32 lines
1.4 KiB
Markdown
32 lines
1.4 KiB
Markdown
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## Usage of objectives
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An objective function (or loss function, or optimization score function) is one of the two parameters required to compile a model:
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```python
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model.compile(loss='mean_squared_error', optimizer='sgd')
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```
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You can either pass the name of an existing objective, or pass a Theano symbolic function that returns a scalar for each data-point and takes the following two arguments:
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- __y_true__: True labels. Theano tensor.
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- __y_pred__: Predictions. Theano tensor of the same shape as y_true.
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The actual optimized objective is the mean of the output array across all datapoints.
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For a few examples of such functions, check out the [objectives source](https://github.com/fchollet/keras/blob/master/keras/objectives.py).
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## Available objectives
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- __mean_squared_error__ / __mse__
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- __root_mean_squared_error__ / __rmse__
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- __mean_absolute_error__ / __mae__
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- __mean_absolute_percentage_error__ / __mape__
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- __mean_squared_logarithmic_error__ / __msle__
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- __squared_hinge__
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- __hinge__
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- __binary_crossentropy__: Also known as logloss.
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- __categorical_crossentropy__: Also known as multiclass logloss. __Note__: using this objective requires that your labels are binary arrays of shape `(nb_samples, nb_classes)`.
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- __poisson__: mean of `(predictions - targets * log(predictions))`
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- __cosine_proximity__: the opposite (negative) of the mean cosine proximity between predictions and targets.
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