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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 4,
  6. "metadata": {},
  7. "outputs": [
  8. {
  9. "name": "stdout",
  10. "output_type": "stream",
  11. "text": [
  12. "MNIST accurancy: 0.9443"
  13. ]
  14. },
  15. {
  16. "name": "stdout",
  17. "output_type": "stream",
  18. "text": [
  19. "\n"
  20. ]
  21. }
  22. ],
  23. "source": [
  24. "## train, predict and save predictions, return learning history ##\n",
  25. "cur_history, name = train_predict(dataset, savepath,\n",
  26. " 'MNIST', verb=0)\n",
  27. "# store learning history to the list\n",
  28. "histories.append(cur_history)\n",
  29. "# store name of the training dataset to the list \n",
  30. "names.append(name)\n"
  31. ]
  32. }
  33. ],
  34. "metadata": {
  35. "kernelspec": {
  36. "display_name": "Python 2",
  37. "language": "python",
  38. "name": "python2"
  39. },
  40. "language_info": {
  41. "codemirror_mode": {
  42. "name": "ipython",
  43. "version": 2
  44. },
  45. "file_extension": ".py",
  46. "mimetype": "text/x-python",
  47. "name": "python",
  48. "nbconvert_exporter": "python",
  49. "pygments_lexer": "ipython2",
  50. "version": "2.7.6"
  51. }
  52. },
  53. "nbformat": 4,
  54. "nbformat_minor": 0
  55. }
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