whats_in_a_question/caffe/monitor/progress_plot.py at master · sidgan/whats_in_a_question · GitHub

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#!/usr/bin/env python

############################
### Reusing Existing code ##
# Unknown author/reference #
############################

import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import math
import pylab
import sys
import argparse
import re
from pylab import figure, show, legend, ylabel

from mpl_toolkits.axes_grid1 import host_subplot


if __name__ == "__main__":

  parser = argparse.ArgumentParser(description='makes a plot from Caffe output')
  parser.add_argument('output_file', help='file of captured stdout and stderr')
  args = parser.parse_args()

  f = open(args.output_file, 'r')

  training_iterations = []
  training_loss = []

  test_iterations = []
  test_accuracy = []
  test_loss = []

  check_test = False
  check_test2 = False
  for line in f:

    if check_test:
      test_accuracy.append(float(line.strip().split(' = ')[-1]))
      check_test = False
      check_test2 = True
    elif check_test2:
      if 'Test net output' in line and 'valid_log_loss' in line:
        #print line
        #print line.strip().split(' ')
        test_loss.append(float(line.strip().split(' ')[-2]))
        check_test2 = False
      else:
        test_loss.append(0)
        check_test2 = False

    if '] Iteration ' in line and 'loss = ' in line:
      arr = re.findall(r'ion \b\d+\b,', line)
      training_iterations.append(int(arr[0].strip(',')[4:]))
      training_loss.append(float(line.strip().split(' = ')[-1]))

    if '] Iteration ' in line and 'Testing net' in line:
      arr = re.findall(r'ion \b\d+\b,', line)
      test_iterations.append(int(arr[0].strip(',')[4:]))
      check_test = True

  print 'train iterations len: ', len(training_iterations)
  print 'train loss len: ', len(training_loss)
  print 'test loss len: ', len(test_loss)
  print 'test iterations len: ', len(test_iterations)
  print 'test accuracy len: ', len(test_accuracy)

  if len(test_iterations) != len(test_accuracy): #awaiting test...
    print 'mis-match'
    print len(test_iterations[0:-1])
    test_iterations = test_iterations[0:-1]

  f.close()
#  plt.plot(training_iterations, training_loss, '-', linewidth=2)
#  plt.plot(test_iterations, test_accuracy, '-', linewidth=2)
#  plt.show()

  host = host_subplot(111)#, axes_class=AA.Axes)
  plt.subplots_adjust(right=0.75)

  par1 = host.twinx()

  host.set_xlabel("iterations")
  host.set_ylabel("log loss")
  par1.set_ylabel("validation accuracy")

  p1, = host.plot(training_iterations, training_loss, label="training log loss")
  p3, = host.plot(test_iterations, test_loss, label="valdation log loss")
  p2, = par1.plot(test_iterations, test_accuracy, label="validation accuracy")

  host.legend(loc=2)

  host.axis["left"].label.set_color(p1.get_color())
  par1.axis["right"].label.set_color(p2.get_color())

  plt.draw()
  plt.show()