Update readme to reflect new loss code

README.md

@@ -30,36 +30,38 @@ Additionally, we provide minibatch iterators for Learning to Rank datasets. Thes
 
 Currently we provide implementations for the following loss functions
 
- * Top-1 ListNet: `shoelace.loss.listwise.ListNetLoss`
- * ListMLE: `shoelace.loss.listwise.ListMLELoss`
- * ListPL: `shoelace.loss.listwise.ListPLLoss`
+ * Top-1 ListNet: `shoelace.loss.listwise.listnet`
+ * ListMLE: `shoelace.loss.listwise.listmle`
+ * ListPL: `shoelace.loss.listwise.listpl`
 
 ## Example
 
 Here is an example script that will train up a single-layer linear neural network with a ListNet loss function:
 
     from shoelace.dataset import LtrDataset
     from shoelace.iterator import LtrIterator
-    from shoelace.loss.listwise import ListNetLoss
-    from chainer import training, optimizers, links
+    from shoelace.loss.listwise import listnet
+    from chainer import training, optimizers, links, Chain
     from chainer.training import extensions
 
     # Load data and set up iterator
-    with open('./path/to/svmrank.txt', 'r') as f:
+    with open('./path/to/ranksvm.txt', 'r') as f:
         training_set = LtrDataset.load_txt(f)
     training_iterator = LtrIterator(training_set, repeat=True, shuffle=True)
-    
-    # Create neural network with chainer and apply our loss function
+
+    # Create neural network with chainer and apply loss function
     predictor = links.Linear(None, 1)
-    loss = ListNetLoss(predictor)
-
+    class Ranker(Chain):
+        def __call__(self, x, t):
+            return listnet(self.predictor(x), t)
+    loss = Ranker(predictor=predictor)
+
     # Build optimizer, updater and trainer
     optimizer = optimizers.Adam()
     optimizer.setup(loss)
     updater = training.StandardUpdater(training_iterator, optimizer)
     trainer = training.Trainer(updater, (40, 'epoch'))
     trainer.extend(extensions.ProgressBar())
-    
+
     # Train neural network
     trainer.run()
-