Alan Lee secsilm

Troubleshooting Convolutional Neural Networks

Intro

This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably Stanford's CS231n course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.

Suppose we are given a convolutional neural network to train and evaluate and assume the evaluation results are worse than expected. The following are steps to troubleshoot and potentially improve performance. The first section corresponds to must-do's and generally good practices before you start troubleshooting. Every subsequent section header corresponds to a problem and the section is devoted to solving it. The sections are ordered to reflect "more common" issues first and under each header the "most-eas

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

1️⃣ Docker daemon 配置代理（推荐）

参考 Docker daemon 配置代理

	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,

	"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
	import tensorflow as tf

	from tensorflow.examples.tutorials.mnist import input_data as mnist_data
	from tensorflow.contrib import slim
	from tensorflow.contrib.learn import ModeKeys
	from tensorflow.contrib.learn import learn_runner


	# Show debugging output

	# -- coding: utf-8 --
	""" rwlock.py

	A class to implement read-write locks on top of the standard threading
	library.

	This is implemented with two mutexes (threading.Lock instances) as per this
	wikipedia pseudocode:

	https://en.wikipedia.org/wiki/Readers%E2%80%93writer_lock#Using_two_mutexes

	#!/usr/bin/env python
	from __future__ import print_function
	import argparse
	import numpy as np
	import time
	tt = time.time()

	import cv2
	from grpc.beta import implementations