Part 2b of renaming SparseTensor.shape -> SparseTensor.dense_shape

Change: 140811785

tensorflow/python/kernel_tests/control_flow_ops_py_test.py

@@ -304,7 +304,7 @@ def testCondSparseTensor(self):
       x = tf.SparseTensor(indices, values, shape=shape)
       pred = tf.less(1, 2)
       fn1 = lambda: tf.SparseTensor(indices + 1, x.values + 1, shape=shape)
-      fn2 = lambda: tf.SparseTensor(indices, x.values - 1, shape=shape)
+      fn2 = lambda: tf.SparseTensor(indices, x.values - 1, dense_shape=shape)
       r = tf.cond(pred, fn1, fn2)
       self.assertAllEqual([3.0, 5.0], r.values.eval())
       self.assertAllEqual([[1], [4]], r.indices.eval())

tensorflow/python/kernel_tests/cwise_ops_test.py

@@ -59,7 +59,7 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64):
   x_shape = x.shape
 
   return tf.SparseTensor(
-      indices=x_indices, values=x_values, shape=x_shape), x_values
+      indices=x_indices, values=x_values, dense_shape=x_shape), x_values
 
 class UnaryOpTest(tf.test.TestCase):
 

tensorflow/python/kernel_tests/functional_ops_test.py

@@ -119,7 +119,7 @@ def testMapSparseTensor(self):
       with self.assertRaises(TypeError):
         tf.map_fn(lambda x: x, tf.SparseTensor(indices=[[0, 0], [0, 1], [1, 0]],
                                                values=tf.constant([0, 1, 2]),
-                                               shape=[2, 2]))
+                                               dense_shape=[2, 2]))
 
   def testMap_Scoped(self):
     with self.test_session() as sess:

tensorflow/python/kernel_tests/shape_ops_test.py

@@ -37,7 +37,7 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64):
   x_shape = x.shape
 
   return tf.SparseTensor(
-      indices=x_indices, values=x_values, shape=x_shape), len(x_values)
+      indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
 
 class ShapeOpsTest(tf.test.TestCase):
 

tensorflow/python/kernel_tests/sparse_add_op_test.py

@@ -33,7 +33,7 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64):
   x_shape = x.shape
 
   return tf.SparseTensor(
-      indices=x_indices, values=x_values, shape=x_shape), len(x_values)
+      indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
 
 
 class SparseAddTest(tf.test.TestCase):

tensorflow/python/kernel_tests/sparse_ops_test.py

@@ -41,7 +41,7 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64):
   x_shape = x.shape
 
   return tf.SparseTensor(
-      indices=x_indices, values=x_values, shape=x_shape), len(x_values)
+      indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
 
 
 class SparseToIndicatorTest(test_util.TensorFlowTestCase):

tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_grad_test.py

@@ -33,7 +33,7 @@ def _sparsify(self, x):
     x_shape = x.shape
 
     return tf.SparseTensor(
-        indices=x_indices, values=x_values, shape=x_shape), len(x_values)
+        indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
 
   def _randomTensor(self, size, np_dtype, adjoint=False, sparse=False):
     n, m = size

tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py

@@ -52,7 +52,7 @@ def _testMatmul(self, x, y, adjoint_a=False, adjoint_b=False):
 
     with self.test_session(use_gpu=True):
       sp_x_value = tf.SparseTensorValue(
-          indices=x_indices, values=x_values, shape=x_shape)
+          indices=x_indices, values=x_values, dense_shape=x_shape)
       tf_value_ans = sparse_ops.sparse_tensor_dense_matmul(
           sp_x_value, y, adjoint_a=adjoint_a, adjoint_b=adjoint_b)
       tf_tensor_ans = sparse_ops.sparse_tensor_dense_matmul(

tensorflow/python/ops/math_ops.py

@@ -295,10 +295,10 @@ def abs(x, name=None):
         x_abs = gen_math_ops.complex_abs(
             x.values, Tout=x.values.dtype.real_dtype, name=name)
         return sparse_tensor.SparseTensor(
-            indices=x.indices, values=x_abs, shape=x.shape)
+            indices=x.indices, values=x_abs, dense_shape=x.shape)
       x_abs = gen_math_ops._abs(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_abs, shape=x.shape)
+          indices=x.indices, values=x_abs, dense_shape=x.shape)
     else:
       x = ops.convert_to_tensor(x, name="x")
       if x.dtype in (dtypes.complex64, dtypes.complex128):
@@ -335,7 +335,7 @@ def neg(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_neg = gen_math_ops.neg(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_neg, shape=x.shape)
+          indices=x.indices, values=x_neg, dense_shape=x.shape)
     else:
       return gen_math_ops.neg(x, name=name)
 
@@ -359,7 +359,7 @@ def sign(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_sign = gen_math_ops.sign(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_sign, shape=x.shape)
+          indices=x.indices, values=x_sign, dense_shape=x.shape)
     else:
       return gen_math_ops.sign(x, name=name)
 
@@ -381,7 +381,7 @@ def square(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_square = gen_math_ops.square(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_square, shape=x.shape)
+          indices=x.indices, values=x_square, dense_shape=x.shape)
     else:
       return gen_math_ops.square(x, name=name)
 
@@ -403,7 +403,7 @@ def sqrt(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_sqrt = gen_math_ops.sqrt(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_sqrt, shape=x.shape)
+          indices=x.indices, values=x_sqrt, dense_shape=x.shape)
     else:
       return gen_math_ops.sqrt(x, name=name)
 
@@ -423,7 +423,7 @@ def erf(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_erf = gen_math_ops.erf(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_erf, shape=x.shape)
+          indices=x.indices, values=x_erf, dense_shape=x.shape)
     else:
       return gen_math_ops.erf(x, name=name)
 
@@ -1997,7 +1997,7 @@ def tanh(x, name=None):
     if isinstance(x, sparse_tensor.SparseTensor):
       x_tanh = gen_math_ops._tanh(x.values, name=name)
       return sparse_tensor.SparseTensor(
-          indices=x.indices, values=x_tanh, shape=x.shape)
+          indices=x.indices, values=x_tanh, dense_shape=x.shape)
     else:
       return gen_math_ops._tanh(x, name=name)
 

tensorflow/python/ops/parsing_ops.py

@@ -291,7 +291,7 @@ def parse_example(serialized, features, name=None, example_names=None):
   ```
   {"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]],
                       values=[1.0, 2.0, 3.0],
-                      shape=(3, 2)) }
+                      dense_shape=(3, 2)) }
   ```
 
   Given two `Example` input protos in `serialized`:

tensorflow/python/ops/sparse_ops.py

@@ -798,7 +798,7 @@ def sparse_merge(sp_ids, sp_values, vocab_size, name=None,
   ```python
     SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
                  values=[-3, 1, 4, 1, 5, 9],
-                 shape=[3, 6])
+                 dense_shape=[3, 6])
   ```
 
   Args:

tensorflow/python/training/input_test.py

@@ -700,13 +700,13 @@ def testCannotInferRankError(self):
         tf.train.batch([x], batch_size=2)
 
   def testBatchedSparseTensorInferredShape(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.batch([sparse], batch_size=2)
     self.assertAllEqual((2,), batched.shape.get_shape().as_list())
 
   def testBatchedSparseTensorInferredShapeEnqueueMany(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.batch([sparse], batch_size=2, enqueue_many=True)
     self.assertAllEqual((1,), batched.shape.get_shape().as_list())
@@ -779,13 +779,13 @@ def testMultipleThreadKeepInputEnqueueMany(self):
     self._testKeepInputHelper(5, True)
 
   def testMaybeBatchedSparseTensorInferredShape(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_batch([sparse], keep_input=True, batch_size=2)
     self.assertAllEqual((2,), batched.shape.get_shape().as_list())
 
   def testMaybeBatchedSparseTensorInferredShapeEnqueueMany(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_batch(
         [sparse], keep_input=True, batch_size=2, enqueue_many=True)
@@ -1248,14 +1248,14 @@ def testMultipleThreadKeepInputEnqueueMany(self):
     self._testKeepInputHelper(5, True)
 
   def testMaybeBatchedSparseTensorInferredShape(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_batch_join(
         [[sparse]], keep_input=True, batch_size=2)
     self.assertAllEqual((2,), batched.shape.get_shape().as_list())
 
   def testMaybeBatchedSparseTensorInferredShapeEnqueueMany(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_batch_join(
         [[sparse]], keep_input=True, batch_size=2, enqueue_many=True)
@@ -1553,13 +1553,13 @@ def testMultipleThreadKeepInputEnqueueMany(self):
     self._testKeepInputHelper(5, True)
 
   def testMaybeBatchedSparseTensorInferredShape(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_shuffle_batch([sparse], 2, 10, 1, True)
     self.assertAllEqual((2,), batched.shape.get_shape().as_list())
 
   def testMaybeBatchedSparseTensorInferredShapeEnqueueMany(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_shuffle_batch(
         [sparse], 2, 10, 1, True, enqueue_many=True)
@@ -1860,13 +1860,13 @@ def testMultipleThreadKeepInputEnqueueMany(self):
     self._testKeepInputHelper(5, True)
 
   def testMaybeBatchedSparseTensorInferredShape(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_shuffle_batch_join([[sparse]], 2, 10, 1, True)
     self.assertAllEqual((2,), batched.shape.get_shape().as_list())
 
   def testMaybeBatchedSparseTensorInferredShapeEnqueueMany(self):
-    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], shape=[1])
+    sparse = tf.SparseTensor(indices=[[0]], values=[1.0], dense_shape=[1])
     self.assertAllEqual((1,), sparse.shape.get_shape().as_list())
     batched = tf.train.maybe_shuffle_batch_join(
         [[sparse]], 2, 10, 1, True, enqueue_many=True)