如果你苦于Tensorflow官方例程的冗长和数据读取的繁杂，那么这篇博客就是你需要看的啦~

Motivation

这段时间学习TensorFlow走了很多弯路，刚开始的MNIST例程还是比较容易啃的。后来钻研im2txt，大概啃了两三个星期，发现结构比MNIST复杂很多，实在难懂。遂转而看CIFAR10例程，但是光数据读取就啃了一个星期，现在都比较迷糊其中队列的用法。
经过这一个月的折腾，我发现Tensorflow虽然网络结构很好定义，而且用户可以方便灵活的自定义层，但是也有一些坑：

例程里有大量函数和方法用于描述代码架构。注意这些东西不是描述网络的，而是仅仅为了让代码更有层次。当然，这也是google工程师的牛逼之处吧。不过对于新手来说却有点迷茫，因为你除了会看到conv、relu、pool，还将会看到一大堆name_space、variable_sapce、tf.collection等等，这些代码量甚至超过模型描述的代码，有喧宾夺主的感觉。
数据读取比较复杂，如果深究的话，往往要理解Tensorflow中的“队列”。例如CIFAR10的数据读取函数，返回的是image节点，而不是image数据。若不能理解Tensorflow中的“队列”，你将难以理解image节点的含义，也就无法自如的对其操作了。（感兴趣的读者可自行阅读官方cifar10_input.py）

———————–因此————————-
我毅然决定自己造轮子，用一种清新脱俗的方式重写官方CIFAR10教程。

本代码优点：

数据读取采用LiFeiFei在cs231n课程上给出的cifar10读取方法，将cifar10数据整个读成numpy.array，熟悉numpy的你可以在数据层随便预处理。并且本代码给出了用numpy进行预处理方式（包括如何用numpy对数据进行shuffle，如何把label变成onehot形式，以及如何每个epoch之后都进行重新shuffle）。
数据预处理借鉴tensorflow里的crop、flip等函数，并解决了tensorflow某些预处理函数只能对一张图片处理，而不能对一个batch处理的问题。
官方教程在训练阶段只能输出train loss，而难以输出test loss（我为了观察过拟合，已经在官方教程上尝试各种方法，均以失败告终，这也是我重写CIFAR10数据层的动机）而我的code可以实时输出train loss、test loss、train accuracy、test accuracy。并在tensorboard上显示。
本代码风格极简，从上到下依次是预处理、构造graph、训练和评测。结构清晰，基本不存在来回跳转，尽量避免使用tensorflow里牛逼但不必须的函数，所以叫做“清新脱俗”
本代码的模型部分与官方教程一致，运行的accuracy和loss曲线也与官方一致，证明代码不是胡写的。
注释比代码还多，而且都是英文注释，方便国际友人阅读（实际上是我的IDE不支持中文输入）。注释中包含了对以上各种trick的详细描述，以及相关trick在stackoverflow上的网址，以及调参遇到过的坑。导致注释比代码还多。。。

本代码缺点：

数据读取采用LiFeiFei在cs231n课程上给出的cifar10读取方法，将cifar10数据整个读成numpy.array。如果数据集更大，将很可能out of memory。所以本代码只是提供了一种小数据集的读取方法。大数据集要用tensorflow的queue来实现啦

运行结果：

刚刚运行时：

step=500k时：

代码（包括cifar10_easy_demo.py和data_utils.py）

cifar10_easy_demo.py

import re
import os
import random
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from data_utils import load_CIFAR10#==============================================================================
# configurations
#==============================================================================
batch=128
INITIAL_LEARNING_RATE=0.1
LEARNING_RATE_DECAY_FACTOR=0.1
NUM_CLASSES=10
decay_steps=130000
MAX_STEPS=500000
log_dir='/home/kcheng/tensorflow_easy_demo/cifar10_log'
checkpoint_dir='/home/kcheng/tensorflow_easy_demo/cifar10_checkpoint'def load_onehot_shuffle():#==============================================================================# this function is to load the data, shufflow it, split it, and transfer label into one-hot label# Notice that following operations are implemented by numpy. In theory ,this operation is implemented by queue, see detial in tensorflow offical code for cifar10# But that offical code really puzzled me, because I can't understand how the queue return a batch, so I decide to preprocess data with numpy instead of queue# This is one of the biggest shortcoming of my code, because my code load all training sets into memory.# Fortunatly, cifar10 datasets is so small that our memory can hold it.# If you try our code on bigger datasets(i.e. ImageNet), you may run out of memory.# All in all, this function shows a easy way to load small datasets as cifar10.# If you have any suggestions for loading or preprocessiong data in tensorflow, please reply me#==============================================================================#==============================================================================# read data#==============================================================================cifar10_dir = '/home/kcheng/tmaster/models-master/tutorials/image/cifar10/winter1516_assignment1/assignment1/cs231n/datasets/cifar-10-batches-py'X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)# As a sanity check, we print out the size of the training and test data.print 'Training data shape: ', X_train.shapeprint 'Training labels shape: ', y_train.shapeprint 'Test data shape: ', X_test.shapeprint 'Test labels shape: ', y_test.shape#==============================================================================# one_hot#==============================================================================y_train_one_hot=np.zeros((50000,10))y_train_one_hot[np.arange(50000), y_train] = 1print y_train_one_hot.shapey_test_one_hot=np.zeros((10000,10))y_test_one_hot[np.arange(10000), y_test] = 1print y_test_one_hot.shapey_train=y_train_one_hoty_test=y_test_one_hot#==============================================================================# shufflow   #==============================================================================p_train = range(50000)p_test=range(10000)np.random.shuffle(p_train)np.random.shuffle(p_test)# here we use deep copy# so we will delete original variable to save memoryX_train_shuffle=X_train[p_train,:,:,:]y_train_shuffle=y_train[p_train,:]X_test_shuffle=X_test[p_test,:,:,:]y_test_shuffle=y_test[p_test,:]del X_traindel y_traindel X_testdel y_test# here is shallow copyX_train=X_train_shuffley_train=y_train_shuffleX_test=X_test_shuffley_test=y_test_shuffleprint 'Train data shape: ', X_train.shapeprint 'Train labels shape: ', y_train.shapeprint 'Test data shape: ', X_test.shapeprint 'Test labels shape: ', y_test.shapeprint '*****************  shuffle    *******************' # tell you a shuffle operation has been donereturn (X_train,y_train,X_test,y_test)def re_shuffle(X_train,y_train,X_test,y_test):#==============================================================================# this function is to re_shufflow images at the beginning of an epoch # without this operation, the order of images will be same between epoches, which will aggravate overfitting and bring down the test accuracy about 5%#==============================================================================p_train = range(50000)p_test=range(10000)np.random.shuffle(p_train)np.random.shuffle(p_test)# here we use deep copy# so we will delete original variable to save memoryX_train_shuffle=X_train[p_train,:,:,:]y_train_shuffle=y_train[p_train,:]X_test_shuffle=X_test[p_test,:,:,:]y_test_shuffle=y_test[p_test,:]del X_traindel y_traindel X_testdel y_test# here is shallow copyX_train=X_train_shuffley_train=y_train_shuffleX_test=X_test_shuffley_test=y_test_shuffleprint '*****************  shuffle    *******************'return (X_train,y_train,X_test,y_test)#==============================================================================
#  Up to now, we have not finished data preprocessing yet. In fact, we will do crop, mirror and other data amplification operations
#  If we implement crop and mirror with numpy, like the above operation, then the crops and mirrors won't change during training, we call it "static data amplification"
#  On the contrast, if we implements crop and mirror in tensorflow graph, in other words, different batchs will use different random seed, we call it "dynamic data amplification"
#  In most cases, we use "dynamic data amplification" ,for it prevents overfitting more efficiently
#  If you want to implement "dynamic data amplification", you should insert tensorflow data amplification oprations into your graph.(otherwise you should write functions like re_shuffle)
#  So, Let's build a graph
#==============================================================================image=tf.placeholder(tf.float32,shape=[None,32,32,3]) #image placeholder
label=tf.placeholder(tf.float32,shape=[None,10]) #label placeholder#==============================================================================
# use tensorflow functions to do Data Amplification
#==============================================================================# Randomly crop the image.
# Note that the tf.random_crop has a paramenter "batch". If you set it '-1', then crop will do in batch dimension too, it will cause an error.
# In my code, I set it to batch. But this means our graph can only receive 128 images each time. Any other batchsize will cause an error.
# In training, we input the graph with a batch of 128. However, when we run on val sets and test sets, we usually input the graph with a batch larger than batchsize(i.e. 1000 in offical code.)
# this is one of the shortcomings of my code. But you can overcome it by cumulating in a loop, in other words, input several times when you run on val sets, 128 images every time.
height = 24
width =  24
distorted_image = tf.random_crop(image, [batch,height, width, 3])# Randomly flip the image horizontally
# ATTENTION:
# tf.image.random_flip_left_right is made for single images (i.e. 3-D tensors with shape [height, width, color-channel]).
# How can we make them work on a batch of images (i.e. 4-D tensors with shape [batch, height, width, color-channel])?
# NEXT LINE is the solution, for more informatioin,SEE http://stackoverflow.com/questions/38920240/tensorflow-image-operations-for-batches
distorted_image = tf.map_fn(lambda img: tf.image.random_flip_left_right(img),distorted_image)# Randomly brightness
distorted_image = tf.image.random_brightness(distorted_image,max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8)# Subtract off the mean and divide by the variance of the pixels.
# ATTENTION:
# tf.image.per_image_standardization is also made for single images, so...
float_image = tf.map_fn(lambda img: tf.image.per_image_standardization(img), distorted_image)#==============================================================================
# # Up to now, we have finished preprocessing the data, and the implemention is different from tensorflow offical code.
# # In fact our implemention is much more easy to understand. These is one of the biggest contribution of this blog.
#==============================================================================#==============================================================================
# Next is CNN model, we implement it similarly to tensorflow offical code
# Notice that offical code implements kernel and biases with a custom function, where they use tf.add_to_collection('losses', weight_decay) to collect L2_loss automatically
# Here, we simplify the code, and implement kernel and biases directly
# This causes another shortcoming of our code, we have to collect L2_loss by hand
#==============================================================================
with tf.variable_scope('conv1') as scope:kernel = tf.Variable(tf.truncated_normal(shape=[5, 5, 3, 64],stddev=5e-2))conv = tf.nn.conv2d(float_image, kernel, [1, 1, 1, 1], padding='SAME')biases = tf.Variable(tf.zeros( [64]))pre_activation = tf.nn.bias_add(conv, biases)conv1 = tf.nn.relu(pre_activation, name=scope.name)# pool1
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],padding='SAME', name='pool1')# norm1
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,name='norm1')# conv2
with tf.variable_scope('conv2') as scope:kernel = tf.Variable(tf.truncated_normal(shape=[5, 5, 64, 64],stddev=5e-2))conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')biases = tf.get_variable(name='biases',shape=[64], initializer=tf.constant_initializer(0.1))pre_activation = tf.nn.bias_add(conv, biases)conv2 = tf.nn.relu(pre_activation, name=scope.name)# norm2
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,name='norm2')# pool2
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],strides=[1, 2, 2, 1], padding='SAME', name='pool2')# local3
with tf.variable_scope('conv3') as scope:# Move everything into depth so we can perform a single matrix multiply.reshape = tf.reshape(pool2, [batch, -1])dim = reshape.get_shape()[1].value
weights3 = tf.Variable(tf.truncated_normal(shape=[dim, 384],stddev=0.04))                          # DON'T FORGET REGULARZATION !!!!!!!!!!!!!
biases3 = tf.get_variable(name='biases3',shape=[384], initializer=tf.constant_initializer(0.1))
local3 = tf.nn.relu(tf.matmul(reshape, weights3) + biases3, name=scope.name)weights4 = tf.Variable(tf.truncated_normal(shape=[384,192],stddev=0.04))                           # DON'T FORGET REGULARZATION !!!!!!!!!!!!!
biases4 = tf.get_variable(name='biases4',shape=[192], initializer=tf.constant_initializer(0.1))
local4 = tf.nn.relu(tf.matmul(local3, weights4) + biases4, name=scope.name)with tf.variable_scope('softmax_linear') as scope:weights = tf.Variable(tf.truncated_normal(shape=[192,10],stddev=1/192))biases = tf.Variable(tf.zeros( [10]))softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)#==============================================================================
# # In offical code, they use tf.nn.sparse_softmax_cross_entropy_with_logits to compute loss
# # But we use tf.nn.softmax_cross_entropy_with_logits instead, because our label is one-hot
#==============================================================================
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label,logits=softmax_linear)) +tf.multiply(tf.nn.l2_loss(weights3),0.004) +tf.multiply(tf.nn.l2_loss(weights4),0.004)#==============================================================================
# Decay the learning rate exponentially based on the number of steps.
# NOTE:
# most training process needs decaying learning rate, so I strongly recommend you use global_step and tf.train.exponential_decay
# DO NOT implement learning_rate_decay DIY in the train loop, because learning rate is not a simple number,but an operation in tensorflow.
# if you still feel confuzed, the following code will show you the reason
#==============================================================================
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(INITIAL_LEARNING_RATE,global_step,decay_steps,LEARNING_RATE_DECAY_FACTOR,staircase=True)# Create the gradient descent optimizer with the given learning rate.
train_op=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)# Calculate accuray
correct_prediction = tf.equal(tf.argmax(label,1), tf.argmax(softmax_linear,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# define a saver to save checkpoints
saver = tf.train.Saver()# Congratulations! We have finished building a graph
# let's define a session
sess=tf.Session()
sess.run(tf.global_variables_initializer())#==============================================================================
# Record the variables you want to observe
# here, we use a trick to observe both train_loss and test_loss, as the following code shows
# for more information about this trick, SEE http://stackoverflow.com/questions/34471563/logging-training-and-validation-loss-in-tensorboard
#==============================================================================
train_loss_summary=tf.summary.scalar('train loss',loss)
test_loss_summary=tf.summary.scalar('test  loss',loss)
train_accuracy_summary=tf.summary.scalar('train accuracy',accuracy)
test_accuracy_summary=tf.summary.scalar('test  accuracy',accuracy)
#merge_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(log_dir,sess.graph)# define some variables used in the upcoming loop
begin=0
train_num=50000use_batch=Nonetrain_losses=[]
test_losses=[]
train_acc=[]
test_acc=[](X_train,y_train,X_test,y_test) = load_onehot_shuffle()for step in range(MAX_STEPS):# get a batch indexuse_batch=range(begin,begin+batch)begin=begin+batch# prevent out of rangeif begin+batch>=train_num:begin=0
#        (X_train,y_train,X_test,y_test) = load_onehot_shuffle()(X_train,y_train,X_test,y_test) = re_shuffle(X_train,y_train,X_test,y_test)# show loss and accuracy every 100 stepsif (step+1)%100 == 0 or (step+1) == MAX_STEPS: # I draw lessons from tensorflow offical code for mnist# traintrain_aaa,train_loss=sess.run([accuracy,loss],feed_dict={image:X_train[use_batch,:,:,:],label:y_train[use_batch,:]})# testtest_accuracy,test_loss=sess.run([accuracy,loss],feed_dict={image:X_test[:batch,:,:,:],label:y_test[:batch,:]})# record loss and accuracytrain_losses.append(train_loss)test_losses.append(test_loss)train_acc.append(train_aaa)test_acc.append(test_loss)print "step %d,learning rate %g, train accuracy %g, test accuracy %g, train loss %g, test loss %g"%(step,sess.run(learning_rate), train_aaa,test_accuracy,train_loss,test_loss)checkpoint_file = os.path.join(checkpoint_dir, 'model.ckpt') #saver.save(sess, checkpoint_file, global_step=step)#==============================================================================
#     LET'S WRITE SUMMARY
#==============================================================================loss_summary_train,sum_train_loss=sess.run([train_loss_summary,loss],feed_dict={image:X_train[use_batch,:,:,:],label:y_train[use_batch,:]})summary_writer.add_summary(loss_summary_train,step)loss_summary_test,sum_test_loss =sess.run([test_loss_summary,loss],feed_dict={image:X_test[:batch,:,:,:],label:y_test[:batch,:]})summary_writer.add_summary(loss_summary_test,step)accuracy_summary_train,sum_train_loss=sess.run([train_accuracy_summary,accuracy],feed_dict={image:X_train[use_batch,:,:,:],label:y_train[use_batch,:]})summary_writer.add_summary(accuracy_summary_train,step)accuracy_summary_test,sum_test_loss =sess.run([test_accuracy_summary,accuracy],feed_dict={image:X_test[:batch,:,:,:],label:y_test[:batch,:]})summary_writer.add_summary(accuracy_summary_test,step)  #==============================================================================
#     THIS IS THE FINAL CODE
#     LET'S TRAIN HAPPYLY ^o^
#==============================================================================sess.run([train_op],feed_dict={image:X_train[use_batch,:,:,:],label:y_train[use_batch,:]})

data_utils.py

import cPickle as pickle
import numpy as np
import os
from scipy.misc import imreaddef load_CIFAR_batch(filename):""" load single batch of cifar """with open(filename, 'rb') as f:datadict = pickle.load(f)X = datadict['data']Y = datadict['labels']X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")Y = np.array(Y)return X, Ydef load_CIFAR10(ROOT):""" load all of cifar """xs = []ys = []for b in range(1,6):f = os.path.join(ROOT, 'data_batch_%d' % (b, ))X, Y = load_CIFAR_batch(f)xs.append(X)ys.append(Y)    Xtr = np.concatenate(xs)Ytr = np.concatenate(ys)del X, YXte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))return Xtr, Ytr, Xte, Ytedef load_tiny_imagenet(path, dtype=np.float32):"""Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, andTinyImageNet-200 have the same directory structure, so this can be usedto load any of them.Inputs:- path: String giving path to the directory to load.- dtype: numpy datatype used to load the data.Returns: A tuple of- class_names: A list where class_names[i] is a list of strings giving theWordNet names for class i in the loaded dataset.- X_train: (N_tr, 3, 64, 64) array of training images- y_train: (N_tr,) array of training labels- X_val: (N_val, 3, 64, 64) array of validation images- y_val: (N_val,) array of validation labels- X_test: (N_test, 3, 64, 64) array of testing images.- y_test: (N_test,) array of test labels; if test labels are not available(such as in student code) then y_test will be None."""# First load wnidswith open(os.path.join(path, 'wnids.txt'), 'r') as f:wnids = [x.strip() for x in f]# Map wnids to integer labelswnid_to_label = {wnid: i for i, wnid in enumerate(wnids)}# Use words.txt to get names for each classwith open(os.path.join(path, 'words.txt'), 'r') as f:wnid_to_words = dict(line.split('\t') for line in f)for wnid, words in wnid_to_words.iteritems():wnid_to_words[wnid] = [w.strip() for w in words.split(',')]class_names = [wnid_to_words[wnid] for wnid in wnids]# Next load training data.X_train = []y_train = []for i, wnid in enumerate(wnids):if (i + 1) % 20 == 0:print 'loading training data for synset %d / %d' % (i + 1, len(wnids))# To figure out the filenames we need to open the boxes fileboxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid)with open(boxes_file, 'r') as f:filenames = [x.split('\t')[0] for x in f]num_images = len(filenames)X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype)y_train_block = wnid_to_label[wnid] * np.ones(num_images, dtype=np.int64)for j, img_file in enumerate(filenames):img_file = os.path.join(path, 'train', wnid, 'images', img_file)img = imread(img_file)if img.ndim == 2:## grayscale fileimg.shape = (64, 64, 1)X_train_block[j] = img.transpose(2, 0, 1)X_train.append(X_train_block)y_train.append(y_train_block)# We need to concatenate all training dataX_train = np.concatenate(X_train, axis=0)y_train = np.concatenate(y_train, axis=0)# Next load validation datawith open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f:img_files = []val_wnids = []for line in f:img_file, wnid = line.split('\t')[:2]img_files.append(img_file)val_wnids.append(wnid)num_val = len(img_files)y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids])X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype)for i, img_file in enumerate(img_files):img_file = os.path.join(path, 'val', 'images', img_file)img = imread(img_file)if img.ndim == 2:img.shape = (64, 64, 1)X_val[i] = img.transpose(2, 0, 1)# Next load test images# Students won't have test labels, so we need to iterate over files in the# images directory.img_files = os.listdir(os.path.join(path, 'test', 'images'))X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype)for i, img_file in enumerate(img_files):img_file = os.path.join(path, 'test', 'images', img_file)img = imread(img_file)if img.ndim == 2:img.shape = (64, 64, 1)X_test[i] = img.transpose(2, 0, 1)y_test = Noney_test_file = os.path.join(path, 'test', 'test_annotations.txt')if os.path.isfile(y_test_file):with open(y_test_file, 'r') as f:img_file_to_wnid = {}for line in f:line = line.split('\t')img_file_to_wnid[line[0]] = line[1]y_test = [wnid_to_label[img_file_to_wnid[img_file]] for img_file in img_files]y_test = np.array(y_test)return class_names, X_train, y_train, X_val, y_val, X_test, y_testdef load_models(models_dir):"""Load saved models from disk. This will attempt to unpickle all files in adirectory; any files that give errors on unpickling (such as README.txt) willbe skipped.Inputs:- models_dir: String giving the path to a directory containing model files.Each model file is a pickled dictionary with a 'model' field.Returns:A dictionary mapping model file names to models."""models = {}for model_file in os.listdir(models_dir):with open(os.path.join(models_dir, model_file), 'rb') as f:try:models[model_file] = pickle.load(f)['model']except pickle.UnpicklingError:continuereturn models

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