65 lines
1.6 KiB
Python
65 lines
1.6 KiB
Python
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import keras.backend as K
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from keras.activations import elu
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def get_custom_objects():
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return dict([
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("precision", precision),
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("recall", recall),
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("f1_score", f1_score),
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("selu", selu)
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])
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def selu(x):
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"""Scaled Exponential Linear Unit. (Klambauer et al., 2017)
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# Arguments
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x: A tensor or variable to compute the activation function for.
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# References
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- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
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# copied from keras.io
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"""
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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return scale * elu(x, alpha)
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def get_metric_functions():
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return [precision, recall, f1_score]
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def precision(y_true, y_pred):
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# Count positive samples.
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true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
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predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
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return true_positives / (predicted_positives + K.epsilon())
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def recall(y_true, y_pred):
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# Count positive samples.
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true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
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possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
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return true_positives / (possible_positives + K.epsilon())
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def f1_score(y_true, y_pred):
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return f_score(1)(y_true, y_pred)
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def f05_score(y_true, y_pred):
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return f_score(0.5)(y_true, y_pred)
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def f_score(beta):
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def _f(y_true, y_pred):
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p = precision(y_true, y_pred)
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r = recall(y_true, y_pred)
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bb = beta ** 2
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fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
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return fbeta_score
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return _f
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