achieve compatibility with tensorflow 1.0rc1 (keras-team#5296)

Author: t-vi

keras/backend/tensorflow_backend.py

@@ -28,6 +28,10 @@
 # Change its value via `manual_variable_initialization(value)`.
 _MANUAL_VAR_INIT = False
 
+# These two integers contain the tensorflow version for coping with API breaks.
+tf_major_version = int(tf.__version__.split('.')[0])
+tf_minor_version = int(tf.__version__.split('.')[1])
+
 
 def clear_session():
     """Destroys the current TF graph and creates a new one.
@@ -240,9 +244,14 @@ def variable(value, dtype=None, name=None):
         sparse_coo = value.tocoo()
         indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
                                   np.expand_dims(sparse_coo.col, 1)), 1)
-        v = tf.SparseTensor(indices=indices,
-                            values=sparse_coo.data,
-                            shape=sparse_coo.shape)
+        if tf_major_version >= 1:
+            v = tf.SparseTensor(indices=indices,
+                                values=sparse_coo.data,
+                                dense_shape=sparse_coo.shape)
+        else:
+            v = tf.SparseTensor(indices=indices,
+                                values=sparse_coo.data,
+                                shape=sparse_coo.shape)
         v._dims = len(sparse_coo.shape)
         v._keras_shape = sparse_coo.shape
         v._uses_learning_phase = False
@@ -1430,10 +1439,13 @@ def concatenate(tensors, axis=-1):
     if py_all([is_sparse(x) for x in tensors]):
         return tf.sparse_concat(axis, tensors)
     else:
-        try:
-            return tf.concat_v2([to_dense(x) for x in tensors], axis)
-        except AttributeError:
-            return tf.concat(axis, [to_dense(x) for x in tensors])
+        if tf_major_version >= 1:
+            return tf.concat([to_dense(x) for x in tensors], axis)
+        else:
+            try:
+                return tf.concat_v2([to_dense(x) for x in tensors], axis)
+            except AttributeError:
+                return tf.concat(axis, [to_dense(x) for x in tensors])
 
 
 def reshape(x, shape):
@@ -3061,8 +3073,12 @@ def ctc_decode(y_pred, input_length, greedy=True, beam_width=100,
             sequence_length=input_length, beam_width=beam_width,
             top_paths=top_paths)
 
-    decoded_dense = [tf.sparse_to_dense(st.indices, st.shape, st.values, default_value=-1)
-                     for st in decoded]
+    if tf_major_version >= 1:
+        decoded_dense = [tf.sparse_to_dense(st.indices, st.dense_shape, st.values, default_value=-1)
+                         for st in decoded]
+    else:
+        decoded_dense = [tf.sparse_to_dense(st.indices, st.shape, st.values, default_value=-1)
+                         for st in decoded]
 
     return (decoded_dense, log_prob)