rbm_dbn_gaussian.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rbm-dbn-gaussian.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVULVDgcDveK",
        "outputId": "da4b37f7-e02d-40d2-e4cb-a2137e748d87"
      },
      "source": [
        "!unzip /content/drive/MyDrive/brain-tumor-2D.zip"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/brain-tumor-2D.zip\n",
            "  inflating: brain_tumor_dataset/no/1 no.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/10 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/11 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/12 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/13 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/14 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/15 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/17 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/18 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/19 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/2 no.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/20 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/21 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/22 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/23 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/24 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/25 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/26 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/27 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/28 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/29 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/3 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/30 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/31 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/32 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/33 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/34 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/35 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/36 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/37 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/38 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/39 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/4 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/40 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/41 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/42 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/43 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/44no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/45 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/46 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/47 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/48 no.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/49 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/5 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/50 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/6 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/7 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/8 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/9 no.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N1.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N11.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N15.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N16.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N17.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N19.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N2.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N20.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N21.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N22.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N26.JPG  \n",
            "  inflating: brain_tumor_dataset/no/N3.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N5.jpg  \n",
            "  inflating: brain_tumor_dataset/no/N6.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No11.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No12.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No13.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No14.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No15.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No16.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No17.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No18.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No19.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No20.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No21.jpg  \n",
            "  inflating: brain_tumor_dataset/no/No22.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 1.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 10.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 100.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 2.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 3.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 4.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 5.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/no 6.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 7.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/no 8.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 89.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 9.png  \n",
            "  inflating: brain_tumor_dataset/no/no 90.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 91.jpeg  \n",
            "  inflating: brain_tumor_dataset/no/no 92.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 923.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 94.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 95.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 96.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 97.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 98.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no 99.jpg  \n",
            "  inflating: brain_tumor_dataset/no/no.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y1.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y10.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y100.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y101.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y102.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y103.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y104.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y105.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y106.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y107.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y108.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y109.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y11.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y111.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y112.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y113.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y114.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y115.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y116.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y117.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y12.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y120.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y13.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y14.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y146.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y147.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y148.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y15.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y153.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y154.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y155.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y156.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y157.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y158.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y159.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y16.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y160.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y161.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y162.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y163.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y164.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y165.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y166.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y167.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y168.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y169.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y17.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y170.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y18.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y180.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y181.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y182.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y183.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y184.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y185.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y186.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y187.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y188.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y19.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y192.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y193.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y194.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y195.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y2.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y20.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y21.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y22.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y23.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y24.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y242.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y243.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y244.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y245.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y246.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y247.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y248.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y249.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y25.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y250.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y251.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y252.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y253.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y254.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y255.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y256.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y257.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y258.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y259.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y26.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y27.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y28.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y29.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y3.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y30.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y31.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y32.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y33.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y34.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y35.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y36.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y37.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y38.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y39.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y4.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y40.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y41.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y42.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y44.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y45.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y46.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y47.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y49.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y50.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y51.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y52.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y53.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y54.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y55.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y56.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y58.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y59.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y6.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y60.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y61.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y62.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y65.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y66.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y67.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y69.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y7.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y70.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y71.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y73.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y74.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y75.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y76.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y77.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y78.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y79.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y8.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y81.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y82.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y85.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y86.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y89.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y9.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y90.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y91.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y92.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y92.png  \n",
            "  inflating: brain_tumor_dataset/yes/Y95.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y96.jpg  \n",
            "  inflating: brain_tumor_dataset/yes/Y97.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y98.JPG  \n",
            "  inflating: brain_tumor_dataset/yes/Y99.JPG  \n",
            "  inflating: no/1 no.jpeg            \n",
            "  inflating: no/10 no.jpg            \n",
            "  inflating: no/11 no.jpg            \n",
            "  inflating: no/12 no.jpg            \n",
            "  inflating: no/13 no.jpg            \n",
            "  inflating: no/14 no.jpg            \n",
            "  inflating: no/15 no.jpg            \n",
            "  inflating: no/17 no.jpg            \n",
            "  inflating: no/18 no.jpg            \n",
            "  inflating: no/19 no.jpg            \n",
            "  inflating: no/2 no.jpeg            \n",
            "  inflating: no/20 no.jpg            \n",
            "  inflating: no/21 no.jpg            \n",
            "  inflating: no/22 no.jpg            \n",
            "  inflating: no/23 no.jpg            \n",
            "  inflating: no/24 no.jpg            \n",
            "  inflating: no/25 no.jpg            \n",
            "  inflating: no/26 no.jpg            \n",
            "  inflating: no/27 no.jpg            \n",
            "  inflating: no/28 no.jpg            \n",
            "  inflating: no/29 no.jpg            \n",
            "  inflating: no/3 no.jpg             \n",
            "  inflating: no/30 no.jpg            \n",
            "  inflating: no/31 no.jpg            \n",
            "  inflating: no/32 no.jpg            \n",
            "  inflating: no/33 no.jpg            \n",
            "  inflating: no/34 no.jpg            \n",
            "  inflating: no/35 no.jpg            \n",
            "  inflating: no/36 no.jpg            \n",
            "  inflating: no/37 no.jpg            \n",
            "  inflating: no/38 no.jpg            \n",
            "  inflating: no/39 no.jpg            \n",
            "  inflating: no/4 no.jpg             \n",
            "  inflating: no/40 no.jpg            \n",
            "  inflating: no/41 no.jpg            \n",
            "  inflating: no/42 no.jpg            \n",
            "  inflating: no/43 no.jpg            \n",
            "  inflating: no/44no.jpg             \n",
            "  inflating: no/45 no.jpg            \n",
            "  inflating: no/46 no.jpg            \n",
            "  inflating: no/47 no.jpg            \n",
            "  inflating: no/48 no.jpeg           \n",
            "  inflating: no/49 no.jpg            \n",
            "  inflating: no/5 no.jpg             \n",
            "  inflating: no/50 no.jpg            \n",
            "  inflating: no/6 no.jpg             \n",
            "  inflating: no/7 no.jpg             \n",
            "  inflating: no/8 no.jpg             \n",
            "  inflating: no/9 no.jpg             \n",
            "  inflating: no/N1.JPG               \n",
            "  inflating: no/N11.jpg              \n",
            "  inflating: no/N15.jpg              \n",
            "  inflating: no/N16.jpg              \n",
            "  inflating: no/N17.jpg              \n",
            "  inflating: no/N19.JPG              \n",
            "  inflating: no/N2.JPG               \n",
            "  inflating: no/N20.JPG              \n",
            "  inflating: no/N21.jpg              \n",
            "  inflating: no/N22.JPG              \n",
            "  inflating: no/N26.JPG              \n",
            "  inflating: no/N3.jpg               \n",
            "  inflating: no/N5.jpg               \n",
            "  inflating: no/N6.jpg               \n",
            "  inflating: no/No11.jpg             \n",
            "  inflating: no/No12.jpg             \n",
            "  inflating: no/No13.jpg             \n",
            "  inflating: no/No14.jpg             \n",
            "  inflating: no/No15.jpg             \n",
            "  inflating: no/No16.jpg             \n",
            "  inflating: no/No17.jpg             \n",
            "  inflating: no/No18.jpg             \n",
            "  inflating: no/No19.jpg             \n",
            "  inflating: no/No20.jpg             \n",
            "  inflating: no/No21.jpg             \n",
            "  inflating: no/No22.jpg             \n",
            "  inflating: no/no 1.jpg             \n",
            "  inflating: no/no 10.jpg            \n",
            "  inflating: no/no 100.jpg           \n",
            "  inflating: no/no 2.jpg             \n",
            "  inflating: no/no 3.jpg             \n",
            "  inflating: no/no 4.jpg             \n",
            "  inflating: no/no 5.jpeg            \n",
            "  inflating: no/no 6.jpg             \n",
            "  inflating: no/no 7.jpeg            \n",
            "  inflating: no/no 8.jpg             \n",
            "  inflating: no/no 89.jpg            \n",
            "  inflating: no/no 9.png             \n",
            "  inflating: no/no 90.jpg            \n",
            "  inflating: no/no 91.jpeg           \n",
            "  inflating: no/no 92.jpg            \n",
            "  inflating: no/no 923.jpg           \n",
            "  inflating: no/no 94.jpg            \n",
            "  inflating: no/no 95.jpg            \n",
            "  inflating: no/no 96.jpg            \n",
            "  inflating: no/no 97.jpg            \n",
            "  inflating: no/no 98.jpg            \n",
            "  inflating: no/no 99.jpg            \n",
            "  inflating: no/no.jpg               \n",
            "  inflating: yes/Y1.jpg              \n",
            "  inflating: yes/Y10.jpg             \n",
            "  inflating: yes/Y100.JPG            \n",
            "  inflating: yes/Y101.jpg            \n",
            "  inflating: yes/Y102.jpg            \n",
            "  inflating: yes/Y103.jpg            \n",
            "  inflating: yes/Y104.jpg            \n",
            "  inflating: yes/Y105.jpg            \n",
            "  inflating: yes/Y106.jpg            \n",
            "  inflating: yes/Y107.jpg            \n",
            "  inflating: yes/Y108.jpg            \n",
            "  inflating: yes/Y109.JPG            \n",
            "  inflating: yes/Y11.jpg             \n",
            "  inflating: yes/Y111.JPG            \n",
            "  inflating: yes/Y112.JPG            \n",
            "  inflating: yes/Y113.JPG            \n",
            "  inflating: yes/Y114.JPG            \n",
            "  inflating: yes/Y115.JPG            \n",
            "  inflating: yes/Y116.JPG            \n",
            "  inflating: yes/Y117.JPG            \n",
            "  inflating: yes/Y12.jpg             \n",
            "  inflating: yes/Y120.JPG            \n",
            "  inflating: yes/Y13.jpg             \n",
            "  inflating: yes/Y14.jpg             \n",
            "  inflating: yes/Y146.JPG            \n",
            "  inflating: yes/Y147.JPG            \n",
            "  inflating: yes/Y148.JPG            \n",
            "  inflating: yes/Y15.jpg             \n",
            "  inflating: yes/Y153.jpg            \n",
            "  inflating: yes/Y154.jpg            \n",
            "  inflating: yes/Y155.JPG            \n",
            "  inflating: yes/Y156.JPG            \n",
            "  inflating: yes/Y157.JPG            \n",
            "  inflating: yes/Y158.JPG            \n",
            "  inflating: yes/Y159.JPG            \n",
            "  inflating: yes/Y16.JPG             \n",
            "  inflating: yes/Y160.JPG            \n",
            "  inflating: yes/Y161.JPG            \n",
            "  inflating: yes/Y162.jpg            \n",
            "  inflating: yes/Y163.JPG            \n",
            "  inflating: yes/Y164.JPG            \n",
            "  inflating: yes/Y165.JPG            \n",
            "  inflating: yes/Y166.JPG            \n",
            "  inflating: yes/Y167.JPG            \n",
            "  inflating: yes/Y168.jpg            \n",
            "  inflating: yes/Y169.jpg            \n",
            "  inflating: yes/Y17.jpg             \n",
            "  inflating: yes/Y170.JPG            \n",
            "  inflating: yes/Y18.JPG             \n",
            "  inflating: yes/Y180.jpg            \n",
            "  inflating: yes/Y181.jpg            \n",
            "  inflating: yes/Y182.JPG            \n",
            "  inflating: yes/Y183.jpg            \n",
            "  inflating: yes/Y184.JPG            \n",
            "  inflating: yes/Y185.jpg            \n",
            "  inflating: yes/Y186.jpg            \n",
            "  inflating: yes/Y187.jpg            \n",
            "  inflating: yes/Y188.jpg            \n",
            "  inflating: yes/Y19.JPG             \n",
            "  inflating: yes/Y192.JPG            \n",
            "  inflating: yes/Y193.JPG            \n",
            "  inflating: yes/Y194.jpg            \n",
            "  inflating: yes/Y195.JPG            \n",
            "  inflating: yes/Y2.jpg              \n",
            "  inflating: yes/Y20.jpg             \n",
            "  inflating: yes/Y21.jpg             \n",
            "  inflating: yes/Y22.jpg             \n",
            "  inflating: yes/Y23.JPG             \n",
            "  inflating: yes/Y24.jpg             \n",
            "  inflating: yes/Y242.JPG            \n",
            "  inflating: yes/Y243.JPG            \n",
            "  inflating: yes/Y244.JPG            \n",
            "  inflating: yes/Y245.jpg            \n",
            "  inflating: yes/Y246.JPG            \n",
            "  inflating: yes/Y247.JPG            \n",
            "  inflating: yes/Y248.JPG            \n",
            "  inflating: yes/Y249.JPG            \n",
            "  inflating: yes/Y25.jpg             \n",
            "  inflating: yes/Y250.jpg            \n",
            "  inflating: yes/Y251.JPG            \n",
            "  inflating: yes/Y252.jpg            \n",
            "  inflating: yes/Y253.JPG            \n",
            "  inflating: yes/Y254.jpg            \n",
            "  inflating: yes/Y255.JPG            \n",
            "  inflating: yes/Y256.JPG            \n",
            "  inflating: yes/Y257.jpg            \n",
            "  inflating: yes/Y258.JPG            \n",
            "  inflating: yes/Y259.JPG            \n",
            "  inflating: yes/Y26.jpg             \n",
            "  inflating: yes/Y27.jpg             \n",
            "  inflating: yes/Y28.jpg             \n",
            "  inflating: yes/Y29.jpg             \n",
            "  inflating: yes/Y3.jpg              \n",
            "  inflating: yes/Y30.jpg             \n",
            "  inflating: yes/Y31.jpg             \n",
            "  inflating: yes/Y32.jpg             \n",
            "  inflating: yes/Y33.jpg             \n",
            "  inflating: yes/Y34.jpg             \n",
            "  inflating: yes/Y35.jpg             \n",
            "  inflating: yes/Y36.JPG             \n",
            "  inflating: yes/Y37.jpg             \n",
            "  inflating: yes/Y38.jpg             \n",
            "  inflating: yes/Y39.jpg             \n",
            "  inflating: yes/Y4.jpg              \n",
            "  inflating: yes/Y40.JPG             \n",
            "  inflating: yes/Y41.jpg             \n",
            "  inflating: yes/Y42.jpg             \n",
            "  inflating: yes/Y44.JPG             \n",
            "  inflating: yes/Y45.JPG             \n",
            "  inflating: yes/Y46.jpg             \n",
            "  inflating: yes/Y47.JPG             \n",
            "  inflating: yes/Y49.JPG             \n",
            "  inflating: yes/Y50.JPG             \n",
            "  inflating: yes/Y51.jpg             \n",
            "  inflating: yes/Y52.jpg             \n",
            "  inflating: yes/Y53.jpg             \n",
            "  inflating: yes/Y54.jpg             \n",
            "  inflating: yes/Y55.jpg             \n",
            "  inflating: yes/Y56.jpg             \n",
            "  inflating: yes/Y58.JPG             \n",
            "  inflating: yes/Y59.JPG             \n",
            "  inflating: yes/Y6.jpg              \n",
            "  inflating: yes/Y60.jpg             \n",
            "  inflating: yes/Y61.jpg             \n",
            "  inflating: yes/Y62.jpg             \n",
            "  inflating: yes/Y65.JPG             \n",
            "  inflating: yes/Y66.JPG             \n",
            "  inflating: yes/Y67.JPG             \n",
            "  inflating: yes/Y69.jpg             \n",
            "  inflating: yes/Y7.jpg              \n",
            "  inflating: yes/Y70.jpg             \n",
            "  inflating: yes/Y71.JPG             \n",
            "  inflating: yes/Y73.jpg             \n",
            "  inflating: yes/Y74.jpg             \n",
            "  inflating: yes/Y75.JPG             \n",
            "  inflating: yes/Y76.jpg             \n",
            "  inflating: yes/Y77.jpg             \n",
            "  inflating: yes/Y78.jpg             \n",
            "  inflating: yes/Y79.jpg             \n",
            "  inflating: yes/Y8.jpg              \n",
            "  inflating: yes/Y81.jpg             \n",
            "  inflating: yes/Y82.jpg             \n",
            "  inflating: yes/Y85.JPG             \n",
            "  inflating: yes/Y86.JPG             \n",
            "  inflating: yes/Y89.JPG             \n",
            "  inflating: yes/Y9.jpg              \n",
            "  inflating: yes/Y90.jpg             \n",
            "  inflating: yes/Y91.jpg             \n",
            "  inflating: yes/Y92.jpg             \n",
            "  inflating: yes/Y92.png             \n",
            "  inflating: yes/Y95.jpg             \n",
            "  inflating: yes/Y96.jpg             \n",
            "  inflating: yes/Y97.JPG             \n",
            "  inflating: yes/Y98.JPG             \n",
            "  inflating: yes/Y99.JPG             \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAE5E6JN_c6o"
      },
      "source": [
        "\r\n",
        "import numpy as np\r\n",
        "import torch\r\n",
        "import pandas as pd\r\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMQJ3vfzI-uR"
      },
      "source": [
        "# Gaussian Bernoulli"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGzcjYUyGFdU"
      },
      "source": [
        "#from RBM import RBM\r\n",
        "import torch\r\n",
        "\r\n",
        "class GaussianBernoulliRBM(RBM):\r\n",
        "\r\n",
        "    '''\r\n",
        "    Visisble layer can assume real values\r\n",
        "    Hidden layer assumes Binarry Values only\r\n",
        "    '''\r\n",
        "\r\n",
        "    def to_visible(self,X):\r\n",
        "        '''\r\n",
        "        the visible units follow gaussian distributions here\r\n",
        "        :params X: torch tensor shape = (n_samples , n_features)\r\n",
        "        :returns X_prob - the new reconstructed layers(probabilities)\r\n",
        "                sample_X_prob - sample of new layer(Gibbs Sampling)\r\n",
        "        '''\r\n",
        "\r\n",
        "        X_prob = torch.matmul(X ,self.W.transpose( 0 , 1) )\r\n",
        "        X_prob = torch.add(X_prob , self.v_bias)\r\n",
        "\r\n",
        "        sample_X_prob = X_prob + torch.randn(X_prob.shape)\r\n",
        "\r\n",
        "        return X_prob,sample_X_prob"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9RyMjyQGHUs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcZEy7WOEzDt"
      },
      "source": [
        "# DBN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCc_6n4HEgCH"
      },
      "source": [
        "import torch\r\n",
        "from torch.autograd import Variable\r\n",
        "import torch.nn as nn\r\n",
        "import torch.nn.functional as F\r\n",
        "#from RBM import RBM\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "class DBN(nn.Module):\r\n",
        "    def __init__(self,\r\n",
        "                visible_units = 256,\r\n",
        "                hidden_units = [64 , 100],\r\n",
        "                k = 2,\r\n",
        "                learning_rate = 1e-5,\r\n",
        "                learning_rate_decay = False,\r\n",
        "                xavier_init = False,\r\n",
        "                increase_to_cd_k = False,\r\n",
        "                use_gpu = False\r\n",
        "                ):\r\n",
        "        super(DBN,self).__init__()\r\n",
        "\r\n",
        "        self.n_layers = len(hidden_units)\r\n",
        "        self.rbm_layers =[]\r\n",
        "        self.rbm_nodes = []\r\n",
        "\r\n",
        "        # Creating different RBM layers\r\n",
        "        for i in range(self.n_layers ):\r\n",
        "            input_size = 0\r\n",
        "            if i==0:\r\n",
        "                input_size = visible_units\r\n",
        "            else:\r\n",
        "                input_size = hidden_units[i-1]\r\n",
        "            rbm = RBM(visible_units = input_size,\r\n",
        "                    hidden_units = hidden_units[i],\r\n",
        "                    k= k,\r\n",
        "                    learning_rate = learning_rate,\r\n",
        "                    learning_rate_decay = learning_rate_decay,\r\n",
        "                    xavier_init = xavier_init,\r\n",
        "                    increase_to_cd_k = increase_to_cd_k,\r\n",
        "                    use_gpu=use_gpu)\r\n",
        "\r\n",
        "            self.rbm_layers.append(rbm)\r\n",
        "\r\n",
        "        # rbm_layers = [RBM(rbn_nodes[i-1] , rbm_nodes[i],use_gpu=use_cuda) for i in range(1,len(rbm_nodes))]\r\n",
        "        self.W_rec = [nn.Parameter(self.rbm_layers[i].W.data.clone()) for i in range(self.n_layers-1)]\r\n",
        "        self.W_gen = [nn.Parameter(self.rbm_layers[i].W.data) for i in range(self.n_layers-1)]\r\n",
        "        self.bias_rec = [nn.Parameter(self.rbm_layers[i].h_bias.data.clone()) for i in range(self.n_layers-1)]\r\n",
        "        self.bias_gen = [nn.Parameter(self.rbm_layers[i].v_bias.data) for i in range(self.n_layers-1)]\r\n",
        "        self.W_mem = nn.Parameter(self.rbm_layers[-1].W.data)\r\n",
        "        self.v_bias_mem = nn.Parameter(self.rbm_layers[-1].v_bias.data)\r\n",
        "        self.h_bias_mem = nn.Parameter(self.rbm_layers[-1].h_bias.data)\r\n",
        "\r\n",
        "        for i in range(self.n_layers-1):\r\n",
        "            self.register_parameter('W_rec%i'%i, self.W_rec[i])\r\n",
        "            self.register_parameter('W_gen%i'%i, self.W_gen[i])\r\n",
        "            self.register_parameter('bias_rec%i'%i, self.bias_rec[i])\r\n",
        "            self.register_parameter('bias_gen%i'%i, self.bias_gen[i])\r\n",
        "\r\n",
        "\r\n",
        "    def forward(self , input_data):\r\n",
        "        '''\r\n",
        "            running the forward pass\r\n",
        "            do not confuse with training this just runs a foward pass\r\n",
        "        '''\r\n",
        "        v = input_data\r\n",
        "        for i in range(len(self.rbm_layers)):\r\n",
        "            v = v.view((v.shape[0] , -1)).type(torch.FloatTensor)#flatten\r\n",
        "            p_v,v = self.rbm_layers[i].to_hidden(v)\r\n",
        "        return p_v,v\r\n",
        "\r\n",
        "    def reconstruct(self,input_data):\r\n",
        "        '''\r\n",
        "        go till the final layer and then reconstruct\r\n",
        "        '''\r\n",
        "        h = input_data\r\n",
        "        p_h = 0\r\n",
        "        for i in range(len(self.rbm_layers)):\r\n",
        "            h = h.view((h.shape[0] , -1)).type(torch.FloatTensor)#flatten\r\n",
        "            p_h,h = self.rbm_layers[i].to_hidden(h)\r\n",
        "\r\n",
        "        v = h\r\n",
        "        for i in range(len(self.rbm_layers)-1,-1,-1):\r\n",
        "            v = v.view((v.shape[0] , -1)).type(torch.FloatTensor)\r\n",
        "            p_v,v = self.rbm_layers[i].to_visible(v)\r\n",
        "        return p_v,v\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "    def train_static(self, train_data,train_labels,num_epochs=50,batch_size=10):\r\n",
        "        '''\r\n",
        "        Greedy Layer By Layer training\r\n",
        "        Keeping previous layers as static\r\n",
        "        '''\r\n",
        "\r\n",
        "        tmp = train_data\r\n",
        "\r\n",
        "        for i in range(len(self.rbm_layers)):\r\n",
        "            print(\"-\"*20)\r\n",
        "            print(\"Training the {} st rbm layer\".format(i+1))\r\n",
        "\r\n",
        "            tensor_x = tmp.type(torch.FloatTensor) # transform to torch tensors\r\n",
        "            tensor_y = train_labels.type(torch.FloatTensor)\r\n",
        "            _dataset = torch.utils.data.TensorDataset(tensor_x,tensor_y) # create your datset\r\n",
        "            _dataloader = torch.utils.data.DataLoader(_dataset,batch_size=batch_size,drop_last = True) # create your dataloader\r\n",
        "\r\n",
        "            self.rbm_layers[i].train(_dataloader , num_epochs,batch_size)\r\n",
        "            # print(train_data.shape)\r\n",
        "            v = tmp.view((tmp.shape[0] , -1)).type(torch.FloatTensor)#flatten\r\n",
        "            p_v , v = self.rbm_layers[i].forward(v)\r\n",
        "            tmp = v\r\n",
        "            # print(v.shape)\r\n",
        "        return\r\n",
        "\r\n",
        "    def train_ith(self, train_data,train_labels,num_epochs,batch_size,ith_layer):\r\n",
        "        '''\r\n",
        "        taking ith layer at once\r\n",
        "        can be used for fine tuning\r\n",
        "        '''\r\n",
        "        if(ith_layer-1>len(self.rbm_layers) or ith_layer<=0):\r\n",
        "            print(\"Layer index out of range\")\r\n",
        "            return\r\n",
        "        ith_layer = ith_layer-1\r\n",
        "        v = train_data.view((train_data.shape[0] , -1)).type(torch.FloatTensor)\r\n",
        "\r\n",
        "        for ith in range(ith_layer):\r\n",
        "            p_v, v = self.rbm_layers[ith].forward(v)\r\n",
        "\r\n",
        "        tmp = v\r\n",
        "        tensor_x = tmp.type(torch.FloatTensor) # transform to torch tensors\r\n",
        "        tensor_y = train_labels.type(torch.FloatTensor)\r\n",
        "        _dataset = torch.utils.data.TensorDataset(tensor_x,tensor_y) # create your datset\r\n",
        "        _dataloader = torch.utils.data.DataLoader(_dataset , batch_size=batch_size,drop_last=True)\r\n",
        "        self.rbm_layers[ith_layer].train(_dataloader, num_epochs,batch_size)\r\n",
        "        return"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "ETsZWAZ0Fb3C",
        "outputId": "76fa23f0-17fa-46da-bc86-a2c2113f41c9"
      },
      "source": [
        "import torch \r\n",
        "import torchvision\r\n",
        "from torchvision import datasets,transforms\r\n",
        "from torch.utils.data import Dataset,DataLoader\r\n",
        "\r\n",
        "import matplotlib\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "import math\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "mnist_data = datasets.MNIST('../data', train=True, download=True,\r\n",
        "                    transform=transforms.Compose(\r\n",
        "                    [transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))]))\r\n",
        "\r\n",
        "mnist_data.data = (mnist_data.data.type(torch.FloatTensor)/255).bernoulli()\r\n",
        "\r\n",
        "#Lets us visualize a number from the data set\r\n",
        "idx = 5\r\n",
        "img = mnist_data.train_data[idx]\r\n",
        "print(\"The number shown is the number: {}\".format(mnist_data.train_labels[idx]) )\r\n",
        "plt.imshow(img , cmap = 'gray')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# I have have set these hyper parameters although you can experiment with them to find better hyperparameters.\r\n",
        "dbn_mnist = DBN(visible_units=28*28 ,\r\n",
        "                hidden_units=[23*23 ,18*18] ,\r\n",
        "                k = 5,\r\n",
        "                learning_rate = 0.01,\r\n",
        "                learning_rate_decay = True,\r\n",
        "                xavier_init = True,\r\n",
        "                increase_to_cd_k = False,\r\n",
        "                use_gpu = False)\r\n",
        "num_epochs = 1\r\n",
        "batch_size = 10\r\n",
        "\r\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The number shown is the number: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n",
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lALVKRHzFzxZ",
        "outputId": "3f2cba1d-2ad4-4523-8775-2db01f6d278e"
      },
      "source": [
        "\r\n",
        "dbn_mnist.train_static(mnist_data.train_data,mnist_data.targets,num_epochs , batch_size)\r\n",
        "\r\n",
        "# visualising layer 1\r\n",
        "learned_weights = dbn_mnist.rbm_layers[0].W.transpose(0,1).numpy()\r\n",
        "plt.show()\r\n",
        "fig = plt.figure(3, figsize=(10,10))\r\n",
        "for i in range(25): \r\n",
        "    sub = fig.add_subplot(5, 5, i+1)\r\n",
        "    sub.imshow(learned_weights[i,:].reshape((28,28)), cmap=plt.cm.gray)\r\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------\n",
            "Training the 1 st rbm layer\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 13it [00:00, 123.29it/s]\u001b[A"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "RBM fitting: 26it [00:00, 123.01it/s]\u001b[A\n",
            "RBM fitting: 39it [00:00, 124.72it/s]\u001b[A\n",
            "RBM fitting: 52it [00:00, 125.77it/s]\u001b[A\n",
            "RBM fitting: 65it [00:00, 126.11it/s]\u001b[A\n",
            "RBM fitting: 78it [00:00, 125.79it/s]\u001b[A\n",
            "RBM fitting: 92it [00:00, 127.11it/s]\u001b[A\n",
            "RBM fitting: 104it [00:00, 122.10it/s]\u001b[A\n",
            "RBM fitting: 117it [00:00, 122.41it/s]\u001b[A\n",
            "RBM fitting: 130it [00:01, 123.61it/s]\u001b[A\n",
            "RBM fitting: 144it [00:01, 126.28it/s]\u001b[A\n",
            "RBM fitting: 157it [00:01, 127.31it/s]\u001b[A\n",
            "RBM fitting: 2617it [00:40, 132.68it/s]\n",
            "RBM fitting: 183it [00:01, 126.20it/s]\u001b[A\n",
            "RBM fitting: 196it [00:01, 126.89it/s]\u001b[A\n",
            "RBM fitting: 209it [00:01, 127.00it/s]\u001b[A\n",
            "RBM fitting: 222it [00:01, 124.20it/s]\u001b[A\n",
            "RBM fitting: 235it [00:01, 125.08it/s]\u001b[A\n",
            "RBM fitting: 248it [00:01, 125.25it/s]\u001b[A\n",
            "RBM fitting: 261it [00:02, 124.45it/s]\u001b[A\n",
            "RBM fitting: 274it [00:02, 123.82it/s]\u001b[A\n",
            "RBM fitting: 287it [00:02, 125.26it/s]\u001b[A\n",
            "RBM fitting: 300it [00:02, 124.80it/s]\u001b[A\n",
            "RBM fitting: 313it [00:02, 125.69it/s]\u001b[A\n",
            "RBM fitting: 326it [00:02, 125.87it/s]\u001b[A\n",
            "RBM fitting: 340it [00:02, 127.44it/s]\u001b[A\n",
            "RBM fitting: 353it [00:02, 124.01it/s]\u001b[A\n",
            "RBM fitting: 366it [00:02, 124.87it/s]\u001b[A\n",
            "RBM fitting: 379it [00:03, 125.74it/s]\u001b[A\n",
            "RBM fitting: 393it [00:03, 127.94it/s]\u001b[A\n",
            "RBM fitting: 406it [00:03, 127.49it/s]\u001b[A\n",
            "RBM fitting: 419it [00:03, 127.57it/s]\u001b[A\n",
            "RBM fitting: 433it [00:03, 128.67it/s]\u001b[A\n",
            "RBM fitting: 446it [00:03, 123.06it/s]\u001b[A\n",
            "RBM fitting: 459it [00:03, 123.03it/s]\u001b[A\n",
            "RBM fitting: 472it [00:03, 123.47it/s]\u001b[A\n",
            "RBM fitting: 485it [00:03, 121.94it/s]\u001b[A\n",
            "RBM fitting: 498it [00:03, 122.56it/s]\u001b[A\n",
            "RBM fitting: 511it [00:04, 123.77it/s]\u001b[A\n",
            "RBM fitting: 525it [00:04, 126.10it/s]\u001b[A\n",
            "RBM fitting: 539it [00:04, 127.77it/s]\u001b[A\n",
            "RBM fitting: 552it [00:04, 127.97it/s]\u001b[A\n",
            "RBM fitting: 565it [00:04, 128.21it/s]\u001b[A\n",
            "RBM fitting: 578it [00:04, 128.22it/s]\u001b[A\n",
            "RBM fitting: 591it [00:04, 128.38it/s]\u001b[A\n",
            "RBM fitting: 604it [00:04, 127.84it/s]\u001b[A\n",
            "RBM fitting: 617it [00:04, 125.04it/s]\u001b[A\n",
            "RBM fitting: 630it [00:05, 125.08it/s]\u001b[A\n",
            "RBM fitting: 643it [00:05, 126.08it/s]\u001b[A\n",
            "RBM fitting: 657it [00:05, 127.54it/s]\u001b[A\n",
            "RBM fitting: 670it [00:05, 123.29it/s]\u001b[A\n",
            "RBM fitting: 683it [00:05, 124.65it/s]\u001b[A\n",
            "RBM fitting: 696it [00:05, 126.09it/s]\u001b[A\n",
            "RBM fitting: 709it [00:05, 124.98it/s]\u001b[A\n",
            "RBM fitting: 722it [00:05, 124.44it/s]\u001b[A\n",
            "RBM fitting: 735it [00:05, 123.83it/s]\u001b[A\n",
            "RBM fitting: 748it [00:05, 125.18it/s]\u001b[A\n",
            "RBM fitting: 761it [00:06, 125.75it/s]\u001b[A\n",
            "RBM fitting: 774it [00:06, 125.14it/s]\u001b[A\n",
            "RBM fitting: 787it [00:06, 126.21it/s]\u001b[A\n",
            "RBM fitting: 801it [00:06, 127.57it/s]\u001b[A\n",
            "RBM fitting: 815it [00:06, 128.86it/s]\u001b[A\n",
            "RBM fitting: 828it [00:06, 127.77it/s]\u001b[A\n",
            "RBM fitting: 841it [00:06, 127.21it/s]\u001b[A\n",
            "RBM fitting: 854it [00:06, 127.68it/s]\u001b[A\n",
            "RBM fitting: 867it [00:06, 125.92it/s]\u001b[A\n",
            "RBM fitting: 880it [00:06, 125.30it/s]\u001b[A\n",
            "RBM fitting: 893it [00:07, 124.54it/s]\u001b[A\n",
            "RBM fitting: 906it [00:07, 124.27it/s]\u001b[A\n",
            "RBM fitting: 919it [00:07, 123.73it/s]\u001b[A\n",
            "RBM fitting: 932it [00:07, 125.48it/s]\u001b[A\n",
            "RBM fitting: 945it [00:07, 126.02it/s]\u001b[A\n",
            "RBM fitting: 958it [00:07, 123.12it/s]\u001b[A\n",
            "RBM fitting: 971it [00:07, 122.63it/s]\u001b[A\n",
            "RBM fitting: 984it [00:07, 124.34it/s]\u001b[A\n",
            "RBM fitting: 997it [00:07, 123.63it/s]\u001b[A\n",
            "RBM fitting: 1010it [00:08, 124.03it/s]\u001b[A\n",
            "RBM fitting: 1023it [00:08, 125.56it/s]\u001b[A\n",
            "RBM fitting: 1036it [00:08, 126.83it/s]\u001b[A\n",
            "RBM fitting: 1049it [00:08, 126.36it/s]\u001b[A\n",
            "RBM fitting: 1063it [00:08, 128.10it/s]\u001b[A\n",
            "RBM fitting: 1077it [00:08, 129.25it/s]\u001b[A\n",
            "RBM fitting: 1090it [00:08, 126.55it/s]\u001b[A\n",
            "RBM fitting: 1103it [00:08, 126.90it/s]\u001b[A\n",
            "RBM fitting: 1116it [00:08, 125.02it/s]\u001b[A\n",
            "RBM fitting: 1129it [00:08, 125.16it/s]\u001b[A\n",
            "RBM fitting: 1142it [00:09, 125.42it/s]\u001b[A\n",
            "RBM fitting: 1155it [00:09, 126.63it/s]\u001b[A\n",
            "RBM fitting: 1169it [00:09, 127.77it/s]\u001b[A\n",
            "RBM fitting: 1182it [00:09, 127.41it/s]\u001b[A\n",
            "RBM fitting: 1195it [00:09, 127.47it/s]\u001b[A\n",
            "RBM fitting: 1208it [00:09, 127.98it/s]\u001b[A\n",
            "RBM fitting: 1221it [00:09, 127.67it/s]\u001b[A\n",
            "RBM fitting: 1234it [00:09, 127.72it/s]\u001b[A\n",
            "RBM fitting: 1247it [00:09, 125.12it/s]\u001b[A\n",
            "RBM fitting: 1260it [00:10, 125.00it/s]\u001b[A\n",
            "RBM fitting: 1273it [00:10, 125.23it/s]\u001b[A\n",
            "RBM fitting: 1286it [00:10, 126.28it/s]\u001b[A\n",
            "RBM fitting: 1300it [00:10, 127.40it/s]\u001b[A\n",
            "RBM fitting: 1313it [00:10, 124.08it/s]\u001b[A\n",
            "RBM fitting: 1326it [00:10, 125.21it/s]\u001b[A\n",
            "RBM fitting: 1339it [00:10, 126.35it/s]\u001b[A\n",
            "RBM fitting: 1352it [00:10, 126.58it/s]\u001b[A\n",
            "RBM fitting: 1365it [00:10, 126.07it/s]\u001b[A\n",
            "RBM fitting: 1378it [00:10, 125.10it/s]\u001b[A\n",
            "RBM fitting: 1391it [00:11, 125.82it/s]\u001b[A\n",
            "RBM fitting: 1404it [00:11, 124.89it/s]\u001b[A\n",
            "RBM fitting: 1417it [00:11, 123.87it/s]\u001b[A\n",
            "RBM fitting: 1430it [00:11, 125.55it/s]\u001b[A\n",
            "RBM fitting: 1443it [00:11, 126.18it/s]\u001b[A\n",
            "RBM fitting: 1456it [00:11, 126.78it/s]\u001b[A\n",
            "RBM fitting: 1469it [00:11, 127.31it/s]\u001b[A\n",
            "RBM fitting: 1483it [00:11, 128.26it/s]\u001b[A\n",
            "RBM fitting: 1497it [00:11, 129.42it/s]\u001b[A\n",
            "RBM fitting: 1510it [00:11, 125.56it/s]\u001b[A\n",
            "RBM fitting: 1523it [00:12, 126.51it/s]\u001b[A\n",
            "RBM fitting: 1536it [00:12, 127.51it/s]\u001b[A\n",
            "RBM fitting: 1550it [00:12, 128.36it/s]\u001b[A\n",
            "RBM fitting: 1563it [00:12, 128.41it/s]\u001b[A\n",
            "RBM fitting: 1577it [00:12, 129.42it/s]\u001b[A\n",
            "RBM fitting: 1590it [00:12, 127.03it/s]\u001b[A\n",
            "RBM fitting: 1603it [00:12, 125.05it/s]\u001b[A\n",
            "RBM fitting: 1616it [00:12, 123.16it/s]\u001b[A\n",
            "RBM fitting: 1629it [00:12, 121.03it/s]\u001b[A\n",
            "RBM fitting: 1642it [00:13, 120.27it/s]\u001b[A\n",
            "RBM fitting: 1655it [00:13, 120.80it/s]\u001b[A\n",
            "RBM fitting: 1668it [00:13, 121.14it/s]\u001b[A\n",
            "RBM fitting: 1681it [00:13, 120.88it/s]\u001b[A\n",
            "RBM fitting: 1694it [00:13, 116.28it/s]\u001b[A\n",
            "RBM fitting: 1707it [00:13, 118.19it/s]\u001b[A\n",
            "RBM fitting: 1720it [00:13, 119.93it/s]\u001b[A\n",
            "RBM fitting: 1733it [00:13, 121.29it/s]\u001b[A\n",
            "RBM fitting: 1746it [00:13, 122.31it/s]\u001b[A\n",
            "RBM fitting: 1759it [00:14, 120.47it/s]\u001b[A\n",
            "RBM fitting: 1772it [00:14, 114.24it/s]\u001b[A\n",
            "RBM fitting: 1785it [00:14, 118.26it/s]\u001b[A\n",
            "RBM fitting: 1798it [00:14, 121.04it/s]\u001b[A\n",
            "RBM fitting: 1811it [00:14, 122.31it/s]\u001b[A\n",
            "RBM fitting: 1824it [00:14, 122.29it/s]\u001b[A\n",
            "RBM fitting: 1837it [00:14, 122.96it/s]\u001b[A\n",
            "RBM fitting: 1850it [00:14, 123.81it/s]\u001b[A\n",
            "RBM fitting: 1863it [00:14, 123.71it/s]\u001b[A\n",
            "RBM fitting: 1876it [00:14, 122.71it/s]\u001b[A\n",
            "RBM fitting: 1889it [00:15, 124.32it/s]\u001b[A\n",
            "RBM fitting: 1902it [00:15, 123.12it/s]\u001b[A\n",
            "RBM fitting: 1915it [00:15, 121.12it/s]\u001b[A\n",
            "RBM fitting: 1928it [00:15, 123.57it/s]\u001b[A\n",
            "RBM fitting: 1941it [00:15, 125.22it/s]\u001b[A\n",
            "RBM fitting: 1954it [00:15, 124.35it/s]\u001b[A\n",
            "RBM fitting: 1967it [00:15, 125.13it/s]\u001b[A\n",
            "RBM fitting: 1980it [00:15, 125.18it/s]\u001b[A\n",
            "RBM fitting: 1993it [00:15, 125.81it/s]\u001b[A\n",
            "RBM fitting: 2006it [00:16, 124.30it/s]\u001b[A\n",
            "RBM fitting: 2019it [00:16, 125.22it/s]\u001b[A\n",
            "RBM fitting: 2032it [00:16, 121.61it/s]\u001b[A\n",
            "RBM fitting: 2045it [00:16, 123.31it/s]\u001b[A\n",
            "RBM fitting: 2058it [00:16, 124.38it/s]\u001b[A\n",
            "RBM fitting: 2071it [00:16, 125.88it/s]\u001b[A\n",
            "RBM fitting: 2084it [00:16, 126.19it/s]\u001b[A\n",
            "RBM fitting: 2097it [00:16, 125.76it/s]\u001b[A\n",
            "RBM fitting: 2110it [00:16, 123.01it/s]\u001b[A\n",
            "RBM fitting: 2123it [00:16, 124.16it/s]\u001b[A\n",
            "RBM fitting: 2136it [00:17, 121.40it/s]\u001b[A\n",
            "RBM fitting: 2149it [00:17, 123.38it/s]\u001b[A\n",
            "RBM fitting: 2162it [00:17, 124.66it/s]\u001b[A\n",
            "RBM fitting: 2175it [00:17, 125.10it/s]\u001b[A\n",
            "RBM fitting: 2188it [00:17, 123.43it/s]\u001b[A\n",
            "RBM fitting: 2201it [00:17, 124.34it/s]\u001b[A\n",
            "RBM fitting: 2214it [00:17, 124.67it/s]\u001b[A\n",
            "RBM fitting: 2227it [00:17, 125.16it/s]\u001b[A\n",
            "RBM fitting: 2240it [00:17, 123.15it/s]\u001b[A\n",
            "RBM fitting: 2253it [00:18, 121.50it/s]\u001b[A\n",
            "RBM fitting: 2266it [00:18, 123.82it/s]\u001b[A\n",
            "RBM fitting: 2279it [00:18, 118.93it/s]\u001b[A\n",
            "RBM fitting: 2292it [00:18, 121.09it/s]\u001b[A\n",
            "RBM fitting: 2305it [00:18, 121.90it/s]\u001b[A\n",
            "RBM fitting: 2318it [00:18, 123.73it/s]\u001b[A\n",
            "RBM fitting: 2331it [00:18, 124.66it/s]\u001b[A\n",
            "RBM fitting: 2344it [00:18, 125.10it/s]\u001b[A\n",
            "RBM fitting: 2357it [00:18, 124.30it/s]\u001b[A\n",
            "RBM fitting: 2370it [00:18, 122.91it/s]\u001b[A\n",
            "RBM fitting: 2383it [00:19, 122.12it/s]\u001b[A\n",
            "RBM fitting: 2396it [00:19, 123.42it/s]\u001b[A\n",
            "RBM fitting: 2409it [00:19, 124.19it/s]\u001b[A\n",
            "RBM fitting: 2423it [00:19, 126.20it/s]\u001b[A\n",
            "RBM fitting: 2436it [00:19, 126.38it/s]\u001b[A\n",
            "RBM fitting: 2449it [00:19, 125.98it/s]\u001b[A\n",
            "RBM fitting: 2462it [00:19, 122.60it/s]\u001b[A\n",
            "RBM fitting: 2475it [00:19, 124.04it/s]\u001b[A\n",
            "RBM fitting: 2488it [00:19, 123.47it/s]\u001b[A\n",
            "RBM fitting: 2501it [00:20, 121.61it/s]\u001b[A\n",
            "RBM fitting: 2514it [00:20, 122.44it/s]\u001b[A\n",
            "RBM fitting: 2527it [00:20, 124.36it/s]\u001b[A\n",
            "RBM fitting: 2540it [00:20, 124.92it/s]\u001b[A\n",
            "RBM fitting: 2553it [00:20, 126.04it/s]\u001b[A\n",
            "RBM fitting: 2567it [00:20, 127.34it/s]\u001b[A\n",
            "RBM fitting: 2580it [00:20, 127.10it/s]\u001b[A\n",
            "RBM fitting: 2593it [00:20, 127.22it/s]\u001b[A\n",
            "RBM fitting: 2606it [00:20, 122.76it/s]\u001b[A\n",
            "RBM fitting: 2619it [00:20, 122.03it/s]\u001b[A\n",
            "RBM fitting: 2632it [00:21, 121.89it/s]\u001b[A\n",
            "RBM fitting: 2645it [00:21, 124.20it/s]\u001b[A\n",
            "RBM fitting: 2658it [00:21, 124.70it/s]\u001b[A\n",
            "RBM fitting: 2672it [00:21, 127.08it/s]\u001b[A\n",
            "RBM fitting: 2686it [00:21, 128.55it/s]\u001b[A\n",
            "RBM fitting: 2700it [00:21, 129.19it/s]\u001b[A\n",
            "RBM fitting: 2713it [00:21, 128.52it/s]\u001b[A\n",
            "RBM fitting: 2726it [00:21, 126.50it/s]\u001b[A\n",
            "RBM fitting: 2739it [00:21, 127.44it/s]\u001b[A\n",
            "RBM fitting: 2752it [00:22, 124.01it/s]\u001b[A\n",
            "RBM fitting: 2765it [00:22, 124.28it/s]\u001b[A\n",
            "RBM fitting: 2778it [00:22, 124.59it/s]\u001b[A\n",
            "RBM fitting: 2791it [00:22, 126.13it/s]\u001b[A\n",
            "RBM fitting: 2805it [00:22, 127.89it/s]\u001b[A\n",
            "RBM fitting: 2819it [00:22, 129.02it/s]\u001b[A\n",
            "RBM fitting: 2832it [00:22, 126.32it/s]\u001b[A\n",
            "RBM fitting: 2845it [00:22, 126.30it/s]\u001b[A\n",
            "RBM fitting: 2858it [00:22, 127.17it/s]\u001b[A\n",
            "RBM fitting: 2872it [00:22, 128.41it/s]\u001b[A\n",
            "RBM fitting: 2885it [00:23, 121.64it/s]\u001b[A\n",
            "RBM fitting: 2898it [00:23, 123.73it/s]\u001b[A\n",
            "RBM fitting: 2912it [00:23, 126.07it/s]\u001b[A\n",
            "RBM fitting: 2925it [00:23, 126.87it/s]\u001b[A\n",
            "RBM fitting: 2938it [00:23, 124.04it/s]\u001b[A\n",
            "RBM fitting: 2951it [00:23, 125.49it/s]\u001b[A\n",
            "RBM fitting: 2964it [00:23, 126.16it/s]\u001b[A\n",
            "RBM fitting: 2977it [00:23, 125.16it/s]\u001b[A\n",
            "RBM fitting: 2990it [00:23, 125.65it/s]\u001b[A\n",
            "RBM fitting: 3003it [00:24, 125.70it/s]\u001b[A\n",
            "RBM fitting: 3016it [00:24, 124.37it/s]\u001b[A\n",
            "RBM fitting: 3029it [00:24, 121.44it/s]\u001b[A\n",
            "RBM fitting: 3042it [00:24, 121.80it/s]\u001b[A\n",
            "RBM fitting: 3055it [00:24, 124.00it/s]\u001b[A\n",
            "RBM fitting: 3068it [00:24, 121.04it/s]\u001b[A\n",
            "RBM fitting: 3081it [00:24, 122.93it/s]\u001b[A\n",
            "RBM fitting: 3094it [00:24, 124.67it/s]\u001b[A\n",
            "RBM fitting: 3107it [00:24, 125.28it/s]\u001b[A\n",
            "RBM fitting: 3121it [00:24, 127.60it/s]\u001b[A\n",
            "RBM fitting: 3135it [00:25, 128.60it/s]\u001b[A\n",
            "RBM fitting: 3148it [00:25, 124.37it/s]\u001b[A\n",
            "RBM fitting: 3161it [00:25, 125.67it/s]\u001b[A\n",
            "RBM fitting: 3174it [00:25, 126.56it/s]\u001b[A\n",
            "RBM fitting: 3188it [00:25, 128.09it/s]\u001b[A\n",
            "RBM fitting: 3201it [00:25, 128.55it/s]\u001b[A\n",
            "RBM fitting: 3214it [00:25, 128.16it/s]\u001b[A\n",
            "RBM fitting: 3227it [00:25, 125.10it/s]\u001b[A\n",
            "RBM fitting: 3241it [00:25, 126.67it/s]\u001b[A\n",
            "RBM fitting: 3254it [00:26, 126.84it/s]\u001b[A\n",
            "RBM fitting: 3267it [00:26, 123.59it/s]\u001b[A\n",
            "RBM fitting: 3280it [00:26, 124.38it/s]\u001b[A\n",
            "RBM fitting: 3294it [00:26, 126.74it/s]\u001b[A\n",
            "RBM fitting: 3308it [00:26, 128.25it/s]\u001b[A\n",
            "RBM fitting: 3321it [00:26, 127.55it/s]\u001b[A\n",
            "RBM fitting: 3334it [00:26, 125.51it/s]\u001b[A\n",
            "RBM fitting: 3347it [00:26, 123.98it/s]\u001b[A\n",
            "RBM fitting: 3360it [00:26, 125.44it/s]\u001b[A\n",
            "RBM fitting: 3374it [00:26, 126.92it/s]\u001b[A\n",
            "RBM fitting: 3387it [00:27, 124.29it/s]\u001b[A\n",
            "RBM fitting: 3400it [00:27, 123.33it/s]\u001b[A\n",
            "RBM fitting: 3413it [00:27, 123.91it/s]\u001b[A\n",
            "RBM fitting: 3426it [00:27, 124.71it/s]\u001b[A\n",
            "RBM fitting: 3440it [00:27, 126.81it/s]\u001b[A\n",
            "RBM fitting: 3453it [00:27, 127.35it/s]\u001b[A\n",
            "RBM fitting: 3466it [00:27, 126.26it/s]\u001b[A\n",
            "RBM fitting: 3479it [00:27, 126.63it/s]\u001b[A\n",
            "RBM fitting: 3492it [00:27, 125.41it/s]\u001b[A\n",
            "RBM fitting: 3505it [00:28, 126.32it/s]\u001b[A\n",
            "RBM fitting: 3519it [00:28, 127.67it/s]\u001b[A\n",
            "RBM fitting: 3532it [00:28, 126.74it/s]\u001b[A\n",
            "RBM fitting: 3545it [00:28, 126.40it/s]\u001b[A\n",
            "RBM fitting: 3558it [00:28, 125.85it/s]\u001b[A\n",
            "RBM fitting: 3571it [00:28, 126.63it/s]\u001b[A\n",
            "RBM fitting: 3584it [00:28, 124.36it/s]\u001b[A\n",
            "RBM fitting: 3597it [00:28, 125.18it/s]\u001b[A\n",
            "RBM fitting: 3610it [00:28, 122.43it/s]\u001b[A\n",
            "RBM fitting: 3623it [00:28, 123.99it/s]\u001b[A\n",
            "RBM fitting: 3636it [00:29, 125.19it/s]\u001b[A\n",
            "RBM fitting: 3650it [00:29, 126.54it/s]\u001b[A\n",
            "RBM fitting: 3663it [00:29, 124.81it/s]\u001b[A\n",
            "RBM fitting: 3676it [00:29, 123.10it/s]\u001b[A\n",
            "RBM fitting: 3689it [00:29, 124.79it/s]\u001b[A\n",
            "RBM fitting: 3702it [00:29, 125.93it/s]\u001b[A\n",
            "RBM fitting: 3715it [00:29, 125.20it/s]\u001b[A\n",
            "RBM fitting: 3728it [00:29, 124.34it/s]\u001b[A\n",
            "RBM fitting: 3741it [00:29, 125.57it/s]\u001b[A\n",
            "RBM fitting: 3754it [00:30, 126.13it/s]\u001b[A\n",
            "RBM fitting: 3767it [00:30, 127.16it/s]\u001b[A\n",
            "RBM fitting: 3780it [00:30, 121.82it/s]\u001b[A\n",
            "RBM fitting: 3793it [00:30, 123.53it/s]\u001b[A\n",
            "RBM fitting: 3807it [00:30, 125.92it/s]\u001b[A\n",
            "RBM fitting: 3820it [00:30, 124.06it/s]\u001b[A\n",
            "RBM fitting: 3833it [00:30, 124.20it/s]\u001b[A\n",
            "RBM fitting: 3846it [00:30, 125.83it/s]\u001b[A\n",
            "RBM fitting: 3859it [00:30, 125.35it/s]\u001b[A\n",
            "RBM fitting: 3872it [00:30, 124.10it/s]\u001b[A\n",
            "RBM fitting: 3885it [00:31, 124.60it/s]\u001b[A\n",
            "RBM fitting: 3898it [00:31, 123.88it/s]\u001b[A\n",
            "RBM fitting: 3911it [00:31, 122.74it/s]\u001b[A\n",
            "RBM fitting: 3924it [00:31, 124.07it/s]\u001b[A\n",
            "RBM fitting: 3937it [00:31, 125.50it/s]\u001b[A\n",
            "RBM fitting: 3950it [00:31, 120.48it/s]\u001b[A\n",
            "RBM fitting: 3963it [00:31, 122.29it/s]\u001b[A\n",
            "RBM fitting: 3976it [00:31, 123.75it/s]\u001b[A\n",
            "RBM fitting: 3990it [00:31, 125.69it/s]\u001b[A\n",
            "RBM fitting: 4003it [00:32, 124.78it/s]\u001b[A\n",
            "RBM fitting: 4016it [00:32, 125.40it/s]\u001b[A\n",
            "RBM fitting: 4030it [00:32, 126.91it/s]\u001b[A\n",
            "RBM fitting: 4043it [00:32, 122.33it/s]\u001b[A\n",
            "RBM fitting: 4056it [00:32, 122.27it/s]\u001b[A\n",
            "RBM fitting: 4069it [00:32, 123.26it/s]\u001b[A\n",
            "RBM fitting: 4082it [00:32, 124.99it/s]\u001b[A\n",
            "RBM fitting: 4096it [00:32, 126.65it/s]\u001b[A\n",
            "RBM fitting: 4109it [00:32, 127.60it/s]\u001b[A\n",
            "RBM fitting: 4122it [00:32, 124.44it/s]\u001b[A\n",
            "RBM fitting: 4135it [00:33, 125.21it/s]\u001b[A\n",
            "RBM fitting: 4148it [00:33, 126.29it/s]\u001b[A\n",
            "RBM fitting: 4161it [00:33, 124.72it/s]\u001b[A\n",
            "RBM fitting: 4174it [00:33, 125.09it/s]\u001b[A\n",
            "RBM fitting: 4187it [00:33, 126.43it/s]\u001b[A\n",
            "RBM fitting: 4200it [00:33, 124.77it/s]\u001b[A\n",
            "RBM fitting: 4213it [00:33, 125.62it/s]\u001b[A\n",
            "RBM fitting: 4226it [00:33, 126.44it/s]\u001b[A\n",
            "RBM fitting: 4239it [00:33, 126.60it/s]\u001b[A\n",
            "RBM fitting: 4252it [00:33, 126.65it/s]\u001b[A\n",
            "RBM fitting: 4266it [00:34, 128.18it/s]\u001b[A\n",
            "RBM fitting: 4279it [00:34, 125.20it/s]\u001b[A\n",
            "RBM fitting: 4292it [00:34, 123.92it/s]\u001b[A\n",
            "RBM fitting: 4305it [00:34, 122.62it/s]\u001b[A\n",
            "RBM fitting: 4319it [00:34, 125.16it/s]\u001b[A\n",
            "RBM fitting: 4332it [00:34, 126.45it/s]\u001b[A\n",
            "RBM fitting: 4345it [00:34, 127.05it/s]\u001b[A\n",
            "RBM fitting: 4358it [00:34, 124.69it/s]\u001b[A\n",
            "RBM fitting: 4371it [00:34, 125.94it/s]\u001b[A\n",
            "RBM fitting: 4384it [00:35, 123.04it/s]\u001b[A\n",
            "RBM fitting: 4397it [00:35, 122.47it/s]\u001b[A\n",
            "RBM fitting: 4411it [00:35, 124.13it/s]\u001b[A\n",
            "RBM fitting: 4424it [00:35, 122.27it/s]\u001b[A\n",
            "RBM fitting: 4437it [00:35, 123.28it/s]\u001b[A\n",
            "RBM fitting: 4450it [00:35, 124.83it/s]\u001b[A\n",
            "RBM fitting: 4463it [00:35, 125.29it/s]\u001b[A\n",
            "RBM fitting: 4476it [00:35, 125.53it/s]\u001b[A\n",
            "RBM fitting: 4489it [00:35, 125.59it/s]\u001b[A\n",
            "RBM fitting: 4502it [00:36, 123.79it/s]\u001b[A\n",
            "RBM fitting: 4515it [00:36, 125.40it/s]\u001b[A\n",
            "RBM fitting: 4529it [00:36, 126.97it/s]\u001b[A\n",
            "RBM fitting: 4542it [00:36, 126.07it/s]\u001b[A\n",
            "RBM fitting: 4555it [00:36, 126.75it/s]\u001b[A\n",
            "RBM fitting: 4568it [00:36, 123.87it/s]\u001b[A\n",
            "RBM fitting: 4581it [00:36, 124.06it/s]\u001b[A\n",
            "RBM fitting: 4594it [00:36, 125.34it/s]\u001b[A\n",
            "RBM fitting: 4607it [00:36, 125.41it/s]\u001b[A\n",
            "RBM fitting: 4620it [00:36, 125.40it/s]\u001b[A\n",
            "RBM fitting: 4633it [00:37, 124.85it/s]\u001b[A\n",
            "RBM fitting: 4647it [00:37, 126.58it/s]\u001b[A\n",
            "RBM fitting: 4660it [00:37, 125.98it/s]\u001b[A\n",
            "RBM fitting: 4673it [00:37, 125.15it/s]\u001b[A\n",
            "RBM fitting: 4686it [00:37, 126.30it/s]\u001b[A\n",
            "RBM fitting: 4699it [00:37, 126.72it/s]\u001b[A\n",
            "RBM fitting: 4712it [00:37, 123.37it/s]\u001b[A\n",
            "RBM fitting: 4725it [00:37, 121.36it/s]\u001b[A\n",
            "RBM fitting: 4738it [00:37, 123.15it/s]\u001b[A\n",
            "RBM fitting: 4751it [00:37, 123.37it/s]\u001b[A\n",
            "RBM fitting: 4764it [00:38, 123.51it/s]\u001b[A\n",
            "RBM fitting: 4778it [00:38, 125.52it/s]\u001b[A\n",
            "RBM fitting: 4791it [00:38, 125.51it/s]\u001b[A\n",
            "RBM fitting: 4804it [00:38, 124.44it/s]\u001b[A\n",
            "RBM fitting: 4817it [00:38, 125.12it/s]\u001b[A\n",
            "RBM fitting: 4830it [00:38, 125.81it/s]\u001b[A\n",
            "RBM fitting: 4843it [00:38, 126.36it/s]\u001b[A\n",
            "RBM fitting: 4856it [00:38, 126.58it/s]\u001b[A\n",
            "RBM fitting: 4869it [00:38, 121.38it/s]\u001b[A\n",
            "RBM fitting: 4882it [00:39, 120.67it/s]\u001b[A\n",
            "RBM fitting: 4895it [00:39, 123.06it/s]\u001b[A\n",
            "RBM fitting: 4909it [00:39, 125.23it/s]\u001b[A\n",
            "RBM fitting: 4922it [00:39, 122.88it/s]\u001b[A\n",
            "RBM fitting: 4935it [00:39, 123.87it/s]\u001b[A\n",
            "RBM fitting: 4948it [00:39, 124.03it/s]\u001b[A\n",
            "RBM fitting: 4961it [00:39, 123.57it/s]\u001b[A\n",
            "RBM fitting: 4974it [00:39, 123.63it/s]\u001b[A\n",
            "RBM fitting: 4987it [00:39, 125.01it/s]\u001b[A\n",
            "RBM fitting: 5000it [00:39, 125.62it/s]\u001b[A\n",
            "RBM fitting: 5013it [00:40, 125.55it/s]\u001b[A\n",
            "RBM fitting: 5026it [00:40, 126.02it/s]\u001b[A\n",
            "RBM fitting: 5039it [00:40, 124.61it/s]\u001b[A\n",
            "RBM fitting: 5052it [00:40, 124.59it/s]\u001b[A\n",
            "RBM fitting: 5066it [00:40, 126.74it/s]\u001b[A\n",
            "RBM fitting: 5079it [00:40, 127.58it/s]\u001b[A\n",
            "RBM fitting: 5092it [00:40, 127.60it/s]\u001b[A\n",
            "RBM fitting: 5105it [00:40, 122.26it/s]\u001b[A\n",
            "RBM fitting: 5118it [00:40, 121.61it/s]\u001b[A\n",
            "RBM fitting: 5131it [00:41, 122.66it/s]\u001b[A\n",
            "RBM fitting: 5144it [00:41, 124.74it/s]\u001b[A\n",
            "RBM fitting: 5157it [00:41, 124.64it/s]\u001b[A\n",
            "RBM fitting: 5170it [00:41, 122.01it/s]\u001b[A\n",
            "RBM fitting: 5183it [00:41, 122.99it/s]\u001b[A\n",
            "RBM fitting: 5196it [00:41, 123.96it/s]\u001b[A\n",
            "RBM fitting: 5209it [00:41, 124.15it/s]\u001b[A\n",
            "RBM fitting: 5222it [00:41, 124.54it/s]\u001b[A\n",
            "RBM fitting: 5235it [00:41, 123.52it/s]\u001b[A\n",
            "RBM fitting: 5248it [00:41, 124.53it/s]\u001b[A\n",
            "RBM fitting: 5261it [00:42, 125.73it/s]\u001b[A\n",
            "RBM fitting: 5274it [00:42, 126.68it/s]\u001b[A\n",
            "RBM fitting: 5288it [00:42, 128.20it/s]\u001b[A\n",
            "RBM fitting: 5301it [00:42, 126.73it/s]\u001b[A\n",
            "RBM fitting: 5314it [00:42, 125.82it/s]\u001b[A\n",
            "RBM fitting: 5327it [00:42, 122.91it/s]\u001b[A\n",
            "RBM fitting: 5340it [00:42, 123.81it/s]\u001b[A\n",
            "RBM fitting: 5353it [00:42, 125.05it/s]\u001b[A\n",
            "RBM fitting: 5366it [00:42, 126.12it/s]\u001b[A\n",
            "RBM fitting: 5379it [00:43, 123.75it/s]\u001b[A\n",
            "RBM fitting: 5392it [00:43, 124.70it/s]\u001b[A\n",
            "RBM fitting: 5405it [00:43, 125.59it/s]\u001b[A\n",
            "RBM fitting: 5418it [00:43, 126.50it/s]\u001b[A\n",
            "RBM fitting: 5431it [00:43, 124.92it/s]\u001b[A\n",
            "RBM fitting: 5444it [00:43, 124.35it/s]\u001b[A\n",
            "RBM fitting: 5457it [00:43, 124.43it/s]\u001b[A\n",
            "RBM fitting: 5470it [00:43, 124.59it/s]\u001b[A\n",
            "RBM fitting: 5483it [00:43, 125.75it/s]\u001b[A\n",
            "RBM fitting: 5496it [00:43, 126.60it/s]\u001b[A\n",
            "RBM fitting: 5509it [00:44, 125.25it/s]\u001b[A\n",
            "RBM fitting: 5522it [00:44, 126.39it/s]\u001b[A\n",
            "RBM fitting: 5536it [00:44, 127.53it/s]\u001b[A\n",
            "RBM fitting: 5549it [00:44, 125.34it/s]\u001b[A\n",
            "RBM fitting: 5562it [00:44, 124.05it/s]\u001b[A\n",
            "RBM fitting: 5575it [00:44, 124.37it/s]\u001b[A\n",
            "RBM fitting: 5588it [00:44, 125.49it/s]\u001b[A\n",
            "RBM fitting: 5601it [00:44, 126.19it/s]\u001b[A\n",
            "RBM fitting: 5614it [00:44, 124.44it/s]\u001b[A\n",
            "RBM fitting: 5627it [00:45, 124.41it/s]\u001b[A\n",
            "RBM fitting: 5640it [00:45, 123.05it/s]\u001b[A\n",
            "RBM fitting: 5653it [00:45, 124.85it/s]\u001b[A\n",
            "RBM fitting: 5666it [00:45, 123.59it/s]\u001b[A\n",
            "RBM fitting: 5679it [00:45, 123.49it/s]\u001b[A\n",
            "RBM fitting: 5692it [00:45, 117.86it/s]\u001b[A\n",
            "RBM fitting: 5705it [00:45, 120.53it/s]\u001b[A\n",
            "RBM fitting: 5718it [00:45, 121.22it/s]\u001b[A\n",
            "RBM fitting: 5731it [00:45, 121.41it/s]\u001b[A\n",
            "RBM fitting: 5744it [00:45, 120.93it/s]\u001b[A\n",
            "RBM fitting: 5757it [00:46, 120.61it/s]\u001b[A\n",
            "RBM fitting: 5771it [00:46, 123.63it/s]\u001b[A\n",
            "RBM fitting: 5784it [00:46, 123.53it/s]\u001b[A\n",
            "RBM fitting: 5797it [00:46, 124.90it/s]\u001b[A\n",
            "RBM fitting: 5810it [00:46, 123.41it/s]\u001b[A\n",
            "RBM fitting: 5823it [00:46, 124.97it/s]\u001b[A\n",
            "RBM fitting: 5836it [00:46, 125.31it/s]\u001b[A\n",
            "RBM fitting: 5849it [00:46, 125.23it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:46, 125.26it/s]\u001b[A\n",
            "RBM fitting: 5875it [00:47, 125.52it/s]\u001b[A\n",
            "RBM fitting: 5889it [00:47, 127.74it/s]\u001b[A\n",
            "RBM fitting: 5902it [00:47, 127.09it/s]\u001b[A\n",
            "RBM fitting: 5915it [00:47, 125.16it/s]\u001b[A\n",
            "RBM fitting: 5929it [00:47, 127.01it/s]\u001b[A\n",
            "RBM fitting: 5942it [00:47, 125.57it/s]\u001b[A\n",
            "RBM fitting: 5955it [00:47, 126.21it/s]\u001b[A\n",
            "RBM fitting: 5968it [00:47, 122.16it/s]\u001b[A\n",
            "RBM fitting: 5981it [00:47, 123.99it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:48, 124.90it/s]\n",
            "Epoch:1 ,avg_cost = 0.49583837389945984 ,std_cost = 0.0549202598631382 ,avg_grad = 11044.9833984375 ,std_grad = 593.8988037109375\n",
            "--------------------\n",
            "Training the 2 st rbm layer\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 22it [00:00, 215.48it/s]\u001b[A\n",
            "RBM fitting: 43it [00:00, 213.21it/s]\u001b[A\n",
            "RBM fitting: 63it [00:00, 206.76it/s]\u001b[A\n",
            "RBM fitting: 85it [00:00, 208.55it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 207.14it/s]\u001b[A\n",
            "RBM fitting: 128it [00:00, 208.39it/s]\u001b[A\n",
            "RBM fitting: 150it [00:00, 210.10it/s]\u001b[A\n",
            "RBM fitting: 172it [00:00, 212.12it/s]\u001b[A\n",
            "RBM fitting: 192it [00:00, 205.71it/s]\u001b[A\n",
            "RBM fitting: 213it [00:01, 204.75it/s]\u001b[A\n",
            "RBM fitting: 235it [00:01, 207.21it/s]\u001b[A\n",
            "RBM fitting: 257it [00:01, 209.11it/s]\u001b[A\n",
            "RBM fitting: 279it [00:01, 210.66it/s]\u001b[A\n",
            "RBM fitting: 300it [00:01, 206.01it/s]\u001b[A\n",
            "RBM fitting: 321it [00:01, 206.83it/s]\u001b[A\n",
            "RBM fitting: 343it [00:01, 209.28it/s]\u001b[A\n",
            "RBM fitting: 365it [00:01, 209.96it/s]\u001b[A\n",
            "RBM fitting: 386it [00:01, 206.74it/s]\u001b[A\n",
            "RBM fitting: 407it [00:01, 205.16it/s]\u001b[A\n",
            "RBM fitting: 428it [00:02, 200.35it/s]\u001b[A\n",
            "RBM fitting: 450it [00:02, 204.14it/s]\u001b[A\n",
            "RBM fitting: 472it [00:02, 206.37it/s]\u001b[A\n",
            "RBM fitting: 493it [00:02, 206.32it/s]\u001b[A\n",
            "RBM fitting: 515it [00:02, 208.55it/s]\u001b[A\n",
            "RBM fitting: 536it [00:02, 208.79it/s]\u001b[A\n",
            "RBM fitting: 557it [00:02, 202.81it/s]\u001b[A\n",
            "RBM fitting: 579it [00:02, 205.84it/s]\u001b[A\n",
            "RBM fitting: 600it [00:02, 204.70it/s]\u001b[A\n",
            "RBM fitting: 622it [00:03, 207.54it/s]\u001b[A\n",
            "RBM fitting: 643it [00:03, 200.22it/s]\u001b[A\n",
            "RBM fitting: 665it [00:03, 204.30it/s]\u001b[A\n",
            "RBM fitting: 687it [00:03, 205.64it/s]\u001b[A\n",
            "RBM fitting: 708it [00:03, 205.67it/s]\u001b[A\n",
            "RBM fitting: 729it [00:03, 205.67it/s]\u001b[A\n",
            "RBM fitting: 751it [00:03, 207.31it/s]\u001b[A\n",
            "RBM fitting: 772it [00:03, 204.98it/s]\u001b[A\n",
            "RBM fitting: 794it [00:03, 207.32it/s]\u001b[A\n",
            "RBM fitting: 815it [00:03, 206.73it/s]\u001b[A\n",
            "RBM fitting: 837it [00:04, 209.75it/s]\u001b[A\n",
            "RBM fitting: 858it [00:04, 206.37it/s]\u001b[A\n",
            "RBM fitting: 879it [00:04, 204.40it/s]\u001b[A\n",
            "RBM fitting: 900it [00:04, 201.10it/s]\u001b[A\n",
            "RBM fitting: 922it [00:04, 204.14it/s]\u001b[A\n",
            "RBM fitting: 943it [00:04, 205.42it/s]\u001b[A\n",
            "RBM fitting: 965it [00:04, 207.72it/s]\u001b[A\n",
            "RBM fitting: 986it [00:04, 201.89it/s]\u001b[A\n",
            "RBM fitting: 1008it [00:04, 205.13it/s]\u001b[A\n",
            "RBM fitting: 1029it [00:04, 203.54it/s]\u001b[A\n",
            "RBM fitting: 1050it [00:05, 202.61it/s]\u001b[A\n",
            "RBM fitting: 1071it [00:05, 203.01it/s]\u001b[A\n",
            "RBM fitting: 1092it [00:05, 203.32it/s]\u001b[A\n",
            "RBM fitting: 1114it [00:05, 205.85it/s]\u001b[A\n",
            "RBM fitting: 1135it [00:05, 201.57it/s]\u001b[A\n",
            "RBM fitting: 1157it [00:05, 205.37it/s]\u001b[A\n",
            "RBM fitting: 1178it [00:05, 204.91it/s]\u001b[A\n",
            "RBM fitting: 1200it [00:05, 207.52it/s]\u001b[A\n",
            "RBM fitting: 1221it [00:05, 205.05it/s]\u001b[A\n",
            "RBM fitting: 1242it [00:06, 205.55it/s]\u001b[A\n",
            "RBM fitting: 1263it [00:06, 200.15it/s]\u001b[A\n",
            "RBM fitting: 1285it [00:06, 203.70it/s]\u001b[A\n",
            "RBM fitting: 1307it [00:06, 206.67it/s]\u001b[A\n",
            "RBM fitting: 1328it [00:06, 207.40it/s]\u001b[A\n",
            "RBM fitting: 1349it [00:06, 205.11it/s]\u001b[A\n",
            "RBM fitting: 1371it [00:06, 207.85it/s]\u001b[A\n",
            "RBM fitting: 1392it [00:06, 201.99it/s]\u001b[A\n",
            "RBM fitting: 1414it [00:06, 204.54it/s]\u001b[A\n",
            "RBM fitting: 1435it [00:06, 204.08it/s]\u001b[A\n",
            "RBM fitting: 1456it [00:07, 204.74it/s]\u001b[A\n",
            "RBM fitting: 1477it [00:07, 203.23it/s]\u001b[A\n",
            "RBM fitting: 1498it [00:07, 204.63it/s]\u001b[A\n",
            "RBM fitting: 1519it [00:07, 196.02it/s]\u001b[A\n",
            "RBM fitting: 1539it [00:07, 195.36it/s]\u001b[A\n",
            "RBM fitting: 1559it [00:07, 195.04it/s]\u001b[A\n",
            "RBM fitting: 1581it [00:07, 199.68it/s]\u001b[A\n",
            "RBM fitting: 1603it [00:07, 203.18it/s]\u001b[A\n",
            "RBM fitting: 1624it [00:07, 203.04it/s]\u001b[A\n",
            "RBM fitting: 1646it [00:08, 205.71it/s]\u001b[A\n",
            "RBM fitting: 1667it [00:08, 201.73it/s]\u001b[A\n",
            "RBM fitting: 1688it [00:08, 203.10it/s]\u001b[A\n",
            "RBM fitting: 1709it [00:08, 202.74it/s]\u001b[A\n",
            "RBM fitting: 1730it [00:08, 203.31it/s]\u001b[A\n",
            "RBM fitting: 1751it [00:08, 204.84it/s]\u001b[A\n",
            "RBM fitting: 1772it [00:08, 205.30it/s]\u001b[A\n",
            "RBM fitting: 1794it [00:08, 207.82it/s]\u001b[A\n",
            "RBM fitting: 1816it [00:08, 210.51it/s]\u001b[A\n",
            "RBM fitting: 1838it [00:08, 208.00it/s]\u001b[A\n",
            "RBM fitting: 1860it [00:09, 210.01it/s]\u001b[A\n",
            "RBM fitting: 1882it [00:09, 206.75it/s]\u001b[A\n",
            "RBM fitting: 1903it [00:09, 206.27it/s]\u001b[A\n",
            "RBM fitting: 1924it [00:09, 205.30it/s]\u001b[A\n",
            "RBM fitting: 1946it [00:09, 209.02it/s]\u001b[A\n",
            "RBM fitting: 1968it [00:09, 210.39it/s]\u001b[A\n",
            "RBM fitting: 1990it [00:09, 209.39it/s]\u001b[A\n",
            "RBM fitting: 2012it [00:09, 210.93it/s]\u001b[A\n",
            "RBM fitting: 2034it [00:09, 209.03it/s]\u001b[A\n",
            "RBM fitting: 2055it [00:10, 202.82it/s]\u001b[A\n",
            "RBM fitting: 2077it [00:10, 205.83it/s]\u001b[A\n",
            "RBM fitting: 2098it [00:10, 199.70it/s]\u001b[A\n",
            "RBM fitting: 2119it [00:10, 195.44it/s]\u001b[A\n",
            "RBM fitting: 2139it [00:10, 196.60it/s]\u001b[A\n",
            "RBM fitting: 2160it [00:10, 199.31it/s]\u001b[A\n",
            "RBM fitting: 2182it [00:10, 202.64it/s]\u001b[A\n",
            "RBM fitting: 2203it [00:10, 203.18it/s]\u001b[A\n",
            "RBM fitting: 2224it [00:10, 198.35it/s]\u001b[A\n",
            "RBM fitting: 2245it [00:10, 200.97it/s]\u001b[A\n",
            "RBM fitting: 2266it [00:11, 203.11it/s]\u001b[A\n",
            "RBM fitting: 2288it [00:11, 205.62it/s]\u001b[A\n",
            "RBM fitting: 2309it [00:11, 201.42it/s]\u001b[A\n",
            "RBM fitting: 2330it [00:11, 203.72it/s]\u001b[A\n",
            "RBM fitting: 2351it [00:11, 205.49it/s]\u001b[A\n",
            "RBM fitting: 2372it [00:11, 206.14it/s]\u001b[A\n",
            "RBM fitting: 2394it [00:11, 208.17it/s]\u001b[A\n",
            "RBM fitting: 2415it [00:11, 207.53it/s]\u001b[A\n",
            "RBM fitting: 2436it [00:11, 205.73it/s]\u001b[A\n",
            "RBM fitting: 2457it [00:11, 203.24it/s]\u001b[A\n",
            "RBM fitting: 2478it [00:12, 204.92it/s]\u001b[A\n",
            "RBM fitting: 2499it [00:12, 202.22it/s]\u001b[A\n",
            "RBM fitting: 2520it [00:12, 200.92it/s]\u001b[A\n",
            "RBM fitting: 2541it [00:12, 201.80it/s]\u001b[A\n",
            "RBM fitting: 2562it [00:12, 203.49it/s]\u001b[A\n",
            "RBM fitting: 2584it [00:12, 206.98it/s]\u001b[A\n",
            "RBM fitting: 2606it [00:12, 209.64it/s]\u001b[A\n",
            "RBM fitting: 2627it [00:12, 208.92it/s]\u001b[A\n",
            "RBM fitting: 2648it [00:12, 204.03it/s]\u001b[A\n",
            "RBM fitting: 2669it [00:13, 204.95it/s]\u001b[A\n",
            "RBM fitting: 2690it [00:13, 205.49it/s]\u001b[A\n",
            "RBM fitting: 2711it [00:13, 199.86it/s]\u001b[A\n",
            "RBM fitting: 2732it [00:13, 198.95it/s]\u001b[A\n",
            "RBM fitting: 2752it [00:13, 193.75it/s]\u001b[A\n",
            "RBM fitting: 2773it [00:13, 197.34it/s]\u001b[A\n",
            "RBM fitting: 2795it [00:13, 201.53it/s]\u001b[A\n",
            "RBM fitting: 2817it [00:13, 205.36it/s]\u001b[A\n",
            "RBM fitting: 2838it [00:13, 204.01it/s]\u001b[A\n",
            "RBM fitting: 2859it [00:13, 204.20it/s]\u001b[A\n",
            "RBM fitting: 2880it [00:14, 202.73it/s]\u001b[A\n",
            "RBM fitting: 2901it [00:14, 204.70it/s]\u001b[A\n",
            "RBM fitting: 2922it [00:14, 201.37it/s]\u001b[A\n",
            "RBM fitting: 2943it [00:14, 201.27it/s]\u001b[A\n",
            "RBM fitting: 2964it [00:14, 202.65it/s]\u001b[A\n",
            "RBM fitting: 2985it [00:14, 203.87it/s]\u001b[A\n",
            "RBM fitting: 3006it [00:14, 205.59it/s]\u001b[A\n",
            "RBM fitting: 3027it [00:14, 205.31it/s]\u001b[A\n",
            "RBM fitting: 3048it [00:14, 200.87it/s]\u001b[A\n",
            "RBM fitting: 3070it [00:15, 204.61it/s]\u001b[A\n",
            "RBM fitting: 3092it [00:15, 206.59it/s]\u001b[A\n",
            "RBM fitting: 3113it [00:15, 205.09it/s]\u001b[A\n",
            "RBM fitting: 3134it [00:15, 202.18it/s]\u001b[A\n",
            "RBM fitting: 3155it [00:15, 202.42it/s]\u001b[A\n",
            "RBM fitting: 3176it [00:15, 202.68it/s]\u001b[A\n",
            "RBM fitting: 3197it [00:15, 201.59it/s]\u001b[A\n",
            "RBM fitting: 3218it [00:15, 199.36it/s]\u001b[A\n",
            "RBM fitting: 3238it [00:15, 196.67it/s]\u001b[A\n",
            "RBM fitting: 3258it [00:15, 196.91it/s]\u001b[A\n",
            "RBM fitting: 3279it [00:16, 200.25it/s]\u001b[A\n",
            "RBM fitting: 3300it [00:16, 201.47it/s]\u001b[A\n",
            "RBM fitting: 3321it [00:16, 199.10it/s]\u001b[A\n",
            "RBM fitting: 3341it [00:16, 196.10it/s]\u001b[A\n",
            "RBM fitting: 3363it [00:16, 200.98it/s]\u001b[A\n",
            "RBM fitting: 3385it [00:16, 204.04it/s]\u001b[A\n",
            "RBM fitting: 3407it [00:16, 207.18it/s]\u001b[A\n",
            "RBM fitting: 3428it [00:16, 205.80it/s]\u001b[A\n",
            "RBM fitting: 3449it [00:16, 204.75it/s]\u001b[A\n",
            "RBM fitting: 3471it [00:16, 207.09it/s]\u001b[A\n",
            "RBM fitting: 3492it [00:17, 207.77it/s]\u001b[A\n",
            "RBM fitting: 3513it [00:17, 201.21it/s]\u001b[A\n",
            "RBM fitting: 3534it [00:17, 199.73it/s]\u001b[A\n",
            "RBM fitting: 3555it [00:17, 198.53it/s]\u001b[A\n",
            "RBM fitting: 3576it [00:17, 201.10it/s]\u001b[A\n",
            "RBM fitting: 3597it [00:17, 202.86it/s]\u001b[A\n",
            "RBM fitting: 3618it [00:17, 202.96it/s]\u001b[A\n",
            "RBM fitting: 3640it [00:17, 206.80it/s]\u001b[A\n",
            "RBM fitting: 3661it [00:17, 205.40it/s]\u001b[A\n",
            "RBM fitting: 3682it [00:18, 206.12it/s]\u001b[A\n",
            "RBM fitting: 3703it [00:18, 204.61it/s]\u001b[A\n",
            "RBM fitting: 3724it [00:18, 195.55it/s]\u001b[A\n",
            "RBM fitting: 3744it [00:18, 195.52it/s]\u001b[A\n",
            "RBM fitting: 3765it [00:18, 197.61it/s]\u001b[A\n",
            "RBM fitting: 3785it [00:18, 198.25it/s]\u001b[A\n",
            "RBM fitting: 3806it [00:18, 201.29it/s]\u001b[A\n",
            "RBM fitting: 3828it [00:18, 204.08it/s]\u001b[A\n",
            "RBM fitting: 3850it [00:18, 205.98it/s]\u001b[A\n",
            "RBM fitting: 3871it [00:18, 204.79it/s]\u001b[A\n",
            "RBM fitting: 3892it [00:19, 202.08it/s]\u001b[A\n",
            "RBM fitting: 3914it [00:19, 205.17it/s]\u001b[A\n",
            "RBM fitting: 3935it [00:19, 206.27it/s]\u001b[A\n",
            "RBM fitting: 3956it [00:19, 203.20it/s]\u001b[A\n",
            "RBM fitting: 3977it [00:19, 203.21it/s]\u001b[A\n",
            "RBM fitting: 3998it [00:19, 204.11it/s]\u001b[A\n",
            "RBM fitting: 4019it [00:19, 203.45it/s]\u001b[A\n",
            "RBM fitting: 4041it [00:19, 205.94it/s]\u001b[A\n",
            "RBM fitting: 4062it [00:19, 205.57it/s]\u001b[A\n",
            "RBM fitting: 4083it [00:19, 206.22it/s]\u001b[A\n",
            "RBM fitting: 4104it [00:20, 207.06it/s]\u001b[A\n",
            "RBM fitting: 4125it [00:20, 203.20it/s]\u001b[A\n",
            "RBM fitting: 4146it [00:20, 200.41it/s]\u001b[A\n",
            "RBM fitting: 4167it [00:20, 201.82it/s]\u001b[A\n",
            "RBM fitting: 4188it [00:20, 202.56it/s]\u001b[A\n",
            "RBM fitting: 4209it [00:20, 204.61it/s]\u001b[A\n",
            "RBM fitting: 4230it [00:20, 204.26it/s]\u001b[A\n",
            "RBM fitting: 4251it [00:20, 201.43it/s]\u001b[A\n",
            "RBM fitting: 4273it [00:20, 204.48it/s]\u001b[A\n",
            "RBM fitting: 4295it [00:21, 207.02it/s]\u001b[A\n",
            "RBM fitting: 4317it [00:21, 208.66it/s]\u001b[A\n",
            "RBM fitting: 4338it [00:21, 206.39it/s]\u001b[A\n",
            "RBM fitting: 4359it [00:21, 199.90it/s]\u001b[A\n",
            "RBM fitting: 4381it [00:21, 203.97it/s]\u001b[A\n",
            "RBM fitting: 4402it [00:21, 204.67it/s]\u001b[A\n",
            "RBM fitting: 4424it [00:21, 208.46it/s]\u001b[A\n",
            "RBM fitting: 4445it [00:21, 206.12it/s]\u001b[A\n",
            "RBM fitting: 4467it [00:21, 208.12it/s]\u001b[A\n",
            "RBM fitting: 4489it [00:21, 210.67it/s]\u001b[A\n",
            "RBM fitting: 4511it [00:22, 209.36it/s]\u001b[A\n",
            "RBM fitting: 4532it [00:22, 206.04it/s]\u001b[A\n",
            "RBM fitting: 4553it [00:22, 205.56it/s]\u001b[A\n",
            "RBM fitting: 4574it [00:22, 203.87it/s]\u001b[A\n",
            "RBM fitting: 4596it [00:22, 206.36it/s]\u001b[A\n",
            "RBM fitting: 4617it [00:22, 206.92it/s]\u001b[A\n",
            "RBM fitting: 4638it [00:22, 200.35it/s]\u001b[A\n",
            "RBM fitting: 4660it [00:22, 203.60it/s]\u001b[A\n",
            "RBM fitting: 4681it [00:22, 204.74it/s]\u001b[A\n",
            "RBM fitting: 4703it [00:23, 207.16it/s]\u001b[A\n",
            "RBM fitting: 4725it [00:23, 209.24it/s]\u001b[A\n",
            "RBM fitting: 4746it [00:23, 205.75it/s]\u001b[A\n",
            "RBM fitting: 4767it [00:23, 200.85it/s]\u001b[A\n",
            "RBM fitting: 4788it [00:23, 199.41it/s]\u001b[A\n",
            "RBM fitting: 4809it [00:23, 200.35it/s]\u001b[A\n",
            "RBM fitting: 4831it [00:23, 203.51it/s]\u001b[A\n",
            "RBM fitting: 4852it [00:23, 202.46it/s]\u001b[A\n",
            "RBM fitting: 4873it [00:23, 203.33it/s]\u001b[A\n",
            "RBM fitting: 4894it [00:23, 202.83it/s]\u001b[A\n",
            "RBM fitting: 4915it [00:24, 201.98it/s]\u001b[A\n",
            "RBM fitting: 4937it [00:24, 204.61it/s]\u001b[A\n",
            "RBM fitting: 4958it [00:24, 202.44it/s]\u001b[A\n",
            "RBM fitting: 4979it [00:24, 201.17it/s]\u001b[A\n",
            "RBM fitting: 5000it [00:24, 203.64it/s]\u001b[A\n",
            "RBM fitting: 5021it [00:24, 204.48it/s]\u001b[A\n",
            "RBM fitting: 5042it [00:24, 204.56it/s]\u001b[A\n",
            "RBM fitting: 5063it [00:24, 205.41it/s]\u001b[A\n",
            "RBM fitting: 5085it [00:24, 207.19it/s]\u001b[A\n",
            "RBM fitting: 5106it [00:24, 207.13it/s]\u001b[A\n",
            "RBM fitting: 5128it [00:25, 208.72it/s]\u001b[A\n",
            "RBM fitting: 5149it [00:25, 208.61it/s]\u001b[A\n",
            "RBM fitting: 5170it [00:25, 202.54it/s]\u001b[A\n",
            "RBM fitting: 5191it [00:25, 202.70it/s]\u001b[A\n",
            "RBM fitting: 5212it [00:25, 203.39it/s]\u001b[A\n",
            "RBM fitting: 5234it [00:25, 206.83it/s]\u001b[A\n",
            "RBM fitting: 5256it [00:25, 209.22it/s]\u001b[A\n",
            "RBM fitting: 5277it [00:25, 204.06it/s]\u001b[A\n",
            "RBM fitting: 5299it [00:25, 206.77it/s]\u001b[A\n",
            "RBM fitting: 5320it [00:26, 206.93it/s]\u001b[A\n",
            "RBM fitting: 5342it [00:26, 208.18it/s]\u001b[A\n",
            "RBM fitting: 5363it [00:26, 207.68it/s]\u001b[A\n",
            "RBM fitting: 5384it [00:26, 206.71it/s]\u001b[A\n",
            "RBM fitting: 5405it [00:26, 200.57it/s]\u001b[A\n",
            "RBM fitting: 5426it [00:26, 201.50it/s]\u001b[A\n",
            "RBM fitting: 5447it [00:26, 203.74it/s]\u001b[A\n",
            "RBM fitting: 5468it [00:26, 205.56it/s]\u001b[A\n",
            "RBM fitting: 5489it [00:26, 197.16it/s]\u001b[A\n",
            "RBM fitting: 5509it [00:26, 197.54it/s]\u001b[A\n",
            "RBM fitting: 5531it [00:27, 201.69it/s]\u001b[A\n",
            "RBM fitting: 5553it [00:27, 205.09it/s]\u001b[A\n",
            "RBM fitting: 5574it [00:27, 205.22it/s]\u001b[A\n",
            "RBM fitting: 5595it [00:27, 202.63it/s]\u001b[A\n",
            "RBM fitting: 5616it [00:27, 198.38it/s]\u001b[A\n",
            "RBM fitting: 5637it [00:27, 201.31it/s]\u001b[A\n",
            "RBM fitting: 5658it [00:27, 197.73it/s]\u001b[A\n",
            "RBM fitting: 5680it [00:27, 201.53it/s]\u001b[A\n",
            "RBM fitting: 5701it [00:27, 201.91it/s]\u001b[A\n",
            "RBM fitting: 5722it [00:28, 201.34it/s]\u001b[A\n",
            "RBM fitting: 5743it [00:28, 203.20it/s]\u001b[A\n",
            "RBM fitting: 5764it [00:28, 203.45it/s]\u001b[A\n",
            "RBM fitting: 5786it [00:28, 205.88it/s]\u001b[A\n",
            "RBM fitting: 5807it [00:28, 205.79it/s]\u001b[A\n",
            "RBM fitting: 5828it [00:28, 198.48it/s]\u001b[A\n",
            "RBM fitting: 5850it [00:28, 202.93it/s]\u001b[A\n",
            "RBM fitting: 5871it [00:28, 203.39it/s]\u001b[A\n",
            "RBM fitting: 5892it [00:28, 204.91it/s]\u001b[A\n",
            "RBM fitting: 5913it [00:28, 204.26it/s]\u001b[A\n",
            "RBM fitting: 5935it [00:29, 208.39it/s]\u001b[A\n",
            "RBM fitting: 5956it [00:29, 208.71it/s]\u001b[A\n",
            "RBM fitting: 5977it [00:29, 206.10it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:29, 204.20it/s]\n",
            "Epoch:1 ,avg_cost = 1.5909719467163086 ,std_cost = 0.17182543873786926 ,avg_grad = 12201.1162109375 ,std_grad = 520.6282348632812\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJBCAYAAABbOIEjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOyda7BlVXXv//M0yKMRaKDpbppXy/upoLwU8QGiogkmH66SlEVSVpmytJJUUqmYez8kH5KK5Yd8S6rCNRapiiG5ZYwSwTKIj6gI0gjIo4HmIW8aEBB5SvdZ90OfNfnNf585e/U5e+8+vR3/Koq5e8+91pxzjDHXOuM/xpip6zoFAoFAIBAIBNqY2dkDCAQCgUAgENgVEC9NgUAgEAgEAgMQL02BQCAQCAQCAxAvTYFAIBAIBAIDEC9NgUAgEAgEAgMQL02BQCAQCAQCA7Col6aU0gdSSnenlO5NKX12VIMK7ByEPKcHIcvpQshzehCy3LWRFlqnKaW0TNI9kt4n6RFJN0q6pOu6O0c3vMCkEPKcHoQspwshz+lByHLXx26L+O2Zku7tuu5+SUop/ZukiyVVhb98+fJu//3311z/4ju+vLHt/fh5y5Yt1X68xszMMIfa7Oxs8XnZsmXbHV9rvH7foXNs3Yt9h77wPvHEE093XbdyO912SJ6Upa+bz6c2Xq7P0PVooXbf1jX8Nz6XHR3TjuhhTc7ej3q+adOmkctSkvbaa6/ujW9847z3r415aL+h+tHCQm24plctu6r9fnvj4L1ruk08//zzevnll4csxg7Jc++9967us/zM8Q61nR2xzaH2TbT29NrYW/etyWR716vpeWu/ePzxx8dim3vssUe39957z/sdxzNUv1trV/uNYxRyr8nGxzf0erV3A/+uNfa+34svvqhXX3113gVYzEvTWkkP4/Mjks5q/WD//ffXpz/96WJwPTiRV199Nbd33333ot9uu70+5F/84he5veeeexb9eA3/rqYML7/8cvG5f4hIpRB+9atfFf02b96c23vssUduv+ENbyj68Rr8DefkY3fhcz1ee+21eWaxFVzPz33ucw9WO76OHZLn/vvvr0996lOSpJdeeqn4jvPm+H0u3Ag457322qvoR7n4WnEdXVcI3ptGSXlJ0gsvvDDvvfzatTHxRVsq57V8+fLiO46jpa8vvvhibn/+858fuSylrbr+0Y9+VNK2a0J74ThrG7lU6h/HL5XrNfTB6DpR2/jcNl955ZXc5rzcdqiztb1IkvbZZ5/q2DlPjrdmp1dcccW8/z4Pdtg2P/GJT0jaVm85T86tJQeO3/c0XsN1n+vdslPe65e//GV1TLwe9xx/GaJe8nrcz328rqPUPdqjj537xV//9V+PxTb33ntvvec975n3O86B43Q7oNx8rjW4rAnK0/d1rjPH4XLiHkq78vFxXrRNfxbw+pS7VOoO9wRH3++///u/q33GHgieUvpkSml9Smn9UGEFliZCltMFytP/YAjsWgjbnC5Qnv7iHti5WIyn6VFJh+HzoXP/VqDrusskXSZJhxxySNf/1eJ/lRD8K803AP6FwWv4X3N8s/Q3Uv6O3qoWfdL6i2rFihXz/sbf9mvwN3V+9rd9vuHzDdwfeu7VGIDtypOyXLt2bZ6oj5FrUPtrUyrXh3/BPf/880U/zrPlXeNfEO6ZqP2l4X911P5ac91gv5o3w+EeOf4lTZ3ydWrZSgU7bJurV6/u+rH7/Wtu9JYLvOVJbXnmWtcnqC9su9ypm+zn+wp1lv1aVIHbd80LXNsHdiCedIdsc82aNV2/dq6PHBfX3nWT3prabxy+59S8sW7DXGPe18dO78EBBxyQ275f8Hr77rtvbrc8ni6LmudxBGe17rBtHnjggV2/37gu0eZaHhTOvea5kYbTnvQmue5wjHxu+nOitofut99+RT9e373wtX6+X9dk3WKDaliMp+lGSceklNallN4g6WOSrlzE9QI7FyHP6UHIcroQ8pwehCx3cSzY09R13eaU0mckfVPSMklf7LrujpGNLDBRhDynByHL6ULIc3oQstz1sRh6Tl3XXS3p6hGNJbCTEfKcHoQspwshz+lByHLXxqJemnYUMzMzmad2HpqcYyuWhJwjOXTnyXm9ViwNOVyPveAYyaE7r1+LR/Gxc4zkhH18rTigoVkA4w4G7bouz6cVZ0R5+frWYkw8LqWVLss1phwYZyaVMmemhmeAkTN/8sknc/vZZ58t+vFzn94tbRtbNjTltsbvS8N49sWi67o8npacGKPg/Wrj9DXmZ4+p4Fq24hf43RNPPJHbnjXDsVM/Dj/88KJfLUPM4zU43ueee674rqanHpvTr9PQMgo7imXLluW4kFZWHPe3ViYSbakVx+bXYDxRLXNYKm2pFWdF3SBcT2rPhVZMje+XtcwxD8r2vWoc4F7ra0x95FjcDrjncS/0tWs9v1qxxgTtomUvnMuBBx6Y2/48Wbt27bzftZ6NrX2Y8/J79frXLKlQ/SYQCAQCgUAgkBEvTYFAIBAIBAIDMFF6bsuWLdkl7C57uslqLnX/jnAXOEsJDE2jdHffypWvF3fl+FouarpB2fZ70X3YSq1tuWM5Dl+XVqHHUSCllO/h61srmeBj4nq3qFauG13+UrmOdOM6amvleki3M+kBd8uzqB2v525hjtfHznnyd16Ez13t40arQGeLPuF31GmnMEh1u35z7ryv28jTTz+d25TZAw88UB0T7+vUA69P+sLTn5966qncbp0MwLbrRK9zC6mMPgSzs7N5TXyetFXaptNRpIi5hr730Q6cVua9qSuPPlpm2JOa4W9a5Va4v3MM/t26dety26ko/s71q1Ybyfe6oWVlFoteBr6HUh7cJ3xfq6Xt+5oMLfzLedMWpdIGqRNOr/L52CogyjAJXqNVJsJ1gnttKxRiCMLTFAgEAoFAIDAA8dIUCAQCgUAgMAATpecIp2DowqfbzakJutZah2LSbemuVrrLmRnirkpm5ZCqcwqA7mW2nXp45pln5h2fu6HpWnXKkG5W0j2ejejUyajRdV1eV3fZc96taq50BZM6aZ3p5XpDdy3v6250yplUIF35kvTQQw/l9oMPvn6UlK8vZcRrOxXF71qVtukKn0S2nIN0q68xx91y33P9KRfvRz1wKpI6TXvx7EXKbcOGDbnd0kXOw93y1AnO0fuRrnMakzrC73z/6amNoQenLgT93uj7IufJ/dOzoThv0i+uG+zna0+ZkZLzMdFGTjnllNz28Ib7778/t4844ojc9vU97LDXC26T6vHrUR9aZ+pxP3I9nET2XEopy8rlxDnRzpzioj7yWeb7WuuMOj4PKcPHH3+86Fc7H86f5QcddJDmw5FHHlkd+6ZNm3Lb91POxeVZq/Jfeya1qPPwNAUCgUAgEAgMQLw0BQKBQCAQCAxAvDQFAoFAIBAIDMDEY5p6vtN5bfKTrTgY8vBse+xBq0o18fOf/7x6L8YvkNdfs2ZN0Y/pl0x1vOeee4p+5O6HxrB4PAjBNfPYkHFXBJe2jdma7985Ro8BqVVw9TRvxsf4PPm5dlq6VOrH0Ucfndtepfb222+fdx5+vdWrV+c25XfIIYcU/ajnfgo89Ybzd12uVSceJbquy2NwOXHctM2WPBlD4PEVXNdWHAxjIDxu4q677srt9evX5/ZRRx1V9DvvvPNym+UCXI9o35xXKzbEx85rMh7L12ncMWszMzNZX32f9VTsHkP3T99XuD5eSmDjxo25zViUQw89tOjHdWOci+99P/vZz+Yd31lnnVX0W7VqVW5z//H4I8YD+bowpqlWDkeazD47Ozub5eNrQnv0/YVgBXzuO14Zv1U+het1880357Y/D2nDtHXfa/n8PuGEE3Lb5UR5Uha33npr0Y/fefwa928+532OfSxTVAQPBAKBQCAQWCTipSkQCAQCgUBgACZ+YG/venM3J11rrXRlulvp5vZqo600UbqimZ76pje9qehHdyJTXD1V+Dvf+U5uMy3W3fCknTgPp6PoWvR70dVPN6u708dN6czOzmbXsLtTOTeWEnBXNvtxzqQ2HE518N6UK6kBqXRdU85OMZKmoRvX02Opv0zhdVnWDop0UF+9ynCLoh0l+rVtHcRLKsX7tdKVCU9zJvg7lhVw1z5lfc455+S26wfd8scdd1xuk+qRSllzfJStVNqj0yHUW9qjy31cB/X26Lou25bTTjVqokUhtsoKtA6J5Wdew/v91m/9Vm6ffvrpue2lXc4444zcJq3kZUNom63Uca6Nl2ihDXLO3M+k9l41KqSUsm15eQXqEmXhB5bzM3/DMAOpnB9LDEgl/cp9zqldlgzg2rVKPtCufA/ld7ye74tuqwTnzL3E3xuGVAgPT1MgEAgEAoHAAMRLUyAQCAQCgcAATJSem52dzW5Pj06nq43fOT1F12vrgEJ+9gMFGe3PQ17d9UrXHV2V7makq7gVmU93JF2Jfl+6WZndJ5Xzp0t50vTczMzMINqI7ms/sJa/56GMTvuQBnHqirLgdz/5yU+Kfm9/+9uLsfdgFpZUyogZP+985zuLfqSpuNaegUlZuiu/pqMuy6GZoIsBKQB3UXOcpDR8rrVDkR9++OGiH+3b9ZT6fvDBB1fHS7tlVtxHPvKRot9jjz2W26R7brvttqIfM3RIL7i+kR5xeVKvaocX87tx0nS1auNce2Z60v6k8mQD0l9vfvObq9fzrDjaFimX0047rej3wQ9+MLe5vk4F3nHHHbnNNX3HO95R9Lv66qtzm3uO03hvfetbc5snAUilznNtnB4bZ1X3+e7TOqSW+uR7aO3UCqe0+Gz0Z+qNN96Y27RTp4Bp+6QFzz777KIfQyhoL56NR1qQc/TnJj+7PtdoWqdl+zm3bDM8TYFAIBAIBAIDEC9NgUAgEAgEAgMQL02BQCAQCAQCAzDxkgN9HISnuDI+grytxxTU0kmdmyQf7pVqGbPAuCg/NZlViBkT4/EyTJNmyqyXVWAcDKsYewo2OX/yz1KZLsmYKY8v8fUYNZjW7Dw75UI5t1LNGSvgMTWUkccvMN6HvzvzzDOLfoxP++IXv5jbHtvCfhdffHFu+8nb5MwZN+H6Snk598/fUV9rldbHia7r8lo6/+9xHD28DADjVvidlx9grJLrLefOGCSPh2MsB2MZbrrppqIfK4RzHl5ehLbP+DKv8M5+bt+10wrcPnpZt6oOLwYsB+KVz7lWtEdP8163bl1u+xoQXEePveO+S9tk6Qep3KsYI+S2RJnT/jxmlfdlCRivFs8TGzw2iXpIvfFnxJAU9cWi67o8PpcT9Yx7lz83GAPGmCGW0pHK+NE777yz+I5rznlTVyTpggsuyG3GTHmcEWMR+Tz0OFPGnnEv8WrmtKdWaQjfc4heh6MieCAQCAQCgcAiES9NgUAgEAgEAgMwUR4gpTTokFe64Dwlme5Rd0ESTGc88cQTi++Ypn7hhRfOOwapdPPSlch0Z0m69957c5uHwZ588slFv69+9avz/ub4448v+pG+8FRYug1ZpdrdxuNOhWWKut+L9A4pRE+9ptubVKjTOTzMkXKQSrqO9/Ux3XLLLblNd76PiWtK3XMqhvpFd69XiSa16PQF51k75FiaDAWQUspUqtsc15Xz8UrwnDvbXnWXNKVTgbRNUkue/kw5/fjHP85tpy+4dkxZd7qV96pVB5dK3WlVCydcnuMuB7Js2bK8Dq19gDrtlAVlxvH7urFUg4Pz5Dr6nvbggw/mNssCeD/SMby2V3fnvbj/uP1xXv5d7TD4FiU7LqSU8n2csnTauofLnbZKW/KTDn7605/m9vXXX198xzU65phjcvvUU08t+rEsBZ9zrVAbPjdbISmcl4cNMBTE9yZ+x2t4mNAQeYanKRAIBAKBQGAAtvvSlFL6YkrpyZTS7fi3A1JK16SUNs79f0XrGoGlg5Dn9CBkOV0IeU4PQpbTiyGepsslfcD+7bOSru267hhJ1859DuwauFwhz2nB5QpZThMuV8hzWnC5QpZTie3GNHVd9z8ppSPtny+W9O659j9L+q6kP9/etbZs2ZL5ZueGyc2y7fEt/I58ZC2tV9o2JuSss87KbfKxzz77bNGPfC9TV8n7SmXKJfliP26FY2fMgMdGMD3S4yFqx6j4Ojl/3GNU8mRMk4+Ra0+5+LEITEHlNTw1n2UBPIWacQ/ksT0WpXYqtx/9wFIFJ510Um5/73vfK/pRHygTPzWc9/KYOY6RulFL8XeM0jZZQsJ5fsYUcP1dx6iPlIWfls7fud1yHRhf4La0YcOG3KbdUi5SWSqEcnI9YrwMZcZjWKT2kT6MleDYfa/r5etzH7U8pW1lSRlxXB4zxu+4V3n5FsYgebwTf8e1uvbaa4t+lAVl6TJifBL3C99XOEfO3+OWGM/oKeq0Ta6F23DjuJqRyTKllMfqusTPXGPfQ2prd9999xX9WLKDJRmk8rlEe/Hjaa666qrcpu372vH5Spvz61EP/Ggygntv6xiVWtmiWn/HQmOaVnVd10fuPiGpGg2YUvpkSml9Smn9uGsHBRaMQfKkLP0lLbBksCDbbG1GgZ2KsM3pwYJsc+gfUYHJYNGB4N3WP2mqlaC6rrus67q3dV33Nv9rJrD00JInZelZYoGlhx2xTc82CSw9hG1OD3bENoccjB6YHBZacmBTSmlN13WPp5TWSHpyu7+YQ++29tQ+vk3TjecuWrq0eQ1/G2elWneh0j35pS99Kbe9Ui2vT3etUwV0+9J97TQeaQ5Scj52ptB6ejLHQZemU2Q7iB2W5+zsbKbR3GVM1yZl6W50fsdTszdt2lT0oxycfmF6K9NgTznllKLfrbfemttMg/Vqx0x9ZeX3f/iHfyj6kUL0khEEdcX1kJ+5hk7b1KjWChZkm62K4Bwbx+x/BHFzZ2kC70dZe4kAuuxbf2GT+mEZCrcDVhVnCRBPsecac89h+Q9Jevjhh3Pb6R7Os3WqwQ5iQbbZe5v8BapW6dj3mVpZiFbaOOkbqZQLbd89YSwZwe/e9ra3Ff1YyZp7hNM5LDHDPcb3baa2u67xO97Ln1srVuxQLPeCbHPLli3ZnrzEANeLa+x2RbBsBku9SCW15vKkTbNauO/J/AOM9si9WirX8vbbc7z8NnQ+wzNY4sL3eD4bnZZu7a/EkFI9C/U0XSnp0rn2pZK+tsDrBJYGQp7Tg5DldCHkOT0IWU4BhpQcuELSjyQdl1J6JKX0CUmfk/S+lNJGSRfMfQ7sAgh5Tg9CltOFkOf0IGQ5vRiSPXdJ5avzF3LD3v3lbk668VqUBl2E/K51oKe7XpkVQErhP//zP4t+5513Xm7T9eluS7od6cr1zAR+55VYCbo3PUCXlBzn6NSXf+4xKnny8GWXEWXL7yg7/65VzZWVnL0aO2kE/s5pFWZMcg09y45u+daByFx7UnyePccsDtdDZuwwScJpn0aGzshsk/J0upXrRQrKKR3aBfXPdfHYY4/NbU8OIf1DGfq6kuIj3eD6UaNP/N9J93G8LXd9q3owKT7X+0b23Ejk2XVdNXuulhXk+s3fcW/2+Braj9sc6dAbb7wxt/2AXYYtUH5eGZpUFGmlc845p+hXsyuXJefoVeuphwylcDqrFgs4SttkRXDfG3j/lt5y3KQzb7jhhqIf5eS0OrMjGULitkS95rr6vnL33XfnNuflc6QeHXbYYbnt2XjUI6fpeU3qsNtgry9Nu69+EwgEAoFAIBDIiJemQCAQCAQCgQGIl6ZAIBAIBAKBAVhoyYEFgVWkPVaHqbGsCuucPD8/8sgjue1xEywfQK5TKksB/PCHP8xtrwpL3pYc58c//vGiH1Nhybt7nBWvR17f03jJ6zsPTD6WPK2v0yRQKx9RqzrcAsfvKcSMqWEZAEm66KKLcptydb6babGsIs4K4FIpF8aiUMZSGe9GXtzTqWtlBaRy3WoVa6VtK9qPA6wI7vKsVbd2vWU/ytPLhjAOhjEKUhn3N7SaPGXt+wrlzgrvntbM9WcsjcfwcEweD8FxcP6e6t7vgUNOVF8IZmZm8j3cljg3xqx4DAf3llbJBMYxtSrEc0/78Ic/XPSjDTLOxWPBGEP3oQ99aN77SGWMKStct+pX+fwZK9nSZX+2jAMzMzNZVh6Txv2Gzy9/HnB+jLVl6QCptB9/HnL9GGfka0J5UMd8D6VN83ntMW8nnHBCbtNufa/lZ99Da7JvlR+oITxNgUAgEAgEAgMQL02BQCAQCAQCAzBReo7wdMbaYZ/uPqN7ji5IP+SVbkx37dNFTbfxlVdeWfTjYa68l1eRpnvyqKOOym2vlMoKuaTufI5cG3eRki6ge99TLBfidtwRsEqtp57XDktuUa1Mx3dKhBSAV4GlrpCyoZtfKg/cZYqsry/d2Kz+zDR5qXRPUw6sNi6VdJPLiGtDeTmtOwnqNaWU6SWnO3h/uu9d7qSnmMLuadns599x/Um/O8VFe+e9brnllqIfbZ325wc11+TkB39y7C5PzoX67JRhr9+tQ0EXA5YccJqiVhbCKRaCVJ2XFWApCKdV2Jdr5WUh+Lu3vvWtue2y5HfcI93WaY/UDX9GUNdaJ09wzfwakzh+qOu6rE/+LKMNco2duqJNc4/y+fAa/ozmOtAenZYl5c77egkJ3vv000/PbZ7mIZXy/PKXv5zbHrbAtfE9s1aCx8sbDKHMw9MUCAQCgUAgMADx0hQIBAKBQCAwADuNnnPXGt2odJG5649uN7rKnWbh9en6k8pqwszkueSSsohrLeOKh4BKZQYFXZBOKdAFzMqr69atK/qRPvIq0jyIkXSDuxXH5frn9ft7tugXuj+9kjpdyDU3s1S6e32tnCLqsXHjxuIzqTa6f1ntVyopAY7PKyYzE5JybWV0+DUIfuc6P4lTzruuy/dtuajpsncbJhXEtlMArWws6gjH8d3vfrfox4OxmRX3lre8pejHe5NSpe34mJi962vPvcOrlBOtderHVKMMRoGeempVNm4dYlrLnvNsMX72fYAnLNCmncKmnEm1ebYVaU7ala8v99ZTTz01t51qpe27LLg23HP9BAGnsseFfj93upXPB8rMx8V+lIVX8/aMRYK6xArjbnNHHHFEbnNvdLqVz0dej6c3SOVe2xor91Dfa2uUrWeX9jrc2gPD0xQIBAKBQCAwAPHSFAgEAoFAIDAA8dIUCAQCgUAgMAATj2nqufNWBdpWPA7TVclDOzdL7vfb3/528R25clZ2ZqVUqTzVmdys86Dk6Bl/42nN73jHO+b9jfOvjC/wOAHOmfE8HicyzngJaSvn24/NY1a4VpyLp7ByjIw38PR+ro+nK7P8w4YNG3Lb40249r/927+d287933PPPbnN+ArGzUilrvA759mp5x4PwnmR3/d18rTYcYBp6m5/jOthXInHV/Czp9kTXAevzM3YC9qpx628+93vzu1jjjkmt9/5zncW/Sgnxir5fbn+PJndY5oY6+DxhpQby0Z4nNuk5Cltuw9wjIzP8dg+fsc4UtcN7oV+L8qZuuGxnrUq/IxBk8r9mPrQipnjWg+N1ZPqZQZcH8YdOyptlWW/j3gMZ63kge9rjMXj2vnvzz///NxetWpV8R1joWhXfvpCLfbMn/k8jYPXuOGGG4p+3Ncfe+yx3Pb4slYVfu61rT1siG2GpykQCAQCgUBgAOKlKRAIBAKBQGAAJk7P9S5Sd4fWUidb1bJbqdhMKXZahOUJSOt5qiPdhHQzeuVbguUIjj766OI7ukVJI7j7ni5Ydz3T7c/5u6ty3BTAli1b8rhJZ/Tf9Will9NlylINTleSguPBjlK5pnS1+vyZNszre/rzbbfdlttMa3f6goeC8hruFq+ViJC2rfzdw6vZjru6e3/P3qXtFCNlSFm7W566SiqsBb8GqbbTTjstt53qpr5w72C6s1TK7brrrqvel/PivVxm7OelMahXlJnTEr18x0XtpJTyWNz+aBfUTd/TuG6U5YMPPlj04+9q5T+kkmrzdSN1znIgTp2wbAhPXvDrkcInfehVv0lN+TrxmrQH15tJHZReKyHBZ4A/R2rgHDzsgHL3NSFlyVIOTstyH/jOd76T2y4nfqacvLwIv2Plfl972q3LifbI73zN+rm09tzwNAUCgUAgEAgMQLw0BQKBQCAQCAzAxOm53iXnruladVqn4Ogep3v1xz/+cdGPNAkj7iXpoosuym1SaJ5Rc+KJJ+Y2XYYnnHBC0Y/uPtI773//+4t+dH2SMvSKp3SbOz3H35GWcFeluydHDVYEdyqMsiWd6pQsqQ+O3w8FpbvdXezMhKD73ilD6hEpVKduSQ+RgqMuSCUdSje2Z42RPvQxUX5e0Z5oZaKNCl3XVTMumXVE2/T+y5cvz2261KnPUikLrrdUrgNpWT/sk2vOLBynmUiZ3Xnnnbnt2TWkjzhH17e77rort13vuR6tPcyz7kaN2dnZvG/4PKmPPOXA9zTOhbL0zGHapuspZXnuuefmNjOMpXr2scuS9BBpembVSeXezwxYtz8+c1wm3HdJIznF5BTiOJBSqoaikF4amh140EEH5bbvO+zn9BzXlbbk689MaO7xTu2eeeaZuU19o15KpR6QfveMeT43nMasHaDt/aIieCAQCAQCgcCIEC9NgUAgEAgEAgMQL02BQCAQCAQCAzDRmKaUUk5LdQ6ZcTDkUr0CNPlItj0l+ZFHHsltj5sgGCuxcePG4ruvfe1ruc3YCO/3rW99K7eZMvvVr3616HfKKafkNvl154QZ4+SxSYzjYkyFc95D008XClYEd/6XYyRX7f0Y40V+mpy7VMYNMG5GKmNMyM8z/kEqq0uT+/ZSAm9/+9vnvQZP4ZZKmZHDZ0qsf/YYII6XcvZYAo+rGQdmZ2ezPXkcDOM7OAcvDUF7pH77Gh9++OG57SnPd9xxR27fdNNNue36fc011+T29ddfn9u/8Ru/UfTjvsAxeVwN50i5eFwN0+q9mjLtkWn6nr48JK15MUgp5RjBVjwc19R1k7E7lLPLkvuM7+nUW+7vtFlJ+ulPf5rbjEv8xje+UfT74Ac/mNuMHT3++OOLfvyO8PWmnXmpEMZ+tdapVpF7lEgp5fXzPZRrTjmxAp/yDs4AACAASURBVHh/jR60by/Rwf3KY4sYz8ZyKa5jjGGkDnhJnzPOOGPeeXi8M+2R7wMeo8Y9tCVPrpOXyehjVVsnamzX05RSOiyl9J2U0p0ppTtSSn809+8HpJSuSSltnPv/iu1dK7BzEbKcLoQ8pwchy+lCyHN6MYSe2yzpT7uuO1HS2ZI+nVI6UdJnJV3bdd0xkq6d+xxY2ghZThdCntODkOV0IeQ5pdguPdd13eOSHp9r/zKltEHSWkkXS3r3XLd/lvRdSX++nWtlt6+79uk+owvS3Yx047EqrFdXpnvZ3cZ005IOcHcw09E5PtILUunCZ4olDxX18ZJy8jnSvemuT7pIWwf2zlepdpSynJ2dzWviLs7aIZnudqVrm6muntZLSu7+++8vvuM8KSN3O9PF7pQvQfc0KTmnSVlygBSvl4ignju1yDlznWouY8co5dm6H935XAcfJ6ka0ji+dpwrU5elkmpr/Tvpd6Yy+2GfLFvA0iNesZyufs7f6VbqW+vAXvZzV39vB7SHUe+z/R7nOkfKmbrp5Va4Huzn+wpt2EMCSLVddtll1WvUQiQYziCVsiQ9zsr9UqlfLCXhtDN1wO2Wqejcj1oUHzGpvZY6R0rKbY5y5/Pq5ptv9nHnts+Nzyw+A1ulN7j+Xt6FYyeN5/bCe1EHXGa0Jz/gmzpMuS+knMsOBYKnlI6UdJqkGyStmlMMSXpC0qrKzwJLECHL6ULIc3oQspwuhDynC4NfmlJK+0j6D0l/3HVd8adot/X1dN6oxpTSJ1NK61NK6z04K7BzELKcLoxCnpMooBnYPsI2pwujkOe4i6EGdgyDsudSSrtrq+C/1HXdV+b+eVNKaU3XdY+nlNZIenK+33Zdd5mkyyRp7dq1Xa2CKl11dLt5xU7ScHQ5uht61ar6CzwptKuuuiq3r7322qIfXZw8VNKrqL7rXe/KbboqfQx0+zPjyukoGokbDNeP7lNfV79mj1HJ8pBDDul6ysvvzTWgO9UfzHTtc63o1pdKty7XTSrnSXl5FXhm59Gd79cj/clrsDq4VLr6OT5fd2bq+cOMrnaujbvFvZI6MSp5HnzwwV3vmneqm2Nr0a2kXVoVdbkOnklF2XAfaGUpsbIwsyT9Gi3ajZQ46QG/Ht3+bt/cq3jIq+tEbze+fqOS5aGHHtr183EaknrLsADfZ2uZb05xsd/3vve94jtSr/fcc09ur1u3ruh38cUX5/Yf/uEfzvt7qdxLSDE5PUR7oY05PUTb90Oq+Wwhte/PmdbB6KOS54oVK7rentyuhh7Mzt9xbk5xUb5Ok3EtGSbBjHG/xo9+9KPc9qrztXcBz4akbpI29mcjbdPXhdfgWvgYelkvqiJ42mox/yRpQ9d1f4evrpR06Vz7Uklf898GlhZCltOFkOf0IGQ5XQh5Ti+GeJreIenjkm5LKd0y92//W9LnJP2/lNInJD0o6X+NZ4iBESJkOV0IeU4PQpbThZDnlGJI9twPJKXK1+ePdjiBcSJkOV0IeU4PQpbThZDn9GKiFcFnZ2dzPEPrtHrGRjjXXDvt2eNlVq9endteYfr73/9+bpN/9Wq35DvPOeec3Pa0U3KpTJ9tpakzrsPjCZge6ZwreVxyzLUYpnEhoUqtc8uMPyF/7rFglCU5aD/RnvEQzsHzd+S7nZNmrBLjZnzslAVLRHg8EuXCGLlWmq7HJjFFmzpfqyA9TqSUsr627sf1ct3kHKiPbptcI5cn7Zb3crtnpW/GJ7mcGMfCvcQrfTO+h9XCXZ7UbZ9XrSKx22a/TuOqCN51XbYLjwXjuLh/eCo75cLYvtNOO63ox+sztszBfeDEE08svjvuuOPmHZ/rBsvD0J49Rooy49q3qmS3dJn7sevDpPbdfl/yfY22yr3QdYvzo115zNuGDRtym/GYUhlTVhuDJN166625zbg/jwejDAnfJykLloFx/WgFzPM7Pq/92evP7PkQZ88FAoFAIBAIDEC8NAUCgUAgEAgMwETpuZmZmezq9JTAWip9q9I33eOeCkv35H333Vd8Rzql5r6XpLPPPnvecbhLj7QT3dVe+ZYUA12+nnrM+Xt1Zv6OblGvxuvXHDVYpdZdmpQLqbYWnUH3aStt3yk+unhZ1dgrgrOCNKlhpyW4bq20cbr6W3QTKUN3mfNzy7Vcqzo8SvAAZpcT9bhWJkEq50rd9IM1KTOnB6jvlKEfuk16jTLz6tB057MSvNsLZcG2UwWt6tB+SHQP3y/6NRyXjSYc2Ov34B5Uo9Gl+oG93o+lGpwmo07Thp1iIp1DuXrJAR6mzb3D90iOnfbosqyFhEj1Q8dd5r5/jAv9fVslB/gMbIWr8JnntDfXweXJzwyZaD1fWbKDZV+kUh60dS8NQRlyXj5Hro1/V7NNh9vqfAhPUyAQCAQCgcAAxEtTIBAIBAKBwADES1MgEAgEAoHAAEw0pomnbzsXTA6SPLz3Y9oi+dzWCcoeE8LYlwceeCC3nd899dRTc5t8sfcj50ru1OO2GEfB8Xk8C3lz51hrqcwtvn4cSCll2XhME1M62XZZMlbAT6UmODePcSNnzlRx57AZC8WYGo/5YIwNxzv0/CePjeC8/Dty8NQVT38eV2o6sWXLlqzHfn/KiWN2u6odPdE6oqJ1TBKv7/KsHbXEGBupjKOgDBnfJJUxLFxvj1FjP4/H4ji4hr6e/XoMiZ9YCLZs2VK9Ry02kzEvUjk36i33TqncZzx2jfsT908fE69BeX34wx8u+vE77jmtOB/asMuS1/B9gGOkLrvOT+LMRsYb+v1rc/A1YT/Kwvdkxq/584ulJ0455ZR5ry2VzzmW8fGjqGpH4bic+Jk27KUOuDYuF/5uSAmJlm2GpykQCAQCgUBgAOKlKRAIBAKBQGAAJk7P9W44pyronmvRc3Sd0/XnLj3SdX6Sfe0Uc09rZmo63Xi1k5GleqqqVM6FLkx3Edaqfvs1Wy7qcVM6XdflcbssuT50k/o8SdtQ5l7VmfP0eXG9a21pW/qyB9NjpVKW1CmXQ+0UbF8LztnpC86F+u96My4ah2A5kFaVY66J09ScD+fg1AfXyOdKu2U/r+ZMnaCd0vUulfQoKxq7LnLsHK+Pr6XPXA/KzNezH+M4Sw70Nuh7FdPzub4uS64pKU8fM+3AKz7TZlgKgpX2pXq5h9Yp9ry2y4jz4l7i16PNedmCms25fk2qIniv762yJZxP6/laK30jlfNp7XkMcfC9kPs6x3H77bcX/Uj3UTYupxbVTVA//H2AOtKiVGulOojwNAUCgUAgEAgMQLw0BQKBQCAQCAzAROk5wt2apE88ap+g645uNnen0s3vdA/dzXRBOoVD6oZuRj9osHYAoM+Rv6sdKimVrkTPCqy5Dd29PskDfP1etYNW3Y3LtWplz9HV6teoueKdOvGsux4tOod0g1+PekOqyF3aXnma4Lw4D3dBt9ZmlOhd/S26lXA7rWUZuc7Sbv3azGoj9eD9arrjVE0tG8j3C+owr+G6TXk6HcXr16rd+3fjAA/TrlUjl+q0mFTS262Dk6nDrqfcZ2l/rCbt1+ce7LZOypB7qd+X60tZtigrR42u9X2gRvuPEpSnz5XypK47BUU94Pq4ftQy1aRS3xny4s9XhiFwvH5INr+jnflBwRxTy65I4beyB6kHrcz1GsLTFAgEAoFAIDAA8dIUCAQCgUAgMADx0hQIBAKBQCAwABONaZqZmcmceCt1lRxpK+WeMUfejzEQzjvzc6s6KMfBlNlW2id/00rFbHHt/J1XOPbU2No1xp2mzhR1jyPh2rcquJJnpkycjz/ooINy2/lzl9l815bKGIiWzBmzwfH6Cd3U31q5DKnNn9fKFnishcfOjAMsIeHj8jiW7f271D79net/4IEHFt/RRmp7gl+DsS4O17kebi+1EwncjnivVrwQv/N4uj5+Y1wlB2ZnZ/N8PBaM68ZYEdcxxlJynK1SHp7m7WOq3YvrxjgX3/t4Ddqzj4mxLZyvx6JSf32/4O84f48p8n1hHNiyZUuOpfO51vYhj8Wr7cnej/PxfYiVxClr7s9SuUa8rz83KV/GCroeUe6tWGXqkc+Lc6FsfX8YYpPhaQoEAoFAIBAYgHhpCgQCgUAgEBiANInDQPPNUnpK0ouSnt5e3wngIO38cUxqDEd0Xbdy+92GY06WD+rXax23h0mMY+SylMI2d9IYxinLsM0Su7o8wzYnO4aqLCf60iRJKaX1Xde9baI3XaLjWApjWCyWwhyWwhiW0jgWiqUy/qUwjqUwhsViKcxhKYxhKY1joVgq418K49jZYwh6LhAIBAKBQGAA4qUpEAgEAoFAYAB2xkvTZTvhnvNhKYxjKYxhsVgKc1gKY5CWzjgWiqUy/qUwjqUwhsViKcxhKYxBWjrjWCiWyviXwjh26hgmHtMUCAQCgUAgsCsi6LlAIBAIBAKBAZjoS1NK6QMppbtTSvemlD47wft+MaX0ZErpdvzbASmla1JKG+f+P9bSrimlw1JK30kp3ZlSuiOl9Ec7YxyjQshyemQphTynSZ4hy+mRpRTyXGrynNhLU0ppmaS/l/RBSSdKuiSldOKEbn+5pA/Yv31W0rVd1x0j6dq5z+PEZkl/2nXdiZLOlvTpuflPehyLRshyemQphTw1RfIMWU6PLKWQp5aiPLuum8h/ks6R9E18/gtJfzHB+x8p6XZ8vlvSmrn2Gkl3T2osc/f8mqT37exxhCx/vWUZ8pwueYYsp0eWIc+lKc9J0nNrJT2Mz4/M/dvOwqqu6x6faz8hadWkbpxSOlLSaZJu2JnjWARClnOYAllKIc+MKZBnyHIOUyBLKeSZsVTkGYHgkrqtr6sTSSNMKe0j6T8k/XHXdc/vrHFMK0KW04WQ5/QgZDld+HWV5yRfmh6VdBg+Hzr3bzsLm1JKayRp7v9PjvuGKaXdtVXwX+q67is7axwjQMhyemQphTynSZ4hy+mRpRTyXHLynORL042SjkkprUspvUHSxyRdOcH7O66UdOlc+1Jt5UrHhpRSkvRPkjZ0Xfd3O2scI0LIcnpkKYU8p0meIcvpkaUU8lx68pxwENdFku6RdJ+k/zPB+14h6XFJr2krJ/wJSQdqa9T9RknfknTAmMdwrra6EH8q6Za5/y6a9DhCliHLkOd0yzNkOT2yDHkuPXlGRfBAIBAIBAKBAYhA8EAgEAgEAoEBWNRL086qVBoYD0Ke04OQ5XQh5Dk9CFnu2lgwPTdXqfQebS009Yi2Bqxd0nXdnaMbXmBSCHlOD0KW04WQ5/QgZLnrY7dF/PZMSfd2XXe/JKWU/k3SxZKqwt9zzz27ffbZR5K0bNmy4rvNmzfn9taA+a2YmSmdYbOzs/P2Wyha1+C9OA7+u1TOpfUSOnTs/M6vx2v4Gtbw1FNPPd113crtdNshee6xxx5VWXLMtbZj6Br6d7W1Grq+LstaP7/e0D82WrIcOg7+7plnnhm5LCVp77337vbbb79txtUai6M216G/8c9cE78GP9M2h+qHo3YNH9+WLVsGXaO2d/Dzc889p5deemnIJrZD8ly+fHm3//77z3vv2pq6DQ/ttxA5t3SdaI2daI2ptV+25Dx0/sQjjzwyNtvs5TlUH1v71W677Tbvvztaz7kW+Cx3GdbG2NKJ1jWIofpcGwP7tWxzMS9N81UqPWueQX1S0iclafny5frN3/xNSVL/wO3x3HPPvT4oCHXvvfcu+r344ou5/YY3vCG3h26q/h3v5Xj55Zdze/ny5bn9y1/+sujXP2z82q7QL730Um7vscce1bFT4H6NV155Jbe5hi3F+vu///sHq1++ju3Kk7Lce++99f73v19SOX8f869+9at52z7mfffdt9qP6/Haa69Vv+N9d99996Jf7UWcMvHvqBuuJ7xX68HBcXAz8WtSri5zzvmKK64YiSylUp777ruvfv/3f3+bMUv1B4hvRJwP5zr0RcOvQfvze1FH9tprr+q9qB++/gSvwWu73F944YXqNbhH1Gxden1P+8IXvlC9lmGHbHO//fbTpz71KUnlvKRy/+Ra0f4k6dVXX81t6h/nKJVy4W/mxpTb1CmOwfsRe+65Z/GZa0q96V8oelDO3Kt9juzncqY9cv5+L+JP/uRPxmKb++23n/7gD/5A0rYvA/4s6uE2zDkcdNBBuU0bm7tv9bs3vvGN897LbfiZZ57JbT6/3f74/PZ7EdTh1h90nKOP1Z8pPXydeh37x3/8x+p9FvPSNAhd110m6TJJOvjgg7veEFzYXFwaom+CNCQqkE+em5s/yPiQ5/VpKFJpIBTq6tWri34UFo3v2WefLfpRkK0HPH/nRsqNiWPyDYYKOSq4LGueCRpHy2g475rxS6XR+DVq1/f1oE5x3fyhwjHR0Fw3uL6Uv+srr+EbHnWF4/Mx+YviqEB5rl69uuvv43NtPTQJypDr75sq+7nuc+6tzbL216zfi2Nn2/vV5O4v1a2/0rluXCd/IPTjGGXmMmW5du3artcnf0Ghfra8u7Qr/sb1m7/zP3A579YDjS+Vv/jFL3K7ZXPUB/7BLZX7O+Xgewc/+0O15in09RxX9jnlecghh3T9uvsL+MqVrzu2OGbfT2mPlEXrRcZtk7KnDP1lmXrFtfNnEn9Hu/K9m/flNXyO3C/44ubgM9X/GOv1peVVW0wg+FKrVBpYHEKe04OQ5XQh5Dk9CFnu4ljMS9NSq1QaWBxCntODkOV0IeQ5PQhZ7uJYMD3Xdd3mlNJnJH1T0jJJX+y67o6RjSwwUYQ8pwchy+lCyHN6ELLc9bGomKau666WdPXQ/rOzs5nndt6S/GaNO5VKPpYcpgdoksv2ID9+R47UuVlyuoxtcO6+FhTsgYfkd/kbHx9/99RTTxXfkWslv+1xEz6XIdhRefZxBc6LMxaB43WZc96ci8eb8Bq+9uxLft+xdu3a3K7FWkilXGrBsFI9fsfjkX7+85/P28+vwfl7LEcrWaGGHZVl13VZjq0gSrY9voLz45i5BpJ0wAEH5LbHFNTijloxb9Q/TzCpxYr5GnOMraSEVtB/7fq+Tn1czNAssrl7DZZnSimPxcdI3a/FcXGMUls3DzzwQI6x+I7ryDijVnIP92bf+6hTmzZtmvf3UhkTWtsvpVJXPJmF+kB78HstJHZ0R21z2bJleS09puz555/Pbe5XtDEHbczlzrn6nld7fvkacJ0ZP+Rxibw3x+7xRE8//fS837Werw7qHPXU3xv6sTez+arfBAKBQCAQCAQy4qUpEAgEAoFAYADGXnLA0bvo3AVHVzHd/J7yS9dfy4XGa7j7kC7qWgqu34vuPbpEpdJN2CqIyPEyTdbnQfey0w10J9Kl6a7KWl2KUaJ3c7qL02moHu4+5drTFezrRvmtWLGi+I7rc+ihh+Z2q+5MrZCqVKaqcg2dsuJn6onTSKQbmE7toE55qu+kD9X2dN2h9XYoC+qw6wPX1XWfOsJx+DVqlM6TTz5Z9KNNt2heUmuUoess18LpnqHlKnrKYmixwB1F13XVujc1qs3nSbqKeut21SpQyDVlbSC/BmVLmXu/GmXodkV7oU66bVJePnZS/bX7TgpbtmzJc2zVPON8XOdq46ZcpFKeDGmQSpqM1/d+3F/Z9n2NIJ24cePG4rtabULff2rhAf65VuuR9wp6LhAIBAKBQGCRiJemQCAQCAQCgQGYKD2XUsouaXeV1sqjuxudbjZSGu6+p1vQXZW17Cmvvn3XXXflNt14zBjxMXJMnv1BMPvD3dCkNtxtfvDBB+d2K6vK5zxqdF2XXZhOM9QyVpzWrI2/dYxI65gS3svdrnTJPvTQQ7ntMiLVyDU84ogjin6Uy7p163K7dVSDg+5u0o6e+ehu6HFgZmYm0ylui9Rv0uWtbJgWOL9WVhzl+dhjjxXf0U3PtXN7qR215JmW1ANSr63jW3zP4Xq0jm/p13BHsud2FP06uB0MPbOPdkAb84wqztn1lGtACsevQUquRqNL5Z75yCOP5PaqVauKfhxvq2J569SIGv3re9NCMlt3FCmlLEenuLh2pEN9jWvHdrWOTHriiSeK72gzrcr4xx57bG5T7i6n9evXzzsPX1Pet3U6AuXptGMtxKG1P9cQnqZAIBAIBAKBAYiXpkAgEAgEAoEBiJemQCAQCAQCgQHYaSUHWidik9N0DpMxBa1U7Icffji3Pd2SHPUZZ5yR2x5fsXr16txes2ZNbt92221FP5YPIA983333Ff3I8XNe3o/z8lR3xiF49WmilvY/KnRdl2N+WvciV92qBNxK62WsWatUBfs5V0/9opwZtyaVHDc5ch8T42juvvvu3PY4lcMOe/1sTi8fwXgT6k3rdPdxYXZ2Nt+X9iGVKd3Uv1YJCcrJY50YB+PXYEwLZebX4Jhuv/323HZ7YWzDIYccktteAbpWLoC2LZU64anulD2v5/fq+9XiOEeB/h6+p3Fv5Rg9joRrT9v06u5cX98HGMPCWD+/Bvc/xsx5KjttjuPzEhlcb+6RHitIXamVaJDKPcGvMYlyICmlLB9fO8qN+tc6VYFjbp0c8aY3van4zHuzvIuX+WD8IcfkMW+HH354bj/44IPVcXBvZGyp/6a2n0rlPPlc92doP8aWbYanKRAIBAKBQGAA4qUpEAgEAoFAYAAmXnKgd6F5+jmpC9ITreqxdDN66njtUF5JOv/883P7+OOPz+1777236Ee37x13vH4Qtbsjzz777NymO9Ld1XSH1w58laQbb7wxt93Nyt+1qpl7+YRRI6WUKauWK5Ny9XXj3ChzrzBON7rTCExvPfHEE3PbKVRSANQb0q5SSblceOGF845VKteb9JCD6bJOHVFGHJNTQpMqOdDrodODtcMuvVo/XeCUk/drpbDXKj273Pm7I488MredxmN5EFJJfqApy0awIrHrIvcSp3RIC3H/YQkRafzUOfdZp5VrByI7rUwdIC3j+wx19dRTTy2+o23R/nwf+MpXvpLbtBfatlTuJaRajz766KIfS3lQ9zy9nPNyCpV7GnXe13MhKes7ipRSthmnk3h/0peut9RB2pI/G0m7uc2RbuXa+b1uueWW3H7rW9+a261SMtwT+DyVpBtuuCG3KRd/N+A1fA+tHUTse1O/X7QO/w1PUyAQCAQCgcAAxEtTIBAIBAKBwABMlJ7rui67xryqNt1udF+7u52fSfGxQqxUuh1PP/304ju6CTds2JDb7m7/9re/ndu33nprbp9zzjlFP9IInJe7UplJVavMK5XuZXfl043eqpQ9bkqHsmxllHCtnfbxDIcefigv18MpAFJHDzzwQG7ff//9RT/KltSd03PUL7qnSd9I0vXXX5/bdAV7hhzpDJ9/jV71arbuah4Xej10Coa2WaumL5WUFPXP6VvqOzNZpJJ+puvc9wtScvyN0yWPP/54bpOCcfqarn5SFE7HMNuyVS2c+4/rRD+vcWVedV2X18EpYeog5ewyqmU/egYsr+drWqMyr7766qIfZcSQCN/TTzrppNymbfr+SblQv3zspKx87yd9yzb3ImkyB6O/9tprmdJ026xlBzq1xnHTRjxDjp9dv6kj3/ve93L7pptuKvrxd7RTz1SmHnzyk5/MbZcTbZDPZKdleV/PAOYewT3f6cl+r20djByepkAgEAgEAoEBiJemQCAQCAQCgQGIl6ZAIBAIBAKBAZhoTNPs7Gzm8z0egvEG5G09voWxEuTTnZNmSqqXIyBvztgXj19gRVrypc6DMlaplU7NNM3jjjsut8npS2Xcjqfnsi/jPLyEg1fEHjWY1uyxOozVIOfvsU+1iuAeX8G4rtZ3rMx98803F/3In1M3vJrwe97zntym/P71X/+16Mf4KcZyeIwAv/MUWcqsVXJhnJWjeY/+Pn6/WhyMxx4QTE/2+ADG+PhcGc/F9fL4BcZsMKbQU8dZNZgxdC4Lwm2JoL657lBf2M9jQ/rvxiXXmZmZLBvfF2slW3y/YNwLddjXlynlrRIrXO+3v/3tRT+Wc+G9eG2pXMd///d/z22WjZFKvTzmmGNy+4QTTij6cb/3WDjqYaty+rjLR0hbba6mr3wWMW7H42kZx8M15rPLf+d2cO211+Y2Yzo9zuqoo47K7euuu27e30ilTlCGLk/q8A9+8IPc9mr3/MwxSKUMuR+5bQ4poxOepkAgEAgEAoEBiJemQCAQCAQCgQGYeEXw3pXnabh01dHd7u5l0jh0t7t7ma5XT7vl9elafOc731n0I7V0zz335Pajjz5a9CPFcNppp+X2FVdcoRqYTksXslS6/d1tXDvs1NPUnRocJ1yWdJNSfp5iTdc25eq0x8EHH5zbLUqIKcSe3kpZ1g6Hlkpa7+STT85tpyB5L1JRTFeX2hWDORe6wp0mnkRF8C1btuT7OOXA8XAdvZoudZCy9uvxdzy0U9rW5d7D6Qlen4dzeyV46tLb3va23P7Rj35U9CN1zPISrrObNm3KbU91r1Ua9lT8cVeRZhiE34shB/zOD4Ll3Gh/TCGXStnSDqRyb+X6Ol1LiugDH/hAbns5Cq7p7/zO76gG6iFPCfB9ls8P31eo59R/3y9aVO6owGr9rnPcl5hm76UQSKvSlryUB5+jfpA891SuA+1Pkt785jfPey+3A8qDVfidAmZFcO4P/tyhHvl+TV3k899DbXo9iIrggUAgEAgEAovEdl+aUkpfTCk9mVK6Hf92QErpmpTSxrn/r2hdI7B0EPKcHoQspwshz+lByHJ6McTTdLmkD9i/fVbStV3XHSPp2rnPgV0DlyvkOS24XCHLacLlCnlOCy5XyHIqsd2Ypq7r/ieldKT988WS3j3X/mdJ35X059u7FlNhPU2R3DB5cuctyVWSk/fYA6bm+71Yjp88vKekXnXVVbnNdEvn7skDP/TQQ7nt5QI4dqbTMi1aKo/N8JgEcq3krZ3DraXCjlKefbqmxyiQJ2YMlsfmMI6Cgw7AGQAAIABJREFUc/EYNJad8NRzxi8wLsHXjdfk9fwYFZYj4CnrHvPhvHsNvJenAbMUBuM1PI6vFp82Slky3tDB8TC+w9eA+j70mAuP36LuMA7mhz/8YdGPsVBMWW8dTcL4JtdZ6h/bHivJMXlasscV9vCyCrVyIKOS57Jly7K+u+5wn2Db14OfKaN777236Ec5+9ozfopxJN/97neLfitXrpz3vhdeeGHRj88I6p7vn+eee25uM27GY/BqZSukUs6tI6/cpvGbkdkm4w099or2wjm0jrThvP2oGtoS44yksswKZcFYQb8X14fPXamMDWas4Ne//vWiH8u4rF+/PrepN1K5d3usEsdL+/bjVvo1HMcxKqu6ruvfSp6QtKrVObDkEfKcHoQspwshz+lByHIKsOhA8G7ra3j1z7uU0idTSutTSuv9L+jA0kNLnpSle7YCSw87Ypv+V3hg6WGobbpnK7D0ELa562KhJQc2pZTWdF33eEppjaQnax27rrtM0mWStHLlyq6nTdzNSGqALlBXGFI8PH3aq2oTfko8aZG3vOUtue1pzU7J9HD66IILLshtuq89nbN2CrrTHJyjj53X4HidjtrB07cHyZOyPOigg7re9e/3pmubcnYXeI2KoVyldvonXfM8ydsryVO/mGp8xBFHFP3oumb6+oYNG4p+pKLodvYHFj/7iyZdwPyDYpHV3Bdkm2vWrOn6+zrNUqNbvTQE7YKpwZ6WTZe96wRtk3KnzHyM1DGngOnab6U/83ocH2kDqdT1FlVSCyOQXqcEBlYE32HbPPTQQ7v+/r4PcE2p605nUB8ZcsC2VFJ3TqdxX2O1fqaQS9K73/3u3Oae6dWqa+VmPAzCZdvDdY3z594hlbrMZ0srXGIAFmSba9eu7fr9y22Jc6UO+5pQ13gahVPy3JP8+ccSEAz/cDmRNqON+HOT+8djjz2mGk455ZTcPv/883PbT/qgLjptSpqauu77dR8ONI6SA1dKunSufamkry3wOoGlgZDn9CBkOV0IeU4PQpZTgCElB66Q9CNJx6WUHkkpfULS5yS9L6W0UdIFc58DuwBCntODkOV0IeQ5PQhZTi+GZM9dUvnq/Mq/t66V3cVe+ZcuM1Ir7g6tHd7qLln2YzVvqaxsSnrHqwTTLUs3o2em0c3I7z72sY8V/X72s5/lNt2gzErwa/iBgvwdKQAfU831Pyp5MhPSaSfKjN95NV267+mWd9coKUqv9E03LF3SnjXEdWNGDTPkpNLVTErO3eKsjEzd9awL0jtOw1LP+buh1dxHaZvS6+vulHitsnPL3V475FcqbcnXhN/dfnsucbONHTB7jnsJK7VLZaZrrVK9VNK01DeXJ7NtPLOXOkK99D2sp5l8TqPea328Ujkf0sCtqu3cn5xG4fWc6qY+cO1ZMVqSzjrrrNxmBrOvPSk0UjN+EDizvphtxWw+/51nPjKzi3rudGcty2qUspydnc00ktsLdZV7vtsc5UuazefDKuD+jObzy+ly4pJLXp86dcLtgLZ655135rYf6Exalc8GP02A9K3bFmVN269lDLcQFcEDgUAgEAgEBiBemgKBQCAQCAQGIF6aAoFAIBAIBAZgoSUHFo15+PzcZuyL86CMlWDcAKuBSmVshPPOTKVkBVSvDs14AMY3eRwMeVHey08E5/Wvv/76eccqlfEErVgljs9TrccNxqe5LBkrwVgll2Wt0rSni5LHvv/++4vvuAbHH398bnvZAsqPY/JUa8ZK8HR3j7Pi2BkH4P0YK+GnttfiIVr3Gid6ObaqSDOOx2OfqAetSui8vn/HufL6LB0glWvJuCXGRkhlPMrNN9+c25StVMqdcWhefoFlCzyFnXKjbD32qe83sOTAgtDfw/cF7jWUl9swf8f97aijjir6cd9txUXxBARPUWdME/f+//t//2/Rj3E03GN8T+B9KQeXJWPQ/LtaFXAvB+L7xziwbNmyvGe5znDfrJXjkUo7o/153BJjufw7pufTDvyUhvPOOy+3WdLny1/+ctGPNsh19XipM844I7cpay95wn3KY78op9Z+OqSERHiaAoFAIBAIBAYgXpoCgUAgEAgEBmCi9NyyZcuyW8/djHSL0X3maer8zJRDVjmVSlrk0EMPLb6jm57X8OrbTLXloYReOfxb3/pWbl900UW57a5bukVZIuEnP/mJavAUarpZ6Xr21NFxuv6lre783g3urlDSFnS7emowaTLSOd6PLmhfe1KgZ599dm6zrIAk3XLLLbn9/e9/P7edimLZAsrvgx/8YNHvuuuum/c3XqWW+uBlC0gr8V5OFbgNjANd12V98lICtcMuW9QEKR2n8WhzTq2QyqJNu9xpP0yFdoqdZSO4Jzg1ylR6uvlbJxf4OhGkqlqV08eFXodcd0iR18qX+O9I03ilaab0O/1MSpyH6Do9x+v/1V/9VW579XFStNwXnTJjWRLuDyxhIbWrWvN5RHv0Eg6Tos57uTl9RH3k2Jxu5bONtuT0bS01XyoP9z355JNz2+k56gjXznWsVnrEDwCunfrgdsS5+N7EefE7pzH7sfv6EeFpCgQCgUAgEBiAeGkKBAKBQCAQGICJ0nOzs7OqHSRJFytdYzz8Tyrd8qR33C1PV667VNetW5fb3/zmN3PbqRVek1SKZ8PQtUjqzykdUgB0JboblHSDUzWk57hO7ob3TJZRY2ZmZhDVSvrLx0jZkk5zeZHecFqFVCCpGNcHjonyJ7UqST/84Q9zm5SCZw2xyjzbXqWW1JTPi+tBeXlW1iIP8B2ElFJ29XvWDN35rSr07Me2Z8OQjvaDlUnpkJY97bTTin7MWGxlytJ+6KLn76XSHrnHOO1PW3UXPvcF0keToOOI2dnZPJ9WRhvtz22Y39GGfX1JhfmeTiqQ6+HUOSkm6hdpGanMauQ8fJ8l1cYQC8+w5l7qB/Zyv6Aeegaw78/jQr9GHrrAdWidkMH5UG9dntxr/LnETDjucx7iQJ3jQc1++DUrw/Oge98nad8cE5+TUkm1ORXIjHnuTa6zPXXXCm8JT1MgEAgEAoHAAMRLUyAQCAQCgcAAxEtTIBAIBAKBwABMvCJ4z62SJ3bU0ij9O/K0Hi9TS1OUyngGVgT31GjGtJx00km57XEYjIMhX9xK4yXX69wxYyN8nThGxu04NzvuCuEsOeAy4mfGx/i6sRQE44w8DZQ8O2PGpDI+hjJnxXWpjCth5XC2pZI/52nb5NylspotdcNT1Kmjrl/UZbZdlycRN5FSyvd1OTFmgTEdrt+1lGePAeR3XsG7dlK7p7rz9PTTTz89txk3KJU6wWts3Lix6Mc4G46BsRBSOReXC2PvGNfhsU/jLiGRUsqxGR6zwjge2hXXUyrLTNB2XJaMnXn44YeL75jazTgSPymBJUBYNuTP/uzPin606csvvzy3mQrv12dsq5d+INw2uTdxjh5T5PE84wBPX6BNSOVeSX30+FzKrXYSgVTGKnksJZ8p733ve3PbY8UYa/SVr3wltxk/LJWxwB/60Idy2/cfViJnxXJ/ThBum5Rh692jl29rzw1PUyAQCAQCgcAAxEtTIBAIBAKBwABMnJ7rXYPuZqQrkOn97kqjO5TuPa8sTFe8UwBMTadr1yuH073McTgtQTch6aNvf/vbRT+6HUkBtA4vdjch3f6t9O9JHCTZj62VIk/XvrvHuQZ037tbmDJnSrpUprPTjesUCN2zlLPTDfxMuZA2kEqairSUV/1mFWqXUc1N7hXWPWV/HCDd6utfm6vbMKlYpqy73LlGXlKEKeI8GNtpMq7Je97znty+7bbbin5f/epXc5u0gc+R5UsoJ6dfSM+4nCjPVpmIfv6tqsOLAalWPxCZMuPcvOI6x0Z78TEzzMBtjvbNtbnmmmuKfqTIuc9SJlJJdX/kIx/JbdchgvSsU2u1MhNSuW9R/12u/iwYB2ibTl1xbJStj5MhIKS4vPo2yzU4/cUwF1YBd51geQlWdfe9ls8Nytopdq4xZe37D6ue+z7MavW0W79XH17ROrg3PE2BQCAQCAQCAxAvTYFAIBAIBAIDMFF6bmZmZptMqx61w/bczVhzs3m2GA9nvOqqq4rv6E6kC/KEE06o9iMt5G7jM888c97xelYcM9zo/vV+dHM7PcDsLK9OS4w7q2PZsmXZ3eou6lqlXXffc+25Hi3Xslf6ZuYG3b1O43Htv/GNb+S2rxPd09RDnyPvSxevUwWkV51iIiXAcTiNMC4ah2DGld+PY6NsfE34HV3lpNulkkZ13WcmImXorvh3vetduU1ZeHYlqUGOyQ+X5RxpY17Nmzrcyo4lXJ8ncQBzD9fv2jydKuY+TXn5enAPbu3VzE7zbDdSg/yNy/yUU07Jbe73559/ftGPVcppcx6yUAsj8HtzP/MD1CdRrX/ZsmV5r/C9gXJr6a0fRt/DQ1daWWO/93u/l9u0aa/MzT2ama1uc9xnmAXNw4Clcp+kvvl+yuv5M5r6x+t51nn/TIuK4IFAIBAIBAKLRLw0BQKBQCAQCAxAvDQFAoFAIBAIDMBEY5p4+rbH46xduza3yRN76h95afLaLCMglbE0zk+S02S6MmOTpJKrJV/KStE+Rvbz2BCOl9VznbtvlQtg2ib52NZp1ePAli1bcqzD0Hs7H89Tr/mdV9Umj33GGWcU31G269evn7ct1Ss0k0uXpBNPPDG3qQ9+MnstLs7LL1BGzrPXKoL7OrUq2I4Ks7OzOUanFQ/HGCSvosw4GOqwx0kwhsVjLWgjlBnjWaRSHrwXKxBLpY4xTbp1kjplxjIKUmnrrAov1VPT3T4mUeG9h8dPUZdoVz5GyqgVR8I4leuuu674jrKlfnt6P8tH8DngVf2ZKs+YNh8T5deK+6xVLPfPtAff090GxoHNmzfn55mXkKA91qq9ez/amOsH1+6ss84qvuNcGefmJ25wzXk9j238wQ9+kNusSM9rS2VZBJ7g4fsUY0u9NAMrnTPO0eU+BOFpCgQCgUAgEBiA7b40pZQOSyl9J6V0Z0rpjpTSH839+wEppWtSShvn/r9ie9cK7FyELKcLIc/pQchyuhDynF4Moec2S/rTrut+klJ6o6SbUkrXSPo9Sdd2Xfe5lNJnJX1W0p+3LpRSym5DpyDokmMqr7uymfJJ15ofUEg3OikXqSwtUEv7lspKtXRRO33Girn/8i//ktuefs7PPDDU0+jp1nZqhuvBOXrqpFN+cxiLLJ1C5TrW6ESpdPeSrnTd4GG+bPvvOGevZkvXNdfX3dhM1aWc6RaWynR4Xs/d9byGu/ZJHdF97qnsFVlKI5TnzMxMlQak3Dgfpz6om6S7fD4tlzipA9KZTm0ed9xxuU03v6ezc068r+tsrdK3y4yycH3mnEl7+B7Wr5v9+8hk2XVdtkGXEfWM1LaHMJCu46G3XpaF6+0Vn7nHcW28wj3DIJgC7zQ9bZPy8/vS9jkPpwVZrd/T4Slb2vcOlAMZmTx32223TDf5XsBxcl2dfuaa0A68cjbX30/Z4F5LPfB9g2tEit3LwPCZ/fGPfzy3/fBovhtw//FSB61q8tQd9vPTCoaU6tmup6nruse7rvvJXPuXkjZIWivpYkn/PNftnyV9ZP4rBJYKQpbThZDn9CBkOV0IeU4vdiimKaV0pKTTJN0gaVXXdf2r4xOSVlV+88mU0vqU0novZBfYeVisLCdx5lJgOBYrz0kEtAaGYbGy9EK/gZ2LkOd0YXD2XEppH0n/IemPu657nu7cruu6lNK8KSFd110m6TJJWrlyZde7i90dTDcjXWYece8uwx48oFcq3WzuRqdbkO5bp9NYWfacc87J7f/6r/8q+vFQSLqrPZOK16f70F2kHFOLdiO15G7i1gvqKGR54IEHdv2Lk2dg0IXayjyhnOnKv++++4p+dMN6RV5mSdAFS0pBKtf+pptuym2n+3gN3peuaamUbe1AW6mUX0smXCd3wW/PZTwKea5atarrX5ycFqlRbU6LkMagHTBzRSopTH9Zq1GBp5566nxTkCT9+Mc/zm0e+CuVtGyLdiNIL/gcKV+vulzbc5y+6Kml+aoOj0KWhx12WFerbMz15nduV1x7UjbMQORcpG3DDGibpIcuv/zyoh+ruJMy832FdkFdc5ujbHkNp2yoy/5HIPdkp+Rq95oPo5Dn2rVrq89N6iCfG36YNDPcuMa+J1GHPXuRdHIrK5jgM9QzZbkvUI94KoNUPvMZ0uCUai3zVirlRFnv6F4rDfQ0pZR211bBf6nruj6nd1NKac3c92skPVn7fWDpIGQ5XQh5Tg9CltOFkOd0Ykj2XJL0T5I2dF33d/jqSkmXzrUvlfS10Q8vMEqELKcLIc/pQchyuhDynF4MoefeIenjkm5LKd0y92//W9LnJP2/lNInJD0o6X+NZ4iBESJkOV0IeU4PQpbThZDnlGK7L01d1/1AUu3I3/Mr/z4vUkrV9OlaKrOXEiA3ydgXj2niNVpVT5lKfs011xT97r777txmTIxzroxpIq/KqrVSyeHWuGi/l68XuV/G1bQ43B6jlOXMzEyO9/D1JU/Oar2e3sl+jB/ywEden2st1av6ekVYxqbwN56GTM781ltvzW2PvWEMCKsYt8oFOKfPuINaZV//jhilPLuuyzrjcRPUOa6dz5UxD1xv2pFUph67PFllm1W/WdZDKvcFVgI+8sgji341ubvO0n4YI+PlAlrzZ+wL+7kN9zFOFt8yMllKr9u/37sWB0m9l8r9lPro/WgjXsmZ+12tVIpUxou2Ytc4JuqJx45SDuw3XwxZD4/pYt+hZSaIUe+1vQz8OefxXD18TRh3RF33GDXaj1fXp90yVokxUn4NjtfLu5x77rm5feyxx+a2n9LANWespJcuaZ2CUYtjdl1s6Uj+zXZ7BAKBQCAQCATipSkQCAQCgUBgCCZ6YG/XdZk2c7ciXbt0eXq6Ll3FTHH19N9W1ed77703t+mO81R3Ujx0QXplU7r4zj777Hl/I5Vu0dYBlnQf+oGhBN2W7lbcXirsKFBzZZK24Bq6a5sUKg/j9AMgeT2nBzhPVon+6Ec/WvTjGp900km5fdVVVxX9SLW10k95PcrSKUjqnusodZ567fTkJGTJiuA+b8qN1IfLn2nlTNl2WpLz8eq/b37zm3P7He94R247jUc9qFWgl0qKiONo0dmco9sm5elyqtF/XpW715chVMBCMDs7myldpyEJUicuc9oc180r4/OgdD8k+7zzzstt6gMPSZdKm2NIAykgqdwXKCMvS0MqkGvsewf3bZcR5Uzb9OfWJOrVbd68Oe8rrVAI6qrPh/KlPL0cCNf44osvLr6rHfDsqf9c5wsuuCC3W3Qay074M49zZCkZ32tp+x7SQFlzv66dJOIhM0Wf6jeBQCAQCAQCgYx4aQoEAoFAIBAYgHhpCgQCgUAgEBiAnRbT5Nws+U1+55wj0/HJL3u8DHlRj4si90uu3Y8BYJwDeXePm+B4+Z3HPHC8jNvyFPNauq9Uzos8tXOwtVTUUYEnqTsYR0C5eml/fsc4BI8jYTkC1wfGTLXiaGqp556iXotR8BgYxu+wrILHLbViHvz4nB4ew1Qr0zFKpJTyHH3tGIvAWAlPt66llfvxRNRNLyXAlHPGqnhcYu1oHaaYS+VcqH/UAamMkeHYvdQE7dHlwmtSX9w2+2uOM1atdu3aETG+vpQt19Cv+5a3vCW3fa0oZ/7OSxNQ5pSr7wPcgxkv6jGQvD7tz+9LG/Z5UWbUee/XihkbFVJKWbf8mULbpP61dIvlCPyoFMraY3Jr9ujPGto07cVjgTkOtv05QVtqxZdxzr5fc29iLJTHFfalGVpyDU9TIBAIBAKBwADES1MgEAgEAoHAAEyUnqOb0Svt0rVLN6NTAHQv0zXpJy3XUo2l0i1LF5+fZE+3LF3F7oauXZvuZKlMgySN46mwrQq0HBPbns45rnTmHjMzM9kF6m5vH0sPro1UypnuXpcXU1q9givXiuvBKtFSSXmSFnPXPvWN82jRbHQFe6pvK62ZY2fbXetOL48L/ficFuF4OB9fE1KlNbpdkk444YTc9hPk+TtS0a3K1occckhue2Vn7jN07bssqIv8jVMApEeciq1Vf/f5j9s2U0pVneG/c1y+p7EfbcLpVOoDKzxLpT5wj3Dah/Q25eLrRDt76KGHcttlRDlTv1yH+J0/Z2p7vO8/rr/jwLJly7KutUoecI9zeopULK/hNkwKzp/R1Gnai1PdDKHwsgAE5U5d9LAFftfqR31rlX4h9erPqn5Mvj8Q4WkKBAKBQCAQGIB4aQoEAoFAIBAYgInSc9LrLr9WxU66z1pVd+keb9FY7qrmd7y+H/JK92GrOjTvzd+4i5TzolvX6Si6ElsZKby+u7LH7TbesmVLHnfrQORWhh/d5ZS/Zydybi4HumiZOeWuW+obr+G6wTGSUnB3bY1ScDd/K/ONc6Yr3N3dLTp4lOjH4/RgLXvI9ZvUGL9zuyJ94t9xvSgLd7fzXi0KhvfiGrvOch8gPekUBb9zaoO6RB32fr19j4umSynle7gsOU/e37N0+R311PtRRp59zPUgDdI6AaGW5SqVNt3KcKyFN/i+Uju8WCr3WdLErvO+HuMAs85dZzg2jtnHRfvhXH1Pbsma37Uy13kv6oA/86kTtGe3Tcqzpr8+Dh877Zj7We0AZqdhifA0BQKBQCAQCAxAvDQFAoFAIBAIDEC8NAUCgUAgEAgMwMRjmnqu0Plq8qysDuppveQayYM6B8k4Ak8rJLdK/rQVZ0Vu1rlUXoP9PFaJv+McnU/nOLw6Kvl7cr0ew+SxNaPGzMxMjhFoxXuRM/dYBoJy9hIGrdgPpptzbXw9ammmHm9CLpzfeaov5cxruw5RDq3YL17DdXnc1d37+/cxI26bHGcrpofjpH67PKnvHrPIdeU4PG6lFY9DeKXrHi4LxjxwfB7z1oq9YywHY7V8D+vXw+OlRoXZ2dl8D58nZdQ6yb0Wx+P6zT2olQ7PfcxPsadOecmI2vXYbsmS8vJ+vIbrCX9HWfqe3kpNHyX6ObnMWMalFlMolfOhfnsZGNqqrxdtmrbpa0J9p5229uTDDjsst31f4Xhbzxp+5/eijlGers/9mFq2GZ6mQCAQCAQCgQGIl6ZAIBAIBAKBAUjjchHPe7OUnpL0oqSnt9d3AjhIO38ckxrDEV3Xrdx+t+GYk+WD+vVax+1hEuMYuSylsM2dNIZxyjJss8SuLs+wzcmOoSrLib40SVJKaX3XdW+b6E2X6DiWwhgWi6Uwh6UwhqU0joViqYx/KYxjKYxhsVgKc1gKY1hK41golsr4l8I4dvYYgp4LBAKBQCAQGIB4aQoEAoFAIBAYgJ3x0nTZTrjnfFgK41gKY1gslsIclsIYpKUzjoViqYx/KYxjKYxhsVgKc1gKY5CWzjgWiqUy/qUwjp06honHNAUCgUAgEAjsigh6LhAIBAKBQGAA4qUpEAgEAoFAYAAm+tKUUvpASunulNK9KaXPTvC+X0wpPZlSuh3/dkBK6ZqU0sa5/69oXWMEYzgspfSdlNKdKaU7Ukp/tDPGMSqELKdHllLIc5rkGbKcHllKIc+lJs+JvTSllJZJ+ntJH5R0oqRLUkonTuj2l0v6gP3bZyVd23XdMZKunfs8TmyW9Kdd150o6WxJn56b/6THsWiELKdHllLIU1Mkz5Dl9MhSCnlqKcqz67qJ/CfpHEnfxOe/kPQXE7z/kZJux+e7Ja2Za6+RdPekxjJ3z69Jet/OHkfI8tdbliHP6ZJnyHJ6ZBnyXJrynCQ9t1bSw/j8yNy/7Sys6rru8bn2E5JWTerGKaUjJZ0m6YadOY5FIGQ5hymQpRTyzJgCeYYs5zAFspRCnhlLRZ4RCC6p2/q6OpHaCymlfST9h6Q/7rru+Z01jmlFyHK6EPKcHoQspwu/rvKc5EvTo5IOw+dD5/5tZ2FTSmmNJM39/8lx3zCltLu2Cv5LXdd9ZWeNYwQIWU6PLKWQ5zTJM2Q5PbKUQp5LTp6TfGm6UdIxKaV1KaU3SPqYpCsneH/HlZIunWtfqq1c6diQUkqS/knShq7r/m5njWNECFlOjyylkOc0yTNkOT2ylEKeS0+eEw7iukjSPZLuk/R/JnjfKyQ9Luk1beWEPyHpQG2Nut8o6VuSDhjzGM7VVhfiTyXdMvffRZMeR8gyZBnynG55hiynR5Yhz6UnzzhGJRAIBAKBQGAAFkXP7ayiW4HxIOQ5PQhZThdCntODkOWujQV7muaKbt2jrTUTHtFW7vWSruvuHN3wApNCyHN6ELKcLoQ8pwchy10fuy3it2dKurfruvslKaX0b5IullQV/l577dXtu+++kqRly5YV3/HlbXZ2Nrdb/YbCf8PPMzOvO9t4X8fWeLRt2617eT9+bs1j6Dhq95XKeT3xxBNPd123snrRrdgheVKWrTFyXD5Gl+0QLGRt/He875YtWwbd18dau15rfC0Z8Xf8d8c4ZCmV8vQ5cDxcLx9nS9a163k/yrC1Ji07q/WrjcHv1bLT2vikul657vTfvfDCC3rllVfqg38dOyTP5cuXdytWrNju+Beyl7b2tJbeDNUNYqH2UhtT63pD7+Xz5+8ee+yxsdgm5dnS2xY47ta8a89Gx9A1qe0d2xtHbeytfx/6TG098/vvnnnmGb3wwgvz3ngxL03zFd06q/WDfffdV7/7u78rSVq+fHnxHRf0lVdeye1WPy5SbWOSpM2bN1e/22OPPXL71VdfrY59t91eXyq/F4X3q1/9Krff8IY3FP34u9deey23XTk5/9Y4KHAX/t57753bf/M3f/Ng9YKvY4fkue++++qSSy6RtK3yct5cU66NJO2///65PVSWL7/8cvEd145r42N66aWXcnu//fbL7eeee67ox9/xvvyNVMpon332yW2fI+VCmUvSXnvtldsvvvjivNdz/O1ZEm/yAAAgAElEQVTf/u3IZSltledHP/pRSeVaSaUuvfDCC7m95557Fv0495Z+c96ut5Qhx+H3qr2U+L04JuqY7yvUq9ZGz/2CayFJb3zjG3P7l7/8ZW77Hxe/+MUvJElf//rXNRA7JM8VK1bo05/+tKRt9z6ub20v9c+tvY+ffd+i3nAcLRup2Z9U36t5Hx8Tdch1jTrltsnvqBuuh/zuL//yL8dimytWrNBnPvMZSaXtSOWaU29d7vwd92fvx3XwdSU4b3/O1WTj9kJ5Uifchv36tX6ci3/H8XIt/HnSf/f5z39+3ntKEyg5kFL6ZEppfUppvQ8wsGshZDldCHlODyhLvoAHdk2EPJcuFuNpGlR0q+u6yyRdJklr1qzp+rfGFi3Cv2wcfCPlW797ifjXxu677+5jym2+WfPN13/H37Q8UhxT6y92zsPf6PmW3KIlOD5fs6G0E7BdeVKWq1at6vqx+V9wHFeLCuNfSS06gzLyv4j5133/F7yPQSr/guVfJC1vIMfkc6Q3iN4qnyPH5/pFbwT7tTyNA7HDtnnwwQd3/br4X7Ncf47T7YBrRM+cexZ4vZa3inA7qMnG+9G2nn/++XnbPg7qhPejDN2DRHlSP1ye/Rq26A/DDtnm2rVru359XL9pB5RDi1obus+4zfFhz7Xye3GMLVnW9gifI/8AoEfR9+PanuDXqHnnpG1tegB22DYPP/zwrp+He8RqYSPuaeda1jzCUrle7hmiTnOPoN5LdX3xfYXf8b5+vdq1vV9LnrRv6qXvP/16NMMLqt9sH0ut6FZgcQh5Tg9CltOFkOf0IGS5i2PBnqau6zanlD4j6ZuSlkn6Ytd1d4xsZIGJIuQ5PQhZThdCntODkOWuj8XQc+q67mpJV49oLIGdjJDn9CBkOV0IeU4PQpa7Nhb10rQQDEmZJ//q8QDkr/mdc+21yHxJOuCAA3KbPLdz4+RFna8nyP3Wsrm8H6/nsSEch/P6HC85Yp9ja7yjQEopxxV4DBLHyDVktpxUztu5ZYJxDh5TwGusXLly3t9IJY9NHfQx1bhv57gZs9OKa3jqqady2/Wrdq9Wxui40HVdXjOP/eAatWJOavEQPh/Oe6jcn3zyyep3rTHVMoU8jpDjZZxHK77L5c65tEoijPsUhpmZmTw/j8liTBpjVFzmtTieVrYjry2VexB/t3r16qIf1762l0qlHnKtfZ995JFHNB98fC05cM6tTOxW/O2o0HVd3t9dH2vPFNe52rOyFaPWKuVAffH9n2vE+/r692UU/NqHHXZY0Y82/MQTT+S22zCfga2sQO5H/uztbbpVxmSSB/YGAoFAIBAI7LKIl6ZAIBAIBAKBAZgoPdd1XXabufurlqrvrn2601qp+by+uzTpKqbrzwsYkjJhP6+bcfDBB+d2q/gd3cFMaXVXIq/vrs+aO9hdzbXU7VEhpZTdwT5Gpv7XUvOlUuYcr1OLLRrhoIMOyu1agUKplFErHZz3ZtvTy5955pnc5tp7YcoWTVNLvXcKxOc8DnRdl22rVRiU+udrxzWn3Tq9yDVxG6nRQp4azX2ANke5SNvSrz2ctqLchlaqd5urUXeToHCILVu25Pm5PtJGqGduc74n1fqRIvF7cd+tlXSQyvVmGjn3EUk6/PDD5x37gw+WNSVrBXVbpQl8Xrx+rTDkJNHbZKuqNuftsqANU2a+19Ae/TnH/Zvr6s/NVlFggjpBqo7hM5L085//PLePPPLI3HbK/tlnn83tmv5KpT36c7L/3bhKDgQCgUAgEAj82iBemgKBQCAQCAQGYOLZcz3cfUa3YO3sIKl0e7eyBVpuWbouOQ53Ja5Zsya36fZ3dzvdnRwHs7mk0r1J16fTVi13Pt2xrcM3x00JMHuuVVWWsmyd20SZuAua/TwDg+7lRx/dprBuRu2cu1aVbq61HzNCVzhpBKcUeA3P9uC6taqA1yimUWJmZibLwMdSy0pxe6nZcItubdEDtFvPqOH6UzYuT16PNuGyID3A75j9KJVUjdvY0EOX+/GOK4supZTv7xQLx08ZOU1KmbX0j+vtlBDXh9fftGlT0e/++++f975HHHFE0Y9yplx87Lw+ddRtk/rlNA33Kuq8y3whh47vKJg9589NrlfrjM5apq4/X0lx+b7O3/H6XpmbNOpjjz2W28cdd1zR75BDDtF84Bikcs1b5wC2aHra2tDzCGsIT1MgEAgEAoHAAMRLUyAQCAQCgcAAxEtTIBAIBAKBwABMvORArboxYwDIEzvnyN+3TkP2+xLkO8llexoyuXzGOTj/feCBB8773c0331z0q1WOdp6aPLyPnfEmjGmadFrz5s2bcyyW88DkvinXVkVsysRlTn1wGZHjblV3p1x4X6awSqXMuKbOkfN6hx56aHV81CGXM79zHp/wmLdxIKWU59uKveKatGLZuMZeroExKG4TXAfqkcfm1EqUHHvssUW/Cy64ILc3btyY27RZads510C98vkzvoRp0h5v2d+rFfe0WPTXbsVoMC7MY3O4Hq1SHq0SK7w392rft2uxYB4jxd898MADuX3TTTcV/Rj3yPZJJ51U9KMsfUzcd71q9KTBeEOXE58HtRIuUn0P8X2H1/M14f5F3fd7Pfzww7l9/PHH53ZrTz766KNz298RGIdHu7r11luLfrVyGlIpQ+q27z9DZB2epkAgEAgEAoEBiJemQCAQCAQCgQGYKKczMzOTXYjuZqS7b6jbmu6+llvN6QG68+mOdCrM3c09PO396aefzm26D0kHSKX7/vTTT89tp9ZY5dppiRo9ME5X/3xYtmxZdhl7Ki9pp9bhy5xbq4IrXa2tSs50tbrMeX26qu+8886iH9f+Jz/5SW47PccUdcrfdYN66dXtWfmWcvV04UmAFcEdtSrpLRqVtuRUGOHp7Ewlf+ihh3KbayyVtrl27drcdhqCB3yybMFdd91V9CNV0KoYTxrBU+drh5g6jTduMAzC9zTaI2kQp8Ko7/yN22mtBIxUHpzL6s2e5k45n3rqqbntex+ppC984Qu57evL/fTkk0/O7VWrVhX9aMNsS6Xcuec4nTPukxekrXbWr4XLs1ZCwmlZfmYZAE/7p/24TnD/ok3XDr2VSttkGENr7F7mg9f72c9+ltteCZ40uI+Jus419P2idfh6j/A0BQKBQCAQCAxAvDQFAoFAIBAIDMBOqwjuFARdu3QleuYJP9OF6q5E0jPuciOtQLcxK4BLpRuZbmk/KJBuRtIzjz/+eHVMd9xxR26/613vKvpxLu6OrR3063TnuKoN95idnR1EI7WqXtPdy/F6P8rrqKOOKr6rVZ72+ZMqJTXqblzK6E1velNuOxVDtzxd3J5xwjXysQ+lhFrVwkeF1mHapA5bGZukG2kTXjmcGYtuI1w/UgDnnntu0Y/0HGkbz4KrVb12G2bGDw93dkqn5ub376izNRt2enNUmJmZyevgVBj3p9WrV+e2U2EE1831u0VDkuIjreIHqL/vfe/Lbe4Jvh/zerRhXlsqqVzqnlPnvJdndlHPa21p27mMA5SnZ5bVMgB9b+YzkJS4z4e26eEprNDOvZEUuFSuK/c13y9+9KMf5fb69etzmyESUkmhcY5Oz3EtWuvUOhGiRmsT4WkKBAKBQCAQGIB4aQoEAoFAIBAYgHhpCgQCgUAgEBiAicc09VzhUD7fuWZyjYxf8HRunrTMtEdJ+sEPfpDb5M09TZPxFozX8PRzxlRwfM5311KSyQ9LZZwN00MdvIanvo47FZZVaj0NmbEN5KCdP6ZsGY/k5RPIT7fiEo455pjc/v73v1/0O++883KbKc7nnHNO0Y8cPOObPEX9wgsvzG3GCNx7771Fv5UrV+a2rxOvT3vwWIIhabCjhNsc5cG2x4Pxd+zndsBYGo8t4jW5Ph5jwDIXjMPwk9S5dkxl9lIHtBfaeqtiuaepezmMHr439f3GFXfI8hE+RuoZ41lcN/mZ+5bHLTEWyteU+9O6deuq96pVAfdSJpTzRz7ykdz+n//5n6Ifr0Fd81gZ6pqPnevE+XsF7aGnUiwGXdfldfdYnVr1c+9HW6J+eywbn0W+l1FfGGfkpSZoS3x+3XjjjUW/WgVzf75SX/jMd3ujfrC8iCQ9+uijud2KaerX2WO9iPA0BQKBQCAQCAxAvDQFAoFAIBAIDMDE6bne7eXph3Txtap009VPV5274+hudaqKLmu67ejKlcrUY7qamXop1Q+mZHVwqXSFnnXWWbntade8l5cSoEuSrvJWaYZxYMuWLdmN6rKkO5WuYF8PupDpPnZ5cc5333138d173/ve3GbqMekASfr/7J1ZrGXFdff/da9HwDZg7KYHZpqhmQczY2yGBGwnnhQrTpQQyTYP8SfFUqSEfHnJQ6T4IY6iRN8LUhzsKHHk2FbAioHgjglmdjN300ADBgw0YLCN54G++3vou4tf/blVvfvec07fPl5/CVHnnjp7V9Vaq/bu9V9r1caNGxe8Fys8S2WKOe/LgyelUg95eKiDrn2uhVTqNnXD0+Y9bXwcSCllV7XT1LRHtmkfUil3rqtXHb7//vtz2w+1ppxInbvukD5hevXtt99e9Hv3u9+d26QUnLYihcZ5uR2R0vExMb2dtERtD5tEFX+/B3WLdKrT3gTpYrcDUmGu37XyMKSspZI2pe0fd9xxRT/KnDSsh3rw+tw/3a5o30PLfLg+jKtsBDEzM5N1pkWP8rvWIfWUux9Y3qqqfd999y3Yz+VUO+DbaWo+o7nHOWXI5wTLSfj4eH2nDPmZv3P76PsFPRcIBAKBQCCwROzwpSml9NmU0vMppY34274ppetTSlvm/79P6xqB5YOQ5/QgZDldCHlOD0KW04shnqYrJV1sf7tc0vqu69ZKWj//ObB74EqFPKcFVypkOU24UiHPacGVCllOJXYY09R13Y0ppYPtz++X9K759uck3SDpz3d0rbm5ucwVe9xE7RgV54zJS7dO2CZv6SmMTDknr+onHjO2gfEynmZ6yimn5DaPY/BYDvKk5HD9qAamtQ49QsM57BrXPip5ppReJcOF7s25tHhixsN4SrLLj2BpgdNOOy23GRsjlWvMcXgaLI9nYOyN47bbbsttysjLBXCNPEWWp36T+3eZ19Zt1LbZxxJ4ujJti3E7Pi7GtDC92+fDIxO++tWvFt8xxpB2euqppxb9GPdw11135bYfp0S0jnahDTM2gmPwa7RizbhmHofSx+24jY7SNvtYHl97xvO1xsjP3HN93TyehWBcG/cBtwPup2y39JDxZB6fRh2gnnj5Fq6/7+k1exxaJmKUtsn7egwOn3Nseyws58r4NeqDVNrtPffcU3zHWCjei888qZQ7S4p4rCrXkrFKPnbqzsMPP5zbPGZJqh8xJpV2y361I7vGcYzKiq7r+ujlZyWtqHVMKV2WUtqQUtow5KyywC7BIHmGLHcLLMo2J3G+XWBR2GnbbJ0jF9ilWJRt+j/EArsWSw4E77a/klVfy7quu6LrulO7rjt1EocbBpaGljxDlrsXdsY2a17DwPLBUNv0LKXA8sPO2GbLexqYPBZbcuC5lNLKruu2ppRWSnp+h7/Qdrdx7/ZspdvSi+EuPboZ6ZZ1ao1prO5qo7tv06ZNue2uSp5sz2uQQpBKmoIblntj6F7m+NxNz37+r0b2rZVp8HEMwE7Lk1VqfX3p2uZ4vfoqdYDj94rUTCF+7LHHiu9YJsIpBoKuXFYadtcyyz/893//d26z2rhU6tCWLVty21N4WyeKk95pVQQftyx79HL0deT9KSdP4WY/UjB0vUuvLhtBvOMd78htyoJUpn939NFH57brDudCezzxxBOLfrVqym5XtFsPCWBf7iWeGu2fd4BFybO3QZdljZrwkgP8zIe278ekwU844YTiO+5d1PXNmzcX/UjhcI9wepxj5x7jZUNYqoKUnD8jKAffw2rhIl6l3CnEHWDRttnDdYfrRVrL9xrOj2P2CucMhfC5Pfvss7nNfe6kk04q+tHeuV6+r5EK5G+c9qYeUVdcZzlHf/bWqHl/9vb6PQ567mpJl863L5V01SKvE1geCHlOD0KW04WQ5/QgZDkFGFJy4AuSbpV0ZErpqZTSxyR9WtJFKaUtki6c/xzYDRDynB6ELKcLIc/pQchyejEke+6jla8uWMwNe7ehuxnpKqZL2akJus3oqnOXL6/h2Wl0M9LN71kdpF3orm0dIsyMOacgSSXRLepuY7od3c1ay2Bw3rtGAYxKnrOzs1k2XhG85vb2udDVTxesu8BJdTC7TZKOOeaY3CYV5hTql770pdxmJpbTL3QTH3bYYbntNAfdupy/u6CZleOHgtKFzH6+Tp4J0mOUtplSyrrl8WocGyk5z16hbj7xxBO57Qd/Uj+8Cj9p0HPPPTe3PUOKByaTxmlRi7RNd+1zji2d5e/cbglSG36Nnu7x/WGU8uzn0Kquz7XxftxPaBM+Zur7o48+WnzHeTPbza/B0xxInfi+4hXYe3i2Fe3lgQceyG3X69pBwVKpA9RXt0WnqPH7kT43ezhlxj2E82tVy64dxiyVdF8rnIJtrrFUUqLUK3++ktodmvXK0xd87akvHmpTOz3Dn+X92kRF8EAgEAgEAoElIl6aAoFAIBAIBAYgXpoCgUAgEAgEBmCxJQeWfmPjqxkjQs7VOVx+rrWlMk20VR2VfP1NN91U9OMJ6YxBcv6VMTJnnnlmbnucAOOnGEPh/GsrRqZ2CrXHcvicR425ublqZWPGJzHuxeMB+DvKi2ndUhkf4+nFtTRTT1End8/1ZdySVMY4sR8rj/vYGeeybt266vW8fATjDFqxBJMobseYplYJCcZledwE48g4Zp837ef8888vvqM8WOHd7ZtxDqxE7jFqtEHahFeZ51wYO7NA1e4FxyCV86zFaEqvjqcaNebm5rK+e/wQbZA21yptwn61+Drp1RW3KTPGkR5yyCFFP8aV8BQGj1PlenNf8bEz/pTy9/gd6oPLktdgP39u7WTJgUWh67p8H6+n5s+Y2rg4P+qEX4+676VCuF8xfq1V5oMVvN02GZ9KG77wwguLftdee21us7yIV6dn3KM//6gj3H98D+vlGzFNgUAgEAgEAktEvDQFAoFAIBAIDMBE6TlWkW6l7dO12KKd6Cr2dG66I5nSKpVuWrrhvDoqXZUsW3DEEUcU/UjJsTrqV77ylaIfDxalO9LpPq6Np9/THUt357jpuIXQr7+7gkk3Un7uCiWdxn5OKVC2TmXSvUy50s3voFv3xhtvLL5jejzX13WDbvpDDz10wfFIpUu75gqWyvnvinPg5ubmMmXs+lijO1pV/enad7qxRQuRurrmmmty2yt4n3POOblNuzrwwAOLfixHcOutt1bHy3IEvIbLjAf4OnXH9aA8a5XDhx7+uhj013Z9pCyo0y4jpnNTH53W5BxclvyOOuVj4tpzTZ3Gu/POO3ObVAzLjkjlQbPc33183C+cMuX+0SpNMIkzOLuuy3roOsP9hWvH54tU7qFOuxG8hlNUq1evzm0+D/161JFjjz02t3myg3/m+FwXWZaEYzjooIOKfq2joCg3nvThe21vq0HPBQKBQCAQCCwR8dIUCAQCgUAgMAC7LHvOszA8e6HWz13dPdxtycMA3d133nnn5TarfrsrnhlAdNE6FUZXKA+AZdaXVLot6ZpsHa7odBQzVFr0yCTQj7t1oCzdn07j0cXbOjyTVIFXHabeMOvOD/ukq5+0q4+J2Vykc2677bai3/HHH5/bnL9fj/N3PazRA15pelLUay9PtznKhtTxUDe/2xWpFdcdHshMW/LMJ1IwXB/PcuRcmMnjNsesR9K3rYrafvAw58lr1DKZWhTAUlE7eYG2RX10m2NYANfXK7jTlmrVlaVyHb2C9IMPPpjbzI7lHi5JRx55ZG6Tinn88ceLfpQR6VSn2LmvOD3HteE8nI4bJ8XKe/RjcAqK4+F8nJ7jc5PrSKrK+zkVSdnQhk855ZSi37333pvbrcr4/MxnrWdPcw8lbepr3wodqNGOfo1+n3HqnQhPUyAQCAQCgcAAxEtTIBAIBAKBwADES1MgEAgEAoHAAEw0pmlmZiZzxc5vknsnn9jijJmy7XEfjAvxqs/kbVlZ9rjjjiv6MaaCnLfHYZBXJm/uMQ+bNm3KbcY8eGot5+VxAozhIdfusQu107dHiX7NW6nGHKOvG2VGOXgK8fPPP5/bTz31VPEd41RqFYOlUi4sQeEVvBkLwLE7v08dJZfu684xOU9eq+7u6+TrOw7Mzs7m+DnXJeo+5+fxHYwloQ54vEirqjhj9mintEWpXtLAx8SYCsYHtlLHeT2PR+K+4nETterTLs9a+vioMDMzk+Xk8Wm1Cum+f9b2GZYH8M+MH5LKE+m5F7osP/KRj+Q292CW/5DKfYC6dsYZZxT9KDPqkJee4akMbreMP63FN0mTiSudmZnJOuQlePiZMvPnBk9IaMUK8ncrV64svuMasTK3x5Qxlpd7l8fDnX766bl91VVX5bbHo3KOjAV2naV9e2kMjp1tn/+QvTY8TYFAIBAIBAIDEC9NgUAgEAgEAgMwUXoupZSpJ3ej16rpuvuT35Hu4GG4Uula9BRX0mF0x3l6MVNemRLp6ZykGO64447c9pIDdE+y6jDTdqXSfegu/Bp14BTAuKtKz8zM5PRXd5NyPej+9DGxuivH727hGk0qlVTmUUcdldvXXXdd0Y+Hf/J6v//7v1/0++hHP5rb1DV323IupABcDnQTu97Q1d9K/24dkjoqsFp/y+aoc61xkWJ1uyJV4DQCbe4///M/c9sr43PNeRis92OFfqYy+8HPpG5op04V1A5+lkrdpE04Lduvx7joua7r8j2cziGFQx32w0/5O+q0h1WQBnf7Jm1D+3NKlmtF+XlJC+6nrAbNMhV+De6zLC8jlXNxOqdG03sJGNe3cYDy9NI8XEs+U902a5Rlq4SBHxTOvuvXr89tp1tvv/32Ba/38Y9/vOjHNececfPNNxf93vWud+X2qaeemtteEZylDnxfoQ1yf1tM6EN4mgKBQCAQCAQGIF6aAoFAIBAIBAZgovTc3NxcdsO5W5Dus9ZBtHQ70t3uVZRbGU100/IaLUqLUfueFUfXJ132TjPVqpT7QYYcu7vDSQ9wbTzLx6mDcaCXmWcgUJZce6eu6EJldXBmyfj1ndatUXysWCuVa08580BJHzuv52MnxcSxMyNHKrO03BVcy/py+mISmJube5Uce9Syh9yG6W6nProbnfS4z/Ub3/hGbtNOncLmWpLGcfrk6aefzm1SGy53ZmBt3LhxwftIJbXhsq4dnupzHHfGFStIu95yP2EGmttVbT/2fYW6QZuQSvmxkrPTldz/WPGf2Xc+RlKhl1xySdGPGZi33HJLbju11aKa+Zn7rO/HnoU5DqSUqgfJUr7UK5cT50pa2CuC17JIpZKKZDgM6XGptFXanz+HaT/c85lVJ0nnnntubvOZ6hXeW9XMuTa8l+tEv74tGw1PUyAQCAQCgcAAxEtTIBAIBAKBwADES1MgEAgEAoHAAEw0polpzZ4KS76T6a/Og5JzZTxL68Rw5ycZY0BunNeTSv50w4YNC/5dkg4++OAFx+HVczlnxgl4ai3n30ppJU/tMU3OR48ac3NzeQ6eOs15klv3+BCmA1Oufr1aWQG/V2s9eG/G1LASrVSm2ZIzZ9ySj4n9WinBHl/iNlC7xiRKDqSUcoySx5zUKvR7PARj1Biv4KUWGG/A+BNJuvHGG3P7zjvvzG2PX+BaUnfWrl1b9KM9svr7CSecUPSjzXG+fl/ey9eJMV6MsXS59/uRx2uOCqwI7uUeOEa2Pb28tp96aj71gfugVMaNse2xo5QZ40280jSfBdyrvXwE46IoV4+5oow8Pq+Wou/xaeMu7eLwPYP3Zzxfq3J57QQLqZyrP0N4TeqLx8Ox3wc/+MHc9tjdb37zm7lN3WGZEKncV6gfHvta020fI3Xb4zj737XKgezQ05RSOiCl9I2U0gMppU0ppT+Z//u+KaXrU0pb5v+/z46uFdi1CFlOF0Ke04OQ5XQh5Dm9GELPvSzpT7uuWyfpDEmfTCmtk3S5pPVd162VtH7+c2B5I2Q5XQh5Tg9CltOFkOeUYof0XNd1WyVtnW//KKW0WdJqSe+X9K75bp+TdIOkP29dixXBHXR70i3obmK6+PgbHgIqlZSAuzSZ/sp7uTuS9+b1nZZwV3+PW2+9tfhMqo3uQncRMhXYx0S3Md3VrQqwPUYty35s7p5llWDO2WVE+XlKOUE6x93hpM1IdzglQhqWOuj00Be/+MUFr+dUK2XEeTllw2u4HrZKSxC1EgSjlCcP0/Y5UJf4nesm14Tp5l5tmuAh1lJpW6St3d1O+6Y73ykYUsDUS7cPVpXmIdBOnZMS8PIGBHW2dji50Z4jkyWv3Uqd5r7TSp0nJef9ODc/YJdV1vmdH8hN2pR64+ESDz74YG5zH/CK4LRHjsH3WVLIrsu155Gj9t0o5Tk3N5fH47pEKp/z8cNxuffQdo4//viiH/cklxOpPB7O3KKAzz777Nz2Zz9tk7SgH8rOZwiv4WEM3Gf8XqRiadOuzz0t1wz3qX6zAFJKB0s6SdLtklbMK4YkPStpReVngWWIkOV0IeQ5PQhZThdCntOFwS9NKaW9JH1Z0qe6rvshv+u2v54tGDmVUrospbQhpbTBPRKBXYOQ5XQh5Dk9GIUsF3OeVmA8CHlOHwZlz6WUXqvtgv/Xruu+Mv/n51JKK7uu25pSWinp+YV+23XdFZKukKT999+/693GrcwT/87GktutzIhDDz00t50WYZVSuvFI20nSF77whdxmpP/73//+oh8zukjJ+SHCdFHTleoZgszOax1szKyCVrVpYlSyXLFiRde7yN3FWZOfZztwzHTF+1w4Z6fT6NYltXPbbbcV/UjrkkbyzPb+lX8AACAASURBVBteg5Sez4l6SBk57UOa0KlFUnetDLkWDTQqeb797W/vejvxcTrV2cNd4NRjrpfTJ/ydH/ZJvaVsvKo4bY72TZe/VFIH1Kv/+I//KPrxGrVKxVJpc25jtexY6iiv4dTZqGS5Zs2artdPP7iUc6Pduv55Nl0Pr9JN2fpBq6S4SG/7Xs2K0rRvZsFJpX6RYnGbo8ypa96Ppwb4/KnzvMbOVOsfpTx7e2qdEME9jiddOEhV+bORa+QH3TOblYfyOj3HdaX9+GHr1157bW5z//fnJudIutXHzj3Hba724uknibRCCXoMyZ5Lkv5J0uau6/4OX10t6dL59qWSrvLfBpYXQpbThZDn9CBkOV0IeU4vhniazpb0B5LuTyndM/+3/yvp05K+mFL6mKQnJH1kPEMMjBAhy+lCyHN6ELKcLoQ8pxRDsuduklQLJb9gtMMJjBMhy+lCyHN6ELKcLoQ8pxcTrQguvRLH4dVwyUeSh3cOmVwleWhPJyVX6TFDTHHlNbxEAFNt+RvnZskfk5P3irZMp2X6s8ctcc6eas04CMbmePplLSZhVGB191ZV3FplXalcN8YSucw5F68uzUqyvL7Hp91www25feKJJ+a2n1RPXeE1nD/neDl/1zVy6a43/K6m19Kr9WMc6Louy8pjdRizQD3zWCvGMjD2xeXJGBaPVaI8qTuehsw1oi15xWrKhqUEfI7sx/X2mCDOuXVqO+3W59/bcKvq8FLAav0+T8Yxcf9wneP4ufYeG0I5+57DGE7Gp3ncyIc+9KHc/upXv5rbGzduLPrRbrl2HufIODnqUGtPdF2mPrTimMZ98oK0PY6p18PaKQLSq5+BBPWYst68eXPRj9/5vbiH3nPPPbnta3DWWWflNk9c8LItjEujze23335FP8aUMabLY9Rof/6s4bzYdrn3e92SKoIHAoFAIBAIBOKlKRAIBAKBQGAQJk7P9WhVYKVrzKkf0m6kCjztka5nPyiQ9AApNJYiWOiaPTzV+vrrr89tpsl65XC6DEmzOQXH+bsLki5Tuird9dyq8DsK0GXcoino/nQ3N13GXIOHHnqo6MdKtL72vOZxxx2X2+7a/9SnPpXb1A3XL8qccnBKgddoyYtwl2+N9nLquuWSHxVIt7ruULda1DH1+7DDDsttrpVUrrkfsMtUb5be8GuwkjjpGKcySYnzer7GvC/bPkd+bsmJc/TSAv16tkqrLAUppTw2pxe5jpRXK23f90+ClKzbEqtSs3I/Qx2kkga/8MILFxyrVO7VtdIzUv2Qc9fr2jNHKqn0VqiHj3Fc6MfnOlM7TNjpVsqG9JcfPt86zJfVwzkOf05yzz/nnHNy29eqFbpAUHe4T/qzgHN2WXNeHK/vF70dtGwzPE2BQCAQCAQCAxAvTYFAIBAIBAIDEC9NgUAgEAgEAgMw0ZimlFLmYJ0zJO/fSvdjHAFTLP16L774Ym7ff//9xXfkxnkvj7lhrATLvHuaJlMpGRvgp3STG+d9PeaBc2kdN0M4h12Lxxol+nF7LAPj1Tg3X1/y2PyOcRJSyWn7sSdcb5bsP++884p+jC9jrIXz7NQb6pfLiJ8ZX+ExJByfy5KxM+T3a8eWjBOMg/EYKsaMUNYud8ZX8Dee1ssYGY+l4Vreddddue16z3IVHIcfM3HEEUfk9t57753bnjrP3zEewmMjGC/jNkabpg14vGEfl+E6NUr0uuZ7KXWL+u16W0vRdjmw3IPj2GOPze3f+Z3fye0LLijLFHFP53g9poZ6ybV3feUcKQcvR0F99dgWjoNxNL6HeVzbuNDrisdUEVwHlxO/e/LJJ3O7lZrv1zjmmGNym6VCfE1YNoRxpg8++GDRjzFJ3CNcFhwjbdhjmlolNHjNVix0v74tuYanKRAIBAKBQGAA4qUpEAgEAoFAYAAmTs/1bi93j9M9TLexu8noxqPr1auh8ncttyzd/H4N0gikkrzaNF25dBm665pzrJ3ELZVUgVM6/K6Vij5ueo6ydJqhVpnVaSd+R4rFU4i53u4y5pqy3IPTbqRK6ar165EualUzr+kQ3cdSKS/XL64H9dX1YZw0zkL3cdqNY+N6+0nqXFeW73Ad5vVch7l+vJf34/5B2ptUrlS64lt0FCvNO8VHUO5Ou9UqR7uO9eMYZ8mBfn5Oz1FGpB6dEuG6kRLyMXO9PRyB+x/bXt2dlPjdd9+d275fcEykh3yfpW7UKFOp1JsWHTP0hIZxIaWU7+O6WRu301PUW66Dl2egfH0PZakC6v7RRx9d9DvwwANzm+tzyCGHFP2oV+znZRBI03K+PkfKumWbrbIw/XdRETwQCAQCgUBgiYiXpkAgEAgEAoEBmCg9Nzc3l13T7uakq41uu5YrrVUVlu41p0WY7dZyy5ImocvRXfs1N7u7D+mCJA3hc+SYhlaHdjpu3BXBW/cmhUo5eD9SApyny5JzdhqhRmU6VUB9o/veqTDemy5ez+Shu5tjd3d9i3ajXrao1laG0ijRy8p1h/rJMfsh0bUq2KRBpHIdnPbkIZ7cB1qHFvM7t3VSP7XKwlIpX97Xx04dczqulpVTO9R1XNQOs5T9QGqGGdSq30v1A4c9E5J7oVOja9asyW2GOjj9S/0ihePrQ9vkPHyP5PVoVz7H1j5L2or24Ht66/k0KszNzWUd98rZ3KNoSz5XfuaaeDYedcLvRV2irjvFxzHR1luUGfXNbZ3XYKiN2x9l489oUr21LEzplXUKei4QCAQCgUBgiYiXpkAgEAgEAoEBiJemQCAQCAQCgQGYeMmBnp9sVQTndy0OmfEPDnLUfi/GGPA0duf/aycqe9wEOXpyoT528rbkVT3WhZ9rpzD79Zyb9riBcaJVGbpVEZxy4Lz8epybl6ogn16rFi/V5edpzdQBys9jHjwWZ6Hx+PV9XvyOsVke7+dzHgdmZmayvnuqMeXEMbcqYreq7jJWwteEfdn2NanF2XhKNmMbaiUBpHLOjJtwOXNevk68dyumqf+uFTexFMzNzeW5euwW7bFVQZo2wpgej+GhXbksH3vssdx+5plnctttjmPiNbx8B/cB9uN9pFIOlLnLknur78GUWavi/yROXkgp5fG4fnPtOBafK39HnXB50pZ8brX4LX++Mj6Q+6HHNPF6XP/WfSkXn2NLFtzXW7FsQ2wyPE2BQCAQCAQCAxAvTYFAIBAIBAIDkMblIl7wZil9V9JPJNV5tclhP+36cUxqDAd1Xfe2HXcbjnlZPqFfr3XcESYxjpHLUgrb3EVjGKcswzZL7O7yDNuc7BiqspzoS5MkpZQ2dF136kRvukzHsRzGsFQshzkshzEsp3EsFstl/MthHMthDEvFcpjDchjDchrHYrFcxr8cxrGrxxD0XCAQCAQCgcAAxEtTIBAIBAKBwADsipemK3bBPRfCchjHchjDUrEc5rAcxiAtn3EsFstl/MthHMthDEvFcpjDchiDtHzGsVgsl/Evh3Hs0jFMPKYpEAgEAoFAYHdE0HOBQCAQCAQCAzDRl6aU0sUppYdSSo+klC6f4H0/m1J6PqW0EX/bN6V0fUppy/z/9xnzGA5IKX0jpfRASmlTSulPdsU4RoWQ5fTIUgp5TpM8Q5bTI0sp5Lnc5Dmxl6aU0qyk/yfpEknrJH00pbRuQre/UtLF9rfLJa3vum6tpPXzn8eJlyX9add16ySdIemT8/Of9DiWjJDl9MhSCnlqiuQZspweWUohTy1HeXZdN5H/JJ0p6Tp8/gtJfzHB+x8saSM+PyRp5Xx7paSHJjWW+XteJemiXT2OkOWvtyxDntMlz5Dl9Mgy5Lk85TlJem61pO/g81Pzf9tVWNF13db59rOSVkzqximlgyWdJOn2XTmOJSBkOY8pkKUU8syYAnmGLOcxBbKUQp4Zy0WeEQguqdv+ujqRNMKU0l6SvizpU13XFcekT3Ic04qQ5XQh5Dk9CFlOF35d5TnJl6anJR2Az2vm/7ar8FxKaaUkzf//+XHfMKX0Wm0X/L92XfeVXTWOESBkOT2ylEKe0yTPkOX0yFIKeS47eU7ypelbktamlA5JKb1O0u9KunqC93dcLenS+fal2s6Vjg0ppSTpnyRt7rru73bVOEaEkOX0yFIKeU6TPEOW0yNLKeS5/OQ54SCu90h6WNKjkv5ygvf9gqStkn6l7ZzwxyS9Vduj7rdI+rqkfcc8hnO03YV4n6R75v97z6THEbIMWYY8p1ueIcvpkWXIc/nJMyqCBwKBQCAQCAxABIIHAoFAIBAIDMCSXpp2VaXSwHgQ8pwehCynCyHP6UHIcvfGoum5+UqlD2t7oamntD1g7aNd1z0wuuEFJoWQ5/QgZDldCHlOD0KWuz9es4Tfnibpka7rHpOklNK/S3q/pKrw99hjj27vvfeWJM3NzRXfbQ+S16u+498dfOGbmRnuNKu9KPq92G8x9/L71K7R6ueYnZ1dsF9rnbZu3fpC13Vv28Fwd0qelKWD4+J4t23bVvTjmFtzaX1Xu4aD6039clm6XtbuO/Q7jql27R1dj2N8+umnRy5LqW2bNV1tzZvXeM1rym2GeuD3GqrfQ+VeG6P/hmPibzieHV2jtR4L3eull17Sz372syE/WrRtttaX3w39x7PPcRS2xM9s+36xmL2vtofvCJwLx+Fz5DWfe+65sdjmG9/4xu4tb3nLq+7nGGoTLSz2ubSz15ZK/ajJVqrPq7VPOYa+U/TfvfTSS/rpT3+6YMelvDQtVKn09AUGcZmkyyTpLW95iz7+8Y9Lkn72s58V/V772tfm9s9//vNXBmgbLifMfm984xuLfv474pe//GVuczHf8IY3FP1+9atfLfib17/+9UW/2gPZhV8brwuf93W86U1vyu2XX345t32+vPdf//VfP1G94CvYoTxdlp/4xCcWHD/Htc8+r5yj+MMfFjXJinXjb173utcV/X7xi1/ktsuI+sB184cdZUbdc7356U9/mttcUze02nfej/PitaX6xuzz32OPPXL78ssvH4ks58ea5fnmN79ZH/vYxyS92jb32muv3KYd+BpzPtR1f7n+yU9+ktu+JrxXS7+5X7Tsxdeyhz+QX3rppQXv9eY3v7l6ba6Fj6n1j4XeDv7lX/6lem3DTtkmZelj3HPPPXObMvJ+lGXrJZLXcNvkd4TLkp9pp5SJVOpG6yHIz9Qhtr2f72G0uR/96Ee57XPkun3mM58Zi22+6U1v0h/8wR9IerUucdzUv9ZcWy8efL5w313o3kPAe/lzk/pBO3N75rw4Xt87KBt/9nJP4/Vq+/o///M/+1Re6VP9ZkTouu4KSVdI0v7779/1E3VhUSCcvBtezYvR+heQC6H2RtraOLjx04ha43Ul4di5mbtytubFvrxX/y+RHnwwjQqU5Zo1a7p+Y/F7ccw//vGPc7v1LwOuh2/MLX2gkXO9XeZuYD18c+Hn2ou8VG7gfBl0eXEurU2jhZ3xou4MKM/Vq1d3vQxaL5JcV38hqT38fLOtvVwsdM0ePqbav3pbDxXOw69HPeJ33/ve94p+nFfrX6zUe9fF3lZbXqydBWW5atWqrtcZt7ma58XXvbY/uQ5zvV0mtNvWPybZr2Z/Dq4pX3Ck8gHJl0T/x0DrZZt7GnXDnxH+EjUqUJ4rVqzo+nX2NeHewO+GPqNcB/1ZRLAv5cS9UJJ+8IMf5Db3BF876hLtxeXJ6/E7lx/n6C/mtRfumk60WIGl7MbLrVJpYGkIeU4PQpbThZDn9CBkuZtjKS9Ny61SaWBpCHlOD0KW04WQ5/QgZLmbY9H0XNd1L6eU/o+k6yTNSvps13WbRjaywEQR8pwehCynCyHP6UHIcvfHkmKauq77mqSvDe2fUsq8a4s7JZ9ITrq/Rg9ys86T8/qt2AHy1c7rkxduBSqTSyWv7LE+7FcLOJbKOAGPvajFEHjQ5NBMHmJn5Nl1XV5j56Brwdnejxw319TjjFr8Mvu+8MILue2xF7WAUJclx/vcc8/ltscufPe7381txpM5b18LbJZK2VKXPW7h+9//vnYWO2ub27ZtyzrUCqJvZdBQB7kmjEno79XD9ZugDft999133wWv5/FflAf1spX04QkLBOXEJAepnCf3rVZSylDsjDy3bduW9y6PdaSMWvE4XAPaiO8zDOD1uEHOuxXDSRmx7fbC/YP64Ner6ZTLgf18XlwbPgdaSUBDsbO2Kb2yB/r+Ugvw9tg+rmUroJvX8H2I12glSKxYsSK3qRO+DxDcc55/vjx7l7Lgd55gwnv5fsHr09b5/JdeWd/W8zMqggcCgUAgEAgMQLw0BQKBQCAQCAzA2EsO1OAUQM316i5ausdb1Afd46RtFrp3j1paulS6Rd0tSNczqSSnfujyI3XXSj337+iqbNW28FTKUWNubi6vibuMWy57gu5xppz6ujHt2ymFZ599NrcpV5cxP9Pt7K5q3ptyJh3n1+D4WmUVnJ4k+F2rhMO4MDMzk13VlIVU6lKNcpFKPWjVWiNl5KnRnLvTXwT1nVSd2zr15e1vf3u1X60YZ0uertvU5xYF2a/NuA5Ln52dzWvs+2ItlMDtoFavx+ko0nitEgHUqVZ9L66972m1sjROQZJCo066vKi/NRn5vVyX3VbGgZmZmbw/+JrU6v21bJP9nJ5qhY1wH6rtCZL0tre9Ut+Tst5///2rY+L1nPrj9blftMoltApzcs1qNaGCngsEAoFAIBBYIuKlKRAIBAKBQGAAJkrPzc3NZReau8/o5qXbzd2Mtaqn7oam282zK+gKbFWFpfuQVKD/5sADD8xtupC9mjBBl6O7hrkWrcw6tp2OG0XGTgszMzPZbe1u71qWhNMtlB/H6xQAXchO57z1rW9d8Br8uyTtt99+C47JXfu8PtfUaVKuPb9z2ofX8yw4yp3jcBpvXDQOkVLKduc0S+3YgVYWDikA129mKjk9wHVonWvGtaQ7nxScVNKq3EtcH7jncHxPPvlk0Y+0f+vYENI2rjv9XrWYYymGgJmQTnVwn+ScfYy1/cPH3AoR4DWZUdU6DYJ77urVq4t+XO9WRjS/a1WBp5359UgT04Z97F4Ne9xwudSegZ51TjlxX2tlnbeyElsZyLRBUucexsFxHHbYYbnt2ascB+fvz3zSsq2QF/6udgRY8xzT6jeBQCAQCAQCgYx4aQoEAoFAIBAYgHhpCgQCgUAgEBiAicY0zc7OZg7YY3BqcUzOTdYqsDrX/uKLL+Z2qzro1q1bc9vjFxj3wNgL5+6POeaY3GYKvHPC5OR5PR87Y4KcB65Vjm7FLowbHttBGXG8LrtVq1YteD3nmRnn4r9hrASvv3bt2qIfefIDDnjlvEyPZaiVu/A4n0cffTS3v/3tb1evx9gIj3HjujG+wuMWxnWSuqOfeyumqlUOhL+jDD1GgfrucSuMmaINe6wS7YzxTffff3/Rb82aNbnNEhJuVxwH5+gxb5Sny5r7AmXm9j0krXkpmJ2dzfuLl6+onVTv+s3Kyxy/xwpSRh6fxngf7ve+pox15DW8+nbt1ADvt3HjxtymTrq+cn/2PYc2yPH6nj6JfTallMfTKiHRigFkjF2tNIZUzs/3nZUrV+Y2dd1jVbnPUa88ppO6RD3y/Yf7B9v+HGb8musp50V9cfvo1yNimgKBQCAQCASWiHhpCgQCgUAgEBiAidJzXddlt3grrZmuRXf90b1MGsurdPMatbRCv7672+mWpWvY6ZOhFcHpNq5VKpZKWvCpp54qvquN192xk6gi3c/V7003KVNO3eVJNzHXyt33XG9Wm5VKt3OrwjHpBlIFnnpOqpVjcuq2Rh057dNydxOkmw4++OBqv3Fh27ZteS3d7U3KpFVNl7IgNePypI089thjxXfUb8raU6hZ5mPLli257RQA5cH1936kFKgfrtv87LKu3auW/jyuUhI8sNf3WVIf3CP8wFqOmRSG0xm0n1alb9rB448/XvTjfsxDslv0HPWLv5FKWXIvciqYa+EyqlGBTs+1qvyPA66PlFutHI9UP7DW9YPPUX/OMRTikEMOye0nnnii6MdnFu3bS9Fwn6MeuX7UwlA8JGXTpk257c98yqlVQqKfc1QEDwQCgUAgEFgi4qUpEAgEAoFAYAAmXhG8d2G3MnRalAYP/WMmlbuNTzvttNz2w1bpMqQL2A8UPPTQQ3P7lltuyW2vAnvjjTfmNl2GN9xwQ9GPLj/SAyeffHLRj25Lp+5YcZpuS692O+6K4F3XZbe105CUJd3JrX6UpVOypGZ8XnfccUdub968Obc9e4J0nVftJo488sjcfuc735nbTl9s2LAht0lLsbKtNCxTw8frbuxJVB1mxpVn+XHNaZsuT86nluUqle5xl0WtmrxTNdwXWhQAs2MffPDB3PY1Jd1HSpltqU3Tk/6hvtQOT22t0VKQUsrXdgqVOsj7O51dywpz2ofXP+qoo5pj6uF0DjMeDz/88Nx2+pP7MfXw1ltvLfpxH2fGl1NrfC44HUP9IHXn69KqTD4qzM3NZcrYKd1aFrbTjbVK+05Tc6/1Z/QzzzyT26RE/dBi0mTUF7dh7pXUI99/aof0egY+KT6fP2laPjdrJz1E9lwgEAgEAoHAEhEvTYFAIBAIBAIDEC9NgUAgEAgEAgMw0ZgmwrlxxuQwvsNLCTCOgPE+njpIrpIVmyXpoIMOym1y/OvWrSv6MQaCXPa1115b9GMcDHnys846q+jH+Ipa+qxU8rut2K9WvBA53HGg67q85h7bQbm0YmA4RnLrHp9FHVi/fn3xHeOYGM/isXCMNSM3//TTTxf9qDe1khOS9J3vfCe3WabAY5rIz3u8TY2fZ8VyafzxaVJZDsRLBFA/KU+P7+A4GV/hsST87PrNSu78zmNzKEOO16tS33vvvblN22HJCKlMp+a8POahVQaBfblmbov9Oo0zpqnX11Y5EM7T41LYr5auLklHH310brvdsi/TyN2WuE/yvl4OhLrB/YKyk8o4JtqpX4/66mPi2Fv77CRimmibjtrfff/jfLjX+Hxoc17Jn2VRKGvamFQ+D3lftyV+x3gnj6/jHk+5n3rqqUU/vgP4/Clr6pjHLvW/i5IDgUAgEAgEAktEvDQFAoFAIBAIDMBE6bmUUnZnuluQrruaa1QqXYvs51Vh+dnTmumSY2VTpyU4Jrom3R1Mlz3dpS3aipSTV7mmm9FT3ek+dZc64dVSRw2mqDv9wnnTzdmqzM4qsr5ujzzySG7/7d/+bfVepMZcvyg/lgvw0gRMm7744otz29Ok//AP/zC3W4dDkwp20NVMqscpEF+3cYCUjtNu/Ex7dHqKLnHOwSkE6r6XA2G5CVI/XpGdaeW1A7gl6fTTT89t6pjvK7Qrjs+pUtISPi/qHG3CadneNsdVtb/ruldRHD1oL9QrTwcnvci18RIMXEf/jmUcuDau3zxUmTrkFAvXi/NolTo4/vjjc9vXhDQN6V6pThe1KMhxot9XXE7cb2qlQaQyTMApbILPSl8vhrlwT/AQGuoO91c/6aAW1uDPPD7LWErI9Y327c9GrhvXzOnVqAgeCAQCgUAgMCLs8KUppfTZlNLzKaWN+Nu+KaXrU0pb5v+/T+sageWDkOf0IGQ5XQh5Tg9CltOLIZ6mKyVdbH+7XNL6ruvWSlo//zmwe+BKhTynBVcqZDlNuFIhz2nBlQpZTiV2GNPUdd2NKaWD7c/vl/Su+fbnJN0g6c+H3LDnwFtxGowl8lglcsqMJfG4AR6v4dwnY5cYU+GxT4x94Tg8HoLlE9j2EgaMiyHH6vEaTI31tE9yra2T5Bsnq49Entu2bcvcs6eNM+6MnLHHgJAz53p4eXwek+FcM2XL63t6MeOi3ve+9+W2x5MxDoO8Pf/uYGzE888/X3zH8bZilRjz0UoTJ0Zpm9u2bcu65vLksQOMM/EjGDhXxhy5zCh3/+6II47Iba7DcccdV/RjfAXv5eU72I/2zXgpqdQXliHxGCnGXHn8GuMoKNvayfQeMzIqec7MzGQ5eWkXyo9jdJtj3Cb3Et9nKT/XU64j421YEsB/x1icu+66q+hHe6TdelwiZc79x2OkaKu+f3JtKCeX+bj3WWm7PHub9PvRNimL2vEg/p2vCe3gtttuK76jLXDfdZujzlFmHgPJ/YNxTNzvpVJfTjzxxNz2dwPO32Naa89Nj33q7aD1frLYmKYVXdf1K/ispBWtzoFlj5Dn9CBkOV0IeU4PQpZTgCUHgnfbX72rp9ullC5LKW1IKW3wf80Elh9a8qQsx108M7B0hG1OF8I2pwc7Y5shz+WFxZYceC6ltLLruq0ppZWSnq917LruCklXSNKKFSu6nqJw9xepFbrR3fXKfqSuvLIz0yqddqPLkK59d2nSdcnUcXdpMp2dbkun3UjPkQJxdzVdia3Kri3qaycfgoPkSVmuWbOm69fY141zo5uU7nqpTFWlTJyy4abh6a28PtPD3WV89tln5zZd8U6RcC433nhj9Xp001O/3AVNusFl6VRHD5elUwc7wKJsc9WqVV0/Pi85QBqcY3GqgtQ5bcLXmNSK6w5d7rRNd8Vz7ZjK7KnjpIXOOeec3PaT1FevXq2FwIrSUkm1OY1ZK7ngc+x1eGA16Z22zVWrVnW9bLx8B3WL1E6LzqiFH0jlqQmu+5QR78vSK1JJedLWfd34LDj00ENz2ylxrj11t1XmxteJc+E6Ocaxz0qlPFeuXNn18vB9iHtqS54E18T1nvp95plnFt898MADuc19zcsb0Fap4/6M5tpxDz3vvPOKftzzOfZ77rmn6Mfnqz9DWLaAuug6269byzYX62m6WtKl8+1LJV21yOsElgdCntODkOV0IeQ5PQhZTgGGlBz4gqRbJR2ZUnoqpfQxSZ+WdFFKaYukC+c/B3YDhDynByHL6ULIc3oQspxeDMme+2jlqwt29mbM6nDXK92JdDN6xlHLVUow0t8zqeiS4zi8lHBrJwAAIABJREFUsiwpBlYvdXcw6Qe6efl7qYzUJ/Xg2TW8nlM6pE5ID7jb3KkT/H0k8uy6Lsum5crkXJyGJL1K+tPd9/zuwgsvLL6ji5cHRW7ZsqXoR/csKQWXZe2w4ZNPPrnot2rVqtym+9jpZGaFuD5QDymv1uGhxChtk2NwnaN+cj6ut8xE5dx8Prfeemtuu2uf63f//ffnttP5pBWYZeUUMF34rP7vewJpIVJ3LcreKytz/i3bbGTPjVSePg6p3IM4Z99XSYNwDTybkLK9++67i+9oS6RQvco6x3T44Yfn9qZNm4p+HAe/85gf2j71oVW5unaoslTK3HXe6Z0eo5RlSinLx/d10lDcC123aLekp5x+PuGEE3KbmWpSaYO8Lw9jlsoQFfYjvSeVFOsf/dEf5baH0/D5ctVVrzjn/DQB0n+tkJdWRnN/r9rzU4qK4IFAIBAIBAKDEC9NgUAgEAgEAgMQL02BQCAQCAQCA7DYkgOLQkopc4YeB8OK0OSXPR6ilmLpHCTjkxg/JJXVncn1ejwEOVfyu/fdd1/Rj/wp066dL65VCfZ4DY7D58+Ub17DT78ed92dlFKeq8d9ME6FcQMev8JYAV7DdePUU08t7ksw9oAxJuTmJelzn/tcbn/rW9/KbU9RJ9//gQ98ILdvuummoh/TYhlf42NnbEHrO8rZ4yuGxvEtFf34PG6Dn9n2WJ1a2QgvrVCrFC1JN998c25TX/wa1AnGsnkV6TVr1uQ2q4rTtqUy3oIxGl7+gXbmFdEZM0N99vipfi6tuImlYG5uLts/9yPp1bEuPWpxc/4bHzP1wfdPxpgwzoUykerxX4yDkkr7pt16rCC/q5UzkEp7dJ2n3LmGvn6uv+PAtm3bctyXxxty7WgjLvcaPKaT6+DlDX7v934vt6+++urcdv3mPsD4JD+Z46yzzlqw30MPPVT0+9///d/cfvjhh3Pb58jxtvYwrqE/k3r9q9mJFJ6mQCAQCAQCgUGIl6ZAIBAIBAKBAZgoPTc3N5fdnu72pquUbjd3qdLNxjRyd/nSLeuHsjKtlfTc5s2bi36sMEo3LO8rla5t0hKe7vuud70rtzlfHjAplS7T1iGYvJe7iZ3GGjW2bduW6Ql341JGHIfTTKQ36Cb19SWV4pQQ05rpvvdq7HTJ0p3stAQP9v3yl7+c25dddlnRj2nTvJ7TqY8++mhuu4yoA5SzU62twyNHhbm5uaxbTreSkqE8W2n7pIedAmilcNNuSe84xc7SBDxcljYrleUDSM+5LDhn6gTHI5X65unmpIlqdADvPS4bnZ2dzbbgusO9tUXJ8TvanK8Hy0L4XsUDy2nTXhma68Z7uQ1zj6fttA4lJlXUOo7EZUQ75j7le50f+Dpu+Fy5V1AfWTJCKvWdeuf0FEMXvPQLdYI2x7ZUrgntz59zDKFhiMN1111X9ONnyumwww4r+rXoOeoB5+x0cx/+Mo6K4IFAIBAIBAK/VoiXpkAgEAgEAoEBmCg913VddoPXKqlKpWvf3Z90m9K15tVeSY0dffTRxXd01THDzSuMkrqji9YpGNJCdBG6+/C0007LbVatdRc6XalOadUOG/Rof3dPjhozMzN5rn5vusE53lZFXo7XXaZ057vblFltzKzw7EHSCKwM7RkdXO/HHnsst93dzbFzvJ5ZSXeyZ4AxE5Iu41aW3bjAqsOtzBuuq2ePcR04b19j6oFTVKRqaD9OAbznPe/J7VqVa6lcSx7AzIw7STrllFNy2w/zraFFC3EtFqj8Pej6i8Xc3FzeZ30f4L35HavzS3Wd8z2SWU9OcXFfvPjii3Pb935mK9LmXJa0uXe+85257fvs17/+9dy+9tprc9vDJUgZ+nd8RpAC8/143LL08Tg9ShvkOJ1iZ1gH9ZbPOKm0pc985jPFdzwVg+vw27/920U/7h+k0X/jN36j6MdwGI7jmmuuKfpRJ0jpOb1Mis9pR4Ky9cPQ+2tGRfBAIBAIBAKBJSJemgKBQCAQCAQGIF6aAoFAIBAIBAZgojFNMzMzmc/2OB5ysIyDca6ZfGyLa2b8iKcw8jumTTuHzjEddNBBue1jZzVh/uab3/xm0Y/xGiyD4OnPjJly3pZcK3lqP3F93DFNhK9Hq1I7UUvt9hR1xld4XNSDDz6Y23//93+f287VM46C1z/ppJOKfmeccUZun3vuubntp2ZTlhyfx/lQHzzmg/E25OA9nmQSJQdSStmePB6M9si4Ep9PrXK4xw2wYrOfkM44E+rHRz7ykaIfSz78wz/8Q27fc889RT/GObBKPOMkpNJ+GPPmcWiUja8T5cS4CS950q/buGy067p8f45DKnWOduqxZdxnKWeP7aPeehV+xrpwT/OYMcZFMS3d+zE2ld+5DXM/5nzdjmrVtP13NZ2U2rEzo0St8jj1lvroz4NafK7HT7LMgO9DvCZjAGkvUhmDevLJJ+e2x0pSN1lewqv1c8+hzLwMCfW0dXJEq9TGkL02PE2BQCAQCAQCAxAvTYFAIBAIBAIDMFF6TnqFrnHahu4z0gEtqoLuSD+ok3SaH+jI35Hu4UG+UunuO/7443Ob6euS9G//9m+5zdTdY489tui3YcOG3KZL2alFUjpOT3LsdA17+vO4kVLKMnPXMWVLV6jLkp9JmdFdL5Vr4Cn91Ae67z2Fmi5jfudu3LPPPju3Se14qvWmTZtymy5ud3czVd7ThVktnPSFy9Irb48bTrtxjalzvnZ0t9OuvEp3Lf1ZkjZu3JjbH/7wh6v9WLLja1/7Wm77Ia+nn356brOitNNWpAlJmznNwXE4vVajB/wg2z4MwMcwKvBgdKcbOGbqqttw7VQGp1qZUn7JJZcU35HeJv3CMgBSuZ9yL+SeK5XlYc4///wFxyeVOkDb8fXm/tMqi0GZe7kErxA+DszOzub7+r5GeXCvdXnyO8rWyzVccMEFue37NcsR+AkcBOlM7uurVq0q+nEP5bXf8Y53FP1IuXOf9H2KsvHwDKJF4/XvHlFyIBAIBAKBQGCJiJemQCAQCAQCgQGYOD3Xu8O8YindyHQtunuZbrxWNVpSAu5iZzVZVul+7rnnqtfgQYZ0K0qlK4+uSadqahH8XvWcbkanAJj5wPs6xTfujKu5ubnsFvesCGY10n3v7nFmwFB+7vJmxo4f9FzLwCM9K5XuZFaV9YwfjpcVpD0rjjLjWvMwaKmUn7u7uU6co7vWfc7jQq9Pbpu17CGnnfgdbdPnw3n74cykYqkHXsGbdDzpd18r6kFrv6CcqM9uw7y+34ufaY9+AHOfoeQ2Oyq0qHOOkVSH22btVAbfS1nl+YMf/GDxHcMTSNN4BizHQZrM+zF8gtWkTzzxxKIf90VmvbaywVoVtLkfj0tmLWzbti1ThH4aBcG5+rOM42aGHA+xlko6c+vWrcV3zDBkmIQ/o6g7vC+foVL5fD3rrLNy2+lWUojUZw/j4Jxbz97WYby9rFuZreFpCgQCgUAgEBiAeGkKBAKBQCAQGIB4aQoEAoFAIBAYgInHNPXw1E1y7YyNcH6TsQjkNO+8886iH3lQ57wvuuii3GZVUY8DYtwE0889HfEb3/hGbjPFfO3atUW/2gnNnvbJOXv1UnLajP9oxSSMA13X5fRzj5uopfJ6rBJjJfidp5czLdRlyVgEpjh7mQny8YyV8BgB6he5/1tvvbXox/WlbniJCMrPqw7X0vc9bsJtYBxIKeW1cP2mnLheHhvA37Ht8QHUCZcTY5xoP14OhGU+uP5HHHFE0Y8p2dwvPA6PY6QOuP1Rtz1dmWOnbN02++/GFXfYdV0ep9sm15565engHBvT3D0uiOn9jAeUynlz7R966KGiH8uvPPXUUwv+RiplxJRylxFPCfDYNYJ67fFTjDFkrKTvF63U9FGB1fpdZ/gc5djc5mhLXC9Pzads7rjjjuI76jfH4c9yyv3ee+/NbY9por5wfF42hHPxkgsE47F8X+E1WvFNrWrh+Vo77BEIBAKBQCAQ2PFLU0rpgJTSN1JKD6SUNqWU/mT+7/umlK5PKW2Z//8+O7pWYNciZDldCHlOD0KW04WQ5/RiCD33sqQ/7brurpTSmyTdmVK6XtIfSVrfdd2nU0qXS7pc0p8PvbFTDrVDaj1dl5V7mfbobjumGjstRCqQ7kg/xJMuPl7D06R5KCFdi37Ia62Ugqcu093Jw02lcp1ID/h6ust0HiOT5ezsbHb1ezo+3aR0X/vhlnSd06XrLnDSZE5lUleoA55Ky6qydEk73UC3PMfrMiINS4rX6RCWVWi5fjkOp+ecSgJGapv9GHyutWr9XnKA35EGcnqK9KsfZkuahOvqKeG0Ld6L1IxUyolp715lnWvOVGivFE0Z+thpg3T7O93cX9PovZHJcmZmJtuWy6h2YK/rLelQUs6+HzFt/5Zbbim+Y8V72hXpVKncd2uHs0r18h2PPPJI0Y9zpkxch7jPuM5T96i/vl806L+RyTOllHXF9wbuUTzE2sMzaqVweLixVJ5awVMUpNIuaFdOBVKXKAsvA0Pak2PyUBvOmTbHMUhlyQE/faF2cLOHTPTPK5czsUNPU9d1W7uuu2u+/SNJmyWtlvR+SZ+b7/Y5SR/Y0bUCuxYhy+lCyHN6ELKcLoQ8pxc7FdOUUjpY0kmSbpe0ouu6vvrVs5JWVH5zWUppQ0ppwyQCWgPDsFRZ+r9gA7sWIc/pQchyuhDPzenC4Oy5lNJekr4s6VNd1/2QruWu67qU0oJpBF3XXSHpCklasWJF17tInXIgjdPKKmHEPV3nTunQZXvTTTcV35G6onvZXXVnnHFGbpPucXc73Yd067mL8Nvf/nZu0/XsFcFbFY7pdqVb1CmQ1sY5ClmuXr26613DTqHSPUuXuFOGdLVyY3A5sJq3X+Puu+/ObVJhThlSlnQ7+7rRPU1KwStSkzIkVcQxSOX8Xa+pNy2qYEeb5ijkuf/++3e1Qy5rmTKeOURqhfbtukh9cQqAYPaq0w082Je2/lu/9VtFP8qJlItTCqR2qb+tQ3l9HyBV0sqq6nVuoT6jkOWqVau6/h6+L/Jzi57j2Ginnm3Ew3ZdlrTN9773vbnt1Cj1i9mObuvcWx9++OHc9v2T8yLd63pI3XCajevBNXOKzz87Rv3c9IxN2hz3Mh5eLpVrzmePU1xf/OIXq9/Rpln93WlqPucoCw/PYMYcD2N2kHbjHuUZj3w38HAdPudpt26D/eclVwRPKb1W2wX/r13XfWX+z8+llFbOf79S0vO13weWD0KW04WQ5/QgZDldCHlOJ4ZkzyVJ/yRpc9d1f4evrpZ06Xz7UklXjX54gVEiZDldCHlOD0KW04WQ5/RiCD13tqQ/kHR/SqlPQfq/kj4t6YsppY9JekLSR8YzxMAIEbKcLoQ8pwchy+lCyHNKscOXpq7rbpKUKl9fsDM3m5mZyfEMzqGTGyYP6hw6eUtWsWU6sVTytp6mWavS7FVx3/Oe9+Q2ee1rrrmm6Pfkk0/mNnll52m/+93v5jZ54Fbcgaepc178zvn/hSqCj1KWCRWknasm78w4Eof/rsehhx5afGaKsld6ZfxPLX5FKmMgmPrqVYcpP17v8MMPL/qxtISnYROUucuSsRi1GAqpHjcxatv0E+x7cE04B58P4yu4/h7zxhRzjx2gPLZs2ZLbXuaD8S2f/OQnc9ttmHGPjF/w+JYbbrght7n+HntJObmN8Xecs6cvLxTTNGrb7MfpusN4Duq3ry9jQrimhxxySNGP1/D09fPOO2/B+x5zzDFFP8bOMC7K9fHqq6/Obeqk732MkWLbS89Qlh7bwmcG19Bt059juN7I5Cm9YiceF0kd9PhMgnM/9thjF2xL0qZNm3Lb5clrsO2lergfMq6NsalSua4sceElB6gHfB/wdwPupx4XSnvnmrn8+veBVlx1VAQPBAKBQCAQGIB4aQoEAoFAIBAYgIke2Ds3N5ddcu6yp5ve3agEUy5ZHdopHaZYero46Rm6Zd3dzurjpIV4WKhUHkTIA2WdAmA6LVPiveowaSunB+h2ZD9fz0YV6ZGAB/Z6GizdxJynUwV0q5OW8RTiVlVxptZyHb1K8Omnn57bTHX1w0OpA61qtrUDkf3vHK+7fKnzlKu7jFuHjo4KrDrsrm1SFWy73Dluztvd6KR4/F60W1JGW7duLfrxkFeuq1PxpIV4L78e7YXUuacuc85OLXKevjZEL89WWvOo4LpEeonlMVpy4Po6/cn1dupu3bp1ud2qzF0rVeHhDdzjeLCvH9JMe6EcXA9bpQTYl9dzmXnZiXFgdnY2r5Hfj9Qvn1f+DCXtyVR9llWRpHPOOSe3WQFcKnVk8+bNC15PKml1js/Xn+Plvuk6Vjuo2WXBz/7spQ7TJnw9e72v0a5SeJoCgUAgEAgEBiFemgKBQCAQCAQGIF6aAoFAIBAIBAZgojFNTFNv8fnkyT2tmVwjj6/wI0tqqeNSyWUzrsbjYO69997cvuOOO3L7vvvuK/qRN2cJA78vOeFWeX/G5nh8D3lhlh/weJkdlfdfKubm5vK4fe0pM3LGHmfFfiwl4ccxcH18nuTMGYfhR4+wFD/hKcSMo6CMWLJAKuVCmXvabyvlmTbA63ncVuvE7VGh67o89taxLRyzrx1tibruMqPcPZaG60d98f2C8RZMcfa1YhwMY188xZxpzdQ3tz/aus+f9shruN73cX7jkmvXdXmNfd0oi9YRR7QX2pXbAX/HGFOpLMXS+jvjGal7vq/UyrS0ygBw7WtxiFK5/0hlLBv3XI+Z81jScWDbtm1ZD11OtCXqtO+hnDvjgqizUqnv559/fvEd4z+pRzfffHPRj/G/3ENPPfXUoh91k89yjyPknkBd9COraMNu37XSDC7Pfl6tY5DC0xQIBAKBQCAwAPHSFAgEAoFAIDAAE6XnmKbuVAXdgnSBe+ou3ZF0rbkbne5DT51kKnPtRGypdLEfcMABuc30dR8T3XqeKk5XLn/j7mVSWq2Tvmuu9oV+N2rMzs5mGrFVPZVzdhqSLmSugbtG6U71NaV+cE39XtQ3nszuLnvSEnT3tqrtEj52zsvXieUYOK+h9xolSAF4CQxSaC2qgm7/VuVerrlTV6RgKFvXb1IApFZ8H+B3HK/rB9ef93LahmNy3eFntmsnqY8Lc3NzWWa+z5L6pX5TF6WSOiH9zLIpUrneToFT30nJ+dpzjyA95/QLbYmydIqpVUqg1s8paV6f43PbnEQ5EMLHyftT51yelAWfIa6LvpY1sJ8/a1j5m/ritFvtOx8Tx96iWLn/e4gD7aBVmqD/XdBzgUAgEAgEAktEvDQFAoFAIBAIDMBE6TnpFbeXV+KsuQXdTVbLjGi5Yd0FVzsc1V1/dH22MqQ4Dl7b6RjSgnQlej/O2bMHa7Seu4ndFTpqbNu2LbuAPcuF7my6753+rGWPuSw5N3c787BPHtrsWUM8OJe0hK8Tx0i6yedIyoryciqq5b6nPtDt7rSX07fjhuscQRtpZcBSB3yNuSY+txr97jQ9KUT+plXJl/uA6yKpoBa1Vqvi7p9bIQbjxuzsbKZM3F6on7Qz11uiFQZAuJwpP1JCfhAvZcFrtKqZ1+gzvxcpIJcDZeu6zL7cm3wfGDfV2o+ln6/vDRwb5+DPMuom6TS3P9qmP6NJcbWeUbUMWB87ba5m91K5N1JPfe3Zz2VN2+e7hutir+stmw1PUyAQCAQCgcAAxEtTIBAIBAKBwADES1MgEAgEAoHAAEw0pmlmZiZznM4ZkoeupVE6yH976jLhaZrk5VspxLW0aefaGZvCMXncBMF7eTxXKyWyFjPl8RUc0zjA6u6M95LK+ZB39n6MReB4W9VcnYOvxWV4nANjG6gPTHGXyvXmdx7zwHHwO493a6U/U89rcRhSGVs1LszMzFTjCjkn6pzbFWMl+J2XMOB3vl6UIefdqrzMOBPfV7j+XGNWoZbqFaD9ehxvK36N9/V+rYrro8Dc3FzWNd8HavP06uRDY0z5O5clPzN2xG2JdsF4Frd12ibl4nskr+FxOQTH4XGl1LdWDOTQFP2lgKcveIxkLW7MwX6MJXL9YCkPj9+iPfJefg2uCeXkNsxnWSv2k/tKKw6PcVG+h1JHqMMeP9XrS6uMTniaAoFAIBAIBAYgXpoCgUAgEAgEBiBNImUy3yyl70r6iaQXdtR3AthPu34ckxrDQV3XvW3H3YZjXpZP6NdrHXeESYxj5LKUwjZ30RjGKcuwzRK7uzzDNic7hqosJ/rSJEkppQ1d1526457TP47lMIalYjnMYTmMYTmNY7FYLuNfDuNYDmNYKpbDHJbDGJbTOBaL5TL+5TCOXT2GoOcCgUAgEAgEBiBemgKBQCAQCAQGYFe8NF2xC+65EJbDOJbDGJaK5TCH5TAGafmMY7FYLuNfDuNYDmNYKpbDHJbDGKTlM47FYrmMfzmMY5eOYeIxTYFAIBAIBAK7I4KeCwQCgUAgEBiAeGkKBAKBQCAQGICJvjSllC5OKT2UUnokpXT5BO/72ZTS8ymljfjbviml61NKW+b/v0/rGiMYwwEppW+klB5IKW1KKf3JrhjHqBCynB5ZSiHPaZJnyHJ6ZCmFPJebPCf20pRSmpX0/yRdImmdpI+mlNZN6PZXSrrY/na5pPVd162VtH7+8zjxsqQ/7bpunaQzJH1yfv6THseSEbKcHllKIU9NkTxDltMjSynkqeUoz67rJvKfpDMlXYfPfyHpLyZ4/4MlbcTnhyStnG+vlPTQpMYyf8+rJF20q8cRsvz1lmXIc7rkGbKcHlmGPJenPCdJz62W9B18fmr+b7sKK7qu2zrfflbSikndOKV0sKSTJN2+K8exBIQs5zEFspRCnhlTIM+Q5TymQJZSyDNjucgzAsElddtfVydSeyGltJekL0v6VNd1P9xV45hWhCynCyHP6UHIcrrw6yrPSb40PS3pAHxeM/+3XYXnUkorJWn+/8+P+4Yppddqu+D/teu6r+yqcYwAIcvpkaUU8pwmeYYsp0eWUshz2clzki9N35K0NqV0SErpdZJ+V9LVE7y/42pJl863L9V2rnRsSCklSf8kaXPXdX+3q8YxIoQsp0eWUshzmuQZspweWUohz+UnzwkHcb1H0sOSHpX0lxO87xckbZX0K23nhD8m6a3aHnW/RdLXJe075jGco+0uxPsk3TP/33smPY6QZcgy5Dnd8gxZTo8sQ57LT55xjEogEAgEAoHAACyJnttVRbcC40HIc3oQspwuhDynByHL3RuL9jTNF916WNtrJjyl7dzrR7uue2B0wwtMCiHP6UHIcroQ8pwehCx3f7xmCb89TdIjXdc9JkkppX+X9H5JVeG/4Q1v6Pbcc09J0sxM6eSam5vL7e2xX6/+uyTNzs4u+J2//PEabHtfjsPvxX5+jSH9WnMkvB+v13qp5e+8H+/1/e9//4Wu695WvdB27JQ899hjj27vvfde8EK1tWqtYU3+0vB5tq7P77Zt27bgtf361DW/b01Gfr3afXcGvMYzzzwzcllK0l577dXtu+++O7w/19vnSgzt1/od0ZJTSz9qttnaLxajU4vBiy++qB//+MdDLrJo23Sdq+2fi53LYn7nMqb8Wns/0doja3twq18LXMPW3rR169ax2Cafm61nWW1cPoeh9rLYvWzoug4dO9HSidYYavt6bR4/+clP9Itf/GLBiSzlpWmholunt36w55576r3vfa8k6fWvf/2rBtmD3/30pz8t+r3lLW/J7R//+Me57ZN/7Wtfm9uve93riu9+/vOf5/Yee+xRvRevyYV+zWvKZfvlL3+54HdvfOMbi36cIxXjDW94Q9GPisGx+vX5u5dffrno97Of/Sy3//3f//0J7Rg7Jc+9995bl1122YLf1TZBXzeuKdeGf5dKGfk8a3rjhsdr/uhHP1rw2lK53m9+85tz2/XrV7/6VW5T/i5z6t4Pf1iUFynk3DJkXvMv//IvRy5LSdp33331Z3/2Zwt+x7Ws2Y5Uyppy8X68nsuTa0n4unL9KXf+XSrXmPrn9+V33AdcZzlH7jGO1sOn/+5v/uZvqr837LRtfuITn5BU6rq0fQ/uwT3C98jauvlDi2vgD8HaP0h5X6mULe/lex9BG3GZc0zcI123eK/Wg5pr6DKnbv/VX/3VWGxzzz331CWXXCLp1XLi3LkmbnPce6gDrX3NbY5rRPt2udfW1W2O96Z+7LXXXkU/2hyf+S2Hgr9fUPbc131P7vH1r3+9eu2xlxxIKV2WUtqQUtrwi1/8Yty3C4wRlKW/YAZ2P1Ce3IwCux/CNqcLlGfr5TEweSzF0zSo6FbXdVdIukKS9t13365XAP/XAd+gW9Qa3wz5pulv4PzO36Zrngv/VwT/lVJ7K5ZK7xfn1fKs8F7+r8E3velNC45BKv8VzHu1/pU3EDuUJ2W5atWqrl8TXzfem2/8bvx8++e/fvztn9f3efF3lGXLje6eLML/ddWjJUvqnv9rirL1f0HVdMr/RTzUJQ3stG0ecMABXX9ftyWOk9/5uDh32pjLveZZ8Gvyev4iQJ3gC5/Lr7auLYqoNUfK3f8RyN/xXr6e/e92Qq47bZv9tbk3LTTmHi6j2l7VYgjcu0G5tLw6lHPL41qjE+k5kMq15/ValI3bHPWI83J9da/ZAOy0be63335dr0MtrxrXy58pNS9ry+PqOlFjR1xOvMYPfvCD3Hb94L0ZGuD7f4298ecO9wjXc/ZthfW4fBfCUjxNy63oVmBpCHlOD0KW04WQ5/QgZLmbY9Gepq7rXk4p/R9J10malfTZrus2jWxkgYki5Dk9CFlOF0Ke04OQ5e6PpdBz6rrua5K+NqKxBHYxQp7Tg5DldCHkOT0IWe7eWNJL085iZmYmc5IeV0JOuZX5VouDca6X3KRz2bwm+VjnSMl3trKGyKXWMgz8et///vcXHINf3znzWsaOc7iLiGmmZzTBAAAgAElEQVTaKczMzORx1jKefFwu81ocm8dxcX18TWvpwK2YFcZA7L///kU/xh0NjTlh/MD3vve94jvO0flzypL8vvdzGxgHUkrNbLDab2qgPboutr6rxRS47lDurVglgvvFULSyxRwcB9fG40b6PXBcNtp1XV5j3xcYE0I5uM1RB1u6yRgnlx3trJVo4OvT461vfWvxmXOptaV6TKzHu3FvdTlzbXj9VqzkJOB7bS1Wx2PPOJ9WzDDn6mtSi3fy2CeOifu17921+Dpf01pMa2uP9/Ip/n7Qw+c/xDYnK/FAIBAIBAKB3RTx0hQIBAKBQCAwABOl5+bm5rIrz928NVrEXbd0O9Kl3HK9u2uO7kS6DD3V0d2JPVoFNzmP/fbbr+j34osvLngNdxtzzu7ypkuTLsRWSvw4sG3btkxLeVozQVn6elJ+tdRZqV2JlutIWtNpMl6D/byqOd26dIW7bjz33HO5TX1ytzjn73pIObOf68Mk6u6klLIOtdztrVIeNaq7ReO5nraou1o/Uj9+vVqqtdsLdZMlP3yfov25PHlvjr1VVHEcwAnxzXm2CvbWKDOnJ0nB1SgQqbQrlxH3AbbdNjkmprK7HpIup7xclrQzt2/aXK1oqzQZem5ubi7ft0V1t0p5tKqAE7WQCaleGLVVFodyf+qpp6rX8xIJtbHzWTO05IdUzpk67OUqau8nxXiq3wQCgUAgEAgEMuKlKRAIBAKBQGAAJkrPSa+4/JxOIyVA16JnqpHGGHoAql+DFIp/R9QqoLrrjt/Rzfjwww8X/ejupJvRswDoAnY3K+k/umbd5d1yL44CKaXsDm0dhEkZuczpQuW6ebZY67yhrVu35jZd7O6eZZYc3djunqXMqRukAyRpzZo1uf3tb387t12Wrfnbocq57RRIjSoZJbquy2NtUQCts7yog7Wz/vwarYrbtXMWpXJd3/72t+e2684BBxyw4G+oN1JJnXMv8j2GGV1OxXKMLUqyv/4iKr0PRk2WHCPl6utGWqV2BqdU2mMr2611+DV/R5l7BhRl1hr7Pvvsk9vPP/98bvt6u/yIWga3U0Ljplp3hNrJElyD1m987RhS4rp/xBFHLPid93vwwQdzu3bmnVTaGb/zjOZaSI7v3Xxutk4roE14dvoQujU8TYFAIBAIBAIDEC9NgUAgEAgEAgMQL02BQCAQCAQCAzBRQpZpzR6nUYuVaFVDJv/ovCpjhjx1lVwqY4QOOuigoh+5VfLuHl/B2KXHH388t//rv/6r6Mf4CvL1rZig1on35NNHcPr2TiGllNe/dfI2ZeRpvTWZe4o9Zcv1lUpZMt7A4xXe9ra35faRRx6Z2624iSeffDK3n3jiiaLfqlWrcpvz8srHXItWiizjp9w2WnF344DHVFHnKE+P26EMKRePg2nFFNTS9hnz5fdiiYCjjz666OflG3q88MILxWfGRbGchOsRZdaK7+F3Ls9xxxt2XZdl5rZJHWRMiMdyMMaE8vf9uFWFnzEmjPvzkha+P/fw/YIy4v5OeUnlvCgH31da1cL5Xa3khtSudD4qzMzM5L2yta9zP3U5UZ58VnhcEOftcuE1GAPoOkbbpJ0deOCBRT+ua2tejz32WG5zH6c++Hg5Pr8+7+s23OtIlBwIBAKBQCAQWCLipSkQCAQCgUBgACZ+YG/vQnQ6ja5cpku6S5V0CtPA6aKXSlclKThJWrFiRW7T5ejuQ9I4HNN3v/vdoh/TWp955pncPvTQQ4t+hx12mBaCU2t0X/v8OWe6lJ0qGWc6c49+3D5Gujxbbld+R31w6oQucHfFk7p69tlnc/v0008v+p199tm5TTk7BfCtb30rt7mG7oa/4YYbcpu65qm+dAU7BUAXMPXQ13MSsuy6LlM3bi81yqx1sCZRq7orvZpG4DWcBieoI1wf2qJUyoP3ah3eyrTr1kHg7trnNVvp7P1e1ypLsBTMzMzk+/sa1g6f9T2oVkHZK2Lzs5dxIK3yne98J7c9DILhDdQN3485F9q90zTcB6ijnvJOmbucSf9Rli7zSZQDmZuby5SX2xx1v2U73FM4ZrdZPke9zMrTTz+d23wGtijKc845J7c9zGDt2rW5zTIFmzdvLvrxdAfK6ZBDDin6Ubf9fYDfcW187HFgbyAQCAQCgcCIEC9NgUAgEAgEAgMwUXqOh7y6q46uU0bftzJ5mN3mWWZ0QTILSirdvnRLk7bza95///25fc899xT9br311tzmoYSefUA3MrOBWF1akh555JHcdhqT1AnH53TDuDOuUkrZve+uYLo8OS6fC12mdME6PcV5+kG8XG+6bp0KpZxJI/j1SK9yvJ7xw+q4pCF8jhy7Z2SQAiCN5LSN33scSCllGsLXn2vXOlCX31FPW3L3w55pt6RPnAahLZE+8erjdNMzA5JZOD72WnatwymAWl+ntPo9bFxZdCmlvG+67vCz75kEaSjuY05n0/78QFbuAx/60Idym7YjldQMsx/dDki70V7uvffeoh9/x73J6T7aut+rdmKDU+WToM5nZ2ezbrUO/aadug3zecBrOAW3adOm3OZzSCrpbVLuTo/SbikzP8D+kksuyW0+52+88caiH5+VtDmOVSpl5vbN+dMea8+uyJ4LBAKBQCAQWCLipSkQCAQCgUBgAOKlKRAIBAKBQGAAJl4RvOcua6l+UrsiOOMGyM97xWCWC/C4FXKujH3x2Iv77rsvtxkrcdNNNxX91q9fn9uPPvpobh977LFFP8ZtMd3Sq5cyRsNPfGZcRit1ctypsEyDrVVd7vv18PgKysw5eILy85gVzvPEE0/Mbaa6SmXJga985Su5ffXVVxf9WLn4tNNOy+1LL7206Pc///M/uc3SEj5Hxhk4908Onjrv6d+TOkm9l5WnyzN2ifFIHtPEciCUi6dpUye80jrXhDbipSYYg8J7eSwC41ZaFcYZ78LxvfTSS9Wxtyoht6pI93vakBPVF4v+nl6FnnsGbc73WbezHl5uhXEvvufQft797nfnto+JNsP19dRz7p+MU/W97/DDD8/t448/PrdZ9sDH4fPnNflc8H6TsE3GAnscHe/P9fdYJcqauu+6SdtvxR3zOy+zwhg42gFlIZXPSq63l4Z45zvfmduMR/V4Ms7FY+9qe5jHsvX3jpIDgUAgEAgEAktEvDQFAoFAIBAIDMAuO7DXQRct3Yyexkuqxt3+BF11q1evrn5Hasxd8bfddltu00XrJQfoKj744INz+5hjjin6rVy5MreZnutp1xyfu0+5HqQHnFpsVVMeBVJKef3dxem0RQ933zNtlWmgngpNV7NTmaQvL7zwwtwmPSuVNMLnP//53HbX/gknnJDbF1xwQW57uiypKM7f6QvKy2VSS412OnncsuzR65rbKKmaltuaOkjXuVNh1AOnO6gTdKO3DlR13SeoO7yXlxeh25/659XMWweVcm1qlBM/j6vkwNzcXL6/607t4FK3Oa4vbcQr/NdCDqRy/6OM7rrrrqIf5fyP//iPue0yoq1TR30dOUZe2/d3zsv3MOoKbdjnX9vrRomZmZms/07/1yqeu11xT6GsjzvuuKIff3fSSScV35FK5zqccsopRb9rrrlmwTE5tcjnJiv5u73UTvdoVd339wbuORyHh4X0/VqlJMLTFAgEAoFAIDAAO3xpSil9NqX0fEppI/62b0rp+pTSlvn/79O6RmD5IOQ5PQhZThdCntODkOX0Yoin6UpJF9vfLpe0vuu6tZLWz38O7B64UiHPacGVCllOE65UyHNacKVCllOJHcY0dV13Y0rpYPvz+yW9a779OUk3SPrzITfsOVjnXJkiyDgC5xzJIb/1rW/Nbee/W+mX5Fkff/zx3Hb+n9wv+dczzzyz6MeYJJYZcP6ZxwUwHsvjJjgvj+FhqidjRTzmo1ZyYFTyZPkIjymoHUnhsTK1eBM/LZ1r5emtZ5xxRm6zrIDHaJCPZ2yEc9eU7apVq3Lbj2rgNTgPP3mbsvX4qVpcho/J4y3wm5HZZkopy8DlydgX6lVtXFIZa9GKg3rhhReKz9Rprj/bUqljjIfwOEfaEvcYtyseu8B4NY9R43cee8Z7cw19nfo19HUe9V4rvXqfZewg9czjwnxPql2P+47LubZ/+ppSLjxag2stlccVUQ+9HMVRRx2V2yw54TE13Eu9DAJ1ns8gP9arFtM0Sll2XZfv488oPssoT7er2j7s8mQsqO/DPOKGNvf0008X/bg3sp8fj8K1ZJywP7tYFohxTK1SJr6Hcv6UtT+TelmP4xiVFV3X9Sv6rKQVtY4ppctSShtSShsmcYZWYFEYJE/K0s/TCiwbLMo2/YUusGyw07bZqnkW2KWI5+YUYMmB4N32V7Lqa1nXdVd0XXdq13WnTioLKLB4tORJWXoBssDyw87YZutg2sDywFDbHPdh3YGlI56buy8WW3LguZTSyq7rtqaUVkp6foe/0HY3de/CbaVV033mlWnpTqT794ADDij6Mb3U3X10XT788MPVfnT/scqsu7IvvvgV6prj5QnPUumW5kuHr0XNNS6Vrn66hn2jdDfyDrAoefYuTHdlct0oV3dl11J53c1PfWDVb0n64z/+49w+77zzcvuLX/xi0W9oGiz/Vfetb30rt3196dblHJ0WJA3kFCr7Uqf8GjtZOXrRsuzH4PrN+9P+3LVfKwHi9FSNxpJKmznooINy2x8cpA4oM5cnr79mzZrcdrqVntN169bltsuMcvdSIbUq5Z6m3uvSQLnutDwpSwf3BY7X6Sl+15oz6U/KSyrp6I0bczz0q9LBWaaFVaMfeuihoh9lRj3h76VyLqT+GIoh1VPZpdLeWyVgJrHPSq/QTe5F5F7G/dX1lmvCebsXi7bpdsvfMYzBK61fd911uU3a++tf/3rRr2ZnrM4vlbQgq41v2bKl6Ecb9r2IttY6rWBIGZDFepqultSfK3GppKsWeZ3A8kDIc3oQspwuhDynByHLKcCQkgNfkHSrpCNTSk+llD4m6dOSLkopbZF04fznwG6AkOf0IGQ5XQh5Tg9CltOLIdlzH618dUHl71XMzc1l96K70WsHRLoLm27U2gGhUknB8RBW/x3dsJ5RQ9cnXc10OUolvUZ3LV3XUr2ys1M/dHd6JWqOg5kEni1Qo0pGKc/efetBxLVKrX6Ioo0rt50qoPv9/PPPL7676KKLcpuZMqy4LpXrw7VxWZJKIU3mGZikUJkt4nNnZonTsNRljq9FTxKjlCXH4LrEObWqdNcCkH0+1BefG+VBCsbXjjZMl73bWC2L1jODeDgs5+tZufzO9yzOv1apmb9b4MDRkchzZmYm7yluc9Q5jtcz1ShbzrNV/d9tjtluXA/f+7nGzITzyuF33313brcywHigOuXv8jriiCNy2/fL2iHkrXARYtT7bK/X/pwjvdYKE+B+RerWDxFnPw95ITXLcTjVTbmTYvW1uvPOO3P7Ax/4QG77aQ7UF2ZBu27XQnek+qHLvp5DqPOoCB4IBAKBQCAwAPHSFAgEAoFAIDAA8dIUCAQCgUAgMACLLTmwKKSUckxA67RspuZ7Smct9oWn2EtlKQGPGSIP/Oijj+a287uMoyDH7TFS5H4Zw8I0W+/3jne8I7edm2UslMdekHNunaS+k2nqOw2epN46IZycsZccqFUB97ggrofz3YxTIbdO+UvlWjHV9eSTT65eb9OmTQv+XpJWrlyZ24xzcL1mBXOXJeVOvfaSE7X08VGCtulzpXwZ++Fzpaxpzx4HwrXzCupMI26daM54HI6DFcWlspI4Yy0YHyOV8SC0ndWrVxf9KCdWO5bKlGfO3+O2+nmNy0ZnZmaybbUqWNP+fP+gzLj2HjPGtXe9ofxquiGVMWncMz1WsjZerxbP3/E3XpC3Vt5CqscH+djdBsYN30P5XOJYPH6I9kK5uL2wLMNZZ51VfMeYJu6NvtdyHGvXrs1tr9zOvZxV3H3/Y2wbbYl6I5Xy9NIYjHGmfvg1+vUdR0XwQCAQCAQCgV8rxEtTIBAIBAKBwABMlJ6TXnF7eQVWum9JabQOkiTt5pQZK4K7e5wUDN2b7nqtpZ2+733vKz7TBX777bfntrura6UJvKwA18LdrHTpc/6e7j3u86dmZmay297TO2sHJ7rM6S7nenj6Lw9B9hTw+++/P7dJ7fi60dVMes6rz3KMlKXTF3Rrs+SAVx2mjPxwSOol+zldW9PDUaLruixHp43obidt4bZJGodz8EOWSaewSrdUHrpMeZIOkEobZj9Pe6c8W/1IFVD/XBfp2ne6h/JtUW/9NVynRoVt27Zles3pNN6zlq4ulfpIuXqoA2kQp9N4TdqF0/nUL9IlTn3xNADanOsQaSCOoXW+olNCfBZQz/0a45JhDa6PpOtqFLN/5t7odsCUfi+zwj2U5QJah/6efvrpue1UGPWPewSf3VK51/JZ0zqU2OdPGVL/XMeGUObhaQoEAoFAIBAYgHhpCgQCgUAgEBiAidJzpADcLcaqn3Tte6VauufoxvNMFtJThx56aPHdYYcdltusQOwVwenupGvYKR26MVnN293GzEjhPJye49jphpZKio8R/u5W9EqnowZl6fQcP3MN3S3v9GUPz5Sia9UzMEhx8Xo+f47puOOOy20eyiuVOkUXvd+XWZLM9mwd+tyaF2XpNPG4ZSltl00/1hblQDrA6Ub+jvbs9CL13fWbc6c736ndO+64I7dbmZek9djPr8eMPmbMuV3xGq7PpJg5Z6cx+9+NO8NVamcBcbyt/YM23Kow7icg8JqkbGgvUrmOlJ9XpGamK/XL5cBxcE+n3UulXLxKOeffyp5rnXIwKvCg+5bNtU4VoC1xT/LnJjNMfW/80pe+lNsMk/A1+dSnPpXbDK34/Oc/X/Rj5izfB1ye3OO5//ieTH32PYzPBq6h70392kRF8EAgEAgEAoElIl6aAoFAIBAIBAYgXpoCgUAgEAgEBmDiJQd6zrBVsZT8q8fLkINk/IlXFibf6RwpeXPGTHlsEXlN8sCezs9KpORLGSfhYKo8T9uWSo7YuVnytuTTJ1E1mkgpZVm0Kn1zrT2Flem7TB/16ryMm/CUW86b8TGMW5PKlFauPU+3l0qdIg/eituivnrMAdPSvfQF+/JezrP7NceN2sntUrnerps+vx4eP8T4Ba/kX6s+7qnejEH58Ic/nNueQn3VVVflNuMwXMdOPfXU3GYMpOss9wSPF6qN3del151WvNFSwPg03xc4rtpeKpVrxX3G41do+15lnXsrdcAreFOPGFNz8MEHF/24PzPmyO9bSz13OfB6fi+Csai+/0wiLo2nL/g+xPFQ/zwOkvZCWbsO8lSMW265pfiO+vLQQw/l9nvf+96iH6/J2M93v/vdRT9W+mZZAX+Wn3nmmbn95JNP5rbvCYxfY+yvVK5bqxJ+/7xqPU/D0xQIBAKBQCAwAPHSFAgEAoFAIDAAEy850LtIvQItQfe9H7xHFyHdbEz1l0pKw12adBXTZetVxen6ZJkB0jtSSQmw7QcAM72TrkU/NLGVCszPrGLr1VFb6zsq9C5gp3N4b66vu0LpQuZcvMyEV5QmvPxDD3ej09XMMblr32XWg7SBVFKv1CefI+Xs86pV8/X05xrtNUp0XZdd0k5nc05cV6cAaqnjnpZdS/+VSvc75+3yPPfcc3ObVYfdhh988MHcrlWUlqSTTjppwXv53tGqKs1q2VwLX0//PA7UKF3uH9x3nHYjKAenWrmnkeKUSrqElI1XFWe1cF6PYQ9SaS/8zqvwk34iDXv44YcX/RgW4Qcz8xq1MA3/bpzodcbXvwbfk2nDpCV9T+I+95u/+ZvFd9yjGHbgezDLfLD8g68dqTvqmO93lBNDIZzGIyXndDPfG1q0+JAwl/A0BQKBQCAQCAxAvDQFAoFAIBAIDMBE6bnZ2dlMw/hhl3Td0VXs7nFSNeznblJSaO6qrlXt3rBhQ9GPGSSsBOxjZ4VbzoNVcKWy+niL5mi5CEkPeFV1YtwHSc7NzeUswpbLmt95RgNdzWw7PcXv3BXsLvweTqEyS45j8mrxpJJIB7jLmNmZRx11VG7zUFj/nVPNnCfv5e5jt4FxgBlXDtoPdd/HSZ0jBUW6WSrn47Qn15U0Hqk1qaRzuXa0U6msAk27Wrt2bdGPtCyzpVxnaX9ut5R1i54bV9Ycr9+vsdP0tSr8fhoCZUT7872POu22SGqGe6ln23pV6h5O69KmSePec889RT/qCjP1nJ4jneg2xnAH2qmvk+9V4wArgrcOouXzwOXM3zG0wHWTGW6+JqTTSMHdfPPNRT9+x6ruv/u7v1v0ow3yAGB/XnNevN4DDzxQ9ONhwz6vWoV+p6X737Uo9PA0BQKBQCAQCAxAvDQFAoFAIBAIDEC8NAUCgUAgEAgMwERjmljZ1HlLcq7ksr06MjlIxrd4iiV5c8Yt+TUZ3+Rpp0xv5L38lG6mV/MaPibGQLCquMd8MB3aYyoYo9CKjRh3TJP0isx8nuSgW/NkrATTkD0Nlt95DAxjTMi5+7oxBXXdunW57eUMhp6GzVgOxuEwbk1qy4jxIYzR8fipSVUd7mMYPLaJcqOsfZwes7XQb/z6vj68F+NFPDaHMSdf/epXc9vlydgExth4zAKrE3O8HlfDCuGe/k19ocw8RnHc8pyZmcnr5fusx2H18BgYzpM67evBOCHfPxl3xNR/jyNhCjzv6/sF781yCR47yvg0yojxbX5f7h1SKTPqucuyVYJilOhtxmPKaD9cH9fNms552j5tzmOaGFPG56vrDnXsoosuyu2zzjqr6Ldx48YFr8eYQqlcc87Dy8PQvj3el+tEudf22ta+vUPrTSkdkFL6RkrpgZTSppTSn8z/fd+U0vUppS3z/68X0wksC4Qspwshz+lByHK6EPKcXgz5J8/Lkv6067p1ks6Q9MmU0jpJl0ta33XdWknr5z8HljdCltOFkOf0IGQ5XQh5Til2SM91XbdV0tb59o9S+v/tnc+vJFUVx7/3GVyxkXEyTPAhQthAAkhcaMaFiTEZ2Rg2RkPMLEhm42JIXAj6D7hi54YEowtiYgIJ7IwSJ8ENkcUw4gwMToCo/NKtK+WVi9ddfurru3fKme7qovh+EkL16+quc++5t7rmfM89t1yWdJukb0n62uq0X0g6L+mHre9i2NiXnTLsxtCih+UZMqQc0Fr+TOlDqm9A6bIQw82U53xZOSUBhv4o20jDNlIucvsY7vTQJ+Uu9kVrI8c1m/Qll6i35By+56Flhra5YSaXlUrD8eDLk2ubabpUdO+99/bH9INvHko7zp8/3x97GJf+Y1jY5Y9aBXtp6PdWOLgmqWzan+t+cemKfuK89ZA/3+MYcEmjVmHcr9XaWPPVV1/tjynF+gbMnJtcau2287qUiHzMso2U5aWhJFDbIJTXZj9v0peSqhv21mR7v89yzHGZvbeF90yWFZCG0h397JIh5+DZs2f7Y998mXOJ/Xv16tXBeZQCmZrhMh793JJh2YdezXyKudl1XTWtheORvnW7+DvHcepSGPvYd5ngvZdyppf5OHfuXH/MVAi/x9Gmixcv9scPP/zw4Dx+ju3yVA3+Rnv5C/qNY8d/e9e0JPT/S1wvpdwh6YuSXpZ0YjUwJOl9SScqHwszJL5cFvHncogvl0X8uSxGPzSVUm6W9Kykx7quGzyWd4ePgkf+U7mUcraU8kop5ZVWMcYwHZvwpf8LPOyOTfhzqoTW0GYTvmQ0OuyWTfizVpA07IZRq+dKKTfp0PHPdF333OrPH5RSTnZd914p5aSkI8u6dl33lKSnJOn48ePdOiTtYflaaN/h55jN7xtwMoznlb5r4T7foPC+++7rjxnG9FAuw9e0z6vHcvBTNvCVWa3wIb+D4X2X52ob9m7Kl/v7+9065OmTmu2pbTDsr7lS0eUhvuche65kvHLlSvVaDz74YH9MGcElFm7sy9U/vhqPG7wyLN4Kn9dWl0nD8LFL163qtJvy5+23316dm5wvlCpa8hRX4fhqU+LyEVfEUBL1VXGUBDgfKa9KQ3mNcoCvluJ5lKB8HtXGtttLf7ossb6WS2Wb8uWtt97are9X7kv6hQ/KtQ1+3U63makKnrZAaYbHPh4eeeSRI+3wuU6pjfIZpVppODb4W+KbunMFnm/wyvspx7mf11qlvCl/Hjt2rFv3i9/n2b7WPZRt4JjgZ6Th76FXO7/rrrv6Y95D+XdpKNnSPq6Wk4Y+5P3ZV8Vx3rLauI8jjmeXUXlP5W9DTaZv+XXM6rki6WlJl7uuexJvvSDpzOr4jKTnr/VdYbfEl8si/lwO8eWyiD+Xy5hI0ylJ35P0x1LKepOfH0n6iaRflVIelfSOpG9vx8SwQeLLZRF/Lof4clnEnwtlzOq530uq6QNf36w5YZvEl8si/lwO8eWyiD+Xy+QVwdd6vucy1HYt9yWR1JS5vJ87HEvDJYeutXOp4unTp/tj13Cpd3p+EqHOzPM88Z0lDLh80/MJ+H2uudZ2kve8iW0n3X/00Ue9b9yXft4az91iu6m5u55cKzMhDfVuatrefvYj81l8WS3zHmg7d+SWhnkvtbwCt91tYn+0kj2nqO5eSun90arSTZ957hXPay0UaC0/Z1kA3hP8PPYJcyi8EjJzL/iej1mOP44jzwlqVTOvVUv3PJR1vsW2KoOXUnpfeKkMtpPHPg9oG489yZx95f3x9ttv98evv/56f3z//fdXv6O2BFwa5jhx7HkVfi9LcpQ90nA+eq4S5yPt83nq420bdF3XjyEv+cB5wd8Dz7tlTiD96dXZ2T6f37y3cV55vidLO/Ce4LnKtJf5an7ehQsX+mOOAR/btVI30rCdbL/fW1u/Zf3nr3lGCCGEEELIQ1MIIYQQwhgmledKKf0SXg9z8nUrfMbwMGUWX2LJ73DZjaUF7rzzzv7Yw5Fc3shwp0t1taX/vuEXhUEAAAkySURBVCTSw5hrfFlza+kk28/wsre/tYR4E+zt7fUhTw/Z1zZY9P6ljexfbzO/z6u2sxr3Aw880B97RVyGeBnGfeuttwbnsRo5r+s+YviXxx4+92X5NRhqdt8dVd19m/ico4xYq9gtDe1ujU36xv3JpceUA7zCL6tN0z6fY5RuOE653FkaztuWpMDXPp5rUnStavS2oNTqcgPHGf3i7azt0OBzndW3fQcEjhXKc770v1YjzOU0Siy8b7tEVqtT5ZIV56a/V/sNckl1Ct/u7e3198SWfMQ55/cQto9L+i9dujQ4j/PF5dZWtXDCkg/sH7+P0YccO14iiG3mmPX7lMt1hP3Bz9UkvlaZl0SaQgghhBBGkIemEEIIIYQR5KEphBBCCGEEk5ccWOvNniNCDbFW8lwalr5nHpRrk9SefWsFLv0/depUf+zaOLfRYJmCl156aXAec6a4Y7dT24LBc6SYp+NLYanN8ti17pYmuwkODg76XITWUtLWljB8Tf383XffHZxHXdz7g9difoznm3DJLTV3bpXjn6O/vH9ry+Y9x6GV71TLi/L+nGovsfWY9L5j+QD2t+dN1M7z3APmXnA+S8MlxcwJ9D7gsvLWVj3MpWH/e+kKtoU5Nq155DkffM17k/fTuv3bKiVxcHBQze9gHg/bxnuONMyBYVv8vs1cM8/34ZhmDoznPjEPjT738h3Mn2I7fL7USrZ4Hq3nThLOYx572ZQp9lNlyQHPC+L1W+1hn9BnPoYvXrzYH/tcqm2/4vmBzIuib/z7mNvG/DXPeeO1aiUzpP9tC6mVSvHxvJ43re9KpCmEEEIIYQR5aAohhBBCGMGk8tze3l4fDnO5o7Y03SUNhkoZQvbwPcOCHoLjtbjk3Cv3MozJkLIvkaU8x1Czt5ESESuduwTAdnnF1tqu7f4drQrTm6BVdZhh2JocIA37gJ9x2YLhWpdz+Dn2h0tM9CUlVB9fDMsy7OxheNrINvoYogzk38H2tyqiu/ywDbqu6/usdX3Ov9bc5PhzSYGV/FvlJdhfXmGc4faW32slHzz8zut6u0irXbzPtEourG3aZkXwdf+4jZyrvH7NRmmYtuD3Ffajjxv6haUk3Ce8f9J/LfmF7fJyAfQlz/P+bu2oQDge/J7e+tym6Lqun1t+L2AfM3XBZUTehzgG/LeMbXXZrdaXPnYuX77cH3Ps+Fynr/m76b95HFet/ubn/D5MOE+9jWPmZCJNIYQQQggjyENTCCGEEMIIJpXnpP+G1zx8xrAjw2weDqbExe/wECHD+R7a5+oshlv9PF67tcEuz+P3eSVyriBhqNPD0K1NIGuSXGvz1G1Aec5XjNEuHvvmywyTUv50eaQVnmVYl6FVDwVzRQf95d9HH/G6vvKjtrrG5RC22WVijl9+zuVOD1dvA/rT+6RWEdklcfqaPnR/8jXnszRsO+ejjzH2Jd9zCZTX4nsuS9TSA1rV0f2eU5PkWqtLt8Xah37/ZL+N3XyZ7fL+4FjxOcf7Gq/r9zfKSpynLZsoybmkUtts2M+rpTpIw77hOPc5PMXcJD432Qba1pJlW1XF2Ue+epGveV5rU2uOF//d5L2R3+0bLteeDfz76Cf3C8cw7XMZsyXrrUmkKYQQQghhBHloCiGEEEIYQR6aQgghhBBGMHlOU23H9toyX9cmqdEzz8T1V2q/rZ3mW9WWqanTDreJdtTyVKThUmtqrG5fS2tnPgTPc212W9WGj7LF7adWTfvdRvYP81I894natedDsA/Yp36tWoVjz1Xi59iHnhtSy9fw86jHez4CK5jzc26753Zsg4ODg74vPTeF+RBsq+fm1MoReLs5R7w6NM/l3GwtF2/l5nCMsV2eu1DLM/I5zDa6TRwTHIstm7YBy0e4jRxnrbzHWoX6VpkFbydzZ/gdfl7NL+4Tvm7d01v2ktY9spaL4zlNXu5gG5RSqr+PHI9st+dFss/Zjz4+6Au/Zi23zX+jan73cjG1Pt7f3x+cx/xDfrffV2hva37X+oL2tvIOE2kKIYQQQhhBHppCCCGEEEZQpqho2l+slL9L+qekf1zr3An4rHZvx1Q2fL7ruuPXPm08K1++o09WP16LKezYuC+lzM0d2bBNX2ZuDvm4+zNzc1obqr6c9KFJkkopr3Rd96VJLzpTO+Zgw40yhzbMwYY52XG9zMX+OdgxBxtulDm0YQ42zMmO62Uu9s/Bjl3bEHkuhBBCCGEEeWgKIYQQQhjBLh6antrBNY9iDnbMwYYbZQ5tmIMN0nzsuF7mYv8c7JiDDTfKHNowBxuk+dhxvczF/jnYsVMbJs9pCiGEEEL4OBJ5LoQQQghhBJM+NJVSTpdS3iil/LmU8viE1/1ZKeXDUspr+NstpZTflFLeXP3/M63v2IAN+6WU35VSLpVS/lRKObcLOzZFfLkcX0rx55L8GV8ux5dS/Dk3f0720FRK+ZSkn0r6pqR7JH23lHLPRJf/uaTT9rfHJb3Ydd3dkl5cvd4m/5b0g67r7pH0ZUnfX7V/ajtumPhyOb6U4k8tyJ/x5XJ8KcWfmqM/u66b5D9JX5H0a7x+QtITE17/Dkmv4fUbkk6ujk9KemMqW1bXfF7SN3ZtR3z5yfZl/Lksf8aXy/Fl/DlPf04pz90m6S94/dfV33bFia7r3lsdvy/pxFQXLqXcIemLkl7epR03QHy5YgG+lOLPngX4M75csQBfSvFnz1z8mURwSd3h4+okywhLKTdLelbSY13XDbbIntKOpRJfLov4cznEl8vik+rPKR+a/iZpH68/t/rbrviglHJSklb//3DbFyyl3KRDxz/Tdd1zu7JjA8SXy/GlFH8uyZ/x5XJ8KcWfs/PnlA9Nf5B0dynlC6WUT0v6jqQXJry+84KkM6vjMzrUSrdGKaVIelrS5a7rntyVHRsivlyOL6X4c0n+jC+X40sp/pyfPydO4npI0hVJVyX9eMLr/lLSe5L+pUNN+FFJx3SYdf+mpN9KumXLNnxVhyHEi5IurP57aGo74sv4Mv5ctj/jy+X4Mv6cnz9TETyEEEIIYQRJBA8hhBBCGEEemkIIIYQQRpCHphBCCCGEEeShKYQQQghhBHloCiGEEEIYQR6aQgghhBBGkIemEEIIIYQR5KEphBBCCGEE/wEXzpYIMkzfuAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJhJ7t4HG7X0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zie_XsQ0F0tR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsnpalvSE460"
      },
      "source": [
        "# RBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuijSjFvE6Yz"
      },
      "source": [
        "import torch\r\n",
        "import torchvision\r\n",
        "import torchvision.transforms as transforms\r\n",
        "from torch.autograd import Variable\r\n",
        "import torch.nn as nn\r\n",
        "import torch.nn.functional as F\r\n",
        "import math\r\n",
        "from tqdm import tqdm\r\n",
        "import sys\r\n",
        "\r\n",
        "BATCH_SIZE = 64\r\n",
        "\r\n",
        "class RBM(nn.Module):\r\n",
        "    '''\r\n",
        "    This class defines all the functions needed for an BinaryRBN model\r\n",
        "    where the visible and hidden units are both considered binary\r\n",
        "    '''\r\n",
        "\r\n",
        "    def __init__(self,\r\n",
        "                visible_units=256,\r\n",
        "                hidden_units = 64,\r\n",
        "                k=2,\r\n",
        "                learning_rate=1e-5,\r\n",
        "                learning_rate_decay = False,\r\n",
        "                xavier_init = False,\r\n",
        "                increase_to_cd_k = False,\r\n",
        "                use_gpu = False\r\n",
        "                ):\r\n",
        "        '''\r\n",
        "        Defines the model\r\n",
        "        W:Wheights shape (visible_units,hidden_units)\r\n",
        "        c:hidden unit bias shape (hidden_units , )\r\n",
        "        b : visible unit bias shape(visisble_units ,)\r\n",
        "        '''\r\n",
        "        super(RBM,self).__init__()\r\n",
        "        self.desc = \"RBM\"\r\n",
        "\r\n",
        "        self.visible_units = visible_units\r\n",
        "        self.hidden_units = hidden_units\r\n",
        "        self.k = k\r\n",
        "        self.learning_rate = learning_rate\r\n",
        "        self.learning_rate_decay = learning_rate_decay\r\n",
        "        self.xavier_init = xavier_init\r\n",
        "        self.increase_to_cd_k = increase_to_cd_k\r\n",
        "        self.use_gpu = use_gpu\r\n",
        "        self.batch_size = 16\r\n",
        "\r\n",
        "\r\n",
        "        # Initialization\r\n",
        "        if not self.xavier_init:\r\n",
        "            self.W = torch.randn(self.visible_units,self.hidden_units) * 0.01 #weights\r\n",
        "        else:\r\n",
        "            self.xavier_value = torch.sqrt(torch.FloatTensor([1.0 / (self.visible_units + self.hidden_units)]))\r\n",
        "            self.W = -self.xavier_value + torch.rand(self.visible_units, self.hidden_units) * (2 * self.xavier_value)\r\n",
        "        self.h_bias = torch.zeros(self.hidden_units) #hidden layer bias\r\n",
        "        self.v_bias = torch.zeros(self.visible_units) #visible layer bias\r\n",
        "\r\n",
        "\r\n",
        "    def to_hidden(self ,X):\r\n",
        "        '''\r\n",
        "        Converts the data in visible layer to hidden layer\r\n",
        "        also does sampling\r\n",
        "        X here is the visible probabilities\r\n",
        "        :param X: torch tensor shape = (n_samples , n_features)\r\n",
        "        :return -  X_prob - new hidden layer (probabilities)\r\n",
        "                    sample_X_prob - Gibbs sampling of hidden (1 or 0) based\r\n",
        "                                on the value\r\n",
        "        '''\r\n",
        "        X_prob = torch.matmul(X,self.W)\r\n",
        "        X_prob = torch.add(X_prob, self.h_bias)#W.x + c\r\n",
        "        X_prob  = torch.sigmoid(X_prob)\r\n",
        "\r\n",
        "        sample_X_prob = self.sampling(X_prob)\r\n",
        "\r\n",
        "        return X_prob,sample_X_prob\r\n",
        "\r\n",
        "    def to_visible(self,X):\r\n",
        "        '''\r\n",
        "        reconstructs data from hidden layer\r\n",
        "        also does sampling\r\n",
        "        X here is the probabilities in the hidden layer\r\n",
        "        :returns - X_prob - the new reconstructed layers(probabilities)\r\n",
        "                    sample_X_prob - sample of new layer(Gibbs Sampling)\r\n",
        "        '''\r\n",
        "        # computing hidden activations and then converting into probabilities\r\n",
        "        X_prob = torch.matmul(X ,self.W.transpose( 0 , 1) )\r\n",
        "        X_prob = torch.add(X_prob , self.v_bias)\r\n",
        "        X_prob = torch.sigmoid(X_prob)\r\n",
        "\r\n",
        "        sample_X_prob = self.sampling(X_prob)\r\n",
        "\r\n",
        "        return X_prob,sample_X_prob\r\n",
        "\r\n",
        "    def sampling(self,prob):\r\n",
        "        '''\r\n",
        "        Bernoulli sampling done based on probabilities s\r\n",
        "        '''\r\n",
        "        s = torch.distributions.Bernoulli(prob).sample()\r\n",
        "        return s\r\n",
        "\r\n",
        "    def reconstruction_error(self , data):\r\n",
        "        '''\r\n",
        "        Computes the reconstruction error for the data\r\n",
        "        handled by pytorch by loss functions\r\n",
        "        '''\r\n",
        "        return self.contrastive_divergence(data, False)\r\n",
        "\r\n",
        "    def reconstruct(self , X,n_gibbs):\r\n",
        "        '''\r\n",
        "        This will reconstruct the sample with k steps of gibbs Sampling\r\n",
        "        '''\r\n",
        "        v = X\r\n",
        "        for i in range(n_gibbs):\r\n",
        "            prob_h_,h = self.to_hidden(v)\r\n",
        "            prob_v_,v = self.to_visible(prob_h_)\r\n",
        "        return prob_v_,v\r\n",
        "\r\n",
        "\r\n",
        "    def contrastive_divergence(self, input_data ,training = True,\r\n",
        "                                n_gibbs_sampling_steps=1,lr = 0.001):\r\n",
        "        # positive phase\r\n",
        "\r\n",
        "        positive_hidden_probabilities,positive_hidden_act  = self.to_hidden(input_data)\r\n",
        "\r\n",
        "        # calculating W via positive side\r\n",
        "        positive_associations = torch.matmul(input_data.t() , positive_hidden_act)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        # negetive phase\r\n",
        "        hidden_activations = positive_hidden_act\r\n",
        "        for i in range(n_gibbs_sampling_steps):\r\n",
        "            visible_probabilities , _ = self.to_visible(hidden_activations)\r\n",
        "            hidden_probabilities,hidden_activations = self.to_hidden(visible_probabilities)\r\n",
        "\r\n",
        "        negative_visible_probabilities = visible_probabilities\r\n",
        "        negative_hidden_probabilities = hidden_probabilities\r\n",
        "\r\n",
        "        # calculating W via negative side\r\n",
        "        negative_associations = torch.matmul(negative_visible_probabilities.t() , negative_hidden_probabilities)\r\n",
        "\r\n",
        "\r\n",
        "        # Update parameters\r\n",
        "        if(training):\r\n",
        "\r\n",
        "            batch_size = self.batch_size\r\n",
        "\r\n",
        "            g = (positive_associations - negative_associations)\r\n",
        "            grad_update = g / batch_size\r\n",
        "            v_bias_update = torch.sum(input_data - negative_visible_probabilities,dim=0)/batch_size\r\n",
        "            h_bias_update = torch.sum(positive_hidden_probabilities - negative_hidden_probabilities,dim=0)/batch_size\r\n",
        "\r\n",
        "            self.W += lr * grad_update\r\n",
        "            self.v_bias += lr * v_bias_update\r\n",
        "            self.h_bias += lr * h_bias_update\r\n",
        "\r\n",
        "\r\n",
        "        # Compute reconstruction error\r\n",
        "        error = torch.mean(torch.sum((input_data - negative_visible_probabilities)**2 , dim = 0))\r\n",
        "\r\n",
        "        return error,torch.sum(torch.abs(grad_update))\r\n",
        "\r\n",
        "\r\n",
        "    def forward(self,input_data):\r\n",
        "        'data->hidden'\r\n",
        "        return  self.to_hidden(input_data)\r\n",
        "    def step(self,input_data,epoch,num_epochs):\r\n",
        "        '''\r\n",
        "            Includes the foward prop plus the gradient descent\r\n",
        "            Use this for training\r\n",
        "        '''\r\n",
        "        if self.increase_to_cd_k:\r\n",
        "            n_gibbs_sampling_steps = int(math.ceil((epoch/num_epochs) * self.k))\r\n",
        "        else:\r\n",
        "            n_gibbs_sampling_steps = self.k\r\n",
        "\r\n",
        "        if self.learning_rate_decay:\r\n",
        "            lr = self.learning_rate / epoch\r\n",
        "        else:\r\n",
        "            lr = self.learning_rate\r\n",
        "\r\n",
        "        return self.contrastive_divergence(input_data , True,n_gibbs_sampling_steps,lr);\r\n",
        "\r\n",
        "\r\n",
        "    def train(self,train_dataloader , num_epochs = 50,batch_size=16):\r\n",
        "\r\n",
        "        self.batch_size = batch_size\r\n",
        "        if(isinstance(train_dataloader ,torch.utils.data.DataLoader)):\r\n",
        "            train_loader = train_dataloader\r\n",
        "        else:\r\n",
        "            train_loader = torch.utils.data.DataLoader(train_dataloader, batch_size=batch_size)\r\n",
        "\r\n",
        "\r\n",
        "        for epoch in range(1 , num_epochs+1):\r\n",
        "            epoch_err = 0.0\r\n",
        "            n_batches = int(len(train_loader))\r\n",
        "            # print(n_batches)\r\n",
        "\r\n",
        "            cost_ = torch.FloatTensor(n_batches , 1)\r\n",
        "            grad_ = torch.FloatTensor(n_batches , 1)\r\n",
        "\r\n",
        "            for i,(batch,_) in tqdm(enumerate(train_loader),ascii=True,\r\n",
        "                                desc=\"RBM fitting\", file=sys.stdout):\r\n",
        "\r\n",
        "                batch = batch.view(len(batch) , self.visible_units)\r\n",
        "                \r\n",
        "                if(self.use_gpu):\r\n",
        "                    batch = batch.cuda()\r\n",
        "                cost_[i-1],grad_[i-1] = self.step(batch,epoch,num_epochs)\r\n",
        "\r\n",
        "\r\n",
        "            print(\"Epoch:{} ,avg_cost = {} ,std_cost = {} ,avg_grad = {} ,std_grad = {}\".format(epoch,\\\r\n",
        "                                                            torch.mean(cost_),\\\r\n",
        "                                                            torch.std(cost_),\\\r\n",
        "                                                            torch.mean(grad_),\\\r\n",
        "                                                            torch.std(grad_)))\r\n",
        "\r\n",
        "        return"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deadSPhlE7HI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hr1iJ-NWJdw7"
      },
      "source": [
        "# RBM on dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yueHG-KB8s3"
      },
      "source": [
        "import torch \r\n",
        "import torchvision\r\n",
        "from torchvision import datasets,transforms\r\n",
        "from torch.utils.data import Dataset,DataLoader\r\n",
        "\r\n",
        "import matplotlib\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "import math\r\n",
        "import numpy as np"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2jZ-NvTKEom"
      },
      "source": [
        "mnist_data = datasets.MNIST('../data', train=True, download=True,\r\n",
        "                    transform=transforms.Compose(\r\n",
        "\r\n",
        "                    [transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))]))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4djj-JWJKH-6"
      },
      "source": [
        "# Need to convert th data into binary variables\r\n",
        "mnist_data.data = (mnist_data.data.type(torch.FloatTensor)/255).bernoulli()"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "9k1X8Dv-Kj3x",
        "outputId": "8468aa03-3ce2-48f6-b433-78cc30af77ec"
      },
      "source": [
        "#Lets us visualize a number from the data set\r\n",
        "idx = 5\r\n",
        "img = mnist_data.train_data[idx]\r\n",
        "print(\"The number shown is the number: {}\".format(mnist_data.train_labels[idx]) )\r\n",
        "plt.imshow(img , cmap = 'gray')\r\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The number shown is the number: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n",
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "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": "7o3ppM_VKp3T"
      },
      "source": [
        "# If we train on the whole set we expect it to learn to detect edges.\r\n",
        "batch_size= 10\r\n",
        "tensor_x = mnist_data.data.type(torch.FloatTensor) # transform to torch tensors\r\n",
        "tensor_y = mnist_data.targets.type(torch.FloatTensor)\r\n",
        "_dataset = torch.utils.data.TensorDataset(tensor_x,tensor_y) # create your datset\r\n",
        "train_loader = torch.utils.data.DataLoader(_dataset,\r\n",
        "                    batch_size=batch_size, shuffle=True,drop_last = True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKfHpDTyKr0F"
      },
      "source": [
        "# I have have set these hyper parameters although you can experiment with them to find better hyperparameters.\r\n",
        "visible_units=28*28\r\n",
        "hidden_units = 500\r\n",
        "k=3\r\n",
        "learning_rate=0.01\r\n",
        "learning_rate_decay = True\r\n",
        "xavier_init = True\r\n",
        "increase_to_cd_k = False\r\n",
        "use_gpu = False\r\n",
        "\r\n",
        "\r\n",
        "rbm_mnist = RBM(visible_units,hidden_units,k ,learning_rate,learning_rate_decay,xavier_init,\r\n",
        "                increase_to_cd_k,use_gpu)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YUmTgyoKzHz",
        "outputId": "26d3581c-2e44-41c6-ba15-3cd7e0b3ab28"
      },
      "source": [
        "epochs = 30\r\n",
        "\r\n",
        "rbm_mnist.train(train_loader , epochs,batch_size)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "RBM fitting: 4517it [00:30, 151.11it/s]\u001b[A\n",
            "RBM fitting: 4533it [00:30, 152.41it/s]\u001b[A\n",
            "RBM fitting: 4549it [00:30, 147.61it/s]\u001b[A\n",
            "RBM fitting: 4564it [00:30, 148.17it/s]\u001b[A\n",
            "RBM fitting: 4580it [00:30, 150.15it/s]\u001b[A\n",
            "RBM fitting: 4596it [00:30, 152.30it/s]\u001b[A\n",
            "RBM fitting: 4612it [00:30, 153.94it/s]\u001b[A\n",
            "RBM fitting: 4628it [00:31, 149.19it/s]\u001b[A\n",
            "RBM fitting: 4643it [00:31, 149.14it/s]\u001b[A\n",
            "RBM fitting: 4659it [00:31, 152.13it/s]\u001b[A\n",
            "RBM fitting: 4675it [00:31, 154.10it/s]\u001b[A\n",
            "RBM fitting: 4691it [00:31, 155.26it/s]\u001b[A\n",
            "RBM fitting: 4707it [00:31, 150.15it/s]\u001b[A\n",
            "RBM fitting: 4723it [00:31, 152.16it/s]\u001b[A\n",
            "RBM fitting: 4739it [00:31, 151.47it/s]\u001b[A\n",
            "RBM fitting: 4755it [00:31, 152.76it/s]\u001b[A\n",
            "RBM fitting: 4771it [00:32, 154.25it/s]\u001b[A\n",
            "RBM fitting: 4787it [00:32, 146.71it/s]\u001b[A\n",
            "RBM fitting: 4803it [00:32, 149.15it/s]\u001b[A\n",
            "RBM fitting: 4819it [00:32, 150.84it/s]\u001b[A\n",
            "RBM fitting: 4835it [00:32, 151.86it/s]\u001b[A\n",
            "RBM fitting: 4851it [00:32, 151.69it/s]\u001b[A\n",
            "RBM fitting: 4867it [00:32, 146.51it/s]\u001b[A\n",
            "RBM fitting: 4883it [00:32, 148.47it/s]\u001b[A\n",
            "RBM fitting: 4898it [00:32, 145.97it/s]\u001b[A\n",
            "RBM fitting: 4913it [00:33, 146.93it/s]\u001b[A\n",
            "RBM fitting: 4929it [00:33, 149.40it/s]\u001b[A\n",
            "RBM fitting: 4944it [00:33, 142.79it/s]\u001b[A\n",
            "RBM fitting: 4960it [00:33, 145.39it/s]\u001b[A\n",
            "RBM fitting: 4976it [00:33, 147.97it/s]\u001b[A\n",
            "RBM fitting: 4991it [00:33, 148.42it/s]\u001b[A\n",
            "RBM fitting: 5006it [00:33, 147.94it/s]\u001b[A\n",
            "RBM fitting: 5021it [00:33, 141.44it/s]\u001b[A\n",
            "RBM fitting: 5037it [00:33, 145.89it/s]\u001b[A\n",
            "RBM fitting: 5053it [00:33, 149.63it/s]\u001b[A\n",
            "RBM fitting: 5069it [00:34, 152.08it/s]\u001b[A\n",
            "RBM fitting: 5085it [00:34, 153.64it/s]\u001b[A\n",
            "RBM fitting: 5101it [00:34, 146.77it/s]\u001b[A\n",
            "RBM fitting: 5117it [00:34, 149.86it/s]\u001b[A\n",
            "RBM fitting: 5133it [00:34, 151.74it/s]\u001b[A\n",
            "RBM fitting: 5149it [00:34, 153.97it/s]\u001b[A\n",
            "RBM fitting: 5165it [00:34, 154.88it/s]\u001b[A\n",
            "RBM fitting: 5181it [00:34, 150.21it/s]\u001b[A\n",
            "RBM fitting: 5197it [00:34, 152.05it/s]\u001b[A\n",
            "RBM fitting: 5213it [00:35, 153.15it/s]\u001b[A\n",
            "RBM fitting: 5229it [00:35, 153.77it/s]\u001b[A\n",
            "RBM fitting: 5245it [00:35, 152.19it/s]\u001b[A\n",
            "RBM fitting: 5261it [00:35, 147.70it/s]\u001b[A\n",
            "RBM fitting: 5276it [00:35, 146.87it/s]\u001b[A\n",
            "RBM fitting: 5291it [00:35, 147.27it/s]\u001b[A\n",
            "RBM fitting: 5307it [00:35, 149.88it/s]\u001b[A\n",
            "RBM fitting: 5323it [00:35, 150.80it/s]\u001b[A\n",
            "RBM fitting: 5339it [00:35, 145.91it/s]\u001b[A\n",
            "RBM fitting: 5355it [00:35, 148.89it/s]\u001b[A\n",
            "RBM fitting: 5371it [00:36, 151.79it/s]\u001b[A\n",
            "RBM fitting: 5387it [00:36, 153.71it/s]\u001b[A\n",
            "RBM fitting: 5403it [00:36, 151.94it/s]\u001b[A\n",
            "RBM fitting: 5419it [00:36, 148.69it/s]\u001b[A\n",
            "RBM fitting: 5435it [00:36, 149.83it/s]\u001b[A\n",
            "RBM fitting: 5451it [00:36, 152.19it/s]\u001b[A\n",
            "RBM fitting: 5467it [00:36, 153.61it/s]\u001b[A\n",
            "RBM fitting: 5483it [00:36, 154.78it/s]\u001b[A\n",
            "RBM fitting: 5499it [00:36, 147.79it/s]\u001b[A\n",
            "RBM fitting: 5515it [00:37, 148.95it/s]\u001b[A\n",
            "RBM fitting: 5531it [00:37, 150.88it/s]\u001b[A\n",
            "RBM fitting: 5547it [00:37, 151.69it/s]\u001b[A\n",
            "RBM fitting: 5563it [00:37, 149.87it/s]\u001b[A\n",
            "RBM fitting: 5579it [00:37, 145.91it/s]\u001b[A\n",
            "RBM fitting: 5595it [00:37, 149.48it/s]\u001b[A\n",
            "RBM fitting: 5611it [00:37, 151.60it/s]\u001b[A\n",
            "RBM fitting: 5628it [00:37, 154.65it/s]\u001b[A\n",
            "RBM fitting: 5645it [00:37, 156.88it/s]\u001b[A\n",
            "RBM fitting: 5661it [00:37, 151.70it/s]\u001b[A\n",
            "RBM fitting: 5677it [00:38, 152.75it/s]\u001b[A\n",
            "RBM fitting: 5693it [00:38, 154.54it/s]\u001b[A\n",
            "RBM fitting: 5709it [00:38, 152.77it/s]\u001b[A\n",
            "RBM fitting: 5725it [00:38, 153.41it/s]\u001b[A\n",
            "RBM fitting: 5741it [00:38, 146.80it/s]\u001b[A\n",
            "RBM fitting: 5757it [00:38, 149.19it/s]\u001b[A\n",
            "RBM fitting: 5773it [00:38, 151.26it/s]\u001b[A\n",
            "RBM fitting: 5789it [00:38, 153.69it/s]\u001b[A\n",
            "RBM fitting: 5805it [00:38, 153.13it/s]\u001b[A\n",
            "RBM fitting: 5821it [00:39, 147.95it/s]\u001b[A\n",
            "RBM fitting: 5837it [00:39, 150.88it/s]\u001b[A\n",
            "RBM fitting: 5853it [00:39, 148.91it/s]\u001b[A\n",
            "RBM fitting: 5868it [00:39, 146.90it/s]\u001b[A\n",
            "RBM fitting: 5884it [00:39, 149.93it/s]\u001b[A\n",
            "RBM fitting: 5900it [00:39, 147.71it/s]\u001b[A\n",
            "RBM fitting: 5915it [00:39, 146.27it/s]\u001b[A\n",
            "RBM fitting: 5930it [00:39, 147.17it/s]\u001b[A\n",
            "RBM fitting: 5946it [00:39, 149.93it/s]\u001b[A\n",
            "RBM fitting: 5962it [00:39, 152.49it/s]\u001b[A\n",
            "RBM fitting: 5978it [00:40, 148.45it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:40, 149.08it/s]\n",
            "Epoch:17 ,avg_cost = 0.3443877398967743 ,std_cost = 0.033900197595357895 ,avg_grad = 10063.046875 ,std_grad = 537.9371948242188\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 13it [00:00, 125.22it/s]\u001b[A\n",
            "RBM fitting: 29it [00:00, 133.26it/s]\u001b[A\n",
            "RBM fitting: 42it [00:00, 129.71it/s]\u001b[A\n",
            "RBM fitting: 58it [00:00, 136.27it/s]\u001b[A\n",
            "RBM fitting: 74it [00:00, 141.60it/s]\u001b[A\n",
            "RBM fitting: 90it [00:00, 145.75it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 149.37it/s]\u001b[A\n",
            "RBM fitting: 121it [00:00, 143.67it/s]\u001b[A\n",
            "RBM fitting: 137it [00:00, 147.68it/s]\u001b[A\n",
            "RBM fitting: 153it [00:01, 150.09it/s]\u001b[A\n",
            "RBM fitting: 168it [00:01, 148.81it/s]\u001b[A\n",
            "RBM fitting: 184it [00:01, 150.40it/s]\u001b[A\n",
            "RBM fitting: 199it [00:01, 144.44it/s]\u001b[A\n",
            "RBM fitting: 215it [00:01, 147.40it/s]\u001b[A\n",
            "RBM fitting: 231it [00:01, 150.09it/s]\u001b[A\n",
            "RBM fitting: 247it [00:01, 152.65it/s]\u001b[A\n",
            "RBM fitting: 263it [00:01, 154.36it/s]\u001b[A\n",
            "RBM fitting: 279it [00:01, 147.40it/s]\u001b[A\n",
            "RBM fitting: 295it [00:01, 148.80it/s]\u001b[A\n",
            "RBM fitting: 310it [00:02, 147.91it/s]\u001b[A\n",
            "RBM fitting: 326it [00:02, 150.93it/s]\u001b[A\n",
            "RBM fitting: 342it [00:02, 153.34it/s]\u001b[A\n",
            "RBM fitting: 358it [00:02, 146.49it/s]\u001b[A\n",
            "RBM fitting: 375it [00:02, 151.35it/s]\u001b[A\n",
            "RBM fitting: 392it [00:02, 154.35it/s]\u001b[A\n",
            "RBM fitting: 409it [00:02, 156.62it/s]\u001b[A\n",
            "RBM fitting: 425it [00:02, 156.82it/s]\u001b[A\n",
            "RBM fitting: 441it [00:02, 150.29it/s]\u001b[A\n",
            "RBM fitting: 457it [00:03, 152.87it/s]\u001b[A\n",
            "RBM fitting: 473it [00:03, 151.80it/s]\u001b[A\n",
            "RBM fitting: 489it [00:03, 153.51it/s]\u001b[A\n",
            "RBM fitting: 505it [00:03, 155.09it/s]\u001b[A\n",
            "RBM fitting: 521it [00:03, 148.66it/s]\u001b[A\n",
            "RBM fitting: 538it [00:03, 152.34it/s]\u001b[A\n",
            "RBM fitting: 554it [00:03, 152.25it/s]\u001b[A\n",
            "RBM fitting: 570it [00:03, 154.27it/s]\u001b[A\n",
            "RBM fitting: 587it [00:03, 156.82it/s]\u001b[A\n",
            "RBM fitting: 603it [00:04, 151.29it/s]\u001b[A\n",
            "RBM fitting: 620it [00:04, 153.78it/s]\u001b[A\n",
            "RBM fitting: 636it [00:04, 153.82it/s]\u001b[A\n",
            "RBM fitting: 653it [00:04, 156.32it/s]\u001b[A\n",
            "RBM fitting: 670it [00:04, 158.22it/s]\u001b[A\n",
            "RBM fitting: 686it [00:04, 150.35it/s]\u001b[A\n",
            "RBM fitting: 702it [00:04, 153.01it/s]\u001b[A\n",
            "RBM fitting: 718it [00:04, 154.03it/s]\u001b[A\n",
            "RBM fitting: 734it [00:04, 155.11it/s]\u001b[A\n",
            "RBM fitting: 750it [00:04, 155.71it/s]\u001b[A\n",
            "RBM fitting: 766it [00:05, 149.21it/s]\u001b[A\n",
            "RBM fitting: 782it [00:05, 149.36it/s]\u001b[A\n",
            "RBM fitting: 798it [00:05, 152.26it/s]\u001b[A\n",
            "RBM fitting: 814it [00:05, 154.21it/s]\u001b[A\n",
            "RBM fitting: 830it [00:05, 153.56it/s]\u001b[A\n",
            "RBM fitting: 846it [00:05, 146.18it/s]\u001b[A\n",
            "RBM fitting: 863it [00:05, 150.66it/s]\u001b[A\n",
            "RBM fitting: 879it [00:05, 152.91it/s]\u001b[A\n",
            "RBM fitting: 895it [00:05, 153.55it/s]\u001b[A\n",
            "RBM fitting: 911it [00:06, 154.56it/s]\u001b[A\n",
            "RBM fitting: 927it [00:06, 149.48it/s]\u001b[A\n",
            "RBM fitting: 943it [00:06, 148.31it/s]\u001b[A\n",
            "RBM fitting: 960it [00:06, 152.13it/s]\u001b[A\n",
            "RBM fitting: 976it [00:06, 154.34it/s]\u001b[A\n",
            "RBM fitting: 993it [00:06, 156.70it/s]\u001b[A\n",
            "RBM fitting: 1009it [00:06, 151.57it/s]\u001b[A\n",
            "RBM fitting: 1026it [00:06, 154.93it/s]\u001b[A\n",
            "RBM fitting: 1043it [00:06, 157.16it/s]\u001b[A\n",
            "RBM fitting: 1059it [00:06, 157.83it/s]\u001b[A\n",
            "RBM fitting: 1075it [00:07, 157.77it/s]\u001b[A\n",
            "RBM fitting: 1091it [00:07, 155.38it/s]\u001b[A\n",
            "RBM fitting: 1107it [00:07, 147.07it/s]\u001b[A\n",
            "RBM fitting: 1123it [00:07, 150.27it/s]\u001b[A\n",
            "RBM fitting: 1139it [00:07, 152.62it/s]\u001b[A\n",
            "RBM fitting: 1155it [00:07, 154.01it/s]\u001b[A\n",
            "RBM fitting: 1171it [00:07, 155.05it/s]\u001b[A\n",
            "RBM fitting: 1187it [00:07, 147.53it/s]\u001b[A\n",
            "RBM fitting: 1202it [00:07, 146.46it/s]\u001b[A\n",
            "RBM fitting: 1218it [00:08, 149.08it/s]\u001b[A\n",
            "RBM fitting: 1234it [00:08, 150.87it/s]\u001b[A\n",
            "RBM fitting: 1250it [00:08, 149.46it/s]\u001b[A\n",
            "RBM fitting: 1265it [00:08, 143.27it/s]\u001b[A\n",
            "RBM fitting: 1281it [00:08, 147.38it/s]\u001b[A\n",
            "RBM fitting: 1297it [00:08, 150.79it/s]\u001b[A\n",
            "RBM fitting: 1313it [00:08, 153.34it/s]\u001b[A\n",
            "RBM fitting: 1329it [00:08, 154.84it/s]\u001b[A\n",
            "RBM fitting: 1345it [00:08, 146.43it/s]\u001b[A\n",
            "RBM fitting: 1360it [00:08, 146.20it/s]\u001b[A\n",
            "RBM fitting: 1376it [00:09, 148.93it/s]\u001b[A\n",
            "RBM fitting: 1392it [00:09, 151.55it/s]\u001b[A\n",
            "RBM fitting: 1408it [00:09, 151.24it/s]\u001b[A\n",
            "RBM fitting: 1424it [00:09, 145.11it/s]\u001b[A\n",
            "RBM fitting: 1440it [00:09, 148.89it/s]\u001b[A\n",
            "RBM fitting: 1456it [00:09, 151.15it/s]\u001b[A\n",
            "RBM fitting: 1473it [00:09, 154.70it/s]\u001b[A\n",
            "RBM fitting: 1489it [00:09, 155.61it/s]\u001b[A\n",
            "RBM fitting: 1505it [00:09, 146.61it/s]\u001b[A\n",
            "RBM fitting: 1521it [00:10, 148.86it/s]\u001b[A\n",
            "RBM fitting: 1537it [00:10, 150.75it/s]\u001b[A\n",
            "RBM fitting: 1553it [00:10, 149.05it/s]\u001b[A\n",
            "RBM fitting: 1569it [00:10, 151.35it/s]\u001b[A\n",
            "RBM fitting: 1585it [00:10, 146.06it/s]\u001b[A\n",
            "RBM fitting: 1601it [00:10, 148.69it/s]\u001b[A\n",
            "RBM fitting: 1617it [00:10, 149.73it/s]\u001b[A\n",
            "RBM fitting: 1633it [00:10, 150.37it/s]\u001b[A\n",
            "RBM fitting: 1650it [00:10, 153.86it/s]\u001b[A\n",
            "RBM fitting: 1666it [00:11, 148.21it/s]\u001b[A\n",
            "RBM fitting: 1683it [00:11, 152.37it/s]\u001b[A\n",
            "RBM fitting: 1699it [00:11, 153.17it/s]\u001b[A\n",
            "RBM fitting: 1715it [00:11, 153.08it/s]\u001b[A\n",
            "RBM fitting: 1731it [00:11, 154.79it/s]\u001b[A\n",
            "RBM fitting: 1747it [00:11, 148.38it/s]\u001b[A\n",
            "RBM fitting: 1763it [00:11, 151.06it/s]\u001b[A\n",
            "RBM fitting: 1779it [00:11, 153.42it/s]\u001b[A\n",
            "RBM fitting: 1795it [00:11, 154.46it/s]\u001b[A\n",
            "RBM fitting: 1811it [00:11, 155.99it/s]\u001b[A\n",
            "RBM fitting: 1827it [00:12, 150.29it/s]\u001b[A\n",
            "RBM fitting: 1843it [00:12, 151.48it/s]\u001b[A\n",
            "RBM fitting: 1859it [00:12, 149.79it/s]\u001b[A\n",
            "RBM fitting: 1875it [00:12, 150.11it/s]\u001b[A\n",
            "RBM fitting: 1891it [00:12, 151.88it/s]\u001b[A\n",
            "RBM fitting: 1907it [00:12, 153.14it/s]\u001b[A\n",
            "RBM fitting: 1923it [00:12, 146.14it/s]\u001b[A\n",
            "RBM fitting: 1939it [00:12, 149.53it/s]\u001b[A\n",
            "RBM fitting: 1955it [00:12, 151.86it/s]\u001b[A\n",
            "RBM fitting: 1972it [00:13, 154.65it/s]\u001b[A\n",
            "RBM fitting: 1988it [00:13, 155.17it/s]\u001b[A\n",
            "RBM fitting: 2004it [00:13, 144.94it/s]\u001b[A\n",
            "RBM fitting: 2019it [00:13, 141.90it/s]\u001b[A\n",
            "RBM fitting: 2034it [00:13, 142.58it/s]\u001b[A\n",
            "RBM fitting: 2050it [00:13, 146.44it/s]\u001b[A\n",
            "RBM fitting: 2066it [00:13, 149.07it/s]\u001b[A\n",
            "RBM fitting: 2081it [00:13, 143.88it/s]\u001b[A\n",
            "RBM fitting: 2097it [00:13, 147.69it/s]\u001b[A\n",
            "RBM fitting: 2113it [00:13, 150.39it/s]\u001b[A\n",
            "RBM fitting: 2129it [00:14, 151.17it/s]\u001b[A\n",
            "RBM fitting: 2145it [00:14, 150.25it/s]\u001b[A\n",
            "RBM fitting: 2161it [00:14, 143.57it/s]\u001b[A\n",
            "RBM fitting: 2177it [00:14, 147.33it/s]\u001b[A\n",
            "RBM fitting: 2193it [00:14, 150.23it/s]\u001b[A\n",
            "RBM fitting: 2209it [00:14, 152.06it/s]\u001b[A\n",
            "RBM fitting: 2225it [00:14, 153.74it/s]\u001b[A\n",
            "RBM fitting: 2241it [00:14, 153.99it/s]\u001b[A\n",
            "RBM fitting: 2257it [00:14, 147.62it/s]\u001b[A\n",
            "RBM fitting: 2273it [00:15, 150.36it/s]\u001b[A\n",
            "RBM fitting: 2289it [00:15, 152.51it/s]\u001b[A\n",
            "RBM fitting: 2305it [00:15, 154.04it/s]\u001b[A\n",
            "RBM fitting: 2321it [00:15, 151.87it/s]\u001b[A\n",
            "RBM fitting: 2337it [00:15, 144.48it/s]\u001b[A\n",
            "RBM fitting: 2352it [00:15, 144.40it/s]\u001b[A\n",
            "RBM fitting: 2367it [00:15, 144.62it/s]\u001b[A\n",
            "RBM fitting: 2383it [00:15, 148.25it/s]\u001b[A\n",
            "RBM fitting: 2398it [00:15, 148.46it/s]\u001b[A\n",
            "RBM fitting: 2413it [00:16, 143.21it/s]\u001b[A\n",
            "RBM fitting: 2429it [00:16, 147.30it/s]\u001b[A\n",
            "RBM fitting: 2445it [00:16, 149.65it/s]\u001b[A\n",
            "RBM fitting: 2461it [00:16, 150.03it/s]\u001b[A\n",
            "RBM fitting: 2477it [00:16, 152.30it/s]\u001b[A\n",
            "RBM fitting: 2493it [00:16, 148.19it/s]\u001b[A\n",
            "RBM fitting: 2509it [00:16, 150.05it/s]\u001b[A\n",
            "RBM fitting: 2525it [00:16, 151.24it/s]\u001b[A\n",
            "RBM fitting: 2541it [00:16, 151.45it/s]\u001b[A\n",
            "RBM fitting: 2557it [00:16, 152.66it/s]\u001b[A\n",
            "RBM fitting: 2574it [00:17, 155.22it/s]\u001b[A\n",
            "RBM fitting: 2590it [00:17, 145.17it/s]\u001b[A\n",
            "RBM fitting: 2606it [00:17, 148.49it/s]\u001b[A\n",
            "RBM fitting: 2621it [00:17, 147.98it/s]\u001b[A\n",
            "RBM fitting: 2637it [00:17, 150.73it/s]\u001b[A\n",
            "RBM fitting: 2653it [00:17, 153.08it/s]\u001b[A\n",
            "RBM fitting: 2669it [00:17, 146.06it/s]\u001b[A\n",
            "RBM fitting: 2684it [00:17, 146.79it/s]\u001b[A\n",
            "RBM fitting: 2700it [00:17, 150.06it/s]\u001b[A\n",
            "RBM fitting: 2716it [00:18, 151.89it/s]\u001b[A\n",
            "RBM fitting: 2732it [00:18, 153.79it/s]\u001b[A\n",
            "RBM fitting: 2748it [00:18, 146.38it/s]\u001b[A\n",
            "RBM fitting: 2764it [00:18, 149.79it/s]\u001b[A\n",
            "RBM fitting: 2780it [00:18, 149.62it/s]\u001b[A\n",
            "RBM fitting: 2796it [00:18, 152.02it/s]\u001b[A\n",
            "RBM fitting: 2812it [00:18, 153.24it/s]\u001b[A\n",
            "RBM fitting: 2828it [00:18, 145.37it/s]\u001b[A\n",
            "RBM fitting: 2844it [00:18, 147.58it/s]\u001b[A\n",
            "RBM fitting: 2860it [00:18, 150.38it/s]\u001b[A\n",
            "RBM fitting: 2876it [00:19, 152.20it/s]\u001b[A\n",
            "RBM fitting: 2892it [00:19, 152.38it/s]\u001b[A\n",
            "RBM fitting: 2908it [00:19, 145.33it/s]\u001b[A\n",
            "RBM fitting: 2923it [00:19, 145.30it/s]\u001b[A\n",
            "RBM fitting: 2939it [00:19, 147.92it/s]\u001b[A\n",
            "RBM fitting: 2955it [00:19, 149.57it/s]\u001b[A\n",
            "RBM fitting: 2971it [00:19, 150.57it/s]\u001b[A\n",
            "RBM fitting: 2987it [00:19, 140.74it/s]\u001b[A\n",
            "RBM fitting: 3003it [00:19, 145.66it/s]\u001b[A\n",
            "RBM fitting: 3019it [00:20, 148.44it/s]\u001b[A\n",
            "RBM fitting: 3035it [00:20, 150.74it/s]\u001b[A\n",
            "RBM fitting: 3051it [00:20, 152.04it/s]\u001b[A\n",
            "RBM fitting: 3067it [00:20, 142.80it/s]\u001b[A\n",
            "RBM fitting: 3083it [00:20, 146.56it/s]\u001b[A\n",
            "RBM fitting: 3099it [00:20, 149.88it/s]\u001b[A\n",
            "RBM fitting: 3115it [00:20, 152.27it/s]\u001b[A\n",
            "RBM fitting: 3131it [00:20, 153.26it/s]\u001b[A\n",
            "RBM fitting: 3147it [00:20, 145.64it/s]\u001b[A\n",
            "RBM fitting: 3162it [00:21, 146.37it/s]\u001b[A\n",
            "RBM fitting: 3178it [00:21, 149.86it/s]\u001b[A\n",
            "RBM fitting: 3194it [00:21, 151.69it/s]\u001b[A\n",
            "RBM fitting: 3211it [00:21, 154.60it/s]\u001b[A\n",
            "RBM fitting: 3227it [00:21, 144.79it/s]\u001b[A\n",
            "RBM fitting: 3243it [00:21, 146.91it/s]\u001b[A\n",
            "RBM fitting: 3259it [00:21, 148.97it/s]\u001b[A\n",
            "RBM fitting: 3275it [00:21, 151.35it/s]\u001b[A\n",
            "RBM fitting: 3292it [00:21, 154.31it/s]\u001b[A\n",
            "RBM fitting: 3308it [00:22, 149.73it/s]\u001b[A\n",
            "RBM fitting: 3324it [00:22, 152.35it/s]\u001b[A\n",
            "RBM fitting: 3340it [00:22, 152.93it/s]\u001b[A\n",
            "RBM fitting: 3356it [00:22, 153.76it/s]\u001b[A\n",
            "RBM fitting: 3372it [00:22, 154.28it/s]\u001b[A\n",
            "RBM fitting: 3388it [00:22, 152.44it/s]\u001b[A\n",
            "RBM fitting: 3404it [00:22, 145.53it/s]\u001b[A\n",
            "RBM fitting: 3419it [00:22, 145.61it/s]\u001b[A\n",
            "RBM fitting: 3435it [00:22, 148.61it/s]\u001b[A\n",
            "RBM fitting: 3451it [00:22, 151.62it/s]\u001b[A\n",
            "RBM fitting: 3467it [00:23, 153.64it/s]\u001b[A\n",
            "RBM fitting: 3483it [00:23, 148.23it/s]\u001b[A\n",
            "RBM fitting: 3499it [00:23, 149.42it/s]\u001b[A\n",
            "RBM fitting: 3515it [00:23, 152.07it/s]\u001b[A\n",
            "RBM fitting: 3531it [00:23, 151.79it/s]\u001b[A\n",
            "RBM fitting: 3547it [00:23, 153.11it/s]\u001b[A\n",
            "RBM fitting: 3563it [00:23, 146.26it/s]\u001b[A\n",
            "RBM fitting: 3579it [00:23, 149.45it/s]\u001b[A\n",
            "RBM fitting: 3595it [00:23, 152.35it/s]\u001b[A\n",
            "RBM fitting: 3611it [00:24, 154.56it/s]\u001b[A\n",
            "RBM fitting: 3627it [00:24, 155.34it/s]\u001b[A\n",
            "RBM fitting: 3643it [00:24, 148.02it/s]\u001b[A\n",
            "RBM fitting: 3659it [00:24, 151.16it/s]\u001b[A\n",
            "RBM fitting: 3675it [00:24, 152.92it/s]\u001b[A\n",
            "RBM fitting: 3691it [00:24, 151.34it/s]\u001b[A\n",
            "RBM fitting: 3707it [00:24, 152.40it/s]\u001b[A\n",
            "RBM fitting: 3723it [00:24, 145.66it/s]\u001b[A\n",
            "RBM fitting: 3739it [00:24, 148.72it/s]\u001b[A\n",
            "RBM fitting: 3754it [00:24, 146.23it/s]\u001b[A\n",
            "RBM fitting: 3770it [00:25, 149.67it/s]\u001b[A\n",
            "RBM fitting: 3786it [00:25, 151.60it/s]\u001b[A\n",
            "RBM fitting: 3802it [00:25, 145.60it/s]\u001b[A\n",
            "RBM fitting: 3819it [00:25, 150.09it/s]\u001b[A\n",
            "RBM fitting: 3835it [00:25, 149.60it/s]\u001b[A\n",
            "RBM fitting: 3852it [00:25, 153.12it/s]\u001b[A\n",
            "RBM fitting: 3868it [00:25, 154.53it/s]\u001b[A\n",
            "RBM fitting: 3884it [00:25, 148.14it/s]\u001b[A\n",
            "RBM fitting: 3900it [00:25, 151.21it/s]\u001b[A\n",
            "RBM fitting: 3916it [00:26, 152.93it/s]\u001b[A\n",
            "RBM fitting: 3933it [00:26, 155.61it/s]\u001b[A\n",
            "RBM fitting: 3949it [00:26, 155.34it/s]\u001b[A\n",
            "RBM fitting: 3965it [00:26, 148.25it/s]\u001b[A\n",
            "RBM fitting: 3981it [00:26, 150.91it/s]\u001b[A\n",
            "RBM fitting: 3997it [00:26, 148.99it/s]\u001b[A\n",
            "RBM fitting: 4013it [00:26, 151.46it/s]\u001b[A\n",
            "RBM fitting: 4029it [00:26, 152.73it/s]\u001b[A\n",
            "RBM fitting: 4045it [00:26, 146.81it/s]\u001b[A\n",
            "RBM fitting: 4061it [00:26, 150.45it/s]\u001b[A\n",
            "RBM fitting: 4077it [00:27, 152.52it/s]\u001b[A\n",
            "RBM fitting: 4093it [00:27, 153.97it/s]\u001b[A\n",
            "RBM fitting: 4109it [00:27, 155.21it/s]\u001b[A\n",
            "RBM fitting: 4125it [00:27, 147.24it/s]\u001b[A\n",
            "RBM fitting: 4141it [00:27, 148.18it/s]\u001b[A\n",
            "RBM fitting: 4157it [00:27, 151.27it/s]\u001b[A\n",
            "RBM fitting: 4173it [00:27, 153.23it/s]\u001b[A\n",
            "RBM fitting: 4189it [00:27, 154.54it/s]\u001b[A\n",
            "RBM fitting: 4205it [00:27, 147.12it/s]\u001b[A\n",
            "RBM fitting: 4221it [00:28, 149.40it/s]\u001b[A\n",
            "RBM fitting: 4237it [00:28, 150.32it/s]\u001b[A\n",
            "RBM fitting: 4253it [00:28, 149.77it/s]\u001b[A\n",
            "RBM fitting: 4269it [00:28, 151.55it/s]\u001b[A\n",
            "RBM fitting: 4285it [00:28, 145.28it/s]\u001b[A\n",
            "RBM fitting: 4300it [00:28, 145.34it/s]\u001b[A\n",
            "RBM fitting: 4316it [00:28, 148.17it/s]\u001b[A\n",
            "RBM fitting: 4332it [00:28, 150.71it/s]\u001b[A\n",
            "RBM fitting: 4349it [00:28, 154.01it/s]\u001b[A\n",
            "RBM fitting: 4365it [00:29, 144.60it/s]\u001b[A\n",
            "RBM fitting: 4381it [00:29, 148.39it/s]\u001b[A\n",
            "RBM fitting: 4397it [00:29, 151.00it/s]\u001b[A\n",
            "RBM fitting: 4414it [00:29, 153.99it/s]\u001b[A\n",
            "RBM fitting: 4430it [00:29, 155.72it/s]\u001b[A\n",
            "RBM fitting: 4446it [00:29, 146.19it/s]\u001b[A\n",
            "RBM fitting: 4462it [00:29, 148.81it/s]\u001b[A\n",
            "RBM fitting: 4479it [00:29, 152.52it/s]\u001b[A\n",
            "RBM fitting: 4495it [00:29, 154.44it/s]\u001b[A\n",
            "RBM fitting: 4511it [00:29, 154.68it/s]\u001b[A\n",
            "RBM fitting: 4527it [00:30, 148.59it/s]\u001b[A\n",
            "RBM fitting: 4543it [00:30, 151.28it/s]\u001b[A\n",
            "RBM fitting: 4559it [00:30, 153.56it/s]\u001b[A\n",
            "RBM fitting: 4575it [00:30, 155.39it/s]\u001b[A\n",
            "RBM fitting: 4592it [00:30, 157.35it/s]\u001b[A\n",
            "RBM fitting: 4608it [00:30, 145.57it/s]\u001b[A\n",
            "RBM fitting: 4624it [00:30, 148.77it/s]\u001b[A\n",
            "RBM fitting: 4640it [00:30, 150.88it/s]\u001b[A\n",
            "RBM fitting: 4656it [00:30, 152.32it/s]\u001b[A\n",
            "RBM fitting: 4672it [00:31, 153.98it/s]\u001b[A\n",
            "RBM fitting: 4688it [00:31, 147.81it/s]\u001b[A\n",
            "RBM fitting: 4704it [00:31, 149.91it/s]\u001b[A\n",
            "RBM fitting: 4720it [00:31, 152.08it/s]\u001b[A\n",
            "RBM fitting: 4736it [00:31, 153.80it/s]\u001b[A\n",
            "RBM fitting: 4752it [00:31, 155.22it/s]\u001b[A\n",
            "RBM fitting: 4768it [00:31, 142.82it/s]\u001b[A\n",
            "RBM fitting: 4784it [00:31, 147.19it/s]\u001b[A\n",
            "RBM fitting: 4800it [00:31, 150.47it/s]\u001b[A\n",
            "RBM fitting: 4816it [00:31, 153.13it/s]\u001b[A\n",
            "RBM fitting: 4832it [00:32, 154.75it/s]\u001b[A\n",
            "RBM fitting: 4848it [00:32, 146.72it/s]\u001b[A\n",
            "RBM fitting: 4864it [00:32, 149.56it/s]\u001b[A\n",
            "RBM fitting: 4880it [00:32, 151.91it/s]\u001b[A\n",
            "RBM fitting: 4896it [00:32, 154.12it/s]\u001b[A\n",
            "RBM fitting: 4912it [00:32, 152.66it/s]\u001b[A\n",
            "RBM fitting: 4928it [00:32, 145.55it/s]\u001b[A\n",
            "RBM fitting: 4944it [00:32, 149.00it/s]\u001b[A\n",
            "RBM fitting: 4960it [00:32, 151.46it/s]\u001b[A\n",
            "RBM fitting: 4976it [00:33, 153.76it/s]\u001b[A\n",
            "RBM fitting: 4992it [00:33, 155.48it/s]\u001b[A\n",
            "RBM fitting: 5008it [00:33, 147.72it/s]\u001b[A\n",
            "RBM fitting: 5024it [00:33, 149.13it/s]\u001b[A\n",
            "RBM fitting: 5040it [00:33, 151.66it/s]\u001b[A\n",
            "RBM fitting: 5056it [00:33, 154.05it/s]\u001b[A\n",
            "RBM fitting: 5072it [00:33, 152.82it/s]\u001b[A\n",
            "RBM fitting: 5088it [00:33, 146.75it/s]\u001b[A\n",
            "RBM fitting: 5104it [00:33, 149.52it/s]\u001b[A\n",
            "RBM fitting: 5121it [00:34, 152.80it/s]\u001b[A\n",
            "RBM fitting: 5137it [00:34, 154.24it/s]\u001b[A\n",
            "RBM fitting: 5153it [00:34, 155.32it/s]\u001b[A\n",
            "RBM fitting: 5169it [00:34, 147.87it/s]\u001b[A\n",
            "RBM fitting: 5185it [00:34, 150.55it/s]\u001b[A\n",
            "RBM fitting: 5201it [00:34, 153.13it/s]\u001b[A\n",
            "RBM fitting: 5217it [00:34, 152.23it/s]\u001b[A\n",
            "RBM fitting: 5233it [00:34, 154.24it/s]\u001b[A\n",
            "RBM fitting: 5249it [00:34, 144.03it/s]\u001b[A\n",
            "RBM fitting: 5265it [00:34, 148.05it/s]\u001b[A\n",
            "RBM fitting: 5280it [00:35, 146.94it/s]\u001b[A\n",
            "RBM fitting: 5296it [00:35, 150.13it/s]\u001b[A\n",
            "RBM fitting: 5312it [00:35, 152.63it/s]\u001b[A\n",
            "RBM fitting: 5328it [00:35, 145.38it/s]\u001b[A\n",
            "RBM fitting: 5344it [00:35, 149.37it/s]\u001b[A\n",
            "RBM fitting: 5360it [00:35, 152.13it/s]\u001b[A\n",
            "RBM fitting: 5376it [00:35, 151.70it/s]\u001b[A\n",
            "RBM fitting: 5392it [00:35, 153.66it/s]\u001b[A\n",
            "RBM fitting: 5408it [00:35, 147.36it/s]\u001b[A\n",
            "RBM fitting: 5424it [00:36, 150.42it/s]\u001b[A\n",
            "RBM fitting: 5440it [00:36, 152.09it/s]\u001b[A\n",
            "RBM fitting: 5456it [00:36, 153.04it/s]\u001b[A\n",
            "RBM fitting: 5472it [00:36, 154.04it/s]\u001b[A\n",
            "RBM fitting: 5488it [00:36, 146.31it/s]\u001b[A\n",
            "RBM fitting: 5504it [00:36, 149.79it/s]\u001b[A\n",
            "RBM fitting: 5520it [00:36, 152.25it/s]\u001b[A\n",
            "RBM fitting: 5536it [00:36, 150.65it/s]\u001b[A\n",
            "RBM fitting: 5553it [00:36, 153.92it/s]\u001b[A\n",
            "RBM fitting: 5569it [00:36, 147.28it/s]\u001b[A\n",
            "RBM fitting: 5585it [00:37, 150.80it/s]\u001b[A\n",
            "RBM fitting: 5601it [00:37, 152.64it/s]\u001b[A\n",
            "RBM fitting: 5617it [00:37, 153.70it/s]\u001b[A\n",
            "RBM fitting: 5633it [00:37, 155.43it/s]\u001b[A\n",
            "RBM fitting: 5649it [00:37, 147.71it/s]\u001b[A\n",
            "RBM fitting: 5665it [00:37, 150.50it/s]\u001b[A\n",
            "RBM fitting: 5681it [00:37, 150.63it/s]\u001b[A\n",
            "RBM fitting: 5697it [00:37, 152.53it/s]\u001b[A\n",
            "RBM fitting: 5713it [00:37, 153.89it/s]\u001b[A\n",
            "RBM fitting: 5729it [00:38, 146.58it/s]\u001b[A\n",
            "RBM fitting: 5745it [00:38, 149.46it/s]\u001b[A\n",
            "RBM fitting: 5761it [00:38, 151.69it/s]\u001b[A\n",
            "RBM fitting: 5777it [00:38, 152.36it/s]\u001b[A\n",
            "RBM fitting: 5793it [00:38, 154.18it/s]\u001b[A\n",
            "RBM fitting: 5809it [00:38, 149.03it/s]\u001b[A\n",
            "RBM fitting: 5824it [00:38, 146.08it/s]\u001b[A\n",
            "RBM fitting: 5839it [00:38, 145.69it/s]\u001b[A\n",
            "RBM fitting: 5855it [00:38, 149.27it/s]\u001b[A\n",
            "RBM fitting: 5871it [00:38, 152.22it/s]\u001b[A\n",
            "RBM fitting: 5887it [00:39, 145.61it/s]\u001b[A\n",
            "RBM fitting: 5903it [00:39, 148.85it/s]\u001b[A\n",
            "RBM fitting: 5919it [00:39, 151.87it/s]\u001b[A\n",
            "RBM fitting: 5935it [00:39, 154.11it/s]\u001b[A\n",
            "RBM fitting: 5952it [00:39, 156.29it/s]\u001b[A\n",
            "RBM fitting: 5968it [00:39, 145.73it/s]\u001b[A\n",
            "RBM fitting: 5983it [00:39, 146.50it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 150.50it/s]\n",
            "Epoch:18 ,avg_cost = 0.3435269296169281 ,std_cost = 0.03419354557991028 ,avg_grad = 10066.9130859375 ,std_grad = 541.3811645507812\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 12it [00:00, 115.79it/s]\u001b[A\n",
            "RBM fitting: 26it [00:00, 122.05it/s]\u001b[A\n",
            "RBM fitting: 42it [00:00, 130.03it/s]\u001b[A\n",
            "RBM fitting: 57it [00:00, 134.71it/s]\u001b[A\n",
            "RBM fitting: 71it [00:00, 133.97it/s]\u001b[A\n",
            "RBM fitting: 87it [00:00, 138.98it/s]\u001b[A\n",
            "RBM fitting: 101it [00:00, 137.74it/s]\u001b[A\n",
            "RBM fitting: 117it [00:00, 142.42it/s]\u001b[A\n",
            "RBM fitting: 132it [00:00, 142.50it/s]\u001b[A\n",
            "RBM fitting: 148it [00:01, 146.40it/s]\u001b[A\n",
            "RBM fitting: 164it [00:01, 149.75it/s]\u001b[A\n",
            "RBM fitting: 180it [00:01, 152.34it/s]\u001b[A\n",
            "RBM fitting: 196it [00:01, 147.27it/s]\u001b[A\n",
            "RBM fitting: 212it [00:01, 149.93it/s]\u001b[A\n",
            "RBM fitting: 228it [00:01, 152.20it/s]\u001b[A\n",
            "RBM fitting: 244it [00:01, 154.44it/s]\u001b[A\n",
            "RBM fitting: 260it [00:01, 154.87it/s]\u001b[A\n",
            "RBM fitting: 276it [00:01, 148.45it/s]\u001b[A\n",
            "RBM fitting: 291it [00:01, 147.49it/s]\u001b[A\n",
            "RBM fitting: 307it [00:02, 148.99it/s]\u001b[A\n",
            "RBM fitting: 323it [00:02, 151.37it/s]\u001b[A\n",
            "RBM fitting: 339it [00:02, 152.25it/s]\u001b[A\n",
            "RBM fitting: 355it [00:02, 153.05it/s]\u001b[A\n",
            "RBM fitting: 371it [00:02, 144.38it/s]\u001b[A\n",
            "RBM fitting: 387it [00:02, 148.29it/s]\u001b[A\n",
            "RBM fitting: 404it [00:02, 151.95it/s]\u001b[A\n",
            "RBM fitting: 421it [00:02, 154.73it/s]\u001b[A\n",
            "RBM fitting: 437it [00:02, 152.70it/s]\u001b[A\n",
            "RBM fitting: 453it [00:03, 147.67it/s]\u001b[A\n",
            "RBM fitting: 469it [00:03, 150.58it/s]\u001b[A\n",
            "RBM fitting: 485it [00:03, 151.66it/s]\u001b[A\n",
            "RBM fitting: 501it [00:03, 152.60it/s]\u001b[A\n",
            "RBM fitting: 517it [00:03, 153.99it/s]\u001b[A\n",
            "RBM fitting: 533it [00:03, 154.76it/s]\u001b[A\n",
            "RBM fitting: 549it [00:03, 147.20it/s]\u001b[A\n",
            "RBM fitting: 565it [00:03, 150.33it/s]\u001b[A\n",
            "RBM fitting: 581it [00:03, 151.97it/s]\u001b[A\n",
            "RBM fitting: 597it [00:04, 149.84it/s]\u001b[A\n",
            "RBM fitting: 613it [00:04, 152.01it/s]\u001b[A\n",
            "RBM fitting: 629it [00:04, 144.44it/s]\u001b[A\n",
            "RBM fitting: 644it [00:04, 144.06it/s]\u001b[A\n",
            "RBM fitting: 659it [00:04, 145.28it/s]\u001b[A\n",
            "RBM fitting: 675it [00:04, 148.22it/s]\u001b[A\n",
            "RBM fitting: 691it [00:04, 150.82it/s]\u001b[A\n",
            "RBM fitting: 707it [00:04, 145.58it/s]\u001b[A\n",
            "RBM fitting: 724it [00:04, 150.13it/s]\u001b[A\n",
            "RBM fitting: 740it [00:04, 147.07it/s]\u001b[A\n",
            "RBM fitting: 756it [00:05, 148.78it/s]\u001b[A\n",
            "RBM fitting: 771it [00:05, 148.78it/s]\u001b[A\n",
            "RBM fitting: 786it [00:05, 140.95it/s]\u001b[A\n",
            "RBM fitting: 802it [00:05, 145.50it/s]\u001b[A\n",
            "RBM fitting: 818it [00:05, 148.40it/s]\u001b[A\n",
            "RBM fitting: 834it [00:05, 150.99it/s]\u001b[A\n",
            "RBM fitting: 850it [00:05, 152.68it/s]\u001b[A\n",
            "RBM fitting: 866it [00:05, 145.40it/s]\u001b[A\n",
            "RBM fitting: 882it [00:05, 148.25it/s]\u001b[A\n",
            "RBM fitting: 897it [00:06, 148.15it/s]\u001b[A\n",
            "RBM fitting: 912it [00:06, 148.69it/s]\u001b[A\n",
            "RBM fitting: 928it [00:06, 151.53it/s]\u001b[A\n",
            "RBM fitting: 944it [00:06, 143.85it/s]\u001b[A\n",
            "RBM fitting: 960it [00:06, 147.78it/s]\u001b[A\n",
            "RBM fitting: 976it [00:06, 150.98it/s]\u001b[A\n",
            "RBM fitting: 992it [00:06, 152.99it/s]\u001b[A\n",
            "RBM fitting: 1008it [00:06, 154.58it/s]\u001b[A\n",
            "RBM fitting: 1024it [00:06, 147.60it/s]\u001b[A\n",
            "RBM fitting: 1039it [00:06, 146.97it/s]\u001b[A\n",
            "RBM fitting: 1055it [00:07, 150.03it/s]\u001b[A\n",
            "RBM fitting: 1071it [00:07, 149.28it/s]\u001b[A\n",
            "RBM fitting: 1087it [00:07, 151.92it/s]\u001b[A\n",
            "RBM fitting: 1103it [00:07, 145.66it/s]\u001b[A\n",
            "RBM fitting: 1119it [00:07, 148.80it/s]\u001b[A\n",
            "RBM fitting: 1136it [00:07, 152.47it/s]\u001b[A\n",
            "RBM fitting: 1153it [00:07, 155.32it/s]\u001b[A\n",
            "RBM fitting: 1169it [00:07, 155.60it/s]\u001b[A\n",
            "RBM fitting: 1185it [00:07, 146.23it/s]\u001b[A\n",
            "RBM fitting: 1200it [00:08, 145.93it/s]\u001b[A\n",
            "RBM fitting: 1216it [00:08, 149.81it/s]\u001b[A\n",
            "RBM fitting: 1232it [00:08, 152.59it/s]\u001b[A\n",
            "RBM fitting: 1248it [00:08, 153.37it/s]\u001b[A\n",
            "RBM fitting: 1264it [00:08, 145.35it/s]\u001b[A\n",
            "RBM fitting: 1280it [00:08, 149.09it/s]\u001b[A\n",
            "RBM fitting: 1296it [00:08, 151.96it/s]\u001b[A\n",
            "RBM fitting: 1313it [00:08, 154.88it/s]\u001b[A\n",
            "RBM fitting: 1329it [00:08, 155.65it/s]\u001b[A\n",
            "RBM fitting: 1345it [00:09, 144.50it/s]\u001b[A\n",
            "RBM fitting: 1361it [00:09, 148.48it/s]\u001b[A\n",
            "RBM fitting: 1378it [00:09, 152.69it/s]\u001b[A\n",
            "RBM fitting: 1394it [00:09, 154.07it/s]\u001b[A\n",
            "RBM fitting: 1410it [00:09, 154.95it/s]\u001b[A\n",
            "RBM fitting: 1426it [00:09, 147.81it/s]\u001b[A\n",
            "RBM fitting: 1442it [00:09, 150.80it/s]\u001b[A\n",
            "RBM fitting: 1458it [00:09, 153.00it/s]\u001b[A\n",
            "RBM fitting: 1475it [00:09, 156.21it/s]\u001b[A\n",
            "RBM fitting: 1491it [00:09, 156.24it/s]\u001b[A\n",
            "RBM fitting: 1507it [00:10, 145.55it/s]\u001b[A\n",
            "RBM fitting: 1523it [00:10, 149.07it/s]\u001b[A\n",
            "RBM fitting: 1539it [00:10, 151.75it/s]\u001b[A\n",
            "RBM fitting: 1555it [00:10, 153.94it/s]\u001b[A\n",
            "RBM fitting: 1571it [00:10, 154.06it/s]\u001b[A\n",
            "RBM fitting: 1587it [00:10, 146.63it/s]\u001b[A\n",
            "RBM fitting: 1603it [00:10, 148.79it/s]\u001b[A\n",
            "RBM fitting: 1619it [00:10, 151.34it/s]\u001b[A\n",
            "RBM fitting: 1635it [00:10, 152.55it/s]\u001b[A\n",
            "RBM fitting: 1651it [00:11, 154.68it/s]\u001b[A\n",
            "RBM fitting: 1667it [00:11, 145.31it/s]\u001b[A\n",
            "RBM fitting: 1684it [00:11, 150.43it/s]\u001b[A\n",
            "RBM fitting: 1700it [00:11, 151.87it/s]\u001b[A\n",
            "RBM fitting: 1716it [00:11, 152.94it/s]\u001b[A\n",
            "RBM fitting: 1732it [00:11, 153.88it/s]\u001b[A\n",
            "RBM fitting: 1748it [00:11, 148.26it/s]\u001b[A\n",
            "RBM fitting: 1764it [00:11, 150.12it/s]\u001b[A\n",
            "RBM fitting: 1780it [00:11, 152.29it/s]\u001b[A\n",
            "RBM fitting: 1796it [00:11, 152.87it/s]\u001b[A\n",
            "RBM fitting: 1812it [00:12, 151.44it/s]\u001b[A\n",
            "RBM fitting: 1828it [00:12, 145.80it/s]\u001b[A\n",
            "RBM fitting: 1844it [00:12, 148.34it/s]\u001b[A\n",
            "RBM fitting: 1860it [00:12, 150.83it/s]\u001b[A\n",
            "RBM fitting: 1876it [00:12, 152.37it/s]\u001b[A\n",
            "RBM fitting: 1892it [00:12, 154.28it/s]\u001b[A\n",
            "RBM fitting: 1908it [00:12, 153.77it/s]\u001b[A\n",
            "RBM fitting: 1924it [00:12, 145.03it/s]\u001b[A\n",
            "RBM fitting: 1939it [00:12, 142.34it/s]\u001b[A\n",
            "RBM fitting: 1955it [00:13, 146.07it/s]\u001b[A\n",
            "RBM fitting: 1970it [00:13, 146.51it/s]\u001b[A\n",
            "RBM fitting: 1986it [00:13, 150.06it/s]\u001b[A\n",
            "RBM fitting: 2002it [00:13, 144.72it/s]\u001b[A\n",
            "RBM fitting: 2018it [00:13, 148.08it/s]\u001b[A\n",
            "RBM fitting: 2034it [00:13, 151.16it/s]\u001b[A\n",
            "RBM fitting: 2051it [00:13, 154.30it/s]\u001b[A\n",
            "RBM fitting: 2067it [00:13, 155.48it/s]\u001b[A\n",
            "RBM fitting: 2083it [00:13, 149.30it/s]\u001b[A\n",
            "RBM fitting: 2099it [00:14, 151.40it/s]\u001b[A\n",
            "RBM fitting: 2115it [00:14, 150.38it/s]\u001b[A\n",
            "RBM fitting: 2131it [00:14, 151.98it/s]\u001b[A\n",
            "RBM fitting: 2147it [00:14, 152.30it/s]\u001b[A\n",
            "RBM fitting: 2163it [00:14, 149.67it/s]\u001b[A\n",
            "RBM fitting: 2178it [00:14, 139.77it/s]\u001b[A\n",
            "RBM fitting: 2193it [00:14, 140.11it/s]\u001b[A\n",
            "RBM fitting: 2208it [00:14, 140.59it/s]\u001b[A\n",
            "RBM fitting: 2223it [00:14, 142.11it/s]\u001b[A\n",
            "RBM fitting: 2239it [00:14, 145.36it/s]\u001b[A\n",
            "RBM fitting: 2254it [00:15, 140.40it/s]\u001b[A\n",
            "RBM fitting: 2269it [00:15, 141.33it/s]\u001b[A\n",
            "RBM fitting: 2284it [00:15, 143.63it/s]\u001b[A\n",
            "RBM fitting: 2299it [00:15, 144.62it/s]\u001b[A\n",
            "RBM fitting: 2315it [00:15, 146.99it/s]\u001b[A\n",
            "RBM fitting: 2330it [00:15, 141.78it/s]\u001b[A\n",
            "RBM fitting: 2345it [00:15, 143.70it/s]\u001b[A\n",
            "RBM fitting: 2361it [00:15, 146.41it/s]\u001b[A\n",
            "RBM fitting: 2376it [00:15, 146.99it/s]\u001b[A\n",
            "RBM fitting: 2392it [00:16, 148.51it/s]\u001b[A\n",
            "RBM fitting: 2407it [00:16, 146.88it/s]\u001b[A\n",
            "RBM fitting: 2422it [00:16, 138.41it/s]\u001b[A\n",
            "RBM fitting: 2436it [00:16, 137.60it/s]\u001b[A\n",
            "RBM fitting: 2451it [00:16, 140.73it/s]\u001b[A\n",
            "RBM fitting: 2467it [00:16, 144.72it/s]\u001b[A\n",
            "RBM fitting: 2483it [00:16, 148.49it/s]\u001b[A\n",
            "RBM fitting: 2498it [00:16, 141.15it/s]\u001b[A\n",
            "RBM fitting: 2513it [00:16, 142.63it/s]\u001b[A\n",
            "RBM fitting: 2529it [00:17, 145.16it/s]\u001b[A\n",
            "RBM fitting: 2545it [00:17, 147.68it/s]\u001b[A\n",
            "RBM fitting: 2560it [00:17, 146.73it/s]\u001b[A\n",
            "RBM fitting: 2575it [00:17, 140.91it/s]\u001b[A\n",
            "RBM fitting: 2591it [00:17, 144.58it/s]\u001b[A\n",
            "RBM fitting: 2607it [00:17, 147.18it/s]\u001b[A\n",
            "RBM fitting: 2623it [00:17, 148.90it/s]\u001b[A\n",
            "RBM fitting: 2639it [00:17, 150.56it/s]\u001b[A\n",
            "RBM fitting: 2655it [00:17, 143.13it/s]\u001b[A\n",
            "RBM fitting: 2671it [00:17, 146.41it/s]\u001b[A\n",
            "RBM fitting: 2687it [00:18, 149.38it/s]\u001b[A\n",
            "RBM fitting: 2703it [00:18, 148.90it/s]\u001b[A\n",
            "RBM fitting: 2719it [00:18, 151.03it/s]\u001b[A\n",
            "RBM fitting: 2735it [00:18, 143.75it/s]\u001b[A\n",
            "RBM fitting: 2751it [00:18, 146.09it/s]\u001b[A\n",
            "RBM fitting: 2767it [00:18, 148.39it/s]\u001b[A\n",
            "RBM fitting: 2783it [00:18, 149.54it/s]\u001b[A\n",
            "RBM fitting: 2799it [00:18, 151.26it/s]\u001b[A\n",
            "RBM fitting: 2815it [00:18, 146.25it/s]\u001b[A\n",
            "RBM fitting: 2830it [00:19, 146.36it/s]\u001b[A\n",
            "RBM fitting: 2846it [00:19, 148.39it/s]\u001b[A\n",
            "RBM fitting: 2861it [00:19, 146.73it/s]\u001b[A\n",
            "RBM fitting: 2877it [00:19, 149.17it/s]\u001b[A\n",
            "RBM fitting: 2893it [00:19, 149.95it/s]\u001b[A\n",
            "RBM fitting: 2909it [00:19, 145.14it/s]\u001b[A\n",
            "RBM fitting: 2925it [00:19, 147.97it/s]\u001b[A\n",
            "RBM fitting: 2940it [00:19, 148.43it/s]\u001b[A\n",
            "RBM fitting: 2955it [00:19, 148.82it/s]\u001b[A\n",
            "RBM fitting: 2970it [00:19, 148.92it/s]\u001b[A\n",
            "RBM fitting: 2986it [00:20, 149.54it/s]\u001b[A\n",
            "RBM fitting: 3001it [00:20, 141.37it/s]\u001b[A\n",
            "RBM fitting: 3016it [00:20, 143.39it/s]\u001b[A\n",
            "RBM fitting: 3032it [00:20, 145.52it/s]\u001b[A\n",
            "RBM fitting: 3047it [00:20, 146.63it/s]\u001b[A\n",
            "RBM fitting: 3063it [00:20, 148.54it/s]\u001b[A\n",
            "RBM fitting: 3079it [00:20, 149.57it/s]\u001b[A\n",
            "RBM fitting: 3094it [00:20, 143.53it/s]\u001b[A\n",
            "RBM fitting: 3109it [00:20, 145.24it/s]\u001b[A\n",
            "RBM fitting: 3125it [00:21, 147.15it/s]\u001b[A\n",
            "RBM fitting: 3140it [00:21, 147.13it/s]\u001b[A\n",
            "RBM fitting: 3155it [00:21, 145.17it/s]\u001b[A\n",
            "RBM fitting: 3171it [00:21, 147.15it/s]\u001b[A\n",
            "RBM fitting: 3186it [00:21, 142.30it/s]\u001b[A\n",
            "RBM fitting: 3201it [00:21, 144.47it/s]\u001b[A\n",
            "RBM fitting: 3217it [00:21, 147.03it/s]\u001b[A\n",
            "RBM fitting: 3232it [00:21, 147.28it/s]\u001b[A\n",
            "RBM fitting: 3248it [00:21, 148.61it/s]\u001b[A\n",
            "RBM fitting: 3264it [00:21, 150.81it/s]\u001b[A\n",
            "RBM fitting: 3280it [00:22, 146.03it/s]\u001b[A\n",
            "RBM fitting: 3296it [00:22, 148.59it/s]\u001b[A\n",
            "RBM fitting: 3311it [00:22, 147.19it/s]\u001b[A\n",
            "RBM fitting: 3327it [00:22, 149.39it/s]\u001b[A\n",
            "RBM fitting: 3343it [00:22, 150.94it/s]\u001b[A\n",
            "RBM fitting: 3359it [00:22, 152.88it/s]\u001b[A\n",
            "RBM fitting: 3375it [00:22, 145.81it/s]\u001b[A\n",
            "RBM fitting: 3390it [00:22, 143.30it/s]\u001b[A\n",
            "RBM fitting: 3405it [00:22, 142.66it/s]\u001b[A\n",
            "RBM fitting: 3420it [00:23, 141.63it/s]\u001b[A\n",
            "RBM fitting: 3436it [00:23, 144.65it/s]\u001b[A\n",
            "RBM fitting: 3451it [00:23, 136.89it/s]\u001b[A\n",
            "RBM fitting: 3467it [00:23, 141.28it/s]\u001b[A\n",
            "RBM fitting: 3482it [00:23, 142.71it/s]\u001b[A\n",
            "RBM fitting: 3497it [00:23, 144.51it/s]\u001b[A\n",
            "RBM fitting: 3512it [00:23, 145.63it/s]\u001b[A\n",
            "RBM fitting: 3527it [00:23, 146.59it/s]\u001b[A\n",
            "RBM fitting: 3542it [00:23, 140.18it/s]\u001b[A\n",
            "RBM fitting: 3557it [00:24, 142.70it/s]\u001b[A\n",
            "RBM fitting: 3572it [00:24, 144.81it/s]\u001b[A\n",
            "RBM fitting: 3588it [00:24, 146.93it/s]\u001b[A\n",
            "RBM fitting: 3603it [00:24, 145.17it/s]\u001b[A\n",
            "RBM fitting: 3619it [00:24, 147.37it/s]\u001b[A\n",
            "RBM fitting: 3634it [00:24, 141.65it/s]\u001b[A\n",
            "RBM fitting: 3650it [00:24, 145.26it/s]\u001b[A\n",
            "RBM fitting: 3666it [00:24, 147.28it/s]\u001b[A\n",
            "RBM fitting: 3681it [00:24, 142.37it/s]\u001b[A\n",
            "RBM fitting: 3696it [00:24, 144.44it/s]\u001b[A\n",
            "RBM fitting: 3712it [00:25, 147.30it/s]\u001b[A\n",
            "RBM fitting: 3727it [00:25, 141.49it/s]\u001b[A\n",
            "RBM fitting: 3742it [00:25, 141.00it/s]\u001b[A\n",
            "RBM fitting: 3758it [00:25, 145.27it/s]\u001b[A\n",
            "RBM fitting: 3774it [00:25, 147.72it/s]\u001b[A\n",
            "RBM fitting: 3790it [00:25, 150.26it/s]\u001b[A\n",
            "RBM fitting: 3806it [00:25, 146.31it/s]\u001b[A\n",
            "RBM fitting: 3822it [00:25, 150.05it/s]\u001b[A\n",
            "RBM fitting: 3838it [00:25, 152.19it/s]\u001b[A\n",
            "RBM fitting: 3854it [00:26, 153.36it/s]\u001b[A\n",
            "RBM fitting: 3870it [00:26, 154.32it/s]\u001b[A\n",
            "RBM fitting: 3886it [00:26, 154.41it/s]\u001b[A\n",
            "RBM fitting: 3902it [00:26, 144.10it/s]\u001b[A\n",
            "RBM fitting: 3917it [00:26, 144.52it/s]\u001b[A\n",
            "RBM fitting: 3932it [00:26, 145.12it/s]\u001b[A\n",
            "RBM fitting: 3948it [00:26, 147.66it/s]\u001b[A\n",
            "RBM fitting: 3963it [00:26, 148.01it/s]\u001b[A\n",
            "RBM fitting: 3978it [00:26, 142.15it/s]\u001b[A\n",
            "RBM fitting: 3994it [00:27, 146.31it/s]\u001b[A\n",
            "RBM fitting: 4010it [00:27, 149.34it/s]\u001b[A\n",
            "RBM fitting: 4026it [00:27, 152.00it/s]\u001b[A\n",
            "RBM fitting: 4042it [00:27, 153.53it/s]\u001b[A\n",
            "RBM fitting: 4058it [00:27, 144.83it/s]\u001b[A\n",
            "RBM fitting: 4074it [00:27, 148.35it/s]\u001b[A\n",
            "RBM fitting: 4090it [00:27, 151.01it/s]\u001b[A\n",
            "RBM fitting: 4106it [00:27, 152.60it/s]\u001b[A\n",
            "RBM fitting: 4122it [00:27, 153.72it/s]\u001b[A\n",
            "RBM fitting: 4138it [00:27, 148.25it/s]\u001b[A\n",
            "RBM fitting: 4154it [00:28, 150.80it/s]\u001b[A\n",
            "RBM fitting: 4170it [00:28, 153.07it/s]\u001b[A\n",
            "RBM fitting: 4186it [00:28, 154.65it/s]\u001b[A\n",
            "RBM fitting: 4202it [00:28, 152.68it/s]\u001b[A\n",
            "RBM fitting: 4218it [00:28, 154.36it/s]\u001b[A\n",
            "RBM fitting: 4234it [00:28, 149.91it/s]\u001b[A\n",
            "RBM fitting: 4250it [00:28, 151.30it/s]\u001b[A\n",
            "RBM fitting: 4267it [00:28, 154.14it/s]\u001b[A\n",
            "RBM fitting: 4284it [00:28, 156.51it/s]\u001b[A\n",
            "RBM fitting: 4300it [00:28, 156.91it/s]\u001b[A\n",
            "RBM fitting: 4316it [00:29, 157.08it/s]\u001b[A\n",
            "RBM fitting: 4332it [00:29, 149.00it/s]\u001b[A\n",
            "RBM fitting: 4348it [00:29, 150.69it/s]\u001b[A\n",
            "RBM fitting: 4364it [00:29, 148.10it/s]\u001b[A\n",
            "RBM fitting: 4379it [00:29, 147.18it/s]\u001b[A\n",
            "RBM fitting: 4394it [00:29, 142.94it/s]\u001b[A\n",
            "RBM fitting: 4410it [00:29, 146.35it/s]\u001b[A\n",
            "RBM fitting: 4425it [00:29, 142.90it/s]\u001b[A\n",
            "RBM fitting: 4441it [00:29, 146.91it/s]\u001b[A\n",
            "RBM fitting: 4457it [00:30, 149.98it/s]\u001b[A\n",
            "RBM fitting: 4473it [00:30, 152.56it/s]\u001b[A\n",
            "RBM fitting: 4489it [00:30, 154.20it/s]\u001b[A\n",
            "RBM fitting: 4505it [00:30, 154.35it/s]\u001b[A\n",
            "RBM fitting: 4521it [00:30, 146.33it/s]\u001b[A\n",
            "RBM fitting: 4537it [00:30, 148.26it/s]\u001b[A\n",
            "RBM fitting: 4553it [00:30, 150.46it/s]\u001b[A\n",
            "RBM fitting: 4569it [00:30, 152.69it/s]\u001b[A\n",
            "RBM fitting: 4585it [00:30, 153.64it/s]\u001b[A\n",
            "RBM fitting: 4601it [00:31, 154.02it/s]\u001b[A\n",
            "RBM fitting: 4617it [00:31, 147.98it/s]\u001b[A\n",
            "RBM fitting: 4633it [00:31, 150.24it/s]\u001b[A\n",
            "RBM fitting: 4649it [00:31, 151.01it/s]\u001b[A\n",
            "RBM fitting: 4665it [00:31, 149.27it/s]\u001b[A\n",
            "RBM fitting: 4681it [00:31, 150.38it/s]\u001b[A\n",
            "RBM fitting: 4697it [00:31, 149.54it/s]\u001b[A\n",
            "RBM fitting: 4712it [00:31, 143.25it/s]\u001b[A\n",
            "RBM fitting: 4728it [00:31, 147.71it/s]\u001b[A\n",
            "RBM fitting: 4744it [00:31, 149.94it/s]\u001b[A\n",
            "RBM fitting: 4760it [00:32, 152.05it/s]\u001b[A\n",
            "RBM fitting: 4776it [00:32, 153.64it/s]\u001b[A\n",
            "RBM fitting: 4792it [00:32, 154.50it/s]\u001b[A\n",
            "RBM fitting: 4808it [00:32, 148.85it/s]\u001b[A\n",
            "RBM fitting: 4823it [00:32, 147.70it/s]\u001b[A\n",
            "RBM fitting: 4839it [00:32, 149.60it/s]\u001b[A\n",
            "RBM fitting: 4855it [00:32, 151.87it/s]\u001b[A\n",
            "RBM fitting: 4871it [00:32, 153.19it/s]\u001b[A\n",
            "RBM fitting: 4887it [00:32, 153.93it/s]\u001b[A\n",
            "RBM fitting: 4903it [00:33, 148.37it/s]\u001b[A\n",
            "RBM fitting: 4919it [00:33, 150.88it/s]\u001b[A\n",
            "RBM fitting: 4935it [00:33, 152.63it/s]\u001b[A\n",
            "RBM fitting: 4951it [00:33, 153.32it/s]\u001b[A\n",
            "RBM fitting: 4967it [00:33, 152.33it/s]\u001b[A\n",
            "RBM fitting: 4983it [00:33, 151.56it/s]\u001b[A\n",
            "RBM fitting: 4999it [00:33, 145.52it/s]\u001b[A\n",
            "RBM fitting: 5015it [00:33, 148.04it/s]\u001b[A\n",
            "RBM fitting: 5032it [00:33, 151.82it/s]\u001b[A\n",
            "RBM fitting: 5048it [00:33, 153.53it/s]\u001b[A\n",
            "RBM fitting: 5064it [00:34, 154.90it/s]\u001b[A\n",
            "RBM fitting: 5081it [00:34, 157.03it/s]\u001b[A\n",
            "RBM fitting: 5097it [00:34, 151.27it/s]\u001b[A\n",
            "RBM fitting: 5114it [00:34, 154.38it/s]\u001b[A\n",
            "RBM fitting: 5130it [00:34, 153.43it/s]\u001b[A\n",
            "RBM fitting: 5146it [00:34, 154.93it/s]\u001b[A\n",
            "RBM fitting: 5162it [00:34, 155.87it/s]\u001b[A\n",
            "RBM fitting: 5178it [00:34, 157.01it/s]\u001b[A\n",
            "RBM fitting: 5194it [00:34, 149.96it/s]\u001b[A\n",
            "RBM fitting: 5210it [00:35, 151.81it/s]\u001b[A\n",
            "RBM fitting: 5226it [00:35, 153.97it/s]\u001b[A\n",
            "RBM fitting: 5242it [00:35, 154.88it/s]\u001b[A\n",
            "RBM fitting: 5258it [00:35, 154.39it/s]\u001b[A\n",
            "RBM fitting: 5274it [00:35, 155.77it/s]\u001b[A\n",
            "RBM fitting: 5290it [00:35, 146.69it/s]\u001b[A\n",
            "RBM fitting: 5305it [00:35, 143.89it/s]\u001b[A\n",
            "RBM fitting: 5320it [00:35, 144.82it/s]\u001b[A\n",
            "RBM fitting: 5336it [00:35, 148.55it/s]\u001b[A\n",
            "RBM fitting: 5352it [00:35, 151.32it/s]\u001b[A\n",
            "RBM fitting: 5369it [00:36, 154.86it/s]\u001b[A\n",
            "RBM fitting: 5385it [00:36, 150.58it/s]\u001b[A\n",
            "RBM fitting: 5401it [00:36, 152.83it/s]\u001b[A\n",
            "RBM fitting: 5417it [00:36, 153.97it/s]\u001b[A\n",
            "RBM fitting: 5433it [00:36, 151.35it/s]\u001b[A\n",
            "RBM fitting: 5449it [00:36, 152.52it/s]\u001b[A\n",
            "RBM fitting: 5465it [00:36, 153.82it/s]\u001b[A\n",
            "RBM fitting: 5481it [00:36, 148.62it/s]\u001b[A\n",
            "RBM fitting: 5497it [00:36, 151.00it/s]\u001b[A\n",
            "RBM fitting: 5513it [00:37, 153.15it/s]\u001b[A\n",
            "RBM fitting: 5529it [00:37, 154.46it/s]\u001b[A\n",
            "RBM fitting: 5545it [00:37, 154.80it/s]\u001b[A\n",
            "RBM fitting: 5561it [00:37, 155.01it/s]\u001b[A\n",
            "RBM fitting: 5577it [00:37, 147.88it/s]\u001b[A\n",
            "RBM fitting: 5592it [00:37, 145.42it/s]\u001b[A\n",
            "RBM fitting: 5608it [00:37, 148.58it/s]\u001b[A\n",
            "RBM fitting: 5624it [00:37, 150.69it/s]\u001b[A\n",
            "RBM fitting: 5641it [00:37, 153.72it/s]\u001b[A\n",
            "RBM fitting: 5657it [00:37, 154.90it/s]\u001b[A\n",
            "RBM fitting: 5673it [00:38, 149.24it/s]\u001b[A\n",
            "RBM fitting: 5689it [00:38, 151.76it/s]\u001b[A\n",
            "RBM fitting: 5705it [00:38, 153.30it/s]\u001b[A\n",
            "RBM fitting: 5721it [00:38, 154.18it/s]\u001b[A\n",
            "RBM fitting: 5737it [00:38, 153.99it/s]\u001b[A\n",
            "RBM fitting: 5753it [00:38, 152.19it/s]\u001b[A\n",
            "RBM fitting: 5769it [00:38, 147.05it/s]\u001b[A\n",
            "RBM fitting: 5785it [00:38, 149.35it/s]\u001b[A\n",
            "RBM fitting: 5801it [00:38, 151.03it/s]\u001b[A\n",
            "RBM fitting: 5817it [00:39, 153.44it/s]\u001b[A\n",
            "RBM fitting: 5833it [00:39, 154.80it/s]\u001b[A\n",
            "RBM fitting: 5849it [00:39, 154.35it/s]\u001b[A\n",
            "RBM fitting: 5865it [00:39, 147.95it/s]\u001b[A\n",
            "RBM fitting: 5881it [00:39, 150.07it/s]\u001b[A\n",
            "RBM fitting: 5897it [00:39, 148.95it/s]\u001b[A\n",
            "RBM fitting: 5913it [00:39, 151.28it/s]\u001b[A\n",
            "RBM fitting: 5929it [00:39, 153.00it/s]\u001b[A\n",
            "RBM fitting: 5945it [00:39, 154.41it/s]\u001b[A\n",
            "RBM fitting: 5961it [00:39, 147.45it/s]\u001b[A\n",
            "RBM fitting: 5977it [00:40, 149.73it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:40, 149.03it/s]\n",
            "Epoch:19 ,avg_cost = 0.3426326811313629 ,std_cost = 0.03421470522880554 ,avg_grad = 10072.2490234375 ,std_grad = 541.135986328125\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 15it [00:00, 144.72it/s]\u001b[A\n",
            "RBM fitting: 31it [00:00, 148.33it/s]\u001b[A\n",
            "RBM fitting: 42it [00:00, 133.53it/s]\u001b[A\n",
            "RBM fitting: 57it [00:00, 137.65it/s]\u001b[A\n",
            "RBM fitting: 73it [00:00, 143.22it/s]\u001b[A\n",
            "RBM fitting: 89it [00:00, 147.87it/s]\u001b[A\n",
            "RBM fitting: 105it [00:00, 150.47it/s]\u001b[A\n",
            "RBM fitting: 120it [00:00, 144.97it/s]\u001b[A\n",
            "RBM fitting: 136it [00:00, 148.41it/s]\u001b[A\n",
            "RBM fitting: 152it [00:01, 149.96it/s]\u001b[A\n",
            "RBM fitting: 167it [00:01, 149.76it/s]\u001b[A\n",
            "RBM fitting: 183it [00:01, 150.90it/s]\u001b[A\n",
            "RBM fitting: 198it [00:01, 149.07it/s]\u001b[A\n",
            "RBM fitting: 213it [00:01, 142.01it/s]\u001b[A\n",
            "RBM fitting: 229it [00:01, 145.42it/s]\u001b[A\n",
            "RBM fitting: 245it [00:01, 147.76it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 149.94it/s]\u001b[A\n",
            "RBM fitting: 277it [00:01, 151.60it/s]\u001b[A\n",
            "RBM fitting: 293it [00:01, 146.18it/s]\u001b[A\n",
            "RBM fitting: 308it [00:02, 147.13it/s]\u001b[A\n",
            "RBM fitting: 324it [00:02, 148.43it/s]\u001b[A\n",
            "RBM fitting: 339it [00:02, 147.45it/s]\u001b[A\n",
            "RBM fitting: 354it [00:02, 145.32it/s]\u001b[A\n",
            "RBM fitting: 370it [00:02, 147.57it/s]\u001b[A\n",
            "RBM fitting: 385it [00:02, 139.18it/s]\u001b[A\n",
            "RBM fitting: 400it [00:02, 141.57it/s]\u001b[A\n",
            "RBM fitting: 415it [00:02, 142.91it/s]\u001b[A\n",
            "RBM fitting: 430it [00:02, 144.50it/s]\u001b[A\n",
            "RBM fitting: 445it [00:03, 144.27it/s]\u001b[A\n",
            "RBM fitting: 460it [00:03, 136.31it/s]\u001b[A\n",
            "RBM fitting: 476it [00:03, 140.60it/s]\u001b[A\n",
            "RBM fitting: 491it [00:03, 141.95it/s]\u001b[A\n",
            "RBM fitting: 507it [00:03, 146.80it/s]\u001b[A\n",
            "RBM fitting: 524it [00:03, 151.59it/s]\u001b[A\n",
            "RBM fitting: 540it [00:03, 152.33it/s]\u001b[A\n",
            "RBM fitting: 556it [00:03, 147.17it/s]\u001b[A\n",
            "RBM fitting: 572it [00:03, 150.52it/s]\u001b[A\n",
            "RBM fitting: 588it [00:03, 152.95it/s]\u001b[A\n",
            "RBM fitting: 604it [00:04, 153.99it/s]\u001b[A\n",
            "RBM fitting: 620it [00:04, 153.99it/s]\u001b[A\n",
            "RBM fitting: 636it [00:04, 154.12it/s]\u001b[A\n",
            "RBM fitting: 652it [00:04, 146.17it/s]\u001b[A\n",
            "RBM fitting: 668it [00:04, 148.94it/s]\u001b[A\n",
            "RBM fitting: 684it [00:04, 150.93it/s]\u001b[A\n",
            "RBM fitting: 700it [00:04, 152.62it/s]\u001b[A\n",
            "RBM fitting: 716it [00:04, 154.09it/s]\u001b[A\n",
            "RBM fitting: 732it [00:04, 155.13it/s]\u001b[A\n",
            "RBM fitting: 748it [00:05, 148.05it/s]\u001b[A\n",
            "RBM fitting: 764it [00:05, 150.86it/s]\u001b[A\n",
            "RBM fitting: 780it [00:05, 151.65it/s]\u001b[A\n",
            "RBM fitting: 796it [00:05, 149.76it/s]\u001b[A\n",
            "RBM fitting: 812it [00:05, 151.96it/s]\u001b[A\n",
            "RBM fitting: 829it [00:05, 154.84it/s]\u001b[A\n",
            "RBM fitting: 845it [00:05, 148.41it/s]\u001b[A\n",
            "RBM fitting: 862it [00:05, 152.17it/s]\u001b[A\n",
            "RBM fitting: 879it [00:05, 155.37it/s]\u001b[A\n",
            "RBM fitting: 896it [00:06, 157.59it/s]\u001b[A\n",
            "RBM fitting: 913it [00:06, 158.87it/s]\u001b[A\n",
            "RBM fitting: 929it [00:06, 157.11it/s]\u001b[A\n",
            "RBM fitting: 945it [00:06, 149.91it/s]\u001b[A\n",
            "RBM fitting: 961it [00:06, 150.29it/s]\u001b[A\n",
            "RBM fitting: 977it [00:06, 152.68it/s]\u001b[A\n",
            "RBM fitting: 993it [00:06, 153.52it/s]\u001b[A\n",
            "RBM fitting: 1009it [00:06, 153.64it/s]\u001b[A\n",
            "RBM fitting: 1025it [00:06, 155.38it/s]\u001b[A\n",
            "RBM fitting: 1041it [00:06, 150.27it/s]\u001b[A\n",
            "RBM fitting: 1057it [00:07, 152.41it/s]\u001b[A\n",
            "RBM fitting: 1074it [00:07, 155.02it/s]\u001b[A\n",
            "RBM fitting: 1090it [00:07, 153.72it/s]\u001b[A\n",
            "RBM fitting: 1106it [00:07, 152.64it/s]\u001b[A\n",
            "RBM fitting: 1122it [00:07, 150.99it/s]\u001b[A\n",
            "RBM fitting: 1138it [00:07, 146.35it/s]\u001b[A\n",
            "RBM fitting: 1154it [00:07, 148.88it/s]\u001b[A\n",
            "RBM fitting: 1170it [00:07, 151.47it/s]\u001b[A\n",
            "RBM fitting: 1186it [00:07, 153.61it/s]\u001b[A\n",
            "RBM fitting: 1202it [00:08, 154.81it/s]\u001b[A\n",
            "RBM fitting: 1219it [00:08, 156.75it/s]\u001b[A\n",
            "RBM fitting: 1235it [00:08, 150.05it/s]\u001b[A\n",
            "RBM fitting: 1251it [00:08, 150.97it/s]\u001b[A\n",
            "RBM fitting: 1267it [00:08, 150.02it/s]\u001b[A\n",
            "RBM fitting: 1283it [00:08, 152.48it/s]\u001b[A\n",
            "RBM fitting: 1299it [00:08, 154.12it/s]\u001b[A\n",
            "RBM fitting: 1315it [00:08, 154.64it/s]\u001b[A\n",
            "RBM fitting: 1331it [00:08, 146.69it/s]\u001b[A\n",
            "RBM fitting: 1347it [00:08, 149.64it/s]\u001b[A\n",
            "RBM fitting: 1363it [00:09, 151.90it/s]\u001b[A\n",
            "RBM fitting: 1380it [00:09, 154.78it/s]\u001b[A\n",
            "RBM fitting: 1396it [00:09, 154.89it/s]\u001b[A\n",
            "RBM fitting: 1412it [00:09, 151.75it/s]\u001b[A\n",
            "RBM fitting: 1428it [00:09, 140.99it/s]\u001b[A\n",
            "RBM fitting: 1444it [00:09, 145.04it/s]\u001b[A\n",
            "RBM fitting: 1460it [00:09, 149.06it/s]\u001b[A\n",
            "RBM fitting: 1476it [00:09, 151.07it/s]\u001b[A\n",
            "RBM fitting: 1493it [00:09, 153.95it/s]\u001b[A\n",
            "RBM fitting: 1509it [00:10, 154.67it/s]\u001b[A\n",
            "RBM fitting: 1525it [00:10, 147.69it/s]\u001b[A\n",
            "RBM fitting: 1541it [00:10, 149.45it/s]\u001b[A\n",
            "RBM fitting: 1557it [00:10, 150.70it/s]\u001b[A\n",
            "RBM fitting: 1573it [00:10, 149.53it/s]\u001b[A\n",
            "RBM fitting: 1589it [00:10, 151.71it/s]\u001b[A\n",
            "RBM fitting: 1605it [00:10, 153.23it/s]\u001b[A\n",
            "RBM fitting: 1621it [00:10, 148.66it/s]\u001b[A\n",
            "RBM fitting: 1637it [00:10, 151.79it/s]\u001b[A\n",
            "RBM fitting: 1653it [00:11, 153.05it/s]\u001b[A\n",
            "RBM fitting: 1669it [00:11, 153.87it/s]\u001b[A\n",
            "RBM fitting: 1685it [00:11, 154.26it/s]\u001b[A\n",
            "RBM fitting: 1702it [00:11, 156.72it/s]\u001b[A\n",
            "RBM fitting: 1718it [00:11, 150.08it/s]\u001b[A\n",
            "RBM fitting: 1734it [00:11, 149.03it/s]\u001b[A\n",
            "RBM fitting: 1750it [00:11, 150.15it/s]\u001b[A\n",
            "RBM fitting: 1766it [00:11, 151.66it/s]\u001b[A\n",
            "RBM fitting: 1782it [00:11, 151.41it/s]\u001b[A\n",
            "RBM fitting: 1798it [00:11, 152.88it/s]\u001b[A\n",
            "RBM fitting: 1814it [00:12, 146.73it/s]\u001b[A\n",
            "RBM fitting: 1829it [00:12, 143.79it/s]\u001b[A\n",
            "RBM fitting: 1844it [00:12, 145.28it/s]\u001b[A\n",
            "RBM fitting: 1860it [00:12, 148.40it/s]\u001b[A\n",
            "RBM fitting: 1875it [00:12, 147.00it/s]\u001b[A\n",
            "RBM fitting: 1891it [00:12, 150.09it/s]\u001b[A\n",
            "RBM fitting: 1907it [00:12, 145.45it/s]\u001b[A\n",
            "RBM fitting: 1923it [00:12, 148.98it/s]\u001b[A\n",
            "RBM fitting: 1939it [00:12, 151.96it/s]\u001b[A\n",
            "RBM fitting: 1955it [00:13, 153.33it/s]\u001b[A\n",
            "RBM fitting: 1971it [00:13, 154.94it/s]\u001b[A\n",
            "RBM fitting: 1987it [00:13, 156.40it/s]\u001b[A\n",
            "RBM fitting: 2003it [00:13, 150.37it/s]\u001b[A\n",
            "RBM fitting: 2019it [00:13, 152.62it/s]\u001b[A\n",
            "RBM fitting: 2035it [00:13, 151.94it/s]\u001b[A\n",
            "RBM fitting: 2051it [00:13, 152.65it/s]\u001b[A\n",
            "RBM fitting: 2067it [00:13, 154.44it/s]\u001b[A\n",
            "RBM fitting: 2083it [00:13, 155.80it/s]\u001b[A\n",
            "RBM fitting: 2099it [00:13, 149.46it/s]\u001b[A\n",
            "RBM fitting: 2115it [00:14, 151.73it/s]\u001b[A\n",
            "RBM fitting: 2131it [00:14, 153.53it/s]\u001b[A\n",
            "RBM fitting: 2147it [00:14, 153.88it/s]\u001b[A\n",
            "RBM fitting: 2163it [00:14, 154.58it/s]\u001b[A\n",
            "RBM fitting: 2179it [00:14, 155.52it/s]\u001b[A\n",
            "RBM fitting: 2195it [00:14, 145.61it/s]\u001b[A\n",
            "RBM fitting: 2211it [00:14, 148.00it/s]\u001b[A\n",
            "RBM fitting: 2226it [00:14, 148.51it/s]\u001b[A\n",
            "RBM fitting: 2242it [00:14, 151.12it/s]\u001b[A\n",
            "RBM fitting: 2258it [00:15, 152.65it/s]\u001b[A\n",
            "RBM fitting: 2274it [00:15, 154.34it/s]\u001b[A\n",
            "RBM fitting: 2290it [00:15, 148.25it/s]\u001b[A\n",
            "RBM fitting: 2306it [00:15, 150.66it/s]\u001b[A\n",
            "RBM fitting: 2322it [00:15, 152.60it/s]\u001b[A\n",
            "RBM fitting: 2338it [00:15, 151.83it/s]\u001b[A\n",
            "RBM fitting: 2354it [00:15, 152.87it/s]\u001b[A\n",
            "RBM fitting: 2370it [00:15, 154.02it/s]\u001b[A\n",
            "RBM fitting: 2386it [00:15, 148.34it/s]\u001b[A\n",
            "RBM fitting: 2402it [00:15, 149.72it/s]\u001b[A\n",
            "RBM fitting: 2418it [00:16, 150.40it/s]\u001b[A\n",
            "RBM fitting: 2434it [00:16, 152.84it/s]\u001b[A\n",
            "RBM fitting: 2450it [00:16, 153.75it/s]\u001b[A\n",
            "RBM fitting: 2466it [00:16, 154.10it/s]\u001b[A\n",
            "RBM fitting: 2482it [00:16, 148.27it/s]\u001b[A\n",
            "RBM fitting: 2497it [00:16, 148.09it/s]\u001b[A\n",
            "RBM fitting: 2513it [00:16, 150.63it/s]\u001b[A\n",
            "RBM fitting: 2529it [00:16, 153.24it/s]\u001b[A\n",
            "RBM fitting: 2546it [00:16, 155.65it/s]\u001b[A\n",
            "RBM fitting: 2562it [00:17, 156.81it/s]\u001b[A\n",
            "RBM fitting: 2578it [00:17, 150.14it/s]\u001b[A\n",
            "RBM fitting: 2594it [00:17, 150.41it/s]\u001b[A\n",
            "RBM fitting: 2610it [00:17, 152.45it/s]\u001b[A\n",
            "RBM fitting: 2626it [00:17, 153.90it/s]\u001b[A\n",
            "RBM fitting: 2642it [00:17, 154.79it/s]\u001b[A\n",
            "RBM fitting: 2658it [00:17, 152.37it/s]\u001b[A\n",
            "RBM fitting: 2674it [00:17, 147.25it/s]\u001b[A\n",
            "RBM fitting: 2690it [00:17, 149.68it/s]\u001b[A\n",
            "RBM fitting: 2706it [00:17, 150.67it/s]\u001b[A\n",
            "RBM fitting: 2722it [00:18, 151.22it/s]\u001b[A\n",
            "RBM fitting: 2738it [00:18, 152.88it/s]\u001b[A\n",
            "RBM fitting: 2754it [00:18, 153.38it/s]\u001b[A\n",
            "RBM fitting: 2770it [00:18, 147.98it/s]\u001b[A\n",
            "RBM fitting: 2786it [00:18, 150.55it/s]\u001b[A\n",
            "RBM fitting: 2802it [00:18, 150.15it/s]\u001b[A\n",
            "RBM fitting: 2818it [00:18, 151.96it/s]\u001b[A\n",
            "RBM fitting: 2835it [00:18, 154.88it/s]\u001b[A\n",
            "RBM fitting: 2852it [00:18, 157.13it/s]\u001b[A\n",
            "RBM fitting: 2868it [00:19, 150.89it/s]\u001b[A\n",
            "RBM fitting: 2885it [00:19, 154.07it/s]\u001b[A\n",
            "RBM fitting: 2901it [00:19, 153.96it/s]\u001b[A\n",
            "RBM fitting: 2917it [00:19, 154.97it/s]\u001b[A\n",
            "RBM fitting: 2933it [00:19, 155.58it/s]\u001b[A\n",
            "RBM fitting: 2949it [00:19, 154.42it/s]\u001b[A\n",
            "RBM fitting: 2965it [00:19, 143.54it/s]\u001b[A\n",
            "RBM fitting: 2981it [00:19, 146.40it/s]\u001b[A\n",
            "RBM fitting: 2997it [00:19, 149.66it/s]\u001b[A\n",
            "RBM fitting: 3013it [00:19, 151.86it/s]\u001b[A\n",
            "RBM fitting: 3029it [00:20, 152.54it/s]\u001b[A\n",
            "RBM fitting: 3045it [00:20, 154.22it/s]\u001b[A\n",
            "RBM fitting: 3061it [00:20, 148.02it/s]\u001b[A\n",
            "RBM fitting: 3077it [00:20, 151.05it/s]\u001b[A\n",
            "RBM fitting: 3093it [00:20, 152.15it/s]\u001b[A\n",
            "RBM fitting: 3109it [00:20, 152.43it/s]\u001b[A\n",
            "RBM fitting: 3125it [00:20, 152.06it/s]\u001b[A\n",
            "RBM fitting: 3141it [00:20, 153.67it/s]\u001b[A\n",
            "RBM fitting: 3157it [00:20, 148.85it/s]\u001b[A\n",
            "RBM fitting: 3173it [00:21, 150.57it/s]\u001b[A\n",
            "RBM fitting: 3189it [00:21, 152.25it/s]\u001b[A\n",
            "RBM fitting: 3205it [00:21, 152.70it/s]\u001b[A\n",
            "RBM fitting: 3221it [00:21, 153.07it/s]\u001b[A\n",
            "RBM fitting: 3237it [00:21, 154.89it/s]\u001b[A\n",
            "RBM fitting: 3253it [00:21, 149.45it/s]\u001b[A\n",
            "RBM fitting: 3268it [00:21, 148.71it/s]\u001b[A\n",
            "RBM fitting: 3284it [00:21, 150.01it/s]\u001b[A\n",
            "RBM fitting: 3300it [00:21, 152.12it/s]\u001b[A\n",
            "RBM fitting: 3316it [00:21, 153.87it/s]\u001b[A\n",
            "RBM fitting: 3332it [00:22, 153.95it/s]\u001b[A\n",
            "RBM fitting: 3348it [00:22, 147.39it/s]\u001b[A\n",
            "RBM fitting: 3364it [00:22, 149.29it/s]\u001b[A\n",
            "RBM fitting: 3380it [00:22, 151.93it/s]\u001b[A\n",
            "RBM fitting: 3396it [00:22, 153.60it/s]\u001b[A\n",
            "RBM fitting: 3412it [00:22, 153.80it/s]\u001b[A\n",
            "RBM fitting: 3428it [00:22, 152.31it/s]\u001b[A\n",
            "RBM fitting: 3444it [00:22, 147.42it/s]\u001b[A\n",
            "RBM fitting: 3460it [00:22, 150.23it/s]\u001b[A\n",
            "RBM fitting: 3476it [00:23, 152.27it/s]\u001b[A\n",
            "RBM fitting: 3493it [00:23, 154.96it/s]\u001b[A\n",
            "RBM fitting: 3509it [00:23, 155.44it/s]\u001b[A\n",
            "RBM fitting: 3525it [00:23, 156.56it/s]\u001b[A\n",
            "RBM fitting: 3541it [00:23, 150.08it/s]\u001b[A\n",
            "RBM fitting: 3557it [00:23, 151.98it/s]\u001b[A\n",
            "RBM fitting: 3573it [00:23, 151.98it/s]\u001b[A\n",
            "RBM fitting: 3589it [00:23, 152.19it/s]\u001b[A\n",
            "RBM fitting: 3605it [00:23, 153.33it/s]\u001b[A\n",
            "RBM fitting: 3622it [00:23, 155.67it/s]\u001b[A\n",
            "RBM fitting: 3638it [00:24, 149.63it/s]\u001b[A\n",
            "RBM fitting: 3654it [00:24, 152.52it/s]\u001b[A\n",
            "RBM fitting: 3670it [00:24, 153.62it/s]\u001b[A\n",
            "RBM fitting: 3686it [00:24, 155.40it/s]\u001b[A\n",
            "RBM fitting: 3703it [00:24, 157.27it/s]\u001b[A\n",
            "RBM fitting: 3719it [00:24, 157.48it/s]\u001b[A\n",
            "RBM fitting: 3735it [00:24, 147.78it/s]\u001b[A\n",
            "RBM fitting: 3751it [00:24, 151.19it/s]\u001b[A\n",
            "RBM fitting: 3767it [00:24, 153.53it/s]\u001b[A\n",
            "RBM fitting: 3783it [00:25, 154.27it/s]\u001b[A\n",
            "RBM fitting: 3799it [00:25, 154.73it/s]\u001b[A\n",
            "RBM fitting: 3815it [00:25, 154.29it/s]\u001b[A\n",
            "RBM fitting: 3831it [00:25, 148.80it/s]\u001b[A\n",
            "RBM fitting: 3847it [00:25, 151.44it/s]\u001b[A\n",
            "RBM fitting: 3863it [00:25, 152.75it/s]\u001b[A\n",
            "RBM fitting: 3879it [00:25, 153.02it/s]\u001b[A\n",
            "RBM fitting: 3895it [00:25, 146.71it/s]\u001b[A\n",
            "RBM fitting: 3911it [00:25, 150.18it/s]\u001b[A\n",
            "RBM fitting: 3927it [00:26, 145.86it/s]\u001b[A\n",
            "RBM fitting: 3943it [00:26, 149.03it/s]\u001b[A\n",
            "RBM fitting: 3958it [00:26, 148.38it/s]\u001b[A\n",
            "RBM fitting: 3974it [00:26, 150.36it/s]\u001b[A\n",
            "RBM fitting: 3990it [00:26, 152.72it/s]\u001b[A\n",
            "RBM fitting: 4006it [00:26, 154.43it/s]\u001b[A\n",
            "RBM fitting: 4022it [00:26, 148.45it/s]\u001b[A\n",
            "RBM fitting: 4037it [00:26, 148.85it/s]\u001b[A\n",
            "RBM fitting: 4053it [00:26, 150.35it/s]\u001b[A\n",
            "RBM fitting: 4069it [00:26, 152.85it/s]\u001b[A\n",
            "RBM fitting: 4085it [00:27, 154.09it/s]\u001b[A\n",
            "RBM fitting: 4101it [00:27, 155.34it/s]\u001b[A\n",
            "RBM fitting: 4117it [00:27, 149.20it/s]\u001b[A\n",
            "RBM fitting: 4133it [00:27, 151.82it/s]\u001b[A\n",
            "RBM fitting: 4149it [00:27, 153.84it/s]\u001b[A\n",
            "RBM fitting: 4165it [00:27, 153.72it/s]\u001b[A\n",
            "RBM fitting: 4181it [00:27, 154.80it/s]\u001b[A\n",
            "RBM fitting: 4197it [00:27, 148.24it/s]\u001b[A\n",
            "RBM fitting: 4212it [00:27, 143.57it/s]\u001b[A\n",
            "RBM fitting: 4228it [00:28, 147.07it/s]\u001b[A\n",
            "RBM fitting: 4244it [00:28, 149.15it/s]\u001b[A\n",
            "RBM fitting: 4260it [00:28, 151.90it/s]\u001b[A\n",
            "RBM fitting: 4276it [00:28, 152.84it/s]\u001b[A\n",
            "RBM fitting: 4292it [00:28, 154.47it/s]\u001b[A\n",
            "RBM fitting: 4308it [00:28, 148.66it/s]\u001b[A\n",
            "RBM fitting: 4324it [00:28, 151.15it/s]\u001b[A\n",
            "RBM fitting: 4340it [00:28, 153.23it/s]\u001b[A\n",
            "RBM fitting: 4356it [00:28, 152.01it/s]\u001b[A\n",
            "RBM fitting: 4372it [00:28, 153.65it/s]\u001b[A\n",
            "RBM fitting: 4388it [00:29, 154.65it/s]\u001b[A\n",
            "RBM fitting: 4404it [00:29, 148.56it/s]\u001b[A\n",
            "RBM fitting: 4420it [00:29, 150.72it/s]\u001b[A\n",
            "RBM fitting: 4436it [00:29, 150.70it/s]\u001b[A\n",
            "RBM fitting: 4452it [00:29, 152.68it/s]\u001b[A\n",
            "RBM fitting: 4468it [00:29, 154.45it/s]\u001b[A\n",
            "RBM fitting: 4484it [00:29, 155.16it/s]\u001b[A\n",
            "RBM fitting: 4500it [00:29, 145.08it/s]\u001b[A\n",
            "RBM fitting: 4516it [00:29, 148.42it/s]\u001b[A\n",
            "RBM fitting: 4533it [00:30, 152.17it/s]\u001b[A\n",
            "RBM fitting: 4549it [00:30, 154.19it/s]\u001b[A\n",
            "RBM fitting: 4566it [00:30, 156.68it/s]\u001b[A\n",
            "RBM fitting: 4582it [00:30, 157.09it/s]\u001b[A\n",
            "RBM fitting: 4598it [00:30, 151.25it/s]\u001b[A\n",
            "RBM fitting: 4614it [00:30, 153.37it/s]\u001b[A\n",
            "RBM fitting: 4630it [00:30, 155.17it/s]\u001b[A\n",
            "RBM fitting: 4646it [00:30, 156.33it/s]\u001b[A\n",
            "RBM fitting: 4662it [00:30, 154.45it/s]\u001b[A\n",
            "RBM fitting: 4678it [00:30, 155.63it/s]\u001b[A\n",
            "RBM fitting: 4694it [00:31, 149.18it/s]\u001b[A\n",
            "RBM fitting: 4710it [00:31, 150.12it/s]\u001b[A\n",
            "RBM fitting: 4726it [00:31, 152.02it/s]\u001b[A\n",
            "RBM fitting: 4742it [00:31, 154.16it/s]\u001b[A\n",
            "RBM fitting: 4758it [00:31, 155.65it/s]\u001b[A\n",
            "RBM fitting: 4774it [00:31, 156.19it/s]\u001b[A\n",
            "RBM fitting: 4790it [00:31, 149.67it/s]\u001b[A\n",
            "RBM fitting: 4806it [00:31, 151.78it/s]\u001b[A\n",
            "RBM fitting: 4822it [00:31, 150.51it/s]\u001b[A\n",
            "RBM fitting: 4838it [00:31, 152.71it/s]\u001b[A\n",
            "RBM fitting: 4854it [00:32, 153.29it/s]\u001b[A\n",
            "RBM fitting: 4870it [00:32, 154.34it/s]\u001b[A\n",
            "RBM fitting: 4886it [00:32, 147.75it/s]\u001b[A\n",
            "RBM fitting: 4902it [00:32, 150.19it/s]\u001b[A\n",
            "RBM fitting: 4918it [00:32, 151.93it/s]\u001b[A\n",
            "RBM fitting: 4934it [00:32, 153.01it/s]\u001b[A\n",
            "RBM fitting: 4950it [00:32, 154.70it/s]\u001b[A\n",
            "RBM fitting: 4966it [00:32, 154.09it/s]\u001b[A\n",
            "RBM fitting: 4982it [00:32, 143.31it/s]\u001b[A\n",
            "RBM fitting: 4998it [00:33, 146.52it/s]\u001b[A\n",
            "RBM fitting: 5013it [00:33, 143.51it/s]\u001b[A\n",
            "RBM fitting: 5029it [00:33, 147.05it/s]\u001b[A\n",
            "RBM fitting: 5045it [00:33, 149.04it/s]\u001b[A\n",
            "RBM fitting: 5061it [00:33, 150.71it/s]\u001b[A\n",
            "RBM fitting: 5077it [00:33, 145.73it/s]\u001b[A\n",
            "RBM fitting: 5093it [00:33, 148.28it/s]\u001b[A\n",
            "RBM fitting: 5109it [00:33, 150.03it/s]\u001b[A\n",
            "RBM fitting: 5125it [00:33, 149.46it/s]\u001b[A\n",
            "RBM fitting: 5141it [00:34, 151.69it/s]\u001b[A\n",
            "RBM fitting: 5157it [00:34, 153.03it/s]\u001b[A\n",
            "RBM fitting: 5173it [00:34, 147.60it/s]\u001b[A\n",
            "RBM fitting: 5189it [00:34, 149.25it/s]\u001b[A\n",
            "RBM fitting: 5205it [00:34, 151.58it/s]\u001b[A\n",
            "RBM fitting: 5221it [00:34, 152.93it/s]\u001b[A\n",
            "RBM fitting: 5237it [00:34, 152.68it/s]\u001b[A\n",
            "RBM fitting: 5253it [00:34, 154.54it/s]\u001b[A\n",
            "RBM fitting: 5269it [00:34, 147.19it/s]\u001b[A\n",
            "RBM fitting: 5285it [00:34, 149.64it/s]\u001b[A\n",
            "RBM fitting: 5301it [00:35, 151.25it/s]\u001b[A\n",
            "RBM fitting: 5318it [00:35, 154.23it/s]\u001b[A\n",
            "RBM fitting: 5334it [00:35, 153.17it/s]\u001b[A\n",
            "RBM fitting: 5350it [00:35, 152.21it/s]\u001b[A\n",
            "RBM fitting: 5366it [00:35, 146.65it/s]\u001b[A\n",
            "RBM fitting: 5382it [00:35, 149.74it/s]\u001b[A\n",
            "RBM fitting: 5398it [00:35, 151.23it/s]\u001b[A\n",
            "RBM fitting: 5414it [00:35, 152.20it/s]\u001b[A\n",
            "RBM fitting: 5430it [00:35, 150.82it/s]\u001b[A\n",
            "RBM fitting: 5446it [00:36, 151.76it/s]\u001b[A\n",
            "RBM fitting: 5462it [00:36, 146.24it/s]\u001b[A\n",
            "RBM fitting: 5478it [00:36, 148.61it/s]\u001b[A\n",
            "RBM fitting: 5493it [00:36, 148.62it/s]\u001b[A\n",
            "RBM fitting: 5509it [00:36, 150.81it/s]\u001b[A\n",
            "RBM fitting: 5525it [00:36, 152.63it/s]\u001b[A\n",
            "RBM fitting: 5541it [00:36, 144.56it/s]\u001b[A\n",
            "RBM fitting: 5557it [00:36, 147.64it/s]\u001b[A\n",
            "RBM fitting: 5573it [00:36, 150.21it/s]\u001b[A\n",
            "RBM fitting: 5589it [00:37, 150.24it/s]\u001b[A\n",
            "RBM fitting: 5605it [00:37, 152.44it/s]\u001b[A\n",
            "RBM fitting: 5621it [00:37, 154.17it/s]\u001b[A\n",
            "RBM fitting: 5637it [00:37, 154.94it/s]\u001b[A\n",
            "RBM fitting: 5653it [00:37, 148.84it/s]\u001b[A\n",
            "RBM fitting: 5668it [00:37, 149.04it/s]\u001b[A\n",
            "RBM fitting: 5683it [00:37, 148.64it/s]\u001b[A\n",
            "RBM fitting: 5699it [00:37, 150.33it/s]\u001b[A\n",
            "RBM fitting: 5715it [00:37, 152.09it/s]\u001b[A\n",
            "RBM fitting: 5731it [00:37, 148.19it/s]\u001b[A\n",
            "RBM fitting: 5746it [00:38, 141.38it/s]\u001b[A\n",
            "RBM fitting: 5761it [00:38, 143.41it/s]\u001b[A\n",
            "RBM fitting: 5776it [00:38, 142.47it/s]\u001b[A\n",
            "RBM fitting: 5791it [00:38, 141.89it/s]\u001b[A\n",
            "RBM fitting: 5807it [00:38, 145.66it/s]\u001b[A\n",
            "RBM fitting: 5823it [00:38, 149.45it/s]\u001b[A\n",
            "RBM fitting: 5839it [00:38, 144.69it/s]\u001b[A\n",
            "RBM fitting: 5855it [00:38, 148.44it/s]\u001b[A\n",
            "RBM fitting: 5871it [00:38, 150.97it/s]\u001b[A\n",
            "RBM fitting: 5887it [00:39, 150.70it/s]\u001b[A\n",
            "RBM fitting: 5903it [00:39, 152.45it/s]\u001b[A\n",
            "RBM fitting: 5919it [00:39, 154.51it/s]\u001b[A\n",
            "RBM fitting: 5935it [00:39, 148.86it/s]\u001b[A\n",
            "RBM fitting: 5951it [00:39, 151.52it/s]\u001b[A\n",
            "RBM fitting: 5967it [00:39, 153.28it/s]\u001b[A\n",
            "RBM fitting: 5983it [00:39, 152.54it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 150.93it/s]\n",
            "Epoch:20 ,avg_cost = 0.34176623821258545 ,std_cost = 0.03347264602780342 ,avg_grad = 10079.5400390625 ,std_grad = 542.1072998046875\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 12it [00:00, 119.93it/s]\u001b[A\n",
            "RBM fitting: 24it [00:00, 118.47it/s]\u001b[A\n",
            "RBM fitting: 40it [00:00, 127.48it/s]\u001b[A\n",
            "RBM fitting: 56it [00:00, 135.36it/s]\u001b[A\n",
            "RBM fitting: 72it [00:00, 140.84it/s]\u001b[A\n",
            "RBM fitting: 88it [00:00, 144.88it/s]\u001b[A\n",
            "RBM fitting: 102it [00:00, 139.30it/s]\u001b[A\n",
            "RBM fitting: 117it [00:00, 141.44it/s]\u001b[A\n",
            "RBM fitting: 133it [00:00, 146.08it/s]\u001b[A\n",
            "RBM fitting: 150it [00:01, 150.41it/s]\u001b[A\n",
            "RBM fitting: 167it [00:01, 153.69it/s]\u001b[A\n",
            "RBM fitting: 183it [00:01, 152.74it/s]\u001b[A\n",
            "RBM fitting: 199it [00:01, 147.08it/s]\u001b[A\n",
            "RBM fitting: 215it [00:01, 150.41it/s]\u001b[A\n",
            "RBM fitting: 231it [00:01, 152.10it/s]\u001b[A\n",
            "RBM fitting: 247it [00:01, 154.18it/s]\u001b[A\n",
            "RBM fitting: 263it [00:01, 150.26it/s]\u001b[A\n",
            "RBM fitting: 279it [00:01, 151.29it/s]\u001b[A\n",
            "RBM fitting: 295it [00:02, 144.62it/s]\u001b[A\n",
            "RBM fitting: 311it [00:02, 147.98it/s]\u001b[A\n",
            "RBM fitting: 327it [00:02, 150.17it/s]\u001b[A\n",
            "RBM fitting: 343it [00:02, 149.21it/s]\u001b[A\n",
            "RBM fitting: 359it [00:02, 151.21it/s]\u001b[A\n",
            "RBM fitting: 375it [00:02, 152.04it/s]\u001b[A\n",
            "RBM fitting: 391it [00:02, 142.24it/s]\u001b[A\n",
            "RBM fitting: 407it [00:02, 145.41it/s]\u001b[A\n",
            "RBM fitting: 423it [00:02, 147.58it/s]\u001b[A\n",
            "RBM fitting: 439it [00:02, 150.79it/s]\u001b[A\n",
            "RBM fitting: 456it [00:03, 154.27it/s]\u001b[A\n",
            "RBM fitting: 472it [00:03, 155.82it/s]\u001b[A\n",
            "RBM fitting: 488it [00:03, 144.23it/s]\u001b[A\n",
            "RBM fitting: 505it [00:03, 149.13it/s]\u001b[A\n",
            "RBM fitting: 521it [00:03, 150.77it/s]\u001b[A\n",
            "RBM fitting: 537it [00:03, 153.08it/s]\u001b[A\n",
            "RBM fitting: 553it [00:03, 154.39it/s]\u001b[A\n",
            "RBM fitting: 570it [00:03, 156.61it/s]\u001b[A\n",
            "RBM fitting: 586it [00:03, 149.51it/s]\u001b[A\n",
            "RBM fitting: 602it [00:04, 151.38it/s]\u001b[A\n",
            "RBM fitting: 618it [00:04, 152.19it/s]\u001b[A\n",
            "RBM fitting: 634it [00:04, 152.03it/s]\u001b[A\n",
            "RBM fitting: 650it [00:04, 150.38it/s]\u001b[A\n",
            "RBM fitting: 666it [00:04, 152.34it/s]\u001b[A\n",
            "RBM fitting: 682it [00:04, 144.22it/s]\u001b[A\n",
            "RBM fitting: 698it [00:04, 148.33it/s]\u001b[A\n",
            "RBM fitting: 714it [00:04, 150.60it/s]\u001b[A\n",
            "RBM fitting: 730it [00:04, 152.05it/s]\u001b[A\n",
            "RBM fitting: 746it [00:04, 154.23it/s]\u001b[A\n",
            "RBM fitting: 762it [00:05, 155.35it/s]\u001b[A\n",
            "RBM fitting: 778it [00:05, 147.88it/s]\u001b[A\n",
            "RBM fitting: 793it [00:05, 146.33it/s]\u001b[A\n",
            "RBM fitting: 808it [00:05, 146.75it/s]\u001b[A\n",
            "RBM fitting: 824it [00:05, 148.11it/s]\u001b[A\n",
            "RBM fitting: 840it [00:05, 150.14it/s]\u001b[A\n",
            "RBM fitting: 856it [00:05, 148.98it/s]\u001b[A\n",
            "RBM fitting: 871it [00:05, 141.57it/s]\u001b[A\n",
            "RBM fitting: 887it [00:05, 144.65it/s]\u001b[A\n",
            "RBM fitting: 903it [00:06, 148.00it/s]\u001b[A\n",
            "RBM fitting: 919it [00:06, 150.46it/s]\u001b[A\n",
            "RBM fitting: 935it [00:06, 152.05it/s]\u001b[A\n",
            "RBM fitting: 951it [00:06, 149.60it/s]\u001b[A\n",
            "RBM fitting: 967it [00:06, 144.36it/s]\u001b[A\n",
            "RBM fitting: 983it [00:06, 147.14it/s]\u001b[A\n",
            "RBM fitting: 999it [00:06, 150.23it/s]\u001b[A\n",
            "RBM fitting: 1015it [00:06, 151.62it/s]\u001b[A\n",
            "RBM fitting: 1031it [00:06, 152.30it/s]\u001b[A\n",
            "RBM fitting: 1047it [00:07, 154.36it/s]\u001b[A\n",
            "RBM fitting: 1063it [00:07, 145.76it/s]\u001b[A\n",
            "RBM fitting: 1079it [00:07, 149.16it/s]\u001b[A\n",
            "RBM fitting: 1095it [00:07, 148.08it/s]\u001b[A\n",
            "RBM fitting: 1111it [00:07, 150.66it/s]\u001b[A\n",
            "RBM fitting: 1127it [00:07, 152.79it/s]\u001b[A\n",
            "RBM fitting: 1143it [00:07, 154.03it/s]\u001b[A\n",
            "RBM fitting: 1159it [00:07, 143.88it/s]\u001b[A\n",
            "RBM fitting: 1175it [00:07, 147.74it/s]\u001b[A\n",
            "RBM fitting: 1191it [00:07, 151.06it/s]\u001b[A\n",
            "RBM fitting: 1207it [00:08, 152.33it/s]\u001b[A\n",
            "RBM fitting: 1223it [00:08, 152.58it/s]\u001b[A\n",
            "RBM fitting: 1239it [00:08, 153.91it/s]\u001b[A\n",
            "RBM fitting: 1255it [00:08, 145.05it/s]\u001b[A\n",
            "RBM fitting: 1271it [00:08, 148.69it/s]\u001b[A\n",
            "RBM fitting: 1287it [00:08, 151.40it/s]\u001b[A\n",
            "RBM fitting: 1303it [00:08, 148.85it/s]\u001b[A\n",
            "RBM fitting: 1319it [00:08, 150.04it/s]\u001b[A\n",
            "RBM fitting: 1335it [00:08, 151.35it/s]\u001b[A\n",
            "RBM fitting: 1351it [00:09, 145.17it/s]\u001b[A\n",
            "RBM fitting: 1368it [00:09, 150.00it/s]\u001b[A\n",
            "RBM fitting: 1384it [00:09, 152.38it/s]\u001b[A\n",
            "RBM fitting: 1400it [00:09, 152.20it/s]\u001b[A\n",
            "RBM fitting: 1416it [00:09, 153.17it/s]\u001b[A\n",
            "RBM fitting: 1432it [00:09, 153.98it/s]\u001b[A\n",
            "RBM fitting: 1448it [00:09, 145.12it/s]\u001b[A\n",
            "RBM fitting: 1465it [00:09, 149.67it/s]\u001b[A\n",
            "RBM fitting: 1481it [00:09, 151.55it/s]\u001b[A\n",
            "RBM fitting: 1497it [00:10, 152.73it/s]\u001b[A\n",
            "RBM fitting: 1513it [00:10, 153.58it/s]\u001b[A\n",
            "RBM fitting: 1529it [00:10, 154.66it/s]\u001b[A\n",
            "RBM fitting: 1545it [00:10, 146.35it/s]\u001b[A\n",
            "RBM fitting: 1560it [00:10, 144.56it/s]\u001b[A\n",
            "RBM fitting: 1576it [00:10, 147.18it/s]\u001b[A\n",
            "RBM fitting: 1592it [00:10, 149.61it/s]\u001b[A\n",
            "RBM fitting: 1608it [00:10, 147.11it/s]\u001b[A\n",
            "RBM fitting: 1624it [00:10, 149.43it/s]\u001b[A\n",
            "RBM fitting: 1639it [00:10, 142.65it/s]\u001b[A\n",
            "RBM fitting: 1655it [00:11, 146.39it/s]\u001b[A\n",
            "RBM fitting: 1671it [00:11, 150.06it/s]\u001b[A\n",
            "RBM fitting: 1687it [00:11, 152.09it/s]\u001b[A\n",
            "RBM fitting: 1703it [00:11, 150.98it/s]\u001b[A\n",
            "RBM fitting: 1719it [00:11, 152.66it/s]\u001b[A\n",
            "RBM fitting: 1735it [00:11, 144.90it/s]\u001b[A\n",
            "RBM fitting: 1751it [00:11, 148.41it/s]\u001b[A\n",
            "RBM fitting: 1767it [00:11, 151.61it/s]\u001b[A\n",
            "RBM fitting: 1783it [00:11, 152.94it/s]\u001b[A\n",
            "RBM fitting: 1800it [00:12, 155.56it/s]\u001b[A\n",
            "RBM fitting: 1816it [00:12, 156.34it/s]\u001b[A\n",
            "RBM fitting: 1832it [00:12, 146.91it/s]\u001b[A\n",
            "RBM fitting: 1848it [00:12, 149.83it/s]\u001b[A\n",
            "RBM fitting: 1864it [00:12, 147.59it/s]\u001b[A\n",
            "RBM fitting: 1880it [00:12, 149.90it/s]\u001b[A\n",
            "RBM fitting: 1896it [00:12, 151.18it/s]\u001b[A\n",
            "RBM fitting: 1912it [00:12, 151.97it/s]\u001b[A\n",
            "RBM fitting: 1928it [00:12, 141.46it/s]\u001b[A\n",
            "RBM fitting: 1944it [00:13, 145.25it/s]\u001b[A\n",
            "RBM fitting: 1960it [00:13, 148.93it/s]\u001b[A\n",
            "RBM fitting: 1976it [00:13, 151.60it/s]\u001b[A\n",
            "RBM fitting: 1992it [00:13, 152.80it/s]\u001b[A\n",
            "RBM fitting: 2008it [00:13, 151.76it/s]\u001b[A\n",
            "RBM fitting: 2024it [00:13, 141.70it/s]\u001b[A\n",
            "RBM fitting: 2040it [00:13, 146.09it/s]\u001b[A\n",
            "RBM fitting: 2056it [00:13, 148.52it/s]\u001b[A\n",
            "RBM fitting: 2072it [00:13, 150.19it/s]\u001b[A\n",
            "RBM fitting: 2088it [00:13, 151.32it/s]\u001b[A\n",
            "RBM fitting: 2104it [00:14, 151.31it/s]\u001b[A\n",
            "RBM fitting: 2120it [00:14, 144.79it/s]\u001b[A\n",
            "RBM fitting: 2136it [00:14, 147.68it/s]\u001b[A\n",
            "RBM fitting: 2152it [00:14, 150.04it/s]\u001b[A\n",
            "RBM fitting: 2168it [00:14, 149.05it/s]\u001b[A\n",
            "RBM fitting: 2184it [00:14, 150.53it/s]\u001b[A\n",
            "RBM fitting: 2200it [00:14, 151.62it/s]\u001b[A\n",
            "RBM fitting: 2216it [00:14, 139.79it/s]\u001b[A\n",
            "RBM fitting: 2232it [00:14, 143.87it/s]\u001b[A\n",
            "RBM fitting: 2248it [00:15, 147.58it/s]\u001b[A\n",
            "RBM fitting: 2265it [00:15, 151.39it/s]\u001b[A\n",
            "RBM fitting: 2281it [00:15, 153.18it/s]\u001b[A\n",
            "RBM fitting: 2297it [00:15, 154.92it/s]\u001b[A\n",
            "RBM fitting: 2313it [00:15, 145.00it/s]\u001b[A\n",
            "RBM fitting: 2329it [00:15, 148.10it/s]\u001b[A\n",
            "RBM fitting: 2345it [00:15, 151.05it/s]\u001b[A\n",
            "RBM fitting: 2361it [00:15, 150.54it/s]\u001b[A\n",
            "RBM fitting: 2377it [00:15, 150.04it/s]\u001b[A\n",
            "RBM fitting: 2393it [00:16, 151.62it/s]\u001b[A\n",
            "RBM fitting: 2409it [00:16, 144.34it/s]\u001b[A\n",
            "RBM fitting: 2425it [00:16, 147.63it/s]\u001b[A\n",
            "RBM fitting: 2441it [00:16, 149.07it/s]\u001b[A\n",
            "RBM fitting: 2457it [00:16, 150.09it/s]\u001b[A\n",
            "RBM fitting: 2473it [00:16, 147.53it/s]\u001b[A\n",
            "RBM fitting: 2489it [00:16, 149.29it/s]\u001b[A\n",
            "RBM fitting: 2504it [00:16, 141.76it/s]\u001b[A\n",
            "RBM fitting: 2520it [00:16, 145.31it/s]\u001b[A\n",
            "RBM fitting: 2536it [00:16, 148.58it/s]\u001b[A\n",
            "RBM fitting: 2551it [00:17, 148.36it/s]\u001b[A\n",
            "RBM fitting: 2567it [00:17, 150.16it/s]\u001b[A\n",
            "RBM fitting: 2583it [00:17, 151.87it/s]\u001b[A\n",
            "RBM fitting: 2599it [00:17, 145.13it/s]\u001b[A\n",
            "RBM fitting: 2614it [00:17, 145.17it/s]\u001b[A\n",
            "RBM fitting: 2630it [00:17, 147.07it/s]\u001b[A\n",
            "RBM fitting: 2646it [00:17, 149.45it/s]\u001b[A\n",
            "RBM fitting: 2662it [00:17, 151.35it/s]\u001b[A\n",
            "RBM fitting: 2678it [00:17, 150.86it/s]\u001b[A\n",
            "RBM fitting: 2694it [00:18, 144.16it/s]\u001b[A\n",
            "RBM fitting: 2710it [00:18, 147.65it/s]\u001b[A\n",
            "RBM fitting: 2726it [00:18, 149.95it/s]\u001b[A\n",
            "RBM fitting: 2742it [00:18, 151.63it/s]\u001b[A\n",
            "RBM fitting: 2758it [00:18, 153.29it/s]\u001b[A\n",
            "RBM fitting: 2774it [00:18, 152.04it/s]\u001b[A\n",
            "RBM fitting: 2790it [00:18, 143.25it/s]\u001b[A\n",
            "RBM fitting: 2806it [00:18, 147.12it/s]\u001b[A\n",
            "RBM fitting: 2822it [00:18, 149.10it/s]\u001b[A\n",
            "RBM fitting: 2838it [00:19, 151.57it/s]\u001b[A\n",
            "RBM fitting: 2854it [00:19, 153.12it/s]\u001b[A\n",
            "RBM fitting: 2870it [00:19, 149.33it/s]\u001b[A\n",
            "RBM fitting: 2885it [00:19, 141.44it/s]\u001b[A\n",
            "RBM fitting: 2900it [00:19, 143.73it/s]\u001b[A\n",
            "RBM fitting: 2915it [00:19, 144.00it/s]\u001b[A\n",
            "RBM fitting: 2930it [00:19, 140.60it/s]\u001b[A\n",
            "RBM fitting: 2946it [00:19, 144.54it/s]\u001b[A\n",
            "RBM fitting: 2962it [00:19, 147.43it/s]\u001b[A\n",
            "RBM fitting: 2977it [00:19, 139.02it/s]\u001b[A\n",
            "RBM fitting: 2992it [00:20, 141.31it/s]\u001b[A\n",
            "RBM fitting: 3008it [00:20, 145.37it/s]\u001b[A\n",
            "RBM fitting: 3024it [00:20, 147.78it/s]\u001b[A\n",
            "RBM fitting: 3040it [00:20, 150.07it/s]\u001b[A\n",
            "RBM fitting: 3056it [00:20, 151.39it/s]\u001b[A\n",
            "RBM fitting: 3072it [00:20, 140.53it/s]\u001b[A\n",
            "RBM fitting: 3087it [00:20, 141.37it/s]\u001b[A\n",
            "RBM fitting: 3103it [00:20, 144.77it/s]\u001b[A\n",
            "RBM fitting: 3119it [00:20, 147.89it/s]\u001b[A\n",
            "RBM fitting: 3135it [00:21, 150.98it/s]\u001b[A\n",
            "RBM fitting: 3151it [00:21, 153.02it/s]\u001b[A\n",
            "RBM fitting: 3167it [00:21, 144.66it/s]\u001b[A\n",
            "RBM fitting: 3183it [00:21, 148.04it/s]\u001b[A\n",
            "RBM fitting: 3199it [00:21, 150.80it/s]\u001b[A\n",
            "RBM fitting: 3215it [00:21, 150.03it/s]\u001b[A\n",
            "RBM fitting: 3231it [00:21, 151.65it/s]\u001b[A\n",
            "RBM fitting: 3247it [00:21, 152.48it/s]\u001b[A\n",
            "RBM fitting: 3263it [00:21, 144.29it/s]\u001b[A\n",
            "RBM fitting: 3279it [00:22, 147.66it/s]\u001b[A\n",
            "RBM fitting: 3295it [00:22, 150.55it/s]\u001b[A\n",
            "RBM fitting: 3311it [00:22, 152.18it/s]\u001b[A\n",
            "RBM fitting: 3327it [00:22, 152.47it/s]\u001b[A\n",
            "RBM fitting: 3343it [00:22, 152.38it/s]\u001b[A\n",
            "RBM fitting: 3359it [00:22, 142.55it/s]\u001b[A\n",
            "RBM fitting: 3374it [00:22, 143.41it/s]\u001b[A\n",
            "RBM fitting: 3390it [00:22, 146.87it/s]\u001b[A\n",
            "RBM fitting: 3406it [00:22, 148.58it/s]\u001b[A\n",
            "RBM fitting: 3422it [00:22, 150.90it/s]\u001b[A\n",
            "RBM fitting: 3438it [00:23, 152.70it/s]\u001b[A\n",
            "RBM fitting: 3454it [00:23, 145.12it/s]\u001b[A\n",
            "RBM fitting: 3470it [00:23, 148.06it/s]\u001b[A\n",
            "RBM fitting: 3485it [00:23, 147.41it/s]\u001b[A\n",
            "RBM fitting: 3501it [00:23, 150.22it/s]\u001b[A\n",
            "RBM fitting: 3517it [00:23, 148.74it/s]\u001b[A\n",
            "RBM fitting: 3533it [00:23, 150.02it/s]\u001b[A\n",
            "RBM fitting: 3549it [00:23, 141.89it/s]\u001b[A\n",
            "RBM fitting: 3565it [00:23, 145.17it/s]\u001b[A\n",
            "RBM fitting: 3581it [00:24, 148.23it/s]\u001b[A\n",
            "RBM fitting: 3597it [00:24, 149.84it/s]\u001b[A\n",
            "RBM fitting: 3613it [00:24, 151.26it/s]\u001b[A\n",
            "RBM fitting: 3629it [00:24, 152.93it/s]\u001b[A\n",
            "RBM fitting: 3645it [00:24, 144.70it/s]\u001b[A\n",
            "RBM fitting: 3661it [00:24, 148.20it/s]\u001b[A\n",
            "RBM fitting: 3676it [00:24, 147.03it/s]\u001b[A\n",
            "RBM fitting: 3692it [00:24, 149.96it/s]\u001b[A\n",
            "RBM fitting: 3708it [00:24, 150.85it/s]\u001b[A\n",
            "RBM fitting: 3724it [00:25, 152.64it/s]\u001b[A\n",
            "RBM fitting: 3740it [00:25, 143.86it/s]\u001b[A\n",
            "RBM fitting: 3756it [00:25, 147.07it/s]\u001b[A\n",
            "RBM fitting: 3772it [00:25, 149.45it/s]\u001b[A\n",
            "RBM fitting: 3788it [00:25, 151.92it/s]\u001b[A\n",
            "RBM fitting: 3804it [00:25, 152.16it/s]\u001b[A\n",
            "RBM fitting: 3820it [00:25, 149.84it/s]\u001b[A\n",
            "RBM fitting: 3836it [00:25, 139.81it/s]\u001b[A\n",
            "RBM fitting: 3852it [00:25, 143.55it/s]\u001b[A\n",
            "RBM fitting: 3868it [00:26, 147.08it/s]\u001b[A\n",
            "RBM fitting: 3884it [00:26, 149.62it/s]\u001b[A\n",
            "RBM fitting: 3900it [00:26, 151.91it/s]\u001b[A\n",
            "RBM fitting: 3916it [00:26, 153.35it/s]\u001b[A\n",
            "RBM fitting: 3932it [00:26, 145.54it/s]\u001b[A\n",
            "RBM fitting: 3948it [00:26, 148.32it/s]\u001b[A\n",
            "RBM fitting: 3964it [00:26, 150.27it/s]\u001b[A\n",
            "RBM fitting: 3980it [00:26, 149.11it/s]\u001b[A\n",
            "RBM fitting: 3996it [00:26, 151.00it/s]\u001b[A\n",
            "RBM fitting: 4012it [00:26, 152.36it/s]\u001b[A\n",
            "RBM fitting: 4028it [00:27, 145.63it/s]\u001b[A\n",
            "RBM fitting: 4044it [00:27, 148.95it/s]\u001b[A\n",
            "RBM fitting: 4060it [00:27, 151.26it/s]\u001b[A\n",
            "RBM fitting: 4077it [00:27, 154.73it/s]\u001b[A\n",
            "RBM fitting: 4093it [00:27, 153.26it/s]\u001b[A\n",
            "RBM fitting: 4109it [00:27, 154.88it/s]\u001b[A\n",
            "RBM fitting: 4125it [00:27, 138.36it/s]\u001b[A\n",
            "RBM fitting: 4141it [00:27, 143.50it/s]\u001b[A\n",
            "RBM fitting: 4157it [00:27, 146.39it/s]\u001b[A\n",
            "RBM fitting: 4173it [00:28, 148.46it/s]\u001b[A\n",
            "RBM fitting: 4189it [00:28, 151.32it/s]\u001b[A\n",
            "RBM fitting: 4205it [00:28, 152.76it/s]\u001b[A\n",
            "RBM fitting: 4221it [00:28, 144.08it/s]\u001b[A\n",
            "RBM fitting: 4237it [00:28, 147.83it/s]\u001b[A\n",
            "RBM fitting: 4253it [00:28, 150.26it/s]\u001b[A\n",
            "RBM fitting: 4269it [00:28, 151.97it/s]\u001b[A\n",
            "RBM fitting: 4285it [00:28, 150.57it/s]\u001b[A\n",
            "RBM fitting: 4301it [00:28, 151.29it/s]\u001b[A\n",
            "RBM fitting: 4317it [00:29, 144.16it/s]\u001b[A\n",
            "RBM fitting: 4332it [00:29, 143.61it/s]\u001b[A\n",
            "RBM fitting: 4348it [00:29, 147.62it/s]\u001b[A\n",
            "RBM fitting: 4364it [00:29, 150.06it/s]\u001b[A\n",
            "RBM fitting: 4380it [00:29, 152.54it/s]\u001b[A\n",
            "RBM fitting: 4396it [00:29, 153.55it/s]\u001b[A\n",
            "RBM fitting: 4412it [00:29, 143.37it/s]\u001b[A\n",
            "RBM fitting: 4427it [00:29, 144.45it/s]\u001b[A\n",
            "RBM fitting: 4443it [00:29, 147.95it/s]\u001b[A\n",
            "RBM fitting: 4459it [00:29, 149.26it/s]\u001b[A\n",
            "RBM fitting: 4475it [00:30, 150.13it/s]\u001b[A\n",
            "RBM fitting: 4491it [00:30, 152.52it/s]\u001b[A\n",
            "RBM fitting: 4507it [00:30, 144.34it/s]\u001b[A\n",
            "RBM fitting: 4523it [00:30, 148.37it/s]\u001b[A\n",
            "RBM fitting: 4539it [00:30, 151.29it/s]\u001b[A\n",
            "RBM fitting: 4555it [00:30, 151.72it/s]\u001b[A\n",
            "RBM fitting: 4571it [00:30, 150.73it/s]\u001b[A\n",
            "RBM fitting: 4587it [00:30, 150.33it/s]\u001b[A\n",
            "RBM fitting: 4603it [00:30, 141.62it/s]\u001b[A\n",
            "RBM fitting: 4619it [00:31, 145.87it/s]\u001b[A\n",
            "RBM fitting: 4635it [00:31, 149.45it/s]\u001b[A\n",
            "RBM fitting: 4651it [00:31, 151.41it/s]\u001b[A\n",
            "RBM fitting: 4667it [00:31, 153.12it/s]\u001b[A\n",
            "RBM fitting: 4683it [00:31, 154.39it/s]\u001b[A\n",
            "RBM fitting: 4699it [00:31, 145.23it/s]\u001b[A\n",
            "RBM fitting: 4715it [00:31, 149.25it/s]\u001b[A\n",
            "RBM fitting: 4731it [00:31, 149.07it/s]\u001b[A\n",
            "RBM fitting: 4747it [00:31, 150.24it/s]\u001b[A\n",
            "RBM fitting: 4763it [00:31, 150.78it/s]\u001b[A\n",
            "RBM fitting: 4779it [00:32, 151.83it/s]\u001b[A\n",
            "RBM fitting: 4795it [00:32, 144.28it/s]\u001b[A\n",
            "RBM fitting: 4810it [00:32, 145.33it/s]\u001b[A\n",
            "RBM fitting: 4826it [00:32, 149.06it/s]\u001b[A\n",
            "RBM fitting: 4842it [00:32, 150.68it/s]\u001b[A\n",
            "RBM fitting: 4858it [00:32, 152.40it/s]\u001b[A\n",
            "RBM fitting: 4874it [00:32, 153.29it/s]\u001b[A\n",
            "RBM fitting: 4890it [00:32, 144.18it/s]\u001b[A\n",
            "RBM fitting: 4906it [00:32, 146.76it/s]\u001b[A\n",
            "RBM fitting: 4922it [00:33, 149.66it/s]\u001b[A\n",
            "RBM fitting: 4938it [00:33, 152.00it/s]\u001b[A\n",
            "RBM fitting: 4954it [00:33, 153.55it/s]\u001b[A\n",
            "RBM fitting: 4970it [00:33, 155.43it/s]\u001b[A\n",
            "RBM fitting: 4986it [00:33, 147.35it/s]\u001b[A\n",
            "RBM fitting: 5002it [00:33, 149.45it/s]\u001b[A\n",
            "RBM fitting: 5018it [00:33, 151.51it/s]\u001b[A\n",
            "RBM fitting: 5034it [00:33, 150.66it/s]\u001b[A\n",
            "RBM fitting: 5050it [00:33, 152.03it/s]\u001b[A\n",
            "RBM fitting: 5066it [00:34, 153.22it/s]\u001b[A\n",
            "RBM fitting: 5082it [00:34, 145.56it/s]\u001b[A\n",
            "RBM fitting: 5098it [00:34, 148.21it/s]\u001b[A\n",
            "RBM fitting: 5114it [00:34, 149.79it/s]\u001b[A\n",
            "RBM fitting: 5130it [00:34, 151.61it/s]\u001b[A\n",
            "RBM fitting: 5146it [00:34, 153.13it/s]\u001b[A\n",
            "RBM fitting: 5162it [00:34, 154.23it/s]\u001b[A\n",
            "RBM fitting: 5178it [00:34, 144.11it/s]\u001b[A\n",
            "RBM fitting: 5193it [00:34, 143.39it/s]\u001b[A\n",
            "RBM fitting: 5209it [00:34, 146.95it/s]\u001b[A\n",
            "RBM fitting: 5225it [00:35, 149.30it/s]\u001b[A\n",
            "RBM fitting: 5241it [00:35, 151.35it/s]\u001b[A\n",
            "RBM fitting: 5257it [00:35, 152.38it/s]\u001b[A\n",
            "RBM fitting: 5273it [00:35, 147.46it/s]\u001b[A\n",
            "RBM fitting: 5289it [00:35, 150.75it/s]\u001b[A\n",
            "RBM fitting: 5305it [00:35, 152.86it/s]\u001b[A\n",
            "RBM fitting: 5321it [00:35, 154.04it/s]\u001b[A\n",
            "RBM fitting: 5337it [00:35, 152.49it/s]\u001b[A\n",
            "RBM fitting: 5353it [00:35, 153.32it/s]\u001b[A\n",
            "RBM fitting: 5369it [00:36, 154.16it/s]\u001b[A\n",
            "RBM fitting: 5385it [00:36, 146.47it/s]\u001b[A\n",
            "RBM fitting: 5401it [00:36, 148.86it/s]\u001b[A\n",
            "RBM fitting: 5417it [00:36, 150.03it/s]\u001b[A\n",
            "RBM fitting: 5433it [00:36, 151.77it/s]\u001b[A\n",
            "RBM fitting: 5449it [00:36, 152.09it/s]\u001b[A\n",
            "RBM fitting: 5465it [00:36, 152.85it/s]\u001b[A\n",
            "RBM fitting: 5481it [00:36, 144.92it/s]\u001b[A\n",
            "RBM fitting: 5496it [00:36, 140.00it/s]\u001b[A\n",
            "RBM fitting: 5511it [00:37, 142.30it/s]\u001b[A\n",
            "RBM fitting: 5527it [00:37, 145.88it/s]\u001b[A\n",
            "RBM fitting: 5543it [00:37, 148.47it/s]\u001b[A\n",
            "RBM fitting: 5559it [00:37, 150.09it/s]\u001b[A\n",
            "RBM fitting: 5575it [00:37, 143.29it/s]\u001b[A\n",
            "RBM fitting: 5591it [00:37, 147.08it/s]\u001b[A\n",
            "RBM fitting: 5607it [00:37, 149.67it/s]\u001b[A\n",
            "RBM fitting: 5623it [00:37, 151.73it/s]\u001b[A\n",
            "RBM fitting: 5639it [00:37, 150.59it/s]\u001b[A\n",
            "RBM fitting: 5655it [00:37, 150.81it/s]\u001b[A\n",
            "RBM fitting: 5671it [00:38, 144.39it/s]\u001b[A\n",
            "RBM fitting: 5687it [00:38, 147.62it/s]\u001b[A\n",
            "RBM fitting: 5703it [00:38, 149.24it/s]\u001b[A\n",
            "RBM fitting: 5719it [00:38, 151.50it/s]\u001b[A\n",
            "RBM fitting: 5735it [00:38, 153.25it/s]\u001b[A\n",
            "RBM fitting: 5751it [00:38, 154.38it/s]\u001b[A\n",
            "RBM fitting: 5767it [00:38, 155.67it/s]\u001b[A\n",
            "RBM fitting: 5783it [00:38, 146.31it/s]\u001b[A\n",
            "RBM fitting: 5798it [00:38, 144.89it/s]\u001b[A\n",
            "RBM fitting: 5814it [00:39, 148.54it/s]\u001b[A\n",
            "RBM fitting: 5830it [00:39, 150.93it/s]\u001b[A\n",
            "RBM fitting: 5846it [00:39, 152.88it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:39, 153.49it/s]\u001b[A\n",
            "RBM fitting: 5878it [00:39, 145.45it/s]\u001b[A\n",
            "RBM fitting: 5894it [00:39, 149.22it/s]\u001b[A\n",
            "RBM fitting: 5910it [00:39, 150.96it/s]\u001b[A\n",
            "RBM fitting: 5926it [00:39, 152.65it/s]\u001b[A\n",
            "RBM fitting: 5942it [00:39, 153.60it/s]\u001b[A\n",
            "RBM fitting: 5958it [00:39, 153.30it/s]\u001b[A\n",
            "RBM fitting: 5974it [00:40, 144.37it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:40, 148.98it/s]\n",
            "Epoch:21 ,avg_cost = 0.34139692783355713 ,std_cost = 0.03366253525018692 ,avg_grad = 10080.5302734375 ,std_grad = 539.3760375976562\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 14it [00:00, 137.10it/s]\u001b[A\n",
            "RBM fitting: 30it [00:00, 142.94it/s]\u001b[A\n",
            "RBM fitting: 43it [00:00, 137.80it/s]\u001b[A\n",
            "RBM fitting: 59it [00:00, 143.19it/s]\u001b[A\n",
            "RBM fitting: 75it [00:00, 146.23it/s]\u001b[A\n",
            "RBM fitting: 91it [00:00, 148.02it/s]\u001b[A\n",
            "RBM fitting: 105it [00:00, 145.28it/s]\u001b[A\n",
            "RBM fitting: 121it [00:00, 148.52it/s]\u001b[A\n",
            "RBM fitting: 136it [00:00, 144.24it/s]\u001b[A\n",
            "RBM fitting: 152it [00:01, 146.84it/s]\u001b[A\n",
            "RBM fitting: 168it [00:01, 149.82it/s]\u001b[A\n",
            "RBM fitting: 184it [00:01, 152.46it/s]\u001b[A\n",
            "RBM fitting: 200it [00:01, 152.95it/s]\u001b[A\n",
            "RBM fitting: 216it [00:01, 153.74it/s]\u001b[A\n",
            "RBM fitting: 232it [00:01, 147.61it/s]\u001b[A\n",
            "RBM fitting: 247it [00:01, 147.39it/s]\u001b[A\n",
            "RBM fitting: 263it [00:01, 149.27it/s]\u001b[A\n",
            "RBM fitting: 279it [00:01, 151.11it/s]\u001b[A\n",
            "RBM fitting: 295it [00:01, 152.60it/s]\u001b[A\n",
            "RBM fitting: 311it [00:02, 153.00it/s]\u001b[A\n",
            "RBM fitting: 327it [00:02, 147.02it/s]\u001b[A\n",
            "RBM fitting: 343it [00:02, 149.90it/s]\u001b[A\n",
            "RBM fitting: 359it [00:02, 152.47it/s]\u001b[A\n",
            "RBM fitting: 375it [00:02, 154.37it/s]\u001b[A\n",
            "RBM fitting: 391it [00:02, 154.27it/s]\u001b[A\n",
            "RBM fitting: 407it [00:02, 152.34it/s]\u001b[A\n",
            "RBM fitting: 423it [00:02, 146.42it/s]\u001b[A\n",
            "RBM fitting: 439it [00:02, 149.25it/s]\u001b[A\n",
            "RBM fitting: 455it [00:03, 150.99it/s]\u001b[A\n",
            "RBM fitting: 471it [00:03, 153.14it/s]\u001b[A\n",
            "RBM fitting: 487it [00:03, 154.38it/s]\u001b[A\n",
            "RBM fitting: 503it [00:03, 154.83it/s]\u001b[A\n",
            "RBM fitting: 519it [00:03, 148.24it/s]\u001b[A\n",
            "RBM fitting: 535it [00:03, 150.67it/s]\u001b[A\n",
            "RBM fitting: 551it [00:03, 152.39it/s]\u001b[A\n",
            "RBM fitting: 567it [00:03, 149.41it/s]\u001b[A\n",
            "RBM fitting: 583it [00:03, 151.08it/s]\u001b[A\n",
            "RBM fitting: 599it [00:03, 152.56it/s]\u001b[A\n",
            "RBM fitting: 615it [00:04, 146.56it/s]\u001b[A\n",
            "RBM fitting: 631it [00:04, 148.71it/s]\u001b[A\n",
            "RBM fitting: 647it [00:04, 150.48it/s]\u001b[A\n",
            "RBM fitting: 663it [00:04, 152.01it/s]\u001b[A\n",
            "RBM fitting: 679it [00:04, 152.76it/s]\u001b[A\n",
            "RBM fitting: 695it [00:04, 151.75it/s]\u001b[A\n",
            "RBM fitting: 711it [00:04, 142.95it/s]\u001b[A\n",
            "RBM fitting: 727it [00:04, 147.00it/s]\u001b[A\n",
            "RBM fitting: 743it [00:04, 149.83it/s]\u001b[A\n",
            "RBM fitting: 759it [00:05, 151.70it/s]\u001b[A\n",
            "RBM fitting: 775it [00:05, 152.87it/s]\u001b[A\n",
            "RBM fitting: 791it [00:05, 153.68it/s]\u001b[A\n",
            "RBM fitting: 807it [00:05, 147.70it/s]\u001b[A\n",
            "RBM fitting: 823it [00:05, 149.69it/s]\u001b[A\n",
            "RBM fitting: 839it [00:05, 152.37it/s]\u001b[A\n",
            "RBM fitting: 855it [00:05, 153.43it/s]\u001b[A\n",
            "RBM fitting: 871it [00:05, 151.19it/s]\u001b[A\n",
            "RBM fitting: 887it [00:05, 153.30it/s]\u001b[A\n",
            "RBM fitting: 903it [00:06, 145.93it/s]\u001b[A\n",
            "RBM fitting: 919it [00:06, 149.23it/s]\u001b[A\n",
            "RBM fitting: 935it [00:06, 150.59it/s]\u001b[A\n",
            "RBM fitting: 951it [00:06, 151.84it/s]\u001b[A\n",
            "RBM fitting: 967it [00:06, 152.43it/s]\u001b[A\n",
            "RBM fitting: 983it [00:06, 153.50it/s]\u001b[A\n",
            "RBM fitting: 999it [00:06, 147.37it/s]\u001b[A\n",
            "RBM fitting: 1014it [00:06, 146.65it/s]\u001b[A\n",
            "RBM fitting: 1031it [00:06, 151.07it/s]\u001b[A\n",
            "RBM fitting: 1048it [00:06, 154.96it/s]\u001b[A\n",
            "RBM fitting: 1064it [00:07, 156.25it/s]\u001b[A\n",
            "RBM fitting: 1081it [00:07, 157.83it/s]\u001b[A\n",
            "RBM fitting: 1097it [00:07, 149.99it/s]\u001b[A\n",
            "RBM fitting: 1113it [00:07, 151.77it/s]\u001b[A\n",
            "RBM fitting: 1129it [00:07, 152.65it/s]\u001b[A\n",
            "RBM fitting: 1145it [00:07, 149.22it/s]\u001b[A\n",
            "RBM fitting: 1161it [00:07, 151.26it/s]\u001b[A\n",
            "RBM fitting: 1177it [00:07, 150.30it/s]\u001b[A\n",
            "RBM fitting: 1193it [00:07, 144.87it/s]\u001b[A\n",
            "RBM fitting: 1209it [00:08, 147.42it/s]\u001b[A\n",
            "RBM fitting: 1225it [00:08, 150.95it/s]\u001b[A\n",
            "RBM fitting: 1241it [00:08, 153.42it/s]\u001b[A\n",
            "RBM fitting: 1257it [00:08, 153.96it/s]\u001b[A\n",
            "RBM fitting: 1273it [00:08, 154.66it/s]\u001b[A\n",
            "RBM fitting: 1289it [00:08, 148.43it/s]\u001b[A\n",
            "RBM fitting: 1305it [00:08, 150.56it/s]\u001b[A\n",
            "RBM fitting: 1322it [00:08, 152.48it/s]\u001b[A\n",
            "RBM fitting: 1338it [00:08, 153.97it/s]\u001b[A\n",
            "RBM fitting: 1354it [00:08, 155.08it/s]\u001b[A\n",
            "RBM fitting: 1371it [00:09, 157.33it/s]\u001b[A\n",
            "RBM fitting: 1387it [00:09, 150.38it/s]\u001b[A\n",
            "RBM fitting: 1403it [00:09, 153.06it/s]\u001b[A\n",
            "RBM fitting: 1419it [00:09, 154.64it/s]\u001b[A\n",
            "RBM fitting: 1435it [00:09, 156.01it/s]\u001b[A\n",
            "RBM fitting: 1451it [00:09, 156.88it/s]\u001b[A\n",
            "RBM fitting: 1468it [00:09, 158.20it/s]\u001b[A\n",
            "RBM fitting: 1484it [00:09, 148.32it/s]\u001b[A\n",
            "RBM fitting: 1501it [00:09, 152.17it/s]\u001b[A\n",
            "RBM fitting: 1517it [00:10, 153.88it/s]\u001b[A\n",
            "RBM fitting: 1533it [00:10, 153.94it/s]\u001b[A\n",
            "RBM fitting: 1549it [00:10, 155.22it/s]\u001b[A\n",
            "RBM fitting: 1565it [00:10, 156.06it/s]\u001b[A\n",
            "RBM fitting: 1581it [00:10, 149.51it/s]\u001b[A\n",
            "RBM fitting: 1597it [00:10, 151.19it/s]\u001b[A\n",
            "RBM fitting: 1613it [00:10, 153.02it/s]\u001b[A\n",
            "RBM fitting: 1630it [00:10, 155.61it/s]\u001b[A\n",
            "RBM fitting: 1646it [00:10, 153.72it/s]\u001b[A\n",
            "RBM fitting: 1662it [00:10, 154.63it/s]\u001b[A\n",
            "RBM fitting: 1678it [00:11, 149.18it/s]\u001b[A\n",
            "RBM fitting: 1694it [00:11, 151.21it/s]\u001b[A\n",
            "RBM fitting: 1710it [00:11, 152.98it/s]\u001b[A\n",
            "RBM fitting: 1726it [00:11, 154.52it/s]\u001b[A\n",
            "RBM fitting: 1742it [00:11, 155.23it/s]\u001b[A\n",
            "RBM fitting: 1758it [00:11, 155.78it/s]\u001b[A\n",
            "RBM fitting: 1774it [00:11, 148.44it/s]\u001b[A\n",
            "RBM fitting: 1790it [00:11, 149.52it/s]\u001b[A\n",
            "RBM fitting: 1806it [00:11, 150.87it/s]\u001b[A\n",
            "RBM fitting: 1822it [00:12, 152.17it/s]\u001b[A\n",
            "RBM fitting: 1838it [00:12, 153.77it/s]\u001b[A\n",
            "RBM fitting: 1854it [00:12, 154.63it/s]\u001b[A\n",
            "RBM fitting: 1870it [00:12, 147.73it/s]\u001b[A\n",
            "RBM fitting: 1886it [00:12, 150.09it/s]\u001b[A\n",
            "RBM fitting: 1902it [00:12, 152.10it/s]\u001b[A\n",
            "RBM fitting: 1918it [00:12, 150.26it/s]\u001b[A\n",
            "RBM fitting: 1934it [00:12, 149.07it/s]\u001b[A\n",
            "RBM fitting: 1949it [00:12, 148.35it/s]\u001b[A\n",
            "RBM fitting: 1964it [00:12, 144.04it/s]\u001b[A\n",
            "RBM fitting: 1980it [00:13, 148.16it/s]\u001b[A\n",
            "RBM fitting: 1996it [00:13, 150.70it/s]\u001b[A\n",
            "RBM fitting: 2012it [00:13, 152.64it/s]\u001b[A\n",
            "RBM fitting: 2028it [00:13, 153.46it/s]\u001b[A\n",
            "RBM fitting: 2044it [00:13, 150.57it/s]\u001b[A\n",
            "RBM fitting: 2060it [00:13, 142.40it/s]\u001b[A\n",
            "RBM fitting: 2075it [00:13, 143.56it/s]\u001b[A\n",
            "RBM fitting: 2091it [00:13, 147.23it/s]\u001b[A\n",
            "RBM fitting: 2106it [00:13, 147.29it/s]\u001b[A\n",
            "RBM fitting: 2122it [00:14, 149.85it/s]\u001b[A\n",
            "RBM fitting: 2138it [00:14, 152.19it/s]\u001b[A\n",
            "RBM fitting: 2154it [00:14, 146.82it/s]\u001b[A\n",
            "RBM fitting: 2170it [00:14, 149.29it/s]\u001b[A\n",
            "RBM fitting: 2186it [00:14, 150.46it/s]\u001b[A\n",
            "RBM fitting: 2202it [00:14, 152.67it/s]\u001b[A\n",
            "RBM fitting: 2218it [00:14, 152.70it/s]\u001b[A\n",
            "RBM fitting: 2234it [00:14, 153.13it/s]\u001b[A\n",
            "RBM fitting: 2250it [00:14, 143.53it/s]\u001b[A\n",
            "RBM fitting: 2266it [00:15, 146.40it/s]\u001b[A\n",
            "RBM fitting: 2282it [00:15, 149.24it/s]\u001b[A\n",
            "RBM fitting: 2298it [00:15, 151.51it/s]\u001b[A\n",
            "RBM fitting: 2314it [00:15, 151.32it/s]\u001b[A\n",
            "RBM fitting: 2330it [00:15, 152.48it/s]\u001b[A\n",
            "RBM fitting: 2346it [00:15, 145.88it/s]\u001b[A\n",
            "RBM fitting: 2362it [00:15, 147.64it/s]\u001b[A\n",
            "RBM fitting: 2378it [00:15, 149.60it/s]\u001b[A\n",
            "RBM fitting: 2394it [00:15, 151.83it/s]\u001b[A\n",
            "RBM fitting: 2410it [00:15, 151.00it/s]\u001b[A\n",
            "RBM fitting: 2426it [00:16, 152.28it/s]\u001b[A\n",
            "RBM fitting: 2442it [00:16, 146.33it/s]\u001b[A\n",
            "RBM fitting: 2458it [00:16, 149.06it/s]\u001b[A\n",
            "RBM fitting: 2474it [00:16, 150.35it/s]\u001b[A\n",
            "RBM fitting: 2490it [00:16, 151.68it/s]\u001b[A\n",
            "RBM fitting: 2506it [00:16, 152.87it/s]\u001b[A\n",
            "RBM fitting: 2522it [00:16, 153.66it/s]\u001b[A\n",
            "RBM fitting: 2538it [00:16, 148.22it/s]\u001b[A\n",
            "RBM fitting: 2553it [00:16, 146.12it/s]\u001b[A\n",
            "RBM fitting: 2569it [00:17, 148.63it/s]\u001b[A\n",
            "RBM fitting: 2585it [00:17, 151.63it/s]\u001b[A\n",
            "RBM fitting: 2601it [00:17, 153.29it/s]\u001b[A\n",
            "RBM fitting: 2617it [00:17, 152.58it/s]\u001b[A\n",
            "RBM fitting: 2633it [00:17, 146.69it/s]\u001b[A\n",
            "RBM fitting: 2649it [00:17, 148.84it/s]\u001b[A\n",
            "RBM fitting: 2665it [00:17, 150.70it/s]\u001b[A\n",
            "RBM fitting: 2681it [00:17, 152.34it/s]\u001b[A\n",
            "RBM fitting: 2697it [00:17, 154.12it/s]\u001b[A\n",
            "RBM fitting: 2713it [00:17, 151.95it/s]\u001b[A\n",
            "RBM fitting: 2729it [00:18, 145.11it/s]\u001b[A\n",
            "RBM fitting: 2744it [00:18, 145.79it/s]\u001b[A\n",
            "RBM fitting: 2760it [00:18, 148.79it/s]\u001b[A\n",
            "RBM fitting: 2776it [00:18, 150.43it/s]\u001b[A\n",
            "RBM fitting: 2792it [00:18, 151.78it/s]\u001b[A\n",
            "RBM fitting: 2808it [00:18, 153.08it/s]\u001b[A\n",
            "RBM fitting: 2824it [00:18, 147.46it/s]\u001b[A\n",
            "RBM fitting: 2840it [00:18, 148.75it/s]\u001b[A\n",
            "RBM fitting: 2857it [00:18, 152.47it/s]\u001b[A\n",
            "RBM fitting: 2873it [00:19, 149.99it/s]\u001b[A\n",
            "RBM fitting: 2889it [00:19, 152.74it/s]\u001b[A\n",
            "RBM fitting: 2905it [00:19, 154.51it/s]\u001b[A\n",
            "RBM fitting: 2921it [00:19, 148.09it/s]\u001b[A\n",
            "RBM fitting: 2937it [00:19, 150.86it/s]\u001b[A\n",
            "RBM fitting: 2953it [00:19, 153.16it/s]\u001b[A\n",
            "RBM fitting: 2969it [00:19, 154.09it/s]\u001b[A\n",
            "RBM fitting: 2985it [00:19, 155.37it/s]\u001b[A\n",
            "RBM fitting: 3001it [00:19, 156.53it/s]\u001b[A\n",
            "RBM fitting: 3017it [00:20, 146.33it/s]\u001b[A\n",
            "RBM fitting: 3033it [00:20, 149.46it/s]\u001b[A\n",
            "RBM fitting: 3049it [00:20, 151.67it/s]\u001b[A\n",
            "RBM fitting: 3065it [00:20, 152.78it/s]\u001b[A\n",
            "RBM fitting: 3081it [00:20, 151.43it/s]\u001b[A\n",
            "RBM fitting: 3097it [00:20, 153.70it/s]\u001b[A\n",
            "RBM fitting: 3113it [00:20, 147.12it/s]\u001b[A\n",
            "RBM fitting: 3128it [00:20, 147.80it/s]\u001b[A\n",
            "RBM fitting: 3144it [00:20, 150.80it/s]\u001b[A\n",
            "RBM fitting: 3160it [00:20, 152.22it/s]\u001b[A\n",
            "RBM fitting: 3176it [00:21, 150.93it/s]\u001b[A\n",
            "RBM fitting: 3193it [00:21, 154.58it/s]\u001b[A\n",
            "RBM fitting: 3209it [00:21, 149.07it/s]\u001b[A\n",
            "RBM fitting: 3225it [00:21, 151.36it/s]\u001b[A\n",
            "RBM fitting: 3241it [00:21, 153.22it/s]\u001b[A\n",
            "RBM fitting: 3258it [00:21, 156.22it/s]\u001b[A\n",
            "RBM fitting: 3274it [00:21, 156.56it/s]\u001b[A\n",
            "RBM fitting: 3290it [00:21, 157.25it/s]\u001b[A\n",
            "RBM fitting: 3306it [00:21, 150.89it/s]\u001b[A\n",
            "RBM fitting: 3322it [00:22, 151.51it/s]\u001b[A\n",
            "RBM fitting: 3338it [00:22, 153.64it/s]\u001b[A\n",
            "RBM fitting: 3355it [00:22, 156.23it/s]\u001b[A\n",
            "RBM fitting: 3371it [00:22, 156.83it/s]\u001b[A\n",
            "RBM fitting: 3387it [00:22, 157.21it/s]\u001b[A\n",
            "RBM fitting: 3403it [00:22, 150.19it/s]\u001b[A\n",
            "RBM fitting: 3419it [00:22, 152.46it/s]\u001b[A\n",
            "RBM fitting: 3435it [00:22, 154.49it/s]\u001b[A\n",
            "RBM fitting: 3452it [00:22, 156.49it/s]\u001b[A\n",
            "RBM fitting: 3468it [00:22, 157.25it/s]\u001b[A\n",
            "RBM fitting: 3484it [00:23, 154.72it/s]\u001b[A\n",
            "RBM fitting: 3500it [00:23, 149.35it/s]\u001b[A\n",
            "RBM fitting: 3517it [00:23, 153.03it/s]\u001b[A\n",
            "RBM fitting: 3533it [00:23, 154.08it/s]\u001b[A\n",
            "RBM fitting: 3549it [00:23, 154.91it/s]\u001b[A\n",
            "RBM fitting: 3565it [00:23, 155.10it/s]\u001b[A\n",
            "RBM fitting: 3581it [00:23, 155.49it/s]\u001b[A\n",
            "RBM fitting: 3597it [00:23, 149.21it/s]\u001b[A\n",
            "RBM fitting: 3613it [00:23, 151.98it/s]\u001b[A\n",
            "RBM fitting: 3629it [00:23, 153.67it/s]\u001b[A\n",
            "RBM fitting: 3645it [00:24, 152.81it/s]\u001b[A\n",
            "RBM fitting: 3661it [00:24, 154.83it/s]\u001b[A\n",
            "RBM fitting: 3677it [00:24, 156.26it/s]\u001b[A\n",
            "RBM fitting: 3693it [00:24, 149.28it/s]\u001b[A\n",
            "RBM fitting: 3709it [00:24, 152.03it/s]\u001b[A\n",
            "RBM fitting: 3725it [00:24, 153.41it/s]\u001b[A\n",
            "RBM fitting: 3741it [00:24, 154.67it/s]\u001b[A\n",
            "RBM fitting: 3757it [00:24, 155.25it/s]\u001b[A\n",
            "RBM fitting: 3773it [00:24, 156.42it/s]\u001b[A\n",
            "RBM fitting: 3789it [00:25, 149.34it/s]\u001b[A\n",
            "RBM fitting: 3805it [00:25, 149.71it/s]\u001b[A\n",
            "RBM fitting: 3821it [00:25, 151.82it/s]\u001b[A\n",
            "RBM fitting: 3837it [00:25, 153.66it/s]\u001b[A\n",
            "RBM fitting: 3853it [00:25, 154.31it/s]\u001b[A\n",
            "RBM fitting: 3869it [00:25, 154.20it/s]\u001b[A\n",
            "RBM fitting: 3885it [00:25, 146.58it/s]\u001b[A\n",
            "RBM fitting: 3901it [00:25, 149.51it/s]\u001b[A\n",
            "RBM fitting: 3917it [00:25, 151.71it/s]\u001b[A\n",
            "RBM fitting: 3933it [00:25, 153.24it/s]\u001b[A\n",
            "RBM fitting: 3949it [00:26, 152.00it/s]\u001b[A\n",
            "RBM fitting: 3965it [00:26, 153.84it/s]\u001b[A\n",
            "RBM fitting: 3981it [00:26, 148.57it/s]\u001b[A\n",
            "RBM fitting: 3997it [00:26, 150.50it/s]\u001b[A\n",
            "RBM fitting: 4013it [00:26, 152.60it/s]\u001b[A\n",
            "RBM fitting: 4029it [00:26, 152.76it/s]\u001b[A\n",
            "RBM fitting: 4045it [00:26, 153.60it/s]\u001b[A\n",
            "RBM fitting: 4061it [00:26, 154.57it/s]\u001b[A\n",
            "RBM fitting: 4077it [00:26, 148.15it/s]\u001b[A\n",
            "RBM fitting: 4093it [00:27, 149.84it/s]\u001b[A\n",
            "RBM fitting: 4109it [00:27, 149.60it/s]\u001b[A\n",
            "RBM fitting: 4125it [00:27, 151.09it/s]\u001b[A\n",
            "RBM fitting: 4141it [00:27, 152.21it/s]\u001b[A\n",
            "RBM fitting: 4157it [00:27, 153.66it/s]\u001b[A\n",
            "RBM fitting: 4173it [00:27, 147.40it/s]\u001b[A\n",
            "RBM fitting: 4189it [00:27, 150.38it/s]\u001b[A\n",
            "RBM fitting: 4205it [00:27, 152.43it/s]\u001b[A\n",
            "RBM fitting: 4221it [00:27, 154.08it/s]\u001b[A\n",
            "RBM fitting: 4237it [00:27, 155.39it/s]\u001b[A\n",
            "RBM fitting: 4253it [00:28, 156.48it/s]\u001b[A\n",
            "RBM fitting: 4269it [00:28, 155.05it/s]\u001b[A\n",
            "RBM fitting: 4285it [00:28, 149.06it/s]\u001b[A\n",
            "RBM fitting: 4301it [00:28, 151.14it/s]\u001b[A\n",
            "RBM fitting: 4317it [00:28, 152.94it/s]\u001b[A\n",
            "RBM fitting: 4333it [00:28, 154.31it/s]\u001b[A\n",
            "RBM fitting: 4349it [00:28, 154.61it/s]\u001b[A\n",
            "RBM fitting: 4365it [00:28, 155.71it/s]\u001b[A\n",
            "RBM fitting: 4381it [00:28, 148.77it/s]\u001b[A\n",
            "RBM fitting: 4397it [00:29, 150.78it/s]\u001b[A\n",
            "RBM fitting: 4413it [00:29, 150.97it/s]\u001b[A\n",
            "RBM fitting: 4429it [00:29, 152.76it/s]\u001b[A\n",
            "RBM fitting: 4445it [00:29, 153.25it/s]\u001b[A\n",
            "RBM fitting: 4461it [00:29, 154.26it/s]\u001b[A\n",
            "RBM fitting: 4477it [00:29, 155.40it/s]\u001b[A\n",
            "RBM fitting: 4493it [00:29, 146.54it/s]\u001b[A\n",
            "RBM fitting: 4509it [00:29, 149.85it/s]\u001b[A\n",
            "RBM fitting: 4525it [00:29, 147.72it/s]\u001b[A\n",
            "RBM fitting: 4541it [00:29, 150.38it/s]\u001b[A\n",
            "RBM fitting: 4557it [00:30, 152.54it/s]\u001b[A\n",
            "RBM fitting: 4573it [00:30, 151.29it/s]\u001b[A\n",
            "RBM fitting: 4589it [00:30, 144.26it/s]\u001b[A\n",
            "RBM fitting: 4605it [00:30, 147.48it/s]\u001b[A\n",
            "RBM fitting: 4622it [00:30, 151.75it/s]\u001b[A\n",
            "RBM fitting: 4638it [00:30, 153.11it/s]\u001b[A\n",
            "RBM fitting: 4654it [00:30, 154.79it/s]\u001b[A\n",
            "RBM fitting: 4670it [00:30, 155.89it/s]\u001b[A\n",
            "RBM fitting: 4686it [00:30, 155.94it/s]\u001b[A\n",
            "RBM fitting: 4702it [00:31, 146.53it/s]\u001b[A\n",
            "RBM fitting: 4718it [00:31, 148.58it/s]\u001b[A\n",
            "RBM fitting: 4734it [00:31, 151.67it/s]\u001b[A\n",
            "RBM fitting: 4750it [00:31, 153.11it/s]\u001b[A\n",
            "RBM fitting: 4766it [00:31, 154.06it/s]\u001b[A\n",
            "RBM fitting: 4783it [00:31, 156.17it/s]\u001b[A\n",
            "RBM fitting: 4799it [00:31, 148.69it/s]\u001b[A\n",
            "RBM fitting: 4815it [00:31, 151.72it/s]\u001b[A\n",
            "RBM fitting: 4832it [00:31, 154.91it/s]\u001b[A\n",
            "RBM fitting: 4848it [00:32, 155.60it/s]\u001b[A\n",
            "RBM fitting: 4864it [00:32, 156.31it/s]\u001b[A\n",
            "RBM fitting: 4880it [00:32, 154.10it/s]\u001b[A\n",
            "RBM fitting: 4896it [00:32, 154.33it/s]\u001b[A\n",
            "RBM fitting: 4912it [00:32, 144.19it/s]\u001b[A\n",
            "RBM fitting: 4928it [00:32, 148.22it/s]\u001b[A\n",
            "RBM fitting: 4944it [00:32, 151.51it/s]\u001b[A\n",
            "RBM fitting: 4960it [00:32, 153.52it/s]\u001b[A\n",
            "RBM fitting: 4976it [00:32, 155.36it/s]\u001b[A\n",
            "RBM fitting: 4992it [00:32, 156.39it/s]\u001b[A\n",
            "RBM fitting: 5008it [00:33, 147.75it/s]\u001b[A\n",
            "RBM fitting: 5024it [00:33, 151.12it/s]\u001b[A\n",
            "RBM fitting: 5040it [00:33, 150.30it/s]\u001b[A\n",
            "RBM fitting: 5056it [00:33, 151.85it/s]\u001b[A\n",
            "RBM fitting: 5072it [00:33, 153.00it/s]\u001b[A\n",
            "RBM fitting: 5088it [00:33, 153.92it/s]\u001b[A\n",
            "RBM fitting: 5104it [00:33, 145.01it/s]\u001b[A\n",
            "RBM fitting: 5120it [00:33, 148.79it/s]\u001b[A\n",
            "RBM fitting: 5136it [00:33, 151.42it/s]\u001b[A\n",
            "RBM fitting: 5152it [00:34, 152.75it/s]\u001b[A\n",
            "RBM fitting: 5168it [00:34, 154.68it/s]\u001b[A\n",
            "RBM fitting: 5184it [00:34, 153.60it/s]\u001b[A\n",
            "RBM fitting: 5200it [00:34, 145.69it/s]\u001b[A\n",
            "RBM fitting: 5216it [00:34, 149.14it/s]\u001b[A\n",
            "RBM fitting: 5233it [00:34, 152.94it/s]\u001b[A\n",
            "RBM fitting: 5249it [00:34, 154.42it/s]\u001b[A\n",
            "RBM fitting: 5265it [00:34, 155.76it/s]\u001b[A\n",
            "RBM fitting: 5281it [00:34, 156.85it/s]\u001b[A\n",
            "RBM fitting: 5297it [00:34, 147.01it/s]\u001b[A\n",
            "RBM fitting: 5313it [00:35, 148.94it/s]\u001b[A\n",
            "RBM fitting: 5329it [00:35, 151.42it/s]\u001b[A\n",
            "RBM fitting: 5345it [00:35, 150.67it/s]\u001b[A\n",
            "RBM fitting: 5361it [00:35, 152.59it/s]\u001b[A\n",
            "RBM fitting: 5377it [00:35, 154.06it/s]\u001b[A\n",
            "RBM fitting: 5393it [00:35, 146.44it/s]\u001b[A\n",
            "RBM fitting: 5409it [00:35, 148.44it/s]\u001b[A\n",
            "RBM fitting: 5425it [00:35, 150.52it/s]\u001b[A\n",
            "RBM fitting: 5441it [00:35, 152.55it/s]\u001b[A\n",
            "RBM fitting: 5457it [00:36, 153.65it/s]\u001b[A\n",
            "RBM fitting: 5474it [00:36, 156.65it/s]\u001b[A\n",
            "RBM fitting: 5490it [00:36, 147.25it/s]\u001b[A\n",
            "RBM fitting: 5506it [00:36, 149.71it/s]\u001b[A\n",
            "RBM fitting: 5522it [00:36, 150.87it/s]\u001b[A\n",
            "RBM fitting: 5538it [00:36, 151.69it/s]\u001b[A\n",
            "RBM fitting: 5554it [00:36, 153.17it/s]\u001b[A\n",
            "RBM fitting: 5570it [00:36, 153.98it/s]\u001b[A\n",
            "RBM fitting: 5587it [00:36, 156.48it/s]\u001b[A\n",
            "RBM fitting: 5603it [00:37, 145.47it/s]\u001b[A\n",
            "RBM fitting: 5619it [00:37, 148.65it/s]\u001b[A\n",
            "RBM fitting: 5635it [00:37, 151.71it/s]\u001b[A\n",
            "RBM fitting: 5651it [00:37, 151.21it/s]\u001b[A\n",
            "RBM fitting: 5667it [00:37, 152.68it/s]\u001b[A\n",
            "RBM fitting: 5683it [00:37, 154.16it/s]\u001b[A\n",
            "RBM fitting: 5699it [00:37, 144.72it/s]\u001b[A\n",
            "RBM fitting: 5715it [00:37, 147.95it/s]\u001b[A\n",
            "RBM fitting: 5731it [00:37, 150.38it/s]\u001b[A\n",
            "RBM fitting: 5747it [00:37, 151.78it/s]\u001b[A\n",
            "RBM fitting: 5763it [00:38, 152.62it/s]\u001b[A\n",
            "RBM fitting: 5779it [00:38, 154.74it/s]\u001b[A\n",
            "RBM fitting: 5795it [00:38, 140.20it/s]\u001b[A\n",
            "RBM fitting: 5811it [00:38, 144.56it/s]\u001b[A\n",
            "RBM fitting: 5827it [00:38, 147.81it/s]\u001b[A\n",
            "RBM fitting: 5843it [00:38, 149.86it/s]\u001b[A\n",
            "RBM fitting: 5859it [00:38, 152.61it/s]\u001b[A\n",
            "RBM fitting: 5875it [00:38, 153.98it/s]\u001b[A\n",
            "RBM fitting: 5891it [00:38, 145.45it/s]\u001b[A\n",
            "RBM fitting: 5907it [00:39, 147.95it/s]\u001b[A\n",
            "RBM fitting: 5923it [00:39, 151.12it/s]\u001b[A\n",
            "RBM fitting: 5939it [00:39, 152.81it/s]\u001b[A\n",
            "RBM fitting: 5955it [00:39, 151.82it/s]\u001b[A\n",
            "RBM fitting: 5971it [00:39, 152.69it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 151.29it/s]\n",
            "Epoch:22 ,avg_cost = 0.3406103253364563 ,std_cost = 0.03370208293199539 ,avg_grad = 10088.2587890625 ,std_grad = 539.18310546875\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 14it [00:00, 137.38it/s]\u001b[A\n",
            "RBM fitting: 30it [00:00, 143.31it/s]\u001b[A\n",
            "RBM fitting: 46it [00:00, 147.24it/s]\u001b[A\n",
            "RBM fitting: 59it [00:00, 141.16it/s]\u001b[A\n",
            "RBM fitting: 75it [00:00, 145.90it/s]\u001b[A\n",
            "RBM fitting: 91it [00:00, 149.79it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 149.31it/s]\u001b[A\n",
            "RBM fitting: 123it [00:00, 152.78it/s]\u001b[A\n",
            "RBM fitting: 139it [00:00, 154.12it/s]\u001b[A\n",
            "RBM fitting: 155it [00:01, 155.03it/s]\u001b[A\n",
            "RBM fitting: 171it [00:01, 147.97it/s]\u001b[A\n",
            "RBM fitting: 187it [00:01, 151.20it/s]\u001b[A\n",
            "RBM fitting: 203it [00:01, 152.70it/s]\u001b[A\n",
            "RBM fitting: 219it [00:01, 154.47it/s]\u001b[A\n",
            "RBM fitting: 235it [00:01, 154.89it/s]\u001b[A\n",
            "RBM fitting: 251it [00:01, 156.33it/s]\u001b[A\n",
            "RBM fitting: 267it [00:01, 154.19it/s]\u001b[A\n",
            "RBM fitting: 283it [00:01, 146.87it/s]\u001b[A\n",
            "RBM fitting: 299it [00:01, 149.53it/s]\u001b[A\n",
            "RBM fitting: 315it [00:02, 151.30it/s]\u001b[A\n",
            "RBM fitting: 331it [00:02, 152.94it/s]\u001b[A\n",
            "RBM fitting: 347it [00:02, 153.28it/s]\u001b[A\n",
            "RBM fitting: 363it [00:02, 152.62it/s]\u001b[A\n",
            "RBM fitting: 379it [00:02, 142.74it/s]\u001b[A\n",
            "RBM fitting: 395it [00:02, 145.73it/s]\u001b[A\n",
            "RBM fitting: 410it [00:02, 145.86it/s]\u001b[A\n",
            "RBM fitting: 427it [00:02, 150.32it/s]\u001b[A\n",
            "RBM fitting: 443it [00:02, 152.94it/s]\u001b[A\n",
            "RBM fitting: 459it [00:03, 154.65it/s]\u001b[A\n",
            "RBM fitting: 475it [00:03, 146.35it/s]\u001b[A\n",
            "RBM fitting: 490it [00:03, 146.59it/s]\u001b[A\n",
            "RBM fitting: 506it [00:03, 149.62it/s]\u001b[A\n",
            "RBM fitting: 523it [00:03, 153.94it/s]\u001b[A\n",
            "RBM fitting: 539it [00:03, 155.58it/s]\u001b[A\n",
            "RBM fitting: 555it [00:03, 156.12it/s]\u001b[A\n",
            "RBM fitting: 571it [00:03, 145.85it/s]\u001b[A\n",
            "RBM fitting: 586it [00:03, 146.33it/s]\u001b[A\n",
            "RBM fitting: 602it [00:03, 149.11it/s]\u001b[A\n",
            "RBM fitting: 618it [00:04, 151.22it/s]\u001b[A\n",
            "RBM fitting: 634it [00:04, 153.62it/s]\u001b[A\n",
            "RBM fitting: 650it [00:04, 154.96it/s]\u001b[A\n",
            "RBM fitting: 666it [00:04, 155.14it/s]\u001b[A\n",
            "RBM fitting: 682it [00:04, 145.35it/s]\u001b[A\n",
            "RBM fitting: 698it [00:04, 147.78it/s]\u001b[A\n",
            "RBM fitting: 713it [00:04, 147.57it/s]\u001b[A\n",
            "RBM fitting: 729it [00:04, 150.46it/s]\u001b[A\n",
            "RBM fitting: 745it [00:04, 152.45it/s]\u001b[A\n",
            "RBM fitting: 761it [00:05, 154.37it/s]\u001b[A\n",
            "RBM fitting: 777it [00:05, 144.87it/s]\u001b[A\n",
            "RBM fitting: 793it [00:05, 148.48it/s]\u001b[A\n",
            "RBM fitting: 809it [00:05, 151.32it/s]\u001b[A\n",
            "RBM fitting: 826it [00:05, 154.47it/s]\u001b[A\n",
            "RBM fitting: 842it [00:05, 154.19it/s]\u001b[A\n",
            "RBM fitting: 858it [00:05, 154.72it/s]\u001b[A\n",
            "RBM fitting: 874it [00:05, 145.02it/s]\u001b[A\n",
            "RBM fitting: 890it [00:05, 147.87it/s]\u001b[A\n",
            "RBM fitting: 906it [00:06, 149.81it/s]\u001b[A\n",
            "RBM fitting: 922it [00:06, 150.92it/s]\u001b[A\n",
            "RBM fitting: 938it [00:06, 152.17it/s]\u001b[A\n",
            "RBM fitting: 954it [00:06, 153.96it/s]\u001b[A\n",
            "RBM fitting: 970it [00:06, 154.75it/s]\u001b[A\n",
            "RBM fitting: 986it [00:06, 148.30it/s]\u001b[A\n",
            "RBM fitting: 1001it [00:06, 148.25it/s]\u001b[A\n",
            "RBM fitting: 1017it [00:06, 150.56it/s]\u001b[A\n",
            "RBM fitting: 1033it [00:06, 150.44it/s]\u001b[A\n",
            "RBM fitting: 1049it [00:06, 152.99it/s]\u001b[A\n",
            "RBM fitting: 1065it [00:07, 154.87it/s]\u001b[A\n",
            "RBM fitting: 1081it [00:07, 155.91it/s]\u001b[A\n",
            "RBM fitting: 1097it [00:07, 147.47it/s]\u001b[A\n",
            "RBM fitting: 1112it [00:07, 145.59it/s]\u001b[A\n",
            "RBM fitting: 1128it [00:07, 147.15it/s]\u001b[A\n",
            "RBM fitting: 1143it [00:07, 147.18it/s]\u001b[A\n",
            "RBM fitting: 1159it [00:07, 149.52it/s]\u001b[A\n",
            "RBM fitting: 1174it [00:07, 149.63it/s]\u001b[A\n",
            "RBM fitting: 1189it [00:07, 141.53it/s]\u001b[A\n",
            "RBM fitting: 1204it [00:08, 143.34it/s]\u001b[A\n",
            "RBM fitting: 1220it [00:08, 147.01it/s]\u001b[A\n",
            "RBM fitting: 1236it [00:08, 148.89it/s]\u001b[A\n",
            "RBM fitting: 1252it [00:08, 150.82it/s]\u001b[A\n",
            "RBM fitting: 1268it [00:08, 151.94it/s]\u001b[A\n",
            "RBM fitting: 1284it [00:08, 144.04it/s]\u001b[A\n",
            "RBM fitting: 1300it [00:08, 147.19it/s]\u001b[A\n",
            "RBM fitting: 1316it [00:08, 149.56it/s]\u001b[A\n",
            "RBM fitting: 1332it [00:08, 149.48it/s]\u001b[A\n",
            "RBM fitting: 1348it [00:08, 150.94it/s]\u001b[A\n",
            "RBM fitting: 1364it [00:09, 153.03it/s]\u001b[A\n",
            "RBM fitting: 1380it [00:09, 143.21it/s]\u001b[A\n",
            "RBM fitting: 1395it [00:09, 145.04it/s]\u001b[A\n",
            "RBM fitting: 1411it [00:09, 148.42it/s]\u001b[A\n",
            "RBM fitting: 1427it [00:09, 151.71it/s]\u001b[A\n",
            "RBM fitting: 1443it [00:09, 153.61it/s]\u001b[A\n",
            "RBM fitting: 1459it [00:09, 152.36it/s]\u001b[A\n",
            "RBM fitting: 1475it [00:09, 137.54it/s]\u001b[A\n",
            "RBM fitting: 1491it [00:09, 143.41it/s]\u001b[A\n",
            "RBM fitting: 1507it [00:10, 146.46it/s]\u001b[A\n",
            "RBM fitting: 1523it [00:10, 148.39it/s]\u001b[A\n",
            "RBM fitting: 1539it [00:10, 150.98it/s]\u001b[A\n",
            "RBM fitting: 1555it [00:10, 152.31it/s]\u001b[A\n",
            "RBM fitting: 1572it [00:10, 155.27it/s]\u001b[A\n",
            "RBM fitting: 1588it [00:10, 149.02it/s]\u001b[A\n",
            "RBM fitting: 1604it [00:10, 150.18it/s]\u001b[A\n",
            "RBM fitting: 1620it [00:10, 151.54it/s]\u001b[A\n",
            "RBM fitting: 1636it [00:10, 151.62it/s]\u001b[A\n",
            "RBM fitting: 1652it [00:10, 152.55it/s]\u001b[A\n",
            "RBM fitting: 1668it [00:11, 153.77it/s]\u001b[A\n",
            "RBM fitting: 1684it [00:11, 154.91it/s]\u001b[A\n",
            "RBM fitting: 1700it [00:11, 147.89it/s]\u001b[A\n",
            "RBM fitting: 1716it [00:11, 150.84it/s]\u001b[A\n",
            "RBM fitting: 1732it [00:11, 153.39it/s]\u001b[A\n",
            "RBM fitting: 1748it [00:11, 154.98it/s]\u001b[A\n",
            "RBM fitting: 1764it [00:11, 155.95it/s]\u001b[A\n",
            "RBM fitting: 1780it [00:11, 156.77it/s]\u001b[A\n",
            "RBM fitting: 1796it [00:11, 155.02it/s]\u001b[A\n",
            "RBM fitting: 1812it [00:12, 147.03it/s]\u001b[A\n",
            "RBM fitting: 1828it [00:12, 149.29it/s]\u001b[A\n",
            "RBM fitting: 1844it [00:12, 150.80it/s]\u001b[A\n",
            "RBM fitting: 1860it [00:12, 151.26it/s]\u001b[A\n",
            "RBM fitting: 1876it [00:12, 153.47it/s]\u001b[A\n",
            "RBM fitting: 1892it [00:12, 154.11it/s]\u001b[A\n",
            "RBM fitting: 1908it [00:12, 154.37it/s]\u001b[A\n",
            "RBM fitting: 1924it [00:12, 147.67it/s]\u001b[A\n",
            "RBM fitting: 1939it [00:12, 147.85it/s]\u001b[A\n",
            "RBM fitting: 1955it [00:12, 150.71it/s]\u001b[A\n",
            "RBM fitting: 1971it [00:13, 152.67it/s]\u001b[A\n",
            "RBM fitting: 1987it [00:13, 154.63it/s]\u001b[A\n",
            "RBM fitting: 2003it [00:13, 155.58it/s]\u001b[A\n",
            "RBM fitting: 2019it [00:13, 155.92it/s]\u001b[A\n",
            "RBM fitting: 2035it [00:13, 148.60it/s]\u001b[A\n",
            "RBM fitting: 2051it [00:13, 151.39it/s]\u001b[A\n",
            "RBM fitting: 2067it [00:13, 153.78it/s]\u001b[A\n",
            "RBM fitting: 2083it [00:13, 153.87it/s]\u001b[A\n",
            "RBM fitting: 2099it [00:13, 152.15it/s]\u001b[A\n",
            "RBM fitting: 2115it [00:14, 153.39it/s]\u001b[A\n",
            "RBM fitting: 2131it [00:14, 154.26it/s]\u001b[A\n",
            "RBM fitting: 2147it [00:14, 147.70it/s]\u001b[A\n",
            "RBM fitting: 2163it [00:14, 149.39it/s]\u001b[A\n",
            "RBM fitting: 2179it [00:14, 151.40it/s]\u001b[A\n",
            "RBM fitting: 2195it [00:14, 153.23it/s]\u001b[A\n",
            "RBM fitting: 2211it [00:14, 153.48it/s]\u001b[A\n",
            "RBM fitting: 2227it [00:14, 154.05it/s]\u001b[A\n",
            "RBM fitting: 2244it [00:14, 156.67it/s]\u001b[A\n",
            "RBM fitting: 2260it [00:15, 146.39it/s]\u001b[A\n",
            "RBM fitting: 2276it [00:15, 149.80it/s]\u001b[A\n",
            "RBM fitting: 2293it [00:15, 153.08it/s]\u001b[A\n",
            "RBM fitting: 2309it [00:15, 154.87it/s]\u001b[A\n",
            "RBM fitting: 2325it [00:15, 155.25it/s]\u001b[A\n",
            "RBM fitting: 2341it [00:15, 156.30it/s]\u001b[A\n",
            "RBM fitting: 2357it [00:15, 157.29it/s]\u001b[A\n",
            "RBM fitting: 2373it [00:15, 149.99it/s]\u001b[A\n",
            "RBM fitting: 2389it [00:15, 149.44it/s]\u001b[A\n",
            "RBM fitting: 2405it [00:15, 148.82it/s]\u001b[A\n",
            "RBM fitting: 2421it [00:16, 151.36it/s]\u001b[A\n",
            "RBM fitting: 2438it [00:16, 154.53it/s]\u001b[A\n",
            "RBM fitting: 2454it [00:16, 154.35it/s]\u001b[A\n",
            "RBM fitting: 2470it [00:16, 153.38it/s]\u001b[A\n",
            "RBM fitting: 2486it [00:16, 146.43it/s]\u001b[A\n",
            "RBM fitting: 2502it [00:16, 149.45it/s]\u001b[A\n",
            "RBM fitting: 2518it [00:16, 151.81it/s]\u001b[A\n",
            "RBM fitting: 2534it [00:16, 152.99it/s]\u001b[A\n",
            "RBM fitting: 2550it [00:16, 154.03it/s]\u001b[A\n",
            "RBM fitting: 2566it [00:17, 152.15it/s]\u001b[A\n",
            "RBM fitting: 2582it [00:17, 152.43it/s]\u001b[A\n",
            "RBM fitting: 2598it [00:17, 146.24it/s]\u001b[A\n",
            "RBM fitting: 2614it [00:17, 148.80it/s]\u001b[A\n",
            "RBM fitting: 2630it [00:17, 150.29it/s]\u001b[A\n",
            "RBM fitting: 2646it [00:17, 151.24it/s]\u001b[A\n",
            "RBM fitting: 2662it [00:17, 152.88it/s]\u001b[A\n",
            "RBM fitting: 2678it [00:17, 154.08it/s]\u001b[A\n",
            "RBM fitting: 2694it [00:17, 155.18it/s]\u001b[A\n",
            "RBM fitting: 2710it [00:17, 145.85it/s]\u001b[A\n",
            "RBM fitting: 2726it [00:18, 148.32it/s]\u001b[A\n",
            "RBM fitting: 2741it [00:18, 148.20it/s]\u001b[A\n",
            "RBM fitting: 2757it [00:18, 150.27it/s]\u001b[A\n",
            "RBM fitting: 2773it [00:18, 151.15it/s]\u001b[A\n",
            "RBM fitting: 2789it [00:18, 152.77it/s]\u001b[A\n",
            "RBM fitting: 2805it [00:18, 152.87it/s]\u001b[A\n",
            "RBM fitting: 2821it [00:18, 144.52it/s]\u001b[A\n",
            "RBM fitting: 2837it [00:18, 147.34it/s]\u001b[A\n",
            "RBM fitting: 2852it [00:18, 147.18it/s]\u001b[A\n",
            "RBM fitting: 2867it [00:19, 146.83it/s]\u001b[A\n",
            "RBM fitting: 2883it [00:19, 149.35it/s]\u001b[A\n",
            "RBM fitting: 2899it [00:19, 150.98it/s]\u001b[A\n",
            "RBM fitting: 2915it [00:19, 152.26it/s]\u001b[A\n",
            "RBM fitting: 2931it [00:19, 143.86it/s]\u001b[A\n",
            "RBM fitting: 2947it [00:19, 146.78it/s]\u001b[A\n",
            "RBM fitting: 2963it [00:19, 149.59it/s]\u001b[A\n",
            "RBM fitting: 2979it [00:19, 150.44it/s]\u001b[A\n",
            "RBM fitting: 2995it [00:19, 151.30it/s]\u001b[A\n",
            "RBM fitting: 3011it [00:19, 152.07it/s]\u001b[A\n",
            "RBM fitting: 3027it [00:20, 143.03it/s]\u001b[A\n",
            "RBM fitting: 3043it [00:20, 146.32it/s]\u001b[A\n",
            "RBM fitting: 3059it [00:20, 148.28it/s]\u001b[A\n",
            "RBM fitting: 3074it [00:20, 148.78it/s]\u001b[A\n",
            "RBM fitting: 3090it [00:20, 150.28it/s]\u001b[A\n",
            "RBM fitting: 3106it [00:20, 151.26it/s]\u001b[A\n",
            "RBM fitting: 3122it [00:20, 152.20it/s]\u001b[A\n",
            "RBM fitting: 3138it [00:20, 145.03it/s]\u001b[A\n",
            "RBM fitting: 3154it [00:20, 147.76it/s]\u001b[A\n",
            "RBM fitting: 3169it [00:21, 146.74it/s]\u001b[A\n",
            "RBM fitting: 3185it [00:21, 149.00it/s]\u001b[A\n",
            "RBM fitting: 3201it [00:21, 150.60it/s]\u001b[A\n",
            "RBM fitting: 3217it [00:21, 151.46it/s]\u001b[A\n",
            "RBM fitting: 3233it [00:21, 153.30it/s]\u001b[A\n",
            "RBM fitting: 3249it [00:21, 147.10it/s]\u001b[A\n",
            "RBM fitting: 3265it [00:21, 149.32it/s]\u001b[A\n",
            "RBM fitting: 3281it [00:21, 150.59it/s]\u001b[A\n",
            "RBM fitting: 3297it [00:21, 152.66it/s]\u001b[A\n",
            "RBM fitting: 3313it [00:21, 154.24it/s]\u001b[A\n",
            "RBM fitting: 3329it [00:22, 152.82it/s]\u001b[A\n",
            "RBM fitting: 3345it [00:22, 154.58it/s]\u001b[A\n",
            "RBM fitting: 3361it [00:22, 145.71it/s]\u001b[A\n",
            "RBM fitting: 3377it [00:22, 148.68it/s]\u001b[A\n",
            "RBM fitting: 3393it [00:22, 150.77it/s]\u001b[A\n",
            "RBM fitting: 3409it [00:22, 153.24it/s]\u001b[A\n",
            "RBM fitting: 3425it [00:22, 154.84it/s]\u001b[A\n",
            "RBM fitting: 3441it [00:22, 155.93it/s]\u001b[A\n",
            "RBM fitting: 3457it [00:22, 156.55it/s]\u001b[A\n",
            "RBM fitting: 3473it [00:23, 146.33it/s]\u001b[A\n",
            "RBM fitting: 3489it [00:23, 149.78it/s]\u001b[A\n",
            "RBM fitting: 3505it [00:23, 152.50it/s]\u001b[A\n",
            "RBM fitting: 3521it [00:23, 153.27it/s]\u001b[A\n",
            "RBM fitting: 3537it [00:23, 154.54it/s]\u001b[A\n",
            "RBM fitting: 3553it [00:23, 155.94it/s]\u001b[A\n",
            "RBM fitting: 3569it [00:23, 157.00it/s]\u001b[A\n",
            "RBM fitting: 3585it [00:23, 148.85it/s]\u001b[A\n",
            "RBM fitting: 3602it [00:23, 152.80it/s]\u001b[A\n",
            "RBM fitting: 3618it [00:23, 153.68it/s]\u001b[A\n",
            "RBM fitting: 3634it [00:24, 152.29it/s]\u001b[A\n",
            "RBM fitting: 3651it [00:24, 155.17it/s]\u001b[A\n",
            "RBM fitting: 3667it [00:24, 155.98it/s]\u001b[A\n",
            "RBM fitting: 3683it [00:24, 154.60it/s]\u001b[A\n",
            "RBM fitting: 3699it [00:24, 147.13it/s]\u001b[A\n",
            "RBM fitting: 3715it [00:24, 150.16it/s]\u001b[A\n",
            "RBM fitting: 3731it [00:24, 152.40it/s]\u001b[A\n",
            "RBM fitting: 3747it [00:24, 153.35it/s]\u001b[A\n",
            "RBM fitting: 3763it [00:24, 154.68it/s]\u001b[A\n",
            "RBM fitting: 3779it [00:25, 155.58it/s]\u001b[A\n",
            "RBM fitting: 3795it [00:25, 153.80it/s]\u001b[A\n",
            "RBM fitting: 3811it [00:25, 147.36it/s]\u001b[A\n",
            "RBM fitting: 3827it [00:25, 149.60it/s]\u001b[A\n",
            "RBM fitting: 3843it [00:25, 151.87it/s]\u001b[A\n",
            "RBM fitting: 3859it [00:25, 153.08it/s]\u001b[A\n",
            "RBM fitting: 3875it [00:25, 153.07it/s]\u001b[A\n",
            "RBM fitting: 3891it [00:25, 153.10it/s]\u001b[A\n",
            "RBM fitting: 3907it [00:25, 154.12it/s]\u001b[A\n",
            "RBM fitting: 3923it [00:26, 147.61it/s]\u001b[A\n",
            "RBM fitting: 3939it [00:26, 148.70it/s]\u001b[A\n",
            "RBM fitting: 3955it [00:26, 151.82it/s]\u001b[A\n",
            "RBM fitting: 3971it [00:26, 153.52it/s]\u001b[A\n",
            "RBM fitting: 3988it [00:26, 156.13it/s]\u001b[A\n",
            "RBM fitting: 4005it [00:26, 158.24it/s]\u001b[A\n",
            "RBM fitting: 4022it [00:26, 159.81it/s]\u001b[A\n",
            "RBM fitting: 4039it [00:26, 152.72it/s]\u001b[A\n",
            "RBM fitting: 4055it [00:26, 153.99it/s]\u001b[A\n",
            "RBM fitting: 4071it [00:26, 155.05it/s]\u001b[A\n",
            "RBM fitting: 4087it [00:27, 155.65it/s]\u001b[A\n",
            "RBM fitting: 4103it [00:27, 153.20it/s]\u001b[A\n",
            "RBM fitting: 4120it [00:27, 155.59it/s]\u001b[A\n",
            "RBM fitting: 4136it [00:27, 156.74it/s]\u001b[A\n",
            "RBM fitting: 4152it [00:27, 149.01it/s]\u001b[A\n",
            "RBM fitting: 4168it [00:27, 151.56it/s]\u001b[A\n",
            "RBM fitting: 4184it [00:27, 153.90it/s]\u001b[A\n",
            "RBM fitting: 4200it [00:27, 154.91it/s]\u001b[A\n",
            "RBM fitting: 4216it [00:27, 155.02it/s]\u001b[A\n",
            "RBM fitting: 4232it [00:27, 156.10it/s]\u001b[A\n",
            "RBM fitting: 4248it [00:28, 157.10it/s]\u001b[A\n",
            "RBM fitting: 4264it [00:28, 147.57it/s]\u001b[A\n",
            "RBM fitting: 4280it [00:28, 150.58it/s]\u001b[A\n",
            "RBM fitting: 4296it [00:28, 151.69it/s]\u001b[A\n",
            "RBM fitting: 4312it [00:28, 153.25it/s]\u001b[A\n",
            "RBM fitting: 4329it [00:28, 155.90it/s]\u001b[A\n",
            "RBM fitting: 4345it [00:28, 157.09it/s]\u001b[A\n",
            "RBM fitting: 4361it [00:28, 157.18it/s]\u001b[A\n",
            "RBM fitting: 4377it [00:28, 149.18it/s]\u001b[A\n",
            "RBM fitting: 4393it [00:29, 151.78it/s]\u001b[A\n",
            "RBM fitting: 4409it [00:29, 151.37it/s]\u001b[A\n",
            "RBM fitting: 4425it [00:29, 153.32it/s]\u001b[A\n",
            "RBM fitting: 4441it [00:29, 150.31it/s]\u001b[A\n",
            "RBM fitting: 4457it [00:29, 152.72it/s]\u001b[A\n",
            "RBM fitting: 4474it [00:29, 155.27it/s]\u001b[A\n",
            "RBM fitting: 4490it [00:29, 145.43it/s]\u001b[A\n",
            "RBM fitting: 4506it [00:29, 148.22it/s]\u001b[A\n",
            "RBM fitting: 4522it [00:29, 151.27it/s]\u001b[A\n",
            "RBM fitting: 4538it [00:30, 152.66it/s]\u001b[A\n",
            "RBM fitting: 4554it [00:30, 153.20it/s]\u001b[A\n",
            "RBM fitting: 4570it [00:30, 152.38it/s]\u001b[A\n",
            "RBM fitting: 4586it [00:30, 153.78it/s]\u001b[A\n",
            "RBM fitting: 4602it [00:30, 147.14it/s]\u001b[A\n",
            "RBM fitting: 4618it [00:30, 150.23it/s]\u001b[A\n",
            "RBM fitting: 4635it [00:30, 153.48it/s]\u001b[A\n",
            "RBM fitting: 4651it [00:30, 152.64it/s]\u001b[A\n",
            "RBM fitting: 4667it [00:30, 154.07it/s]\u001b[A\n",
            "RBM fitting: 4683it [00:30, 155.26it/s]\u001b[A\n",
            "RBM fitting: 4700it [00:31, 157.67it/s]\u001b[A\n",
            "RBM fitting: 4716it [00:31, 149.56it/s]\u001b[A\n",
            "RBM fitting: 4732it [00:31, 151.56it/s]\u001b[A\n",
            "RBM fitting: 4748it [00:31, 152.90it/s]\u001b[A\n",
            "RBM fitting: 4764it [00:31, 154.17it/s]\u001b[A\n",
            "RBM fitting: 4780it [00:31, 155.73it/s]\u001b[A\n",
            "RBM fitting: 4797it [00:31, 157.44it/s]\u001b[A\n",
            "RBM fitting: 4813it [00:31, 156.75it/s]\u001b[A\n",
            "RBM fitting: 4829it [00:31, 147.90it/s]\u001b[A\n",
            "RBM fitting: 4845it [00:32, 150.20it/s]\u001b[A\n",
            "RBM fitting: 4861it [00:32, 152.76it/s]\u001b[A\n",
            "RBM fitting: 4877it [00:32, 151.79it/s]\u001b[A\n",
            "RBM fitting: 4893it [00:32, 153.30it/s]\u001b[A\n",
            "RBM fitting: 4909it [00:32, 154.60it/s]\u001b[A\n",
            "RBM fitting: 4925it [00:32, 155.21it/s]\u001b[A\n",
            "RBM fitting: 4941it [00:32, 148.83it/s]\u001b[A\n",
            "RBM fitting: 4957it [00:32, 151.74it/s]\u001b[A\n",
            "RBM fitting: 4973it [00:32, 153.25it/s]\u001b[A\n",
            "RBM fitting: 4989it [00:32, 154.33it/s]\u001b[A\n",
            "RBM fitting: 5005it [00:33, 155.92it/s]\u001b[A\n",
            "RBM fitting: 5021it [00:33, 156.43it/s]\u001b[A\n",
            "RBM fitting: 5037it [00:33, 154.56it/s]\u001b[A\n",
            "RBM fitting: 5053it [00:33, 145.74it/s]\u001b[A\n",
            "RBM fitting: 5069it [00:33, 149.56it/s]\u001b[A\n",
            "RBM fitting: 5085it [00:33, 151.65it/s]\u001b[A\n",
            "RBM fitting: 5101it [00:33, 153.30it/s]\u001b[A\n",
            "RBM fitting: 5117it [00:33, 153.99it/s]\u001b[A\n",
            "RBM fitting: 5133it [00:33, 154.92it/s]\u001b[A\n",
            "RBM fitting: 5149it [00:34, 151.02it/s]\u001b[A\n",
            "RBM fitting: 5165it [00:34, 144.80it/s]\u001b[A\n",
            "RBM fitting: 5180it [00:34, 144.41it/s]\u001b[A\n",
            "RBM fitting: 5196it [00:34, 147.31it/s]\u001b[A\n",
            "RBM fitting: 5212it [00:34, 150.39it/s]\u001b[A\n",
            "RBM fitting: 5228it [00:34, 151.51it/s]\u001b[A\n",
            "RBM fitting: 5244it [00:34, 152.44it/s]\u001b[A\n",
            "RBM fitting: 5260it [00:34, 152.06it/s]\u001b[A\n",
            "RBM fitting: 5276it [00:34, 144.79it/s]\u001b[A\n",
            "RBM fitting: 5292it [00:34, 148.06it/s]\u001b[A\n",
            "RBM fitting: 5308it [00:35, 151.07it/s]\u001b[A\n",
            "RBM fitting: 5325it [00:35, 154.50it/s]\u001b[A\n",
            "RBM fitting: 5341it [00:35, 153.04it/s]\u001b[A\n",
            "RBM fitting: 5357it [00:35, 154.59it/s]\u001b[A\n",
            "RBM fitting: 5374it [00:35, 157.15it/s]\u001b[A\n",
            "RBM fitting: 5390it [00:35, 149.72it/s]\u001b[A\n",
            "RBM fitting: 5407it [00:35, 153.14it/s]\u001b[A\n",
            "RBM fitting: 5423it [00:35, 154.04it/s]\u001b[A\n",
            "RBM fitting: 5439it [00:35, 155.61it/s]\u001b[A\n",
            "RBM fitting: 5455it [00:36, 156.69it/s]\u001b[A\n",
            "RBM fitting: 5471it [00:36, 156.83it/s]\u001b[A\n",
            "RBM fitting: 5487it [00:36, 157.48it/s]\u001b[A\n",
            "RBM fitting: 5503it [00:36, 147.38it/s]\u001b[A\n",
            "RBM fitting: 5519it [00:36, 150.37it/s]\u001b[A\n",
            "RBM fitting: 5535it [00:36, 153.08it/s]\u001b[A\n",
            "RBM fitting: 5551it [00:36, 154.92it/s]\u001b[A\n",
            "RBM fitting: 5567it [00:36, 156.20it/s]\u001b[A\n",
            "RBM fitting: 5583it [00:36, 156.85it/s]\u001b[A\n",
            "RBM fitting: 5599it [00:36, 157.14it/s]\u001b[A\n",
            "RBM fitting: 5615it [00:37, 149.76it/s]\u001b[A\n",
            "RBM fitting: 5631it [00:37, 152.64it/s]\u001b[A\n",
            "RBM fitting: 5647it [00:37, 152.70it/s]\u001b[A\n",
            "RBM fitting: 5663it [00:37, 153.24it/s]\u001b[A\n",
            "RBM fitting: 5679it [00:37, 155.04it/s]\u001b[A\n",
            "RBM fitting: 5695it [00:37, 155.67it/s]\u001b[A\n",
            "RBM fitting: 5711it [00:37, 153.83it/s]\u001b[A\n",
            "RBM fitting: 5727it [00:37, 146.96it/s]\u001b[A\n",
            "RBM fitting: 5743it [00:37, 149.89it/s]\u001b[A\n",
            "RBM fitting: 5759it [00:38, 152.18it/s]\u001b[A\n",
            "RBM fitting: 5775it [00:38, 153.85it/s]\u001b[A\n",
            "RBM fitting: 5791it [00:38, 155.35it/s]\u001b[A\n",
            "RBM fitting: 5807it [00:38, 153.80it/s]\u001b[A\n",
            "RBM fitting: 5823it [00:38, 155.35it/s]\u001b[A\n",
            "RBM fitting: 5839it [00:38, 148.01it/s]\u001b[A\n",
            "RBM fitting: 5855it [00:38, 150.81it/s]\u001b[A\n",
            "RBM fitting: 5871it [00:38, 152.85it/s]\u001b[A\n",
            "RBM fitting: 5887it [00:38, 154.37it/s]\u001b[A\n",
            "RBM fitting: 5903it [00:38, 155.49it/s]\u001b[A\n",
            "RBM fitting: 5919it [00:39, 156.26it/s]\u001b[A\n",
            "RBM fitting: 5935it [00:39, 156.44it/s]\u001b[A\n",
            "RBM fitting: 5951it [00:39, 149.66it/s]\u001b[A\n",
            "RBM fitting: 5967it [00:39, 147.92it/s]\u001b[A\n",
            "RBM fitting: 5983it [00:39, 150.60it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 151.52it/s]\n",
            "Epoch:23 ,avg_cost = 0.3401847183704376 ,std_cost = 0.03243827074766159 ,avg_grad = 10090.775390625 ,std_grad = 524.3545532226562\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 13it [00:00, 129.58it/s]\u001b[A\n",
            "RBM fitting: 26it [00:00, 128.93it/s]\u001b[A\n",
            "RBM fitting: 42it [00:00, 136.54it/s]\u001b[A\n",
            "RBM fitting: 58it [00:00, 142.64it/s]\u001b[A\n",
            "RBM fitting: 74it [00:00, 146.87it/s]\u001b[A\n",
            "RBM fitting: 91it [00:00, 151.29it/s]\u001b[A\n",
            "RBM fitting: 107it [00:00, 152.59it/s]\u001b[A\n",
            "RBM fitting: 122it [00:00, 151.39it/s]\u001b[A\n",
            "RBM fitting: 137it [00:00, 145.40it/s]\u001b[A\n",
            "RBM fitting: 153it [00:01, 147.58it/s]\u001b[A\n",
            "RBM fitting: 170it [00:01, 151.55it/s]\u001b[A\n",
            "RBM fitting: 186it [00:01, 153.89it/s]\u001b[A\n",
            "RBM fitting: 202it [00:01, 155.61it/s]\u001b[A\n",
            "RBM fitting: 218it [00:01, 156.82it/s]\u001b[A\n",
            "RBM fitting: 235it [00:01, 158.49it/s]\u001b[A\n",
            "RBM fitting: 251it [00:01, 150.52it/s]\u001b[A\n",
            "RBM fitting: 267it [00:01, 150.24it/s]\u001b[A\n",
            "RBM fitting: 284it [00:01, 153.92it/s]\u001b[A\n",
            "RBM fitting: 301it [00:01, 156.78it/s]\u001b[A\n",
            "RBM fitting: 318it [00:02, 158.38it/s]\u001b[A\n",
            "RBM fitting: 334it [00:02, 157.89it/s]\u001b[A\n",
            "RBM fitting: 351it [00:02, 158.90it/s]\u001b[A\n",
            "RBM fitting: 367it [00:02, 150.58it/s]\u001b[A\n",
            "RBM fitting: 383it [00:02, 152.44it/s]\u001b[A\n",
            "RBM fitting: 399it [00:02, 153.82it/s]\u001b[A\n",
            "RBM fitting: 415it [00:02, 155.24it/s]\u001b[A\n",
            "RBM fitting: 431it [00:02, 153.03it/s]\u001b[A\n",
            "RBM fitting: 447it [00:02, 154.77it/s]\u001b[A\n",
            "RBM fitting: 463it [00:03, 156.01it/s]\u001b[A\n",
            "RBM fitting: 479it [00:03, 147.20it/s]\u001b[A\n",
            "RBM fitting: 494it [00:03, 144.09it/s]\u001b[A\n",
            "RBM fitting: 510it [00:03, 148.33it/s]\u001b[A\n",
            "RBM fitting: 526it [00:03, 151.23it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 153.52it/s]\u001b[A\n",
            "RBM fitting: 558it [00:03, 154.14it/s]\u001b[A\n",
            "RBM fitting: 574it [00:03, 154.15it/s]\u001b[A\n",
            "RBM fitting: 590it [00:03, 145.23it/s]\u001b[A\n",
            "RBM fitting: 606it [00:03, 148.85it/s]\u001b[A\n",
            "RBM fitting: 622it [00:04, 150.56it/s]\u001b[A\n",
            "RBM fitting: 638it [00:04, 152.00it/s]\u001b[A\n",
            "RBM fitting: 654it [00:04, 153.62it/s]\u001b[A\n",
            "RBM fitting: 670it [00:04, 155.00it/s]\u001b[A\n",
            "RBM fitting: 686it [00:04, 156.35it/s]\u001b[A\n",
            "RBM fitting: 702it [00:04, 149.34it/s]\u001b[A\n",
            "RBM fitting: 718it [00:04, 151.83it/s]\u001b[A\n",
            "RBM fitting: 734it [00:04, 150.42it/s]\u001b[A\n",
            "RBM fitting: 750it [00:04, 152.34it/s]\u001b[A\n",
            "RBM fitting: 767it [00:05, 155.04it/s]\u001b[A\n",
            "RBM fitting: 783it [00:05, 155.77it/s]\u001b[A\n",
            "RBM fitting: 799it [00:05, 156.26it/s]\u001b[A\n",
            "RBM fitting: 815it [00:05, 148.83it/s]\u001b[A\n",
            "RBM fitting: 831it [00:05, 151.87it/s]\u001b[A\n",
            "RBM fitting: 847it [00:05, 154.05it/s]\u001b[A\n",
            "RBM fitting: 864it [00:05, 156.54it/s]\u001b[A\n",
            "RBM fitting: 880it [00:05, 156.81it/s]\u001b[A\n",
            "RBM fitting: 896it [00:05, 155.05it/s]\u001b[A\n",
            "RBM fitting: 912it [00:05, 155.58it/s]\u001b[A\n",
            "RBM fitting: 928it [00:06, 149.37it/s]\u001b[A\n",
            "RBM fitting: 944it [00:06, 151.22it/s]\u001b[A\n",
            "RBM fitting: 960it [00:06, 153.50it/s]\u001b[A\n",
            "RBM fitting: 976it [00:06, 155.35it/s]\u001b[A\n",
            "RBM fitting: 992it [00:06, 156.42it/s]\u001b[A\n",
            "RBM fitting: 1009it [00:06, 158.01it/s]\u001b[A\n",
            "RBM fitting: 1026it [00:06, 159.30it/s]\u001b[A\n",
            "RBM fitting: 1042it [00:06, 148.27it/s]\u001b[A\n",
            "RBM fitting: 1058it [00:06, 151.58it/s]\u001b[A\n",
            "RBM fitting: 1074it [00:07, 153.97it/s]\u001b[A\n",
            "RBM fitting: 1091it [00:07, 156.21it/s]\u001b[A\n",
            "RBM fitting: 1107it [00:07, 156.98it/s]\u001b[A\n",
            "RBM fitting: 1123it [00:07, 156.21it/s]\u001b[A\n",
            "RBM fitting: 1139it [00:07, 156.73it/s]\u001b[A\n",
            "RBM fitting: 1155it [00:07, 149.34it/s]\u001b[A\n",
            "RBM fitting: 1171it [00:07, 151.72it/s]\u001b[A\n",
            "RBM fitting: 1187it [00:07, 153.42it/s]\u001b[A\n",
            "RBM fitting: 1203it [00:07, 152.96it/s]\u001b[A\n",
            "RBM fitting: 1219it [00:07, 154.54it/s]\u001b[A\n",
            "RBM fitting: 1235it [00:08, 155.48it/s]\u001b[A\n",
            "RBM fitting: 1251it [00:08, 156.07it/s]\u001b[A\n",
            "RBM fitting: 1267it [00:08, 146.95it/s]\u001b[A\n",
            "RBM fitting: 1283it [00:08, 149.49it/s]\u001b[A\n",
            "RBM fitting: 1299it [00:08, 152.18it/s]\u001b[A\n",
            "RBM fitting: 1315it [00:08, 153.93it/s]\u001b[A\n",
            "RBM fitting: 1331it [00:08, 154.39it/s]\u001b[A\n",
            "RBM fitting: 1347it [00:08, 154.13it/s]\u001b[A\n",
            "RBM fitting: 1363it [00:08, 152.51it/s]\u001b[A\n",
            "RBM fitting: 1379it [00:09, 146.24it/s]\u001b[A\n",
            "RBM fitting: 1395it [00:09, 149.91it/s]\u001b[A\n",
            "RBM fitting: 1411it [00:09, 151.57it/s]\u001b[A\n",
            "RBM fitting: 1427it [00:09, 153.24it/s]\u001b[A\n",
            "RBM fitting: 1443it [00:09, 153.96it/s]\u001b[A\n",
            "RBM fitting: 1459it [00:09, 154.56it/s]\u001b[A\n",
            "RBM fitting: 1475it [00:09, 155.94it/s]\u001b[A\n",
            "RBM fitting: 1491it [00:09, 147.84it/s]\u001b[A\n",
            "RBM fitting: 1507it [00:09, 150.03it/s]\u001b[A\n",
            "RBM fitting: 1523it [00:09, 151.62it/s]\u001b[A\n",
            "RBM fitting: 1539it [00:10, 153.72it/s]\u001b[A\n",
            "RBM fitting: 1555it [00:10, 154.43it/s]\u001b[A\n",
            "RBM fitting: 1571it [00:10, 155.07it/s]\u001b[A\n",
            "RBM fitting: 1587it [00:10, 155.86it/s]\u001b[A\n",
            "RBM fitting: 1603it [00:10, 148.61it/s]\u001b[A\n",
            "RBM fitting: 1619it [00:10, 151.80it/s]\u001b[A\n",
            "RBM fitting: 1635it [00:10, 153.43it/s]\u001b[A\n",
            "RBM fitting: 1651it [00:10, 151.99it/s]\u001b[A\n",
            "RBM fitting: 1667it [00:10, 151.53it/s]\u001b[A\n",
            "RBM fitting: 1683it [00:11, 152.85it/s]\u001b[A\n",
            "RBM fitting: 1699it [00:11, 154.24it/s]\u001b[A\n",
            "RBM fitting: 1715it [00:11, 146.66it/s]\u001b[A\n",
            "RBM fitting: 1731it [00:11, 149.09it/s]\u001b[A\n",
            "RBM fitting: 1748it [00:11, 152.94it/s]\u001b[A\n",
            "RBM fitting: 1764it [00:11, 154.81it/s]\u001b[A\n",
            "RBM fitting: 1781it [00:11, 157.33it/s]\u001b[A\n",
            "RBM fitting: 1797it [00:11, 157.55it/s]\u001b[A\n",
            "RBM fitting: 1814it [00:11, 158.99it/s]\u001b[A\n",
            "RBM fitting: 1830it [00:11, 149.28it/s]\u001b[A\n",
            "RBM fitting: 1847it [00:12, 152.82it/s]\u001b[A\n",
            "RBM fitting: 1863it [00:12, 152.37it/s]\u001b[A\n",
            "RBM fitting: 1879it [00:12, 153.58it/s]\u001b[A\n",
            "RBM fitting: 1895it [00:12, 154.57it/s]\u001b[A\n",
            "RBM fitting: 1912it [00:12, 157.21it/s]\u001b[A\n",
            "RBM fitting: 1929it [00:12, 159.59it/s]\u001b[A\n",
            "RBM fitting: 1945it [00:12, 151.44it/s]\u001b[A\n",
            "RBM fitting: 1961it [00:12, 153.82it/s]\u001b[A\n",
            "RBM fitting: 1977it [00:12, 154.22it/s]\u001b[A\n",
            "RBM fitting: 1993it [00:13, 155.87it/s]\u001b[A\n",
            "RBM fitting: 2010it [00:13, 157.93it/s]\u001b[A\n",
            "RBM fitting: 2026it [00:13, 158.37it/s]\u001b[A\n",
            "RBM fitting: 2042it [00:13, 157.85it/s]\u001b[A\n",
            "RBM fitting: 2058it [00:13, 149.73it/s]\u001b[A\n",
            "RBM fitting: 2074it [00:13, 152.20it/s]\u001b[A\n",
            "RBM fitting: 2091it [00:13, 154.96it/s]\u001b[A\n",
            "RBM fitting: 2107it [00:13, 156.06it/s]\u001b[A\n",
            "RBM fitting: 2123it [00:13, 156.91it/s]\u001b[A\n",
            "RBM fitting: 2139it [00:13, 153.97it/s]\u001b[A\n",
            "RBM fitting: 2155it [00:14, 155.42it/s]\u001b[A\n",
            "RBM fitting: 2171it [00:14, 148.06it/s]\u001b[A\n",
            "RBM fitting: 2187it [00:14, 151.00it/s]\u001b[A\n",
            "RBM fitting: 2203it [00:14, 153.08it/s]\u001b[A\n",
            "RBM fitting: 2220it [00:14, 155.52it/s]\u001b[A\n",
            "RBM fitting: 2236it [00:14, 156.81it/s]\u001b[A\n",
            "RBM fitting: 2253it [00:14, 158.32it/s]\u001b[A\n",
            "RBM fitting: 2269it [00:14, 156.03it/s]\u001b[A\n",
            "RBM fitting: 2285it [00:14, 148.52it/s]\u001b[A\n",
            "RBM fitting: 2300it [00:15, 148.10it/s]\u001b[A\n",
            "RBM fitting: 2316it [00:15, 150.62it/s]\u001b[A\n",
            "RBM fitting: 2332it [00:15, 151.90it/s]\u001b[A\n",
            "RBM fitting: 2348it [00:15, 148.19it/s]\u001b[A\n",
            "RBM fitting: 2363it [00:15, 147.91it/s]\u001b[A\n",
            "RBM fitting: 2379it [00:15, 149.63it/s]\u001b[A\n",
            "RBM fitting: 2394it [00:15, 143.32it/s]\u001b[A\n",
            "RBM fitting: 2410it [00:15, 145.80it/s]\u001b[A\n",
            "RBM fitting: 2426it [00:15, 148.91it/s]\u001b[A\n",
            "RBM fitting: 2442it [00:15, 149.81it/s]\u001b[A\n",
            "RBM fitting: 2458it [00:16, 151.63it/s]\u001b[A\n",
            "RBM fitting: 2474it [00:16, 151.12it/s]\u001b[A\n",
            "RBM fitting: 2490it [00:16, 151.33it/s]\u001b[A\n",
            "RBM fitting: 2506it [00:16, 146.27it/s]\u001b[A\n",
            "RBM fitting: 2522it [00:16, 150.10it/s]\u001b[A\n",
            "RBM fitting: 2538it [00:16, 152.02it/s]\u001b[A\n",
            "RBM fitting: 2554it [00:16, 150.42it/s]\u001b[A\n",
            "RBM fitting: 2570it [00:16, 151.69it/s]\u001b[A\n",
            "RBM fitting: 2586it [00:16, 153.03it/s]\u001b[A\n",
            "RBM fitting: 2602it [00:17, 151.46it/s]\u001b[A\n",
            "RBM fitting: 2618it [00:17, 145.19it/s]\u001b[A\n",
            "RBM fitting: 2634it [00:17, 147.19it/s]\u001b[A\n",
            "RBM fitting: 2650it [00:17, 149.82it/s]\u001b[A\n",
            "RBM fitting: 2666it [00:17, 151.01it/s]\u001b[A\n",
            "RBM fitting: 2682it [00:17, 153.09it/s]\u001b[A\n",
            "RBM fitting: 2698it [00:17, 155.09it/s]\u001b[A\n",
            "RBM fitting: 2714it [00:17, 155.57it/s]\u001b[A\n",
            "RBM fitting: 2730it [00:17, 148.29it/s]\u001b[A\n",
            "RBM fitting: 2746it [00:17, 148.63it/s]\u001b[A\n",
            "RBM fitting: 2763it [00:18, 152.57it/s]\u001b[A\n",
            "RBM fitting: 2779it [00:18, 153.52it/s]\u001b[A\n",
            "RBM fitting: 2795it [00:18, 154.17it/s]\u001b[A\n",
            "RBM fitting: 2811it [00:18, 154.85it/s]\u001b[A\n",
            "RBM fitting: 2827it [00:18, 155.59it/s]\u001b[A\n",
            "RBM fitting: 2843it [00:18, 149.56it/s]\u001b[A\n",
            "RBM fitting: 2859it [00:18, 151.83it/s]\u001b[A\n",
            "RBM fitting: 2875it [00:18, 152.78it/s]\u001b[A\n",
            "RBM fitting: 2891it [00:18, 154.69it/s]\u001b[A\n",
            "RBM fitting: 2907it [00:19, 153.07it/s]\u001b[A\n",
            "RBM fitting: 2923it [00:19, 153.11it/s]\u001b[A\n",
            "RBM fitting: 2939it [00:19, 152.25it/s]\u001b[A\n",
            "RBM fitting: 2955it [00:19, 145.76it/s]\u001b[A\n",
            "RBM fitting: 2971it [00:19, 149.05it/s]\u001b[A\n",
            "RBM fitting: 2987it [00:19, 151.70it/s]\u001b[A\n",
            "RBM fitting: 3004it [00:19, 154.50it/s]\u001b[A\n",
            "RBM fitting: 3020it [00:19, 155.23it/s]\u001b[A\n",
            "RBM fitting: 3036it [00:19, 156.51it/s]\u001b[A\n",
            "RBM fitting: 3052it [00:19, 157.03it/s]\u001b[A\n",
            "RBM fitting: 3068it [00:20, 147.26it/s]\u001b[A\n",
            "RBM fitting: 3084it [00:20, 148.89it/s]\u001b[A\n",
            "RBM fitting: 3100it [00:20, 151.29it/s]\u001b[A\n",
            "RBM fitting: 3116it [00:20, 151.52it/s]\u001b[A\n",
            "RBM fitting: 3132it [00:20, 151.05it/s]\u001b[A\n",
            "RBM fitting: 3148it [00:20, 153.25it/s]\u001b[A\n",
            "RBM fitting: 3164it [00:20, 154.21it/s]\u001b[A\n",
            "RBM fitting: 3180it [00:20, 146.82it/s]\u001b[A\n",
            "RBM fitting: 3196it [00:20, 149.84it/s]\u001b[A\n",
            "RBM fitting: 3212it [00:21, 150.26it/s]\u001b[A\n",
            "RBM fitting: 3228it [00:21, 152.12it/s]\u001b[A\n",
            "RBM fitting: 3244it [00:21, 153.00it/s]\u001b[A\n",
            "RBM fitting: 3260it [00:21, 154.75it/s]\u001b[A\n",
            "RBM fitting: 3276it [00:21, 153.91it/s]\u001b[A\n",
            "RBM fitting: 3292it [00:21, 147.99it/s]\u001b[A\n",
            "RBM fitting: 3309it [00:21, 151.89it/s]\u001b[A\n",
            "RBM fitting: 3325it [00:21, 152.52it/s]\u001b[A\n",
            "RBM fitting: 3341it [00:21, 153.98it/s]\u001b[A\n",
            "RBM fitting: 3357it [00:21, 155.40it/s]\u001b[A\n",
            "RBM fitting: 3373it [00:22, 153.88it/s]\u001b[A\n",
            "RBM fitting: 3389it [00:22, 154.84it/s]\u001b[A\n",
            "RBM fitting: 3405it [00:22, 148.33it/s]\u001b[A\n",
            "RBM fitting: 3421it [00:22, 150.58it/s]\u001b[A\n",
            "RBM fitting: 3437it [00:22, 152.26it/s]\u001b[A\n",
            "RBM fitting: 3454it [00:22, 154.84it/s]\u001b[A\n",
            "RBM fitting: 3470it [00:22, 155.13it/s]\u001b[A\n",
            "RBM fitting: 3486it [00:22, 155.49it/s]\u001b[A\n",
            "RBM fitting: 3502it [00:22, 155.56it/s]\u001b[A\n",
            "RBM fitting: 3518it [00:23, 145.80it/s]\u001b[A\n",
            "RBM fitting: 3534it [00:23, 148.47it/s]\u001b[A\n",
            "RBM fitting: 3550it [00:23, 150.63it/s]\u001b[A\n",
            "RBM fitting: 3566it [00:23, 152.57it/s]\u001b[A\n",
            "RBM fitting: 3582it [00:23, 151.40it/s]\u001b[A\n",
            "RBM fitting: 3598it [00:23, 153.35it/s]\u001b[A\n",
            "RBM fitting: 3614it [00:23, 154.83it/s]\u001b[A\n",
            "RBM fitting: 3630it [00:23, 147.78it/s]\u001b[A\n",
            "RBM fitting: 3646it [00:23, 150.64it/s]\u001b[A\n",
            "RBM fitting: 3662it [00:24, 152.54it/s]\u001b[A\n",
            "RBM fitting: 3678it [00:24, 150.83it/s]\u001b[A\n",
            "RBM fitting: 3694it [00:24, 151.10it/s]\u001b[A\n",
            "RBM fitting: 3710it [00:24, 153.46it/s]\u001b[A\n",
            "RBM fitting: 3726it [00:24, 154.96it/s]\u001b[A\n",
            "RBM fitting: 3742it [00:24, 144.57it/s]\u001b[A\n",
            "RBM fitting: 3758it [00:24, 148.84it/s]\u001b[A\n",
            "RBM fitting: 3774it [00:24, 151.12it/s]\u001b[A\n",
            "RBM fitting: 3790it [00:24, 153.54it/s]\u001b[A\n",
            "RBM fitting: 3807it [00:24, 155.90it/s]\u001b[A\n",
            "RBM fitting: 3824it [00:25, 157.59it/s]\u001b[A\n",
            "RBM fitting: 3840it [00:25, 155.86it/s]\u001b[A\n",
            "RBM fitting: 3856it [00:25, 149.06it/s]\u001b[A\n",
            "RBM fitting: 3872it [00:25, 150.54it/s]\u001b[A\n",
            "RBM fitting: 3889it [00:25, 153.72it/s]\u001b[A\n",
            "RBM fitting: 3906it [00:25, 157.37it/s]\u001b[A\n",
            "RBM fitting: 3923it [00:25, 158.73it/s]\u001b[A\n",
            "RBM fitting: 3939it [00:25, 158.80it/s]\u001b[A\n",
            "RBM fitting: 3956it [00:25, 160.39it/s]\u001b[A\n",
            "RBM fitting: 3973it [00:26, 152.22it/s]\u001b[A\n",
            "RBM fitting: 3989it [00:26, 152.00it/s]\u001b[A\n",
            "RBM fitting: 4005it [00:26, 152.87it/s]\u001b[A\n",
            "RBM fitting: 4021it [00:26, 154.44it/s]\u001b[A\n",
            "RBM fitting: 4037it [00:26, 155.06it/s]\u001b[A\n",
            "RBM fitting: 4053it [00:26, 156.29it/s]\u001b[A\n",
            "RBM fitting: 4069it [00:26, 157.00it/s]\u001b[A\n",
            "RBM fitting: 4085it [00:26, 148.52it/s]\u001b[A\n",
            "RBM fitting: 4101it [00:26, 151.51it/s]\u001b[A\n",
            "RBM fitting: 4117it [00:26, 153.63it/s]\u001b[A\n",
            "RBM fitting: 4133it [00:27, 150.35it/s]\u001b[A\n",
            "RBM fitting: 4149it [00:27, 149.28it/s]\u001b[A\n",
            "RBM fitting: 4165it [00:27, 151.42it/s]\u001b[A\n",
            "RBM fitting: 4181it [00:27, 153.55it/s]\u001b[A\n",
            "RBM fitting: 4197it [00:27, 146.96it/s]\u001b[A\n",
            "RBM fitting: 4213it [00:27, 149.99it/s]\u001b[A\n",
            "RBM fitting: 4229it [00:27, 152.17it/s]\u001b[A\n",
            "RBM fitting: 4245it [00:27, 151.36it/s]\u001b[A\n",
            "RBM fitting: 4261it [00:27, 151.53it/s]\u001b[A\n",
            "RBM fitting: 4277it [00:28, 153.26it/s]\u001b[A\n",
            "RBM fitting: 4293it [00:28, 154.65it/s]\u001b[A\n",
            "RBM fitting: 4309it [00:28, 144.74it/s]\u001b[A\n",
            "RBM fitting: 4325it [00:28, 148.13it/s]\u001b[A\n",
            "RBM fitting: 4341it [00:28, 150.98it/s]\u001b[A\n",
            "RBM fitting: 4357it [00:28, 152.73it/s]\u001b[A\n",
            "RBM fitting: 4373it [00:28, 154.77it/s]\u001b[A\n",
            "RBM fitting: 4389it [00:28, 155.69it/s]\u001b[A\n",
            "RBM fitting: 4406it [00:28, 157.65it/s]\u001b[A\n",
            "RBM fitting: 4422it [00:28, 149.62it/s]\u001b[A\n",
            "RBM fitting: 4438it [00:29, 152.04it/s]\u001b[A\n",
            "RBM fitting: 4454it [00:29, 151.51it/s]\u001b[A\n",
            "RBM fitting: 4470it [00:29, 153.11it/s]\u001b[A\n",
            "RBM fitting: 4486it [00:29, 154.59it/s]\u001b[A\n",
            "RBM fitting: 4502it [00:29, 153.98it/s]\u001b[A\n",
            "RBM fitting: 4518it [00:29, 155.04it/s]\u001b[A\n",
            "RBM fitting: 4534it [00:29, 148.66it/s]\u001b[A\n",
            "RBM fitting: 4551it [00:29, 152.67it/s]\u001b[A\n",
            "RBM fitting: 4567it [00:29, 154.77it/s]\u001b[A\n",
            "RBM fitting: 4583it [00:30, 156.04it/s]\u001b[A\n",
            "RBM fitting: 4599it [00:30, 157.01it/s]\u001b[A\n",
            "RBM fitting: 4615it [00:30, 154.44it/s]\u001b[A\n",
            "RBM fitting: 4631it [00:30, 153.47it/s]\u001b[A\n",
            "RBM fitting: 4647it [00:30, 147.51it/s]\u001b[A\n",
            "RBM fitting: 4663it [00:30, 150.18it/s]\u001b[A\n",
            "RBM fitting: 4679it [00:30, 152.70it/s]\u001b[A\n",
            "RBM fitting: 4695it [00:30, 154.45it/s]\u001b[A\n",
            "RBM fitting: 4712it [00:30, 156.63it/s]\u001b[A\n",
            "RBM fitting: 4728it [00:30, 157.49it/s]\u001b[A\n",
            "RBM fitting: 4744it [00:31, 158.21it/s]\u001b[A\n",
            "RBM fitting: 4760it [00:31, 150.41it/s]\u001b[A\n",
            "RBM fitting: 4776it [00:31, 149.65it/s]\u001b[A\n",
            "RBM fitting: 4792it [00:31, 151.88it/s]\u001b[A\n",
            "RBM fitting: 4808it [00:31, 154.13it/s]\u001b[A\n",
            "RBM fitting: 4825it [00:31, 156.55it/s]\u001b[A\n",
            "RBM fitting: 4841it [00:31, 157.18it/s]\u001b[A\n",
            "RBM fitting: 4857it [00:31, 157.42it/s]\u001b[A\n",
            "RBM fitting: 4873it [00:31, 150.23it/s]\u001b[A\n",
            "RBM fitting: 4889it [00:32, 153.01it/s]\u001b[A\n",
            "RBM fitting: 4906it [00:32, 155.48it/s]\u001b[A\n",
            "RBM fitting: 4922it [00:32, 153.74it/s]\u001b[A\n",
            "RBM fitting: 4938it [00:32, 154.93it/s]\u001b[A\n",
            "RBM fitting: 4954it [00:32, 154.27it/s]\u001b[A\n",
            "RBM fitting: 4970it [00:32, 154.92it/s]\u001b[A\n",
            "RBM fitting: 4986it [00:32, 147.88it/s]\u001b[A\n",
            "RBM fitting: 5002it [00:32, 149.98it/s]\u001b[A\n",
            "RBM fitting: 5019it [00:32, 153.39it/s]\u001b[A\n",
            "RBM fitting: 5036it [00:32, 155.67it/s]\u001b[A\n",
            "RBM fitting: 5053it [00:33, 158.31it/s]\u001b[A\n",
            "RBM fitting: 5070it [00:33, 159.64it/s]\u001b[A\n",
            "RBM fitting: 5086it [00:33, 156.54it/s]\u001b[A\n",
            "RBM fitting: 5102it [00:33, 149.00it/s]\u001b[A\n",
            "RBM fitting: 5118it [00:33, 149.92it/s]\u001b[A\n",
            "RBM fitting: 5134it [00:33, 152.65it/s]\u001b[A\n",
            "RBM fitting: 5150it [00:33, 154.23it/s]\u001b[A\n",
            "RBM fitting: 5166it [00:33, 155.78it/s]\u001b[A\n",
            "RBM fitting: 5183it [00:33, 157.97it/s]\u001b[A\n",
            "RBM fitting: 5200it [00:34, 159.51it/s]\u001b[A\n",
            "RBM fitting: 5216it [00:34, 152.02it/s]\u001b[A\n",
            "RBM fitting: 5232it [00:34, 152.29it/s]\u001b[A\n",
            "RBM fitting: 5248it [00:34, 150.74it/s]\u001b[A\n",
            "RBM fitting: 5264it [00:34, 152.97it/s]\u001b[A\n",
            "RBM fitting: 5281it [00:34, 155.37it/s]\u001b[A\n",
            "RBM fitting: 5298it [00:34, 157.21it/s]\u001b[A\n",
            "RBM fitting: 5314it [00:34, 157.39it/s]\u001b[A\n",
            "RBM fitting: 5330it [00:34, 150.12it/s]\u001b[A\n",
            "RBM fitting: 5346it [00:34, 152.62it/s]\u001b[A\n",
            "RBM fitting: 5363it [00:35, 155.43it/s]\u001b[A\n",
            "RBM fitting: 5380it [00:35, 157.38it/s]\u001b[A\n",
            "RBM fitting: 5396it [00:35, 155.74it/s]\u001b[A\n",
            "RBM fitting: 5412it [00:35, 156.29it/s]\u001b[A\n",
            "RBM fitting: 5428it [00:35, 156.27it/s]\u001b[A\n",
            "RBM fitting: 5444it [00:35, 148.91it/s]\u001b[A\n",
            "RBM fitting: 5460it [00:35, 150.66it/s]\u001b[A\n",
            "RBM fitting: 5476it [00:35, 151.57it/s]\u001b[A\n",
            "RBM fitting: 5492it [00:35, 153.38it/s]\u001b[A\n",
            "RBM fitting: 5509it [00:36, 155.75it/s]\u001b[A\n",
            "RBM fitting: 5526it [00:36, 158.05it/s]\u001b[A\n",
            "RBM fitting: 5542it [00:36, 158.00it/s]\u001b[A\n",
            "RBM fitting: 5558it [00:36, 144.21it/s]\u001b[A\n",
            "RBM fitting: 5574it [00:36, 148.23it/s]\u001b[A\n",
            "RBM fitting: 5590it [00:36, 151.47it/s]\u001b[A\n",
            "RBM fitting: 5607it [00:36, 154.94it/s]\u001b[A\n",
            "RBM fitting: 5623it [00:36, 155.34it/s]\u001b[A\n",
            "RBM fitting: 5640it [00:36, 157.29it/s]\u001b[A\n",
            "RBM fitting: 5656it [00:36, 157.85it/s]\u001b[A\n",
            "RBM fitting: 5672it [00:37, 149.91it/s]\u001b[A\n",
            "RBM fitting: 5688it [00:37, 152.23it/s]\u001b[A\n",
            "RBM fitting: 5704it [00:37, 150.64it/s]\u001b[A\n",
            "RBM fitting: 5720it [00:37, 151.99it/s]\u001b[A\n",
            "RBM fitting: 5736it [00:37, 152.99it/s]\u001b[A\n",
            "RBM fitting: 5752it [00:37, 151.05it/s]\u001b[A\n",
            "RBM fitting: 5768it [00:37, 150.20it/s]\u001b[A\n",
            "RBM fitting: 5784it [00:37, 144.88it/s]\u001b[A\n",
            "RBM fitting: 5800it [00:37, 148.14it/s]\u001b[A\n",
            "RBM fitting: 5817it [00:38, 152.63it/s]\u001b[A\n",
            "RBM fitting: 5833it [00:38, 153.51it/s]\u001b[A\n",
            "RBM fitting: 5849it [00:38, 154.26it/s]\u001b[A\n",
            "RBM fitting: 5865it [00:38, 152.17it/s]\u001b[A\n",
            "RBM fitting: 5881it [00:38, 153.03it/s]\u001b[A\n",
            "RBM fitting: 5897it [00:38, 145.88it/s]\u001b[A\n",
            "RBM fitting: 5913it [00:38, 148.80it/s]\u001b[A\n",
            "RBM fitting: 5929it [00:38, 151.21it/s]\u001b[A\n",
            "RBM fitting: 5945it [00:38, 153.56it/s]\u001b[A\n",
            "RBM fitting: 5962it [00:39, 155.99it/s]\u001b[A\n",
            "RBM fitting: 5978it [00:39, 157.14it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 152.78it/s]\n",
            "Epoch:24 ,avg_cost = 0.3392358124256134 ,std_cost = 0.03337264806032181 ,avg_grad = 10080.8828125 ,std_grad = 545.2388305664062\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 10it [00:00, 98.48it/s]\u001b[A\n",
            "RBM fitting: 24it [00:00, 107.41it/s]\u001b[A\n",
            "RBM fitting: 41it [00:00, 119.52it/s]\u001b[A\n",
            "RBM fitting: 57it [00:00, 129.23it/s]\u001b[A\n",
            "RBM fitting: 73it [00:00, 136.47it/s]\u001b[A\n",
            "RBM fitting: 88it [00:00, 139.41it/s]\u001b[A\n",
            "RBM fitting: 102it [00:00, 135.05it/s]\u001b[A\n",
            "RBM fitting: 118it [00:00, 141.35it/s]\u001b[A\n",
            "RBM fitting: 132it [00:00, 140.65it/s]\u001b[A\n",
            "RBM fitting: 147it [00:01, 142.06it/s]\u001b[A\n",
            "RBM fitting: 162it [00:01, 142.23it/s]\u001b[A\n",
            "RBM fitting: 178it [00:01, 146.73it/s]\u001b[A\n",
            "RBM fitting: 194it [00:01, 150.47it/s]\u001b[A\n",
            "RBM fitting: 210it [00:01, 145.11it/s]\u001b[A\n",
            "RBM fitting: 226it [00:01, 148.59it/s]\u001b[A\n",
            "RBM fitting: 242it [00:01, 151.44it/s]\u001b[A\n",
            "RBM fitting: 258it [00:01, 153.24it/s]\u001b[A\n",
            "RBM fitting: 274it [00:01, 154.81it/s]\u001b[A\n",
            "RBM fitting: 290it [00:01, 155.71it/s]\u001b[A\n",
            "RBM fitting: 306it [00:02, 156.00it/s]\u001b[A\n",
            "RBM fitting: 322it [00:02, 145.47it/s]\u001b[A\n",
            "RBM fitting: 338it [00:02, 148.78it/s]\u001b[A\n",
            "RBM fitting: 355it [00:02, 152.53it/s]\u001b[A\n",
            "RBM fitting: 371it [00:02, 153.74it/s]\u001b[A\n",
            "RBM fitting: 387it [00:02, 152.87it/s]\u001b[A\n",
            "RBM fitting: 403it [00:02, 153.95it/s]\u001b[A\n",
            "RBM fitting: 419it [00:02, 155.16it/s]\u001b[A\n",
            "RBM fitting: 435it [00:02, 148.20it/s]\u001b[A\n",
            "RBM fitting: 451it [00:03, 150.46it/s]\u001b[A\n",
            "RBM fitting: 467it [00:03, 149.49it/s]\u001b[A\n",
            "RBM fitting: 483it [00:03, 150.99it/s]\u001b[A\n",
            "RBM fitting: 499it [00:03, 153.06it/s]\u001b[A\n",
            "RBM fitting: 515it [00:03, 153.70it/s]\u001b[A\n",
            "RBM fitting: 531it [00:03, 154.45it/s]\u001b[A\n",
            "RBM fitting: 547it [00:03, 146.41it/s]\u001b[A\n",
            "RBM fitting: 563it [00:03, 149.92it/s]\u001b[A\n",
            "RBM fitting: 579it [00:03, 152.45it/s]\u001b[A\n",
            "RBM fitting: 595it [00:03, 153.42it/s]\u001b[A\n",
            "RBM fitting: 611it [00:04, 153.79it/s]\u001b[A\n",
            "RBM fitting: 627it [00:04, 152.14it/s]\u001b[A\n",
            "RBM fitting: 643it [00:04, 153.75it/s]\u001b[A\n",
            "RBM fitting: 659it [00:04, 147.84it/s]\u001b[A\n",
            "RBM fitting: 674it [00:04, 148.46it/s]\u001b[A\n",
            "RBM fitting: 690it [00:04, 151.34it/s]\u001b[A\n",
            "RBM fitting: 706it [00:04, 153.56it/s]\u001b[A\n",
            "RBM fitting: 722it [00:04, 155.00it/s]\u001b[A\n",
            "RBM fitting: 739it [00:04, 156.86it/s]\u001b[A\n",
            "RBM fitting: 755it [00:05, 157.00it/s]\u001b[A\n",
            "RBM fitting: 771it [00:05, 149.29it/s]\u001b[A\n",
            "RBM fitting: 787it [00:05, 149.67it/s]\u001b[A\n",
            "RBM fitting: 804it [00:05, 153.25it/s]\u001b[A\n",
            "RBM fitting: 820it [00:05, 154.92it/s]\u001b[A\n",
            "RBM fitting: 837it [00:05, 156.88it/s]\u001b[A\n",
            "RBM fitting: 854it [00:05, 159.23it/s]\u001b[A\n",
            "RBM fitting: 870it [00:05, 159.07it/s]\u001b[A\n",
            "RBM fitting: 886it [00:05, 150.43it/s]\u001b[A\n",
            "RBM fitting: 902it [00:05, 152.17it/s]\u001b[A\n",
            "RBM fitting: 918it [00:06, 152.73it/s]\u001b[A\n",
            "RBM fitting: 934it [00:06, 151.51it/s]\u001b[A\n",
            "RBM fitting: 950it [00:06, 152.61it/s]\u001b[A\n",
            "RBM fitting: 966it [00:06, 154.62it/s]\u001b[A\n",
            "RBM fitting: 982it [00:06, 155.82it/s]\u001b[A\n",
            "RBM fitting: 998it [00:06, 148.75it/s]\u001b[A\n",
            "RBM fitting: 1014it [00:06, 150.82it/s]\u001b[A\n",
            "RBM fitting: 1030it [00:06, 152.92it/s]\u001b[A\n",
            "RBM fitting: 1046it [00:06, 153.73it/s]\u001b[A\n",
            "RBM fitting: 1062it [00:07, 153.44it/s]\u001b[A\n",
            "RBM fitting: 1078it [00:07, 153.52it/s]\u001b[A\n",
            "RBM fitting: 1094it [00:07, 151.59it/s]\u001b[A\n",
            "RBM fitting: 1110it [00:07, 145.23it/s]\u001b[A\n",
            "RBM fitting: 1126it [00:07, 147.59it/s]\u001b[A\n",
            "RBM fitting: 1142it [00:07, 149.61it/s]\u001b[A\n",
            "RBM fitting: 1158it [00:07, 150.93it/s]\u001b[A\n",
            "RBM fitting: 1174it [00:07, 152.52it/s]\u001b[A\n",
            "RBM fitting: 1190it [00:07, 154.25it/s]\u001b[A\n",
            "RBM fitting: 1206it [00:07, 154.67it/s]\u001b[A\n",
            "RBM fitting: 1222it [00:08, 146.54it/s]\u001b[A\n",
            "RBM fitting: 1237it [00:08, 141.71it/s]\u001b[A\n",
            "RBM fitting: 1253it [00:08, 145.92it/s]\u001b[A\n",
            "RBM fitting: 1269it [00:08, 149.11it/s]\u001b[A\n",
            "RBM fitting: 1285it [00:08, 149.85it/s]\u001b[A\n",
            "RBM fitting: 1301it [00:08, 149.07it/s]\u001b[A\n",
            "RBM fitting: 1317it [00:08, 150.58it/s]\u001b[A\n",
            "RBM fitting: 1333it [00:08, 145.14it/s]\u001b[A\n",
            "RBM fitting: 1349it [00:08, 147.56it/s]\u001b[A\n",
            "RBM fitting: 1365it [00:09, 149.46it/s]\u001b[A\n",
            "RBM fitting: 1381it [00:09, 152.19it/s]\u001b[A\n",
            "RBM fitting: 1397it [00:09, 152.28it/s]\u001b[A\n",
            "RBM fitting: 1414it [00:09, 154.35it/s]\u001b[A\n",
            "RBM fitting: 1430it [00:09, 155.75it/s]\u001b[A\n",
            "RBM fitting: 1446it [00:09, 148.96it/s]\u001b[A\n",
            "RBM fitting: 1462it [00:09, 151.70it/s]\u001b[A\n",
            "RBM fitting: 1478it [00:09, 153.25it/s]\u001b[A\n",
            "RBM fitting: 1494it [00:09, 152.71it/s]\u001b[A\n",
            "RBM fitting: 1510it [00:10, 151.97it/s]\u001b[A\n",
            "RBM fitting: 1526it [00:10, 153.12it/s]\u001b[A\n",
            "RBM fitting: 1542it [00:10, 154.06it/s]\u001b[A\n",
            "RBM fitting: 1558it [00:10, 145.20it/s]\u001b[A\n",
            "RBM fitting: 1575it [00:10, 149.66it/s]\u001b[A\n",
            "RBM fitting: 1592it [00:10, 153.23it/s]\u001b[A\n",
            "RBM fitting: 1609it [00:10, 155.53it/s]\u001b[A\n",
            "RBM fitting: 1626it [00:10, 157.41it/s]\u001b[A\n",
            "RBM fitting: 1642it [00:10, 158.01it/s]\u001b[A\n",
            "RBM fitting: 1658it [00:10, 158.07it/s]\u001b[A\n",
            "RBM fitting: 1674it [00:11, 149.01it/s]\u001b[A\n",
            "RBM fitting: 1690it [00:11, 151.07it/s]\u001b[A\n",
            "RBM fitting: 1706it [00:11, 150.24it/s]\u001b[A\n",
            "RBM fitting: 1722it [00:11, 152.39it/s]\u001b[A\n",
            "RBM fitting: 1738it [00:11, 152.80it/s]\u001b[A\n",
            "RBM fitting: 1754it [00:11, 151.54it/s]\u001b[A\n",
            "RBM fitting: 1770it [00:11, 153.54it/s]\u001b[A\n",
            "RBM fitting: 1786it [00:11, 147.18it/s]\u001b[A\n",
            "RBM fitting: 1801it [00:11, 145.71it/s]\u001b[A\n",
            "RBM fitting: 1816it [00:12, 146.45it/s]\u001b[A\n",
            "RBM fitting: 1832it [00:12, 149.19it/s]\u001b[A\n",
            "RBM fitting: 1848it [00:12, 151.09it/s]\u001b[A\n",
            "RBM fitting: 1864it [00:12, 151.33it/s]\u001b[A\n",
            "RBM fitting: 1880it [00:12, 153.40it/s]\u001b[A\n",
            "RBM fitting: 1896it [00:12, 147.52it/s]\u001b[A\n",
            "RBM fitting: 1912it [00:12, 151.01it/s]\u001b[A\n",
            "RBM fitting: 1928it [00:12, 153.53it/s]\u001b[A\n",
            "RBM fitting: 1945it [00:12, 156.36it/s]\u001b[A\n",
            "RBM fitting: 1962it [00:12, 158.03it/s]\u001b[A\n",
            "RBM fitting: 1978it [00:13, 156.97it/s]\u001b[A\n",
            "RBM fitting: 1994it [00:13, 157.67it/s]\u001b[A\n",
            "RBM fitting: 2010it [00:13, 145.40it/s]\u001b[A\n",
            "RBM fitting: 2026it [00:13, 148.96it/s]\u001b[A\n",
            "RBM fitting: 2042it [00:13, 151.77it/s]\u001b[A\n",
            "RBM fitting: 2058it [00:13, 153.33it/s]\u001b[A\n",
            "RBM fitting: 2074it [00:13, 154.90it/s]\u001b[A\n",
            "RBM fitting: 2090it [00:13, 155.75it/s]\u001b[A\n",
            "RBM fitting: 2106it [00:13, 156.68it/s]\u001b[A\n",
            "RBM fitting: 2122it [00:14, 148.76it/s]\u001b[A\n",
            "RBM fitting: 2138it [00:14, 151.88it/s]\u001b[A\n",
            "RBM fitting: 2155it [00:14, 154.74it/s]\u001b[A\n",
            "RBM fitting: 2171it [00:14, 152.75it/s]\u001b[A\n",
            "RBM fitting: 2187it [00:14, 153.88it/s]\u001b[A\n",
            "RBM fitting: 2203it [00:14, 153.46it/s]\u001b[A\n",
            "RBM fitting: 2219it [00:14, 154.52it/s]\u001b[A\n",
            "RBM fitting: 2235it [00:14, 148.01it/s]\u001b[A\n",
            "RBM fitting: 2251it [00:14, 149.16it/s]\u001b[A\n",
            "RBM fitting: 2266it [00:14, 149.33it/s]\u001b[A\n",
            "RBM fitting: 2282it [00:15, 150.40it/s]\u001b[A\n",
            "RBM fitting: 2298it [00:15, 151.16it/s]\u001b[A\n",
            "RBM fitting: 2314it [00:15, 147.64it/s]\u001b[A\n",
            "RBM fitting: 2329it [00:15, 146.16it/s]\u001b[A\n",
            "RBM fitting: 2344it [00:15, 138.96it/s]\u001b[A\n",
            "RBM fitting: 2360it [00:15, 142.84it/s]\u001b[A\n",
            "RBM fitting: 2376it [00:15, 146.52it/s]\u001b[A\n",
            "RBM fitting: 2392it [00:15, 149.74it/s]\u001b[A\n",
            "RBM fitting: 2408it [00:15, 149.82it/s]\u001b[A\n",
            "RBM fitting: 2424it [00:16, 149.45it/s]\u001b[A\n",
            "RBM fitting: 2440it [00:16, 150.33it/s]\u001b[A\n",
            "RBM fitting: 2456it [00:16, 143.50it/s]\u001b[A\n",
            "RBM fitting: 2471it [00:16, 144.34it/s]\u001b[A\n",
            "RBM fitting: 2486it [00:16, 144.66it/s]\u001b[A\n",
            "RBM fitting: 2501it [00:16, 146.13it/s]\u001b[A\n",
            "RBM fitting: 2517it [00:16, 148.57it/s]\u001b[A\n",
            "RBM fitting: 2532it [00:16, 148.47it/s]\u001b[A\n",
            "RBM fitting: 2548it [00:16, 151.01it/s]\u001b[A\n",
            "RBM fitting: 2564it [00:17, 144.49it/s]\u001b[A\n",
            "RBM fitting: 2580it [00:17, 147.85it/s]\u001b[A\n",
            "RBM fitting: 2596it [00:17, 150.99it/s]\u001b[A\n",
            "RBM fitting: 2613it [00:17, 154.06it/s]\u001b[A\n",
            "RBM fitting: 2629it [00:17, 153.23it/s]\u001b[A\n",
            "RBM fitting: 2645it [00:17, 154.34it/s]\u001b[A\n",
            "RBM fitting: 2661it [00:17, 155.33it/s]\u001b[A\n",
            "RBM fitting: 2677it [00:17, 147.60it/s]\u001b[A\n",
            "RBM fitting: 2692it [00:17, 148.31it/s]\u001b[A\n",
            "RBM fitting: 2707it [00:17, 148.71it/s]\u001b[A\n",
            "RBM fitting: 2723it [00:18, 150.54it/s]\u001b[A\n",
            "RBM fitting: 2739it [00:18, 152.14it/s]\u001b[A\n",
            "RBM fitting: 2755it [00:18, 153.52it/s]\u001b[A\n",
            "RBM fitting: 2772it [00:18, 155.25it/s]\u001b[A\n",
            "RBM fitting: 2788it [00:18, 145.19it/s]\u001b[A\n",
            "RBM fitting: 2804it [00:18, 148.81it/s]\u001b[A\n",
            "RBM fitting: 2820it [00:18, 150.90it/s]\u001b[A\n",
            "RBM fitting: 2836it [00:18, 153.12it/s]\u001b[A\n",
            "RBM fitting: 2852it [00:18, 154.70it/s]\u001b[A\n",
            "RBM fitting: 2868it [00:19, 154.35it/s]\u001b[A\n",
            "RBM fitting: 2884it [00:19, 154.72it/s]\u001b[A\n",
            "RBM fitting: 2900it [00:19, 147.57it/s]\u001b[A\n",
            "RBM fitting: 2916it [00:19, 149.68it/s]\u001b[A\n",
            "RBM fitting: 2932it [00:19, 150.12it/s]\u001b[A\n",
            "RBM fitting: 2948it [00:19, 150.99it/s]\u001b[A\n",
            "RBM fitting: 2965it [00:19, 154.04it/s]\u001b[A\n",
            "RBM fitting: 2982it [00:19, 156.18it/s]\u001b[A\n",
            "RBM fitting: 2998it [00:19, 156.28it/s]\u001b[A\n",
            "RBM fitting: 3014it [00:19, 149.01it/s]\u001b[A\n",
            "RBM fitting: 3029it [00:20, 149.19it/s]\u001b[A\n",
            "RBM fitting: 3045it [00:20, 151.32it/s]\u001b[A\n",
            "RBM fitting: 3061it [00:20, 152.21it/s]\u001b[A\n",
            "RBM fitting: 3077it [00:20, 152.75it/s]\u001b[A\n",
            "RBM fitting: 3093it [00:20, 150.73it/s]\u001b[A\n",
            "RBM fitting: 3109it [00:20, 152.85it/s]\u001b[A\n",
            "RBM fitting: 3125it [00:20, 147.06it/s]\u001b[A\n",
            "RBM fitting: 3141it [00:20, 149.10it/s]\u001b[A\n",
            "RBM fitting: 3157it [00:20, 151.51it/s]\u001b[A\n",
            "RBM fitting: 3173it [00:21, 152.24it/s]\u001b[A\n",
            "RBM fitting: 3189it [00:21, 153.61it/s]\u001b[A\n",
            "RBM fitting: 3205it [00:21, 154.41it/s]\u001b[A\n",
            "RBM fitting: 3221it [00:21, 155.64it/s]\u001b[A\n",
            "RBM fitting: 3237it [00:21, 144.71it/s]\u001b[A\n",
            "RBM fitting: 3253it [00:21, 148.62it/s]\u001b[A\n",
            "RBM fitting: 3269it [00:21, 151.85it/s]\u001b[A\n",
            "RBM fitting: 3285it [00:21, 152.52it/s]\u001b[A\n",
            "RBM fitting: 3301it [00:21, 153.38it/s]\u001b[A\n",
            "RBM fitting: 3317it [00:21, 154.46it/s]\u001b[A\n",
            "RBM fitting: 3333it [00:22, 153.60it/s]\u001b[A\n",
            "RBM fitting: 3349it [00:22, 147.34it/s]\u001b[A\n",
            "RBM fitting: 3365it [00:22, 150.11it/s]\u001b[A\n",
            "RBM fitting: 3381it [00:22, 152.72it/s]\u001b[A\n",
            "RBM fitting: 3397it [00:22, 151.19it/s]\u001b[A\n",
            "RBM fitting: 3413it [00:22, 152.81it/s]\u001b[A\n",
            "RBM fitting: 3429it [00:22, 152.22it/s]\u001b[A\n",
            "RBM fitting: 3445it [00:22, 154.34it/s]\u001b[A\n",
            "RBM fitting: 3461it [00:22, 147.09it/s]\u001b[A\n",
            "RBM fitting: 3477it [00:23, 149.00it/s]\u001b[A\n",
            "RBM fitting: 3493it [00:23, 151.46it/s]\u001b[A\n",
            "RBM fitting: 3509it [00:23, 153.41it/s]\u001b[A\n",
            "RBM fitting: 3525it [00:23, 154.20it/s]\u001b[A\n",
            "RBM fitting: 3541it [00:23, 152.98it/s]\u001b[A\n",
            "RBM fitting: 3557it [00:23, 151.83it/s]\u001b[A\n",
            "RBM fitting: 3573it [00:23, 145.91it/s]\u001b[A\n",
            "RBM fitting: 3589it [00:23, 149.38it/s]\u001b[A\n",
            "RBM fitting: 3605it [00:23, 151.76it/s]\u001b[A\n",
            "RBM fitting: 3621it [00:23, 152.92it/s]\u001b[A\n",
            "RBM fitting: 3637it [00:24, 152.96it/s]\u001b[A\n",
            "RBM fitting: 3653it [00:24, 153.97it/s]\u001b[A\n",
            "RBM fitting: 3669it [00:24, 155.00it/s]\u001b[A\n",
            "RBM fitting: 3685it [00:24, 147.70it/s]\u001b[A\n",
            "RBM fitting: 3700it [00:24, 147.04it/s]\u001b[A\n",
            "RBM fitting: 3716it [00:24, 150.00it/s]\u001b[A\n",
            "RBM fitting: 3732it [00:24, 152.54it/s]\u001b[A\n",
            "RBM fitting: 3748it [00:24, 154.13it/s]\u001b[A\n",
            "RBM fitting: 3764it [00:24, 153.88it/s]\u001b[A\n",
            "RBM fitting: 3780it [00:25, 154.52it/s]\u001b[A\n",
            "RBM fitting: 3796it [00:25, 147.08it/s]\u001b[A\n",
            "RBM fitting: 3812it [00:25, 150.04it/s]\u001b[A\n",
            "RBM fitting: 3828it [00:25, 152.59it/s]\u001b[A\n",
            "RBM fitting: 3844it [00:25, 154.23it/s]\u001b[A\n",
            "RBM fitting: 3860it [00:25, 152.30it/s]\u001b[A\n",
            "RBM fitting: 3876it [00:25, 153.56it/s]\u001b[A\n",
            "RBM fitting: 3892it [00:25, 150.46it/s]\u001b[A\n",
            "RBM fitting: 3908it [00:25, 145.14it/s]\u001b[A\n",
            "RBM fitting: 3924it [00:25, 148.48it/s]\u001b[A\n",
            "RBM fitting: 3940it [00:26, 150.41it/s]\u001b[A\n",
            "RBM fitting: 3956it [00:26, 152.87it/s]\u001b[A\n",
            "RBM fitting: 3972it [00:26, 153.92it/s]\u001b[A\n",
            "RBM fitting: 3989it [00:26, 156.15it/s]\u001b[A\n",
            "RBM fitting: 4005it [00:26, 156.29it/s]\u001b[A\n",
            "RBM fitting: 4021it [00:26, 146.81it/s]\u001b[A\n",
            "RBM fitting: 4037it [00:26, 149.36it/s]\u001b[A\n",
            "RBM fitting: 4053it [00:26, 151.79it/s]\u001b[A\n",
            "RBM fitting: 4069it [00:26, 154.08it/s]\u001b[A\n",
            "RBM fitting: 4085it [00:27, 154.37it/s]\u001b[A\n",
            "RBM fitting: 4101it [00:27, 155.30it/s]\u001b[A\n",
            "RBM fitting: 4117it [00:27, 155.59it/s]\u001b[A\n",
            "RBM fitting: 4133it [00:27, 147.99it/s]\u001b[A\n",
            "RBM fitting: 4149it [00:27, 150.83it/s]\u001b[A\n",
            "RBM fitting: 4165it [00:27, 150.49it/s]\u001b[A\n",
            "RBM fitting: 4181it [00:27, 152.88it/s]\u001b[A\n",
            "RBM fitting: 4197it [00:27, 153.40it/s]\u001b[A\n",
            "RBM fitting: 4213it [00:27, 152.20it/s]\u001b[A\n",
            "RBM fitting: 4229it [00:27, 154.11it/s]\u001b[A\n",
            "RBM fitting: 4245it [00:28, 146.48it/s]\u001b[A\n",
            "RBM fitting: 4261it [00:28, 149.64it/s]\u001b[A\n",
            "RBM fitting: 4277it [00:28, 150.98it/s]\u001b[A\n",
            "RBM fitting: 4293it [00:28, 152.07it/s]\u001b[A\n",
            "RBM fitting: 4309it [00:28, 152.30it/s]\u001b[A\n",
            "RBM fitting: 4325it [00:28, 150.29it/s]\u001b[A\n",
            "RBM fitting: 4341it [00:28, 151.80it/s]\u001b[A\n",
            "RBM fitting: 4357it [00:28, 145.39it/s]\u001b[A\n",
            "RBM fitting: 4373it [00:28, 148.87it/s]\u001b[A\n",
            "RBM fitting: 4389it [00:29, 151.03it/s]\u001b[A\n",
            "RBM fitting: 4405it [00:29, 153.12it/s]\u001b[A\n",
            "RBM fitting: 4421it [00:29, 154.21it/s]\u001b[A\n",
            "RBM fitting: 4437it [00:29, 155.63it/s]\u001b[A\n",
            "RBM fitting: 4453it [00:29, 156.16it/s]\u001b[A\n",
            "RBM fitting: 4469it [00:29, 146.32it/s]\u001b[A\n",
            "RBM fitting: 4485it [00:29, 149.73it/s]\u001b[A\n",
            "RBM fitting: 4501it [00:29, 152.24it/s]\u001b[A\n",
            "RBM fitting: 4517it [00:29, 151.33it/s]\u001b[A\n",
            "RBM fitting: 4533it [00:29, 151.59it/s]\u001b[A\n",
            "RBM fitting: 4549it [00:30, 151.75it/s]\u001b[A\n",
            "RBM fitting: 4565it [00:30, 143.62it/s]\u001b[A\n",
            "RBM fitting: 4581it [00:30, 146.99it/s]\u001b[A\n",
            "RBM fitting: 4597it [00:30, 149.60it/s]\u001b[A\n",
            "RBM fitting: 4613it [00:30, 151.27it/s]\u001b[A\n",
            "RBM fitting: 4629it [00:30, 149.19it/s]\u001b[A\n",
            "RBM fitting: 4644it [00:30, 149.18it/s]\u001b[A\n",
            "RBM fitting: 4660it [00:30, 150.94it/s]\u001b[A\n",
            "RBM fitting: 4676it [00:30, 143.94it/s]\u001b[A\n",
            "RBM fitting: 4692it [00:31, 146.88it/s]\u001b[A\n",
            "RBM fitting: 4708it [00:31, 150.39it/s]\u001b[A\n",
            "RBM fitting: 4725it [00:31, 153.77it/s]\u001b[A\n",
            "RBM fitting: 4741it [00:31, 155.48it/s]\u001b[A\n",
            "RBM fitting: 4757it [00:31, 156.18it/s]\u001b[A\n",
            "RBM fitting: 4773it [00:31, 156.69it/s]\u001b[A\n",
            "RBM fitting: 4789it [00:31, 150.92it/s]\u001b[A\n",
            "RBM fitting: 4805it [00:31, 144.75it/s]\u001b[A\n",
            "RBM fitting: 4821it [00:31, 148.09it/s]\u001b[A\n",
            "RBM fitting: 4837it [00:32, 150.68it/s]\u001b[A\n",
            "RBM fitting: 4853it [00:32, 152.16it/s]\u001b[A\n",
            "RBM fitting: 4869it [00:32, 153.96it/s]\u001b[A\n",
            "RBM fitting: 4886it [00:32, 156.41it/s]\u001b[A\n",
            "RBM fitting: 4902it [00:32, 157.04it/s]\u001b[A\n",
            "RBM fitting: 4918it [00:32, 149.18it/s]\u001b[A\n",
            "RBM fitting: 4934it [00:32, 148.47it/s]\u001b[A\n",
            "RBM fitting: 4950it [00:32, 150.53it/s]\u001b[A\n",
            "RBM fitting: 4966it [00:32, 152.71it/s]\u001b[A\n",
            "RBM fitting: 4982it [00:32, 154.63it/s]\u001b[A\n",
            "RBM fitting: 4998it [00:33, 155.01it/s]\u001b[A\n",
            "RBM fitting: 5014it [00:33, 154.97it/s]\u001b[A\n",
            "RBM fitting: 5030it [00:33, 155.26it/s]\u001b[A\n",
            "RBM fitting: 5046it [00:33, 146.54it/s]\u001b[A\n",
            "RBM fitting: 5061it [00:33, 146.50it/s]\u001b[A\n",
            "RBM fitting: 5078it [00:33, 150.73it/s]\u001b[A\n",
            "RBM fitting: 5094it [00:33, 149.76it/s]\u001b[A\n",
            "RBM fitting: 5110it [00:33, 151.89it/s]\u001b[A\n",
            "RBM fitting: 5126it [00:33, 153.39it/s]\u001b[A\n",
            "RBM fitting: 5142it [00:34, 153.92it/s]\u001b[A\n",
            "RBM fitting: 5158it [00:34, 138.61it/s]\u001b[A\n",
            "RBM fitting: 5173it [00:34, 141.70it/s]\u001b[A\n",
            "RBM fitting: 5189it [00:34, 145.88it/s]\u001b[A\n",
            "RBM fitting: 5206it [00:34, 150.31it/s]\u001b[A\n",
            "RBM fitting: 5223it [00:34, 153.98it/s]\u001b[A\n",
            "RBM fitting: 5239it [00:34, 152.72it/s]\u001b[A\n",
            "RBM fitting: 5256it [00:34, 155.39it/s]\u001b[A\n",
            "RBM fitting: 5272it [00:34, 146.41it/s]\u001b[A\n",
            "RBM fitting: 5288it [00:35, 149.04it/s]\u001b[A\n",
            "RBM fitting: 5304it [00:35, 150.75it/s]\u001b[A\n",
            "RBM fitting: 5320it [00:35, 151.68it/s]\u001b[A\n",
            "RBM fitting: 5336it [00:35, 153.28it/s]\u001b[A\n",
            "RBM fitting: 5352it [00:35, 154.28it/s]\u001b[A\n",
            "RBM fitting: 5368it [00:35, 154.69it/s]\u001b[A\n",
            "RBM fitting: 5384it [00:35, 145.39it/s]\u001b[A\n",
            "RBM fitting: 5400it [00:35, 147.49it/s]\u001b[A\n",
            "RBM fitting: 5416it [00:35, 149.85it/s]\u001b[A\n",
            "RBM fitting: 5432it [00:35, 151.68it/s]\u001b[A\n",
            "RBM fitting: 5448it [00:36, 152.47it/s]\u001b[A\n",
            "RBM fitting: 5464it [00:36, 151.65it/s]\u001b[A\n",
            "RBM fitting: 5480it [00:36, 153.49it/s]\u001b[A\n",
            "RBM fitting: 5496it [00:36, 154.73it/s]\u001b[A\n",
            "RBM fitting: 5512it [00:36, 145.51it/s]\u001b[A\n",
            "RBM fitting: 5528it [00:36, 148.22it/s]\u001b[A\n",
            "RBM fitting: 5543it [00:36, 148.39it/s]\u001b[A\n",
            "RBM fitting: 5559it [00:36, 150.65it/s]\u001b[A\n",
            "RBM fitting: 5575it [00:36, 148.33it/s]\u001b[A\n",
            "RBM fitting: 5591it [00:37, 149.85it/s]\u001b[A\n",
            "RBM fitting: 5607it [00:37, 151.04it/s]\u001b[A\n",
            "RBM fitting: 5623it [00:37, 142.84it/s]\u001b[A\n",
            "RBM fitting: 5639it [00:37, 145.85it/s]\u001b[A\n",
            "RBM fitting: 5655it [00:37, 148.46it/s]\u001b[A\n",
            "RBM fitting: 5671it [00:37, 150.60it/s]\u001b[A\n",
            "RBM fitting: 5687it [00:37, 152.72it/s]\u001b[A\n",
            "RBM fitting: 5703it [00:37, 151.88it/s]\u001b[A\n",
            "RBM fitting: 5719it [00:37, 154.19it/s]\u001b[A\n",
            "RBM fitting: 5735it [00:37, 145.66it/s]\u001b[A\n",
            "RBM fitting: 5750it [00:38, 146.87it/s]\u001b[A\n",
            "RBM fitting: 5766it [00:38, 149.48it/s]\u001b[A\n",
            "RBM fitting: 5782it [00:38, 152.23it/s]\u001b[A\n",
            "RBM fitting: 5798it [00:38, 153.69it/s]\u001b[A\n",
            "RBM fitting: 5814it [00:38, 153.47it/s]\u001b[A\n",
            "RBM fitting: 5830it [00:38, 153.75it/s]\u001b[A\n",
            "RBM fitting: 5846it [00:38, 141.56it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:38, 146.10it/s]\u001b[A\n",
            "RBM fitting: 5878it [00:38, 148.73it/s]\u001b[A\n",
            "RBM fitting: 5894it [00:39, 150.81it/s]\u001b[A\n",
            "RBM fitting: 5910it [00:39, 153.25it/s]\u001b[A\n",
            "RBM fitting: 5926it [00:39, 154.69it/s]\u001b[A\n",
            "RBM fitting: 5943it [00:39, 156.61it/s]\u001b[A\n",
            "RBM fitting: 5959it [00:39, 146.76it/s]\u001b[A\n",
            "RBM fitting: 5975it [00:39, 148.99it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 150.90it/s]\n",
            "Epoch:25 ,avg_cost = 0.33869314193725586 ,std_cost = 0.032871998846530914 ,avg_grad = 10082.14453125 ,std_grad = 535.6273803710938\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 15it [00:00, 144.07it/s]\u001b[A\n",
            "RBM fitting: 31it [00:00, 146.66it/s]\u001b[A\n",
            "RBM fitting: 43it [00:00, 134.32it/s]\u001b[A\n",
            "RBM fitting: 59it [00:00, 140.52it/s]\u001b[A\n",
            "RBM fitting: 75it [00:00, 145.61it/s]\u001b[A\n",
            "RBM fitting: 92it [00:00, 150.47it/s]\u001b[A\n",
            "RBM fitting: 108it [00:00, 152.37it/s]\u001b[A\n",
            "RBM fitting: 125it [00:00, 155.29it/s]\u001b[A\n",
            "RBM fitting: 141it [00:00, 154.19it/s]\u001b[A\n",
            "RBM fitting: 156it [00:01, 151.60it/s]\u001b[A\n",
            "RBM fitting: 171it [00:01, 144.41it/s]\u001b[A\n",
            "RBM fitting: 187it [00:01, 147.54it/s]\u001b[A\n",
            "RBM fitting: 203it [00:01, 149.95it/s]\u001b[A\n",
            "RBM fitting: 219it [00:01, 151.80it/s]\u001b[A\n",
            "RBM fitting: 235it [00:01, 153.84it/s]\u001b[A\n",
            "RBM fitting: 252it [00:01, 155.93it/s]\u001b[A\n",
            "RBM fitting: 268it [00:01, 156.23it/s]\u001b[A\n",
            "RBM fitting: 284it [00:01, 147.93it/s]\u001b[A\n",
            "RBM fitting: 300it [00:01, 149.65it/s]\u001b[A\n",
            "RBM fitting: 316it [00:02, 149.24it/s]\u001b[A\n",
            "RBM fitting: 332it [00:02, 150.56it/s]\u001b[A\n",
            "RBM fitting: 348it [00:02, 150.99it/s]\u001b[A\n",
            "RBM fitting: 364it [00:02, 153.23it/s]\u001b[A\n",
            "RBM fitting: 380it [00:02, 154.61it/s]\u001b[A\n",
            "RBM fitting: 396it [00:02, 155.82it/s]\u001b[A\n",
            "RBM fitting: 412it [00:02, 146.43it/s]\u001b[A\n",
            "RBM fitting: 427it [00:02, 145.53it/s]\u001b[A\n",
            "RBM fitting: 443it [00:02, 147.77it/s]\u001b[A\n",
            "RBM fitting: 458it [00:03, 147.10it/s]\u001b[A\n",
            "RBM fitting: 474it [00:03, 149.88it/s]\u001b[A\n",
            "RBM fitting: 490it [00:03, 152.04it/s]\u001b[A\n",
            "RBM fitting: 506it [00:03, 153.47it/s]\u001b[A\n",
            "RBM fitting: 522it [00:03, 146.71it/s]\u001b[A\n",
            "RBM fitting: 538it [00:03, 150.21it/s]\u001b[A\n",
            "RBM fitting: 554it [00:03, 151.73it/s]\u001b[A\n",
            "RBM fitting: 570it [00:03, 152.96it/s]\u001b[A\n",
            "RBM fitting: 586it [00:03, 151.40it/s]\u001b[A\n",
            "RBM fitting: 602it [00:03, 151.74it/s]\u001b[A\n",
            "RBM fitting: 618it [00:04, 151.08it/s]\u001b[A\n",
            "RBM fitting: 634it [00:04, 153.17it/s]\u001b[A\n",
            "RBM fitting: 650it [00:04, 144.58it/s]\u001b[A\n",
            "RBM fitting: 666it [00:04, 147.78it/s]\u001b[A\n",
            "RBM fitting: 683it [00:04, 151.69it/s]\u001b[A\n",
            "RBM fitting: 699it [00:04, 153.65it/s]\u001b[A\n",
            "RBM fitting: 715it [00:04, 154.08it/s]\u001b[A\n",
            "RBM fitting: 732it [00:04, 155.97it/s]\u001b[A\n",
            "RBM fitting: 748it [00:04, 155.51it/s]\u001b[A\n",
            "RBM fitting: 764it [00:05, 141.45it/s]\u001b[A\n",
            "RBM fitting: 780it [00:05, 145.63it/s]\u001b[A\n",
            "RBM fitting: 796it [00:05, 148.84it/s]\u001b[A\n",
            "RBM fitting: 812it [00:05, 150.54it/s]\u001b[A\n",
            "RBM fitting: 828it [00:05, 152.03it/s]\u001b[A\n",
            "RBM fitting: 844it [00:05, 149.33it/s]\u001b[A\n",
            "RBM fitting: 860it [00:05, 148.67it/s]\u001b[A\n",
            "RBM fitting: 875it [00:05, 141.75it/s]\u001b[A\n",
            "RBM fitting: 890it [00:05, 143.01it/s]\u001b[A\n",
            "RBM fitting: 906it [00:06, 146.94it/s]\u001b[A\n",
            "RBM fitting: 921it [00:06, 147.11it/s]\u001b[A\n",
            "RBM fitting: 937it [00:06, 150.70it/s]\u001b[A\n",
            "RBM fitting: 953it [00:06, 152.17it/s]\u001b[A\n",
            "RBM fitting: 969it [00:06, 153.98it/s]\u001b[A\n",
            "RBM fitting: 985it [00:06, 146.00it/s]\u001b[A\n",
            "RBM fitting: 1000it [00:06, 146.45it/s]\u001b[A\n",
            "RBM fitting: 1016it [00:06, 149.39it/s]\u001b[A\n",
            "RBM fitting: 1032it [00:06, 151.92it/s]\u001b[A\n",
            "RBM fitting: 1048it [00:06, 153.07it/s]\u001b[A\n",
            "RBM fitting: 1064it [00:07, 154.32it/s]\u001b[A\n",
            "RBM fitting: 1080it [00:07, 152.74it/s]\u001b[A\n",
            "RBM fitting: 1096it [00:07, 136.22it/s]\u001b[A\n",
            "RBM fitting: 1112it [00:07, 141.96it/s]\u001b[A\n",
            "RBM fitting: 1128it [00:07, 146.02it/s]\u001b[A\n",
            "RBM fitting: 1145it [00:07, 150.34it/s]\u001b[A\n",
            "RBM fitting: 1161it [00:07, 152.35it/s]\u001b[A\n",
            "RBM fitting: 1177it [00:07, 153.72it/s]\u001b[A\n",
            "RBM fitting: 1193it [00:07, 153.54it/s]\u001b[A\n",
            "RBM fitting: 1209it [00:08, 142.76it/s]\u001b[A\n",
            "RBM fitting: 1224it [00:08, 142.40it/s]\u001b[A\n",
            "RBM fitting: 1240it [00:08, 146.99it/s]\u001b[A\n",
            "RBM fitting: 1256it [00:08, 150.34it/s]\u001b[A\n",
            "RBM fitting: 1272it [00:08, 152.12it/s]\u001b[A\n",
            "RBM fitting: 1288it [00:08, 153.47it/s]\u001b[A\n",
            "RBM fitting: 1304it [00:08, 154.53it/s]\u001b[A\n",
            "RBM fitting: 1320it [00:08, 146.11it/s]\u001b[A\n",
            "RBM fitting: 1335it [00:08, 145.38it/s]\u001b[A\n",
            "RBM fitting: 1351it [00:09, 147.65it/s]\u001b[A\n",
            "RBM fitting: 1367it [00:09, 149.22it/s]\u001b[A\n",
            "RBM fitting: 1383it [00:09, 152.00it/s]\u001b[A\n",
            "RBM fitting: 1399it [00:09, 152.75it/s]\u001b[A\n",
            "RBM fitting: 1415it [00:09, 153.87it/s]\u001b[A\n",
            "RBM fitting: 1431it [00:09, 144.23it/s]\u001b[A\n",
            "RBM fitting: 1447it [00:09, 147.40it/s]\u001b[A\n",
            "RBM fitting: 1463it [00:09, 149.99it/s]\u001b[A\n",
            "RBM fitting: 1479it [00:09, 152.46it/s]\u001b[A\n",
            "RBM fitting: 1495it [00:09, 153.29it/s]\u001b[A\n",
            "RBM fitting: 1511it [00:10, 153.46it/s]\u001b[A\n",
            "RBM fitting: 1527it [00:10, 147.25it/s]\u001b[A\n",
            "RBM fitting: 1542it [00:10, 138.30it/s]\u001b[A\n",
            "RBM fitting: 1558it [00:10, 142.62it/s]\u001b[A\n",
            "RBM fitting: 1573it [00:10, 144.37it/s]\u001b[A\n",
            "RBM fitting: 1589it [00:10, 148.18it/s]\u001b[A\n",
            "RBM fitting: 1605it [00:10, 150.64it/s]\u001b[A\n",
            "RBM fitting: 1621it [00:10, 153.20it/s]\u001b[A\n",
            "RBM fitting: 1637it [00:10, 154.56it/s]\u001b[A\n",
            "RBM fitting: 1653it [00:11, 144.69it/s]\u001b[A\n",
            "RBM fitting: 1668it [00:11, 141.03it/s]\u001b[A\n",
            "RBM fitting: 1683it [00:11, 143.26it/s]\u001b[A\n",
            "RBM fitting: 1699it [00:11, 147.15it/s]\u001b[A\n",
            "RBM fitting: 1715it [00:11, 148.71it/s]\u001b[A\n",
            "RBM fitting: 1731it [00:11, 151.25it/s]\u001b[A\n",
            "RBM fitting: 1747it [00:11, 152.59it/s]\u001b[A\n",
            "RBM fitting: 1763it [00:11, 143.44it/s]\u001b[A\n",
            "RBM fitting: 1778it [00:11, 144.46it/s]\u001b[A\n",
            "RBM fitting: 1793it [00:12, 146.05it/s]\u001b[A\n",
            "RBM fitting: 1809it [00:12, 148.52it/s]\u001b[A\n",
            "RBM fitting: 1824it [00:12, 148.21it/s]\u001b[A\n",
            "RBM fitting: 1840it [00:12, 150.21it/s]\u001b[A\n",
            "RBM fitting: 1856it [00:12, 152.39it/s]\u001b[A\n",
            "RBM fitting: 1872it [00:12, 145.27it/s]\u001b[A\n",
            "RBM fitting: 1887it [00:12, 144.74it/s]\u001b[A\n",
            "RBM fitting: 1902it [00:12, 146.04it/s]\u001b[A\n",
            "RBM fitting: 1918it [00:12, 148.84it/s]\u001b[A\n",
            "RBM fitting: 1934it [00:12, 151.61it/s]\u001b[A\n",
            "RBM fitting: 1950it [00:13, 153.75it/s]\u001b[A\n",
            "RBM fitting: 1966it [00:13, 154.97it/s]\u001b[A\n",
            "RBM fitting: 1982it [00:13, 153.58it/s]\u001b[A\n",
            "RBM fitting: 1998it [00:13, 144.74it/s]\u001b[A\n",
            "RBM fitting: 2014it [00:13, 147.30it/s]\u001b[A\n",
            "RBM fitting: 2030it [00:13, 150.34it/s]\u001b[A\n",
            "RBM fitting: 2046it [00:13, 152.48it/s]\u001b[A\n",
            "RBM fitting: 2062it [00:13, 153.53it/s]\u001b[A\n",
            "RBM fitting: 2078it [00:13, 154.23it/s]\u001b[A\n",
            "RBM fitting: 2094it [00:13, 155.84it/s]\u001b[A\n",
            "RBM fitting: 2110it [00:14, 147.21it/s]\u001b[A\n",
            "RBM fitting: 2125it [00:14, 147.11it/s]\u001b[A\n",
            "RBM fitting: 2141it [00:14, 149.65it/s]\u001b[A\n",
            "RBM fitting: 2157it [00:14, 151.89it/s]\u001b[A\n",
            "RBM fitting: 2173it [00:14, 153.89it/s]\u001b[A\n",
            "RBM fitting: 2189it [00:14, 155.48it/s]\u001b[A\n",
            "RBM fitting: 2205it [00:14, 154.39it/s]\u001b[A\n",
            "RBM fitting: 2221it [00:14, 147.59it/s]\u001b[A\n",
            "RBM fitting: 2238it [00:14, 152.03it/s]\u001b[A\n",
            "RBM fitting: 2254it [00:15, 154.15it/s]\u001b[A\n",
            "RBM fitting: 2271it [00:15, 156.39it/s]\u001b[A\n",
            "RBM fitting: 2287it [00:15, 154.77it/s]\u001b[A\n",
            "RBM fitting: 2303it [00:15, 154.87it/s]\u001b[A\n",
            "RBM fitting: 2319it [00:15, 155.99it/s]\u001b[A\n",
            "RBM fitting: 2335it [00:15, 156.62it/s]\u001b[A\n",
            "RBM fitting: 2351it [00:15, 148.01it/s]\u001b[A\n",
            "RBM fitting: 2367it [00:15, 150.89it/s]\u001b[A\n",
            "RBM fitting: 2383it [00:15, 153.39it/s]\u001b[A\n",
            "RBM fitting: 2399it [00:15, 155.16it/s]\u001b[A\n",
            "RBM fitting: 2415it [00:16, 154.88it/s]\u001b[A\n",
            "RBM fitting: 2431it [00:16, 156.01it/s]\u001b[A\n",
            "RBM fitting: 2447it [00:16, 152.80it/s]\u001b[A\n",
            "RBM fitting: 2463it [00:16, 154.64it/s]\u001b[A\n",
            "RBM fitting: 2479it [00:16, 145.52it/s]\u001b[A\n",
            "RBM fitting: 2495it [00:16, 149.26it/s]\u001b[A\n",
            "RBM fitting: 2511it [00:16, 152.05it/s]\u001b[A\n",
            "RBM fitting: 2527it [00:16, 153.92it/s]\u001b[A\n",
            "RBM fitting: 2544it [00:16, 156.29it/s]\u001b[A\n",
            "RBM fitting: 2560it [00:17, 156.39it/s]\u001b[A\n",
            "RBM fitting: 2576it [00:17, 156.58it/s]\u001b[A\n",
            "RBM fitting: 2592it [00:17, 157.05it/s]\u001b[A\n",
            "RBM fitting: 2608it [00:17, 145.14it/s]\u001b[A\n",
            "RBM fitting: 2624it [00:17, 148.66it/s]\u001b[A\n",
            "RBM fitting: 2640it [00:17, 150.48it/s]\u001b[A\n",
            "RBM fitting: 2656it [00:17, 151.91it/s]\u001b[A\n",
            "RBM fitting: 2672it [00:17, 153.37it/s]\u001b[A\n",
            "RBM fitting: 2688it [00:17, 154.48it/s]\u001b[A\n",
            "RBM fitting: 2704it [00:17, 154.95it/s]\u001b[A\n",
            "RBM fitting: 2720it [00:18, 155.76it/s]\u001b[A\n",
            "RBM fitting: 2736it [00:18, 146.95it/s]\u001b[A\n",
            "RBM fitting: 2751it [00:18, 142.85it/s]\u001b[A\n",
            "RBM fitting: 2767it [00:18, 146.34it/s]\u001b[A\n",
            "RBM fitting: 2784it [00:18, 150.67it/s]\u001b[A\n",
            "RBM fitting: 2800it [00:18, 153.08it/s]\u001b[A\n",
            "RBM fitting: 2816it [00:18, 152.63it/s]\u001b[A\n",
            "RBM fitting: 2832it [00:18, 154.45it/s]\u001b[A\n",
            "RBM fitting: 2848it [00:18, 144.84it/s]\u001b[A\n",
            "RBM fitting: 2864it [00:19, 147.28it/s]\u001b[A\n",
            "RBM fitting: 2880it [00:19, 149.91it/s]\u001b[A\n",
            "RBM fitting: 2896it [00:19, 151.38it/s]\u001b[A\n",
            "RBM fitting: 2912it [00:19, 150.50it/s]\u001b[A\n",
            "RBM fitting: 2928it [00:19, 153.01it/s]\u001b[A\n",
            "RBM fitting: 2945it [00:19, 155.40it/s]\u001b[A\n",
            "RBM fitting: 2961it [00:19, 145.68it/s]\u001b[A\n",
            "RBM fitting: 2976it [00:19, 146.41it/s]\u001b[A\n",
            "RBM fitting: 2992it [00:19, 149.49it/s]\u001b[A\n",
            "RBM fitting: 3009it [00:20, 153.29it/s]\u001b[A\n",
            "RBM fitting: 3025it [00:20, 154.66it/s]\u001b[A\n",
            "RBM fitting: 3041it [00:20, 154.74it/s]\u001b[A\n",
            "RBM fitting: 3057it [00:20, 151.57it/s]\u001b[A\n",
            "RBM fitting: 3073it [00:20, 149.45it/s]\u001b[A\n",
            "RBM fitting: 3088it [00:20, 144.06it/s]\u001b[A\n",
            "RBM fitting: 3104it [00:20, 148.25it/s]\u001b[A\n",
            "RBM fitting: 3119it [00:20, 148.24it/s]\u001b[A\n",
            "RBM fitting: 3135it [00:20, 151.41it/s]\u001b[A\n",
            "RBM fitting: 3152it [00:20, 154.64it/s]\u001b[A\n",
            "RBM fitting: 3168it [00:21, 155.84it/s]\u001b[A\n",
            "RBM fitting: 3184it [00:21, 147.67it/s]\u001b[A\n",
            "RBM fitting: 3200it [00:21, 149.30it/s]\u001b[A\n",
            "RBM fitting: 3216it [00:21, 149.25it/s]\u001b[A\n",
            "RBM fitting: 3232it [00:21, 152.21it/s]\u001b[A\n",
            "RBM fitting: 3249it [00:21, 155.18it/s]\u001b[A\n",
            "RBM fitting: 3265it [00:21, 155.86it/s]\u001b[A\n",
            "RBM fitting: 3281it [00:21, 156.71it/s]\u001b[A\n",
            "RBM fitting: 3297it [00:21, 149.30it/s]\u001b[A\n",
            "RBM fitting: 3314it [00:22, 152.79it/s]\u001b[A\n",
            "RBM fitting: 3330it [00:22, 152.23it/s]\u001b[A\n",
            "RBM fitting: 3346it [00:22, 153.71it/s]\u001b[A\n",
            "RBM fitting: 3362it [00:22, 153.59it/s]\u001b[A\n",
            "RBM fitting: 3378it [00:22, 150.66it/s]\u001b[A\n",
            "RBM fitting: 3395it [00:22, 154.24it/s]\u001b[A\n",
            "RBM fitting: 3412it [00:22, 156.98it/s]\u001b[A\n",
            "RBM fitting: 3428it [00:22, 149.02it/s]\u001b[A\n",
            "RBM fitting: 3444it [00:22, 151.59it/s]\u001b[A\n",
            "RBM fitting: 3461it [00:22, 154.45it/s]\u001b[A\n",
            "RBM fitting: 3477it [00:23, 155.58it/s]\u001b[A\n",
            "RBM fitting: 3493it [00:23, 156.30it/s]\u001b[A\n",
            "RBM fitting: 3509it [00:23, 156.71it/s]\u001b[A\n",
            "RBM fitting: 3525it [00:23, 153.10it/s]\u001b[A\n",
            "RBM fitting: 3541it [00:23, 154.56it/s]\u001b[A\n",
            "RBM fitting: 3557it [00:23, 147.02it/s]\u001b[A\n",
            "RBM fitting: 3573it [00:23, 149.60it/s]\u001b[A\n",
            "RBM fitting: 3589it [00:23, 151.79it/s]\u001b[A\n",
            "RBM fitting: 3605it [00:23, 152.95it/s]\u001b[A\n",
            "RBM fitting: 3621it [00:24, 153.80it/s]\u001b[A\n",
            "RBM fitting: 3637it [00:24, 154.75it/s]\u001b[A\n",
            "RBM fitting: 3653it [00:24, 153.70it/s]\u001b[A\n",
            "RBM fitting: 3669it [00:24, 151.06it/s]\u001b[A\n",
            "RBM fitting: 3685it [00:24, 142.60it/s]\u001b[A\n",
            "RBM fitting: 3701it [00:24, 146.93it/s]\u001b[A\n",
            "RBM fitting: 3717it [00:24, 150.04it/s]\u001b[A\n",
            "RBM fitting: 3734it [00:24, 153.60it/s]\u001b[A\n",
            "RBM fitting: 3750it [00:24, 153.92it/s]\u001b[A\n",
            "RBM fitting: 3766it [00:25, 154.91it/s]\u001b[A\n",
            "RBM fitting: 3782it [00:25, 156.26it/s]\u001b[A\n",
            "RBM fitting: 3798it [00:25, 156.82it/s]\u001b[A\n",
            "RBM fitting: 3814it [00:25, 146.20it/s]\u001b[A\n",
            "RBM fitting: 3829it [00:25, 145.86it/s]\u001b[A\n",
            "RBM fitting: 3845it [00:25, 148.21it/s]\u001b[A\n",
            "RBM fitting: 3861it [00:25, 150.54it/s]\u001b[A\n",
            "RBM fitting: 3877it [00:25, 151.76it/s]\u001b[A\n",
            "RBM fitting: 3893it [00:25, 153.03it/s]\u001b[A\n",
            "RBM fitting: 3909it [00:25, 153.75it/s]\u001b[A\n",
            "RBM fitting: 3925it [00:26, 154.47it/s]\u001b[A\n",
            "RBM fitting: 3941it [00:26, 146.37it/s]\u001b[A\n",
            "RBM fitting: 3957it [00:26, 149.05it/s]\u001b[A\n",
            "RBM fitting: 3973it [00:26, 151.31it/s]\u001b[A\n",
            "RBM fitting: 3989it [00:26, 149.61it/s]\u001b[A\n",
            "RBM fitting: 4005it [00:26, 152.01it/s]\u001b[A\n",
            "RBM fitting: 4021it [00:26, 153.34it/s]\u001b[A\n",
            "RBM fitting: 4037it [00:26, 155.13it/s]\u001b[A\n",
            "RBM fitting: 4054it [00:26, 156.71it/s]\u001b[A\n",
            "RBM fitting: 4070it [00:27, 149.18it/s]\u001b[A\n",
            "RBM fitting: 4087it [00:27, 152.81it/s]\u001b[A\n",
            "RBM fitting: 4103it [00:27, 154.39it/s]\u001b[A\n",
            "RBM fitting: 4119it [00:27, 154.91it/s]\u001b[A\n",
            "RBM fitting: 4135it [00:27, 155.11it/s]\u001b[A\n",
            "RBM fitting: 4151it [00:27, 153.70it/s]\u001b[A\n",
            "RBM fitting: 4168it [00:27, 156.39it/s]\u001b[A\n",
            "RBM fitting: 4184it [00:27, 156.77it/s]\u001b[A\n",
            "RBM fitting: 4200it [00:27, 148.32it/s]\u001b[A\n",
            "RBM fitting: 4216it [00:27, 151.56it/s]\u001b[A\n",
            "RBM fitting: 4233it [00:28, 154.59it/s]\u001b[A\n",
            "RBM fitting: 4250it [00:28, 157.40it/s]\u001b[A\n",
            "RBM fitting: 4267it [00:28, 158.84it/s]\u001b[A\n",
            "RBM fitting: 4283it [00:28, 157.69it/s]\u001b[A\n",
            "RBM fitting: 4299it [00:28, 155.24it/s]\u001b[A\n",
            "RBM fitting: 4315it [00:28, 156.34it/s]\u001b[A\n",
            "RBM fitting: 4331it [00:28, 147.14it/s]\u001b[A\n",
            "RBM fitting: 4347it [00:28, 150.56it/s]\u001b[A\n",
            "RBM fitting: 4363it [00:28, 152.96it/s]\u001b[A\n",
            "RBM fitting: 4380it [00:29, 155.35it/s]\u001b[A\n",
            "RBM fitting: 4396it [00:29, 156.09it/s]\u001b[A\n",
            "RBM fitting: 4412it [00:29, 156.58it/s]\u001b[A\n",
            "RBM fitting: 4428it [00:29, 156.90it/s]\u001b[A\n",
            "RBM fitting: 4444it [00:29, 156.19it/s]\u001b[A\n",
            "RBM fitting: 4460it [00:29, 144.87it/s]\u001b[A\n",
            "RBM fitting: 4476it [00:29, 148.34it/s]\u001b[A\n",
            "RBM fitting: 4492it [00:29, 150.10it/s]\u001b[A\n",
            "RBM fitting: 4508it [00:29, 152.40it/s]\u001b[A\n",
            "RBM fitting: 4524it [00:29, 153.68it/s]\u001b[A\n",
            "RBM fitting: 4540it [00:30, 154.37it/s]\u001b[A\n",
            "RBM fitting: 4557it [00:30, 156.40it/s]\u001b[A\n",
            "RBM fitting: 4573it [00:30, 156.93it/s]\u001b[A\n",
            "RBM fitting: 4589it [00:30, 148.22it/s]\u001b[A\n",
            "RBM fitting: 4605it [00:30, 150.93it/s]\u001b[A\n",
            "RBM fitting: 4621it [00:30, 150.68it/s]\u001b[A\n",
            "RBM fitting: 4637it [00:30, 152.85it/s]\u001b[A\n",
            "RBM fitting: 4653it [00:30, 154.77it/s]\u001b[A\n",
            "RBM fitting: 4669it [00:30, 155.64it/s]\u001b[A\n",
            "RBM fitting: 4685it [00:31, 156.86it/s]\u001b[A\n",
            "RBM fitting: 4701it [00:31, 156.81it/s]\u001b[A\n",
            "RBM fitting: 4717it [00:31, 143.58it/s]\u001b[A\n",
            "RBM fitting: 4733it [00:31, 147.19it/s]\u001b[A\n",
            "RBM fitting: 4749it [00:31, 148.80it/s]\u001b[A\n",
            "RBM fitting: 4765it [00:31, 146.63it/s]\u001b[A\n",
            "RBM fitting: 4781it [00:31, 148.76it/s]\u001b[A\n",
            "RBM fitting: 4797it [00:31, 150.80it/s]\u001b[A\n",
            "RBM fitting: 4813it [00:31, 153.39it/s]\u001b[A\n",
            "RBM fitting: 4829it [00:31, 155.16it/s]\u001b[A\n",
            "RBM fitting: 4845it [00:32, 147.00it/s]\u001b[A\n",
            "RBM fitting: 4861it [00:32, 149.91it/s]\u001b[A\n",
            "RBM fitting: 4877it [00:32, 152.05it/s]\u001b[A\n",
            "RBM fitting: 4893it [00:32, 153.43it/s]\u001b[A\n",
            "RBM fitting: 4910it [00:32, 155.88it/s]\u001b[A\n",
            "RBM fitting: 4926it [00:32, 154.26it/s]\u001b[A\n",
            "RBM fitting: 4942it [00:32, 155.44it/s]\u001b[A\n",
            "RBM fitting: 4959it [00:32, 157.46it/s]\u001b[A\n",
            "RBM fitting: 4975it [00:32, 148.22it/s]\u001b[A\n",
            "RBM fitting: 4991it [00:33, 150.90it/s]\u001b[A\n",
            "RBM fitting: 5008it [00:33, 153.97it/s]\u001b[A\n",
            "RBM fitting: 5025it [00:33, 156.41it/s]\u001b[A\n",
            "RBM fitting: 5041it [00:33, 156.93it/s]\u001b[A\n",
            "RBM fitting: 5057it [00:33, 157.29it/s]\u001b[A\n",
            "RBM fitting: 5073it [00:33, 156.36it/s]\u001b[A\n",
            "RBM fitting: 5089it [00:33, 152.39it/s]\u001b[A\n",
            "RBM fitting: 5105it [00:33, 144.89it/s]\u001b[A\n",
            "RBM fitting: 5121it [00:33, 148.59it/s]\u001b[A\n",
            "RBM fitting: 5137it [00:33, 151.64it/s]\u001b[A\n",
            "RBM fitting: 5153it [00:34, 153.71it/s]\u001b[A\n",
            "RBM fitting: 5169it [00:34, 155.17it/s]\u001b[A\n",
            "RBM fitting: 5185it [00:34, 155.92it/s]\u001b[A\n",
            "RBM fitting: 5201it [00:34, 155.66it/s]\u001b[A\n",
            "RBM fitting: 5217it [00:34, 156.61it/s]\u001b[A\n",
            "RBM fitting: 5233it [00:34, 146.29it/s]\u001b[A\n",
            "RBM fitting: 5249it [00:34, 149.48it/s]\u001b[A\n",
            "RBM fitting: 5265it [00:34, 151.87it/s]\u001b[A\n",
            "RBM fitting: 5282it [00:34, 154.75it/s]\u001b[A\n",
            "RBM fitting: 5298it [00:35, 156.23it/s]\u001b[A\n",
            "RBM fitting: 5315it [00:35, 157.74it/s]\u001b[A\n",
            "RBM fitting: 5332it [00:35, 158.83it/s]\u001b[A\n",
            "RBM fitting: 5348it [00:35, 158.19it/s]\u001b[A\n",
            "RBM fitting: 5364it [00:35, 149.36it/s]\u001b[A\n",
            "RBM fitting: 5380it [00:35, 152.20it/s]\u001b[A\n",
            "RBM fitting: 5396it [00:35, 148.14it/s]\u001b[A\n",
            "RBM fitting: 5411it [00:35, 145.40it/s]\u001b[A\n",
            "RBM fitting: 5427it [00:35, 149.04it/s]\u001b[A\n",
            "RBM fitting: 5444it [00:35, 152.45it/s]\u001b[A\n",
            "RBM fitting: 5460it [00:36, 153.75it/s]\u001b[A\n",
            "RBM fitting: 5476it [00:36, 155.25it/s]\u001b[A\n",
            "RBM fitting: 5492it [00:36, 146.06it/s]\u001b[A\n",
            "RBM fitting: 5507it [00:36, 147.17it/s]\u001b[A\n",
            "RBM fitting: 5523it [00:36, 149.92it/s]\u001b[A\n",
            "RBM fitting: 5539it [00:36, 150.47it/s]\u001b[A\n",
            "RBM fitting: 5555it [00:36, 151.15it/s]\u001b[A\n",
            "RBM fitting: 5572it [00:36, 154.31it/s]\u001b[A\n",
            "RBM fitting: 5588it [00:36, 155.67it/s]\u001b[A\n",
            "RBM fitting: 5604it [00:37, 147.62it/s]\u001b[A\n",
            "RBM fitting: 5621it [00:37, 151.59it/s]\u001b[A\n",
            "RBM fitting: 5637it [00:37, 153.80it/s]\u001b[A\n",
            "RBM fitting: 5653it [00:37, 155.01it/s]\u001b[A\n",
            "RBM fitting: 5670it [00:37, 156.71it/s]\u001b[A\n",
            "RBM fitting: 5686it [00:37, 157.24it/s]\u001b[A\n",
            "RBM fitting: 5702it [00:37, 154.74it/s]\u001b[A\n",
            "RBM fitting: 5718it [00:37, 155.53it/s]\u001b[A\n",
            "RBM fitting: 5734it [00:37, 148.22it/s]\u001b[A\n",
            "RBM fitting: 5750it [00:38, 151.17it/s]\u001b[A\n",
            "RBM fitting: 5766it [00:38, 153.38it/s]\u001b[A\n",
            "RBM fitting: 5782it [00:38, 155.22it/s]\u001b[A\n",
            "RBM fitting: 5798it [00:38, 154.25it/s]\u001b[A\n",
            "RBM fitting: 5814it [00:38, 155.20it/s]\u001b[A\n",
            "RBM fitting: 5830it [00:38, 156.20it/s]\u001b[A\n",
            "RBM fitting: 5846it [00:38, 157.13it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:38, 145.62it/s]\u001b[A\n",
            "RBM fitting: 5878it [00:38, 149.09it/s]\u001b[A\n",
            "RBM fitting: 5894it [00:38, 151.18it/s]\u001b[A\n",
            "RBM fitting: 5910it [00:39, 153.72it/s]\u001b[A\n",
            "RBM fitting: 5926it [00:39, 154.74it/s]\u001b[A\n",
            "RBM fitting: 5943it [00:39, 156.80it/s]\u001b[A\n",
            "RBM fitting: 5959it [00:39, 156.68it/s]\u001b[A\n",
            "RBM fitting: 5976it [00:39, 158.32it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 151.38it/s]\n",
            "Epoch:26 ,avg_cost = 0.33861833810806274 ,std_cost = 0.033613529056310654 ,avg_grad = 10089.9697265625 ,std_grad = 547.4337768554688\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 13it [00:00, 124.14it/s]\u001b[A\n",
            "RBM fitting: 29it [00:00, 132.89it/s]\u001b[A\n",
            "RBM fitting: 46it [00:00, 140.53it/s]\u001b[A\n",
            "RBM fitting: 62it [00:00, 145.55it/s]\u001b[A\n",
            "RBM fitting: 79it [00:00, 150.35it/s]\u001b[A\n",
            "RBM fitting: 93it [00:00, 143.60it/s]\u001b[A\n",
            "RBM fitting: 109it [00:00, 146.84it/s]\u001b[A\n",
            "RBM fitting: 125it [00:00, 149.85it/s]\u001b[A\n",
            "RBM fitting: 141it [00:00, 152.39it/s]\u001b[A\n",
            "RBM fitting: 158it [00:01, 154.85it/s]\u001b[A\n",
            "RBM fitting: 174it [00:01, 153.08it/s]\u001b[A\n",
            "RBM fitting: 190it [00:01, 154.63it/s]\u001b[A\n",
            "RBM fitting: 206it [00:01, 155.87it/s]\u001b[A\n",
            "RBM fitting: 222it [00:01, 145.18it/s]\u001b[A\n",
            "RBM fitting: 238it [00:01, 147.71it/s]\u001b[A\n",
            "RBM fitting: 254it [00:01, 150.61it/s]\u001b[A\n",
            "RBM fitting: 270it [00:01, 152.81it/s]\u001b[A\n",
            "RBM fitting: 286it [00:01, 153.98it/s]\u001b[A\n",
            "RBM fitting: 302it [00:01, 155.45it/s]\u001b[A\n",
            "RBM fitting: 318it [00:02, 153.77it/s]\u001b[A\n",
            "RBM fitting: 334it [00:02, 144.96it/s]\u001b[A\n",
            "RBM fitting: 350it [00:02, 148.11it/s]\u001b[A\n",
            "RBM fitting: 366it [00:02, 150.70it/s]\u001b[A\n",
            "RBM fitting: 382it [00:02, 152.87it/s]\u001b[A\n",
            "RBM fitting: 398it [00:02, 154.39it/s]\u001b[A\n",
            "RBM fitting: 414it [00:02, 154.46it/s]\u001b[A\n",
            "RBM fitting: 430it [00:02, 155.45it/s]\u001b[A\n",
            "RBM fitting: 447it [00:02, 157.20it/s]\u001b[A\n",
            "RBM fitting: 463it [00:03, 146.97it/s]\u001b[A\n",
            "RBM fitting: 478it [00:03, 145.90it/s]\u001b[A\n",
            "RBM fitting: 494it [00:03, 149.78it/s]\u001b[A\n",
            "RBM fitting: 511it [00:03, 153.28it/s]\u001b[A\n",
            "RBM fitting: 527it [00:03, 155.10it/s]\u001b[A\n",
            "RBM fitting: 543it [00:03, 156.37it/s]\u001b[A\n",
            "RBM fitting: 559it [00:03, 156.28it/s]\u001b[A\n",
            "RBM fitting: 575it [00:03, 147.07it/s]\u001b[A\n",
            "RBM fitting: 591it [00:03, 149.65it/s]\u001b[A\n",
            "RBM fitting: 607it [00:03, 152.18it/s]\u001b[A\n",
            "RBM fitting: 623it [00:04, 153.10it/s]\u001b[A\n",
            "RBM fitting: 639it [00:04, 151.99it/s]\u001b[A\n",
            "RBM fitting: 655it [00:04, 154.17it/s]\u001b[A\n",
            "RBM fitting: 671it [00:04, 155.63it/s]\u001b[A\n",
            "RBM fitting: 687it [00:04, 156.75it/s]\u001b[A\n",
            "RBM fitting: 703it [00:04, 148.18it/s]\u001b[A\n",
            "RBM fitting: 718it [00:04, 148.54it/s]\u001b[A\n",
            "RBM fitting: 734it [00:04, 150.44it/s]\u001b[A\n",
            "RBM fitting: 750it [00:04, 151.77it/s]\u001b[A\n",
            "RBM fitting: 766it [00:05, 151.91it/s]\u001b[A\n",
            "RBM fitting: 782it [00:05, 149.32it/s]\u001b[A\n",
            "RBM fitting: 798it [00:05, 150.15it/s]\u001b[A\n",
            "RBM fitting: 814it [00:05, 150.06it/s]\u001b[A\n",
            "RBM fitting: 830it [00:05, 143.43it/s]\u001b[A\n",
            "RBM fitting: 846it [00:05, 146.41it/s]\u001b[A\n",
            "RBM fitting: 862it [00:05, 149.25it/s]\u001b[A\n",
            "RBM fitting: 878it [00:05, 151.69it/s]\u001b[A\n",
            "RBM fitting: 894it [00:05, 153.74it/s]\u001b[A\n",
            "RBM fitting: 911it [00:05, 156.29it/s]\u001b[A\n",
            "RBM fitting: 927it [00:06, 155.99it/s]\u001b[A\n",
            "RBM fitting: 943it [00:06, 154.46it/s]\u001b[A\n",
            "RBM fitting: 959it [00:06, 147.14it/s]\u001b[A\n",
            "RBM fitting: 975it [00:06, 150.41it/s]\u001b[A\n",
            "RBM fitting: 992it [00:06, 153.83it/s]\u001b[A\n",
            "RBM fitting: 1008it [00:06, 155.37it/s]\u001b[A\n",
            "RBM fitting: 1024it [00:06, 155.96it/s]\u001b[A\n",
            "RBM fitting: 1040it [00:06, 156.67it/s]\u001b[A\n",
            "RBM fitting: 1056it [00:06, 156.18it/s]\u001b[A\n",
            "RBM fitting: 1072it [00:07, 153.46it/s]\u001b[A\n",
            "RBM fitting: 1088it [00:07, 139.43it/s]\u001b[A\n",
            "RBM fitting: 1103it [00:07, 140.63it/s]\u001b[A\n",
            "RBM fitting: 1119it [00:07, 144.86it/s]\u001b[A\n",
            "RBM fitting: 1135it [00:07, 148.21it/s]\u001b[A\n",
            "RBM fitting: 1151it [00:07, 150.58it/s]\u001b[A\n",
            "RBM fitting: 1167it [00:07, 152.49it/s]\u001b[A\n",
            "RBM fitting: 1183it [00:07, 153.95it/s]\u001b[A\n",
            "RBM fitting: 1200it [00:07, 156.21it/s]\u001b[A\n",
            "RBM fitting: 1216it [00:08, 148.26it/s]\u001b[A\n",
            "RBM fitting: 1231it [00:08, 148.58it/s]\u001b[A\n",
            "RBM fitting: 1246it [00:08, 148.19it/s]\u001b[A\n",
            "RBM fitting: 1262it [00:08, 150.97it/s]\u001b[A\n",
            "RBM fitting: 1278it [00:08, 152.58it/s]\u001b[A\n",
            "RBM fitting: 1294it [00:08, 152.05it/s]\u001b[A\n",
            "RBM fitting: 1310it [00:08, 151.02it/s]\u001b[A\n",
            "RBM fitting: 1326it [00:08, 151.21it/s]\u001b[A\n",
            "RBM fitting: 1342it [00:08, 144.40it/s]\u001b[A\n",
            "RBM fitting: 1358it [00:08, 148.07it/s]\u001b[A\n",
            "RBM fitting: 1373it [00:09, 148.08it/s]\u001b[A\n",
            "RBM fitting: 1389it [00:09, 150.26it/s]\u001b[A\n",
            "RBM fitting: 1405it [00:09, 149.24it/s]\u001b[A\n",
            "RBM fitting: 1420it [00:09, 145.01it/s]\u001b[A\n",
            "RBM fitting: 1436it [00:09, 147.44it/s]\u001b[A\n",
            "RBM fitting: 1452it [00:09, 148.89it/s]\u001b[A\n",
            "RBM fitting: 1467it [00:09, 140.30it/s]\u001b[A\n",
            "RBM fitting: 1483it [00:09, 144.43it/s]\u001b[A\n",
            "RBM fitting: 1499it [00:09, 147.55it/s]\u001b[A\n",
            "RBM fitting: 1515it [00:10, 149.42it/s]\u001b[A\n",
            "RBM fitting: 1531it [00:10, 150.74it/s]\u001b[A\n",
            "RBM fitting: 1547it [00:10, 148.80it/s]\u001b[A\n",
            "RBM fitting: 1563it [00:10, 150.13it/s]\u001b[A\n",
            "RBM fitting: 1579it [00:10, 151.60it/s]\u001b[A\n",
            "RBM fitting: 1595it [00:10, 144.30it/s]\u001b[A\n",
            "RBM fitting: 1610it [00:10, 144.30it/s]\u001b[A\n",
            "RBM fitting: 1625it [00:10, 145.41it/s]\u001b[A\n",
            "RBM fitting: 1641it [00:10, 148.74it/s]\u001b[A\n",
            "RBM fitting: 1657it [00:10, 151.03it/s]\u001b[A\n",
            "RBM fitting: 1673it [00:11, 151.69it/s]\u001b[A\n",
            "RBM fitting: 1689it [00:11, 152.85it/s]\u001b[A\n",
            "RBM fitting: 1705it [00:11, 150.49it/s]\u001b[A\n",
            "RBM fitting: 1721it [00:11, 143.14it/s]\u001b[A\n",
            "RBM fitting: 1737it [00:11, 146.61it/s]\u001b[A\n",
            "RBM fitting: 1753it [00:11, 149.17it/s]\u001b[A\n",
            "RBM fitting: 1769it [00:11, 149.98it/s]\u001b[A\n",
            "RBM fitting: 1785it [00:11, 150.91it/s]\u001b[A\n",
            "RBM fitting: 1801it [00:11, 152.24it/s]\u001b[A\n",
            "RBM fitting: 1817it [00:12, 152.49it/s]\u001b[A\n",
            "RBM fitting: 1833it [00:12, 153.36it/s]\u001b[A\n",
            "RBM fitting: 1849it [00:12, 142.59it/s]\u001b[A\n",
            "RBM fitting: 1865it [00:12, 146.28it/s]\u001b[A\n",
            "RBM fitting: 1881it [00:12, 148.53it/s]\u001b[A\n",
            "RBM fitting: 1897it [00:12, 150.63it/s]\u001b[A\n",
            "RBM fitting: 1913it [00:12, 151.73it/s]\u001b[A\n",
            "RBM fitting: 1929it [00:12, 151.85it/s]\u001b[A\n",
            "RBM fitting: 1945it [00:12, 153.22it/s]\u001b[A\n",
            "RBM fitting: 1961it [00:13, 154.27it/s]\u001b[A\n",
            "RBM fitting: 1977it [00:13, 144.23it/s]\u001b[A\n",
            "RBM fitting: 1992it [00:13, 145.51it/s]\u001b[A\n",
            "RBM fitting: 2007it [00:13, 145.43it/s]\u001b[A\n",
            "RBM fitting: 2023it [00:13, 147.58it/s]\u001b[A\n",
            "RBM fitting: 2039it [00:13, 149.38it/s]\u001b[A\n",
            "RBM fitting: 2055it [00:13, 150.58it/s]\u001b[A\n",
            "RBM fitting: 2071it [00:13, 152.27it/s]\u001b[A\n",
            "RBM fitting: 2087it [00:13, 154.44it/s]\u001b[A\n",
            "RBM fitting: 2103it [00:13, 146.73it/s]\u001b[A\n",
            "RBM fitting: 2119it [00:14, 149.62it/s]\u001b[A\n",
            "RBM fitting: 2135it [00:14, 152.42it/s]\u001b[A\n",
            "RBM fitting: 2151it [00:14, 153.60it/s]\u001b[A\n",
            "RBM fitting: 2167it [00:14, 152.52it/s]\u001b[A\n",
            "RBM fitting: 2183it [00:14, 153.72it/s]\u001b[A\n",
            "RBM fitting: 2199it [00:14, 155.01it/s]\u001b[A\n",
            "RBM fitting: 2215it [00:14, 155.87it/s]\u001b[A\n",
            "RBM fitting: 2231it [00:14, 146.69it/s]\u001b[A\n",
            "RBM fitting: 2247it [00:14, 149.58it/s]\u001b[A\n",
            "RBM fitting: 2263it [00:15, 152.12it/s]\u001b[A\n",
            "RBM fitting: 2279it [00:15, 152.98it/s]\u001b[A\n",
            "RBM fitting: 2295it [00:15, 153.84it/s]\u001b[A\n",
            "RBM fitting: 2311it [00:15, 151.39it/s]\u001b[A\n",
            "RBM fitting: 2327it [00:15, 150.35it/s]\u001b[A\n",
            "RBM fitting: 2343it [00:15, 151.78it/s]\u001b[A\n",
            "RBM fitting: 2359it [00:15, 144.44it/s]\u001b[A\n",
            "RBM fitting: 2375it [00:15, 146.73it/s]\u001b[A\n",
            "RBM fitting: 2391it [00:15, 149.42it/s]\u001b[A\n",
            "RBM fitting: 2407it [00:15, 151.24it/s]\u001b[A\n",
            "RBM fitting: 2423it [00:16, 151.68it/s]\u001b[A\n",
            "RBM fitting: 2439it [00:16, 152.95it/s]\u001b[A\n",
            "RBM fitting: 2455it [00:16, 153.35it/s]\u001b[A\n",
            "RBM fitting: 2471it [00:16, 150.94it/s]\u001b[A\n",
            "RBM fitting: 2487it [00:16, 143.19it/s]\u001b[A\n",
            "RBM fitting: 2503it [00:16, 146.25it/s]\u001b[A\n",
            "RBM fitting: 2518it [00:16, 146.55it/s]\u001b[A\n",
            "RBM fitting: 2534it [00:16, 148.01it/s]\u001b[A\n",
            "RBM fitting: 2550it [00:16, 149.95it/s]\u001b[A\n",
            "RBM fitting: 2566it [00:17, 151.04it/s]\u001b[A\n",
            "RBM fitting: 2582it [00:17, 151.95it/s]\u001b[A\n",
            "RBM fitting: 2598it [00:17, 151.68it/s]\u001b[A\n",
            "RBM fitting: 2614it [00:17, 139.64it/s]\u001b[A\n",
            "RBM fitting: 2630it [00:17, 143.89it/s]\u001b[A\n",
            "RBM fitting: 2645it [00:17, 144.82it/s]\u001b[A\n",
            "RBM fitting: 2661it [00:17, 147.72it/s]\u001b[A\n",
            "RBM fitting: 2677it [00:17, 150.30it/s]\u001b[A\n",
            "RBM fitting: 2693it [00:17, 152.52it/s]\u001b[A\n",
            "RBM fitting: 2710it [00:18, 155.25it/s]\u001b[A\n",
            "RBM fitting: 2726it [00:18, 154.64it/s]\u001b[A\n",
            "RBM fitting: 2742it [00:18, 145.79it/s]\u001b[A\n",
            "RBM fitting: 2758it [00:18, 148.60it/s]\u001b[A\n",
            "RBM fitting: 2773it [00:18, 148.21it/s]\u001b[A\n",
            "RBM fitting: 2789it [00:18, 150.62it/s]\u001b[A\n",
            "RBM fitting: 2805it [00:18, 151.20it/s]\u001b[A\n",
            "RBM fitting: 2821it [00:18, 152.91it/s]\u001b[A\n",
            "RBM fitting: 2837it [00:18, 152.11it/s]\u001b[A\n",
            "RBM fitting: 2853it [00:18, 153.09it/s]\u001b[A\n",
            "RBM fitting: 2869it [00:19, 144.80it/s]\u001b[A\n",
            "RBM fitting: 2885it [00:19, 148.52it/s]\u001b[A\n",
            "RBM fitting: 2901it [00:19, 150.69it/s]\u001b[A\n",
            "RBM fitting: 2917it [00:19, 149.08it/s]\u001b[A\n",
            "RBM fitting: 2933it [00:19, 150.96it/s]\u001b[A\n",
            "RBM fitting: 2949it [00:19, 151.27it/s]\u001b[A\n",
            "RBM fitting: 2965it [00:19, 152.64it/s]\u001b[A\n",
            "RBM fitting: 2981it [00:19, 153.06it/s]\u001b[A\n",
            "RBM fitting: 2997it [00:19, 145.09it/s]\u001b[A\n",
            "RBM fitting: 3013it [00:20, 148.68it/s]\u001b[A\n",
            "RBM fitting: 3028it [00:20, 148.98it/s]\u001b[A\n",
            "RBM fitting: 3044it [00:20, 149.90it/s]\u001b[A\n",
            "RBM fitting: 3060it [00:20, 151.78it/s]\u001b[A\n",
            "RBM fitting: 3076it [00:20, 151.19it/s]\u001b[A\n",
            "RBM fitting: 3092it [00:20, 151.51it/s]\u001b[A\n",
            "RBM fitting: 3108it [00:20, 152.37it/s]\u001b[A\n",
            "RBM fitting: 3124it [00:20, 144.58it/s]\u001b[A\n",
            "RBM fitting: 3140it [00:20, 147.31it/s]\u001b[A\n",
            "RBM fitting: 3156it [00:21, 149.37it/s]\u001b[A\n",
            "RBM fitting: 3172it [00:21, 151.20it/s]\u001b[A\n",
            "RBM fitting: 3188it [00:21, 148.80it/s]\u001b[A\n",
            "RBM fitting: 3204it [00:21, 151.94it/s]\u001b[A\n",
            "RBM fitting: 3220it [00:21, 152.37it/s]\u001b[A\n",
            "RBM fitting: 3236it [00:21, 153.52it/s]\u001b[A\n",
            "RBM fitting: 3252it [00:21, 146.62it/s]\u001b[A\n",
            "RBM fitting: 3268it [00:21, 148.88it/s]\u001b[A\n",
            "RBM fitting: 3284it [00:21, 151.15it/s]\u001b[A\n",
            "RBM fitting: 3300it [00:21, 152.86it/s]\u001b[A\n",
            "RBM fitting: 3316it [00:22, 154.18it/s]\u001b[A\n",
            "RBM fitting: 3332it [00:22, 155.86it/s]\u001b[A\n",
            "RBM fitting: 3348it [00:22, 156.07it/s]\u001b[A\n",
            "RBM fitting: 3364it [00:22, 155.61it/s]\u001b[A\n",
            "RBM fitting: 3380it [00:22, 144.95it/s]\u001b[A\n",
            "RBM fitting: 3396it [00:22, 148.42it/s]\u001b[A\n",
            "RBM fitting: 3412it [00:22, 151.71it/s]\u001b[A\n",
            "RBM fitting: 3428it [00:22, 153.31it/s]\u001b[A\n",
            "RBM fitting: 3444it [00:22, 154.40it/s]\u001b[A\n",
            "RBM fitting: 3460it [00:22, 154.09it/s]\u001b[A\n",
            "RBM fitting: 3476it [00:23, 154.69it/s]\u001b[A\n",
            "RBM fitting: 3493it [00:23, 157.32it/s]\u001b[A\n",
            "RBM fitting: 3509it [00:23, 148.49it/s]\u001b[A\n",
            "RBM fitting: 3525it [00:23, 151.27it/s]\u001b[A\n",
            "RBM fitting: 3541it [00:23, 151.19it/s]\u001b[A\n",
            "RBM fitting: 3557it [00:23, 153.06it/s]\u001b[A\n",
            "RBM fitting: 3573it [00:23, 154.63it/s]\u001b[A\n",
            "RBM fitting: 3589it [00:23, 154.92it/s]\u001b[A\n",
            "RBM fitting: 3605it [00:23, 155.47it/s]\u001b[A\n",
            "RBM fitting: 3621it [00:24, 154.37it/s]\u001b[A\n",
            "RBM fitting: 3637it [00:24, 145.69it/s]\u001b[A\n",
            "RBM fitting: 3653it [00:24, 148.09it/s]\u001b[A\n",
            "RBM fitting: 3669it [00:24, 150.33it/s]\u001b[A\n",
            "RBM fitting: 3685it [00:24, 151.51it/s]\u001b[A\n",
            "RBM fitting: 3701it [00:24, 150.16it/s]\u001b[A\n",
            "RBM fitting: 3717it [00:24, 148.85it/s]\u001b[A\n",
            "RBM fitting: 3733it [00:24, 151.41it/s]\u001b[A\n",
            "RBM fitting: 3749it [00:24, 153.30it/s]\u001b[A\n",
            "RBM fitting: 3765it [00:25, 145.61it/s]\u001b[A\n",
            "RBM fitting: 3781it [00:25, 148.06it/s]\u001b[A\n",
            "RBM fitting: 3797it [00:25, 150.78it/s]\u001b[A\n",
            "RBM fitting: 3813it [00:25, 152.57it/s]\u001b[A\n",
            "RBM fitting: 3830it [00:25, 154.95it/s]\u001b[A\n",
            "RBM fitting: 3846it [00:25, 152.15it/s]\u001b[A\n",
            "RBM fitting: 3862it [00:25, 152.14it/s]\u001b[A\n",
            "RBM fitting: 3878it [00:25, 153.31it/s]\u001b[A\n",
            "RBM fitting: 3894it [00:25, 146.13it/s]\u001b[A\n",
            "RBM fitting: 3910it [00:25, 149.27it/s]\u001b[A\n",
            "RBM fitting: 3926it [00:26, 151.45it/s]\u001b[A\n",
            "RBM fitting: 3942it [00:26, 150.39it/s]\u001b[A\n",
            "RBM fitting: 3958it [00:26, 150.03it/s]\u001b[A\n",
            "RBM fitting: 3974it [00:26, 152.37it/s]\u001b[A\n",
            "RBM fitting: 3990it [00:26, 153.16it/s]\u001b[A\n",
            "RBM fitting: 4006it [00:26, 151.94it/s]\u001b[A\n",
            "RBM fitting: 4022it [00:26, 144.31it/s]\u001b[A\n",
            "RBM fitting: 4038it [00:26, 146.80it/s]\u001b[A\n",
            "RBM fitting: 4054it [00:26, 149.91it/s]\u001b[A\n",
            "RBM fitting: 4070it [00:27, 150.78it/s]\u001b[A\n",
            "RBM fitting: 4086it [00:27, 151.30it/s]\u001b[A\n",
            "RBM fitting: 4102it [00:27, 152.06it/s]\u001b[A\n",
            "RBM fitting: 4118it [00:27, 152.45it/s]\u001b[A\n",
            "RBM fitting: 4134it [00:27, 153.55it/s]\u001b[A\n",
            "RBM fitting: 4150it [00:27, 142.43it/s]\u001b[A\n",
            "RBM fitting: 4166it [00:27, 145.86it/s]\u001b[A\n",
            "RBM fitting: 4182it [00:27, 148.87it/s]\u001b[A\n",
            "RBM fitting: 4198it [00:27, 151.77it/s]\u001b[A\n",
            "RBM fitting: 4214it [00:28, 150.98it/s]\u001b[A\n",
            "RBM fitting: 4230it [00:28, 150.17it/s]\u001b[A\n",
            "RBM fitting: 4246it [00:28, 152.16it/s]\u001b[A\n",
            "RBM fitting: 4262it [00:28, 153.48it/s]\u001b[A\n",
            "RBM fitting: 4278it [00:28, 145.63it/s]\u001b[A\n",
            "RBM fitting: 4294it [00:28, 148.08it/s]\u001b[A\n",
            "RBM fitting: 4309it [00:28, 147.93it/s]\u001b[A\n",
            "RBM fitting: 4325it [00:28, 150.53it/s]\u001b[A\n",
            "RBM fitting: 4341it [00:28, 151.90it/s]\u001b[A\n",
            "RBM fitting: 4357it [00:28, 153.20it/s]\u001b[A\n",
            "RBM fitting: 4373it [00:29, 154.27it/s]\u001b[A\n",
            "RBM fitting: 4389it [00:29, 155.32it/s]\u001b[A\n",
            "RBM fitting: 4405it [00:29, 146.56it/s]\u001b[A\n",
            "RBM fitting: 4421it [00:29, 149.64it/s]\u001b[A\n",
            "RBM fitting: 4437it [00:29, 151.14it/s]\u001b[A\n",
            "RBM fitting: 4453it [00:29, 150.43it/s]\u001b[A\n",
            "RBM fitting: 4469it [00:29, 152.60it/s]\u001b[A\n",
            "RBM fitting: 4485it [00:29, 153.12it/s]\u001b[A\n",
            "RBM fitting: 4501it [00:29, 154.56it/s]\u001b[A\n",
            "RBM fitting: 4517it [00:29, 155.78it/s]\u001b[A\n",
            "RBM fitting: 4533it [00:30, 147.09it/s]\u001b[A\n",
            "RBM fitting: 4550it [00:30, 151.98it/s]\u001b[A\n",
            "RBM fitting: 4567it [00:30, 155.17it/s]\u001b[A\n",
            "RBM fitting: 4583it [00:30, 156.47it/s]\u001b[A\n",
            "RBM fitting: 4600it [00:30, 159.04it/s]\u001b[A\n",
            "RBM fitting: 4616it [00:30, 156.46it/s]\u001b[A\n",
            "RBM fitting: 4633it [00:30, 158.27it/s]\u001b[A\n",
            "RBM fitting: 4649it [00:30, 156.88it/s]\u001b[A\n",
            "RBM fitting: 4665it [00:30, 149.06it/s]\u001b[A\n",
            "RBM fitting: 4681it [00:31, 151.63it/s]\u001b[A\n",
            "RBM fitting: 4697it [00:31, 153.34it/s]\u001b[A\n",
            "RBM fitting: 4713it [00:31, 154.89it/s]\u001b[A\n",
            "RBM fitting: 4729it [00:31, 154.54it/s]\u001b[A\n",
            "RBM fitting: 4745it [00:31, 155.80it/s]\u001b[A\n",
            "RBM fitting: 4761it [00:31, 156.71it/s]\u001b[A\n",
            "RBM fitting: 4777it [00:31, 153.69it/s]\u001b[A\n",
            "RBM fitting: 4793it [00:31, 145.66it/s]\u001b[A\n",
            "RBM fitting: 4810it [00:31, 150.84it/s]\u001b[A\n",
            "RBM fitting: 4827it [00:32, 154.66it/s]\u001b[A\n",
            "RBM fitting: 4843it [00:32, 154.55it/s]\u001b[A\n",
            "RBM fitting: 4859it [00:32, 155.35it/s]\u001b[A\n",
            "RBM fitting: 4875it [00:32, 156.70it/s]\u001b[A\n",
            "RBM fitting: 4892it [00:32, 158.04it/s]\u001b[A\n",
            "RBM fitting: 4908it [00:32, 158.46it/s]\u001b[A\n",
            "RBM fitting: 4924it [00:32, 146.78it/s]\u001b[A\n",
            "RBM fitting: 4940it [00:32, 150.09it/s]\u001b[A\n",
            "RBM fitting: 4956it [00:32, 152.01it/s]\u001b[A\n",
            "RBM fitting: 4972it [00:32, 153.99it/s]\u001b[A\n",
            "RBM fitting: 4988it [00:33, 155.23it/s]\u001b[A\n",
            "RBM fitting: 5004it [00:33, 156.40it/s]\u001b[A\n",
            "RBM fitting: 5020it [00:33, 156.61it/s]\u001b[A\n",
            "RBM fitting: 5036it [00:33, 157.18it/s]\u001b[A\n",
            "RBM fitting: 5052it [00:33, 147.86it/s]\u001b[A\n",
            "RBM fitting: 5068it [00:33, 151.22it/s]\u001b[A\n",
            "RBM fitting: 5084it [00:33, 150.38it/s]\u001b[A\n",
            "RBM fitting: 5100it [00:33, 152.33it/s]\u001b[A\n",
            "RBM fitting: 5116it [00:33, 154.15it/s]\u001b[A\n",
            "RBM fitting: 5133it [00:33, 156.72it/s]\u001b[A\n",
            "RBM fitting: 5149it [00:34, 156.55it/s]\u001b[A\n",
            "RBM fitting: 5166it [00:34, 158.28it/s]\u001b[A\n",
            "RBM fitting: 5182it [00:34, 147.86it/s]\u001b[A\n",
            "RBM fitting: 5198it [00:34, 150.14it/s]\u001b[A\n",
            "RBM fitting: 5214it [00:34, 152.49it/s]\u001b[A\n",
            "RBM fitting: 5230it [00:34, 154.01it/s]\u001b[A\n",
            "RBM fitting: 5246it [00:34, 152.26it/s]\u001b[A\n",
            "RBM fitting: 5262it [00:34, 153.21it/s]\u001b[A\n",
            "RBM fitting: 5279it [00:34, 155.57it/s]\u001b[A\n",
            "RBM fitting: 5295it [00:35, 156.55it/s]\u001b[A\n",
            "RBM fitting: 5311it [00:35, 148.83it/s]\u001b[A\n",
            "RBM fitting: 5327it [00:35, 151.87it/s]\u001b[A\n",
            "RBM fitting: 5343it [00:35, 153.53it/s]\u001b[A\n",
            "RBM fitting: 5359it [00:35, 154.67it/s]\u001b[A\n",
            "RBM fitting: 5375it [00:35, 156.07it/s]\u001b[A\n",
            "RBM fitting: 5391it [00:35, 155.66it/s]\u001b[A\n",
            "RBM fitting: 5407it [00:35, 154.43it/s]\u001b[A\n",
            "RBM fitting: 5423it [00:35, 154.95it/s]\u001b[A\n",
            "RBM fitting: 5439it [00:36, 146.97it/s]\u001b[A\n",
            "RBM fitting: 5455it [00:36, 149.22it/s]\u001b[A\n",
            "RBM fitting: 5471it [00:36, 151.44it/s]\u001b[A\n",
            "RBM fitting: 5487it [00:36, 153.77it/s]\u001b[A\n",
            "RBM fitting: 5504it [00:36, 155.91it/s]\u001b[A\n",
            "RBM fitting: 5520it [00:36, 156.74it/s]\u001b[A\n",
            "RBM fitting: 5536it [00:36, 157.22it/s]\u001b[A\n",
            "RBM fitting: 5552it [00:36, 154.93it/s]\u001b[A\n",
            "RBM fitting: 5568it [00:36, 146.34it/s]\u001b[A\n",
            "RBM fitting: 5584it [00:36, 149.96it/s]\u001b[A\n",
            "RBM fitting: 5600it [00:37, 152.20it/s]\u001b[A\n",
            "RBM fitting: 5616it [00:37, 152.05it/s]\u001b[A\n",
            "RBM fitting: 5632it [00:37, 154.29it/s]\u001b[A\n",
            "RBM fitting: 5648it [00:37, 155.95it/s]\u001b[A\n",
            "RBM fitting: 5665it [00:37, 158.06it/s]\u001b[A\n",
            "RBM fitting: 5682it [00:37, 159.71it/s]\u001b[A\n",
            "RBM fitting: 5698it [00:37, 150.11it/s]\u001b[A\n",
            "RBM fitting: 5714it [00:37, 148.96it/s]\u001b[A\n",
            "RBM fitting: 5731it [00:37, 152.91it/s]\u001b[A\n",
            "RBM fitting: 5747it [00:38, 154.09it/s]\u001b[A\n",
            "RBM fitting: 5763it [00:38, 154.95it/s]\u001b[A\n",
            "RBM fitting: 5780it [00:38, 157.52it/s]\u001b[A\n",
            "RBM fitting: 5796it [00:38, 157.92it/s]\u001b[A\n",
            "RBM fitting: 5813it [00:38, 159.49it/s]\u001b[A\n",
            "RBM fitting: 5829it [00:38, 148.01it/s]\u001b[A\n",
            "RBM fitting: 5845it [00:38, 150.22it/s]\u001b[A\n",
            "RBM fitting: 5861it [00:38, 150.64it/s]\u001b[A\n",
            "RBM fitting: 5877it [00:38, 153.09it/s]\u001b[A\n",
            "RBM fitting: 5893it [00:38, 154.86it/s]\u001b[A\n",
            "RBM fitting: 5909it [00:39, 156.11it/s]\u001b[A\n",
            "RBM fitting: 5925it [00:39, 156.87it/s]\u001b[A\n",
            "RBM fitting: 5941it [00:39, 156.82it/s]\u001b[A\n",
            "RBM fitting: 5957it [00:39, 148.26it/s]\u001b[A\n",
            "RBM fitting: 5973it [00:39, 151.15it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 151.30it/s]\n",
            "Epoch:27 ,avg_cost = 0.3378734290599823 ,std_cost = 0.03319889307022095 ,avg_grad = 10089.3525390625 ,std_grad = 544.0367431640625\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 13it [00:00, 126.01it/s]\u001b[A\n",
            "RBM fitting: 28it [00:00, 131.48it/s]\u001b[A\n",
            "RBM fitting: 43it [00:00, 134.86it/s]\u001b[A\n",
            "RBM fitting: 59it [00:00, 140.35it/s]\u001b[A\n",
            "RBM fitting: 72it [00:00, 134.99it/s]\u001b[A\n",
            "RBM fitting: 86it [00:00, 135.96it/s]\u001b[A\n",
            "RBM fitting: 102it [00:00, 140.65it/s]\u001b[A\n",
            "RBM fitting: 118it [00:00, 145.34it/s]\u001b[A\n",
            "RBM fitting: 134it [00:00, 149.37it/s]\u001b[A\n",
            "RBM fitting: 150it [00:01, 151.62it/s]\u001b[A\n",
            "RBM fitting: 165it [00:01, 150.65it/s]\u001b[A\n",
            "RBM fitting: 181it [00:01, 152.83it/s]\u001b[A\n",
            "RBM fitting: 197it [00:01, 146.15it/s]\u001b[A\n",
            "RBM fitting: 213it [00:01, 147.97it/s]\u001b[A\n",
            "RBM fitting: 229it [00:01, 149.86it/s]\u001b[A\n",
            "RBM fitting: 245it [00:01, 152.54it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 154.60it/s]\u001b[A\n",
            "RBM fitting: 277it [00:01, 154.38it/s]\u001b[A\n",
            "RBM fitting: 293it [00:01, 155.09it/s]\u001b[A\n",
            "RBM fitting: 309it [00:02, 155.32it/s]\u001b[A\n",
            "RBM fitting: 325it [00:02, 144.66it/s]\u001b[A\n",
            "RBM fitting: 340it [00:02, 144.03it/s]\u001b[A\n",
            "RBM fitting: 356it [00:02, 147.67it/s]\u001b[A\n",
            "RBM fitting: 372it [00:02, 150.66it/s]\u001b[A\n",
            "RBM fitting: 388it [00:02, 153.24it/s]\u001b[A\n",
            "RBM fitting: 404it [00:02, 155.02it/s]\u001b[A\n",
            "RBM fitting: 420it [00:02, 156.36it/s]\u001b[A\n",
            "RBM fitting: 436it [00:02, 153.47it/s]\u001b[A\n",
            "RBM fitting: 452it [00:03, 145.38it/s]\u001b[A\n",
            "RBM fitting: 467it [00:03, 146.65it/s]\u001b[A\n",
            "RBM fitting: 482it [00:03, 146.58it/s]\u001b[A\n",
            "RBM fitting: 499it [00:03, 151.21it/s]\u001b[A\n",
            "RBM fitting: 515it [00:03, 152.56it/s]\u001b[A\n",
            "RBM fitting: 532it [00:03, 155.32it/s]\u001b[A\n",
            "RBM fitting: 548it [00:03, 156.60it/s]\u001b[A\n",
            "RBM fitting: 565it [00:03, 158.16it/s]\u001b[A\n",
            "RBM fitting: 581it [00:03, 147.30it/s]\u001b[A\n",
            "RBM fitting: 596it [00:03, 145.22it/s]\u001b[A\n",
            "RBM fitting: 612it [00:04, 148.69it/s]\u001b[A\n",
            "RBM fitting: 627it [00:04, 148.13it/s]\u001b[A\n",
            "RBM fitting: 643it [00:04, 151.18it/s]\u001b[A\n",
            "RBM fitting: 659it [00:04, 153.04it/s]\u001b[A\n",
            "RBM fitting: 675it [00:04, 154.19it/s]\u001b[A\n",
            "RBM fitting: 691it [00:04, 155.14it/s]\u001b[A\n",
            "RBM fitting: 707it [00:04, 144.76it/s]\u001b[A\n",
            "RBM fitting: 723it [00:04, 148.30it/s]\u001b[A\n",
            "RBM fitting: 739it [00:04, 151.16it/s]\u001b[A\n",
            "RBM fitting: 755it [00:05, 152.73it/s]\u001b[A\n",
            "RBM fitting: 771it [00:05, 154.04it/s]\u001b[A\n",
            "RBM fitting: 787it [00:05, 152.60it/s]\u001b[A\n",
            "RBM fitting: 803it [00:05, 154.35it/s]\u001b[A\n",
            "RBM fitting: 819it [00:05, 152.65it/s]\u001b[A\n",
            "RBM fitting: 835it [00:05, 144.26it/s]\u001b[A\n",
            "RBM fitting: 851it [00:05, 146.94it/s]\u001b[A\n",
            "RBM fitting: 867it [00:05, 149.38it/s]\u001b[A\n",
            "RBM fitting: 883it [00:05, 151.36it/s]\u001b[A\n",
            "RBM fitting: 899it [00:05, 152.56it/s]\u001b[A\n",
            "RBM fitting: 915it [00:06, 153.76it/s]\u001b[A\n",
            "RBM fitting: 931it [00:06, 152.33it/s]\u001b[A\n",
            "RBM fitting: 947it [00:06, 152.49it/s]\u001b[A\n",
            "RBM fitting: 963it [00:06, 142.21it/s]\u001b[A\n",
            "RBM fitting: 978it [00:06, 143.73it/s]\u001b[A\n",
            "RBM fitting: 994it [00:06, 147.83it/s]\u001b[A\n",
            "RBM fitting: 1009it [00:06, 144.16it/s]\u001b[A\n",
            "RBM fitting: 1024it [00:06, 145.40it/s]\u001b[A\n",
            "RBM fitting: 1040it [00:06, 148.71it/s]\u001b[A\n",
            "RBM fitting: 1056it [00:07, 151.89it/s]\u001b[A\n",
            "RBM fitting: 1072it [00:07, 153.38it/s]\u001b[A\n",
            "RBM fitting: 1088it [00:07, 142.61it/s]\u001b[A\n",
            "RBM fitting: 1104it [00:07, 146.85it/s]\u001b[A\n",
            "RBM fitting: 1120it [00:07, 150.42it/s]\u001b[A\n",
            "RBM fitting: 1137it [00:07, 154.10it/s]\u001b[A\n",
            "RBM fitting: 1154it [00:07, 156.26it/s]\u001b[A\n",
            "RBM fitting: 1171it [00:07, 158.08it/s]\u001b[A\n",
            "RBM fitting: 1187it [00:07, 158.53it/s]\u001b[A\n",
            "RBM fitting: 1203it [00:08, 157.50it/s]\u001b[A\n",
            "RBM fitting: 1219it [00:08, 147.30it/s]\u001b[A\n",
            "RBM fitting: 1234it [00:08, 140.05it/s]\u001b[A\n",
            "RBM fitting: 1250it [00:08, 144.77it/s]\u001b[A\n",
            "RBM fitting: 1266it [00:08, 147.64it/s]\u001b[A\n",
            "RBM fitting: 1282it [00:08, 151.00it/s]\u001b[A\n",
            "RBM fitting: 1298it [00:08, 153.37it/s]\u001b[A\n",
            "RBM fitting: 1314it [00:08, 154.14it/s]\u001b[A\n",
            "RBM fitting: 1331it [00:08, 156.30it/s]\u001b[A\n",
            "RBM fitting: 1347it [00:08, 147.72it/s]\u001b[A\n",
            "RBM fitting: 1362it [00:09, 148.11it/s]\u001b[A\n",
            "RBM fitting: 1378it [00:09, 150.57it/s]\u001b[A\n",
            "RBM fitting: 1394it [00:09, 150.13it/s]\u001b[A\n",
            "RBM fitting: 1411it [00:09, 153.66it/s]\u001b[A\n",
            "RBM fitting: 1428it [00:09, 156.90it/s]\u001b[A\n",
            "RBM fitting: 1445it [00:09, 158.04it/s]\u001b[A\n",
            "RBM fitting: 1461it [00:09, 158.26it/s]\u001b[A\n",
            "RBM fitting: 1477it [00:09, 148.22it/s]\u001b[A\n",
            "RBM fitting: 1493it [00:09, 149.21it/s]\u001b[A\n",
            "RBM fitting: 1509it [00:10, 150.57it/s]\u001b[A\n",
            "RBM fitting: 1525it [00:10, 151.63it/s]\u001b[A\n",
            "RBM fitting: 1541it [00:10, 153.11it/s]\u001b[A\n",
            "RBM fitting: 1557it [00:10, 150.98it/s]\u001b[A\n",
            "RBM fitting: 1573it [00:10, 150.74it/s]\u001b[A\n",
            "RBM fitting: 1589it [00:10, 148.48it/s]\u001b[A\n",
            "RBM fitting: 1604it [00:10, 141.32it/s]\u001b[A\n",
            "RBM fitting: 1619it [00:10, 142.19it/s]\u001b[A\n",
            "RBM fitting: 1635it [00:10, 146.82it/s]\u001b[A\n",
            "RBM fitting: 1651it [00:10, 149.32it/s]\u001b[A\n",
            "RBM fitting: 1667it [00:11, 152.33it/s]\u001b[A\n",
            "RBM fitting: 1683it [00:11, 154.11it/s]\u001b[A\n",
            "RBM fitting: 1699it [00:11, 153.21it/s]\u001b[A\n",
            "RBM fitting: 1715it [00:11, 154.26it/s]\u001b[A\n",
            "RBM fitting: 1731it [00:11, 145.29it/s]\u001b[A\n",
            "RBM fitting: 1747it [00:11, 148.19it/s]\u001b[A\n",
            "RBM fitting: 1763it [00:11, 151.10it/s]\u001b[A\n",
            "RBM fitting: 1779it [00:11, 152.25it/s]\u001b[A\n",
            "RBM fitting: 1795it [00:11, 152.16it/s]\u001b[A\n",
            "RBM fitting: 1811it [00:12, 152.09it/s]\u001b[A\n",
            "RBM fitting: 1827it [00:12, 149.87it/s]\u001b[A\n",
            "RBM fitting: 1843it [00:12, 151.86it/s]\u001b[A\n",
            "RBM fitting: 1859it [00:12, 143.44it/s]\u001b[A\n",
            "RBM fitting: 1874it [00:12, 141.67it/s]\u001b[A\n",
            "RBM fitting: 1890it [00:12, 146.55it/s]\u001b[A\n",
            "RBM fitting: 1907it [00:12, 150.71it/s]\u001b[A\n",
            "RBM fitting: 1923it [00:12, 152.37it/s]\u001b[A\n",
            "RBM fitting: 1939it [00:12, 152.87it/s]\u001b[A\n",
            "RBM fitting: 1955it [00:13, 153.87it/s]\u001b[A\n",
            "RBM fitting: 1972it [00:13, 156.25it/s]\u001b[A\n",
            "RBM fitting: 1988it [00:13, 146.97it/s]\u001b[A\n",
            "RBM fitting: 2003it [00:13, 145.68it/s]\u001b[A\n",
            "RBM fitting: 2019it [00:13, 149.31it/s]\u001b[A\n",
            "RBM fitting: 2035it [00:13, 151.50it/s]\u001b[A\n",
            "RBM fitting: 2051it [00:13, 153.19it/s]\u001b[A\n",
            "RBM fitting: 2067it [00:13, 154.45it/s]\u001b[A\n",
            "RBM fitting: 2083it [00:13, 155.31it/s]\u001b[A\n",
            "RBM fitting: 2099it [00:13, 155.56it/s]\u001b[A\n",
            "RBM fitting: 2115it [00:14, 146.29it/s]\u001b[A\n",
            "RBM fitting: 2130it [00:14, 147.00it/s]\u001b[A\n",
            "RBM fitting: 2147it [00:14, 151.21it/s]\u001b[A\n",
            "RBM fitting: 2163it [00:14, 151.59it/s]\u001b[A\n",
            "RBM fitting: 2180it [00:14, 154.53it/s]\u001b[A\n",
            "RBM fitting: 2197it [00:14, 156.83it/s]\u001b[A\n",
            "RBM fitting: 2213it [00:14, 157.41it/s]\u001b[A\n",
            "RBM fitting: 2229it [00:14, 158.03it/s]\u001b[A\n",
            "RBM fitting: 2245it [00:14, 147.98it/s]\u001b[A\n",
            "RBM fitting: 2260it [00:15, 147.16it/s]\u001b[A\n",
            "RBM fitting: 2276it [00:15, 150.11it/s]\u001b[A\n",
            "RBM fitting: 2292it [00:15, 152.38it/s]\u001b[A\n",
            "RBM fitting: 2308it [00:15, 154.49it/s]\u001b[A\n",
            "RBM fitting: 2324it [00:15, 151.53it/s]\u001b[A\n",
            "RBM fitting: 2340it [00:15, 151.20it/s]\u001b[A\n",
            "RBM fitting: 2356it [00:15, 152.73it/s]\u001b[A\n",
            "RBM fitting: 2372it [00:15, 140.03it/s]\u001b[A\n",
            "RBM fitting: 2388it [00:15, 144.92it/s]\u001b[A\n",
            "RBM fitting: 2404it [00:15, 147.16it/s]\u001b[A\n",
            "RBM fitting: 2420it [00:16, 149.99it/s]\u001b[A\n",
            "RBM fitting: 2436it [00:16, 151.35it/s]\u001b[A\n",
            "RBM fitting: 2452it [00:16, 152.74it/s]\u001b[A\n",
            "RBM fitting: 2468it [00:16, 151.66it/s]\u001b[A\n",
            "RBM fitting: 2485it [00:16, 154.74it/s]\u001b[A\n",
            "RBM fitting: 2501it [00:16, 146.55it/s]\u001b[A\n",
            "RBM fitting: 2516it [00:16, 145.77it/s]\u001b[A\n",
            "RBM fitting: 2531it [00:16, 145.17it/s]\u001b[A\n",
            "RBM fitting: 2547it [00:16, 148.42it/s]\u001b[A\n",
            "RBM fitting: 2563it [00:17, 150.36it/s]\u001b[A\n",
            "RBM fitting: 2579it [00:17, 152.10it/s]\u001b[A\n",
            "RBM fitting: 2595it [00:17, 153.87it/s]\u001b[A\n",
            "RBM fitting: 2611it [00:17, 155.13it/s]\u001b[A\n",
            "RBM fitting: 2627it [00:17, 141.93it/s]\u001b[A\n",
            "RBM fitting: 2643it [00:17, 146.28it/s]\u001b[A\n",
            "RBM fitting: 2659it [00:17, 148.83it/s]\u001b[A\n",
            "RBM fitting: 2675it [00:17, 151.59it/s]\u001b[A\n",
            "RBM fitting: 2691it [00:17, 153.61it/s]\u001b[A\n",
            "RBM fitting: 2707it [00:17, 154.79it/s]\u001b[A\n",
            "RBM fitting: 2724it [00:18, 157.02it/s]\u001b[A\n",
            "RBM fitting: 2740it [00:18, 156.98it/s]\u001b[A\n",
            "RBM fitting: 2756it [00:18, 148.32it/s]\u001b[A\n",
            "RBM fitting: 2771it [00:18, 145.90it/s]\u001b[A\n",
            "RBM fitting: 2787it [00:18, 149.56it/s]\u001b[A\n",
            "RBM fitting: 2803it [00:18, 151.43it/s]\u001b[A\n",
            "RBM fitting: 2819it [00:18, 153.03it/s]\u001b[A\n",
            "RBM fitting: 2835it [00:18, 153.99it/s]\u001b[A\n",
            "RBM fitting: 2851it [00:18, 154.74it/s]\u001b[A\n",
            "RBM fitting: 2867it [00:19, 154.95it/s]\u001b[A\n",
            "RBM fitting: 2883it [00:19, 146.09it/s]\u001b[A\n",
            "RBM fitting: 2899it [00:19, 148.45it/s]\u001b[A\n",
            "RBM fitting: 2915it [00:19, 151.17it/s]\u001b[A\n",
            "RBM fitting: 2931it [00:19, 150.56it/s]\u001b[A\n",
            "RBM fitting: 2947it [00:19, 150.56it/s]\u001b[A\n",
            "RBM fitting: 2963it [00:19, 152.61it/s]\u001b[A\n",
            "RBM fitting: 2980it [00:19, 155.27it/s]\u001b[A\n",
            "RBM fitting: 2996it [00:19, 156.20it/s]\u001b[A\n",
            "RBM fitting: 3012it [00:20, 147.30it/s]\u001b[A\n",
            "RBM fitting: 3027it [00:20, 146.87it/s]\u001b[A\n",
            "RBM fitting: 3043it [00:20, 149.57it/s]\u001b[A\n",
            "RBM fitting: 3060it [00:20, 153.02it/s]\u001b[A\n",
            "RBM fitting: 3076it [00:20, 151.73it/s]\u001b[A\n",
            "RBM fitting: 3092it [00:20, 153.80it/s]\u001b[A\n",
            "RBM fitting: 3108it [00:20, 155.29it/s]\u001b[A\n",
            "RBM fitting: 3124it [00:20, 155.87it/s]\u001b[A\n",
            "RBM fitting: 3140it [00:20, 147.17it/s]\u001b[A\n",
            "RBM fitting: 3156it [00:20, 150.05it/s]\u001b[A\n",
            "RBM fitting: 3172it [00:21, 152.87it/s]\u001b[A\n",
            "RBM fitting: 3188it [00:21, 153.43it/s]\u001b[A\n",
            "RBM fitting: 3204it [00:21, 155.07it/s]\u001b[A\n",
            "RBM fitting: 3221it [00:21, 157.34it/s]\u001b[A\n",
            "RBM fitting: 3237it [00:21, 151.37it/s]\u001b[A\n",
            "RBM fitting: 3253it [00:21, 153.07it/s]\u001b[A\n",
            "RBM fitting: 3269it [00:21, 145.20it/s]\u001b[A\n",
            "RBM fitting: 3284it [00:21, 146.17it/s]\u001b[A\n",
            "RBM fitting: 3300it [00:21, 147.47it/s]\u001b[A\n",
            "RBM fitting: 3315it [00:22, 146.18it/s]\u001b[A\n",
            "RBM fitting: 3331it [00:22, 149.11it/s]\u001b[A\n",
            "RBM fitting: 3347it [00:22, 150.96it/s]\u001b[A\n",
            "RBM fitting: 3363it [00:22, 152.67it/s]\u001b[A\n",
            "RBM fitting: 3379it [00:22, 152.59it/s]\u001b[A\n",
            "RBM fitting: 3395it [00:22, 141.58it/s]\u001b[A\n",
            "RBM fitting: 3410it [00:22, 142.69it/s]\u001b[A\n",
            "RBM fitting: 3426it [00:22, 146.18it/s]\u001b[A\n",
            "RBM fitting: 3442it [00:22, 149.09it/s]\u001b[A\n",
            "RBM fitting: 3458it [00:22, 151.51it/s]\u001b[A\n",
            "RBM fitting: 3474it [00:23, 152.59it/s]\u001b[A\n",
            "RBM fitting: 3490it [00:23, 153.31it/s]\u001b[A\n",
            "RBM fitting: 3506it [00:23, 154.44it/s]\u001b[A\n",
            "RBM fitting: 3522it [00:23, 145.84it/s]\u001b[A\n",
            "RBM fitting: 3537it [00:23, 145.57it/s]\u001b[A\n",
            "RBM fitting: 3552it [00:23, 146.03it/s]\u001b[A\n",
            "RBM fitting: 3568it [00:23, 149.25it/s]\u001b[A\n",
            "RBM fitting: 3584it [00:23, 151.32it/s]\u001b[A\n",
            "RBM fitting: 3600it [00:23, 153.57it/s]\u001b[A\n",
            "RBM fitting: 3616it [00:24, 154.25it/s]\u001b[A\n",
            "RBM fitting: 3632it [00:24, 154.96it/s]\u001b[A\n",
            "RBM fitting: 3648it [00:24, 144.19it/s]\u001b[A\n",
            "RBM fitting: 3663it [00:24, 145.54it/s]\u001b[A\n",
            "RBM fitting: 3679it [00:24, 148.35it/s]\u001b[A\n",
            "RBM fitting: 3694it [00:24, 147.42it/s]\u001b[A\n",
            "RBM fitting: 3710it [00:24, 150.18it/s]\u001b[A\n",
            "RBM fitting: 3726it [00:24, 152.73it/s]\u001b[A\n",
            "RBM fitting: 3742it [00:24, 154.63it/s]\u001b[A\n",
            "RBM fitting: 3758it [00:24, 155.18it/s]\u001b[A\n",
            "RBM fitting: 3774it [00:25, 145.75it/s]\u001b[A\n",
            "RBM fitting: 3789it [00:25, 145.99it/s]\u001b[A\n",
            "RBM fitting: 3805it [00:25, 149.27it/s]\u001b[A\n",
            "RBM fitting: 3821it [00:25, 151.38it/s]\u001b[A\n",
            "RBM fitting: 3837it [00:25, 153.39it/s]\u001b[A\n",
            "RBM fitting: 3853it [00:25, 152.52it/s]\u001b[A\n",
            "RBM fitting: 3869it [00:25, 152.48it/s]\u001b[A\n",
            "RBM fitting: 3885it [00:25, 153.81it/s]\u001b[A\n",
            "RBM fitting: 3901it [00:25, 146.19it/s]\u001b[A\n",
            "RBM fitting: 3917it [00:26, 148.23it/s]\u001b[A\n",
            "RBM fitting: 3933it [00:26, 150.73it/s]\u001b[A\n",
            "RBM fitting: 3949it [00:26, 151.66it/s]\u001b[A\n",
            "RBM fitting: 3966it [00:26, 154.55it/s]\u001b[A\n",
            "RBM fitting: 3982it [00:26, 154.88it/s]\u001b[A\n",
            "RBM fitting: 3998it [00:26, 154.23it/s]\u001b[A\n",
            "RBM fitting: 4015it [00:26, 156.89it/s]\u001b[A\n",
            "RBM fitting: 4031it [00:26, 147.93it/s]\u001b[A\n",
            "RBM fitting: 4046it [00:26, 148.25it/s]\u001b[A\n",
            "RBM fitting: 4062it [00:26, 150.54it/s]\u001b[A\n",
            "RBM fitting: 4078it [00:27, 152.80it/s]\u001b[A\n",
            "RBM fitting: 4094it [00:27, 153.29it/s]\u001b[A\n",
            "RBM fitting: 4110it [00:27, 155.00it/s]\u001b[A\n",
            "RBM fitting: 4126it [00:27, 155.23it/s]\u001b[A\n",
            "RBM fitting: 4142it [00:27, 155.81it/s]\u001b[A\n",
            "RBM fitting: 4158it [00:27, 145.44it/s]\u001b[A\n",
            "RBM fitting: 4173it [00:27, 144.88it/s]\u001b[A\n",
            "RBM fitting: 4190it [00:27, 149.46it/s]\u001b[A\n",
            "RBM fitting: 4206it [00:27, 151.69it/s]\u001b[A\n",
            "RBM fitting: 4222it [00:28, 151.57it/s]\u001b[A\n",
            "RBM fitting: 4238it [00:28, 152.23it/s]\u001b[A\n",
            "RBM fitting: 4254it [00:28, 153.33it/s]\u001b[A\n",
            "RBM fitting: 4270it [00:28, 154.93it/s]\u001b[A\n",
            "RBM fitting: 4286it [00:28, 145.82it/s]\u001b[A\n",
            "RBM fitting: 4301it [00:28, 145.22it/s]\u001b[A\n",
            "RBM fitting: 4317it [00:28, 148.09it/s]\u001b[A\n",
            "RBM fitting: 4333it [00:28, 150.85it/s]\u001b[A\n",
            "RBM fitting: 4349it [00:28, 153.40it/s]\u001b[A\n",
            "RBM fitting: 4365it [00:28, 154.76it/s]\u001b[A\n",
            "RBM fitting: 4381it [00:29, 155.86it/s]\u001b[A\n",
            "RBM fitting: 4397it [00:29, 156.16it/s]\u001b[A\n",
            "RBM fitting: 4413it [00:29, 147.60it/s]\u001b[A\n",
            "RBM fitting: 4428it [00:29, 148.31it/s]\u001b[A\n",
            "RBM fitting: 4445it [00:29, 152.25it/s]\u001b[A\n",
            "RBM fitting: 4461it [00:29, 151.36it/s]\u001b[A\n",
            "RBM fitting: 4477it [00:29, 153.40it/s]\u001b[A\n",
            "RBM fitting: 4493it [00:29, 155.05it/s]\u001b[A\n",
            "RBM fitting: 4510it [00:29, 157.36it/s]\u001b[A\n",
            "RBM fitting: 4526it [00:30, 156.10it/s]\u001b[A\n",
            "RBM fitting: 4542it [00:30, 145.03it/s]\u001b[A\n",
            "RBM fitting: 4558it [00:30, 147.20it/s]\u001b[A\n",
            "RBM fitting: 4574it [00:30, 150.35it/s]\u001b[A\n",
            "RBM fitting: 4590it [00:30, 151.98it/s]\u001b[A\n",
            "RBM fitting: 4606it [00:30, 154.21it/s]\u001b[A\n",
            "RBM fitting: 4622it [00:30, 153.57it/s]\u001b[A\n",
            "RBM fitting: 4639it [00:30, 156.31it/s]\u001b[A\n",
            "RBM fitting: 4656it [00:30, 158.48it/s]\u001b[A\n",
            "RBM fitting: 4672it [00:31, 149.17it/s]\u001b[A\n",
            "RBM fitting: 4688it [00:31, 149.50it/s]\u001b[A\n",
            "RBM fitting: 4704it [00:31, 152.11it/s]\u001b[A\n",
            "RBM fitting: 4721it [00:31, 154.84it/s]\u001b[A\n",
            "RBM fitting: 4737it [00:31, 156.11it/s]\u001b[A\n",
            "RBM fitting: 4754it [00:31, 157.71it/s]\u001b[A\n",
            "RBM fitting: 4770it [00:31, 154.46it/s]\u001b[A\n",
            "RBM fitting: 4786it [00:31, 155.10it/s]\u001b[A\n",
            "RBM fitting: 4802it [00:31, 139.58it/s]\u001b[A\n",
            "RBM fitting: 4818it [00:31, 144.16it/s]\u001b[A\n",
            "RBM fitting: 4833it [00:32, 141.85it/s]\u001b[A\n",
            "RBM fitting: 4849it [00:32, 145.26it/s]\u001b[A\n",
            "RBM fitting: 4865it [00:32, 148.40it/s]\u001b[A\n",
            "RBM fitting: 4881it [00:32, 151.18it/s]\u001b[A\n",
            "RBM fitting: 4897it [00:32, 153.44it/s]\u001b[A\n",
            "RBM fitting: 4913it [00:32, 154.78it/s]\u001b[A\n",
            "RBM fitting: 4929it [00:32, 143.61it/s]\u001b[A\n",
            "RBM fitting: 4944it [00:32, 142.84it/s]\u001b[A\n",
            "RBM fitting: 4961it [00:32, 148.48it/s]\u001b[A\n",
            "RBM fitting: 4977it [00:33, 151.37it/s]\u001b[A\n",
            "RBM fitting: 4994it [00:33, 154.55it/s]\u001b[A\n",
            "RBM fitting: 5010it [00:33, 155.94it/s]\u001b[A\n",
            "RBM fitting: 5027it [00:33, 158.38it/s]\u001b[A\n",
            "RBM fitting: 5043it [00:33, 158.40it/s]\u001b[A\n",
            "RBM fitting: 5059it [00:33, 149.47it/s]\u001b[A\n",
            "RBM fitting: 5075it [00:33, 148.99it/s]\u001b[A\n",
            "RBM fitting: 5091it [00:33, 152.10it/s]\u001b[A\n",
            "RBM fitting: 5108it [00:33, 154.82it/s]\u001b[A\n",
            "RBM fitting: 5124it [00:33, 155.93it/s]\u001b[A\n",
            "RBM fitting: 5140it [00:34, 154.38it/s]\u001b[A\n",
            "RBM fitting: 5156it [00:34, 154.18it/s]\u001b[A\n",
            "RBM fitting: 5172it [00:34, 155.13it/s]\u001b[A\n",
            "RBM fitting: 5188it [00:34, 145.72it/s]\u001b[A\n",
            "RBM fitting: 5203it [00:34, 146.28it/s]\u001b[A\n",
            "RBM fitting: 5219it [00:34, 150.00it/s]\u001b[A\n",
            "RBM fitting: 5235it [00:34, 149.42it/s]\u001b[A\n",
            "RBM fitting: 5251it [00:34, 152.36it/s]\u001b[A\n",
            "RBM fitting: 5268it [00:34, 155.28it/s]\u001b[A\n",
            "RBM fitting: 5284it [00:35, 155.83it/s]\u001b[A\n",
            "RBM fitting: 5300it [00:35, 156.17it/s]\u001b[A\n",
            "RBM fitting: 5316it [00:35, 146.38it/s]\u001b[A\n",
            "RBM fitting: 5331it [00:35, 144.59it/s]\u001b[A\n",
            "RBM fitting: 5347it [00:35, 148.61it/s]\u001b[A\n",
            "RBM fitting: 5362it [00:35, 148.66it/s]\u001b[A\n",
            "RBM fitting: 5378it [00:35, 151.20it/s]\u001b[A\n",
            "RBM fitting: 5394it [00:35, 150.16it/s]\u001b[A\n",
            "RBM fitting: 5410it [00:35, 152.67it/s]\u001b[A\n",
            "RBM fitting: 5426it [00:35, 153.09it/s]\u001b[A\n",
            "RBM fitting: 5442it [00:36, 144.13it/s]\u001b[A\n",
            "RBM fitting: 5458it [00:36, 146.93it/s]\u001b[A\n",
            "RBM fitting: 5474it [00:36, 150.26it/s]\u001b[A\n",
            "RBM fitting: 5490it [00:36, 152.69it/s]\u001b[A\n",
            "RBM fitting: 5507it [00:36, 155.20it/s]\u001b[A\n",
            "RBM fitting: 5523it [00:36, 155.40it/s]\u001b[A\n",
            "RBM fitting: 5539it [00:36, 153.88it/s]\u001b[A\n",
            "RBM fitting: 5555it [00:36, 155.21it/s]\u001b[A\n",
            "RBM fitting: 5571it [00:36, 146.66it/s]\u001b[A\n",
            "RBM fitting: 5586it [00:37, 145.54it/s]\u001b[A\n",
            "RBM fitting: 5603it [00:37, 149.89it/s]\u001b[A\n",
            "RBM fitting: 5619it [00:37, 151.84it/s]\u001b[A\n",
            "RBM fitting: 5636it [00:37, 154.62it/s]\u001b[A\n",
            "RBM fitting: 5652it [00:37, 154.97it/s]\u001b[A\n",
            "RBM fitting: 5668it [00:37, 155.65it/s]\u001b[A\n",
            "RBM fitting: 5684it [00:37, 156.85it/s]\u001b[A\n",
            "RBM fitting: 5700it [00:37, 146.72it/s]\u001b[A\n",
            "RBM fitting: 5715it [00:37, 146.85it/s]\u001b[A\n",
            "RBM fitting: 5731it [00:38, 148.37it/s]\u001b[A\n",
            "RBM fitting: 5747it [00:38, 151.55it/s]\u001b[A\n",
            "RBM fitting: 5763it [00:38, 152.54it/s]\u001b[A\n",
            "RBM fitting: 5779it [00:38, 154.31it/s]\u001b[A\n",
            "RBM fitting: 5795it [00:38, 155.38it/s]\u001b[A\n",
            "RBM fitting: 5811it [00:38, 156.17it/s]\u001b[A\n",
            "RBM fitting: 5827it [00:38, 146.43it/s]\u001b[A\n",
            "RBM fitting: 5842it [00:38, 144.99it/s]\u001b[A\n",
            "RBM fitting: 5858it [00:38, 148.96it/s]\u001b[A\n",
            "RBM fitting: 5874it [00:38, 150.87it/s]\u001b[A\n",
            "RBM fitting: 5890it [00:39, 152.79it/s]\u001b[A\n",
            "RBM fitting: 5906it [00:39, 154.10it/s]\u001b[A\n",
            "RBM fitting: 5922it [00:39, 154.83it/s]\u001b[A\n",
            "RBM fitting: 5939it [00:39, 156.76it/s]\u001b[A\n",
            "RBM fitting: 5955it [00:39, 147.35it/s]\u001b[A\n",
            "RBM fitting: 5970it [00:39, 147.50it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 150.80it/s]\n",
            "Epoch:28 ,avg_cost = 0.3374193608760834 ,std_cost = 0.03309265524148941 ,avg_grad = 10089.53515625 ,std_grad = 538.5001831054688\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 14it [00:00, 137.49it/s]\u001b[A\n",
            "RBM fitting: 30it [00:00, 141.84it/s]\u001b[A\n",
            "RBM fitting: 46it [00:00, 145.29it/s]\u001b[A\n",
            "RBM fitting: 59it [00:00, 139.42it/s]\u001b[A\n",
            "RBM fitting: 73it [00:00, 137.67it/s]\u001b[A\n",
            "RBM fitting: 89it [00:00, 143.10it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 148.69it/s]\u001b[A\n",
            "RBM fitting: 123it [00:00, 152.99it/s]\u001b[A\n",
            "RBM fitting: 140it [00:00, 156.41it/s]\u001b[A\n",
            "RBM fitting: 156it [00:01, 155.32it/s]\u001b[A\n",
            "RBM fitting: 172it [00:01, 156.47it/s]\u001b[A\n",
            "RBM fitting: 188it [00:01, 147.79it/s]\u001b[A\n",
            "RBM fitting: 204it [00:01, 150.61it/s]\u001b[A\n",
            "RBM fitting: 220it [00:01, 152.80it/s]\u001b[A\n",
            "RBM fitting: 236it [00:01, 154.47it/s]\u001b[A\n",
            "RBM fitting: 252it [00:01, 154.95it/s]\u001b[A\n",
            "RBM fitting: 269it [00:01, 157.21it/s]\u001b[A\n",
            "RBM fitting: 286it [00:01, 158.44it/s]\u001b[A\n",
            "RBM fitting: 303it [00:01, 159.51it/s]\u001b[A\n",
            "RBM fitting: 319it [00:02, 146.26it/s]\u001b[A\n",
            "RBM fitting: 334it [00:02, 144.18it/s]\u001b[A\n",
            "RBM fitting: 351it [00:02, 149.18it/s]\u001b[A\n",
            "RBM fitting: 367it [00:02, 151.71it/s]\u001b[A\n",
            "RBM fitting: 383it [00:02, 153.94it/s]\u001b[A\n",
            "RBM fitting: 399it [00:02, 154.96it/s]\u001b[A\n",
            "RBM fitting: 416it [00:02, 156.90it/s]\u001b[A\n",
            "RBM fitting: 433it [00:02, 158.40it/s]\u001b[A\n",
            "RBM fitting: 449it [00:02, 149.45it/s]\u001b[A\n",
            "RBM fitting: 465it [00:03, 147.90it/s]\u001b[A\n",
            "RBM fitting: 482it [00:03, 151.82it/s]\u001b[A\n",
            "RBM fitting: 499it [00:03, 154.87it/s]\u001b[A\n",
            "RBM fitting: 516it [00:03, 157.74it/s]\u001b[A\n",
            "RBM fitting: 532it [00:03, 158.40it/s]\u001b[A\n",
            "RBM fitting: 549it [00:03, 159.48it/s]\u001b[A\n",
            "RBM fitting: 565it [00:03, 159.50it/s]\u001b[A\n",
            "RBM fitting: 581it [00:03, 149.63it/s]\u001b[A\n",
            "RBM fitting: 597it [00:03, 149.10it/s]\u001b[A\n",
            "RBM fitting: 614it [00:04, 152.97it/s]\u001b[A\n",
            "RBM fitting: 630it [00:04, 151.72it/s]\u001b[A\n",
            "RBM fitting: 646it [00:04, 153.60it/s]\u001b[A\n",
            "RBM fitting: 662it [00:04, 155.06it/s]\u001b[A\n",
            "RBM fitting: 678it [00:04, 156.07it/s]\u001b[A\n",
            "RBM fitting: 694it [00:04, 156.27it/s]\u001b[A\n",
            "RBM fitting: 710it [00:04, 146.50it/s]\u001b[A\n",
            "RBM fitting: 725it [00:04, 145.93it/s]\u001b[A\n",
            "RBM fitting: 740it [00:04, 147.07it/s]\u001b[A\n",
            "RBM fitting: 756it [00:04, 149.65it/s]\u001b[A\n",
            "RBM fitting: 772it [00:05, 150.41it/s]\u001b[A\n",
            "RBM fitting: 788it [00:05, 152.95it/s]\u001b[A\n",
            "RBM fitting: 804it [00:05, 154.93it/s]\u001b[A\n",
            "RBM fitting: 821it [00:05, 156.86it/s]\u001b[A\n",
            "RBM fitting: 837it [00:05, 147.15it/s]\u001b[A\n",
            "RBM fitting: 852it [00:05, 145.63it/s]\u001b[A\n",
            "RBM fitting: 868it [00:05, 148.97it/s]\u001b[A\n",
            "RBM fitting: 884it [00:05, 150.05it/s]\u001b[A\n",
            "RBM fitting: 900it [00:05, 151.85it/s]\u001b[A\n",
            "RBM fitting: 917it [00:06, 154.77it/s]\u001b[A\n",
            "RBM fitting: 933it [00:06, 153.73it/s]\u001b[A\n",
            "RBM fitting: 949it [00:06, 154.99it/s]\u001b[A\n",
            "RBM fitting: 965it [00:06, 146.08it/s]\u001b[A\n",
            "RBM fitting: 980it [00:06, 146.58it/s]\u001b[A\n",
            "RBM fitting: 996it [00:06, 150.10it/s]\u001b[A\n",
            "RBM fitting: 1012it [00:06, 146.61it/s]\u001b[A\n",
            "RBM fitting: 1028it [00:06, 149.31it/s]\u001b[A\n",
            "RBM fitting: 1044it [00:06, 150.46it/s]\u001b[A\n",
            "RBM fitting: 1060it [00:06, 152.45it/s]\u001b[A\n",
            "RBM fitting: 1076it [00:07, 153.46it/s]\u001b[A\n",
            "RBM fitting: 1092it [00:07, 137.00it/s]\u001b[A\n",
            "RBM fitting: 1108it [00:07, 141.80it/s]\u001b[A\n",
            "RBM fitting: 1124it [00:07, 145.18it/s]\u001b[A\n",
            "RBM fitting: 1140it [00:07, 148.77it/s]\u001b[A\n",
            "RBM fitting: 1156it [00:07, 151.73it/s]\u001b[A\n",
            "RBM fitting: 1173it [00:07, 154.92it/s]\u001b[A\n",
            "RBM fitting: 1189it [00:07, 156.06it/s]\u001b[A\n",
            "RBM fitting: 1205it [00:07, 155.64it/s]\u001b[A\n",
            "RBM fitting: 1221it [00:08, 146.67it/s]\u001b[A\n",
            "RBM fitting: 1236it [00:08, 144.40it/s]\u001b[A\n",
            "RBM fitting: 1252it [00:08, 148.47it/s]\u001b[A\n",
            "RBM fitting: 1269it [00:08, 152.30it/s]\u001b[A\n",
            "RBM fitting: 1285it [00:08, 153.71it/s]\u001b[A\n",
            "RBM fitting: 1301it [00:08, 155.27it/s]\u001b[A\n",
            "RBM fitting: 1317it [00:08, 155.83it/s]\u001b[A\n",
            "RBM fitting: 1333it [00:08, 156.88it/s]\u001b[A\n",
            "RBM fitting: 1349it [00:08, 147.67it/s]\u001b[A\n",
            "RBM fitting: 1365it [00:09, 149.04it/s]\u001b[A\n",
            "RBM fitting: 1380it [00:09, 147.27it/s]\u001b[A\n",
            "RBM fitting: 1395it [00:09, 145.85it/s]\u001b[A\n",
            "RBM fitting: 1411it [00:09, 149.81it/s]\u001b[A\n",
            "RBM fitting: 1428it [00:09, 153.17it/s]\u001b[A\n",
            "RBM fitting: 1444it [00:09, 153.49it/s]\u001b[A\n",
            "RBM fitting: 1460it [00:09, 153.71it/s]\u001b[A\n",
            "RBM fitting: 1476it [00:09, 143.49it/s]\u001b[A\n",
            "RBM fitting: 1492it [00:09, 146.12it/s]\u001b[A\n",
            "RBM fitting: 1508it [00:09, 149.13it/s]\u001b[A\n",
            "RBM fitting: 1525it [00:10, 152.72it/s]\u001b[A\n",
            "RBM fitting: 1541it [00:10, 151.86it/s]\u001b[A\n",
            "RBM fitting: 1557it [00:10, 153.89it/s]\u001b[A\n",
            "RBM fitting: 1573it [00:10, 155.14it/s]\u001b[A\n",
            "RBM fitting: 1589it [00:10, 155.73it/s]\u001b[A\n",
            "RBM fitting: 1605it [00:10, 146.14it/s]\u001b[A\n",
            "RBM fitting: 1620it [00:10, 146.77it/s]\u001b[A\n",
            "RBM fitting: 1636it [00:10, 149.98it/s]\u001b[A\n",
            "RBM fitting: 1652it [00:10, 151.32it/s]\u001b[A\n",
            "RBM fitting: 1668it [00:11, 152.32it/s]\u001b[A\n",
            "RBM fitting: 1685it [00:11, 155.91it/s]\u001b[A\n",
            "RBM fitting: 1701it [00:11, 153.93it/s]\u001b[A\n",
            "RBM fitting: 1717it [00:11, 154.91it/s]\u001b[A\n",
            "RBM fitting: 1733it [00:11, 144.62it/s]\u001b[A\n",
            "RBM fitting: 1749it [00:11, 148.57it/s]\u001b[A\n",
            "RBM fitting: 1765it [00:11, 150.92it/s]\u001b[A\n",
            "RBM fitting: 1781it [00:11, 152.80it/s]\u001b[A\n",
            "RBM fitting: 1797it [00:11, 153.93it/s]\u001b[A\n",
            "RBM fitting: 1813it [00:11, 155.50it/s]\u001b[A\n",
            "RBM fitting: 1829it [00:12, 155.17it/s]\u001b[A\n",
            "RBM fitting: 1845it [00:12, 151.33it/s]\u001b[A\n",
            "RBM fitting: 1861it [00:12, 143.09it/s]\u001b[A\n",
            "RBM fitting: 1877it [00:12, 146.96it/s]\u001b[A\n",
            "RBM fitting: 1894it [00:12, 150.99it/s]\u001b[A\n",
            "RBM fitting: 1910it [00:12, 152.62it/s]\u001b[A\n",
            "RBM fitting: 1926it [00:12, 153.35it/s]\u001b[A\n",
            "RBM fitting: 1942it [00:12, 153.84it/s]\u001b[A\n",
            "RBM fitting: 1958it [00:12, 154.66it/s]\u001b[A\n",
            "RBM fitting: 1974it [00:13, 154.82it/s]\u001b[A\n",
            "RBM fitting: 1990it [00:13, 145.79it/s]\u001b[A\n",
            "RBM fitting: 2005it [00:13, 143.22it/s]\u001b[A\n",
            "RBM fitting: 2022it [00:13, 148.39it/s]\u001b[A\n",
            "RBM fitting: 2038it [00:13, 151.60it/s]\u001b[A\n",
            "RBM fitting: 2055it [00:13, 154.57it/s]\u001b[A\n",
            "RBM fitting: 2071it [00:13, 156.02it/s]\u001b[A\n",
            "RBM fitting: 2088it [00:13, 158.74it/s]\u001b[A\n",
            "RBM fitting: 2105it [00:13, 159.68it/s]\u001b[A\n",
            "RBM fitting: 2122it [00:14, 150.81it/s]\u001b[A\n",
            "RBM fitting: 2138it [00:14, 151.10it/s]\u001b[A\n",
            "RBM fitting: 2154it [00:14, 150.56it/s]\u001b[A\n",
            "RBM fitting: 2170it [00:14, 152.81it/s]\u001b[A\n",
            "RBM fitting: 2186it [00:14, 152.55it/s]\u001b[A\n",
            "RBM fitting: 2202it [00:14, 154.67it/s]\u001b[A\n",
            "RBM fitting: 2218it [00:14, 155.92it/s]\u001b[A\n",
            "RBM fitting: 2235it [00:14, 157.87it/s]\u001b[A\n",
            "RBM fitting: 2251it [00:14, 148.15it/s]\u001b[A\n",
            "RBM fitting: 2266it [00:14, 148.61it/s]\u001b[A\n",
            "RBM fitting: 2282it [00:15, 151.26it/s]\u001b[A\n",
            "RBM fitting: 2298it [00:15, 152.26it/s]\u001b[A\n",
            "RBM fitting: 2314it [00:15, 151.21it/s]\u001b[A\n",
            "RBM fitting: 2331it [00:15, 154.36it/s]\u001b[A\n",
            "RBM fitting: 2347it [00:15, 155.73it/s]\u001b[A\n",
            "RBM fitting: 2364it [00:15, 157.62it/s]\u001b[A\n",
            "RBM fitting: 2380it [00:15, 148.05it/s]\u001b[A\n",
            "RBM fitting: 2395it [00:15, 147.66it/s]\u001b[A\n",
            "RBM fitting: 2412it [00:15, 151.56it/s]\u001b[A\n",
            "RBM fitting: 2428it [00:16, 153.77it/s]\u001b[A\n",
            "RBM fitting: 2445it [00:16, 156.16it/s]\u001b[A\n",
            "RBM fitting: 2461it [00:16, 153.77it/s]\u001b[A\n",
            "RBM fitting: 2477it [00:16, 153.04it/s]\u001b[A\n",
            "RBM fitting: 2494it [00:16, 155.87it/s]\u001b[A\n",
            "RBM fitting: 2510it [00:16, 146.79it/s]\u001b[A\n",
            "RBM fitting: 2525it [00:16, 145.98it/s]\u001b[A\n",
            "RBM fitting: 2542it [00:16, 151.36it/s]\u001b[A\n",
            "RBM fitting: 2559it [00:16, 154.82it/s]\u001b[A\n",
            "RBM fitting: 2576it [00:16, 157.71it/s]\u001b[A\n",
            "RBM fitting: 2592it [00:17, 158.09it/s]\u001b[A\n",
            "RBM fitting: 2608it [00:17, 157.03it/s]\u001b[A\n",
            "RBM fitting: 2624it [00:17, 154.48it/s]\u001b[A\n",
            "RBM fitting: 2640it [00:17, 146.36it/s]\u001b[A\n",
            "RBM fitting: 2655it [00:17, 146.09it/s]\u001b[A\n",
            "RBM fitting: 2671it [00:17, 149.96it/s]\u001b[A\n",
            "RBM fitting: 2688it [00:17, 153.61it/s]\u001b[A\n",
            "RBM fitting: 2704it [00:17, 154.29it/s]\u001b[A\n",
            "RBM fitting: 2720it [00:17, 152.99it/s]\u001b[A\n",
            "RBM fitting: 2737it [00:18, 156.12it/s]\u001b[A\n",
            "RBM fitting: 2753it [00:18, 156.91it/s]\u001b[A\n",
            "RBM fitting: 2769it [00:18, 147.55it/s]\u001b[A\n",
            "RBM fitting: 2784it [00:18, 144.15it/s]\u001b[A\n",
            "RBM fitting: 2800it [00:18, 147.42it/s]\u001b[A\n",
            "RBM fitting: 2817it [00:18, 151.39it/s]\u001b[A\n",
            "RBM fitting: 2833it [00:18, 152.57it/s]\u001b[A\n",
            "RBM fitting: 2849it [00:18, 153.71it/s]\u001b[A\n",
            "RBM fitting: 2865it [00:18, 153.83it/s]\u001b[A\n",
            "RBM fitting: 2881it [00:18, 154.60it/s]\u001b[A\n",
            "RBM fitting: 2897it [00:19, 144.66it/s]\u001b[A\n",
            "RBM fitting: 2912it [00:19, 144.43it/s]\u001b[A\n",
            "RBM fitting: 2927it [00:19, 144.81it/s]\u001b[A\n",
            "RBM fitting: 2943it [00:19, 148.56it/s]\u001b[A\n",
            "RBM fitting: 2959it [00:19, 151.29it/s]\u001b[A\n",
            "RBM fitting: 2975it [00:19, 152.27it/s]\u001b[A\n",
            "RBM fitting: 2992it [00:19, 155.27it/s]\u001b[A\n",
            "RBM fitting: 3008it [00:19, 155.23it/s]\u001b[A\n",
            "RBM fitting: 3024it [00:19, 145.98it/s]\u001b[A\n",
            "RBM fitting: 3039it [00:20, 146.10it/s]\u001b[A\n",
            "RBM fitting: 3055it [00:20, 149.83it/s]\u001b[A\n",
            "RBM fitting: 3071it [00:20, 151.72it/s]\u001b[A\n",
            "RBM fitting: 3087it [00:20, 149.84it/s]\u001b[A\n",
            "RBM fitting: 3103it [00:20, 151.78it/s]\u001b[A\n",
            "RBM fitting: 3119it [00:20, 153.35it/s]\u001b[A\n",
            "RBM fitting: 3135it [00:20, 154.17it/s]\u001b[A\n",
            "RBM fitting: 3151it [00:20, 145.74it/s]\u001b[A\n",
            "RBM fitting: 3166it [00:20, 144.46it/s]\u001b[A\n",
            "RBM fitting: 3182it [00:21, 148.37it/s]\u001b[A\n",
            "RBM fitting: 3199it [00:21, 151.99it/s]\u001b[A\n",
            "RBM fitting: 3215it [00:21, 152.17it/s]\u001b[A\n",
            "RBM fitting: 3231it [00:21, 152.49it/s]\u001b[A\n",
            "RBM fitting: 3247it [00:21, 152.24it/s]\u001b[A\n",
            "RBM fitting: 3263it [00:21, 153.44it/s]\u001b[A\n",
            "RBM fitting: 3279it [00:21, 145.39it/s]\u001b[A\n",
            "RBM fitting: 3294it [00:21, 146.33it/s]\u001b[A\n",
            "RBM fitting: 3310it [00:21, 150.14it/s]\u001b[A\n",
            "RBM fitting: 3326it [00:21, 151.99it/s]\u001b[A\n",
            "RBM fitting: 3342it [00:22, 153.31it/s]\u001b[A\n",
            "RBM fitting: 3358it [00:22, 154.79it/s]\u001b[A\n",
            "RBM fitting: 3374it [00:22, 155.36it/s]\u001b[A\n",
            "RBM fitting: 3390it [00:22, 153.89it/s]\u001b[A\n",
            "RBM fitting: 3406it [00:22, 145.32it/s]\u001b[A\n",
            "RBM fitting: 3421it [00:22, 144.97it/s]\u001b[A\n",
            "RBM fitting: 3437it [00:22, 148.45it/s]\u001b[A\n",
            "RBM fitting: 3454it [00:22, 152.12it/s]\u001b[A\n",
            "RBM fitting: 3470it [00:22, 152.67it/s]\u001b[A\n",
            "RBM fitting: 3487it [00:23, 155.31it/s]\u001b[A\n",
            "RBM fitting: 3504it [00:23, 157.64it/s]\u001b[A\n",
            "RBM fitting: 3520it [00:23, 157.74it/s]\u001b[A\n",
            "RBM fitting: 3536it [00:23, 147.22it/s]\u001b[A\n",
            "RBM fitting: 3551it [00:23, 142.14it/s]\u001b[A\n",
            "RBM fitting: 3567it [00:23, 145.89it/s]\u001b[A\n",
            "RBM fitting: 3583it [00:23, 148.84it/s]\u001b[A\n",
            "RBM fitting: 3599it [00:23, 150.71it/s]\u001b[A\n",
            "RBM fitting: 3615it [00:23, 152.07it/s]\u001b[A\n",
            "RBM fitting: 3631it [00:23, 153.25it/s]\u001b[A\n",
            "RBM fitting: 3647it [00:24, 154.62it/s]\u001b[A\n",
            "RBM fitting: 3663it [00:24, 145.95it/s]\u001b[A\n",
            "RBM fitting: 3678it [00:24, 146.38it/s]\u001b[A\n",
            "RBM fitting: 3693it [00:24, 146.64it/s]\u001b[A\n",
            "RBM fitting: 3709it [00:24, 149.01it/s]\u001b[A\n",
            "RBM fitting: 3725it [00:24, 151.77it/s]\u001b[A\n",
            "RBM fitting: 3741it [00:24, 152.98it/s]\u001b[A\n",
            "RBM fitting: 3757it [00:24, 153.43it/s]\u001b[A\n",
            "RBM fitting: 3773it [00:24, 154.64it/s]\u001b[A\n",
            "RBM fitting: 3789it [00:25, 146.17it/s]\u001b[A\n",
            "RBM fitting: 3804it [00:25, 146.27it/s]\u001b[A\n",
            "RBM fitting: 3820it [00:25, 148.34it/s]\u001b[A\n",
            "RBM fitting: 3836it [00:25, 150.75it/s]\u001b[A\n",
            "RBM fitting: 3852it [00:25, 150.11it/s]\u001b[A\n",
            "RBM fitting: 3868it [00:25, 151.86it/s]\u001b[A\n",
            "RBM fitting: 3884it [00:25, 152.94it/s]\u001b[A\n",
            "RBM fitting: 3900it [00:25, 154.51it/s]\u001b[A\n",
            "RBM fitting: 3916it [00:25, 145.64it/s]\u001b[A\n",
            "RBM fitting: 3931it [00:25, 146.33it/s]\u001b[A\n",
            "RBM fitting: 3947it [00:26, 149.46it/s]\u001b[A\n",
            "RBM fitting: 3963it [00:26, 151.82it/s]\u001b[A\n",
            "RBM fitting: 3980it [00:26, 154.83it/s]\u001b[A\n",
            "RBM fitting: 3997it [00:26, 156.83it/s]\u001b[A\n",
            "RBM fitting: 4013it [00:26, 152.49it/s]\u001b[A\n",
            "RBM fitting: 4029it [00:26, 153.16it/s]\u001b[A\n",
            "RBM fitting: 4045it [00:26, 145.40it/s]\u001b[A\n",
            "RBM fitting: 4060it [00:26, 145.72it/s]\u001b[A\n",
            "RBM fitting: 4076it [00:26, 148.67it/s]\u001b[A\n",
            "RBM fitting: 4092it [00:27, 151.54it/s]\u001b[A\n",
            "RBM fitting: 4108it [00:27, 153.68it/s]\u001b[A\n",
            "RBM fitting: 4124it [00:27, 154.49it/s]\u001b[A\n",
            "RBM fitting: 4140it [00:27, 155.40it/s]\u001b[A\n",
            "RBM fitting: 4156it [00:27, 153.61it/s]\u001b[A\n",
            "RBM fitting: 4172it [00:27, 146.21it/s]\u001b[A\n",
            "RBM fitting: 4187it [00:27, 145.01it/s]\u001b[A\n",
            "RBM fitting: 4204it [00:27, 149.79it/s]\u001b[A\n",
            "RBM fitting: 4220it [00:27, 152.49it/s]\u001b[A\n",
            "RBM fitting: 4236it [00:27, 154.18it/s]\u001b[A\n",
            "RBM fitting: 4252it [00:28, 155.05it/s]\u001b[A\n",
            "RBM fitting: 4268it [00:28, 152.78it/s]\u001b[A\n",
            "RBM fitting: 4285it [00:28, 155.50it/s]\u001b[A\n",
            "RBM fitting: 4301it [00:28, 146.62it/s]\u001b[A\n",
            "RBM fitting: 4316it [00:28, 141.85it/s]\u001b[A\n",
            "RBM fitting: 4332it [00:28, 145.53it/s]\u001b[A\n",
            "RBM fitting: 4348it [00:28, 149.27it/s]\u001b[A\n",
            "RBM fitting: 4364it [00:28, 151.92it/s]\u001b[A\n",
            "RBM fitting: 4380it [00:28, 154.12it/s]\u001b[A\n",
            "RBM fitting: 4396it [00:29, 154.85it/s]\u001b[A\n",
            "RBM fitting: 4412it [00:29, 156.14it/s]\u001b[A\n",
            "RBM fitting: 4428it [00:29, 144.09it/s]\u001b[A\n",
            "RBM fitting: 4444it [00:29, 146.41it/s]\u001b[A\n",
            "RBM fitting: 4459it [00:29, 147.00it/s]\u001b[A\n",
            "RBM fitting: 4475it [00:29, 149.81it/s]\u001b[A\n",
            "RBM fitting: 4491it [00:29, 149.89it/s]\u001b[A\n",
            "RBM fitting: 4507it [00:29, 151.65it/s]\u001b[A\n",
            "RBM fitting: 4523it [00:29, 152.38it/s]\u001b[A\n",
            "RBM fitting: 4539it [00:29, 154.53it/s]\u001b[A\n",
            "RBM fitting: 4555it [00:30, 146.66it/s]\u001b[A\n",
            "RBM fitting: 4570it [00:30, 146.13it/s]\u001b[A\n",
            "RBM fitting: 4586it [00:30, 149.61it/s]\u001b[A\n",
            "RBM fitting: 4602it [00:30, 151.19it/s]\u001b[A\n",
            "RBM fitting: 4618it [00:30, 149.76it/s]\u001b[A\n",
            "RBM fitting: 4634it [00:30, 151.07it/s]\u001b[A\n",
            "RBM fitting: 4650it [00:30, 153.04it/s]\u001b[A\n",
            "RBM fitting: 4666it [00:30, 154.78it/s]\u001b[A\n",
            "RBM fitting: 4682it [00:30, 147.46it/s]\u001b[A\n",
            "RBM fitting: 4697it [00:31, 146.42it/s]\u001b[A\n",
            "RBM fitting: 4713it [00:31, 148.23it/s]\u001b[A\n",
            "RBM fitting: 4730it [00:31, 152.35it/s]\u001b[A\n",
            "RBM fitting: 4747it [00:31, 155.32it/s]\u001b[A\n",
            "RBM fitting: 4763it [00:31, 155.15it/s]\u001b[A\n",
            "RBM fitting: 4779it [00:31, 153.92it/s]\u001b[A\n",
            "RBM fitting: 4795it [00:31, 154.56it/s]\u001b[A\n",
            "RBM fitting: 4811it [00:31, 146.37it/s]\u001b[A\n",
            "RBM fitting: 4826it [00:31, 145.93it/s]\u001b[A\n",
            "RBM fitting: 4842it [00:32, 148.71it/s]\u001b[A\n",
            "RBM fitting: 4858it [00:32, 151.12it/s]\u001b[A\n",
            "RBM fitting: 4874it [00:32, 152.60it/s]\u001b[A\n",
            "RBM fitting: 4890it [00:32, 153.92it/s]\u001b[A\n",
            "RBM fitting: 4906it [00:32, 153.94it/s]\u001b[A\n",
            "RBM fitting: 4922it [00:32, 154.40it/s]\u001b[A\n",
            "RBM fitting: 4938it [00:32, 146.01it/s]\u001b[A\n",
            "RBM fitting: 4953it [00:32, 146.77it/s]\u001b[A\n",
            "RBM fitting: 4969it [00:32, 150.29it/s]\u001b[A\n",
            "RBM fitting: 4986it [00:32, 154.26it/s]\u001b[A\n",
            "RBM fitting: 5003it [00:33, 156.64it/s]\u001b[A\n",
            "RBM fitting: 5020it [00:33, 158.39it/s]\u001b[A\n",
            "RBM fitting: 5036it [00:33, 157.88it/s]\u001b[A\n",
            "RBM fitting: 5052it [00:33, 156.74it/s]\u001b[A\n",
            "RBM fitting: 5068it [00:33, 148.75it/s]\u001b[A\n",
            "RBM fitting: 5083it [00:33, 144.65it/s]\u001b[A\n",
            "RBM fitting: 5100it [00:33, 149.69it/s]\u001b[A\n",
            "RBM fitting: 5116it [00:33, 152.16it/s]\u001b[A\n",
            "RBM fitting: 5133it [00:33, 155.26it/s]\u001b[A\n",
            "RBM fitting: 5150it [00:34, 157.35it/s]\u001b[A\n",
            "RBM fitting: 5166it [00:34, 157.91it/s]\u001b[A\n",
            "RBM fitting: 5182it [00:34, 157.72it/s]\u001b[A\n",
            "RBM fitting: 5198it [00:34, 148.05it/s]\u001b[A\n",
            "RBM fitting: 5214it [00:34, 149.42it/s]\u001b[A\n",
            "RBM fitting: 5230it [00:34, 149.81it/s]\u001b[A\n",
            "RBM fitting: 5246it [00:34, 151.45it/s]\u001b[A\n",
            "RBM fitting: 5262it [00:34, 153.25it/s]\u001b[A\n",
            "RBM fitting: 5278it [00:34, 153.85it/s]\u001b[A\n",
            "RBM fitting: 5294it [00:34, 154.46it/s]\u001b[A\n",
            "RBM fitting: 5310it [00:35, 153.75it/s]\u001b[A\n",
            "RBM fitting: 5327it [00:35, 155.99it/s]\u001b[A\n",
            "RBM fitting: 5343it [00:35, 146.40it/s]\u001b[A\n",
            "RBM fitting: 5358it [00:35, 145.51it/s]\u001b[A\n",
            "RBM fitting: 5374it [00:35, 147.89it/s]\u001b[A\n",
            "RBM fitting: 5389it [00:35, 147.39it/s]\u001b[A\n",
            "RBM fitting: 5405it [00:35, 150.65it/s]\u001b[A\n",
            "RBM fitting: 5421it [00:35, 153.27it/s]\u001b[A\n",
            "RBM fitting: 5437it [00:35, 154.44it/s]\u001b[A\n",
            "RBM fitting: 5453it [00:36, 155.79it/s]\u001b[A\n",
            "RBM fitting: 5469it [00:36, 148.41it/s]\u001b[A\n",
            "RBM fitting: 5484it [00:36, 148.15it/s]\u001b[A\n",
            "RBM fitting: 5500it [00:36, 150.64it/s]\u001b[A\n",
            "RBM fitting: 5516it [00:36, 152.87it/s]\u001b[A\n",
            "RBM fitting: 5532it [00:36, 154.00it/s]\u001b[A\n",
            "RBM fitting: 5548it [00:36, 151.65it/s]\u001b[A\n",
            "RBM fitting: 5564it [00:36, 151.30it/s]\u001b[A\n",
            "RBM fitting: 5580it [00:36, 149.69it/s]\u001b[A\n",
            "RBM fitting: 5595it [00:36, 141.33it/s]\u001b[A\n",
            "RBM fitting: 5610it [00:37, 140.76it/s]\u001b[A\n",
            "RBM fitting: 5626it [00:37, 143.98it/s]\u001b[A\n",
            "RBM fitting: 5641it [00:37, 141.73it/s]\u001b[A\n",
            "RBM fitting: 5656it [00:37, 142.84it/s]\u001b[A\n",
            "RBM fitting: 5672it [00:37, 145.84it/s]\u001b[A\n",
            "RBM fitting: 5687it [00:37, 145.85it/s]\u001b[A\n",
            "RBM fitting: 5704it [00:37, 150.43it/s]\u001b[A\n",
            "RBM fitting: 5720it [00:37, 144.25it/s]\u001b[A\n",
            "RBM fitting: 5735it [00:37, 143.26it/s]\u001b[A\n",
            "RBM fitting: 5751it [00:38, 146.86it/s]\u001b[A\n",
            "RBM fitting: 5767it [00:38, 148.85it/s]\u001b[A\n",
            "RBM fitting: 5783it [00:38, 150.60it/s]\u001b[A\n",
            "RBM fitting: 5799it [00:38, 152.76it/s]\u001b[A\n",
            "RBM fitting: 5815it [00:38, 152.35it/s]\u001b[A\n",
            "RBM fitting: 5831it [00:38, 151.27it/s]\u001b[A\n",
            "RBM fitting: 5847it [00:38, 138.30it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:38, 139.17it/s]\u001b[A\n",
            "RBM fitting: 5878it [00:38, 142.79it/s]\u001b[A\n",
            "RBM fitting: 5893it [00:39, 143.96it/s]\u001b[A\n",
            "RBM fitting: 5909it [00:39, 147.00it/s]\u001b[A\n",
            "RBM fitting: 5925it [00:39, 149.08it/s]\u001b[A\n",
            "RBM fitting: 5941it [00:39, 151.98it/s]\u001b[A\n",
            "RBM fitting: 5958it [00:39, 154.69it/s]\u001b[A\n",
            "RBM fitting: 5974it [00:39, 146.42it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 150.96it/s]\n",
            "Epoch:29 ,avg_cost = 0.33693403005599976 ,std_cost = 0.03310143202543259 ,avg_grad = 10092.4697265625 ,std_grad = 548.7925415039062\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 14it [00:00, 137.92it/s]\u001b[A\n",
            "RBM fitting: 30it [00:00, 143.81it/s]\u001b[A\n",
            "RBM fitting: 46it [00:00, 147.91it/s]\u001b[A\n",
            "RBM fitting: 61it [00:00, 148.19it/s]\u001b[A\n",
            "RBM fitting: 77it [00:00, 149.80it/s]\u001b[A\n",
            "RBM fitting: 90it [00:00, 142.95it/s]\u001b[A\n",
            "RBM fitting: 104it [00:00, 140.70it/s]\u001b[A\n",
            "RBM fitting: 120it [00:00, 145.39it/s]\u001b[A\n",
            "RBM fitting: 135it [00:00, 146.56it/s]\u001b[A\n",
            "RBM fitting: 151it [00:01, 150.19it/s]\u001b[A\n",
            "RBM fitting: 167it [00:01, 152.71it/s]\u001b[A\n",
            "RBM fitting: 183it [00:01, 154.43it/s]\u001b[A\n",
            "RBM fitting: 200it [00:01, 156.46it/s]\u001b[A\n",
            "RBM fitting: 216it [00:01, 157.07it/s]\u001b[A\n",
            "RBM fitting: 232it [00:01, 146.49it/s]\u001b[A\n",
            "RBM fitting: 248it [00:01, 149.17it/s]\u001b[A\n",
            "RBM fitting: 264it [00:01, 150.08it/s]\u001b[A\n",
            "RBM fitting: 280it [00:01, 149.74it/s]\u001b[A\n",
            "RBM fitting: 296it [00:01, 149.14it/s]\u001b[A\n",
            "RBM fitting: 312it [00:02, 151.93it/s]\u001b[A\n",
            "RBM fitting: 328it [00:02, 152.10it/s]\u001b[A\n",
            "RBM fitting: 344it [00:02, 154.19it/s]\u001b[A\n",
            "RBM fitting: 361it [00:02, 157.36it/s]\u001b[A\n",
            "RBM fitting: 377it [00:02, 141.52it/s]\u001b[A\n",
            "RBM fitting: 392it [00:02, 137.36it/s]\u001b[A\n",
            "RBM fitting: 408it [00:02, 142.68it/s]\u001b[A\n",
            "RBM fitting: 424it [00:02, 146.92it/s]\u001b[A\n",
            "RBM fitting: 440it [00:02, 147.91it/s]\u001b[A\n",
            "RBM fitting: 456it [00:03, 150.85it/s]\u001b[A\n",
            "RBM fitting: 472it [00:03, 152.22it/s]\u001b[A\n",
            "RBM fitting: 488it [00:03, 153.59it/s]\u001b[A\n",
            "RBM fitting: 504it [00:03, 144.19it/s]\u001b[A\n",
            "RBM fitting: 520it [00:03, 147.78it/s]\u001b[A\n",
            "RBM fitting: 536it [00:03, 151.12it/s]\u001b[A\n",
            "RBM fitting: 552it [00:03, 153.02it/s]\u001b[A\n",
            "RBM fitting: 568it [00:03, 153.35it/s]\u001b[A\n",
            "RBM fitting: 584it [00:03, 153.96it/s]\u001b[A\n",
            "RBM fitting: 600it [00:03, 153.28it/s]\u001b[A\n",
            "RBM fitting: 617it [00:04, 155.87it/s]\u001b[A\n",
            "RBM fitting: 633it [00:04, 156.64it/s]\u001b[A\n",
            "RBM fitting: 649it [00:04, 146.92it/s]\u001b[A\n",
            "RBM fitting: 664it [00:04, 146.76it/s]\u001b[A\n",
            "RBM fitting: 680it [00:04, 149.93it/s]\u001b[A\n",
            "RBM fitting: 696it [00:04, 152.74it/s]\u001b[A\n",
            "RBM fitting: 712it [00:04, 153.72it/s]\u001b[A\n",
            "RBM fitting: 728it [00:04, 154.05it/s]\u001b[A\n",
            "RBM fitting: 744it [00:04, 154.99it/s]\u001b[A\n",
            "RBM fitting: 760it [00:05, 153.96it/s]\u001b[A\n",
            "RBM fitting: 776it [00:05, 146.42it/s]\u001b[A\n",
            "RBM fitting: 791it [00:05, 145.93it/s]\u001b[A\n",
            "RBM fitting: 807it [00:05, 149.26it/s]\u001b[A\n",
            "RBM fitting: 823it [00:05, 151.77it/s]\u001b[A\n",
            "RBM fitting: 840it [00:05, 154.41it/s]\u001b[A\n",
            "RBM fitting: 856it [00:05, 155.21it/s]\u001b[A\n",
            "RBM fitting: 872it [00:05, 155.78it/s]\u001b[A\n",
            "RBM fitting: 888it [00:05, 154.70it/s]\u001b[A\n",
            "RBM fitting: 904it [00:06, 143.61it/s]\u001b[A\n",
            "RBM fitting: 919it [00:06, 143.62it/s]\u001b[A\n",
            "RBM fitting: 935it [00:06, 148.14it/s]\u001b[A\n",
            "RBM fitting: 951it [00:06, 150.79it/s]\u001b[A\n",
            "RBM fitting: 967it [00:06, 152.87it/s]\u001b[A\n",
            "RBM fitting: 983it [00:06, 154.57it/s]\u001b[A\n",
            "RBM fitting: 999it [00:06, 155.85it/s]\u001b[A\n",
            "RBM fitting: 1015it [00:06, 156.35it/s]\u001b[A\n",
            "RBM fitting: 1031it [00:06, 144.76it/s]\u001b[A\n",
            "RBM fitting: 1047it [00:06, 146.93it/s]\u001b[A\n",
            "RBM fitting: 1062it [00:07, 146.70it/s]\u001b[A\n",
            "RBM fitting: 1079it [00:07, 151.17it/s]\u001b[A\n",
            "RBM fitting: 1096it [00:07, 154.68it/s]\u001b[A\n",
            "RBM fitting: 1112it [00:07, 156.13it/s]\u001b[A\n",
            "RBM fitting: 1128it [00:07, 155.12it/s]\u001b[A\n",
            "RBM fitting: 1144it [00:07, 155.88it/s]\u001b[A\n",
            "RBM fitting: 1160it [00:07, 156.99it/s]\u001b[A\n",
            "RBM fitting: 1176it [00:07, 148.53it/s]\u001b[A\n",
            "RBM fitting: 1191it [00:07, 147.08it/s]\u001b[A\n",
            "RBM fitting: 1208it [00:08, 151.97it/s]\u001b[A\n",
            "RBM fitting: 1224it [00:08, 151.58it/s]\u001b[A\n",
            "RBM fitting: 1241it [00:08, 155.05it/s]\u001b[A\n",
            "RBM fitting: 1257it [00:08, 156.27it/s]\u001b[A\n",
            "RBM fitting: 1274it [00:08, 157.96it/s]\u001b[A\n",
            "RBM fitting: 1290it [00:08, 157.34it/s]\u001b[A\n",
            "RBM fitting: 1306it [00:08, 146.04it/s]\u001b[A\n",
            "RBM fitting: 1321it [00:08, 146.01it/s]\u001b[A\n",
            "RBM fitting: 1337it [00:08, 148.74it/s]\u001b[A\n",
            "RBM fitting: 1353it [00:08, 151.55it/s]\u001b[A\n",
            "RBM fitting: 1369it [00:09, 151.44it/s]\u001b[A\n",
            "RBM fitting: 1385it [00:09, 152.74it/s]\u001b[A\n",
            "RBM fitting: 1401it [00:09, 152.86it/s]\u001b[A\n",
            "RBM fitting: 1417it [00:09, 154.50it/s]\u001b[A\n",
            "RBM fitting: 1433it [00:09, 146.36it/s]\u001b[A\n",
            "RBM fitting: 1448it [00:09, 146.82it/s]\u001b[A\n",
            "RBM fitting: 1464it [00:09, 150.33it/s]\u001b[A\n",
            "RBM fitting: 1480it [00:09, 152.91it/s]\u001b[A\n",
            "RBM fitting: 1496it [00:09, 154.42it/s]\u001b[A\n",
            "RBM fitting: 1513it [00:10, 157.48it/s]\u001b[A\n",
            "RBM fitting: 1529it [00:10, 156.30it/s]\u001b[A\n",
            "RBM fitting: 1545it [00:10, 157.11it/s]\u001b[A\n",
            "RBM fitting: 1561it [00:10, 147.19it/s]\u001b[A\n",
            "RBM fitting: 1577it [00:10, 149.27it/s]\u001b[A\n",
            "RBM fitting: 1593it [00:10, 151.57it/s]\u001b[A\n",
            "RBM fitting: 1609it [00:10, 153.87it/s]\u001b[A\n",
            "RBM fitting: 1625it [00:10, 155.03it/s]\u001b[A\n",
            "RBM fitting: 1641it [00:10, 156.23it/s]\u001b[A\n",
            "RBM fitting: 1657it [00:10, 156.15it/s]\u001b[A\n",
            "RBM fitting: 1673it [00:11, 155.87it/s]\u001b[A\n",
            "RBM fitting: 1689it [00:11, 153.55it/s]\u001b[A\n",
            "RBM fitting: 1705it [00:11, 144.43it/s]\u001b[A\n",
            "RBM fitting: 1720it [00:11, 142.22it/s]\u001b[A\n",
            "RBM fitting: 1736it [00:11, 146.43it/s]\u001b[A\n",
            "RBM fitting: 1753it [00:11, 151.43it/s]\u001b[A\n",
            "RBM fitting: 1770it [00:11, 154.47it/s]\u001b[A\n",
            "RBM fitting: 1786it [00:11, 155.51it/s]\u001b[A\n",
            "RBM fitting: 1802it [00:11, 156.55it/s]\u001b[A\n",
            "RBM fitting: 1819it [00:12, 157.91it/s]\u001b[A\n",
            "RBM fitting: 1835it [00:12, 147.44it/s]\u001b[A\n",
            "RBM fitting: 1850it [00:12, 146.45it/s]\u001b[A\n",
            "RBM fitting: 1866it [00:12, 149.61it/s]\u001b[A\n",
            "RBM fitting: 1882it [00:12, 147.44it/s]\u001b[A\n",
            "RBM fitting: 1898it [00:12, 150.87it/s]\u001b[A\n",
            "RBM fitting: 1914it [00:12, 153.19it/s]\u001b[A\n",
            "RBM fitting: 1930it [00:12, 152.72it/s]\u001b[A\n",
            "RBM fitting: 1946it [00:12, 154.39it/s]\u001b[A\n",
            "RBM fitting: 1962it [00:12, 145.44it/s]\u001b[A\n",
            "RBM fitting: 1978it [00:13, 147.89it/s]\u001b[A\n",
            "RBM fitting: 1994it [00:13, 149.16it/s]\u001b[A\n",
            "RBM fitting: 2010it [00:13, 152.13it/s]\u001b[A\n",
            "RBM fitting: 2027it [00:13, 154.83it/s]\u001b[A\n",
            "RBM fitting: 2044it [00:13, 157.00it/s]\u001b[A\n",
            "RBM fitting: 2061it [00:13, 158.88it/s]\u001b[A\n",
            "RBM fitting: 2078it [00:13, 159.97it/s]\u001b[A\n",
            "RBM fitting: 2095it [00:13, 150.09it/s]\u001b[A\n",
            "RBM fitting: 2111it [00:13, 151.48it/s]\u001b[A\n",
            "RBM fitting: 2128it [00:14, 154.77it/s]\u001b[A\n",
            "RBM fitting: 2144it [00:14, 154.50it/s]\u001b[A\n",
            "RBM fitting: 2160it [00:14, 155.78it/s]\u001b[A\n",
            "RBM fitting: 2176it [00:14, 156.83it/s]\u001b[A\n",
            "RBM fitting: 2192it [00:14, 155.53it/s]\u001b[A\n",
            "RBM fitting: 2209it [00:14, 157.60it/s]\u001b[A\n",
            "RBM fitting: 2225it [00:14, 149.08it/s]\u001b[A\n",
            "RBM fitting: 2241it [00:14, 148.78it/s]\u001b[A\n",
            "RBM fitting: 2257it [00:14, 151.86it/s]\u001b[A\n",
            "RBM fitting: 2274it [00:14, 154.85it/s]\u001b[A\n",
            "RBM fitting: 2291it [00:15, 157.92it/s]\u001b[A\n",
            "RBM fitting: 2307it [00:15, 156.91it/s]\u001b[A\n",
            "RBM fitting: 2324it [00:15, 158.59it/s]\u001b[A\n",
            "RBM fitting: 2340it [00:15, 158.79it/s]\u001b[A\n",
            "RBM fitting: 2356it [00:15, 149.57it/s]\u001b[A\n",
            "RBM fitting: 2372it [00:15, 150.14it/s]\u001b[A\n",
            "RBM fitting: 2389it [00:15, 153.92it/s]\u001b[A\n",
            "RBM fitting: 2406it [00:15, 156.50it/s]\u001b[A\n",
            "RBM fitting: 2422it [00:15, 157.48it/s]\u001b[A\n",
            "RBM fitting: 2438it [00:16, 157.88it/s]\u001b[A\n",
            "RBM fitting: 2454it [00:16, 155.19it/s]\u001b[A\n",
            "RBM fitting: 2470it [00:16, 154.05it/s]\u001b[A\n",
            "RBM fitting: 2486it [00:16, 146.55it/s]\u001b[A\n",
            "RBM fitting: 2501it [00:16, 147.55it/s]\u001b[A\n",
            "RBM fitting: 2518it [00:16, 151.46it/s]\u001b[A\n",
            "RBM fitting: 2535it [00:16, 154.52it/s]\u001b[A\n",
            "RBM fitting: 2551it [00:16, 155.99it/s]\u001b[A\n",
            "RBM fitting: 2567it [00:16, 156.63it/s]\u001b[A\n",
            "RBM fitting: 2583it [00:16, 157.40it/s]\u001b[A\n",
            "RBM fitting: 2599it [00:17, 157.28it/s]\u001b[A\n",
            "RBM fitting: 2615it [00:17, 141.13it/s]\u001b[A\n",
            "RBM fitting: 2631it [00:17, 146.00it/s]\u001b[A\n",
            "RBM fitting: 2647it [00:17, 149.93it/s]\u001b[A\n",
            "RBM fitting: 2664it [00:17, 153.37it/s]\u001b[A\n",
            "RBM fitting: 2681it [00:17, 156.22it/s]\u001b[A\n",
            "RBM fitting: 2698it [00:17, 157.91it/s]\u001b[A\n",
            "RBM fitting: 2714it [00:17, 157.42it/s]\u001b[A\n",
            "RBM fitting: 2730it [00:17, 158.03it/s]\u001b[A\n",
            "RBM fitting: 2746it [00:18, 149.81it/s]\u001b[A\n",
            "RBM fitting: 2762it [00:18, 151.40it/s]\u001b[A\n",
            "RBM fitting: 2778it [00:18, 151.44it/s]\u001b[A\n",
            "RBM fitting: 2795it [00:18, 154.56it/s]\u001b[A\n",
            "RBM fitting: 2811it [00:18, 154.32it/s]\u001b[A\n",
            "RBM fitting: 2827it [00:18, 153.76it/s]\u001b[A\n",
            "RBM fitting: 2843it [00:18, 155.25it/s]\u001b[A\n",
            "RBM fitting: 2860it [00:18, 157.86it/s]\u001b[A\n",
            "RBM fitting: 2876it [00:18, 148.74it/s]\u001b[A\n",
            "RBM fitting: 2892it [00:19, 151.50it/s]\u001b[A\n",
            "RBM fitting: 2909it [00:19, 155.95it/s]\u001b[A\n",
            "RBM fitting: 2925it [00:19, 156.18it/s]\u001b[A\n",
            "RBM fitting: 2942it [00:19, 159.18it/s]\u001b[A\n",
            "RBM fitting: 2959it [00:19, 160.13it/s]\u001b[A\n",
            "RBM fitting: 2976it [00:19, 159.99it/s]\u001b[A\n",
            "RBM fitting: 2993it [00:19, 160.95it/s]\u001b[A\n",
            "RBM fitting: 3010it [00:19, 151.48it/s]\u001b[A\n",
            "RBM fitting: 3026it [00:19, 152.14it/s]\u001b[A\n",
            "RBM fitting: 3043it [00:19, 156.14it/s]\u001b[A\n",
            "RBM fitting: 3060it [00:20, 157.97it/s]\u001b[A\n",
            "RBM fitting: 3077it [00:20, 160.07it/s]\u001b[A\n",
            "RBM fitting: 3094it [00:20, 158.49it/s]\u001b[A\n",
            "RBM fitting: 3111it [00:20, 160.64it/s]\u001b[A\n",
            "RBM fitting: 3128it [00:20, 161.36it/s]\u001b[A\n",
            "RBM fitting: 3145it [00:20, 152.31it/s]\u001b[A\n",
            "RBM fitting: 3161it [00:20, 152.68it/s]\u001b[A\n",
            "RBM fitting: 3178it [00:20, 156.15it/s]\u001b[A\n",
            "RBM fitting: 3194it [00:20, 156.77it/s]\u001b[A\n",
            "RBM fitting: 3211it [00:21, 159.70it/s]\u001b[A\n",
            "RBM fitting: 3228it [00:21, 161.39it/s]\u001b[A\n",
            "RBM fitting: 3245it [00:21, 157.70it/s]\u001b[A\n",
            "RBM fitting: 3262it [00:21, 159.85it/s]\u001b[A\n",
            "RBM fitting: 3279it [00:21, 147.91it/s]\u001b[A\n",
            "RBM fitting: 3296it [00:21, 153.06it/s]\u001b[A\n",
            "RBM fitting: 3313it [00:21, 156.16it/s]\u001b[A\n",
            "RBM fitting: 3330it [00:21, 158.43it/s]\u001b[A\n",
            "RBM fitting: 3346it [00:21, 158.26it/s]\u001b[A\n",
            "RBM fitting: 3363it [00:22, 160.66it/s]\u001b[A\n",
            "RBM fitting: 3380it [00:22, 161.24it/s]\u001b[A\n",
            "RBM fitting: 3397it [00:22, 162.71it/s]\u001b[A\n",
            "RBM fitting: 3414it [00:22, 151.29it/s]\u001b[A\n",
            "RBM fitting: 3430it [00:22, 149.40it/s]\u001b[A\n",
            "RBM fitting: 3447it [00:22, 153.88it/s]\u001b[A\n",
            "RBM fitting: 3464it [00:22, 157.05it/s]\u001b[A\n",
            "RBM fitting: 3481it [00:22, 158.88it/s]\u001b[A\n",
            "RBM fitting: 3497it [00:22, 159.01it/s]\u001b[A\n",
            "RBM fitting: 3513it [00:22, 158.41it/s]\u001b[A\n",
            "RBM fitting: 3530it [00:23, 160.72it/s]\u001b[A\n",
            "RBM fitting: 3547it [00:23, 145.51it/s]\u001b[A\n",
            "RBM fitting: 3562it [00:23, 145.90it/s]\u001b[A\n",
            "RBM fitting: 3579it [00:23, 150.27it/s]\u001b[A\n",
            "RBM fitting: 3596it [00:23, 153.71it/s]\u001b[A\n",
            "RBM fitting: 3613it [00:23, 156.53it/s]\u001b[A\n",
            "RBM fitting: 3629it [00:23, 157.04it/s]\u001b[A\n",
            "RBM fitting: 3645it [00:23, 157.67it/s]\u001b[A\n",
            "RBM fitting: 3661it [00:23, 157.31it/s]\u001b[A\n",
            "RBM fitting: 3677it [00:24, 148.39it/s]\u001b[A\n",
            "RBM fitting: 3692it [00:24, 147.94it/s]\u001b[A\n",
            "RBM fitting: 3709it [00:24, 151.82it/s]\u001b[A\n",
            "RBM fitting: 3725it [00:24, 151.71it/s]\u001b[A\n",
            "RBM fitting: 3742it [00:24, 154.83it/s]\u001b[A\n",
            "RBM fitting: 3758it [00:24, 154.96it/s]\u001b[A\n",
            "RBM fitting: 3775it [00:24, 157.01it/s]\u001b[A\n",
            "RBM fitting: 3791it [00:24, 157.85it/s]\u001b[A\n",
            "RBM fitting: 3807it [00:24, 147.90it/s]\u001b[A\n",
            "RBM fitting: 3822it [00:24, 147.96it/s]\u001b[A\n",
            "RBM fitting: 3838it [00:25, 150.63it/s]\u001b[A\n",
            "RBM fitting: 3854it [00:25, 152.72it/s]\u001b[A\n",
            "RBM fitting: 3871it [00:25, 155.30it/s]\u001b[A\n",
            "RBM fitting: 3887it [00:25, 156.22it/s]\u001b[A\n",
            "RBM fitting: 3904it [00:25, 158.92it/s]\u001b[A\n",
            "RBM fitting: 3921it [00:25, 160.97it/s]\u001b[A\n",
            "RBM fitting: 3938it [00:25, 150.77it/s]\u001b[A\n",
            "RBM fitting: 3954it [00:25, 150.95it/s]\u001b[A\n",
            "RBM fitting: 3971it [00:25, 154.48it/s]\u001b[A\n",
            "RBM fitting: 3988it [00:26, 157.91it/s]\u001b[A\n",
            "RBM fitting: 4005it [00:26, 160.24it/s]\u001b[A\n",
            "RBM fitting: 4022it [00:26, 159.76it/s]\u001b[A\n",
            "RBM fitting: 4039it [00:26, 159.10it/s]\u001b[A\n",
            "RBM fitting: 4056it [00:26, 160.60it/s]\u001b[A\n",
            "RBM fitting: 4073it [00:26, 150.82it/s]\u001b[A\n",
            "RBM fitting: 4089it [00:26, 152.62it/s]\u001b[A\n",
            "RBM fitting: 4106it [00:26, 155.57it/s]\u001b[A\n",
            "RBM fitting: 4122it [00:26, 156.62it/s]\u001b[A\n",
            "RBM fitting: 4139it [00:27, 159.32it/s]\u001b[A\n",
            "RBM fitting: 4156it [00:27, 161.85it/s]\u001b[A\n",
            "RBM fitting: 4173it [00:27, 163.32it/s]\u001b[A\n",
            "RBM fitting: 4190it [00:27, 163.22it/s]\u001b[A\n",
            "RBM fitting: 4207it [00:27, 150.86it/s]\u001b[A\n",
            "RBM fitting: 4223it [00:27, 149.59it/s]\u001b[A\n",
            "RBM fitting: 4239it [00:27, 150.47it/s]\u001b[A\n",
            "RBM fitting: 4255it [00:27, 153.01it/s]\u001b[A\n",
            "RBM fitting: 4271it [00:27, 149.41it/s]\u001b[A\n",
            "RBM fitting: 4288it [00:27, 153.34it/s]\u001b[A\n",
            "RBM fitting: 4305it [00:28, 156.77it/s]\u001b[A\n",
            "RBM fitting: 4321it [00:28, 157.45it/s]\u001b[A\n",
            "RBM fitting: 4337it [00:28, 147.89it/s]\u001b[A\n",
            "RBM fitting: 4352it [00:28, 144.15it/s]\u001b[A\n",
            "RBM fitting: 4369it [00:28, 149.19it/s]\u001b[A\n",
            "RBM fitting: 4386it [00:28, 153.11it/s]\u001b[A\n",
            "RBM fitting: 4402it [00:28, 154.66it/s]\u001b[A\n",
            "RBM fitting: 4418it [00:28, 154.49it/s]\u001b[A\n",
            "RBM fitting: 4434it [00:28, 155.09it/s]\u001b[A\n",
            "RBM fitting: 4451it [00:29, 157.73it/s]\u001b[A\n",
            "RBM fitting: 4467it [00:29, 149.39it/s]\u001b[A\n",
            "RBM fitting: 4483it [00:29, 151.14it/s]\u001b[A\n",
            "RBM fitting: 4499it [00:29, 149.73it/s]\u001b[A\n",
            "RBM fitting: 4515it [00:29, 146.81it/s]\u001b[A\n",
            "RBM fitting: 4532it [00:29, 151.67it/s]\u001b[A\n",
            "RBM fitting: 4549it [00:29, 155.90it/s]\u001b[A\n",
            "RBM fitting: 4566it [00:29, 158.48it/s]\u001b[A\n",
            "RBM fitting: 4583it [00:29, 160.19it/s]\u001b[A\n",
            "RBM fitting: 4600it [00:30, 161.60it/s]\u001b[A\n",
            "RBM fitting: 4617it [00:30, 151.80it/s]\u001b[A\n",
            "RBM fitting: 4633it [00:30, 153.68it/s]\u001b[A\n",
            "RBM fitting: 4650it [00:30, 156.80it/s]\u001b[A\n",
            "RBM fitting: 4666it [00:30, 155.43it/s]\u001b[A\n",
            "RBM fitting: 4683it [00:30, 158.60it/s]\u001b[A\n",
            "RBM fitting: 4700it [00:30, 160.53it/s]\u001b[A\n",
            "RBM fitting: 4717it [00:30, 163.18it/s]\u001b[A\n",
            "RBM fitting: 4734it [00:30, 161.96it/s]\u001b[A\n",
            "RBM fitting: 4751it [00:30, 152.95it/s]\u001b[A\n",
            "RBM fitting: 4767it [00:31, 153.47it/s]\u001b[A\n",
            "RBM fitting: 4784it [00:31, 156.98it/s]\u001b[A\n",
            "RBM fitting: 4801it [00:31, 158.90it/s]\u001b[A\n",
            "RBM fitting: 4818it [00:31, 159.63it/s]\u001b[A\n",
            "RBM fitting: 4835it [00:31, 158.43it/s]\u001b[A\n",
            "RBM fitting: 4851it [00:31, 158.85it/s]\u001b[A\n",
            "RBM fitting: 4867it [00:31, 159.13it/s]\u001b[A\n",
            "RBM fitting: 4883it [00:31, 150.42it/s]\u001b[A\n",
            "RBM fitting: 4899it [00:31, 149.37it/s]\u001b[A\n",
            "RBM fitting: 4916it [00:32, 153.81it/s]\u001b[A\n",
            "RBM fitting: 4933it [00:32, 156.78it/s]\u001b[A\n",
            "RBM fitting: 4949it [00:32, 156.88it/s]\u001b[A\n",
            "RBM fitting: 4966it [00:32, 158.77it/s]\u001b[A\n",
            "RBM fitting: 4982it [00:32, 156.53it/s]\u001b[A\n",
            "RBM fitting: 4999it [00:32, 158.24it/s]\u001b[A\n",
            "RBM fitting: 5015it [00:32, 148.59it/s]\u001b[A\n",
            "RBM fitting: 5031it [00:32, 149.82it/s]\u001b[A\n",
            "RBM fitting: 5047it [00:32, 151.92it/s]\u001b[A\n",
            "RBM fitting: 5064it [00:32, 155.59it/s]\u001b[A\n",
            "RBM fitting: 5081it [00:33, 158.55it/s]\u001b[A\n",
            "RBM fitting: 5098it [00:33, 159.76it/s]\u001b[A\n",
            "RBM fitting: 5115it [00:33, 158.92it/s]\u001b[A\n",
            "RBM fitting: 5132it [00:33, 160.59it/s]\u001b[A\n",
            "RBM fitting: 5149it [00:33, 147.74it/s]\u001b[A\n",
            "RBM fitting: 5164it [00:33, 148.08it/s]\u001b[A\n",
            "RBM fitting: 5181it [00:33, 152.21it/s]\u001b[A\n",
            "RBM fitting: 5197it [00:33, 152.04it/s]\u001b[A\n",
            "RBM fitting: 5214it [00:33, 154.96it/s]\u001b[A\n",
            "RBM fitting: 5231it [00:34, 158.46it/s]\u001b[A\n",
            "RBM fitting: 5248it [00:34, 160.73it/s]\u001b[A\n",
            "RBM fitting: 5265it [00:34, 161.79it/s]\u001b[A\n",
            "RBM fitting: 5282it [00:34, 151.63it/s]\u001b[A\n",
            "RBM fitting: 5298it [00:34, 149.50it/s]\u001b[A\n",
            "RBM fitting: 5315it [00:34, 153.89it/s]\u001b[A\n",
            "RBM fitting: 5332it [00:34, 156.60it/s]\u001b[A\n",
            "RBM fitting: 5348it [00:34, 156.86it/s]\u001b[A\n",
            "RBM fitting: 5364it [00:34, 156.63it/s]\u001b[A\n",
            "RBM fitting: 5381it [00:35, 158.54it/s]\u001b[A\n",
            "RBM fitting: 5398it [00:35, 160.37it/s]\u001b[A\n",
            "RBM fitting: 5415it [00:35, 150.64it/s]\u001b[A\n",
            "RBM fitting: 5431it [00:35, 150.23it/s]\u001b[A\n",
            "RBM fitting: 5447it [00:35, 151.45it/s]\u001b[A\n",
            "RBM fitting: 5463it [00:35, 151.38it/s]\u001b[A\n",
            "RBM fitting: 5480it [00:35, 155.05it/s]\u001b[A\n",
            "RBM fitting: 5497it [00:35, 157.54it/s]\u001b[A\n",
            "RBM fitting: 5514it [00:35, 159.00it/s]\u001b[A\n",
            "RBM fitting: 5531it [00:35, 160.36it/s]\u001b[A\n",
            "RBM fitting: 5548it [00:36, 151.71it/s]\u001b[A\n",
            "RBM fitting: 5564it [00:36, 151.67it/s]\u001b[A\n",
            "RBM fitting: 5580it [00:36, 153.36it/s]\u001b[A\n",
            "RBM fitting: 5596it [00:36, 154.39it/s]\u001b[A\n",
            "RBM fitting: 5612it [00:36, 153.97it/s]\u001b[A\n",
            "RBM fitting: 5629it [00:36, 157.54it/s]\u001b[A\n",
            "RBM fitting: 5646it [00:36, 159.86it/s]\u001b[A\n",
            "RBM fitting: 5663it [00:36, 161.02it/s]\u001b[A\n",
            "RBM fitting: 5680it [00:36, 149.56it/s]\u001b[A\n",
            "RBM fitting: 5697it [00:37, 153.11it/s]\u001b[A\n",
            "RBM fitting: 5713it [00:37, 153.11it/s]\u001b[A\n",
            "RBM fitting: 5729it [00:37, 151.80it/s]\u001b[A\n",
            "RBM fitting: 5746it [00:37, 154.97it/s]\u001b[A\n",
            "RBM fitting: 5762it [00:37, 156.43it/s]\u001b[A\n",
            "RBM fitting: 5778it [00:37, 155.82it/s]\u001b[A\n",
            "RBM fitting: 5795it [00:37, 159.54it/s]\u001b[A\n",
            "RBM fitting: 5812it [00:37, 150.89it/s]\u001b[A\n",
            "RBM fitting: 5828it [00:37, 150.92it/s]\u001b[A\n",
            "RBM fitting: 5845it [00:38, 155.34it/s]\u001b[A\n",
            "RBM fitting: 5862it [00:38, 157.75it/s]\u001b[A\n",
            "RBM fitting: 5879it [00:38, 160.92it/s]\u001b[A\n",
            "RBM fitting: 5896it [00:38, 162.29it/s]\u001b[A\n",
            "RBM fitting: 5913it [00:38, 163.25it/s]\u001b[A\n",
            "RBM fitting: 5930it [00:38, 163.68it/s]\u001b[A\n",
            "RBM fitting: 5947it [00:38, 150.87it/s]\u001b[A\n",
            "RBM fitting: 5963it [00:38, 150.34it/s]\u001b[A\n",
            "RBM fitting: 5979it [00:38, 151.08it/s]\u001b[A\n",
            "RBM fitting: 6000it [00:39, 153.74it/s]\n",
            "Epoch:30 ,avg_cost = 0.3366420865058899 ,std_cost = 0.03308003023266792 ,avg_grad = 10090.6728515625 ,std_grad = 537.0645751953125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "dooEe4mvK1_x",
        "outputId": "644354ee-9245-47f6-fe78-e544d3e1223f"
      },
      "source": [
        "learned_weights = rbm_mnist.W.transpose(0,1).numpy()\r\n",
        "plt.show()\r\n",
        "fig = plt.figure(3, figsize=(10,10))\r\n",
        "for i in range(25): \r\n",
        "    sub = fig.add_subplot(5, 5, i+1)\r\n",
        "    sub.imshow(learned_weights[i,:].reshape((28,28)), cmap=plt.cm.gray)\r\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEq9-WVQK73D",
        "outputId": "b57a04ea-a382-44de-fb42-a997e0ea9bf8"
      },
      "source": [
        "#This is an unsupervised learning algorithm. So let us try training on one particular number.But first\r\n",
        "# we need to seperate the data.\r\n",
        "\r\n",
        "number = 5 #A number between 0 and 10.\r\n",
        "\r\n",
        "particular_mnist = []\r\n",
        "\r\n",
        "limit = mnist_data.train_data.shape[0]\r\n",
        "# limit = 60000\r\n",
        "for i in range(limit):\r\n",
        "    if(mnist_data.train_labels[i] == number):\r\n",
        "        particular_mnist.append(mnist_data.train_data[i].numpy())\r\n",
        "# particular_mnist = np.array(particular_mnist)\r\n",
        "len(particular_mnist)\r\n",
        "# mnist_data.train_data"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n",
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5421"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXDdLZ0ZK8SD"
      },
      "source": [
        "tensor_x = torch.stack([torch.Tensor(i) for i in particular_mnist]).type(torch.FloatTensor)\r\n",
        "tensor_y = torch.stack([torch.Tensor(number) for i in range(len(particular_mnist))]).type(torch.FloatTensor)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQkgztFKK-io"
      },
      "source": [
        "mnist_particular_dataset = torch.utils.data.TensorDataset(tensor_x,tensor_y)\r\n",
        "mnist_particular_dataloader = torch.utils.data.DataLoader(mnist_particular_dataset,batch_size = batch_size,drop_last=True,num_workers=0)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qutJdBQLAUy",
        "outputId": "c484f860-358b-424b-fc87-75a5f8eba8a4"
      },
      "source": [
        "visible_units=28*28\r\n",
        "hidden_units = 500\r\n",
        "k=3\r\n",
        "learning_rate=0.01\r\n",
        "learning_rate_decay = False\r\n",
        "xavier_init = True\r\n",
        "increase_to_cd_k = False\r\n",
        "use_gpu = False\r\n",
        "\r\n",
        "\r\n",
        "rbm_mnist = RBM(visible_units,hidden_units,k ,learning_rate,learning_rate_decay,xavier_init,\r\n",
        "                increase_to_cd_k,use_gpu)\r\n",
        "\r\n",
        "\r\n",
        "epochs = 10\r\n",
        "\r\n",
        "rbm_mnist.train(mnist_particular_dataloader , epochs)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 17it [00:00, 160.82it/s]\u001b[A\n",
            "RBM fitting: 34it [00:00, 162.80it/s]\u001b[A\n",
            "RBM fitting: 51it [00:00, 164.08it/s]\u001b[A\n",
            "RBM fitting: 68it [00:00, 164.69it/s]\u001b[A\n",
            "RBM fitting: 85it [00:00, 165.55it/s]\u001b[A\n",
            "RBM fitting: 103it [00:00, 167.53it/s]\u001b[A\n",
            "RBM fitting: 120it [00:00, 166.03it/s]\u001b[A\n",
            "RBM fitting: 137it [00:00, 166.61it/s]\u001b[A\n",
            "RBM fitting: 154it [00:00, 166.81it/s]\u001b[A\n",
            "RBM fitting: 171it [00:01, 166.57it/s]\u001b[A\n",
            "RBM fitting: 189it [00:01, 168.41it/s]\u001b[A\n",
            "RBM fitting: 207it [00:01, 169.84it/s]\u001b[A\n",
            "RBM fitting: 225it [00:01, 170.63it/s]\u001b[A\n",
            "RBM fitting: 243it [00:01, 171.27it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 169.90it/s]\u001b[A\n",
            "RBM fitting: 278it [00:01, 168.78it/s]\u001b[A\n",
            "RBM fitting: 295it [00:01, 164.32it/s]\u001b[A\n",
            "RBM fitting: 312it [00:01, 165.32it/s]\u001b[A\n",
            "RBM fitting: 329it [00:01, 166.15it/s]\u001b[A\n",
            "RBM fitting: 346it [00:02, 167.19it/s]\u001b[A\n",
            "RBM fitting: 363it [00:02, 165.90it/s]\u001b[A\n",
            "RBM fitting: 380it [00:02, 166.85it/s]\u001b[A\n",
            "RBM fitting: 398it [00:02, 168.75it/s]\u001b[A\n",
            "RBM fitting: 416it [00:02, 169.65it/s]\u001b[A\n",
            "RBM fitting: 433it [00:02, 167.58it/s]\u001b[A\n",
            "RBM fitting: 450it [00:02, 167.05it/s]\u001b[A\n",
            "RBM fitting: 467it [00:02, 166.38it/s]\u001b[A\n",
            "RBM fitting: 484it [00:02, 166.47it/s]\u001b[A\n",
            "RBM fitting: 501it [00:02, 166.97it/s]\u001b[A\n",
            "RBM fitting: 518it [00:03, 166.92it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 167.37it/s]\n",
            "Epoch:1 ,avg_cost = 0.7555854916572571 ,std_cost = 0.16582675278186798 ,avg_grad = 9999.982421875 ,std_grad = 3281.38623046875\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 17it [00:00, 168.43it/s]\u001b[A\n",
            "RBM fitting: 35it [00:00, 169.95it/s]\u001b[A\n",
            "RBM fitting: 52it [00:00, 167.85it/s]\u001b[A\n",
            "RBM fitting: 70it [00:00, 169.44it/s]\u001b[A\n",
            "RBM fitting: 87it [00:00, 167.65it/s]\u001b[A\n",
            "RBM fitting: 105it [00:00, 169.86it/s]\u001b[A\n",
            "RBM fitting: 122it [00:00, 168.15it/s]\u001b[A\n",
            "RBM fitting: 140it [00:00, 170.68it/s]\u001b[A\n",
            "RBM fitting: 158it [00:00, 171.86it/s]\u001b[A\n",
            "RBM fitting: 176it [00:01, 172.52it/s]\u001b[A\n",
            "RBM fitting: 193it [00:01, 169.83it/s]\u001b[A\n",
            "RBM fitting: 210it [00:01, 168.15it/s]\u001b[A\n",
            "RBM fitting: 227it [00:01, 164.99it/s]\u001b[A\n",
            "RBM fitting: 244it [00:01, 166.04it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 162.44it/s]\u001b[A\n",
            "RBM fitting: 278it [00:01, 160.38it/s]\u001b[A\n",
            "RBM fitting: 296it [00:01, 163.92it/s]\u001b[A\n",
            "RBM fitting: 314it [00:01, 167.21it/s]\u001b[A\n",
            "RBM fitting: 332it [00:01, 169.53it/s]\u001b[A\n",
            "RBM fitting: 349it [00:02, 164.75it/s]\u001b[A\n",
            "RBM fitting: 367it [00:02, 166.63it/s]\u001b[A\n",
            "RBM fitting: 385it [00:02, 168.33it/s]\u001b[A\n",
            "RBM fitting: 402it [00:02, 168.82it/s]\u001b[A\n",
            "RBM fitting: 419it [00:02, 165.43it/s]\u001b[A\n",
            "RBM fitting: 437it [00:02, 167.08it/s]\u001b[A\n",
            "RBM fitting: 454it [00:02, 167.39it/s]\u001b[A\n",
            "RBM fitting: 471it [00:02, 163.89it/s]\u001b[A\n",
            "RBM fitting: 488it [00:02, 160.83it/s]\u001b[A\n",
            "RBM fitting: 505it [00:03, 161.33it/s]\u001b[A\n",
            "RBM fitting: 522it [00:03, 161.57it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 165.84it/s]\n",
            "Epoch:2 ,avg_cost = 0.6143175959587097 ,std_cost = 0.0584806427359581 ,avg_grad = 7901.9482421875 ,std_grad = 443.58709716796875\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 17it [00:00, 164.91it/s]\u001b[A\n",
            "RBM fitting: 35it [00:00, 166.98it/s]\u001b[A\n",
            "RBM fitting: 52it [00:00, 165.47it/s]\u001b[A\n",
            "RBM fitting: 70it [00:00, 166.98it/s]\u001b[A\n",
            "RBM fitting: 88it [00:00, 167.94it/s]\u001b[A\n",
            "RBM fitting: 104it [00:00, 165.11it/s]\u001b[A\n",
            "RBM fitting: 122it [00:00, 166.79it/s]\u001b[A\n",
            "RBM fitting: 140it [00:00, 168.43it/s]\u001b[A\n",
            "RBM fitting: 157it [00:00, 167.77it/s]\u001b[A\n",
            "RBM fitting: 174it [00:01, 166.72it/s]\u001b[A\n",
            "RBM fitting: 192it [00:01, 168.20it/s]\u001b[A\n",
            "RBM fitting: 210it [00:01, 168.49it/s]\u001b[A\n",
            "RBM fitting: 228it [00:01, 169.67it/s]\u001b[A\n",
            "RBM fitting: 245it [00:01, 168.33it/s]\u001b[A\n",
            "RBM fitting: 262it [00:01, 167.53it/s]\u001b[A\n",
            "RBM fitting: 280it [00:01, 168.99it/s]\u001b[A\n",
            "RBM fitting: 298it [00:01, 170.41it/s]\u001b[A\n",
            "RBM fitting: 316it [00:01, 168.54it/s]\u001b[A\n",
            "RBM fitting: 333it [00:01, 168.93it/s]\u001b[A\n",
            "RBM fitting: 351it [00:02, 169.76it/s]\u001b[A\n",
            "RBM fitting: 369it [00:02, 170.23it/s]\u001b[A\n",
            "RBM fitting: 387it [00:02, 167.48it/s]\u001b[A\n",
            "RBM fitting: 404it [00:02, 165.82it/s]\u001b[A\n",
            "RBM fitting: 421it [00:02, 166.99it/s]\u001b[A\n",
            "RBM fitting: 438it [00:02, 167.24it/s]\u001b[A\n",
            "RBM fitting: 455it [00:02, 167.41it/s]\u001b[A\n",
            "RBM fitting: 472it [00:02, 165.22it/s]\u001b[A\n",
            "RBM fitting: 490it [00:02, 167.49it/s]\u001b[A\n",
            "RBM fitting: 507it [00:03, 167.95it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 168.14it/s]\n",
            "Epoch:3 ,avg_cost = 0.5567048788070679 ,std_cost = 0.05134524777531624 ,avg_grad = 7372.70849609375 ,std_grad = 378.0453186035156\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 16it [00:00, 155.97it/s]\u001b[A\n",
            "RBM fitting: 33it [00:00, 159.82it/s]\u001b[A\n",
            "RBM fitting: 51it [00:00, 164.41it/s]\u001b[A\n",
            "RBM fitting: 69it [00:00, 166.57it/s]\u001b[A\n",
            "RBM fitting: 86it [00:00, 167.13it/s]\u001b[A\n",
            "RBM fitting: 103it [00:00, 167.29it/s]\u001b[A\n",
            "RBM fitting: 121it [00:00, 169.73it/s]\u001b[A\n",
            "RBM fitting: 138it [00:00, 169.70it/s]\u001b[A\n",
            "RBM fitting: 155it [00:00, 169.65it/s]\u001b[A\n",
            "RBM fitting: 172it [00:01, 164.48it/s]\u001b[A\n",
            "RBM fitting: 189it [00:01, 162.73it/s]\u001b[A\n",
            "RBM fitting: 206it [00:01, 164.53it/s]\u001b[A\n",
            "RBM fitting: 224it [00:01, 166.71it/s]\u001b[A\n",
            "RBM fitting: 241it [00:01, 165.21it/s]\u001b[A\n",
            "RBM fitting: 258it [00:01, 165.99it/s]\u001b[A\n",
            "RBM fitting: 275it [00:01, 166.08it/s]\u001b[A\n",
            "RBM fitting: 293it [00:01, 167.88it/s]\u001b[A\n",
            "RBM fitting: 311it [00:01, 169.20it/s]\u001b[A\n",
            "RBM fitting: 328it [00:01, 169.19it/s]\u001b[A\n",
            "RBM fitting: 346it [00:02, 170.16it/s]\u001b[A\n",
            "RBM fitting: 364it [00:02, 167.85it/s]\u001b[A\n",
            "RBM fitting: 382it [00:02, 169.68it/s]\u001b[A\n",
            "RBM fitting: 400it [00:02, 171.14it/s]\u001b[A\n",
            "RBM fitting: 418it [00:02, 169.69it/s]\u001b[A\n",
            "RBM fitting: 435it [00:02, 169.42it/s]\u001b[A\n",
            "RBM fitting: 453it [00:02, 170.83it/s]\u001b[A\n",
            "RBM fitting: 471it [00:02, 168.75it/s]\u001b[A\n",
            "RBM fitting: 488it [00:02, 163.99it/s]\u001b[A\n",
            "RBM fitting: 505it [00:03, 160.57it/s]\u001b[A\n",
            "RBM fitting: 522it [00:03, 156.30it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 165.62it/s]\n",
            "Epoch:4 ,avg_cost = 0.5265102982521057 ,std_cost = 0.04821872338652611 ,avg_grad = 7074.17333984375 ,std_grad = 348.0127868652344\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 15it [00:00, 147.76it/s]\u001b[A\n",
            "RBM fitting: 31it [00:00, 148.83it/s]\u001b[A\n",
            "RBM fitting: 47it [00:00, 149.39it/s]\u001b[A\n",
            "RBM fitting: 63it [00:00, 150.63it/s]\u001b[A\n",
            "RBM fitting: 78it [00:00, 149.75it/s]\u001b[A\n",
            "RBM fitting: 94it [00:00, 150.64it/s]\u001b[A\n",
            "RBM fitting: 109it [00:00, 148.82it/s]\u001b[A\n",
            "RBM fitting: 125it [00:00, 149.79it/s]\u001b[A\n",
            "RBM fitting: 140it [00:00, 147.30it/s]\u001b[A\n",
            "RBM fitting: 156it [00:01, 149.45it/s]\u001b[A\n",
            "RBM fitting: 172it [00:01, 150.79it/s]\u001b[A\n",
            "RBM fitting: 188it [00:01, 152.95it/s]\u001b[A\n",
            "RBM fitting: 204it [00:01, 152.29it/s]\u001b[A\n",
            "RBM fitting: 220it [00:01, 151.57it/s]\u001b[A\n",
            "RBM fitting: 236it [00:01, 152.57it/s]\u001b[A\n",
            "RBM fitting: 252it [00:01, 153.05it/s]\u001b[A\n",
            "RBM fitting: 268it [00:01, 153.51it/s]\u001b[A\n",
            "RBM fitting: 284it [00:01, 150.49it/s]\u001b[A\n",
            "RBM fitting: 300it [00:01, 150.98it/s]\u001b[A\n",
            "RBM fitting: 316it [00:02, 147.59it/s]\u001b[A\n",
            "RBM fitting: 331it [00:02, 148.09it/s]\u001b[A\n",
            "RBM fitting: 347it [00:02, 148.92it/s]\u001b[A\n",
            "RBM fitting: 363it [00:02, 150.61it/s]\u001b[A\n",
            "RBM fitting: 379it [00:02, 151.28it/s]\u001b[A\n",
            "RBM fitting: 395it [00:02, 146.97it/s]\u001b[A\n",
            "RBM fitting: 411it [00:02, 148.58it/s]\u001b[A\n",
            "RBM fitting: 427it [00:02, 150.09it/s]\u001b[A\n",
            "RBM fitting: 443it [00:02, 148.16it/s]\u001b[A\n",
            "RBM fitting: 458it [00:03, 147.77it/s]\u001b[A\n",
            "RBM fitting: 474it [00:03, 149.33it/s]\u001b[A\n",
            "RBM fitting: 490it [00:03, 150.27it/s]\u001b[A\n",
            "RBM fitting: 506it [00:03, 150.75it/s]\u001b[A\n",
            "RBM fitting: 522it [00:03, 150.51it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 149.85it/s]\n",
            "Epoch:5 ,avg_cost = 0.5057640671730042 ,std_cost = 0.0463857464492321 ,avg_grad = 6878.62939453125 ,std_grad = 329.1927490234375\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 16it [00:00, 153.92it/s]\u001b[A\n",
            "RBM fitting: 32it [00:00, 153.84it/s]\u001b[A\n",
            "RBM fitting: 47it [00:00, 150.78it/s]\u001b[A\n",
            "RBM fitting: 62it [00:00, 150.13it/s]\u001b[A\n",
            "RBM fitting: 78it [00:00, 151.77it/s]\u001b[A\n",
            "RBM fitting: 94it [00:00, 152.75it/s]\u001b[A\n",
            "RBM fitting: 110it [00:00, 153.90it/s]\u001b[A\n",
            "RBM fitting: 125it [00:00, 151.66it/s]\u001b[A\n",
            "RBM fitting: 141it [00:00, 151.69it/s]\u001b[A\n",
            "RBM fitting: 156it [00:01, 150.90it/s]\u001b[A\n",
            "RBM fitting: 171it [00:01, 148.50it/s]\u001b[A\n",
            "RBM fitting: 187it [00:01, 150.11it/s]\u001b[A\n",
            "RBM fitting: 202it [00:01, 149.21it/s]\u001b[A\n",
            "RBM fitting: 218it [00:01, 150.86it/s]\u001b[A\n",
            "RBM fitting: 234it [00:01, 152.41it/s]\u001b[A\n",
            "RBM fitting: 250it [00:01, 154.20it/s]\u001b[A\n",
            "RBM fitting: 266it [00:01, 154.71it/s]\u001b[A\n",
            "RBM fitting: 282it [00:01, 150.22it/s]\u001b[A\n",
            "RBM fitting: 298it [00:01, 152.37it/s]\u001b[A\n",
            "RBM fitting: 314it [00:02, 153.68it/s]\u001b[A\n",
            "RBM fitting: 330it [00:02, 155.13it/s]\u001b[A\n",
            "RBM fitting: 346it [00:02, 153.24it/s]\u001b[A\n",
            "RBM fitting: 362it [00:02, 150.80it/s]\u001b[A\n",
            "RBM fitting: 378it [00:02, 152.88it/s]\u001b[A\n",
            "RBM fitting: 394it [00:02, 154.42it/s]\u001b[A\n",
            "RBM fitting: 410it [00:02, 153.66it/s]\u001b[A\n",
            "RBM fitting: 426it [00:02, 155.03it/s]\u001b[A\n",
            "RBM fitting: 443it [00:02, 156.74it/s]\u001b[A\n",
            "RBM fitting: 459it [00:03, 155.37it/s]\u001b[A\n",
            "RBM fitting: 477it [00:03, 160.19it/s]\u001b[A\n",
            "RBM fitting: 494it [00:03, 162.10it/s]\u001b[A\n",
            "RBM fitting: 511it [00:03, 163.50it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 155.14it/s]\n",
            "Epoch:6 ,avg_cost = 0.4884074926376343 ,std_cost = 0.04558286443352699 ,avg_grad = 6759.3095703125 ,std_grad = 330.5162658691406\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 18it [00:00, 174.28it/s]\u001b[A\n",
            "RBM fitting: 36it [00:00, 173.91it/s]\u001b[A\n",
            "RBM fitting: 54it [00:00, 173.51it/s]\u001b[A\n",
            "RBM fitting: 70it [00:00, 167.93it/s]\u001b[A\n",
            "RBM fitting: 88it [00:00, 168.73it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 169.54it/s]\u001b[A\n",
            "RBM fitting: 123it [00:00, 168.84it/s]\u001b[A\n",
            "RBM fitting: 140it [00:00, 168.74it/s]\u001b[A\n",
            "RBM fitting: 157it [00:00, 167.03it/s]\u001b[A\n",
            "RBM fitting: 175it [00:01, 169.29it/s]\u001b[A\n",
            "RBM fitting: 193it [00:01, 170.54it/s]\u001b[A\n",
            "RBM fitting: 210it [00:01, 166.59it/s]\u001b[A\n",
            "RBM fitting: 227it [00:01, 167.10it/s]\u001b[A\n",
            "RBM fitting: 244it [00:01, 167.69it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 162.33it/s]\u001b[A\n",
            "RBM fitting: 278it [00:01, 162.93it/s]\u001b[A\n",
            "RBM fitting: 295it [00:01, 164.80it/s]\u001b[A\n",
            "RBM fitting: 312it [00:01, 163.88it/s]\u001b[A\n",
            "RBM fitting: 330it [00:01, 166.36it/s]\u001b[A\n",
            "RBM fitting: 348it [00:02, 168.46it/s]\u001b[A\n",
            "RBM fitting: 365it [00:02, 164.79it/s]\u001b[A\n",
            "RBM fitting: 383it [00:02, 167.32it/s]\u001b[A\n",
            "RBM fitting: 400it [00:02, 165.98it/s]\u001b[A\n",
            "RBM fitting: 417it [00:02, 162.78it/s]\u001b[A\n",
            "RBM fitting: 435it [00:02, 165.88it/s]\u001b[A\n",
            "RBM fitting: 453it [00:02, 169.34it/s]\u001b[A\n",
            "RBM fitting: 471it [00:02, 170.11it/s]\u001b[A\n",
            "RBM fitting: 489it [00:02, 166.70it/s]\u001b[A\n",
            "RBM fitting: 506it [00:03, 167.63it/s]\u001b[A\n",
            "RBM fitting: 523it [00:03, 167.19it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 167.03it/s]\n",
            "Epoch:7 ,avg_cost = 0.47435262799263 ,std_cost = 0.04349464550614357 ,avg_grad = 6636.52392578125 ,std_grad = 316.8775939941406\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 17it [00:00, 164.34it/s]\u001b[A\n",
            "RBM fitting: 33it [00:00, 161.41it/s]\u001b[A\n",
            "RBM fitting: 51it [00:00, 164.05it/s]\u001b[A\n",
            "RBM fitting: 67it [00:00, 162.05it/s]\u001b[A\n",
            "RBM fitting: 84it [00:00, 162.83it/s]\u001b[A\n",
            "RBM fitting: 102it [00:00, 164.83it/s]\u001b[A\n",
            "RBM fitting: 119it [00:00, 164.17it/s]\u001b[A\n",
            "RBM fitting: 137it [00:00, 167.36it/s]\u001b[A\n",
            "RBM fitting: 155it [00:00, 169.08it/s]\u001b[A\n",
            "RBM fitting: 172it [00:01, 165.50it/s]\u001b[A\n",
            "RBM fitting: 189it [00:01, 165.91it/s]\u001b[A\n",
            "RBM fitting: 206it [00:01, 165.60it/s]\u001b[A\n",
            "RBM fitting: 223it [00:01, 166.23it/s]\u001b[A\n",
            "RBM fitting: 241it [00:01, 168.54it/s]\u001b[A\n",
            "RBM fitting: 259it [00:01, 169.78it/s]\u001b[A\n",
            "RBM fitting: 276it [00:01, 164.66it/s]\u001b[A\n",
            "RBM fitting: 294it [00:01, 166.74it/s]\u001b[A\n",
            "RBM fitting: 311it [00:01, 167.53it/s]\u001b[A\n",
            "RBM fitting: 328it [00:01, 165.58it/s]\u001b[A\n",
            "RBM fitting: 346it [00:02, 168.12it/s]\u001b[A\n",
            "RBM fitting: 364it [00:02, 169.86it/s]\u001b[A\n",
            "RBM fitting: 382it [00:02, 170.93it/s]\u001b[A\n",
            "RBM fitting: 400it [00:02, 169.82it/s]\u001b[A\n",
            "RBM fitting: 417it [00:02, 167.73it/s]\u001b[A\n",
            "RBM fitting: 435it [00:02, 169.15it/s]\u001b[A\n",
            "RBM fitting: 452it [00:02, 164.00it/s]\u001b[A\n",
            "RBM fitting: 470it [00:02, 167.00it/s]\u001b[A\n",
            "RBM fitting: 487it [00:02, 166.15it/s]\u001b[A\n",
            "RBM fitting: 505it [00:03, 168.05it/s]\u001b[A\n",
            "RBM fitting: 522it [00:03, 166.01it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 166.36it/s]\n",
            "Epoch:8 ,avg_cost = 0.46288061141967773 ,std_cost = 0.04356443136930466 ,avg_grad = 6522.7197265625 ,std_grad = 321.2811279296875\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 16it [00:00, 152.92it/s]\u001b[A\n",
            "RBM fitting: 34it [00:00, 158.35it/s]\u001b[A\n",
            "RBM fitting: 52it [00:00, 161.92it/s]\u001b[A\n",
            "RBM fitting: 69it [00:00, 162.30it/s]\u001b[A\n",
            "RBM fitting: 87it [00:00, 166.02it/s]\u001b[A\n",
            "RBM fitting: 106it [00:00, 170.28it/s]\u001b[A\n",
            "RBM fitting: 124it [00:00, 172.61it/s]\u001b[A\n",
            "RBM fitting: 141it [00:00, 170.75it/s]\u001b[A\n",
            "RBM fitting: 158it [00:00, 168.07it/s]\u001b[A\n",
            "RBM fitting: 176it [00:01, 169.77it/s]\u001b[A\n",
            "RBM fitting: 194it [00:01, 170.66it/s]\u001b[A\n",
            "RBM fitting: 211it [00:01, 169.65it/s]\u001b[A\n",
            "RBM fitting: 229it [00:01, 171.52it/s]\u001b[A\n",
            "RBM fitting: 247it [00:01, 169.26it/s]\u001b[A\n",
            "RBM fitting: 264it [00:01, 169.01it/s]\u001b[A\n",
            "RBM fitting: 281it [00:01, 166.39it/s]\u001b[A\n",
            "RBM fitting: 299it [00:01, 167.98it/s]\u001b[A\n",
            "RBM fitting: 316it [00:01, 167.56it/s]\u001b[A\n",
            "RBM fitting: 334it [00:01, 169.59it/s]\u001b[A\n",
            "RBM fitting: 352it [00:02, 171.11it/s]\u001b[A\n",
            "RBM fitting: 370it [00:02, 172.27it/s]\u001b[A\n",
            "RBM fitting: 388it [00:02, 167.82it/s]\u001b[A\n",
            "RBM fitting: 405it [00:02, 168.40it/s]\u001b[A\n",
            "RBM fitting: 422it [00:02, 166.26it/s]\u001b[A\n",
            "RBM fitting: 440it [00:02, 168.87it/s]\u001b[A\n",
            "RBM fitting: 458it [00:02, 169.62it/s]\u001b[A\n",
            "RBM fitting: 476it [00:02, 170.43it/s]\u001b[A\n",
            "RBM fitting: 494it [00:02, 168.79it/s]\u001b[A\n",
            "RBM fitting: 511it [00:03, 164.44it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 169.26it/s]\n",
            "Epoch:9 ,avg_cost = 0.4532868564128876 ,std_cost = 0.04323573410511017 ,avg_grad = 6430.6044921875 ,std_grad = 321.41455078125\n",
            "\n",
            "RBM fitting: 0it [00:00, ?it/s]\u001b[A\n",
            "RBM fitting: 18it [00:00, 171.65it/s]\u001b[A\n",
            "RBM fitting: 35it [00:00, 170.58it/s]\u001b[A\n",
            "RBM fitting: 51it [00:00, 166.73it/s]\u001b[A\n",
            "RBM fitting: 67it [00:00, 164.27it/s]\u001b[A\n",
            "RBM fitting: 85it [00:00, 167.63it/s]\u001b[A\n",
            "RBM fitting: 103it [00:00, 169.41it/s]\u001b[A\n",
            "RBM fitting: 120it [00:00, 169.18it/s]\u001b[A\n",
            "RBM fitting: 137it [00:00, 168.98it/s]\u001b[A\n",
            "RBM fitting: 155it [00:00, 169.94it/s]\u001b[A\n",
            "RBM fitting: 172it [00:01, 166.76it/s]\u001b[A\n",
            "RBM fitting: 190it [00:01, 169.24it/s]\u001b[A\n",
            "RBM fitting: 208it [00:01, 171.02it/s]\u001b[A\n",
            "RBM fitting: 225it [00:01, 168.84it/s]\u001b[A\n",
            "RBM fitting: 243it [00:01, 171.25it/s]\u001b[A\n",
            "RBM fitting: 261it [00:01, 172.71it/s]\u001b[A\n",
            "RBM fitting: 279it [00:01, 168.91it/s]\u001b[A\n",
            "RBM fitting: 297it [00:01, 170.42it/s]\u001b[A\n",
            "RBM fitting: 315it [00:01, 172.57it/s]\u001b[A\n",
            "RBM fitting: 333it [00:01, 174.59it/s]\u001b[A\n",
            "RBM fitting: 351it [00:02, 174.60it/s]\u001b[A\n",
            "RBM fitting: 369it [00:02, 173.13it/s]\u001b[A\n",
            "RBM fitting: 387it [00:02, 170.99it/s]\u001b[A\n",
            "RBM fitting: 405it [00:02, 169.85it/s]\u001b[A\n",
            "RBM fitting: 423it [00:02, 170.07it/s]\u001b[A\n",
            "RBM fitting: 441it [00:02, 171.55it/s]\u001b[A\n",
            "RBM fitting: 459it [00:02, 167.00it/s]\u001b[A\n",
            "RBM fitting: 476it [00:02, 166.55it/s]\u001b[A\n",
            "RBM fitting: 493it [00:02, 165.75it/s]\u001b[A\n",
            "RBM fitting: 511it [00:03, 167.97it/s]\u001b[A\n",
            "RBM fitting: 542it [00:03, 169.33it/s]\n",
            "Epoch:10 ,avg_cost = 0.4452744722366333 ,std_cost = 0.04112632945179939 ,avg_grad = 6346.1435546875 ,std_grad = 304.56378173828125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "f1w1BfzuLCiN",
        "outputId": "40e588ee-98db-4480-f2cb-5abc60c6ca32"
      },
      "source": [
        "# This shows the weights for each of the 64 hidden neurons and give an idea how each neuron is activated.\r\n",
        "\r\n",
        "learned_weights = rbm_mnist.W.transpose(0,1).numpy()\r\n",
        "plt.show()\r\n",
        "fig = plt.figure(3, figsize=(10,10))\r\n",
        "for i in range(25): \r\n",
        "    sub = fig.add_subplot(5, 5, i+1)\r\n",
        "    sub.imshow(learned_weights[i, :].reshape((28,28)), cmap=plt.cm.gray)\r\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "VSpA-qPxLE8Q",
        "outputId": "ba185e1c-1dd6-429d-e31f-dc763c1d18ef"
      },
      "source": [
        "#Lets try reconstructing a random number from this model which has learned 5\r\n",
        "idx = 7\r\n",
        "img = mnist_data.train_data[idx]\r\n",
        "reconstructed_img = img.view(-1).type(torch.FloatTensor)\r\n",
        "\r\n",
        "# _ , reconstructed_img = rbm_mnist.to_hidden(reconstructed_img)\r\n",
        "# _ , reconstructed_img = rbm_mnist.to_visible(reconstructed_img)\r\n",
        "\r\n",
        "_,reconstructed_img = rbm_mnist.reconstruct(reconstructed_img,1)\r\n",
        "# print(reconstructed_img)\r\n",
        "reconstructed_img = reconstructed_img.view((28,28))\r\n",
        "print(\"The original number: {}\".format(mnist_data.train_labels[idx]))\r\n",
        "plt.imshow(img , cmap = 'gray')\r\n",
        "plt.show()\r\n",
        "print(\"The reconstructed image\")\r\n",
        "plt.imshow(reconstructed_img , cmap = 'gray')\r\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The original number: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n",
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "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": "stream",
          "text": [
            "The reconstructed image\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALoklEQVR4nO3dT6hc5R3G8eepfzYqNKn0comxscWdCy2SVSh2oaTZRDeiq4iF66IWuzPoQkEEKa1dFiIG02IVwViDlGoqYlxJboKN+YMmlYi5XHMJaWlcWc2vizk3jMn8u3POmXNmft8PDDNz7syZ35zkue973vfOvI4IAZh932u6AACTQdiBJAg7kARhB5Ig7EASV0/yxWwz9A/ULCLca3uplt32Vtuf2D5le2eZfQGol8edZ7d9laRPJd0t6Yykg5IejIjjA55Dyw7UrI6WfbOkUxHxWUR8LelVSdtL7A9AjcqEfYOkL7runym2fYftBduLthdLvBaAkmofoIuIXZJ2SXTjgSaVadmXJG3sun9TsQ1AC5UJ+0FJt9q+xfa1kh6QtK+asgBUbexufER8Y/tRSW9LukrS7og4VlllACo19tTbWC/GOTtQu1r+qAbA9CDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgibGXbEZ7THIl3kmyey5Gesmw9z3s+dmUCrvt05IuSPpW0jcRcWcVRQGoXhUt+88j4lwF+wFQI87ZgSTKhj0kvWP7kO2FXg+wvWB70fZiydcCUILLDO7Y3hARS7Z/KGm/pF9HxIEBj5/NkaSGMUA33vNnVUT0fOOlWvaIWCquVyS9IWlzmf0BqM/YYbd9ne0bVm9LukfS0aoKA1CtMqPxc5LeKLpKV0v6S0T8vZKqsCaDuqvT3MWnm16tUufsa34xztknbprDPgxh762Wc3YA04OwA0kQdiAJwg4kQdiBJPiIawuUnWKa1hH3su+rzvdddqS/jdOGtOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATz7C3Q5k9v1TnfXPffF5T56G/Z2tr4b0rLDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM8+49o437uqbG1lnt/m41IXWnYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJ59inQ5HeQt/H7zzGeoS277d22V2wf7dq23vZ+2yeL63X1lgmgrFG68S9J2nrZtp2S3o2IWyW9W9wH0GJDwx4RBySdv2zzdkl7itt7JN1bcV0AKjbuOftcRCwXt7+UNNfvgbYXJC2M+ToAKlJ6gC4iwnbfUZyI2CVplyQNehyAeo079XbW9rwkFdcr1ZUEoA7jhn2fpB3F7R2S3qymHAB18QjzqK9IukvSjZLOSnpK0l8lvSbpZkmfS7o/Ii4fxOu1L7rxM4Z5+PaJiJ4HfWjYq0TYZw9hb59+YefPZYEkCDuQBGEHkiDsQBKEHUiCj7jOuLKzLXUu2Vznksy4Ei07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBPPsMqPOTi3XOdU/yE5egZQfSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJJhnnwJl5rrrnstmrnx60LIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBLMs8+AOue6y363e5PKfGf9LBrastvebXvF9tGubU/bXrL9UXHZVm+ZAMoapRv/kqStPbb/ISJuLy5/q7YsAFUbGvaIOCDp/ARqAVCjMgN0j9o+UnTz1/V7kO0F24u2F0u8FoCSPMoAi+1Nkt6KiNuK+3OSzkkKSc9Imo+Ih0fYT3tHc1qsyUGwNg/QlaltlgfoIqLnmxurZY+IsxHxbURclPSCpM1ligNQv7HCbnu+6+59ko72eyyAdhg6z277FUl3SbrR9hlJT0m6y/bt6nTjT0t6pMYaZ17Ta6jX+drT+p32s2ikc/bKXoxz9p6mOezDtPmcflZVes4OYPoQdiAJwg4kQdiBJAg7kAQfcW2BWR41bvJrrvFdtOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATz7CNq87eeTGttmCxadiAJwg4kQdiBJAg7kARhB5Ig7EAShB1Ignn2ETU9Xz1Ik7Xx7bHTg5YdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Jgnj25Nn/enHn0ag1t2W1vtP2e7eO2j9l+rNi+3vZ+2yeL63X1lwtgXEPXZ7c9L2k+Ig7bvkHSIUn3SnpI0vmIeM72TknrIuLxIftqbzOSFC377Bl7ffaIWI6Iw8XtC5JOSNogabukPcXD9qjzCwBAS63pnN32Jkl3SPpQ0lxELBc/+lLSXJ/nLEhaGL9EAFUY2o2/9ED7eknvS3o2Ivba/k9EfL/r5/+OiIHn7XTj24du/OwZuxsvSbavkfS6pJcjYm+x+WxxPr96Xr9SRaEA6jG0G+/Or9cXJZ2IiOe7frRP0g5JzxXXb9ZS4YTMags3q+8LazfKaPwWSR9I+ljSxWLzE+qct78m6WZJn0u6PyLOD9lXa//nzWooZvV9ob9+3fiRz9mrQNjHQ9ixFqXO2QFMP8IOJEHYgSQIO5AEYQeS4COuhTaPeJfZ/7D3VXftg15/hGnfqstJjZYdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Jgnn1Ebf702CBtnstmHn2yaNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnm2UdU5+eyy87h1/lZfObCZwctO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kMTTstjfafs/2cdvHbD9WbH/a9pLtj4rLtvrLbSfbAy9ln192/3XWjukxyvrs85LmI+Kw7RskHZJ0r6T7JX0VEb8b+cVavGQzMCv6Ldk89C/oImJZ0nJx+4LtE5I2VFsegLqt6Zzd9iZJd0j6sNj0qO0jtnfbXtfnOQu2F20vlqoUQClDu/GXHmhfL+l9Sc9GxF7bc5LOSQpJz6jT1X94yD7oxgM169eNHynstq+R9JaktyPi+R4/3yTprYi4bch+CDtQs35hH2U03pJelHSiO+jFwN2q+yQdLVskgPqMMhq/RdIHkj6WdLHY/ISkByXdrk43/rSkR4rBvEH7omUHalaqG18Vwg7Ub+xuPIDZQNiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUhi0ks2n5P0edf9G4ttbdTW2tpal0Rt46qyth/1+8FEP89+xYvbixFxZ2MFDNDW2tpal0Rt45pUbXTjgSQIO5BE02Hf1fDrD9LW2tpal0Rt45pIbY2eswOYnKZbdgATQtiBJBoJu+2ttj+xfcr2ziZq6Mf2adsfF8tQN7o+XbGG3orto13b1tveb/tkcd1zjb2GamvFMt4Dlhlv9Ng1vfz5xM/ZbV8l6VNJd0s6I+mgpAcj4vhEC+nD9mlJd0ZE43+AYftnkr6S9KfVpbVs/1bS+Yh4rvhFuS4iHm9JbU9rjct411Rbv2XGH1KDx67K5c/H0UTLvlnSqYj4LCK+lvSqpO0N1NF6EXFA0vnLNm+XtKe4vUed/ywT16e2VoiI5Yg4XNy+IGl1mfFGj92AuiaiibBvkPRF1/0zatd67yHpHduHbC80XUwPc13LbH0paa7JYnoYuoz3JF22zHhrjt04y5+XxQDdlbZExE8l/ULSr4ruaitF5xysTXOnf5T0E3XWAFyW9PsmiymWGX9d0m8i4r/dP2vy2PWoayLHrYmwL0na2HX/pmJbK0TEUnG9IukNdU472uTs6gq6xfVKw/VcEhFnI+LbiLgo6QU1eOyKZcZfl/RyROwtNjd+7HrVNanj1kTYD0q61fYttq+V9ICkfQ3UcQXb1xUDJ7J9naR71L6lqPdJ2lHc3iHpzQZr+Y62LOPdb5lxNXzsGl/+PCImfpG0TZ0R+X9JerKJGvrU9WNJ/ywux5quTdIr6nTr/qfO2MYvJf1A0ruSTkr6h6T1Lartz+os7X1EnWDNN1TbFnW66EckfVRctjV97AbUNZHjxp/LAkkwQAckQdiBJAg7kARhB5Ig7EAShB1IgrADSfwfrt4x28DFv7QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wa4K34i0LHXX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}