CIFAR 10 image dataset
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.
wget -c https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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
imsave.py uses from scipy.misc import imsave
which is deprecated
this requires sudo pip install scipy==0.16.1
save.py PIL version