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Edit and run 





In [2]:



    


import graphlab



    











In [3]:



    


image_train = graphlab.SFrame('image_train_data/')



    


















    






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










    






[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1489783636.log

















In [4]:



    


image_test = graphlab.SFrame('image_test_data/')



    











In [5]:



    


graphlab.canvas.set_target('ipynb')



    











In [6]:



    


image_train['image'].show()

train a classifier on the raw picture pixels





In [7]:



    


raw_pixel_model = graphlab.logistic_classifier.create(image_train, target='label', features=['image_array'])



    


















    






PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.











    







WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.










    







Logistic regression:










    







--------------------------------------------------------










    







Number of examples          : 1890










    







Number of classes           : 4










    







Number of feature columns   : 1










    







Number of unpacked features : 3072










    







Number of coefficients    : 9219










    







Starting L-BFGS










    







--------------------------------------------------------










    







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










    







| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |










    







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










    







| 1         | 6        | 0.000004  | 5.984170     | 0.259788          | 0.165217            |










    







| 2         | 9        | 5.000000  | 8.711652     | 0.404762          | 0.373913            |










    







| 3         | 10       | 5.000000  | 10.275751    | 0.441270          | 0.408696            |










    







| 4         | 11       | 5.000000  | 11.783001    | 0.297884          | 0.286957            |










    







| 5         | 13       | 1.000000  | 14.188616    | 0.402646          | 0.365217            |










    







| 6         | 14       | 1.000000  | 15.671265    | 0.235979          | 0.278261            |










    







| 7         | 16       | 1.000000  | 18.304505    | 0.454497          | 0.417391            |










    







| 8         | 17       | 1.000000  | 19.766691    | 0.457143          | 0.417391            |










    







| 9         | 18       | 1.000000  | 21.504897    | 0.461905          | 0.408696            |










    







| 10        | 19       | 1.000000  | 23.040814    | 0.471958          | 0.391304            |










    







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










    







TERMINATED: Iteration limit reached.










    







This model may not be optimal. To improve it, consider increasing `max_iterations`.

predict





In [8]:



    


image_test[0:3]['image'].show()



    


















    



























In [9]:



    


image_test[0:3]['label']



    


















    
Out[9]:








dtype: str
Rows: 3
['cat', 'automobile', 'cat']

















In [10]:



    


raw_pixel_model.predict(image_test[0:3])



    


















    
Out[10]:








dtype: str
Rows: 3
['bird', 'cat', 'bird']

evaluate all model





In [11]:



    


raw_pixel_model.evaluate(image_test)



    


















    
Out[11]:








{'accuracy': 0.431, 'auc': 0.7134548333333341, 'confusion_matrix': Columns:
 	target_label	str
 	predicted_label	str
 	count	int
 
 Rows: 16
 
 Data:
 +--------------+-----------------+-------+
 | target_label | predicted_label | count |
 +--------------+-----------------+-------+
 |     bird     |       dog       |  213  |
 |     dog      |       cat       |   95  |
 |     bird     |    automobile   |   35  |
 |  automobile  |    automobile   |  371  |
 |     cat      |       dog       |  384  |
 |     dog      |       dog       |  488  |
 |     bird     |       bird      |  715  |
 |  automobile  |       bird      |  346  |
 |     bird     |       cat       |   37  |
 |  automobile  |       cat       |   71  |
 +--------------+-----------------+-------+
 [16 rows x 3 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'f1_score': 0.4114369755764029, 'log_loss': 1.2646741643055923, 'precision': 0.48806633476245403, 'recall': 0.43099999999999994, 'roc_curve': Columns:
 	threshold	float
 	fpr	float
 	tpr	float
 	p	int
 	n	int
 	class	int
 
 Rows: 400004
 
 Data:
 +-----------+-----+-----+------+------+-------+
 | threshold | fpr | tpr |  p   |  n   | class |
 +-----------+-----+-----+------+------+-------+
 |    0.0    | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   1e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   2e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   3e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   4e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   5e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   6e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   7e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   8e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 |   9e-05   | 1.0 | 1.0 | 1000 | 3000 |   0   |
 +-----------+-----+-----+------+------+-------+
 [400004 rows x 6 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}

can we use deep features to improve the model





In [12]:



    


len(image_train)



    


















    
Out[12]:








2005

















In [14]:



    


#deep_learning_model = graphlab.load_model('http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45')
image_train.head()



    


















    
Out[14]:








    

        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]

Given the deep features, let's train a classifier





In [15]:



    


deep_features_model = graphlab.logistic_classifier.create(image_train, target='label', features=['deep_features'])



    


















    






PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
          You can set ``validation_set=None`` to disable validation tracking.











    







WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.










    







WARNING: Detected extremely low variance for feature(s) 'deep_features' because all entries are nearly the same.
Proceeding with model training using all features. If the model does not provide results of adequate quality, exclude the above mentioned feature(s) from the input dataset.










    







Logistic regression:










    







--------------------------------------------------------










    







Number of examples          : 1910










    







Number of classes           : 4










    







Number of feature columns   : 1










    







Number of unpacked features : 4096










    







Number of coefficients    : 12291










    







Starting L-BFGS










    







--------------------------------------------------------










    







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










    







| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |










    







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










    







| 1         | 5        | 0.000131  | 5.387331     | 0.765445          | 0.736842            |










    







| 2         | 9        | 0.250000  | 9.727010     | 0.770157          | 0.715789            |










    







| 3         | 10       | 0.250000  | 11.121171    | 0.773298          | 0.726316            |










    







| 4         | 11       | 0.250000  | 12.512048    | 0.780105          | 0.736842            |










    







| 5         | 12       | 0.250000  | 13.866592    | 0.790576          | 0.736842            |










    







| 6         | 13       | 0.250000  | 15.268560    | 0.804188          | 0.757895            |










    







| 7         | 14       | 0.250000  | 16.752407    | 0.826702          | 0.747368            |










    







| 8         | 15       | 0.250000  | 18.138179    | 0.840314          | 0.747368            |










    







| 9         | 16       | 0.250000  | 19.552293    | 0.858639          | 0.747368            |










    







| 10        | 17       | 0.250000  | 20.984161    | 0.879581          | 0.747368            |










    







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










    







TERMINATED: Iteration limit reached.










    







This model may not be optimal. To improve it, consider increasing `max_iterations`.

















In [16]:



    


deep_features_model.predict(image_test[0:3])



    


















    
Out[16]:








dtype: str
Rows: 3
['cat', 'automobile', 'cat']

















In [17]:



    


deep_features_model.evaluate(image_test)



    


















    
Out[17]:








{'accuracy': 0.7915, 'auc': 0.9418345833333305, 'confusion_matrix': Columns:
 	target_label	str
 	predicted_label	str
 	count	int
 
 Rows: 16
 
 Data:
 +--------------+-----------------+-------+
 | target_label | predicted_label | count |
 +--------------+-----------------+-------+
 |  automobile  |       cat       |   13  |
 |     bird     |       dog       |   66  |
 |     cat      |       bird      |   63  |
 |  automobile  |       dog       |   7   |
 |     cat      |    automobile   |   33  |
 |     dog      |       bird      |   35  |
 |     bird     |       cat       |  117  |
 |     dog      |    automobile   |   18  |
 |     dog      |       dog       |  740  |
 |     cat      |       dog       |  229  |
 +--------------+-----------------+-------+
 [16 rows x 3 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'f1_score': 0.7917247987795695, 'log_loss': 0.5598031974693009, 'precision': 0.7932749787417366, 'recall': 0.7915, 'roc_curve': Columns:
 	threshold	float
 	fpr	float
 	tpr	float
 	p	int
 	n	int
 	class	int
 
 Rows: 400004
 
 Data:
 +-----------+----------------+-----+------+------+-------+
 | threshold |      fpr       | tpr |  p   |  n   | class |
 +-----------+----------------+-----+------+------+-------+
 |    0.0    |      1.0       | 1.0 | 1000 | 3000 |   0   |
 |   1e-05   | 0.974333333333 | 1.0 | 1000 | 3000 |   0   |
 |   2e-05   | 0.964333333333 | 1.0 | 1000 | 3000 |   0   |
 |   3e-05   | 0.956333333333 | 1.0 | 1000 | 3000 |   0   |
 |   4e-05   | 0.948333333333 | 1.0 | 1000 | 3000 |   0   |
 |   5e-05   |     0.945      | 1.0 | 1000 | 3000 |   0   |
 |   6e-05   | 0.939666666667 | 1.0 | 1000 | 3000 |   0   |
 |   7e-05   |     0.934      | 1.0 | 1000 | 3000 |   0   |
 |   8e-05   | 0.931666666667 | 1.0 | 1000 | 3000 |   0   |
 |   9e-05   | 0.929333333333 | 1.0 | 1000 | 3000 |   0   |
 +-----------+----------------+-----+------+------+-------+
 [400004 rows x 6 columns]
 Note: Only the head of the SFrame is printed.
 You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}

















In [ ]:

Content source: quoniammm/happy-machine-learning

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