In [1]:

    

import graphlab


    

    




A newer version of GraphLab Create (v1.7.1) is available! Your current version is v1.6.1.

You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.

In [2]:

    

image_train = graphlab.SFrame('image_train_data/')


    

    




[INFO] This non-commercial license of GraphLab Create is assigned to sandipto.neogi@gmail.com and will expire on October 11, 2016. For commercial licensing options, visit https://dato.com/buy/.

[INFO] Start server at: ipc:///tmp/graphlab_server-10710 - Server binary: /Users/sandiptoneogi/anaconda/envs/dato/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1448084408.log
[INFO] GraphLab Server Version: 1.6.1

In [3]:

    

image_train.head(1)


    

    
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, ...
    

[1 rows x 5 columns]

In [4]:

    

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


    

    




PROGRESS: Starting brute force nearest neighbors model training.

In [10]:

    

cat = image_train[18:19]


    

In [11]:

    

graphlab.canvas.set_target('ipynb')


    

In [12]:

    

cat['image'].show()


    

In [13]:

    

knn_model.query(cat)


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 30.657ms     |
PROGRESS: | Done         |         | 100         | 259.532ms    |
PROGRESS: +--------------+---------+-------------+--------------+






    
Out[13]:





    

        
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 [17]:

    

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


    

In [19]:

    

cat_neighbours = get_images_ids(knn_model.query(cat))


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 20.096ms     |
PROGRESS: | Done         |         | 100         | 247.07ms     |
PROGRESS: +--------------+---------+-------------+--------------+

In [20]:

    

cat_neighbours['image'].show()


    

In [21]:

    

car = image_train[8:9]


    

In [22]:

    

car['image'].show()


    

In [23]:

    

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   | 30.164ms     |
PROGRESS: | Done         |         | 100         | 247.301ms    |
PROGRESS: +--------------+---------+-------------+--------------+






    

In [24]:

    

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


    

In [25]:

    

show_neighbours(8)


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 26.734ms     |
PROGRESS: | Done         |         | 100         | 250.298ms    |
PROGRESS: +--------------+---------+-------------+--------------+






    

In [29]:

    

show_neighbours(2000)


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 24.613ms     |
PROGRESS: | Done         |         | 100         | 250.336ms    |
PROGRESS: +--------------+---------+-------------+--------------+






    

In [39]:

    

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


    

In [40]:

    

len(dog_sframe)


    

    
Out[40]:





509

In [37]:

    

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


    

In [38]:

    

len(cat_sframe)


    

    
Out[38]:





509

In [41]:

    

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


    

In [42]:

    

len(bird_sframe)


    

    
Out[42]:





478

In [43]:

    

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


    

In [45]:

    

len(automobile_sframe)


    

    
Out[45]:





509

In [53]:

    

print len(image_train)
print 509 * 3 + 478


    

    




2005
2005

In [54]:

    

dog_only_knn_model = graphlab.nearest_neighbors.create(dog_sframe, features=['deep_features'], label='id')


    

    




PROGRESS: Starting brute force nearest neighbors model training.

In [55]:

    

cat_only_knn_model = graphlab.nearest_neighbors.create(cat_sframe, features=['deep_features'], label='id')


    

    




PROGRESS: Starting brute force nearest neighbors model training.

In [56]:

    

bird_only_knn_model = graphlab.nearest_neighbors.create(bird_sframe, features=['deep_features'], label='id')


    

    




PROGRESS: Starting brute force nearest neighbors model training.

In [57]:

    

automobile_only_knn_model = graphlab.nearest_neighbors.create(automobile_sframe, features=['deep_features'], label='id')


    

    




PROGRESS: Starting brute force nearest neighbors model training.

In [58]:

    

image_test = graphlab.SFrame('image_test_data/')


    

In [59]:

    

image_test[0:1]['image'].show()


    

In [73]:

    

q2 = cat_only_knn_model.query(image_test[0:1])


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 28.432ms     |
PROGRESS: | Done         |         | 100         | 105.853ms    |
PROGRESS: +--------------+---------+-------------+--------------+

In [74]:

    

q2 = q2['rank' == 1]
# Filter
cat_neighbors = image_train.filter_by(q2['reference_label'],'id')
# Get the image
cat_neighbors['image'].show()


    

In [79]:

    

q3 = dog_only_knn_model.query(image_test[0:1])


    

    




PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 20.881ms     |
PROGRESS: | Done         |         | 100         | 95.941ms     |
PROGRESS: +--------------+---------+-------------+--------------+

In [80]:

    

q3 = q3['rank' == 1]
# Filter
dog_neighbors = image_train.filter_by(q3['reference_label'], 'id')
# Get the image
dog_neighbors['image'].show()


    

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