Patch required to make reverse_words work with LSTM

lstm.diff

diff --git a/reverse_words/__init__.py b/reverse_words/__init__.py
index b649ab5..ac296e7 100644
--- a/reverse_words/__init__.py
+++ b/reverse_words/__init__.py
@@ -14,7 +14,7 @@ from theano import tensor
 from blocks.bricks import Tanh, Initializable
 from blocks.bricks.base import application
 from blocks.bricks.lookup import LookupTable
-from blocks.bricks.recurrent import SimpleRecurrent, Bidirectional
+from blocks.bricks.recurrent import SimpleRecurrent, LSTM, Bidirectional
 from blocks.bricks.attention import SequenceContentAttention
 from blocks.bricks.parallel import Fork
 from blocks.bricks.sequence_generators import (
@@ -94,13 +94,13 @@ class WordReverser(Initializable):
     def __init__(self, dimension, alphabet_size, **kwargs):
         super(WordReverser, self).__init__(**kwargs)
         encoder = Bidirectional(
-            SimpleRecurrent(dim=dimension, activation=Tanh()))
+            LSTM(dim=dimension, activation=Tanh()))
         fork = Fork([name for name in encoder.prototype.apply.sequences
                     if name != 'mask'])
         fork.input_dim = dimension
         fork.output_dims = [encoder.prototype.get_dim(name) for name in fork.input_names]
         lookup = LookupTable(alphabet_size, dimension)
-        transition = SimpleRecurrent(
+        transition = LSTM(
             activation=Tanh(),
             dim=dimension, name="transition")
         attention = SequenceContentAttention(
@@ -130,7 +130,7 @@ class WordReverser(Initializable):
             attended=self.encoder.apply(
                 **dict_union(
                     self.fork.apply(self.lookup.apply(chars), as_dict=True),
-                    mask=chars_mask)),
+                    mask=chars_mask))[0],
             attended_mask=chars_mask)
 
     @application
@@ -139,7 +139,7 @@ class WordReverser(Initializable):
             n_steps=3 * chars.shape[0], batch_size=chars.shape[1],
             attended=self.encoder.apply(
                 **dict_union(
-                    self.fork.apply(self.lookup.apply(chars), as_dict=True))),
+                    self.fork.apply(self.lookup.apply(chars), as_dict=True)))[0],
             attended_mask=tensor.ones(chars.shape))
 
 
@@ -228,7 +228,7 @@ def main(mode, save_path, num_batches, data_path=None):
         # Construct the main loop and start training!
         average_monitoring = TrainingDataMonitoring(
             observables, prefix="average", every_n_batches=10)
-        
+
         main_loop = MainLoop(
             model=model,
             data_stream=data_stream,
(END)

Thanks Dima for sharing your experiences with me. It helped me a lot.
Did you try the sampling or beam search as well? Because I am getting an error during generation of sequences. For example in the reverse_word example, I got a value error from here.