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a plot script for caffe to show loss/training curves
# In the name of GOD the most compassionate the most merciful
# Originally developed by Yasse Souri
# Just added the search for current directory so that users dont have to use command prompts anymore!
# and also shows the top 4 accuracies achieved so far, and displaying the highest in the plot title
# Coded By: Seyyed Hossein Hasan Pour (Coderx7@gmail.com)
# -------How to Use ---------------
# 1.Just place your caffe's traning/test log file (with .log extension) next to this script
# and then run the script.If you have multiple logs placed next to the script, it will plot all of them
# you may also copy this script to your working directory, where you generate/keep your train/test logs
# and easily execute the script and see the curve plotted.
# this script is standalone.
# 2. you can use command line arguments as well, just feed the script with different log files separated by space
# and you are good to go.
#----------------------------------
import numpy as np
import re
import click
import glob, os
from matplotlib import pylab as plt
import operator
import ntpath
@click.command()
@click.argument('files', nargs=-1, type=click.Path(exists=True))
def main(files):
plt.style.use('ggplot')
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.set_xlabel('iteration')
ax1.set_ylabel('loss')
ax2.set_ylabel('accuracy %')
if not files:
print 'no args found'
print '\n\rloading all files with .log extension from current directory'
os.chdir(".")
files = glob.glob("*.log")
for i, log_file in enumerate(files):
loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName = parse_log(log_file)
disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=i)
plt.show()
def parse_log(log_file):
with open(log_file, 'r') as log_file2:
log = log_file2.read()
loss_pattern = r"Iteration (?P<iter_num>\d+), loss = (?P<loss_val>[+-]?(\d+(\.\d*)?|\.\d+)([eE][+-]?\d+)?)"
losses = []
loss_iterations = []
fileName= os.path.basename(log_file)
for r in re.findall(loss_pattern, log):
loss_iterations.append(int(r[0]))
losses.append(float(r[1]))
loss_iterations = np.array(loss_iterations)
losses = np.array(losses)
accuracy_pattern = r"Iteration (?P<iter_num>\d+), Testing net \(#0\)\n.* accuracy = (?P<accuracy>[+-]?(\d+(\.\d*)?|\.\d+)([eE][+-]?\d+)?)"
accuracies = []
accuracy_iterations = []
accuracies_iteration_checkpoints_ind = []
for r in re.findall(accuracy_pattern, log):
iteration = int(r[0])
accuracy = float(r[1]) * 100
if iteration % 10000 == 0 and iteration > 0:
accuracies_iteration_checkpoints_ind.append(len(accuracy_iterations))
accuracy_iterations.append(iteration)
accuracies.append(accuracy)
accuracy_iterations = np.array(accuracy_iterations)
accuracies = np.array(accuracies)
return loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName
def disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=0):
modula = len(plt.rcParams['axes.color_cycle'])
acrIterations =[]
top_acrs={}
if accuracies.size:
if accuracies.size>4:
top_n = 4
else:
top_n = accuracies.size -1
temp = np.argpartition(-accuracies, top_n)
result_indexces = temp[:top_n]
temp = np.partition(-accuracies, top_n)
result = -temp[:top_n]
for acr in result_indexces:
acrIterations.append(accuracy_iterations[acr])
top_acrs[str(accuracy_iterations[acr])]=str(accuracies[acr])
sorted_top4 = sorted(top_acrs.items(), key=operator.itemgetter(1))
maxAcc = np.amax(accuracies, axis=0)
iterIndx = np.argmax(accuracies)
maxAccIter = accuracy_iterations[iterIndx]
maxIter = accuracy_iterations[-1]
consoleInfo = format('\n[%s]:maximum accuracy [from 0 to %s ] = [Iteration %s]: %s ' %(fileName,maxIter,maxAccIter ,maxAcc))
plotTitle = format('max accuracy(%s) [Iteration %s]: %s ' % (fileName,maxAccIter, maxAcc))
print (consoleInfo)
#print (str(result))
#print(acrIterations)
# print 'Top 4 accuracies:'
print ('Top 4 accuracies:'+str(sorted_top4))
plt.title(plotTitle)
ax1.plot(loss_iterations, losses, color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 0) % modula])
ax2.plot(accuracy_iterations, accuracies, plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula], label=str(fileName))
ax2.plot(accuracy_iterations[accuracies_iteration_checkpoints_ind], accuracies[accuracies_iteration_checkpoints_ind], 'o', color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula])
plt.legend(loc='lower right')
if __name__ == '__main__':
main()
@ProGamerGov
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ProGamerGov commented May 10, 2017

axes.color_cycle was replaced with axes.prop_cycle in Matplotlib. As a result, the script no longer works. Can you please fix this?

@MFarooqAit
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MFarooqAit commented May 22, 2017

How to create caffe's traning/test log file (with .log extension)?
I was unable to create log file by using "tee" command as shown below
"....... |tee caffe_loss_history.log"

Can you help me in this regards?

@ktl014
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ktl014 commented Jul 17, 2017

ProGamerGov commented on May 10
axes.color_cycle was replaced with axes.prop_cycle in Matplotlib. As a result, the script no longer works. Can you please fix this?

Could you resolve this issue? I'm also facing the same problem^^

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