Add BiRNN for entailment

Hastily add bidirectional RNN to entailment example

examples/keras_parikh_entailment/keras_decomposable_attention.py

@@ -12,7 +12,7 @@ from keras.optimizers import Adam
 from keras.layers.normalization import BatchNormalization
 
 
-def build_model(vectors, shape, settings):
+def build_model(vectors, shape, settings, use_rnn_encoding=False):
     '''Compile the model.'''
     max_length, nr_hidden, nr_class = shape
     # Declare inputs.
@@ -21,6 +21,8 @@ def build_model(vectors, shape, settings):
 
     # Construct operations, which we'll chain together.
     embed = _StaticEmbedding(vectors, max_length, nr_hidden)
+    if use_rnn_encoding:
+        encode = _BiLSTMEncode(max_length, nr_hidden)
     attend = _Attention(max_length, nr_hidden)
     align = _SoftAlignment(max_length, nr_hidden)
     compare = _Comparison(max_length, nr_hidden)
@@ -29,6 +31,10 @@ def build_model(vectors, shape, settings):
     # Declare the model as a computational graph.
     sent1 = embed(ids1) # Shape: (i, n)
     sent2 = embed(ids2) # Shape: (j, n)
+    
+    if use_rnn_encoding:
+        sent1 = encode(sent1)
+        sent2 = encode(sent2)
 
     attention = attend(sent1, sent2)  # Shape: (i, j)
 
@@ -72,7 +78,14 @@ class _StaticEmbedding(object):
 
     def __call__(self, sentence):
         return self.project(self.embed(sentence))
+    
+class _BiRNNEncoding(object):
+    def __init__(self, max_length, nr_out):
+        self.model = Sequential()
+        self.model.add(Bidirectional(LSTM(nr_out, input_length=max_length)))
 
+    def __call__(self, sentence):
+        return self.model(sentence)
 
 class _Attention(object):
     def __init__(self, max_length, nr_hidden, dropout=0.0, L2=1e-4, activation='relu'):