--- a
+++ b/dsb2018_topcoders/selim/losses.py
@@ -0,0 +1,73 @@
+import keras.backend as K
+from keras.losses import categorical_crossentropy
+
+
+def hard_dice_coef(y_true, y_pred, smooth=1e-3):
+    y_true_f = K.flatten(K.round(y_true[..., 0]))
+    y_pred_f = K.flatten(K.round(y_pred[..., 0]))
+    intersection = K.sum(y_true_f * y_pred_f)
+    return 100. * (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
+
+
+def hard_dice_coef_ch1(y_true, y_pred, smooth=1e-3):
+    y_true_f = K.flatten(K.round(y_true[..., 1]))
+    y_pred_f = K.flatten(K.round(y_pred[..., 1]))
+    intersection = K.sum(y_true_f * y_pred_f)
+    return 100. * (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
+
+
+def dice_coef(y_true, y_pred, smooth=1e-3):
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = K.sum(y_true_f * y_pred_f)
+    return K.mean((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
+
+
+def dice_coef_loss(y_true, y_pred):
+    return 1 - dice_coef(y_true, y_pred)
+
+
+def dice_coef_loss_bce(y_true, y_pred, dice=0.5, bce=0.5):
+    return binary_crossentropy(y_true, y_pred) * bce + dice_coef_loss(y_true, y_pred) * dice
+
+
+def binary_crossentropy(y, p):
+    return K.mean(K.binary_crossentropy(y, p))
+
+
+def double_head_loss(y_true, y_pred):
+    mask_loss = dice_coef_loss_bce(y_true[..., 0], y_pred[..., 0])
+    contour_loss = dice_coef_loss_bce(y_true[..., 1], y_pred[..., 1])
+    return mask_loss + contour_loss
+
+
+def mask_contour_mask_loss(y_true, y_pred):
+    mask_loss = dice_coef_loss_bce(y_true[..., 0], y_pred[..., 0])
+    contour_loss = dice_coef_loss_bce(y_true[..., 1], y_pred[..., 1])
+    full_mask = dice_coef_loss_bce(y_true[..., 2], y_pred[..., 2])
+    return mask_loss + 2 * contour_loss + full_mask
+
+
+def softmax_dice_loss(y_true, y_pred):
+    return categorical_crossentropy(y_true, y_pred) * 0.6 + dice_coef_loss(y_true[..., 0], y_pred[..., 0]) * 0.2 + dice_coef_loss(y_true[..., 1], y_pred[..., 1]) * 0.2
+
+
+def make_loss(loss_name):
+    if loss_name == 'bce_dice':
+        def loss(y, p):
+            return dice_coef_loss_bce(y, p, dice=0.5, bce=0.5)
+
+        return loss
+    elif loss_name == 'bce':
+        def loss(y, p):
+            return dice_coef_loss_bce(y, p, dice=0, bce=1)
+
+        return loss
+    elif loss_name == 'categorical_dice':
+        return softmax_dice_loss
+    elif loss_name == 'double_head_loss':
+        return double_head_loss
+    elif loss_name == 'mask_contour_mask_loss':
+        return mask_contour_mask_loss
+    else:
+        ValueError("Unknown loss.")