Comments (1)
you can change your code like this for the metrics and you are good to go
def dice_coef(y_true, y_pred, smooth=1):
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 iou_coef(y_true, y_pred, smooth=1):
intersection = K.sum(K.abs(y_true * y_pred), axis=[1,2,3])
union = K.sum(y_true,[1,2,3])+K.sum(y_pred,[1,2,3])-intersection
iou = K.mean((intersection + smooth) / (union + smooth), axis=0)
return iou
def dice_loss(y_true, y_pred):
smooth = 1.
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = y_true_f * y_pred_f
score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
return 1. - score
def bce_dice_loss(y_true, y_pred):
return binary_crossentropy(tf.cast(y_true, tf.float32), y_pred) + 0.5 * dice_loss(tf.cast(y_true, tf.float32), y_pred)
from machinelearning-deeplearning-code-for-my-youtube-channel.
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from machinelearning-deeplearning-code-for-my-youtube-channel.