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#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)

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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);

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# Dinesh Jayaraman

# Based on code by
# Authors: Fabian Pedregosa <[email protected]>
#          Olivier Grisel <[email protected]>
#          Mathieu Blondel <[email protected]>
#          Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2011

print(__doc__)

Coderx7

#برای نمایش بلادرنگ نمودار ترینینگ و تست ما 
import numpy as np
from matplotlib import pyplot as plt

class LivePlotNotebook(object):
    """
    Live plot using %matplotlib notebook in jupyter notebook
    original url : https://gist.github.com/wassname/04e77eb821447705b399e8e7a6d082ce
    """

Coderx7

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

Coderx7

Here's my experience of installing the NVIDIA CUDA kit 8.0 on a fresh install of Ubuntu Desktop 16.04.3 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

1. Install NVIDIA Graphics Driver via apt-get

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# https://github.com/pytorch/vision/blob/master/torchvision/models/__init__.py
import argparse
import os
import shutil
import time
import os, sys, pdb, shutil, time, random, datetime

import torch
import torch.nn as nn
import torch.nn.parallel

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# https://github.com/pytorch/vision/blob/master/torchvision/models/__init__.py
import argparse
import os
import shutil
import time
import os, sys, pdb, shutil, time, random, datetime

import torch
import torch.nn as nn
import torch.nn.parallel

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=> creating model 'simplenetv1_imagenet_3p'
=> Model : simplenetv1_imagenet_3p(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=[3, 3], stride=(2, 2), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.05, affine=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 128, kernel_size=[3, 3], stride=(2, 2), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True)
    (5): ReLU(inplace)
    (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))

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workbench.main.js:3272 [Extension Host] debugger listening on port 16367
workbench.main.js:3270 Extension Host
workbench.main.js:3270 Debugger listening on ws://127.0.0.1:16367/d86fbd32-94fb-45fe-a384-4880d4073811
For help, see: https://nodejs.org/en/docs/inspector

workbench.main.js:238 [Extension Host] [vscode-icons] v8.7.0 activated!
workbench.main.js:238 [Extension Host] (node:8044) [DEP0005] DeprecationWarning: Buffer() is deprecated due to security and usability issues. Please use the Buffer.alloc(), Buffer.allocUnsafe(), or Buffer.from() methods instead.
t.log @ workbench.main.js:238
workbench.main.js:238 [Extension Host] Info Python Extension: 2019-06-04 01:29:01: Display locator refreshing progress, Class name = p, completed in 1ms, , Return Value: undefined
workbench.main.js:238 [Extension Host] Info Python Extension: 2019-06-04 01:29:01: Notify locators are locating, Class name = p, completed in 2ms, , Return Value: undefined