--- a
+++ b/losses.py
@@ -0,0 +1,84 @@
+import numpy as np
+import keras.backend as K
+
+
+def dice(y_true, y_pred):
+    #computes the dice score on two tensors
+
+    sum_p=K.sum(y_pred,axis=0)
+    sum_r=K.sum(y_true,axis=0)
+    sum_pr=K.sum(y_true * y_pred,axis=0)
+    dice_numerator =2*sum_pr
+    dice_denominator =sum_r+sum_p
+    dice_score =(dice_numerator+K.epsilon() )/(dice_denominator+K.epsilon())
+    return dice_score
+
+
+def dice_whole_metric(y_true, y_pred):
+    #computes the dice for the whole tumor
+
+    y_true_f = K.reshape(y_true,shape=(-1,4))
+    y_pred_f = K.reshape(y_pred,shape=(-1,4))
+    y_whole=K.sum(y_true_f[:,1:],axis=1)
+    p_whole=K.sum(y_pred_f[:,1:],axis=1)
+    dice_whole=dice(y_whole,p_whole)
+    return dice_whole
+
+def dice_en_metric(y_true, y_pred):
+    #computes the dice for the enhancing region
+
+    y_true_f = K.reshape(y_true,shape=(-1,4))
+    y_pred_f = K.reshape(y_pred,shape=(-1,4))
+    y_enh=y_true_f[:,-1]
+    p_enh=y_pred_f[:,-1]
+    dice_en=dice(y_enh,p_enh)
+    return dice_en
+
+def dice_core_metric(y_true, y_pred):
+    ##computes the dice for the core region
+
+    y_true_f = K.reshape(y_true,shape=(-1,4))
+    y_pred_f = K.reshape(y_pred,shape=(-1,4))
+    
+    #workaround for tf
+    #y_core=K.sum(tf.gather(y_true_f, [1,3],axis =1),axis=1)
+    #p_core=K.sum(tf.gather(y_pred_f, [1,3],axis =1),axis=1)
+    
+    y_core=K.sum(y_true_f[:,[1,3]],axis=1)
+    p_core=K.sum(y_pred_f[:,[1,3]],axis=1)
+    dice_core=dice(y_core,p_core)
+    return dice_core
+
+
+
+def weighted_log_loss(y_true, y_pred):
+    # scale predictions so that the class probas of each sample sum to 1
+    y_pred /= K.sum(y_pred, axis=-1, keepdims=True)
+    # clip to prevent NaN's and Inf's
+    y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())
+    # weights are assigned in this order : normal,necrotic,edema,enhancing 
+    weights=np.array([1,5,2,4])
+    weights = K.variable(weights)
+    loss = y_true * K.log(y_pred) * weights
+    loss = K.mean(-K.sum(loss, -1))
+    return loss
+
+def gen_dice_loss(y_true, y_pred):
+    '''
+    computes the sum of two losses : generalised dice loss and weighted cross entropy
+    '''
+
+    #generalised dice score is calculated as in this paper : https://arxiv.org/pdf/1707.03237
+    y_true_f = K.reshape(y_true,shape=(-1,4))
+    y_pred_f = K.reshape(y_pred,shape=(-1,4))
+    sum_p=K.sum(y_pred_f,axis=-2)
+    sum_r=K.sum(y_true_f,axis=-2)
+    sum_pr=K.sum(y_true_f * y_pred_f,axis=-2)
+    weights=K.pow(K.square(sum_r)+K.epsilon(),-1)
+    generalised_dice_numerator =2*K.sum(weights*sum_pr)
+    generalised_dice_denominator =K.sum(weights*(sum_r+sum_p))
+    generalised_dice_score =generalised_dice_numerator /generalised_dice_denominator
+    GDL=1-generalised_dice_score
+    del sum_p,sum_r,sum_pr,weights
+
+    return GDL+weighted_log_loss(y_true,y_pred)