6531, 6537, 6541, 6563, 6578, 6580, 6582, 6591, 6592, 6599, 6606, 6607, 6609, 6610, 6613, 6628, 6629, 6636, 6640, 6642, 6648, 6660, 6661, 6665, 6667, 6668, 6671, 6683, 6692, 6698, 6703, 6718, 6722, 6741, 6743, 6746, 6749, 6758, 6760, 6761, 6763, 6764, 6765, 6766, 6767, 6774, 6777, 6784, 6801, 6803, 6808, 6813, 6814, 6816, 6822, 6825, 6826, 6828, 6831, 6838, 6843, 6857} [361, 236, 266] [203, 22, 733] [540, 0, 129, 11, 42, 0, 25, 4] [0]\n",
            "Begin new epoch: 5\n",
            "WRONG faking object 1 [46]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [21]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [57]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [277]\n",
            "WRONG faking object 1 [0]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 5 epoch with average cost 1.266760\n",
            "There are 99.154272 % left in this 5 epoch with average cost 0.885901\n",
            "There are 98.425197 % left in this 5 epoch with average cost 0.635681\n",
            "There are 97.696121 % left in this 5 epoch with average cost 0.514091\n",
            "There are 96.967046 % left in this 5 epoch with average cost 0.476628\n",
            "There are 96.237970 % left in this 5 epoch with average cost 0.548389\n",
            "There are 95.508895 % left in this 5 epoch with average cost 0.382958\n",
            "There are 94.779819 % left in this 5 epoch with average cost 0.457621\n",
            "There are 94.050744 % left in this 5 epoch with average cost 0.352998\n",
            "There are 93.321668 % left in this 5 epoch with average cost 0.659087\n",
            "There are 92.592593 % left in this 5 epoch with average cost 0.528853\n",
            "There are 91.863517 % left in this 5 epoch with average cost 0.687282\n",
            "There are 91.134442 % left in this 5 epoch with average cost 0.408913\n",
            "There are 90.405366 % left in this 5 epoch with average cost 0.569453\n",
            "There are 89.676290 % left in this 5 epoch with average cost 0.503122\n",
            "There are 88.947215 % left in this 5 epoch with average cost 0.598404\n",
            "There are 88.218139 % left in this 5 epoch with average cost 0.501665\n",
            "There are 87.489064 % left in this 5 epoch with average cost 0.465226\n",
            "There are 86.759988 % left in this 5 epoch with average cost 0.664344\n",
            "There are 86.030913 % left in this 5 epoch with average cost 0.543912\n",
            "There are 85.301837 % left in this 5 epoch with average cost 0.336506\n",
            "There are 84.572762 % left in this 5 epoch with average cost 0.836075\n",
            "There are 83.843686 % left in this 5 epoch with average cost 0.554829\n",
            "There are 83.114611 % left in this 5 epoch with average cost 0.486044\n",
            "There are 82.385535 % left in this 5 epoch with average cost 0.528083\n",
            "There are 81.656460 % left in this 5 epoch with average cost 0.383386\n",
            "There are 80.927384 % left in this 5 epoch with average cost 0.573854\n",
            "There are 80.198309 % left in this 5 epoch with average cost 0.454890\n",
            "There are 79.469233 % left in this 5 epoch with average cost 0.522454\n",
            "There are 78.740157 % left in this 5 epoch with average cost 0.391647\n",
            "There are 78.011082 % left in this 5 epoch with average cost 0.387232\n",
            "There are 77.282006 % left in this 5 epoch with average cost 0.470852\n",
            "There are 76.552931 % left in this 5 epoch with average cost 0.487269\n",
            "There are 75.823855 % left in this 5 epoch with average cost 0.536389\n",
            "There are 75.094780 % left in this 5 epoch with average cost 0.696874\n",
            "There are 74.365704 % left in this 5 epoch with average cost 0.380960\n",
            "There are 73.636629 % left in this 5 epoch with average cost 0.683696\n",
            "There are 72.907553 % left in this 5 epoch with average cost 0.678312\n",
            "There are 72.178478 % left in this 5 epoch with average cost 0.565642\n",
            "There are 71.449402 % left in this 5 epoch with average cost 0.598229\n",
            "There are 70.720327 % left in this 5 epoch with average cost 0.373913\n",
            "There are 69.991251 % left in this 5 epoch with average cost 0.853290\n",
            "There are 69.262176 % left in this 5 epoch with average cost 0.418656\n",
            "There are 68.533100 % left in this 5 epoch with average cost 0.639049\n",
            "There are 67.804024 % left in this 5 epoch with average cost 0.378481\n",
            "There are 67.074949 % left in this 5 epoch with average cost 0.463904\n",
            "There are 66.345873 % left in this 5 epoch with average cost 0.451248\n",
            "There are 65.616798 % left in this 5 epoch with average cost 0.700562\n",
            "There are 64.887722 % left in this 5 epoch with average cost 0.390496\n",
            "There are 64.158647 % left in this 5 epoch with average cost 0.374486\n",
            "There are 63.429571 % left in this 5 epoch with average cost 0.530297\n",
            "There are 62.700496 % left in this 5 epoch with average cost 0.436599\n",
            "There are 61.971420 % left in this 5 epoch with average cost 0.459738\n",
            "There are 61.242345 % left in this 5 epoch with average cost 0.635461\n",
            "There are 60.513269 % left in this 5 epoch with average cost 0.404731\n",
            "There are 59.784194 % left in this 5 epoch with average cost 0.411527\n",
            "There are 59.055118 % left in this 5 epoch with average cost 0.514324\n",
            "There are 58.326043 % left in this 5 epoch with average cost 0.619877\n",
            "There are 57.596967 % left in this 5 epoch with average cost 0.567705\n",
            "There are 56.867892 % left in this 5 epoch with average cost 0.281444\n",
            "There are 56.138816 % left in this 5 epoch with average cost 0.412917\n",
            "There are 55.409740 % left in this 5 epoch with average cost 0.299980\n",
            "There are 54.680665 % left in this 5 epoch with average cost 0.602966\n",
            "There are 53.951589 % left in this 5 epoch with average cost 0.511483\n",
            "There are 53.222514 % left in this 5 epoch with average cost 0.430672\n",
            "There are 52.493438 % left in this 5 epoch with average cost 0.387783\n",
            "There are 51.764363 % left in this 5 epoch with average cost 0.475402\n",
            "There are 51.035287 % left in this 5 epoch with average cost 0.535026\n",
            "There are 50.306212 % left in this 5 epoch with average cost 0.500967\n",
            "There are 49.577136 % left in this 5 epoch with average cost 0.586886\n",
            "There are 48.848061 % left in this 5 epoch with average cost 0.660515\n",
            "There are 48.118985 % left in this 5 epoch with average cost 0.345739\n",
            "There are 47.389910 % left in this 5 epoch with average cost 0.449016\n",
            "There are 46.660834 % left in this 5 epoch with average cost 0.583448\n",
            "There are 45.931759 % left in this 5 epoch with average cost 0.513154\n",
            "There are 45.202683 % left in this 5 epoch with average cost 0.456368\n",
            "There are 44.473607 % left in this 5 epoch with average cost 0.581428\n",
            "There are 43.744532 % left in this 5 epoch with average cost 0.482489\n",
            "There are 43.015456 % left in this 5 epoch with average cost 0.540853\n",
            "There are 42.286381 % left in this 5 epoch with average cost 0.585611\n",
            "There are 41.557305 % left in this 5 epoch with average cost 0.687278\n",
            "There are 40.828230 % left in this 5 epoch with average cost 0.786855\n",
            "There are 40.099154 % left in this 5 epoch with average cost 0.783669\n",
            "There are 39.370079 % left in this 5 epoch with average cost 1.067023\n",
            "There are 38.641003 % left in this 5 epoch with average cost 1.104169\n",
            "There are 37.911928 % left in this 5 epoch with average cost 0.687945\n",
            "There are 37.182852 % left in this 5 epoch with average cost 0.622301\n",
            "There are 36.453777 % left in this 5 epoch with average cost 0.974790\n",
            "There are 35.724701 % left in this 5 epoch with average cost 1.047506\n",
            "There are 34.995626 % left in this 5 epoch with average cost 0.920539\n",
            "There are 34.266550 % left in this 5 epoch with average cost 0.817659\n",
            "There are 33.537474 % left in this 5 epoch with average cost 0.917221\n",
            "There are 32.808399 % left in this 5 epoch with average cost 0.858222\n",
            "There are 32.079323 % left in this 5 epoch with average cost 0.934860\n",
            "There are 31.350248 % left in this 5 epoch with average cost 0.679802\n",
            "There are 30.621172 % left in this 5 epoch with average cost 0.891828\n",
            "There are 29.892097 % left in this 5 epoch with average cost 0.965880\n",
            "There are 29.163021 % left in this 5 epoch with average cost 1.104782\n",
            "There are 28.433946 % left in this 5 epoch with average cost 1.001824\n",
            "There are 27.704870 % left in this 5 epoch with average cost 1.007622\n",
            "There are 26.975795 % left in this 5 epoch with average cost 0.955964\n",
            "There are 26.246719 % left in this 5 epoch with average cost 0.992623\n",
            "There are 25.517644 % left in this 5 epoch with average cost 0.910655\n",
            "There are 24.788568 % left in this 5 epoch with average cost 1.039249\n",
            "There are 24.059493 % left in this 5 epoch with average cost 0.997684\n",
            "There are 23.330417 % left in this 5 epoch with average cost 0.965548\n",
            "There are 22.601341 % left in this 5 epoch with average cost 0.907335\n",
            "{4, 5, 6, 7, 8, 10, 12, 13, 14, 16, 29, 33, 42, 43, 46, 50, 51, 59, 75, 79, 80, 83, 84, 88, 95, 98, 100, 104, 105, 110, 114, 117, 126, 127, 129, 130, 145, 150, 152, 158, 162, 164, 171, 179, 189, 192, 196, 199, 201, 202, 203, 204, 208, 210, 214, 218, 223, 230, 231, 236, 239, 241, 247, 251, 256, 259, 261, 262, 263, 271, 272, 273, 274, 278, 282, 285, 289, 290, 291, 292, 295, 308, 310, 314, 316, 317, 320, 321, 323, 327, 355, 360, 364, 369, 373, 382, 387, 392, 396, 409, 416, 417, 418, 420, 428, 430, 432, 433, 442, 443, 447, 450, 452, 459, 465, 471, 487, 489, 496, 508, 509, 513, 521, 543, 545, 546, 547, 548, 551, 562, 567, 570, 572, 574, 575, 577, 579, 580, 581, 588, 594, 600, 608, 609, 610, 612, 620, 627, 629, 632, 639, 642, 656, 668, 670, 673, 675, 676, 688, 690, 691, 692, 693, 696, 698, 700, 701, 704, 708, 713, 714, 717, 718, 719, 720, 721, 724, 726, 727, 730, 734, 735, 736, 739, 742, 752, 756, 766, 768, 769, 779, 781, 783, 784, 785, 786, 789, 803, 822, 846, 850, 852, 866, 867, 869, 870, 874, 877, 878, 896, 897, 906, 910, 926, 936, 937, 941, 947, 950, 956, 959, 960, 971, 972, 978, 979, 983, 986, 990, 994, 1001, 1005, 1006, 1007, 1013, 1015, 1020, 1021, 1022, 1023, 1032, 1033, 1042, 1043, 1047, 1064, 1065, 1080, 1082, 1087, 1094, 1095, 1103, 1104, 1105, 1106, 1116, 1125, 1126, 1131, 1136, 1149, 1152, 1169, 1170, 1172, 1174, 1180, 1183, 1186, 1191, 1195, 1198, 1200, 1201, 1202, 1203, 1213, 1219, 1236, 1237, 1245, 1250, 1253, 1262, 1276, 1279, 1283, 1293, 1302, 1313, 1316, 1322, 1325, 1326, 1328, 1329, 1335, 1344, 1346, 1347, 1348, 1357, 1360, 1369, 1371, 1374, 1380, 1392, 1395, 1397, 1398, 1405, 1406, 1410, 1421, 1432, 1437, 1442, 1447, 1455, 1466, 1471, 1474, 1476, 1482, 1486, 1487, 1488, 1489, 1504, 1506, 1507, 1508, 1511, 1512, 1513, 1516, 1517, 1518, 1524, 1529, 1543, 1556, 1558, 1561, 1568, 1570, 1573, 1576, 1580, 1593, 1598, 1605, 1606, 1607, 1620, 1633, 1634, 1636, 1637, 1641, 1642, 1643, 1646, 1648, 1649, 1651, 1652, 1655, 1656, 1662, 1666, 1682, 1688, 1693, 1694, 1695, 1701, 1704, 1706, 1720, 1723, 1727, 1728, 1730, 1734, 1736, 1737, 1740, 1741, 1744, 1753, 1754, 1755, 1762, 1770, 1771, 1774, 1776, 1777, 1785, 1791, 1792, 1794, 1797, 1798, 1801, 1802, 1803, 1812, 1814, 1816, 1817, 1821, 1824, 1825, 1831, 1846, 1848, 1849, 1856, 1863, 1866, 1868, 1869, 1870, 1877, 1878, 1879, 1885, 1889, 1894, 1897, 1901, 1904, 1905, 1910, 1914, 1916, 1920, 1921, 1923, 1924, 1925, 1927, 1934, 1937, 1940, 1941, 1942, 1950, 1954, 1956, 1960, 1962, 1979, 1980, 1994, 1995, 1996, 1997, 1998, 2000, 2002, 2003, 2004, 2010, 2014, 2016, 2017, 2018, 2025, 2027, 2028, 2030, 2033, 2035, 2036, 2037, 2045, 2046, 2052, 2054, 2055, 2061, 2062, 2064, 2065, 2066, 2074, 2078, 2079, 2081, 2085, 2099, 2101, 2102, 2103, 2104, 2106, 2114, 2119, 2120, 2127, 2129, 2130, 2131, 2134, 2140, 2143, 2144, 2145, 2153, 2156, 2163, 2165, 2168, 2172, 2183, 2184, 2194, 2201, 2202, 2203, 2204, 2205, 2207, 2210, 2213, 2215, 2218, 2224, 2230, 2232, 2237, 2239, 2246, 2253, 2255, 2257, 2261, 2262, 2264, 2267, 2268, 2269, 2272, 2275, 2284, 2288, 2290, 2291, 2294, 2298, 2309, 2310, 2312, 2314, 2324, 2327, 2347, 2349, 2354, 2367, 2368, 2373, 2378, 2390, 2392, 2398, 2410, 2412, 2419, 2421, 2424, 2427, 2432, 2434, 2435, 2437, 2438, 2450, 2455, 2458, 2464, 2465, 2466, 2475, 2483, 2494, 2495, 2496, 2497, 2508, 2513, 2515, 2519, 2524, 2538, 2541, 2546, 2547, 2556, 2566, 2567, 2570, 2573, 2574, 2578, 2584, 2604, 2605, 2609, 2612, 2618, 2621, 2632, 2647, 2660, 2663, 2664, 2670, 2672, 2675, 2686, 2687, 2688, 2696, 2704, 2705, 2706, 2720, 2721, 2723, 2746, 2748, 2749, 2750, 2755, 2761, 2762, 2765, 2766, 2779, 2780, 2784, 2787, 2792, 2802, 2804, 2814, 2826, 2828, 2830, 2837, 2841, 2842, 2851, 2854, 2861, 2869, 2870, 2885, 2887, 2896, 2900, 2912, 2919, 2935, 2936, 2951, 2964, 2976, 2982, 2990, 3010, 3021, 3029, 3030, 3031, 3050, 3058, 3064, 3065, 3070, 3072, 3075, 3076, 3077, 3078, 3079, 3084, 3085, 3094, 3095, 3107, 3108, 3109, 3111, 3117, 3125, 3131, 3133, 3138, 3139, 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6857} [122, 0, 745] [731, 285, 226] [14, 0, 3] [2]\n",
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[581] [14, 44, 0, 12] [501]\n",
            "Begin new epoch: 6\n",
            "WRONG faking object 1 [140]\n",
            "WRONG faking object 1 [2]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [145]\n",
            "WRONG faking object 1 [736]\n",
            "There are 3 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 6 epoch with average cost 1.062270\n",
            "There are 99.154272 % left in this 6 epoch with average cost 0.635097\n",
            "There are 98.425197 % left in this 6 epoch with average cost 0.541310\n",
            "There are 97.696121 % left in this 6 epoch with average cost 0.652795\n",
            "There are 96.967046 % left in this 6 epoch with average cost 0.527791\n",
            "There are 96.237970 % left in this 6 epoch with average cost 0.640301\n",
            "There are 95.508895 % left in this 6 epoch with average cost 0.383809\n",
            "There are 94.779819 % left in this 6 epoch with average cost 0.637832\n",
            "There are 94.050744 % left in this 6 epoch with average cost 0.553703\n",
            "There are 93.321668 % left in this 6 epoch with average cost 0.517472\n",
            "There are 92.592593 % left in this 6 epoch with average cost 0.495667\n",
            "There are 91.863517 % left in this 6 epoch with average cost 0.549428\n",
            "There are 91.134442 % left in this 6 epoch with average cost 0.618395\n",
            "There are 90.405366 % left in this 6 epoch with average cost 0.496671\n",
            "There are 89.676290 % left in this 6 epoch with average cost 0.555124\n",
            "There are 88.947215 % left in this 6 epoch with average cost 0.405516\n",
            "There are 88.218139 % left in this 6 epoch with average cost 0.377090\n",
            "There are 87.489064 % left in this 6 epoch with average cost 0.338020\n",
            "There are 86.759988 % left in this 6 epoch with average cost 0.514755\n",
            "There are 86.030913 % left in this 6 epoch with average cost 0.425704\n",
            "There are 85.301837 % left in this 6 epoch with average cost 0.611460\n",
            "There are 84.572762 % left in this 6 epoch with average cost 0.521949\n",
            "There are 83.843686 % left in this 6 epoch with average cost 0.553557\n",
            "There are 83.114611 % left in this 6 epoch with average cost 0.522429\n",
            "There are 82.385535 % left in this 6 epoch with average cost 0.626431\n",
            "There are 81.656460 % left in this 6 epoch with average cost 0.615254\n",
            "There are 80.927384 % left in this 6 epoch with average cost 0.562570\n",
            "There are 80.198309 % left in this 6 epoch with average cost 0.489302\n",
            "There are 79.469233 % left in this 6 epoch with average cost 0.599136\n",
            "There are 78.740157 % left in this 6 epoch with average cost 0.588152\n",
            "There are 78.011082 % left in this 6 epoch with average cost 0.285639\n",
            "There are 77.282006 % left in this 6 epoch with average cost 0.533006\n",
            "There are 76.552931 % left in this 6 epoch with average cost 0.510204\n",
            "There are 75.823855 % left in this 6 epoch with average cost 0.462077\n",
            "There are 75.094780 % left in this 6 epoch with average cost 0.528530\n",
            "There are 74.365704 % left in this 6 epoch with average cost 0.552629\n",
            "There are 73.636629 % left in this 6 epoch with average cost 0.335966\n",
            "There are 72.907553 % left in this 6 epoch with average cost 0.506220\n",
            "There are 72.178478 % left in this 6 epoch with average cost 0.667824\n",
            "There are 71.449402 % left in this 6 epoch with average cost 0.532217\n",
            "There are 70.720327 % left in this 6 epoch with average cost 0.752009\n",
            "There are 69.991251 % left in this 6 epoch with average cost 0.610140\n",
            "There are 69.262176 % left in this 6 epoch with average cost 0.612523\n",
            "There are 68.533100 % left in this 6 epoch with average cost 0.600140\n",
            "There are 67.804024 % left in this 6 epoch with average cost 0.598961\n",
            "There are 67.074949 % left in this 6 epoch with average cost 0.465196\n",
            "There are 66.345873 % left in this 6 epoch with average cost 0.510046\n",
            "There are 65.616798 % left in this 6 epoch with average cost 0.669790\n",
            "There are 64.887722 % left in this 6 epoch with average cost 0.649482\n",
            "There are 64.158647 % left in this 6 epoch with average cost 0.618925\n",
            "There are 63.429571 % left in this 6 epoch with average cost 0.472038\n",
            "There are 62.700496 % left in this 6 epoch with average cost 0.293117\n",
            "There are 61.971420 % left in this 6 epoch with average cost 0.298104\n",
            "There are 61.242345 % left in this 6 epoch with average cost 0.559356\n",
            "There are 60.513269 % left in this 6 epoch with average cost 0.515135\n",
            "There are 59.784194 % left in this 6 epoch with average cost 0.414202\n",
            "There are 59.055118 % left in this 6 epoch with average cost 0.432066\n",
            "There are 58.326043 % left in this 6 epoch with average cost 0.503897\n",
            "There are 57.596967 % left in this 6 epoch with average cost 0.519442\n",
            "There are 56.867892 % left in this 6 epoch with average cost 0.464344\n",
            "There are 56.138816 % left in this 6 epoch with average cost 0.443533\n",
            "There are 55.409740 % left in this 6 epoch with average cost 0.519882\n",
            "There are 54.680665 % left in this 6 epoch with average cost 0.525981\n",
            "There are 53.951589 % left in this 6 epoch with average cost 0.226779\n",
            "There are 53.222514 % left in this 6 epoch with average cost 0.675612\n",
            "There are 52.493438 % left in this 6 epoch with average cost 0.382770\n",
            "There are 51.764363 % left in this 6 epoch with average cost 0.385151\n",
            "There are 51.035287 % left in this 6 epoch with average cost 0.333429\n",
            "There are 50.306212 % left in this 6 epoch with average cost 0.578305\n",
            "There are 49.577136 % left in this 6 epoch with average cost 0.460929\n",
            "There are 48.848061 % left in this 6 epoch with average cost 0.348563\n",
            "There are 48.118985 % left in this 6 epoch with average cost 0.383555\n",
            "There are 47.389910 % left in this 6 epoch with average cost 0.795778\n",
            "There are 46.660834 % left in this 6 epoch with average cost 0.556841\n",
            "There are 45.931759 % left in this 6 epoch with average cost 0.514914\n",
            "There are 45.202683 % left in this 6 epoch with average cost 0.339013\n",
            "There are 44.473607 % left in this 6 epoch with average cost 0.425399\n",
            "There are 43.744532 % left in this 6 epoch with average cost 0.585875\n",
            "There are 43.015456 % left in this 6 epoch with average cost 0.422769\n",
            "There are 42.286381 % left in this 6 epoch with average cost 0.795615\n",
            "There are 41.557305 % left in this 6 epoch with average cost 0.650449\n",
            "There are 40.828230 % left in this 6 epoch with average cost 0.826243\n",
            "There are 40.099154 % left in this 6 epoch with average cost 0.860418\n",
            "There are 39.370079 % left in this 6 epoch with average cost 0.912664\n",
            "There are 38.641003 % left in this 6 epoch with average cost 1.025367\n",
            "There are 37.911928 % left in this 6 epoch with average cost 0.934355\n",
            "There are 37.182852 % left in this 6 epoch with average cost 1.015177\n",
            "There are 36.453777 % left in this 6 epoch with average cost 0.855159\n",
            "There are 35.724701 % left in this 6 epoch with average cost 1.045213\n",
            "There are 34.995626 % left in this 6 epoch with average cost 0.865353\n",
            "There are 34.266550 % left in this 6 epoch with average cost 0.853499\n",
            "There are 33.537474 % left in this 6 epoch with average cost 0.914911\n",
            "There are 32.808399 % left in this 6 epoch with average cost 0.793868\n",
            "There are 32.079323 % left in this 6 epoch with average cost 0.946202\n",
            "There are 31.350248 % left in this 6 epoch with average cost 0.935124\n",
            "There are 30.621172 % left in this 6 epoch with average cost 0.988541\n",
            "There are 29.892097 % left in this 6 epoch with average cost 0.796681\n",
            "There are 29.163021 % left in this 6 epoch with average cost 0.995370\n",
            "There are 28.433946 % left in this 6 epoch with average cost 1.128414\n",
            "There are 27.704870 % left in this 6 epoch with average cost 0.813106\n",
            "There are 26.975795 % left in this 6 epoch with average cost 0.971380\n",
            "There are 26.246719 % left in this 6 epoch with average cost 0.879183\n",
            "There are 25.517644 % left in this 6 epoch with average cost 1.041538\n",
            "There are 24.788568 % left in this 6 epoch with average cost 0.882933\n",
            "{4, 6, 13, 14, 16, 25, 29, 33, 47, 49, 54, 55, 59, 60, 64, 67, 69, 75, 79, 80, 83, 84, 86, 105, 110, 114, 115, 123, 129, 130, 138, 145, 147, 149, 150, 151, 152, 158, 161, 164, 170, 174, 182, 186, 190, 192, 196, 202, 203, 206, 230, 231, 234, 236, 238, 241, 243, 246, 247, 248, 254, 255, 256, 271, 279, 295, 298, 299, 314, 315, 320, 321, 340, 345, 348, 357, 358, 360, 365, 369, 373, 378, 387, 420, 424, 430, 432, 433, 443, 447, 450, 451, 455, 456, 459, 462, 473, 489, 490, 491, 496, 497, 499, 500, 510, 511, 519, 520, 535, 544, 545, 546, 561, 564, 566, 568, 577, 578, 580, 581, 582, 588, 594, 608, 609, 612, 613, 616, 620, 627, 631, 632, 634, 639, 640, 643, 654, 660, 668, 676, 679, 685, 690, 692, 693, 694, 695, 696, 697, 698, 700, 701, 703, 706, 708, 709, 714, 719, 720, 721, 726, 730, 734, 741, 752, 756, 760, 765, 768, 769, 775, 781, 783, 789, 797, 799, 803, 815, 845, 846, 852, 858, 864, 870, 873, 877, 878, 882, 893, 901, 912, 918, 922, 923, 926, 927, 929, 932, 939, 941, 944, 950, 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            "{4, 6, 13, 14, 16, 25, 29, 33, 47, 49, 54, 55, 59, 60, 64, 67, 69, 75, 79, 80, 83, 84, 86, 105, 110, 114, 115, 123, 129, 130, 138, 145, 147, 149, 150, 151, 152, 158, 161, 164, 170, 174, 182, 186, 190, 192, 196, 202, 203, 206, 230, 231, 234, 236, 238, 241, 243, 246, 247, 248, 254, 255, 256, 271, 279, 295, 298, 299, 314, 315, 320, 321, 340, 345, 348, 357, 358, 360, 365, 369, 373, 378, 387, 420, 424, 430, 432, 433, 443, 447, 450, 451, 455, 456, 459, 462, 473, 489, 490, 491, 496, 497, 499, 500, 510, 511, 519, 520, 535, 544, 545, 546, 561, 564, 566, 568, 577, 578, 580, 581, 582, 588, 594, 608, 609, 612, 613, 616, 620, 627, 631, 632, 634, 639, 640, 643, 654, 660, 668, 676, 679, 685, 690, 692, 693, 694, 695, 696, 697, 698, 700, 701, 703, 706, 708, 709, 714, 719, 720, 721, 726, 730, 734, 741, 752, 756, 760, 765, 768, 769, 775, 781, 783, 789, 797, 799, 803, 815, 845, 846, 852, 858, 864, 870, 873, 877, 878, 882, 893, 901, 912, 918, 922, 923, 926, 927, 929, 932, 939, 941, 944, 950, 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6380, 6386, 6389, 6397, 6399, 6410, 6411, 6414, 6415, 6417, 6421, 6425, 6436, 6446, 6447, 6455, 6457, 6461, 6464, 6466, 6467, 6472, 6475, 6476, 6477, 6485, 6486, 6487, 6488, 6489, 6500, 6502, 6503, 6517, 6521, 6522, 6523, 6531, 6538, 6544, 6554, 6575, 6580, 6581, 6590, 6591, 6592, 6595, 6597, 6598, 6601, 6602, 6606, 6607, 6608, 6609, 6610, 6611, 6613, 6614, 6615, 6616, 6617, 6618, 6629, 6630, 6631, 6634, 6636, 6640, 6642, 6648, 6653, 6655, 6661, 6667, 6668, 6669, 6679, 6687, 6688, 6690, 6692, 6693, 6694, 6698, 6702, 6703, 6705, 6708, 6714, 6716, 6721, 6722, 6723, 6724, 6733, 6734, 6736, 6743, 6749, 6751, 6754, 6759, 6760, 6764, 6784, 6789, 6794, 6795, 6798, 6800, 6801, 6802, 6808, 6809, 6810, 6815, 6839, 6843, 6856} [145] [232] [32, 0, 36, 719, 150, 25, 7] [0]\n",
            "Begin new epoch: 7\n",
            "WRONG faking object 1 [46]\n",
            "WRONG faking object 1 [0]\n",
            "WRONG faking object 1 [10]\n",
            "WRONG faking object 1 [198]\n",
            "There are 4 wrong random loops\n",
            "************************************************************************\n",
            "\n",
            "************************************************************************\n",
            "\n",
            "There are 99.883348 % left in this 7 epoch with average cost 0.985148\n",
            "There are 99.154272 % left in this 7 epoch with average cost 0.501177\n",
            "There are 98.425197 % left in this 7 epoch with average cost 0.744166\n",
            "There are 97.696121 % left in this 7 epoch with average cost 0.514853\n",
            "There are 96.967046 % left in this 7 epoch with average cost 0.590658\n",
            "There are 96.237970 % left in this 7 epoch with average cost 0.560398\n",
            "There are 95.508895 % left in this 7 epoch with average cost 0.440598\n",
            "There are 94.779819 % left in this 7 epoch with average cost 0.492689\n",
            "There are 94.050744 % left in this 7 epoch with average cost 0.410296\n",
            "There are 93.321668 % left in this 7 epoch with average cost 0.378176\n",
            "There are 92.592593 % left in this 7 epoch with average cost 0.577347\n",
            "There are 91.863517 % left in this 7 epoch with average cost 0.439431\n",
            "There are 91.134442 % left in this 7 epoch with average cost 0.449346\n",
            "There are 90.405366 % left in this 7 epoch with average cost 0.401449\n",
            "There are 89.676290 % left in this 7 epoch with average cost 0.358543\n",
            "There are 88.947215 % left in this 7 epoch with average cost 0.535879\n",
            "There are 88.218139 % left in this 7 epoch with average cost 0.325549\n",
            "There are 87.489064 % left in this 7 epoch with average cost 0.449849\n",
            "There are 86.759988 % left in this 7 epoch with average cost 0.742025\n",
            "There are 86.030913 % left in this 7 epoch with average cost 0.579091\n",
            "There are 85.301837 % left in this 7 epoch with average cost 0.414966\n",
            "There are 84.572762 % left in this 7 epoch with average cost 0.444602\n",
            "There are 83.843686 % left in this 7 epoch with average cost 0.640072\n",
            "There are 83.114611 % left in this 7 epoch with average cost 0.739188\n",
            "There are 82.385535 % left in this 7 epoch with average cost 0.508246\n",
            "There are 81.656460 % left in this 7 epoch with average cost 0.441102\n",
            "There are 80.927384 % left in this 7 epoch with average cost 0.457279\n",
            "There are 80.198309 % left in this 7 epoch with average cost 0.675321\n",
            "There are 79.469233 % left in this 7 epoch with average cost 0.602008\n",
            "There are 78.740157 % left in this 7 epoch with average cost 0.522284\n",
            "There are 78.011082 % left in this 7 epoch with average cost 0.457391\n",
            "There are 77.282006 % left in this 7 epoch with average cost 0.562924\n",
            "There are 76.552931 % left in this 7 epoch with average cost 0.382034\n",
            "There are 75.823855 % left in this 7 epoch with average cost 0.456490\n",
            "There are 75.094780 % left in this 7 epoch with average cost 0.556750\n",
            "There are 74.365704 % left in this 7 epoch with average cost 0.513758\n",
            "There are 73.636629 % left in this 7 epoch with average cost 0.481285\n",
            "There are 72.907553 % left in this 7 epoch with average cost 0.459460\n",
            "There are 72.178478 % left in this 7 epoch with average cost 0.438546\n",
            "There are 71.449402 % left in this 7 epoch with average cost 0.388726\n",
            "There are 70.720327 % left in this 7 epoch with average cost 0.685338\n",
            "There are 69.991251 % left in this 7 epoch with average cost 0.480135\n",
            "There are 69.262176 % left in this 7 epoch with average cost 0.492813\n",
            "There are 68.533100 % left in this 7 epoch with average cost 0.483031\n",
            "There are 67.804024 % left in this 7 epoch with average cost 0.458133\n",
            "There are 67.074949 % left in this 7 epoch with average cost 0.616550\n",
            "There are 66.345873 % left in this 7 epoch with average cost 0.258794\n",
            "There are 65.616798 % left in this 7 epoch with average cost 0.411238\n",
            "There are 64.887722 % left in this 7 epoch with average cost 0.606120\n",
            "There are 64.158647 % left in this 7 epoch with average cost 0.379001\n",
            "There are 63.429571 % left in this 7 epoch with average cost 0.293759\n",
            "There are 62.700496 % left in this 7 epoch with average cost 0.428207\n",
            "There are 61.971420 % left in this 7 epoch with average cost 0.558738\n",
            "There are 61.242345 % left in this 7 epoch with average cost 0.588101\n",
            "There are 60.513269 % left in this 7 epoch with average cost 0.296537\n",
            "There are 59.784194 % left in this 7 epoch with average cost 0.431084\n",
            "There are 59.055118 % left in this 7 epoch with average cost 0.527780\n",
            "There are 58.326043 % left in this 7 epoch with average cost 0.383964\n",
            "There are 57.596967 % left in this 7 epoch with average cost 0.500473\n",
            "There are 56.867892 % left in this 7 epoch with average cost 0.462234\n",
            "There are 56.138816 % left in this 7 epoch with average cost 0.472587\n",
            "There are 55.409740 % left in this 7 epoch with average cost 0.663501\n",
            "There are 54.680665 % left in this 7 epoch with average cost 0.497651\n",
            "There are 53.951589 % left in this 7 epoch with average cost 0.462913\n",
            "There are 53.222514 % left in this 7 epoch with average cost 0.467401\n",
            "There are 52.493438 % left in this 7 epoch with average cost 0.424613\n",
            "There are 51.764363 % left in this 7 epoch with average cost 0.459451\n",
            "There are 51.035287 % left in this 7 epoch with average cost 0.689859\n",
            "There are 50.306212 % left in this 7 epoch with average cost 0.313092\n",
            "There are 49.577136 % left in this 7 epoch with average cost 0.459900\n",
            "There are 48.848061 % left in this 7 epoch with average cost 0.605887\n",
            "There are 48.118985 % left in this 7 epoch with average cost 0.588843\n",
            "There are 47.389910 % left in this 7 epoch with average cost 0.392297\n",
            "There are 46.660834 % left in this 7 epoch with average cost 0.526659\n",
            "There are 45.931759 % left in this 7 epoch with average cost 0.508156\n",
            "There are 45.202683 % left in this 7 epoch with average cost 0.428162\n",
            "There are 44.473607 % left in this 7 epoch with average cost 0.430885\n",
            "There are 43.744532 % left in this 7 epoch with average cost 0.433784\n",
            "There are 43.015456 % left in this 7 epoch with average cost 0.649802\n",
            "There are 42.286381 % left in this 7 epoch with average cost 0.923321\n",
            "There are 41.557305 % left in this 7 epoch with average cost 0.845680\n",
            "There are 40.828230 % left in this 7 epoch with average cost 0.805085\n",
            "There are 40.099154 % left in this 7 epoch with average cost 0.709973\n",
            "There are 39.370079 % left in this 7 epoch with average cost 0.956151\n",
            "There are 38.641003 % left in this 7 epoch with average cost 0.867619\n",
            "There are 37.911928 % left in this 7 epoch with average cost 0.789462\n",
            "There are 37.182852 % left in this 7 epoch with average cost 0.862991\n",
            "There are 36.453777 % left in this 7 epoch with average cost 0.883359\n",
            "There are 35.724701 % left in this 7 epoch with average cost 0.923000\n",
            "There are 34.995626 % left in this 7 epoch with average cost 0.887459\n",
            "There are 34.266550 % left in this 7 epoch with average cost 0.746812\n",
            "There are 33.537474 % left in this 7 epoch with average cost 1.101383\n",
            "There are 32.808399 % left in this 7 epoch with average cost 1.021463\n",
            "There are 32.079323 % left in this 7 epoch with average cost 1.070575\n",
            "There are 31.350248 % left in this 7 epoch with average cost 1.024437\n",
            "There are 30.621172 % left in this 7 epoch with average cost 0.866207\n",
            "There are 29.892097 % left in this 7 epoch with average cost 0.849904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QT3moyqk8CP5",
        "colab_type": "code",
        "outputId": "1c5ae062-6d25-4eb3-b911-36b9186cedf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "y = np.load(\"./gdrive/My Drive/capstone 2/event_embedding/Thesis_data/ModelEvents_result/resultEmbeding.pickle\", allow_pickle=True)\n",
        "print(len(y.keys()))\n",
        "\n",
        "y = np.load(\"./gdrive/My Drive/capstone 2/event_embedding/Thesis_data/ModelEvents_result/DailyReturn.npy\", allow_pickle=True)\n",
        "print(y.shape)\n",
        "\n",
        "y = np.load(\"./gdrive/My Drive/capstone 2/event_embedding/Thesis_data/ModelEvents_result/DailyVector.npy\", allow_pickle=True)\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5838\n",
            "(897, 2)\n",
            "[[[-1.00000000e+00 -1.00000000e+00  1.00000000e+00 ...  3.22047137e-01\n",
            "   -9.93534328e-01  1.00000000e+00]\n",
            "  [ 5.77206438e-06 -2.51900728e-01  2.50000000e-01 ... -2.52788487e-01\n",
            "    2.49982285e-01 -2.49999811e-01]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "    6.12165874e-12  1.00000000e+00]\n",
            "  [-7.05797839e-01 -1.00000000e+00 -4.28571429e-01 ... -7.14289177e-01\n",
            "    4.28577051e-01  2.17422037e-01]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -3.33333335e-01 ... -1.00000000e+00\n",
            "    3.33333334e-01  1.00000000e+00]]\n",
            "\n",
            " [[-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "    6.12165874e-12  1.00000000e+00]\n",
            "  [-7.05797839e-01 -1.00000000e+00 -4.28571429e-01 ... -7.14289177e-01\n",
            "    4.28577051e-01  2.17422037e-01]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -3.33333335e-01 ... -1.00000000e+00\n",
            "    3.33333334e-01  1.00000000e+00]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "    1.00000000e+00 -1.00000000e+00]\n",
            "  [ 1.00000000e+00 -9.99999991e-01 -1.00000000e+00 ...  1.00000000e+00\n",
            "   -7.01086860e-01  1.00000000e+00]]\n",
            "\n",
            " [[-7.05797839e-01 -1.00000000e+00 -4.28571429e-01 ... -7.14289177e-01\n",
            "    4.28577051e-01  2.17422037e-01]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -3.33333335e-01 ... -1.00000000e+00\n",
            "    3.33333334e-01  1.00000000e+00]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "    1.00000000e+00 -1.00000000e+00]\n",
            "  [ 1.00000000e+00 -9.99999991e-01 -1.00000000e+00 ...  1.00000000e+00\n",
            "   -7.01086860e-01  1.00000000e+00]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "    1.00000000e+00  1.00000000e+00]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[-6.00013293e-01 -5.68278526e-01 -3.59061922e-01 ... -5.13148933e-01\n",
            "   -1.78238875e-01  6.49535644e-01]\n",
            "  [-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "   -1.00000000e+00  1.00000000e+00]\n",
            "  [-8.01348080e-01 -9.99447325e-01 -4.54407316e-01 ...  4.33951444e-01\n",
            "    9.09088693e-02  4.54545456e-01]\n",
            "  [ 1.23109540e-07  5.75256026e-09 -2.50000441e-01 ...  5.09094814e-03\n",
            "   -2.58646606e-01  7.49989167e-01]\n",
            "  [-6.70301225e-01 -1.00000000e+00 -6.66666667e-01 ... -9.99999996e-01\n",
            "   -6.66666476e-01  6.66666667e-01]]\n",
            "\n",
            " [[-1.00000000e+00 -1.00000000e+00 -1.00000000e+00 ... -1.00000000e+00\n",
            "   -1.00000000e+00  1.00000000e+00]\n",
            "  [-8.01348080e-01 -9.99447325e-01 -4.54407316e-01 ...  4.33951444e-01\n",
            "    9.09088693e-02  4.54545456e-01]\n",
            "  [ 1.23109540e-07  5.75256026e-09 -2.50000441e-01 ...  5.09094814e-03\n",
            "   -2.58646606e-01  7.49989167e-01]\n",
            "  [-6.70301225e-01 -1.00000000e+00 -6.66666667e-01 ... -9.99999996e-01\n",
            "   -6.66666476e-01  6.66666667e-01]\n",
            "  [-3.33056374e-01 -6.66666667e-01 -3.32994502e-01 ...  3.21354356e-01\n",
            "    2.07197656e-03  3.05966118e-01]]\n",
            "\n",
            " [[-8.01348080e-01 -9.99447325e-01 -4.54407316e-01 ...  4.33951444e-01\n",
            "    9.09088693e-02  4.54545456e-01]\n",
            "  [ 1.23109540e-07  5.75256026e-09 -2.50000441e-01 ...  5.09094814e-03\n",
            "   -2.58646606e-01  7.49989167e-01]\n",
            "  [-6.70301225e-01 -1.00000000e+00 -6.66666667e-01 ... -9.99999996e-01\n",
            "   -6.66666476e-01  6.66666667e-01]\n",
            "  [-3.33056374e-01 -6.66666667e-01 -3.32994502e-01 ...  3.21354356e-01\n",
            "    2.07197656e-03  3.05966118e-01]\n",
            "  [-1.99900605e-01 -5.99965706e-01 -5.99628636e-01 ...  2.00000000e-01\n",
            "   -2.00000000e-01  6.00000001e-01]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVpsCg_VdKat",
        "colab_type": "code",
        "outputId": "7659ac72-b4b1-4163-8954-9bd1146204b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import os\n",
        "os.environ['THEANO_FLAGS'] = \"device=cuda,force_device=True,floatX=float32\"\n",
        "import theano\n",
        "print(theano.config.__getattribute__('device'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR (theano.gpuarray): pygpu was configured but could not be imported or is too old (version 0.7 or higher required)\n",
            "NoneType: None\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlhDayPdFlGt",
        "colab_type": "code",
        "outputId": "fc05a6e5-d305-4463-9f10-fa45c3bc3507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%run ./gdrive/'My Drive'/'capstone 2'/event_embedding/Code/Model_StockMovePredict/train_model_CNN.py ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyVector.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyReturn.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/ 100 80 5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 726 samples, validate on 81 samples\n",
            "Epoch 1/100\n",
            " - 0s - loss: 0.8002 - accuracy: 0.5083 - recall: 0.4312 - precision: 0.4525 - fbeta_score: 0.3922 - val_loss: 0.6735 - val_accuracy: 0.6173 - val_recall: 0.8678 - val_precision: 0.6471 - val_fbeta_score: 0.7377\n",
            "\n",
            "Epoch 00001: val_fbeta_score improved from -inf to 0.73774, saving model to ./gdrive/My Drive/capstone 2/event_embedding/model2_price_move_predict.hdf5\n",
            "Epoch 2/100\n",
            " - 0s - loss: 0.6816 - accuracy: 0.5620 - recall: 0.3570 - precision: 0.5608 - fbeta_score: 0.4026 - val_loss: 0.6845 - val_accuracy: 0.5679 - val_recall: 0.6938 - val_precision: 0.6454 - val_fbeta_score: 0.6634\n",
            "\n",
            "Epoch 00002: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 3/100\n",
            " - 0s - loss: 0.6380 - accuracy: 0.6488 - recall: 0.5117 - precision: 0.6308 - fbeta_score: 0.5491 - val_loss: 0.7063 - val_accuracy: 0.4568 - val_recall: 0.1672 - val_precision: 0.8148 - val_fbeta_score: 0.2496\n",
            "\n",
            "Epoch 00003: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 4/100\n",
            " - 0s - loss: 0.5977 - accuracy: 0.6901 - recall: 0.4938 - precision: 0.7291 - fbeta_score: 0.5714 - val_loss: 0.6789 - val_accuracy: 0.5802 - val_recall: 0.6789 - val_precision: 0.6440 - val_fbeta_score: 0.6472\n",
            "\n",
            "Epoch 00004: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 5/100\n",
            " - 0s - loss: 0.5744 - accuracy: 0.7107 - recall: 0.6114 - precision: 0.7474 - fbeta_score: 0.6370 - val_loss: 0.6700 - val_accuracy: 0.5432 - val_recall: 0.8131 - val_precision: 0.6118 - val_fbeta_score: 0.6856\n",
            "\n",
            "Epoch 00005: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 6/100\n",
            " - 0s - loss: 0.5395 - accuracy: 0.7590 - recall: 0.6780 - precision: 0.7597 - fbeta_score: 0.7064 - val_loss: 0.6998 - val_accuracy: 0.5309 - val_recall: 0.4955 - val_precision: 0.6346 - val_fbeta_score: 0.5353\n",
            "\n",
            "Epoch 00006: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 7/100\n",
            " - 0s - loss: 0.5164 - accuracy: 0.7521 - recall: 0.6524 - precision: 0.7826 - fbeta_score: 0.6893 - val_loss: 0.7405 - val_accuracy: 0.5185 - val_recall: 0.2514 - val_precision: 0.8788 - val_fbeta_score: 0.3430\n",
            "\n",
            "Epoch 00007: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 8/100\n",
            " - 0s - loss: 0.5018 - accuracy: 0.7631 - recall: 0.6295 - precision: 0.8355 - fbeta_score: 0.6861 - val_loss: 0.6973 - val_accuracy: 0.4938 - val_recall: 0.6054 - val_precision: 0.5789 - val_fbeta_score: 0.5693\n",
            "\n",
            "Epoch 00008: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 9/100\n",
            " - 0s - loss: 0.4500 - accuracy: 0.8154 - recall: 0.7843 - precision: 0.8131 - fbeta_score: 0.7835 - val_loss: 0.6939 - val_accuracy: 0.5432 - val_recall: 0.7572 - val_precision: 0.6091 - val_fbeta_score: 0.6609\n",
            "\n",
            "Epoch 00009: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 10/100\n",
            " - 0s - loss: 0.4093 - accuracy: 0.8416 - recall: 0.8147 - precision: 0.8288 - fbeta_score: 0.8159 - val_loss: 0.7229 - val_accuracy: 0.4938 - val_recall: 0.6871 - val_precision: 0.5800 - val_fbeta_score: 0.6135\n",
            "\n",
            "Epoch 00010: val_fbeta_score did not improve from 0.73774\n",
            "Epoch 00010: early stopping\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.50      0.54        52\n",
            "           1       0.42      0.50      0.46        38\n",
            "\n",
            "   micro avg       0.50      0.50      0.50        90\n",
            "   macro avg       0.50      0.50      0.50        90\n",
            "weighted avg       0.51      0.50      0.50        90\n",
            " samples avg       0.50      0.50      0.50        90\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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "ImIWBEmiGNiz",
        "colab_type": "code",
        "outputId": "16fd8592-bb3a-42c5-9b06-92adb30f6291",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%run ./gdrive/'My Drive'/'capstone 2'/event_embedding/Code/Model_StockMovePredict/train_model_LSTM.py ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyVector.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyReturn.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/ 100 80 5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 726 samples, validate on 81 samples\n",
            "Epoch 1/100\n",
            " - 1s - loss: 0.8674 - accuracy: 0.5096 - recall: 0.6258 - precision: 0.5541 - fbeta_score: 0.5416 - val_loss: 0.8472 - val_accuracy: 0.4198 - val_recall: 0.9091 - val_precision: 0.3864 - val_fbeta_score: 0.5179\n",
            "\n",
            "Epoch 00001: val_fbeta_score improved from -inf to 0.51787, saving model to ./gdrive/My Drive/capstone 2/event_embedding/model2_price_move_predict.hdf5\n",
            "Epoch 2/100\n",
            " - 1s - loss: 0.7835 - accuracy: 0.5689 - recall: 0.7625 - precision: 0.5976 - fbeta_score: 0.6383 - val_loss: 0.7828 - val_accuracy: 0.4815 - val_recall: 0.5121 - val_precision: 0.4045 - val_fbeta_score: 0.3839\n",
            "\n",
            "Epoch 00002: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 3/100\n",
            " - 1s - loss: 0.7621 - accuracy: 0.5785 - recall: 0.7970 - precision: 0.5948 - fbeta_score: 0.6486 - val_loss: 0.7579 - val_accuracy: 0.5926 - val_recall: 0.0152 - val_precision: 0.0909 - val_fbeta_score: 0.0260\n",
            "\n",
            "Epoch 00003: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 4/100\n",
            " - 1s - loss: 0.7394 - accuracy: 0.5992 - recall: 0.7313 - precision: 0.6189 - fbeta_score: 0.6303 - val_loss: 0.7392 - val_accuracy: 0.5679 - val_recall: 0.2227 - val_precision: 0.4394 - val_fbeta_score: 0.2535\n",
            "\n",
            "Epoch 00004: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 5/100\n",
            " - 1s - loss: 0.7299 - accuracy: 0.5964 - recall: 0.7513 - precision: 0.6226 - fbeta_score: 0.6494 - val_loss: 0.7386 - val_accuracy: 0.5556 - val_recall: 0.3303 - val_precision: 0.3652 - val_fbeta_score: 0.3139\n",
            "\n",
            "Epoch 00005: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 6/100\n",
            " - 1s - loss: 0.7178 - accuracy: 0.5923 - recall: 0.7481 - precision: 0.6042 - fbeta_score: 0.6364 - val_loss: 0.7388 - val_accuracy: 0.5802 - val_recall: 0.0152 - val_precision: 0.0455 - val_fbeta_score: 0.0227\n",
            "\n",
            "Epoch 00006: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 7/100\n",
            " - 1s - loss: 0.7081 - accuracy: 0.6226 - recall: 0.7185 - precision: 0.6487 - fbeta_score: 0.6433 - val_loss: 0.7383 - val_accuracy: 0.5062 - val_recall: 0.3788 - val_precision: 0.3407 - val_fbeta_score: 0.3260\n",
            "\n",
            "Epoch 00007: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 8/100\n",
            " - 1s - loss: 0.6992 - accuracy: 0.6198 - recall: 0.7683 - precision: 0.6340 - fbeta_score: 0.6623 - val_loss: 0.7267 - val_accuracy: 0.5309 - val_recall: 0.2455 - val_precision: 0.3485 - val_fbeta_score: 0.2388\n",
            "\n",
            "Epoch 00008: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 9/100\n",
            " - 1s - loss: 0.6879 - accuracy: 0.6446 - recall: 0.7856 - precision: 0.6477 - fbeta_score: 0.6805 - val_loss: 0.7284 - val_accuracy: 0.5926 - val_recall: 0.1364 - val_precision: 0.2273 - val_fbeta_score: 0.1320\n",
            "\n",
            "Epoch 00009: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 10/100\n",
            " - 1s - loss: 0.6747 - accuracy: 0.6433 - recall: 0.7673 - precision: 0.6578 - fbeta_score: 0.6700 - val_loss: 0.7310 - val_accuracy: 0.5926 - val_recall: 0.2227 - val_precision: 0.3788 - val_fbeta_score: 0.2186\n",
            "\n",
            "Epoch 00010: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 11/100\n",
            " - 1s - loss: 0.6614 - accuracy: 0.6598 - recall: 0.7161 - precision: 0.6777 - fbeta_score: 0.6687 - val_loss: 0.7467 - val_accuracy: 0.5432 - val_recall: 0.2682 - val_precision: 0.3788 - val_fbeta_score: 0.2669\n",
            "\n",
            "Epoch 00011: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 12/100\n",
            " - 1s - loss: 0.6521 - accuracy: 0.6708 - recall: 0.7755 - precision: 0.6804 - fbeta_score: 0.6972 - val_loss: 0.7574 - val_accuracy: 0.5556 - val_recall: 0.3273 - val_precision: 0.4773 - val_fbeta_score: 0.3266\n",
            "\n",
            "Epoch 00012: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 13/100\n",
            " - 1s - loss: 0.5993 - accuracy: 0.7163 - recall: 0.7789 - precision: 0.7368 - fbeta_score: 0.7277 - val_loss: 0.7974 - val_accuracy: 0.5185 - val_recall: 0.2939 - val_precision: 0.4727 - val_fbeta_score: 0.2853\n",
            "\n",
            "Epoch 00013: val_fbeta_score did not improve from 0.51787\n",
            "Epoch 00013: early stopping\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.44      0.51        52\n",
            "           1       0.43      0.58      0.49        38\n",
            "\n",
            "   micro avg       0.50      0.50      0.50        90\n",
            "   macro avg       0.51      0.51      0.50        90\n",
            "weighted avg       0.52      0.50      0.50        90\n",
            " samples avg       0.50      0.50      0.50        90\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SilXPwrdvQnRPq1XBw2oS9BuABBGJF5FArM7VBedtcxAYCSAiPbGCPs+93RQRCRKReCAB+MZTxderTkMhYRQTi9+j5GQ+S3cetbsipZQvSnvferVp0q0w4nf1copLBr0xphK4H/gC2Ik1uma7iDwpIuPdmz0M3C0iW4B3gB8by3asO/0dwOfAz40x3vO+vpEz8a84xfSwz3T6YqWU5x1cDx/eBx0Gw4QXrdaEeiCNbfKu5ORkk5qaancZ3/ngHirTPmJoyd+Y++uJxEeF2V2RUsoXHNsLr14LwS3gJ0shtFWdDiciG40xydV9pk/GXsqI3+HAxS8DPuBtvatXSnlCyTF461YwLrj9vTqH/KVo0F9Ky05I8p3c6ljON6nrKa3wnpYnpVQjVFkG706DwoMw5W1rqvR6pkFfE1dNB/8Q7q18m4VbD9ldjVLKWxkDCx6EA6thwkvQcUiDnFaDviaaReM39H7GOr5h/arFdlejlPJWXz8DW+fCiEehz60NdloN+hqSIQ9wOqAlN+a/wvYcfSmJUuoybXkXlj9tTbNy1fQGPbUGfU0FhcNV0xni2MHST+dqW71Squb2r4YF90OnYXDDC/U2jPJiNOgvQ8jgn3AiKIZJuX9mxrMvs1ZntlRKXUp+Osy9DVp0hMn/Bf/ABi9Bg/5y+AcR8eN3aREezt9OP8Y3rz/M9Hc3cqxY30SllKpGcYE1jNLP3xpGGdLSljI06C9Xuz6EPLAa+kzmIf8PmbL9p0z76zzeS82isT18ppSyUUWpdSd/MhemvgOt4m0rRYO+NoLCcUx8GW7+F/2CD/Eev2H5B3OYMmcdmXlFdlenlLKbywUf/wyy1sFNL0Ocva/h0KCvi6RbcNy3itCYXrwY+AKTD/2Zic8t4e9L9mhnrVJN2fKnrcnKRs6ExIl2V6NBX2ctOyF3fgbDfs1NLGdx2OMs/WoxY59fyZrMfLurU0o1tG/fghV/gf4/gh/80u5qAA16z3AEwMjHkTsW0CbIySchs5hY9hG3v7KWh+dt0c5apZqKvV/DJw9C5+Hww783+DDKi9Gg96T4q+C+1fgljOL+itf5su2LrNq8nWv+tpx52lmrlG/L2w3v/ggiu8Kk/1g3gI2EBr2nhbaCKW/BuL/RuehbVkc8zs3Nd/Ob+VuZMmcdGUe1s1Ypn1N0FN66BfyD4LZ5EBxhd0Xn0KCvDyKQ8hO4exn+4a15vPAxPu/5ORmHChj7/Eqe1c5apXxHxWl4ZyoU5cHUudCyo90VXUCDvj616QV3fwUpd9Nj339Y13o2d3Qr54Uv0xnz/ErWZGhnrVJezeWCD++FnI1w8ysQO8DuiqqlQV/fAkJg3F9hyjsEnMrh0eyfsvjq/TidLm57dT2/mreZgqIyu6tUStXGl0/Ajo9h1B+g5w12V3NRGvQNpcdYuG81xCbTbf3vWNbp3/xqWGsWbM5l5LNfa2etUt5m4xuw+jlIvhMG3293Nd9Lg74hNY+BH30EI2fi2P0pD+65k68mBdM1upl21irlTTK+hE9/BV2vhTF/aTTDKC9Gg76h+Tlg2K/gzsXg50eHj29mXo8V/PmmXuw8dJIxz6/QzlqlGrMjO2DeHdC6J9zyOjj87a7okjTo7RI7AO5dCUm34vf1n5i8/Wcsu7sr45Lane2sXb9Xp0FWqlE5dRjengSBYXDbuxDc3O6KakSD3k7BzWHiHLjp/+DwViL/O4Ln+hzkv3cNpNLlYvKcdcz8OI3iskq7K1VKlRfDO1OgpMAK+YhYuyuqMQ36xqDvFLh3BbTqAvP+h2E7/8DnP0vmx0M68e+1B7j+uRWs1qGYStnH5YT374bczXDLaxDTz+6KLosGfWMR2QXu/AKG/gI2/YewN0Yyq+ch5t0ziACHH7e/up5HPtjGqdIKuytVqmkpPQmf/RZ2L4TRs6H7GLsrumzS2Ib0JScnm9TUVLvLsNfe5fDJQ3B8P8RfTenwmfwtLYR/rdpH2+bBPD0xieHdW9tdpVK+62Qu7F4EuxbBvhXgqoArfwpj/mx3ZRclIhuNMcnVfqZB30hVlkPqa7DiGatNMPEW0no+xC++OE7G0SJuGRDL4+N6ERHaeCZOUsprGQN5u2DXp1a4526y1rfqDD3GQfdx0GFQox5GqUHvzUpPwurnYe2L4KqkcsCd/NM1kefWHiMyLJCnb0ri2l5t7K5SKe/jckLWeti10Po6vs9a3z7ZesCx+ziI7t6ow72qOge9iIwGngccwKvGmNnnff53YIR7MRRobYxp4f7MCWxzf3bQGDP++86lQX8RJw/B8j/Bt/+FwGYcTvop96ansOVIBRP6xTDrht60DGv4t8sr5VXKS2DvMivY93xu/bXsCIT4q61w7zYGmrezu8paqVPQi4gD2ANcB2QDG4CpxpgdF9n+AaC/MeZO93KRMaZZTYvVoL+EvN2w9AnYvRAT3o4lbe7igR09CA8N5g8TEhmT5J3/SJWqN8X5VqjvWgiZy6DyNARFQLdRVrNM12shKNzuKuvs+4K+Jo90DQQyjDF73QebC0wAqg16YCowszaFqhqI7g5T34YDa5Elv2dUxlNsadONP1dM5r63yhib1I4nJyQS1SzI7kqVsk9BprszdaHVPGNc0DwWrviRFe4dhzaqF4PUt5rc0d8CjDbG/MS9/CPgSmPMBbP4iEhHYB0Qa4xxutdVApuBSmC2Meajava7B7gHoEOHDgMOHDhQp4tqMoyxOo+WzoKCDHIj+vNgwUQyA3swa3xvxveNQbykfVGpOnG5IPdbawjkroVWxypAmySrSabHOGjbx2va22ujrnf0l2MKMP9MyLt1NMbkiEhn4CsR2WaMyay6kzFmDjAHrKYbD9fku0SsqVG7jYZN/yFm+Wzm+z/OGv+hPPruRD7d2o8/3phI6+bBdleqVP3I2WT1W+3+DE4dAnFAxyEw4MfQfWyjfAmIHWoS9DlAXJXlWPe66kwBfl51hTEmx/19r4gsB/oDmRfuqmrNEQApd0GfybD2RQaveYEvg9fzTsY1THr2Fu6/YSg3X9Fe7+6VbzAG0pfAmhdg/0oICLXa2XuMg4RR1us81Tlq0nTjj9UZOxIr4DcAtxljtp+3XQ/gcyDeuA8qIi2BEmNMmYhEAWuBCRfryAXtjPWIoqPw9TOYja9TagJ4uXwsuzvfwcxbBtIuIsTu6pSqncpy2PYerPkH5O2E8BgYdJ919+4lk4vVJ08MrxwLPIc1vPI1Y8wfReRJINUYs8C9zSwg2Bgzo8p+Q4D/A1xY0y08Z4z51/edS4PegwoyMUufQHZ+TJ6J4GVuJWHMz5h8ZWe9u1feo/QEpL4O61+2mmda94YhD0DizeCvQ4rP0AemmrrsVEoXPUpw7joyXe34JPpubr7tp8RFhtldmVIXdyIH1r0EG/8N5acg/ioY8hB0HenTnaq1pUGvwBhcuz/n5KeP0qIok29NN45c+QijRt+En5/+R6MakSPbreaZbe9Z7fG9b4QhD3rdjJENTYNefcfl5NiaN2DZ07Ry5lOBP0YciCMAh38Afg5/8AsAP3/rzTl+53053J/5OapsF+Berm7fABA/wFj/0V70O5fYhktsi1WDOMDPzzq3OL6rVfyq1O1fzXZn1lfZ7uz+ft9df5ve0LJTQ/2/1XQYY00etuYFyFhqdbBe8T8w6Gc6cqaGGnJ4pWrs/By0+sFdmIFT2L7on+Rm7SX3WBGVpeU4cBId6kf75gG0C/cnKtSBAyc4K6x5QVwV4Kq0fq4sB1eJe53TWu8883lllWUnGCcg7j+3BYTzlt3f4cJ1F/sO5x0H66EYl/O7c7oqrfHVrsoqy07cvzVqL6Y/9J5o3Wm26FC3YzV1zkrY8ZEV8Ie2QFg0XPMYJN+lo2c8SO/oFS6XYcehk6xMz2dleh6p+49T7nQR5O/HwPhWXN0tmmEJ0XRr08w3OnFdLnfwO6v8Ejj/F4Tz3PWuSnCWwf5VsP1D6+EcsCbASpwIvSZ41RuHbFdeDJv+C+tehMKDENnV6mDtMwUC9LmP2tCmG3VZSsorWb/3GCvS81ixJ4/MvGIA2jQPYlhCNMMSovhB1ygim/I0C8f2wvaPrNA/vNVaF3eldaffa4LXToxV74qOwjdz4JtXoLTQ+t9syIPWw01++h6kutCgV3WSU3iaVel5rNiTz6qMfE6crkAEEmMiGJYQxbCEaAZ0bEmgfxP9DzU/A3Z8CGkfwtHtgECHwdadfs/xEK7TSJOfAWv/AZvfAWe59XDTkAehw5V2V+YzNOiVxzhdhm05J1ixJ4+V6XlsOliI02UIDXQwuHMkwxKiuKpbNPFRYb7RzHO58na77/Q/sOZbET9rAq3eN1mh3yza7gob1sH1Vvv7roXWdMD9psLgByCqq92V+RwNelVvTpZWsDazgJXpeaxMz+dAQQkA7VuEcFW3KK5KiGZI1ygiQprOTIFnHd0JaR9YoV+QYYV+/FVW6Pe4AcIi7a7w8hhjta2fPgYlx6p8P37esvt7cT6cOAghLSHlJzDwHmimr8CsLxr0qsEcKChmRXo+K/fksSazgKKySgIcwvDurbmxX3tG9mxNcIDD7jIbljHW2PDtH1ht+sf2WkM3Ow933+n/0ArDhuSstNrIqwvoc74fP3fZWX7xYwZFQGhLCGlljZgJaQWxKdD/dgjUh/Pqmwa9skWF08XmrEI+TzvMJ1tyOXqqjLBAB9cntmVCv/YM7RKJv6OJtesbY3XeprlDv/CA9axBlxFW6HcfCyEtLtzPWWk9HVpeDGVFUF4EZe7lC34uqmZb9/czP1eevniNfgHfBXVoK+uX0DnL1XwPadGk5ndvjDTole2cLsP6vQV8vDmXRWmHOFVaSWRYID/s047x/dpzRYcWTa9N3xjrJdTbP7Ta9U9kWe3YbZOgsuzc8K4srdkxxQ8Cm7m/wiDI/XNQuLUc2My9LrxKgJ8X5IHNdIoBL6RBrxqVskony3fnsWBzLkt3HqGs0kVsyxAm9IthQr/2dGvj/a91u2zGQHaq1bxzZHuVQD4T2OHnhXe4+3tYlSBvBgEhGtJNlAa9arROlVawePsRPt6Sy6r0PFwGerQNZ0K/9tzQtx2xLUPtLlEpr6BBr7xC3qkyFm7N5eMtuXx7sBCAlE4tmdCvPWOT2tEqTKekVepiNOiV1zlYUMKCLTl8tDmXjKNF+PsJV3WLZkK/GK7t2YawIJ2mSamqNOiV1zLGsPPQKT7eksMnm3PJPVFKSICD63q1YUK/GIYlRDfdJ3KVqkKDXvkEl8uQeuA4H2/OYeG2QxSWVNAiNICxSe0Y3zeG5I4tm95wTaXcNOiVzymvdLEqI4+PN+eyePsRTlc4iQgJ4AcJUVzdLZqru0XTprnOgqiaDp2PXvmcQH8/runRhmt6tKGkvJJlu/JYvvsoX+/JY+HWQwD0bNf8bOg36UnXVJOnd/TKpxhj2HX4FMt35/H1nqOk7j9OpcvQLMifIV0iubq7Ffw6bFP5Gm26UU3WqdIK1mQW8PWePL7enUdOofXof9fWzc7e7Q+Mb9X05t9RPkeDXimsu/3MvCL33X4e6/cdo7zSRXCAH4M7R3J1t2iGd29NpyidgEt5Hw16papx5k1aX++x2vf3u6dY7hgZ6g79aAZ1jiQ0ULuyVOOnQa9UDezPL2ZFeh7Ld+exNrOA0xVOAh3We3OHu9v2u7b2kffmKp+jQa/UZSqtcJK6//jZkTzpR4sAaNs8mCFdIhncJZIhXaNo3yLE5kqVsmjQK1VHOYWn+Xp3Hqsz81mbWcCxYusFHB0jQxnSJeps+Ec15RemK1tp0CvlQS6XYc/RU6zJKGBNZgHr9xZwqqwSgO5twhncJZKhXaMYGN+qab5CUdlCg16pelTpdJGWe5I17rv9DfuPUVrhwk8gqX0Eg913/CmdWhESqMM4Vf2oc9CLyGjgecABvGqMmX3e538HRrgXQ4HWxpgW7s/uAB5zf/aUMebf33cuDXrl7coqnWw+WMiazALWZObz7cFCKl2GAIfQv0NLhnSJZEiXKPrFtdCndZXH1CnoRcQB7AGuA7KBDcBUY8yOi2z/ANDfGHOniLQCUoFkwAAbgQHGmOMXO58GvfI1JeWVbNh//Owd/7acExgDIaLK4TYAABCqSURBVAEOUuJbuYM/kt4xETj8dESPqp26znUzEMgwxux1H2wuMAGoNuiBqcBM98/XA0uMMcfc+y4BRgPv1Lx8pbxbaKD/2adwAU6UVLB+X8HZO/7Zn+0CIDzYn0GdIxmWEMX1vdvqpGzKY2oS9O2BrCrL2cCV1W0oIh2BeOCr79m3fTX73QPcA9ChQ4calKSU94oIDWBU77aM6t0WsN6stXZvAWsz81mTWcCSHUf4/cfbSe7YktGJbRmT1E6Hcao68fQjf1OA+cYY5+XsZIyZA8wBq+nGwzUp1ahFhwcxvm8M4/vGAJBxtIjP0w6xaNthnlq4k6cW7qRvXAvGJrZlTGI7OkTqhGzq8tQk6HOAuCrLse511ZkC/Py8fYeft+/ympenVNPTtXUz7r8mgfuvSWB/fjGfpR3ms7RD/OmzXfzps10ktm/OmMR2jElsS+foZnaXq7xATTpj/bE6Y0diBfcG4DZjzPbztusBfA7EG/dB3Z2xG4Er3JttwuqMPXax82lnrFLVyzpWwudph1mUdujsy9N7tA1nbJIV+gltwm2uUNnJE8MrxwLPYQ2vfM0Y80cReRJINcYscG8zCwg2xsw4b987gd+5F/9ojHn9+86lQa/UpeUWnuZz951+6oHjGGP9JTDW3abfo224zsnTxOgDU0r5sKMnS/li+2EWbTvM+n0FuAzER4UxJrEtY5Pa0TumuYZ+E6BBr1QTkV9UxuLtR/gs7RBrMgtwugxxrULOtun3i2uhoe+jNOiVaoKOF5ezZMcRFqUdYnVGPhVOQ0xEMKMT2zE2qS0DOrbU0PchGvRKNXEnSipYutO601+xJ59yp4uOkaHcOiCWmwfE0i5Cx+l7Ow16pdRZp0orWLLjCO+lZrN2bwF+AsMSopmcEsfInq0J8teJ17yRBr1SqloHCoqZvzGb+RuzOXSilJahAdzYvz2TU+Lo0ba53eWpy6BBr5T6Xk6XYWV6Hu+lZrN4x2EqnIY+sRHcmhzH+L4xOq++F9CgV0rV2LHicj76Nod5qVnsOnyKIH8/xiS2ZVJyHIM6R+KnM2w2Shr0SqnLZowhLeck76Ye5OPNuZwqrSSuVQi3Dojj5gGxOtFaI6NBr5Sqk9IKJ19sP8y81CxWZxQgAj/oGsXklDiu69VGO3AbAQ16pZTHZB0r4b2N2cxPzSL3RCktQgO4sV97JiXH0StGO3DtokGvlPI4p8uwOiOfealZLN5+hHKni8T2zZmUHMeEvu2JCNUO3IakQa+UqleFJVYH7rup2ew8dJJAfz9G97Y6cId00Q7chqBBr5RqMGk5J5iXmsVH3+ZwsrSS2JZWB+4tydqBW5806JVSDa66DtxhCdFMTo7j2l76BK6nadArpWyVdayE91KzeK/KE7g39Y9lckoc3dvqC1M8QYNeKdUonHkCd15qFkt2HKHCaegb14LJyXHc0Lcd4cHagVtbGvRKqUanoKiMD91P4O45UkRIgIOxSe2YnBJHSiedQvlyadArpRotYwybswqZl5rFgs25FJc76RwVxq3Jcdw8oD2tw4PtLtEraNArpbxCSXklC7ceYl5qFhv2H8fhJ4zo3prJKXGM6B6Nv8PP7hIbLQ16pZTXycwrYl5qFu9vzCG/qIzo8CBuvsLqwI2PCrO7vEZHg14p5bUqnC6W7TrKvNQslu3Ow+kyDOzUikkpcYxNaktooL/dJTYKGvRKKZ9w5GQp72/KZt6GLPYXlNAsyJ9xSe0Y0SOawV2imvS8+Rr0SimfYozhm33HeDc1iy/SDlNc7sRPoG9cC4Z1jWJYt2j6xbUgoAm16WvQK6V8VoXTxbcHC1mVnsfKjHy2ZBXiMtAsyJ9BnVsxLCGaHyRE0TkqzKeHbGrQK6WajBMlFazdm8/KdOvr4LESAGIigs+G/tCuUbQKC7S5Us/SoFdKNVkHC0pYmZHHyj35rMnM52RpJSKQGBPBDxKiGJYQxYCOLb1+7h0NeqWUAiqdLrblnGBlej6r0vPZdPA4lS5DSICDgfGtGJYQxbCEaLq1aeZ1zTx1DnoRGQ08DziAV40xs6vZZhIwCzDAFmPMbe71TmCbe7ODxpjx33cuDXqlVEMpKqtkXWYBqzLyWZmeR2ZeMQCtw4PO3u0P7RrlFU/n1inoRcQB7AGuA7KBDcBUY8yOKtskAPOAa4wxx0WktTHmqPuzImNMs5oWq0GvlLJLbuFpVqXnsyI9j9UZ+RwvqQCgZ7vmzJ6YRN+4FjZXeHHfF/Q1edJgIJBhjNnrPthcYAKwo8o2dwMvGmOOA5wJeaWU8iYxLUKYlBLHpJQ4XC7DjkMnWZGex3/WHOAX727ms4eGERzgfW35NRlk2h7IqrKc7V5XVTegm4isFpF17qaeM4JFJNW9/sY61quUUg3Cz09IbB/Bz4Z35W+T+rIvv5i/L91jd1m14qmnCfyBBGA4MBV4RUTO/I3T0f3nxG3AcyLS5fydReQe9y+D1Ly8PA+VpJRSnjG0axRTUuJ4ZcVetmYX2l3OZatJ0OcAcVWWY93rqsoGFhhjKowx+7Da9BMAjDE57u97geVA//NPYIyZY4xJNsYkR0dHX/ZFKKVUffvduJ5Ehwfxm/lbKa902V3OZalJ0G8AEkQkXkQCgSnAgvO2+Qjrbh4RicJqytkrIi1FJKjK+qGc27avlFJeoXlwAH+8MYldh0/xz+WZdpdzWS4Z9MaYSuB+4AtgJzDPGLNdRJ4UkTNDJb8ACkRkB7AMmG6MKQB6AqkissW9fnbV0TpKKeVNru3Vhhv6xvC/y9LZffiU3eXUmD4wpZRSl6GgqIzr/r6CuFahfHDfEBx+jePBqu8bXtl0pnZTSikPiGwWxMwberElq5DXVu2zu5wa0aBXSqnLNL5vDNf2bM3fluxmf36x3eVckga9UkpdJhHhqRuTCPDzY8YHW3G5GlcT+Pk06JVSqhbaRgTz6LierNt7jHc2HLS7nO+lQa+UUrU0OSWOoV0j+dOiXeQWnra7nIvSoFdKqVoSEf50Ux+cLsOjH26jsY1iPEODXiml6qBDZCi/vr47y3bn8dHm8ycNaBw06JVSqo5+PKQTV3RowROf7CDvVJnd5VxAg14pperI4Sc8c0sfSsqczFqw3e5yLqBBr5RSHtC1dTgPjuzKwm2H+DztsN3lnEODXimlPOTeq7vQq11zHv84jRPut1M1Bhr0SinlIQEOP565pQ/Hist5amHjmb9Rg14ppTwosX0E917Vmfc2ZrNiT+N4kZIGvVJKediDIxPoEh3GIx9so7is0u5yNOiVUsrTggMc/PnmPuSeOM0zn++yuxwNeqWUqg/JnVpxx+BO/HvtATbsP2ZrLRr0SilVT6Zf353YliH8dv5WSiucttWhQa+UUvUkLMifP01MYm9+Mc9/mW5bHRr0SilVj4YlRDMpOZY5K/aSlnPClho06JVSqp49Oq4XkWGBTJ+/lQqnq8HPr0GvlFL1LCIkgKduTGTnoZO8vDyzwc+vQa+UUg1gVO+2/LBPO/7xVQbpR0416Lk16JVSqoHMGt+bsCAH0+dvxdmA75nVoFdKqQYS1SyImTf0ZnNWIa+v3tdg59WgV0qpBjShXwzX9GjNXxfv5kBBcYOcU4NeKaUakIjwx5sSCfDzY8b7DfOeWQ16pZRqYO0iQnhkbE/W7i1g7oasej+fBr1SStlg6sA4BneO5OmFOzl04nS9nqtGQS8io0Vkt4hkiMiMi2wzSUR2iMh2EXm7yvo7RCTd/XWHpwpXSilvJiLMvjmJCpeLxz5Mq9cmnEsGvYg4gBeBMUAvYKqI9DpvmwTgEWCoMaY38Av3+lbATOBKYCAwU0RaevQKlFLKS3WMDOPXo7rz5a6jLNiSW2/nqckd/UAgwxiz1xhTDswFJpy3zd3Ai8aY4wDGmKPu9dcDS4wxx9yfLQFGe6Z0pZTyfv9vaDz9O7Rg1oLt5BeV1cs5ahL07YGqvQXZ7nVVdQO6ichqEVknIqMvY19E5B4RSRWR1Ly8xvHqLaWUaggOP+GZm/tQXOZk1oLt9XIOfw8eJwEYDsQCK0QkqaY7G2PmAHMAkpOTG+5xMaWUagQS2oTzy+u6cbrCictl8PMTjx6/JkGfA8RVWY51r6sqG1hvjKkA9onIHqzgz8EK/6r7Lq9tsUop5avuG96l3o5dk6abDUCCiMSLSCAwBVhw3jYf4Q50EYnCasrZC3wBjBKRlu5O2FHudUoppRrIJe/ojTGVInI/VkA7gNeMMdtF5Ekg1RizgO8CfQfgBKYbYwoAROQPWL8sAJ40xtj78kSllGpipCEev70cycnJJjU11e4ylFLKq4jIRmNMcnWf6ZOxSinl4zTolVLKx2nQK6WUj9OgV0opH6dBr5RSPq7RjboRkTzgQB0OEQXke6gcO/nKdYBeS2PlK9fiK9cBdbuWjsaY6Oo+aHRBX1ciknqxIUbexFeuA/RaGitfuRZfuQ6ov2vRphullPJxGvRKKeXjfDHo59hdgIf4ynWAXktj5SvX4ivXAfV0LT7XRq+UUupcvnhHr5RSqgoNeqWU8nE+E/QiMlpEdotIhojMsLue2hKROBFZJiI7RGS7iDxkd011ISIOEflWRD61u5a6EJEWIjJfRHaJyE4RGWx3TbUlIr90/9tKE5F3RCTY7ppqSkReE5GjIpJWZV0rEVkiIunu7y3trLGmLnItf3H/G9sqIh+KSAtPnMsngl5EHMCLwBigFzBVRHrZW1WtVQIPG2N6AYOAn3vxtQA8BOy0uwgPeB743BjTA+iLl16TiLQHHgSSjTGJWO+YmGJvVZflDWD0eetmAF8aYxKAL93L3uANLryWJUCiMaYPsAd4xBMn8omgBwYCGcaYvcaYcmAuMMHmmmrFGHPIGLPJ/fMprEC54IXq3kBEYoFxwKt211IXIhIBXAX8C8AYU26MKbS3qjrxB0JExB8IBXJtrqfGjDErgPNfXjQB+Lf7538DNzZoUbVU3bUYYxYbYyrdi+uwXr9aZ74S9O2BrCrL2XhpOFYlIp2A/sB6eyupteeA3wAuuwupo3ggD3jd3Qz1qoiE2V1UbRhjcoC/AgeBQ8AJY8xie6uqszbGmEPunw8DbewsxoPuBD7zxIF8Jeh9jog0A94HfmGMOWl3PZdLRH4IHDXGbLS7Fg/wB64A/mmM6Q8U4z3NA+dwt19PwPrlFQOEicg0e6vyHGONF/f6MeMi8ihWM+5bnjierwR9DhBXZTnWvc4riUgAVsi/ZYz5wO56amkoMF5E9mM1pV0jIm/aW1KtZQPZxpgzf1nNxwp+b3QtsM8Yk2eMqQA+AIbYXFNdHRGRdgDu70dtrqdOROTHwA+B242HHnTylaDfACSISLyIBGJ1Li2wuaZaERHBagveaYx51u56assY84gxJtYY0wnr/4+vjDFeeedojDkMZIlId/eqkcAOG0uqi4PAIBEJdf9bG4mXdixXsQC4w/3zHcDHNtZSJyIyGqu5c7wxpsRTx/WJoHd3XtwPfIH1j3aeMWa7vVXV2lDgR1h3wJvdX2PtLkrxAPCWiGwF+gFP21xPrbj/KpkPbAK2YWWA10whICLvAGuB7iKSLSJ3AbOB60QkHesvltl21lhTF7mW/wXCgSXu//Zf9si5dAoEpZTybT5xR6+UUuriNOiVUsrHadArpZSP06BXSikfp0GvlFI+ToNeKaV8nAa9Ukr5uP8P4Ayn2fit7GgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "grvrBcwHHv0-",
        "colab_type": "code",
        "outputId": "e846cf49-4800-4ddb-da19-d1d146a15b9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%run ./gdrive/'My Drive'/'capstone 2'/event_embedding/Code/Model_StockMovePredict/train_model_fullly_connected.py ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyVector.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/Thesis_data/ModelEvents_result/DailyReturn.npy ./gdrive/'My Drive'/'capstone 2'/event_embedding/ 200 80 5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 726 samples, validate on 81 samples\n",
            "Epoch 1/200\n",
            " - 1s - loss: 1.0864 - accuracy: 0.5000 - recall: 0.5299 - precision: 0.5791 - fbeta_score: 0.5123 - val_loss: 0.7011 - val_accuracy: 0.4444 - val_recall: 1.0000 - val_precision: 0.4015 - val_fbeta_score: 0.5620\n",
            "\n",
            "Epoch 00001: val_fbeta_score improved from -inf to 0.56204, saving model to ./gdrive/My Drive/capstone 2/event_embedding/model2_price_move_predict.hdf5\n",
            "Epoch 2/200\n",
            " - 0s - loss: 0.9595 - accuracy: 0.5041 - recall: 0.6196 - precision: 0.5424 - fbeta_score: 0.5687 - val_loss: 0.7596 - val_accuracy: 0.4198 - val_recall: 1.0000 - val_precision: 0.3909 - val_fbeta_score: 0.5509\n",
            "\n",
            "Epoch 00002: val_fbeta_score did not improve from 0.56204\n",
            "Epoch 3/200\n",
            " - 0s - loss: 0.9338 - accuracy: 0.5138 - recall: 0.5803 - precision: 0.5617 - fbeta_score: 0.5538 - val_loss: 0.6995 - val_accuracy: 0.5062 - val_recall: 0.8910 - val_precision: 0.4222 - val_fbeta_score: 0.5700\n",
            "\n",
            "Epoch 00003: val_fbeta_score improved from 0.56204 to 0.57004, saving model to ./gdrive/My Drive/capstone 2/event_embedding/model2_price_move_predict.hdf5\n",
            "Epoch 4/200\n",
            " - 0s - loss: 0.8124 - accuracy: 0.5317 - recall: 0.6070 - precision: 0.5677 - fbeta_score: 0.5770 - val_loss: 0.6774 - val_accuracy: 0.5802 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_fbeta_score: 0.0000e+00\n",
            "\n",
            "Epoch 00004: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 5/200\n",
            " - 0s - loss: 0.7799 - accuracy: 0.5565 - recall: 0.6478 - precision: 0.6019 - fbeta_score: 0.6074 - val_loss: 0.7170 - val_accuracy: 0.4321 - val_recall: 1.0000 - val_precision: 0.3966 - val_fbeta_score: 0.5557\n",
            "\n",
            "Epoch 00005: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 6/200\n",
            " - 0s - loss: 0.7743 - accuracy: 0.5069 - recall: 0.6088 - precision: 0.5563 - fbeta_score: 0.5694 - val_loss: 0.7190 - val_accuracy: 0.4444 - val_recall: 1.0000 - val_precision: 0.4010 - val_fbeta_score: 0.5601\n",
            "\n",
            "Epoch 00006: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 7/200\n",
            " - 0s - loss: 0.7323 - accuracy: 0.5992 - recall: 0.6946 - precision: 0.6233 - fbeta_score: 0.6486 - val_loss: 0.6829 - val_accuracy: 0.5802 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_fbeta_score: 0.0000e+00\n",
            "\n",
            "Epoch 00007: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 8/200\n",
            " - 0s - loss: 0.7512 - accuracy: 0.5482 - recall: 0.5894 - precision: 0.5891 - fbeta_score: 0.5723 - val_loss: 0.6734 - val_accuracy: 0.5802 - val_recall: 0.0196 - val_precision: 0.1667 - val_fbeta_score: 0.0351\n",
            "\n",
            "Epoch 00008: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 9/200\n",
            " - 0s - loss: 0.7158 - accuracy: 0.5771 - recall: 0.6349 - precision: 0.6242 - fbeta_score: 0.6067 - val_loss: 0.8343 - val_accuracy: 0.4198 - val_recall: 1.0000 - val_precision: 0.3909 - val_fbeta_score: 0.5509\n",
            "\n",
            "Epoch 00009: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 10/200\n",
            " - 0s - loss: 0.7435 - accuracy: 0.5592 - recall: 0.7051 - precision: 0.6026 - fbeta_score: 0.6087 - val_loss: 0.6883 - val_accuracy: 0.5926 - val_recall: 0.0196 - val_precision: 0.3333 - val_fbeta_score: 0.0370\n",
            "\n",
            "Epoch 00010: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 11/200\n",
            " - 0s - loss: 0.6896 - accuracy: 0.6019 - recall: 0.6033 - precision: 0.6929 - fbeta_score: 0.6012 - val_loss: 0.6833 - val_accuracy: 0.5062 - val_recall: 0.0845 - val_precision: 0.1984 - val_fbeta_score: 0.1184\n",
            "\n",
            "Epoch 00011: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 12/200\n",
            " - 0s - loss: 0.7054 - accuracy: 0.5826 - recall: 0.7394 - precision: 0.6183 - fbeta_score: 0.6484 - val_loss: 0.6943 - val_accuracy: 0.5062 - val_recall: 0.4713 - val_precision: 0.4076 - val_fbeta_score: 0.4259\n",
            "\n",
            "Epoch 00012: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 13/200\n",
            " - 0s - loss: 0.6285 - accuracy: 0.6680 - recall: 0.8010 - precision: 0.6833 - fbeta_score: 0.7257 - val_loss: 0.6925 - val_accuracy: 0.5679 - val_recall: 0.0196 - val_precision: 0.1111 - val_fbeta_score: 0.0333\n",
            "\n",
            "Epoch 00013: val_fbeta_score did not improve from 0.57004\n",
            "Epoch 00013: early stopping\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.12      0.19        52\n",
            "           1       0.42      0.89      0.58        38\n",
            "\n",
            "   micro avg       0.44      0.44      0.44        90\n",
            "   macro avg       0.51      0.51      0.38        90\n",
            "weighted avg       0.53      0.44      0.36        90\n",
            " samples avg       0.44      0.44      0.44        90\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}