CIFAR - 10

Decode data

Activate virtual environment

In [1]:
source ~/kerai/bin/activate


In [2]:
%matplotlib inline
from helper import get_class_names, get_train_data, get_test_data, plot_images

Using TensorFlow backend.

Get class names

In [3]:
class_names = get_class_names()

Decoding file: data/batches.meta

Decode and fetch the training data

In [4]:
images_train, labels_train, class_train = get_train_data()

Decoding file: data/data_batch_1
Decoding file: data/data_batch_2
Decoding file: data/data_batch_3
Decoding file: data/data_batch_4
Decoding file: data/data_batch_5

Decode and fetch the testing data

In [5]:
images_test, labels_test, class_test = get_test_data()

Decoding file: data/test_batch

In [6]:
print("Training set size:\t",len(images_train))
print("Testing set size:\t",len(images_test))

Training set size:	 50000
Testing set size:	 10000

Display a few examples from the dataset

In [7]:
# Get the first images from the test-set.
images = images_test[0:9]

# Get the true classes for those images.
labels_true = labels_test[0:9]

# Plot the images and labels using our helper-function above.
plot_images(images=images, labels_true=labels_true, class_names=class_names)