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change the loss function #150

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change the loss function #150

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8937163
change the loss function
Easonrust Sep 22, 2019
a111262
add jaccard, dice, specificity, sensitivity
Easonrust Sep 28, 2019
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43 changes: 39 additions & 4 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,8 +9,45 @@
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras

def jaccard_distance(y_true, y_pred, smooth=100):
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth

def unet(pretrained_weights = None,input_size = (256,256,1)):
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 dice_coef_loss(y_true, y_pred):
print("dice loss")
return 1-dice_coef(y_true, y_pred)
def specificity(y_true,y_pred):
specificity=0
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
TP=K.sum(y_true_f*y_pred_f)
TN=K.sum((1-y_true_f)*(1-y_pred_f))
FP=K.sum((1-y_true_f)*y_pred_f)
FN=K.sum(y_true_f*(1-y_pred_f))
specificity=(TN)/((TN+FP))
return specificity
def sensitivity(y_true,y_pred):
sensitivity=0
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
TP=K.sum(y_true_f*y_pred_f)
TN=K.sum((1-y_true_f)*(1-y_pred_f))
FP=K.sum((1-y_true_f)*y_pred_f)
FN=K.sum(y_true_f*(1-y_pred_f))
sensitivity=(TP)/((TP+FN))

return sensitivity

def unet(pretrained_weights = None,input_size = (256,256,3)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
Expand Down Expand Up @@ -54,13 +91,11 @@ def unet(pretrained_weights = None,input_size = (256,256,1)):

model = Model(input = inputs, output = conv10)

model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
model.compile(optimizer = Adam(lr = 1e-4), loss = dice_coef_loss, metrics = ['accuracy'])

#model.summary()

if(pretrained_weights):
model.load_weights(pretrained_weights)

return model