metrics.py

from keras import backend as K

def precision(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    _precision = true_positives / (predicted_positives + K.epsilon())
    return _precision

def recall(y_true, y_pred):
    """Compute recall metric"""
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    return true_positives / (possible_positives + K.epsilon())

def f1_score(y_true, y_pred):
    """Compute f1-score metric"""
    _precision = precision(y_true, y_pred)
    _recall = recall(y_true, y_pred)
    f1_score = 2 * ((_precision * _recall) / (_precision + _recall + K.epsilon()))
    return f1_score