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July 19, 2018 00:02
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Keras Stateful Custom Metric Recall
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| class Recall(keras.layers.Layer): | |
| """Stateful Metric to count the total recall over all batches. | |
| Assumes predictions and targets of shape `(samples, 1)`. | |
| # Arguments | |
| name: String, name for the metric. | |
| """ | |
| def __init__(self, name='recall', **kwargs): | |
| super(Recall, self).__init__(name=name, **kwargs) | |
| self.stateful = True | |
| self.recall = K.variable(value=0.0, dtype='float32') | |
| self.true_positives = K.variable(value=0, dtype='int32') | |
| self.false_negatives = K.variable(value=0, dtype='int32') | |
| def reset_states(self): | |
| K.set_value(self.recall, 0.0) | |
| K.set_value(self.true_positives, 0) | |
| K.set_value(self.false_negatives, 0) | |
| def __call__(self, y_true, y_pred): | |
| """Computes the number of true positives in a batch. | |
| # Arguments | |
| y_true: Tensor, batch_wise labels | |
| y_pred: Tensor, batch_wise predictions | |
| # Returns | |
| The total number of true positives seen this epoch at the | |
| completion of the batch. | |
| """ | |
| y_true = K.cast(y_true, 'int32') | |
| y_pred = K.cast(K.round(y_pred), 'int32') | |
| # False negative calculations | |
| y_true = K.cast(y_true, 'int32') | |
| y_pred = K.cast(K.round(y_pred), 'int32') | |
| false_neg = K.cast(K.sum(K.cast(K.greater(y_pred, y_true), 'int32')), 'int32') | |
| current_false_neg = self.false_negatives * 1 | |
| self.add_update(K.update_add(self.false_negatives, | |
| false_neg), | |
| inputs=[y_true, y_pred]) | |
| # True positive calculations | |
| correct_preds = K.cast(K.equal(y_pred, y_true), 'int32') | |
| true_pos = K.cast(K.sum(correct_preds * y_true), 'int32') | |
| current_true_pos = self.true_positives * 1 | |
| self.add_update(K.update_add(self.true_positives, | |
| true_pos), | |
| inputs=[y_true, y_pred]) | |
| # Combine | |
| recall = (K.cast(self.true_positives, 'float32') / (K.cast(self.true_positives, 'float32') + K.cast(self.false_negatives, 'float32') + K.cast(K.epsilon(), 'float32'))) | |
| self.add_update(K.update(self.recall, | |
| recall), | |
| inputs=[y_true, y_pred]) | |
| return recall |
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Shouldn't false negative be where y_true > y_pred? Hence:
false_neg = K.cast(K.sum(K.cast(K.greater(y_true, y_pred), 'int32')), 'int32')