My model always predict under probability 0.5 for all pixels.
I dropped all images without ships and have tried focal loss,iou loss,weighted loss to deal with imbalance .
But the result is same.After few batches the masks i predicted gradually became all zeros.
Here is my notebook: enter link description here
Kaggle discussion:enter link description here
In the notebook , basically what i did is :
（1）discard all samples where there is no ship
（2）build a plain u-net
（3）define three custom loss function(iouloss,focal_binarycrossentropy,biased_crossentropy), all of which i have tried.
（4）train and submit
#define different losses to try
def iouloss(y_true,y_pred):
intersection = K.sum(y_true * y_pred, axis=-1)
sum_ = K.sum(y_true + y_pred, axis=-1)
jac = intersection / (sum_ - intersection)
return 1 - jac
def focal_binarycrossentropy(y_true,y_pred):
#focal loss with gamma 8
t1=K.binary_crossentropy(y_true, y_pred)
t2=tf.where(tf.equal(y_true,0),t1*(y_pred**8),t1*((1-y_pred)**8))
return t2
def biased_crossentropy(y_true,y_pred):
#apply 1000 times heavier punishment to ship pixels
t1=K.binary_crossentropy(y_true, y_pred)
t2=tf.where(tf.equal(y_true,0),t1*1000,t1)
return t2
...
#try different loss function
unet.compile(loss=iouloss, optimizer="adam", metrics=[ioumetric])
or
unet.compile(loss=focal_binarycrossentropy, optimizer="adam", metrics=[ioumetric])
or
unet.compile(loss=biased_crossentropy, optimizer="adam", metrics=[ioumetric])
...
#start training
unet.train_on_batch(x=image_batch,y=mask_batch)
