metrics.py

from tensorflow.keras import backend as K


def recall_m(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_m(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_m(y_true, y_pred):
    precision = precision_m(y_true, y_pred)
    recall = recall_m(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))


def sens_m(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    false_negatives = K.sum(K.round(K.clip(y_true - y_pred, 0, 1)))
    return true_positives / (true_positives + false_negatives + K.epsilon())


def spec_m(y_true, y_pred):
    true_negatives = K.sum(K.round(K.clip(1 - (y_true + y_pred), 0, 1)))
    false_positives = K.sum(K.round(K.clip(y_pred - y_true, 0, 1)))
    return true_negatives / (false_positives + true_negatives + K.epsilon())


def g_mean_m(y_true, y_pred):
    sens = sens_m(y_true, y_pred)
    spec = spec_m(y_true, y_pred)
    return K.sqrt(sens * spec)