turicreate.image_classifier.ImageClassifier.predict_topk

ImageClassifier.predict_topk(dataset, output_type='probability', k=3, batch_size=64)

Return top-k predictions for the dataset, using the trained model. Predictions are returned as an SFrame with three columns: id, class, and probability, margin, or rank, depending on the output_type parameter. Input dataset size must be the same as for training of the model.

Parameters:
dataset : SFrame | SArray | turicreate.Image

Images to be classified. If dataset is an SFrame, it must include columns with the same names as the features used for model training, but does not require a target column. Additional columns are ignored.

output_type : {‘probability’, ‘rank’, ‘margin’}, optional

Choose the return type of the prediction:

  • probability: Probability associated with each label in the prediction.
  • rank : Rank associated with each label in the prediction.
  • margin : Margin associated with each label in the prediction.
k : int, optional

Number of classes to return for each input example.
Returns:
out : SFrame

An SFrame with model predictions.

See also

predict, classify, evaluate

Examples

>>> pred = m.predict_topk(validation_data, k=3)
>>> pred
+----+-------+-------------------+
| id | class |   probability     |
+----+-------+-------------------+
| 0  |   4   |   0.995623886585  |
| 0  |   9   |  0.0038311756216  |
| 0  |   7   | 0.000301006948575 |
| 1  |   1   |   0.928708016872  |
| 1  |   3   |  0.0440889261663  |
| 1  |   2   |  0.0176190119237  |
| 2  |   3   |   0.996967732906  |
| 2  |   2   |  0.00151345680933 |
| 2  |   7   | 0.000637513934635 |
| 3  |   1   |   0.998070061207  |
| .. |  ...  |        ...        |
+----+-------+-------------------+
[35688 rows x 3 columns]