In [1]:

    

import sys
sys.path.append('../../')
import time


    

In [2]:

    

from library.datasets.cifar.cifar10 import CIFAR10
from library.utils import file_utils


    

In [3]:

    

total_time = 0
exp_no = 1


    

In [4]:

    

output_directory = '../../models/cifar10/load_cifar10_dataset/'
dataset_file = output_directory + 'cifar10_dataset.h5'
file_utils.mkdir_p(output_directory)
num_train_images = 1.0


    

In [5]:

    

start = time.time()
cifar10 = CIFAR10(num_images=num_train_images, save_h5py='')
end = time.time()
total_time += (end-start)
print('CIFAR10 class constructor - %.4f seconds' % (end-start))


    

    




CIFAR10 class constructor - 0.0002 seconds

In [6]:

    

start = time.time()
cifar10.load_data(data_directory='../../datasets/cifar10/')
end = time.time()
total_time += (end-start)
print('Loaded CIFAR10 dataset in %.4f seconds' % (end-start))


    

    




Loading CIFAR 10 Dataset
Downloading and extracting CIFAR 10 file
MD5sum of the file: ../../datasets/cifar10/cifar-10.tar.gz is verified

Loading 50000 train images
Loading CIFAR 10 Training Dataset
Reading unpicked data file: ../../datasets/cifar10/cifar-10-batches/data_batch_1
Reading unpicked data file: ../../datasets/cifar10/cifar-10-batches/data_batch_2
Reading unpicked data file: ../../datasets/cifar10/cifar-10-batches/data_batch_3
Reading unpicked data file: ../../datasets/cifar10/cifar-10-batches/data_batch_4
Reading unpicked data file: ../../datasets/cifar10/cifar-10-batches/data_batch_5

Loading 10000 test images
Loading CIFAR 10 Test Dataset
Unpickling test file: ../../datasets/cifar10/cifar-10-batches/test_batch
Reading unpicked test file: ../../datasets/cifar10/cifar-10-batches/test_batch

Loaded CIFAR 10 Dataset in 2.4267 seconds
Loaded CIFAR10 dataset in 2.4277 seconds

In [7]:

    

cifar10.plot_sample(plot_train=True, plot_test=False)


    

In [9]:

    

cifar10.plot_images(cifar10.train.images, cifar10.train.fine_class_names, cls_pred_fine=None, nrows=3, ncols=3, 
                    fig_size=(7,7), fontsize=15, convert=False, type='rgb')


    

    













    
Out[9]:





True

In [10]:

    

cifar10.plot_sample(plot_train=False, plot_test=True)


    

In [12]:

    

cifar10.plot_images(cifar10.test.images, cifar10.test.fine_class_names, cls_pred_fine=None, nrows=3, ncols=3, 
                    fig_size=(7,7), fontsize=15, convert=False, type='rgb')


    

    













    
Out[12]:





True

In [13]:

    

def output_HTML(read_file, output_file):
    from nbconvert import HTMLExporter
    import codecs
    import nbformat
    exporter = HTMLExporter()
    output_notebook = nbformat.read(read_file, as_version=4)
    print()
    output, resources = exporter.from_notebook_node(output_notebook)
    codecs.open(output_file, 'w', encoding='utf-8').write(output)


    

In [14]:

    

%%javascript
var notebook = IPython.notebook
notebook.save_notebook()


    

In [15]:

    

%%javascript
var kernel = IPython.notebook.kernel;
var thename = window.document.getElementById("notebook_name").innerHTML;
var command = "theNotebook = " + "'"+thename+"'";
kernel.execute(command);


    

In [16]:

    

current_file = './' + theNotebook + '.ipynb'
output_file = output_directory + 'exp_no_' + str(exp_no).zfill(2) + '_' + theNotebook + '.html'
print('Current file: ' + str(current_file))
print('Output file: ' + str(output_file))
file_utils.mkdir_p(output_directory) 
output_HTML(current_file, output_file)


    

    




Current file: ./load_cifar10_dataset.ipynb
Output file: ../../models/cifar10/load_cifar10_dataset/exp_no_01_load_cifar10_dataset.html

In [17]:

    

print('Code took %.6f s to run on training with %d examples' % (total_time,num_train_images))


    

    




Code took 2.427942 s to run on training with 1 examples

In [ ]: