diff --git a/losses.py b/losses.py
index f56ac23..43cb446 100644
--- a/losses.py
+++ b/losses.py
@@ -1,5 +1,5 @@
 """
-Useful definitions of common ml losses.
+Useful definitions of common image segmentation losses.
 """
 
 import torch
@@ -86,10 +86,10 @@ def dice_loss(true, logits, eps=1e-7):
     intersection = torch.sum(probas * true_1_hot, dims)
     cardinality = torch.sum(probas + true_1_hot, dims)
     dice_loss = (2. * intersection / (cardinality + eps)).mean()
-    return (-1 * dice_loss)
+    return (1 - dice_loss)
 
 
-def jaccard_loss(true, pred, eps=1e-7):
+def jaccard_loss(true, logits, eps=1e-7):
     """Computes the Jaccard loss, a.k.a the IoU loss.
 
     Note that PyTorch optimizers minimize a loss. In this
@@ -124,7 +124,53 @@ def jaccard_loss(true, pred, eps=1e-7):
     cardinality = torch.sum(probas + true_1_hot, dims)
     union = cardinality - intersection
     jacc_loss = (intersection / (union + eps)).mean()
-    return (-1 * jacc_loss)
+    return (1 - jacc_loss)
+
+
+def tversky_loss(true, logits, alpha, beta, eps=1e-7):
+    """Computes the Tversky loss [1].
+
+    Args:
+        true: a tensor of shape [B, H, W] or [B, 1, H, W].
+        logits: a tensor of shape [B, C, H, W]. Corresponds to
+            the raw output or logits of the model.
+        alpha: controls the penalty for false positives.
+        beta: controls the penalty for false negatives.
+        eps: added to the denominator for numerical stability.
+
+    Returns:
+        tversky_loss: the Tversky loss.
+
+    Notes:
+        alpha = beta = 0.5 => dice coeff
+        alpha = beta = 1 => tanimoto coeff
+        alpha + beta = 1 => F beta coeff
+
+    References:
+        [1]: https://arxiv.org/abs/1706.05721
+    """
+    num_classes = logits.shape[1]
+    if num_classes == 1:
+        true_1_hot = torch.eye(num_classes + 1)[true.squeeze(1)]
+        true_1_hot = true_1_hot.permute(0, 3, 1, 2).float()
+        true_1_hot_f = true_1_hot[:, 0:1, :, :]
+        true_1_hot_s = true_1_hot[:, 1:2, :, :]
+        true_1_hot = torch.cat([true_1_hot_s, true_1_hot_f], dim=1)
+        pos_prob = torch.sigmoid(logits)
+        neg_prob = 1 - pos_prob
+        probas = torch.cat([pos_prob, neg_prob], dim=1)
+    else:
+        true_1_hot = torch.eye(num_classes)[true.squeeze(1)]
+        true_1_hot = true_1_hot.permute(0, 3, 1, 2).float()
+        probas = F.softmax(probas, dim=1)
+    dims = (0,) + tuple(range(2, true.ndimension()))
+    intersection = torch.sum(probas * true_1_hot, dims)
+    fps = torch.sum(probas * (1 - true_1_hot), dims)
+    fns = torch.sum((1 - probas) * true_1_hot, dims)
+    num = intersection
+    denom = intersection + alpha*fps + beta*fns
+    tversky_loss = (num / (denom + eps)).mean()
+    return (1 - tversky_loss)
 
 
 def ce_dice(true, pred, log=False, w1=1, w2=1):