Metric.py

from keras import backend as K
import tensorflow as tf

## Evaluate Method
def k_recall(y_target, y_pred):
    y_target_yn = K.round(K.clip(y_target, 0, 1))
    y_pred_yn = K.round(K.clip(y_pred, 0, 1))
    count_true_positive = K.sum(y_target_yn * y_pred_yn)
    count_true_positive_false_negative = K.sum(y_target_yn)
    recall = count_true_positive / (count_true_positive_false_negative + K.epsilon())
    return recall

def k_precision(y_target, y_pred):
    y_pred_yn = K.round(K.clip(y_pred, 0, 1))
    y_target_yn = K.round(K.clip(y_target, 0, 1))
    count_true_positive = K.sum(y_target_yn * y_pred_yn)
    count_true_positive_false_positive = K.sum(y_pred_yn)
    precision = count_true_positive / (count_true_positive_false_positive + K.epsilon())
    return precision

def k_f1score(y_target, y_pred):
    _recall = k_recall(y_target, y_pred)
    _precision = k_precision(y_target, y_pred)
    _f1score = ( 2 * _recall * _precision) / (_recall + _precision+ K.epsilon())
    return _f1score

#### LOSS #####
def focal_loss(gamma=2., alpha=.25):
    def focal_loss_fixed(y_true, y_pred):
        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) - K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
    return focal_loss_fixed