Coderx7

import argparse
import time

start_time=time.time();
################## Argument Parsing #####################################
parser=argparse.ArgumentParser();
parser.add_argument('-s','--solver', default='', type=str); # if empty, solver is created, else read
parser.add_argument('-res', '--resume_from', default='', type=str); #if not empty, resumes training from given file
parser.add_argument('-ft', '--finetune_from', default='', type=str);

Coderx7

#in the name of God the most compassionate the most merciful
# added mean subtraction so that, the accuracy can be reported accurately just like caffe when doing a mean subtraction
# Seyyed Hossein Hasan Pour
# [email protected]
# 7/3/2016 
# Added Recall/Precision/F1-Score as well 
# 01/03/2017
# added classification using caffe.classifier, which lets you do oversampling (allows for multiple preprocessing options and crops) + the manual center crop
# method. its sloppy at the moment. for some reason, caffe.classifier's predict method, provides less accuracy compared to the caffes test accuracy
# the manuall center crop (i.e. the one which uses Caffe.Net, works just well and achieves the same accuracy as Caffe's)

Coderx7

#in the name of God the most compassionate the most merciful
# added mean subtraction so that, the accuracy can be reported accurately just like caffe when doing a mean subtraction
# Seyyed Hossein Hasan Pour
# [email protected]
# 7/3/2016 
# Added Recall/Precision/F1-Score as well 
# 01/03/2017
# Added batch processing, not what used to take a minute or so, takes only several seconds!
# 07/25/2017
#info: 

Coderx7

# Seyyed Hossein Hasan Pour
# [email protected]
# 7/3/2016 
# Added Recall/Precision/F1-Score as well 
# 01/03/2017
#info: 
#if on windows, one can use these command in a batch file and ease him/her self 
#REM Calculating Confusing Matrix 
#python confusionMatrix_convnet_test.py --proto cifar10_deploy.prototxt --model cifar10_.caffemodel --mean mean.binaryproto --lmdb cifar10_test_lmdb  
#pause

Coderx7

#In the name of God
#Use this script to convert a binaryproto mean file to an equivalent python npy mean file.
#simply use the script like this : 
#python convert_protomean.py mean.binaryproto mean.npy"
import caffe
import numpy as np
import sys

if len(sys.argv) != 3:
    print "Usage: python convert_protomean.py mean.binaryproto mean.npy"

Coderx7

name: "wrn_128_v8"
layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TRAIN
  }
  transform_param {

Coderx7

name: "wrn_151_v7"
layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TRAIN
  }
  transform_param {

Coderx7

#in the name of Allah, the most compassionate the most merciful
#run this scrip under python2. python3 would work, but not prefectly. e.g. running this script and setting oversample to true in 
#net.predict([input_image], oversample=True) will cause a crash. but it works just fine under python2.

import numpy as np
import os, sys, getopt

# Main path to your caffe installation
caffe_root = '/path/to/your/caffe/'
# Model prototxt file

Coderx7

# In the name of GOD the most compassionate the most merciful
# Last Updated : 4/9/2017 , updated the regex for the latest caffe (rc5) logs
# 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

Coderx7

# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed