The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images.

CIFAR-10

wget -c https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

• CIFAR 10 Python

tar -xvzf cifar-10-python.tar.gz

• sigmoid.py : Based on DL.ai cats.py

• 5_layer.py: : Based on DL.ai 5_layer_model.py

• load_cifar.py : load data_batch_1 data (images) and labels (classes) into a np.array

• imsave.py : save an image from CIFAR-10 as JPG

• class_labels.py : load batches.meta label_names (classes) and print them

• filter_class.py : write class label index from data_batch_1 to class_label.txt

load_cifar.py uses random.seed(1) for consistant accuracy

sigmoid.py:

train accuracy: 89.0 %
test accuracy: 72.0 %

5_layer.py:

train accuracy: 99.0 %
test accuracy: 70.0 %

0 : airplane
1 : automobile
2 : bird
3 : cat
4 : deer
5 : dog
6 : frog
7 : horse
8 : ship
9 : truck

Batch to Array Code

imsave.py uses from scipy.misc import imsave which is deprecated
this requires sudo pip install scipy==0.16.1
save.py PIL version