remove theano dependency, using keras backend functions

examples/keras_parikh_entailment/keras_decomposable_attention.py

@@ -6,7 +6,6 @@ from keras.layers import InputSpec, Layer, Input, Dense, merge
 from keras.layers import Activation, Dropout, Embedding, TimeDistributed
 from keras.layers import Bidirectional, GRU
 import keras.backend as K
-import theano.tensor as T
 from keras.models import Sequential, Model, model_from_json
 from keras.regularizers import l2
 from keras.optimizers import Adam
@@ -106,8 +105,8 @@ class _Attention(object):
     
     def __call__(self, sent1, sent2):
         def _outer(AB):
-            att_ji = T.batched_dot(AB[1], AB[0].dimshuffle((0, 2, 1)))
-            return att_ji.dimshuffle((0, 2, 1))
+            att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
+            return K.permute_dimensions(att_ji,(0, 2, 1))
 
 
         return merge(
@@ -126,12 +125,12 @@ class _SoftAlignment(object):
             att = attmat[0]
             mat = attmat[1]
             if transpose:
-                att = att.dimshuffle((0, 2, 1))
+                att = K.permute_dimensions(att,(0, 2, 1))
             # 3d softmax
             e = K.exp(att - K.max(att, axis=-1, keepdims=True))
             s = K.sum(e, axis=-1, keepdims=True)
             sm_att = e / s
-            return T.batched_dot(sm_att, mat)
+            return K.batch_dot(sm_att, mat)
         return merge([attention, sentence], mode=_normalize_attention,
                       output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
  
@@ -191,7 +190,7 @@ class _GlobalSumPooling1D(Layer):
 
     def call(self, x, mask=None):
         if mask is not None:
-            return K.sum(x * T.clip(mask, 0, 1), axis=1)
+            return K.sum(x * K.clip(mask, 0, 1), axis=1)
         else:
             return K.sum(x, axis=1)
 
@@ -204,6 +203,7 @@ def test_build_model():
 
 
 def test_fit_model():
+
     def _generate_X(nr_example, length, nr_vector):
         X1 = numpy.ndarray((nr_example, length), dtype='int32')
         X1 *= X1 < nr_vector
@@ -212,6 +212,7 @@ def test_fit_model():
         X2 *= X2 < nr_vector
         X2 *= 0 <= X2
         return [X1, X2]
+
     def _generate_Y(nr_example, nr_class):
         ys = numpy.zeros((nr_example, nr_class), dtype='int32')
         for i in range(nr_example):