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In [1]:



    


import graphlab

Load CIFAR-10 dataset





In [2]:



    


image_train = graphlab.SFrame('image_train_data_2/')



    


















    






[INFO] This non-commercial license of GraphLab Create is assigned to akshay.narayan@u.nus.edu and will expire on September 26, 2016. For commercial licensing options, visit https://dato.com/buy/.

[INFO] Start server at: ipc:///tmp/graphlab_server-7626 - Server binary: /usr/local/lib/python2.7/dist-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1449378909.log
[INFO] GraphLab Server Version: 1.6.1

















In [3]:



    


image_train.head()



    


















    
Out[3]:








    

        id
        image
        label
        deep_features
        image_array
    

    

        24
        Height: 32 Width: 32
        bird
        [0.242871761322,
1.09545373917, 0.0, ...		
        [73.0, 77.0, 58.0, 71.0,
68.0, 50.0, 77.0, 69.0, ...		
    

    

        33
        Height: 32 Width: 32
        cat
        [0.525087952614, 0.0,
0.0, 0.0, 0.0, 0.0, ...		
        [7.0, 5.0, 8.0, 7.0, 5.0,
8.0, 5.0, 4.0, 6.0, 7.0, ...		
    

    

        36
        Height: 32 Width: 32
        cat
        [0.566015958786, 0.0,
0.0, 0.0, 0.0, 0.0, ...		
        [169.0, 122.0, 65.0,
131.0, 108.0, 75.0, ...		
    

    

        70
        Height: 32 Width: 32
        dog
        [1.12979578972, 0.0, 0.0,
0.778194487095, 0.0, ...		
        [154.0, 179.0, 152.0,
159.0, 183.0, 157.0, ...		
    

    

        90
        Height: 32 Width: 32
        bird
        [1.71786928177, 0.0, 0.0,
0.0, 0.0, 0.0, ...		
        [216.0, 195.0, 180.0,
201.0, 178.0, 160.0, ...		
    

    

        97
        Height: 32 Width: 32
        automobile
        [1.57818555832, 0.0, 0.0,
0.0, 0.0, 0.0, ...		
        [33.0, 44.0, 27.0, 29.0,
44.0, 31.0, 32.0, 45.0, ...		
    

    

        107
        Height: 32 Width: 32
        dog
        [0.0, 0.0,
0.220677852631, 0.0,  ...		
        [97.0, 51.0, 31.0, 104.0,
58.0, 38.0, 107.0, 61.0, ...		
    

    

        121
        Height: 32 Width: 32
        bird
        [0.0, 0.23753464222, 0.0,
0.0, 0.0, 0.0, ...		
        [93.0, 96.0, 88.0, 102.0,
106.0, 97.0, 117.0, ...		
    

    

        136
        Height: 32 Width: 32
        automobile
        [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 7.5737862587, 0.0, ...		
        [35.0, 59.0, 53.0, 36.0,
56.0, 56.0, 42.0, 62.0, ...		
    

    

        138
        Height: 32 Width: 32
        bird
        [0.658935725689, 0.0,
0.0, 0.0, 0.0, 0.0, ...		
        [205.0, 193.0, 195.0,
200.0, 187.0, 193.0, ...		
    



[10 rows x 5 columns]

Creating nearest neighbors model for retrieving images using deep features





In [4]:



    


knn_model = graphlab.nearest_neighbors.create(image_train,
                                             features=['deep_features'],
                                             label='id')



    


















    






PROGRESS: Starting brute force nearest neighbors model training.

Using knn model to find similar images





In [5]:



    


cat = image_train[18:19]



    











In [6]:



    


graphlab.canvas.set_target('ipynb')



    











In [7]:



    


cat['image'].show()



    


















    



























In [8]:



    


knn_model.query(cat)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 10.652ms     |
PROGRESS: | Done         |         | 100         | 186.896ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    
Out[8]:








    

        query_label
        reference_label
        distance
        rank
    

    

        0
        384
        0.0
        1
    

    

        0
        6910
        36.9403137951
        2
    

    

        0
        39777
        38.4634888975
        3
    

    

        0
        36870
        39.7559623119
        4
    

    

        0
        41734
        39.7866014148
        5
    



[5 rows x 4 columns]



















In [10]:



    


def get_images_from_ids(query_result):
    return image_train.filter_by(query_result['reference_label'], 
                                'id')



    











In [11]:



    


cat_neighbors = get_images_from_ids(knn_model.query(cat))



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 8.325ms      |
PROGRESS: | Done         |         | 100         | 187.995ms    |
PROGRESS: +--------------+---------+-------------+--------------+

















In [12]:



    


cat_neighbors['image'].show()



    


















    



























In [13]:



    


car = image_train[8:9]



    











In [14]:



    


car['image'].show()



    


















    



























In [15]:



    


get_images_from_ids(knn_model.query(car))['image'].show()



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 14.863ms     |
PROGRESS: | Done         |         | 100         | 185.29ms     |
PROGRESS: +--------------+---------+-------------+--------------+

Create a lambda to find and show the NN images





In [16]:



    


show_neighbors = lambda i: get_images_from_ids(knn_model.query(image_train[i:i+1]))['image'].show()



    











In [17]:



    


show_neighbors(8)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 9.391ms      |
PROGRESS: | Done         |         | 100         | 182.124ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    



























In [18]:



    


show_neighbors(26)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 11.755ms     |
PROGRESS: | Done         |         | 100         | 196.706ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    



























In [19]:



    


show_neighbors(122)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 9.103ms      |
PROGRESS: | Done         |         | 100         | 190.016ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    



























In [20]:



    


show_neighbors(1222)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 8.623ms      |
PROGRESS: | Done         |         | 100         | 181.369ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    



























In [21]:



    


show_neighbors(2000)



    


















    






PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 10.369ms     |
PROGRESS: | Done         |         | 100         | 183.209ms    |
PROGRESS: +--------------+---------+-------------+--------------+










    



























In [23]:



    


image_train['label'].sketch_summary()



    


















    
Out[23]:








+------------------+-------+----------+
|       item       | value | is exact |
+------------------+-------+----------+
|      Length      |  2005 |   Yes    |
| # Missing Values |   0   |   Yes    |
| # unique values  |   4   |    No    |
+------------------+-------+----------+

Most frequent items:
+-------+------------+-----+-----+------+
| value | automobile | cat | dog | bird |
+-------+------------+-----+-----+------+
| count |    509     | 509 | 509 | 478  |
+-------+------------+-----+-----+------+


















In [30]:



    


dog_sframe = image_train[image_train['label'] == 'dog']



    











In [31]:



    


dog_sframe.show()



    


















    



























In [32]:



    


len(dog_sframe)



    


















    
Out[32]:








509

















In [33]:



    


cat_sframe = image_train[image_train['label'] == 'cat']



    











In [34]:



    


bird_sframe = image_train[image_train['label'] == 'bird']



    











In [35]:



    


automobile_sframe = image_train[image_train['label'] == 'automobile']



    











In [38]:



    


len(cat_sframe), len(bird_sframe), len(automobile_sframe), len(dog_sframe)



    


















    
Out[38]:








(509, 478, 509, 509)

















In [39]:



    


cat_sframe.show()



    


















    



























In [40]:



    


dog_sframe.show()



    


















    



























In [41]:



    


bird_sframe.show()



    


















    



























In [42]:



    


automobile_sframe.show()