metrics.py

from keras import backend as K


def dice_coef(y_true, y_pred):
    smooth = K.epsilon()
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)

    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)


def recall(y_true, y_pred):
    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)))

    recall = true_positives / (possible_positives + K.epsilon())

    return recall


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 f1(y_true, y_pred):

    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)

    pre = precision(y_true_f, y_pred_f)
    rec = recall(y_true_f, y_pred_f)

    return 2 * ((pre * rec) / (pre + rec + K.epsilon()))