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Diffstat (limited to 'tensorflow/python/ops/math_grad.py')
| -rw-r--r-- | tensorflow/python/ops/math_grad.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 55dd0c0e0d..e2ee9e4fe4 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -52,14 +52,14 @@ def _SumGrad(op, grad): if axes is not None: rank = len(input_0_shape) if np.array_equal(axes, np.arange(rank)): # Reduce all dims. - if context.in_graph_mode(): - new_shape = [1] * rank - else: + if context.executing_eagerly(): ctx = context.context() new_shape = ctx.ones_rank_cache().get(rank) if new_shape is None: new_shape = constant_op.constant([1] * rank, dtype=dtypes.int32) ctx.ones_rank_cache().put(rank, new_shape) + else: + new_shape = [1] * rank grad = array_ops.reshape(grad, new_shape) # If shape is not fully defined (but rank is), we use Shape. if None not in input_0_shape: @@ -997,7 +997,7 @@ def _SparseMatMulGrad(op, grad): op.inputs[0]: op.get_attr("a_is_sparse"), op.inputs[1]: op.get_attr("b_is_sparse"), # Use heuristic to figure out if grad might be sparse - grad: context.in_graph_mode() and (grad.op.type == "ReluGrad") + grad: not context.executing_eagerly() and (grad.op.type == "ReluGrad") } def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False): |