def matthews_correlation(y_true, y_pred):
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
y_pred_neg = 1 - y_pred_pos
y_pos = K.round(K.clip(y_true, 0, 1))
y_neg = 1 - y_pos
tp = K.sum(y_pos * y_pred_pos)
tn = K.sum(y_neg * y_pred_neg)
fp = K.sum(y_neg * y_pred_pos)
fn = K.sum(y_pos * y_pred_neg)
numerator = (tp * tn - fp * fn)
denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
return numerator / (denominator + K.epsilon())
def FocalLoss(y_true, y_pred):
"""
:param y_true: A tensor of the same shape as `y_pred`
:param y_pred: A tensor resulting from a sigmoid
:return: Output tensor.
"""
gamma = 2.0
alpha = 0.25
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))
epsilon = K.epsilon()
# clip to prevent NaN's and Inf's
pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)
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))
def Kaggle_IoU_Precision(y_true, y_pred, threshold=0.5):
y_pred = K.squeeze(tf.to_int32(y_pred > threshold), -1)
y_true = K.cast(y_true[..., 0], K.floatx())
y_pred = K.cast(y_pred, K.floatx())
truth_areas = K.sum(y_true, axis=[1, 2])
pred_areas = K.sum(y_pred, axis=[1, 2])
intersection = K.sum(y_true * y_pred, axis=[1, 2])
union = K.clip(truth_areas + pred_areas - intersection, 1e-9, 128 * 128)
check = K.map_fn(lambda x: K.equal(x, 0),
truth_areas + pred_areas,
dtype=tf.bool)
p = intersection / union
iou = K.switch(check, p + 1., p)
prec = K.map_fn(lambda x: K.mean(K.greater(x, np.arange(0.5, 1.0, 0.05))),
iou,
dtype=tf.float32)
prec_iou = K.mean(prec)
return prec_iou
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 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