CIFAR-10 and CIFAR-100 Dataset in TensorFlow

The CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Geoffrey Hinton and Vinod Nair. The dataset is divided into five training batches and only one test batch, each with 10000 images.

The test batch contains 1000 randomly-selected images from each class. The training batches contain the remaining images in a random order, but some training batches contain the remaining images in a random order, but some training batches contain more images from one class to another. Between them, the training batches contain exactly 5000 images for each class.

The classes will be entirely mutually exclusive. There will be no overlapping between automobiles and trucks. Automobiles include things which are similar to sedans and SUVs. Trucks class includes only big trucks, and it neither includes pickup trucks. If we are looked through the CIFAR dataset, we realize that is not just one type of bird or cat. The bird and cat class contains many different types of birds and cat. The bird and Cat class provide many kinds of birds and cat varying in size, color, magnification, different angles, and different poses.

With endless datasets, there are many ways by which we can write number one and number two. It just wasn’t as diverse, and on top of that, the endless dataset is a gray scalar. The CIFAR dataset consists of 32 larger by 32 color images, and each photograph with three different color channels. Now our most important question is that the LeNet model that has performed so well on an endless dataset will it be enough to classify the CIFAR dataset?

CIFAR-100 Dataset

It is just like CIFAR-10 dataset. The only difference is that it has 100 classes containing 600 images per class. There are 100 testing images and 500 training images in per class. These 100 classes are grouped into 20 superclasses, and each image comes with a “coarse” label (the superclass to which it belongs) and a “fine” label (the class which it belongs to) and a “fine” label (the class to which it belongs to).

Below classes in the CIFAR-100 dataset:

S. No	Superclass	Classes
1.	Flowers	Orchids, poppies, roses, sunflowers, tulips
2.	Fish	Aquarium fish, flatfish, ray, shark, trout
3.	Aquatic mammals	Beaver, dolphin, otter, seal, whale
4.	food containers	Bottles, bowls, cans, cups, plates
5.	Household electrical devices	Clock, lamp, telephone, television, computer keyboard
6.	Fruit and vegetables	Apples, mushrooms, oranges, pears, sweet peppers
7.	Household furniture	Table, Chair, couch, wardrobe, bed,
8.	Insects bee, beetle, butterfly, caterpillar, cockroach
9.	Large natural outdoor scenes	Cloud, forest, mountain, plain, sea
10.	Large human-made outdoor things	Bridge, castle, house, road, skyscraper
11.	Large carnivores	Bear, leopard, lion, tiger, wolf
12.	Medium-sized mammals	Fox, porcupine, possum, raccoon, skunk
13.	Large Omnivores and herbivores	Camel, cattle, chimpanzee, elephant, kangaroo
14.	Non-insect invertebrates	Crab, lobster, snail, spider, worm
15.	reptiles	Crocodile, dinosaur, lizards, snake, turtle
16.	trees	Maple, oak, palm, pine, willow
17.	people	girl, man, women, baby, boy
18.	Small mammals	Hamster, rabbit, mouse, shrew, squirrel
19.	Vehicles 1	Bicycle, bus, motorcycle, pickup truck, train
20.	Vehicles 2	Lawn-mower, rocket, streetcar, tractor, tank

Use-Case: Implementation of CIFAR10 with the help of Convolutional Neural Networks Using TensorFlow

Now, train a network to classify images from the CIFAR10 Dataset using a Convolution Neural Network built-in TensorFlow.

Consider the following Flowchart to understand the working of the use-case:

Img

Install Necessary packages:

Train The Network:

  import numpy as np  import tensorflow as tf  from time import time  import math  from include .data import get_data_set  from include.model import model, lr  train_x, train_y= get_data_set(“train”)  test_x, test_y = get_data_set(“test”)  tf. set_random_seed(21)  x, y, output, y_pred_cls, global_step, learning_rate=model()  global_accuracy =0  epoch_start=0  #PARAM  _BATCH_SIZE=128  _EPOCH=60  _SAVE_PATH=”./tensorboard/cifar-10-v1.0.0/”  #LOSS AND OPTIMIZER  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y))  optimizer=tf.train.AdamOptimizer(learning_rate= learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08). Minimize(loss, global_step=global_step)  #PREDICTION AND ACCURACY CALCULATION  correct_prediction=tf.equal(y_pred_cls, tf.argmax(y, axis=1))  accuracy = tf.reduce_mean(tf.cast(correct_predictiction, tf.float32))  # SAVER  merged = tf.summary.merge_all()  saver = tf.train.Saver()  sess = tf.Session()  train_writer= tf.summary.FileWriter(_SAVE_PATH, sess.graph)  try:  print(” Trying to restore last checkpoint?”)  last_chk_path= tf.train.latest_checkpoint(checkpoint_dir=SAVE_PATH)  saver.restore(sess, save_path=last_chk_path)  print(“Restored checkpoint from:”, last_chk_path)  except ValueError:  print(“Failed to restore checkpoint. Initializing variable instead.”)  sess.run(tf.global_variables_initializer())  def train(epoch):      global epoch_start      epoch_start= time()      batch_size=int(math.ceil(len(train_x)/_BATCH_SIZE))      i_global = 0  for s in range(batch_size):      batch_xs= train_x[s*_BATCH_SIZE: (s+1)*_BATCH_SIZE]      batch_ys = train_y[s*_BATCH_SIZE: (s+1)*_BATCH_SIZE]     start_time= time()  i_global, _, batch_loss, batch_acc=sess.run( [global_step, optimizer, loss, accuracy],  feed_dict={x: batch_xs, y: batch_ys, learning_rate: lr(epoch)})  duration = time() – start_time  if s% 10== 0:  percentage = int(round((s/batch_size)*100))  bar_len=29  filled_len= int ((bar_len*int(percentage))/100)  bar=’=’ *filled_len + ?>’ + ?-? * (bar_len _filled_len)  msg= “Global step: { :>5} – [{}] {:>3}% -acc: {:.{:>4f} – loss: {:.4f} -{:.1f} sample/sec”  print(msg.format(i_global, bar, percentage, batch_acc, batch_loss, _BATCH_SIZE/duration))  test_and_save(i_global, epoch)  def test_and_save(_global_step, epoch):  global global_accuracy  global epoch_start  i=0  predicted_class=np.zeroes(shape=len(test_x), dtype=np.int)  while i< len (test_x) : j=min(i+_BATCH_SIZE, len(test_x)) batch_xs=test_x[I:j, :] batch_ys=test_y[i:j,:] predicted_class[i:j]=sess.run(y_pred_cls, feed_dict=x:batch_xs, y: batch_ys, learning_rate: lr(epoch)} ) i=j correct= (np.argmax(test_y, axis=1) == predicted_class) acc = correct.mean()*100 correct_numbers = correct.sum() hours, rem = divmod(time() – epoch_start, 3600) minutes, seconds = divmod(rem, 60) mes = ”  Epoch {} – accuracy: {: .2f}% ({}/{})- time: {:0>2}:{:0.2}:{:05.2f}”  print(mes.format((epoch+1), acc, correct_numbers, len(test_x), int(hours), int(minutes), seconds))  if global_accuracy != 0 and global_accuracy < acc: summary = tf.Summary(value=[ tf.Summary.Value(tag=”Accuracy/test”, simple_value=acc), ]) train_writer.add_summary(summary, _global_step) saver.save(sess, save_path=_SAVE_PATH, global_step=_global_step) mes = “This epoch receive better accuracy: {:.2f} > {:.2f}. Saving session…    print(mes.format((acc, global_accuracy))  global_accuracy = acc  elif global_accuracy==0:  global_accuracy=acc  print(“################################################################  def main():  train_start=time()  for i in range(_EPOCH):  print(” Epoch: {}/{}”.format(( i+1),_EPOCH))  train(i)  hours, rem=divmod(time()-train_start, 3600 minutes, seconds=divmod(rem,60)  mes= “Best accuracy per session: {:.2f}, time: {:0>2}:{:0>2}:{:05.2f}”  print(mes.format(global_accuracy, int(hours), int(minutes), seconds))  if _name_ ==”_main_”:  main()  sess.close()  

Output:

Epoch: 60/60    Global step: 23070 - [>-----------------------------]   0% - acc: 0.9531 - loss: 1.5081 - 7045.4 sample/sec  Global step: 23080 - [>-----------------------------]   3% - acc: 0.9453 - loss: 1.5159 - 7147.6 sample/sec  Global step: 23090 - [=>----------------------------]   5% - acc: 0.9844 - loss: 1.4764 - 7154.6 sample/sec  Global step: 23100 - [==>---------------------------]   8% - acc: 0.9297 - loss: 1.5307 - 7104.4 sample/sec  Global step: 23110 - [==>---------------------------]  10% - acc: 0.9141 - loss: 1.5462 - 7091.4 sample/sec  Global step: 23120 - [===>--------------------------]  13% - acc: 0.9297 - loss: 1.5314 - 7162.9 sample/sec  Global step: 23130 - [====>-------------------------]  15% - acc: 0.9297 - loss: 1.5307 - 7174.8 sample/sec  Global step: 23140 - [=====>------------------------]  18% - acc: 0.9375 - loss: 1.5231 - 7140.0 sample/sec  Global step: 23150 - [=====>------------------------]  20% - acc: 0.9297 - loss: 1.5301 - 7152.8 sample/sec  Global step: 23160 - [======>-----------------------]  23% - acc: 0.9531 - loss: 1.5080 - 7112.3 sample/sec  Global step: 23170 - [=======>----------------------]  26% - acc: 0.9609 - loss: 1.5000 - 7154.0 sample/sec  Global step: 23180 - [========>---------------------]  28% - acc: 0.9531 - loss: 1.5074 - 6862.2 sample/sec  Global step: 23190 - [========>---------------------]  31% - acc: 0.9609 - loss: 1.4993 - 7134.5 sample/sec  Global step: 23200 - [=========>--------------------]  33% - acc: 0.9609 - loss: 1.4995 - 7166.0 sample/sec  Global step: 23210 - [==========>-------------------]  36% - acc: 0.9375 - loss: 1.5231 - 7116.7 sample/sec  Global step: 23220 - [===========>------------------]  38% - acc: 0.9453 - loss: 1.5153 - 7134.1 sample/sec  Global step: 23230 - [===========>------------------]  41% - acc: 0.9375 - loss: 1.5233 - 7074.5 sample/sec  Global step: 23240 - [============>-----------------]  43% - acc: 0.9219 - loss: 1.5387 - 7176.9 sample/sec  Global step: 23250 - [=============>----------------]  46% - acc: 0.8828 - loss: 1.5769 - 7144.1 sample/sec  Global step: 23260 - [==============>---------------]  49% - acc: 0.9219 - loss: 1.5383 - 7059.7 sample/sec  Global step: 23270 - [==============>---------------]  51% - acc: 0.8984 - loss: 1.5618 - 6638.6 sample/sec  Global step: 23280 - [===============>--------------]  54% - acc: 0.9453 - loss: 1.5151 - 7035.7 sample/sec  Global step: 23290 - [================>-------------]  56% - acc: 0.9609 - loss: 1.4996 - 7129.0 sample/sec  Global step: 23300 - [=================>------------]  59% - acc: 0.9609 - loss: 1.4997 - 7075.4 sample/sec  Global step: 23310 - [=================>------------]  61% - acc: 0.8750 - loss:1.5842 - 7117.8 sample/sec  Global step: 23320 - [==================>-----------]  64% - acc: 0.9141 - loss:1.5463 - 7157.2 sample/sec  Global step: 23330 - [===================>----------]  66% - acc: 0.9062 - loss: 1.5549 - 7169.3 sample/sec  Global step: 23340 - [====================>---------]  69% - acc: 0.9219 - loss: 1.5389 - 7164.4 sample/sec  Global step: 23350 - [====================>---------]  72% - acc: 0.9609 - loss: 1.5002 - 7135.4 sample/sec  Global step: 23360 - [=====================>--------]  74% - acc: 0.9766 - loss: 1.4842 - 7124.2 sample/sec  Global step: 23370 - [======================>-------]  77% - acc: 0.9375 - loss: 1.5231 - 7168.5 sample/sec  Global step: 23380 - [======================>-------]  79% - acc: 0.8906 - loss: 1.5695 - 7175.2 sample/sec  Global step: 23390 - [=======================>------]  82% - acc: 0.9375 - loss: 1.5225 - 7132.1 sample/sec  Global step: 23400 - [========================>-----]  84% - acc: 0.9844 - loss: 1.4768 - 7100.1 sample/sec  Global step: 23410 - [=========================>----]  87% - acc: 0.9766 - loss: 1.4840 - 7172.0 sample/sec  Global step: 23420 - [==========================>---]  90% - acc: 0.9062 - loss: 1.5542 - 7122.1 sample/sec  Global step: 23430 - [==========================>---]  92% - acc: 0.9297 - loss: 1.5313 - 7145.3 sample/sec  Global step: 23440 - [===========================>--]  95% - acc: 0.9297 - loss: 1.5301 - 7133.3 sample/sec  Global step: 23450 - [============================>-]  97% - acc: 0.9375 - loss: 1.5231 - 7135.7 sample/sec  Global step: 23460 - [=============================>] 100% - acc: 0.9250 - loss: 1.5362 - 10297.5 sample/sec    Epoch 60 - accuracy: 78.81% (7881/10000)  This epoch receive better accuracy: 78.81 > 78.78. Saving session...  ##################################################################################################

Run Network on Test Dataset:

  import numpy as np  import tensorflow as tf  from include.data import get_data_set  from include.model import model  test_x, test_y= get_data_set(“test”)  x, y, output, y_pred_cls, global_step, learning_rate =model()  _BATCH_SIZE = 128  _CLASS_SIZE = 10  _SAVE_PATH = “./tensorboard/cifar-10-v1.0.0/”  saver= tf.train.Saver()  Sess=tf.Session()  try;   print(” Trying to restore last checkpoint …”)  last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH  saver.restore(sess, save_path=last_chk_path)  print(“Restored checkpoint from:”, last_chk_path)  expect ValueError:  print(”  Failed to restore checkpoint. Initializing variables instead.”)  sess.run(tf.global_variables_initializer())  def main():  i=0  predicted_class= np.zeros(shape=len(test_x), dtype=np.int)  while i< lens(test_x):  j=min(i+_BATCH_SIZE, len(test_x))  batch_xs=test_x[i:j,:]  batch_xs=test_y[i:j,:]  pre dicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})  i=j  corr ect = (np.argmax(test_y, axis=1) == predicted_class)  acc=correct.mean()*100  correct_numbers=correct.sum()  print()  print(“Accuracy is on Test-Set: {0:.2f}% ({1} / {2})”.format(acc, correct_numbers, len(test_x)))  if__name__==”__main__”:  main()  sess.close()  

Simple Output

Trying to restore last checkpoint ...  Restored checkpoint from: ./tensorboard/cifar-10-v1.0.0/-23460    Accuracy on Test-Set: 78.81% (7881 / 10000)

Training Time

Here, we see that how much time takes 60 epoch:

Device	Batch	Time	Accuracy[%]
NVidia	128	8m4s	79.12
Inteli77700HQ	128	3h30m	78.91

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CIFAR-10 and CIFAR-100 Dataset in TensorFlow

CIFAR-10 and CIFAR-100 Dataset in TensorFlow

CIFAR-100 Dataset

Use-Case: Implementation of CIFAR10 with the help of Convolutional Neural Networks Using TensorFlow

Install Necessary packages:

Train The Network:

Run Network on Test Dataset:

Training Time

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