In [1]:

    

import graphlab


    

In [2]:

    

image_train = graphlab.SFrame('image_train_data/')


    

    




[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1489784161.log






    




This non-commercial license of GraphLab Create for academic use is assigned to y_xwang@163.com and will expire on March 13, 2018.

In [5]:

    

image_train.head()


    

    
Out[5]:





    

        
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]

In [4]:

    

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


    

    





Starting brute force nearest neighbors model training.

In [6]:

    

graphlab.canvas.set_target('ipynb')


    

In [7]:

    

cat = image_train[18:19]


    

In [8]:

    

cat['image'].show()


    

In [9]:

    

knn_model.query(cat)


    

    





Starting pairwise querying.






    





+--------------+---------+-------------+--------------+






    





| Query points | # Pairs | % Complete. | Elapsed Time |






    





+--------------+---------+-------------+--------------+






    





| 0            | 1       | 0.0498753   | 66.697ms     |






    





| 0            | 1140    | 56.8579     | 430.828ms    |






    





| Done         |         | 100         | 715.18ms     |






    





+--------------+---------+-------------+--------------+






    
Out[9]:





    

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


    

In [ ]: