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- def P(y_true, y_pred):
- true_positives = K.sum(K.cast(K.greater(K.clip(y_true * y_pred, 0, 1), 0.20), 'float32'))
- pred_positives = K.sum(K.cast(K.greater(K.clip(y_pred, 0, 1), 0.20), 'float32'))
- precision = true_positives / (pred_positives + K.epsilon())
- return precision
- def R(y_true, y_pred):
- true_positives = K.sum(K.cast(K.greater(K.clip(y_true * y_pred, 0, 1), 0.20), 'float32'))
- poss_positives = K.sum(K.cast(K.greater(K.clip(y_true, 0, 1), 0.20), 'float32'))
- recall = true_positives / (poss_positives + K.epsilon())
- return recall
- def F(y_true, y_pred):
- p_val = P(y_true, y_pred)
- r_val = R(y_true, y_pred)
- f_val = 2*p_val*r_val / (p_val + r_val)
- return f_val
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