Fix TF RNN issues

examples/imdb_bidirectional_lstm.py

@@ -41,7 +41,7 @@ y_test = np.array(y_test)
 
 print('Build model...')
 model = Graph()
-model.add_input(name='input', input_shape=(1,), dtype=int)
+model.add_input(name='input', input_shape=(maxlen,), dtype=int)
 model.add_node(Embedding(max_features, 128, input_length=maxlen),
                name='embedding', input='input')
 model.add_node(LSTM(64), name='forward', input='embedding')

keras/backend/tensorflow_backend.py

@@ -381,7 +381,7 @@ def rnn(step_function, inputs, initial_states,
     successive_states = []
     successive_outputs = []
     if go_backwards:
-        input_list = input_list.reverse()
+        input_list.reverse()
     for input in input_list:
         output, new_states = step_function(input, states)
         if masking:

keras/layers/containers.py

@@ -224,12 +224,13 @@ class Graph(Layer):
         self.input_order.append(name)
         layer = Layer()  # empty layer
         layer.set_input_shape(input_shape)
-        ndim = len(input_shape) + 1
         if dtype == 'float':
-            layer.input = K.placeholder(ndim=ndim, name=name)
+            layer.input = K.placeholder(shape=layer.input_shape, name=name)
         else:
-            if ndim == 2:
-                layer.input = K.placeholder(ndim=2, dtype='int32', name=name)
+            if len(input_shape) == 1:
+                layer.input = K.placeholder(shape=layer.input_shape,
+                                            dtype='int32',
+                                            name=name)
             else:
                 raise Exception('Type "int" can only be used with ndim==2 (Embedding).')
         self.inputs[name] = layer

keras/layers/embeddings.py

@@ -17,9 +17,12 @@ class Embedding(Layer):
     '''
     input_ndim = 2
 
-    def __init__(self, input_dim, output_dim, init='uniform', input_length=None,
-                 W_regularizer=None, activity_regularizer=None, W_constraint=None,
-                 mask_zero=False, weights=None, **kwargs):
+    def __init__(self, input_dim, output_dim,
+                 init='uniform', input_length=None,
+                 W_regularizer=None, activity_regularizer=None,
+                 W_constraint=None,
+                 mask_zero=False,
+                 weights=None, **kwargs):
         self.input_dim = input_dim
         self.output_dim = output_dim
         self.init = initializations.get(init)