diff options
Diffstat (limited to 'tensorflow/compiler/tests/tensor_array_ops_test.py')
| -rw-r--r-- | tensorflow/compiler/tests/tensor_array_ops_test.py | 56 |
1 files changed, 30 insertions, 26 deletions
diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index b3067be51d..f277314352 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -139,7 +139,7 @@ class TensorArrayTest(xla_test.XLATestCase): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) - # Unpack a matrix into vectors + # Unpack a matrix into vectors. w1 = ta.unstack(convert([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]])) r0 = w1.read(0) r1 = w1.read(1) @@ -180,7 +180,7 @@ class TensorArrayTest(xla_test.XLATestCase): convert = _make_converter(tf_dtype) - # Split an empty vector + # Split an empty vector. lengths = constant_op.constant([0, 0, 0]) w0 = ta.split(convert([]), lengths=lengths) r0 = w0.read(0) @@ -192,7 +192,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(convert([]), d1) self.assertAllEqual(convert([]), d2) - # Split a vector + # Split a vector. ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) lengths = constant_op.constant([1, 1, 1]) @@ -206,7 +206,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(convert([2.0]), d1) self.assertAllEqual(convert([3.0]), d2) - # Split a matrix + # Split a matrix. ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) lengths = constant_op.constant([1, 1, 1]) @@ -319,27 +319,31 @@ class TensorArrayTest(xla_test.XLATestCase): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) - # Test writing the wrong datatype + # Test writing the wrong datatype. with self.assertRaisesOpError( "TensorArray dtype is float but op has dtype int32"): ta.write(-1, np.int32(7)).flow.eval() def testTensorArrayReadWrongIndexOrDataTypeFails(self): - with self.test_session(), self.test_scope(): - ta = tensor_array_ops.TensorArray( - dtype=dtypes.float32, tensor_array_name="foo", size=3) - - w0 = ta.write(0, [[4.0, 5.0]]) - - # Test reading wrong datatype - r0_bad = gen_data_flow_ops._tensor_array_read_v3( - handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow) - with self.assertRaisesOpError( - "TensorArray dtype is float but op has dtype double."): - r0_bad.eval() - - # Test reading from a different index than the one we wrote to - w0.read(1) + # Find two different floating point types, create an array of + # the first type, but try to read the other type. + if len(self.float_types) > 1: + dtype1 = self.float_types[0] + dtype2 = self.float_types[1] + with self.test_session(), self.test_scope(): + ta = tensor_array_ops.TensorArray( + dtype=dtype1, tensor_array_name="foo", size=3) + + w0 = ta.write(0, [[4.0, 5.0]]) + + # Test reading wrong datatype. + r0_bad = gen_data_flow_ops._tensor_array_read_v3( + handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow) + with self.assertRaisesOpError("TensorArray dtype is "): + r0_bad.eval() + + # Test reading from a different index than the one we wrote to + w0.read(1) def testTensorArraySplitIncompatibleShapesFails(self): with self.test_session(), self.test_scope(): @@ -487,7 +491,7 @@ class TensorArrayTest(xla_test.XLATestCase): r0 = w1.read(0) s0 = w1.concat() - # Test gradient accumulation between read(0), pack(), and concat() + # Test gradient accumulation between read(0), pack(), and concat(). with ops.control_dependencies([p0, r0, s0]): grad_r = gradients_impl.gradients( ys=[p0, r0, s0], @@ -536,7 +540,7 @@ class TensorArrayTest(xla_test.XLATestCase): r0_1 = w.read(0) r1 = w.read(1) - # Test combined gradients + aggregation of read(0) + # Test combined gradients + aggregation of read(0). grad = gradients_impl.gradients( ys=[r0, r0_1, r1], xs=[value], @@ -744,7 +748,7 @@ class TensorArrayTest(xla_test.XLATestCase): grad_b_t, = session.run([grad_b]) self.assertAllEqual(grad_b_t, g0) - # Test gradients calculated jointly + # Test gradients calculated jointly. joint_grad_a_t, joint_grad_b_t = session.run([grad_a, grad_b]) self.assertAllEqual(joint_grad_a_t, g0) self.assertAllEqual(joint_grad_b_t, g0) @@ -877,7 +881,7 @@ class TensorArrayTest(xla_test.XLATestCase): x = constant_op.constant([2.0, 3.0]) w = ta.unstack(x) r0 = w.read(0) - # calculate (dr0/dx0, dr0/dx1). since r0 = x0, gradients are (1, 0). + # Calculate (dr0/dx0, dr0/dx1). since r0 = x0, gradients are (1, 0). grad_r0 = gradients_impl.gradients(ys=[r0], xs=[x], grad_ys=[1.0]) grad_r0_vals = session.run(grad_r0)[0] self.assertAllEqual(grad_r0_vals, [1.0, 0.0]) @@ -927,7 +931,7 @@ class TensorArrayTest(xla_test.XLATestCase): r0 = w.read(1) r1 = w.read(8) - # Test combined gradients + aggregation of read(0) + # Test combined gradients + aggregation of read(0). grad = gradients_impl.gradients( ys=[r0, r1], xs=[value], grad_ys=[[2.0, 3.0], [4.0, 5.0]]) read_vals, grad_vals = session.run([[r0, r1], grad]) @@ -951,7 +955,7 @@ class TensorArrayTest(xla_test.XLATestCase): w = ta.unstack(values) g = w.gather(indices) - # Test combined gradients + aggregation of read(0) + # Test combined gradients + aggregation of read(0). grad = gradients_impl.gradients( ys=[g], xs=[values], grad_ys=[[[2.0, 3.0], [4.0, 5.0]]]) g_vals, grad_vals = session.run([[g], grad]) |