Created
June 6, 2016 04:10
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a plot script for caffe to show loss/training curves
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# 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 ([email protected]) | |
# -------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 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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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?