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

Coderx7

# 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 
# Added train/test plot!
# Coded By: Seyyed Hossein Hasan Pour ([email protected])
# -------How to Use ---------------
# 0.First of all make sure your test layers name is 'accuracy' and the layer which reports your training accuracy is
# 'accuracy_training'. then you are good to go. if you are using different names for your test/train layers, make sure to edit
# the regex yourself. I might remove this limitation later, but at the moment this is a hacky edition to get my job done!

Coderx7

# 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 ---------------
# 0.First of all make sure your test layers name is 'accuracy' and the layer which reports your training accuracy is
# 'accuracy_training'. then you are good to go. if you are using different names for your test/train layers, make sure to edit
# the regex yourself. I might remove this limitation later, but at the moment this is a hacky edition to get my job done!
# so the code isnot polished! but is straight forward!

Coderx7

#in the name of GOD
#pylearn2 cifar10 convertor to lmdb
#by:Seyyed Hossein Hasanpour
#[email protected] 
#2/14/2017
import numpy as np
import cPickle
import lmdb
import caffe
from caffe.proto import caffe_pb2

Coderx7

#in the name of God, the most compassionate the most merciful
#Seyyed Hossein Hasanpour 
#[email protected]
#script for zeropadding and normalizing CIFAR10 dataset (can also be used for CIFAR100)
import math
import caffe
import lmdb
import numpy as np
from caffe.proto import caffe_pb2
import cv2

Coderx7

#In the name of God, the most compassionate the most merciful
import math
import numpy as np
#English: a simple handy snippet which I specifically wrote for calculating mean for a batch of images,
#in semi-vectorized and unvectorized fashion, along with the fully numpy example to test the output!
#Farsi: 
#mohasebe mean batchi az tasavir be sorate vectorized, unvectorized and semi vectorized
#age version vectorized error kambod hafeze dad, behtare az semi vectorized estefade beshe
#chon unvectorized ya hamoon loop mamoli sooratesh kheyli kame. 
#[email protected]

Coderx7

#In the name of God, the most compassionate the most merciful
#a non-vectorized and semi-vectorized implementation for calculating std for a batch of images.
#the semi-vectorized is the one, one should use, since its as fast as the numpy.std  
#Seyyed Hossein Hasan pour 
#[email protected]
#1/12/2017
import math
#unvectorized version --really slow!
def calc_std_classic(a):
    #sum all elements in each channel and divide by the number of elements