diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2018-10-04 11:59:37 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-10-04 12:09:21 -0700 |
| commit | 2667ed3bf01e7153f466b27c450fc2b662c00bdd (patch) | |
| tree | acabe1fc78fe4bc215463def3ae0f815425c978a /tensorflow/python | |
| parent | b82c4dad705bffac6d14a189605c9ece89f8c17b (diff) | |
Makes sure Keras Layer's `__call__` is always used in Eager.
Currently if a Layer is invoked with the Functional API in Eager, `__call__` is only used
during setup, and thereafter `call` is used internally. This limits the ability
to add pre/post processing steps to `call` in Eager in the future.
Additionally, the Subclassed Model API already always uses `__call__` in Eager.
PiperOrigin-RevId: 215778408
Diffstat (limited to 'tensorflow/python')
| -rw-r--r-- | tensorflow/python/keras/engine/network.py | 10 |
1 files changed, 8 insertions, 2 deletions
diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 8d34006967..918488bd7a 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -1028,7 +1028,10 @@ class Network(base_layer.Layer): output_tensors, output_masks = layer._call_and_compute_mask( computed_tensor, **kwargs) else: - output_tensors = layer.call(computed_tensor, **kwargs) + if context.executing_eagerly(): + output_tensors = layer(computed_tensor, **kwargs) + else: + output_tensors = layer.call(computed_tensor, **kwargs) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensor, computed_mask) @@ -1049,7 +1052,10 @@ class Network(base_layer.Layer): output_tensors, output_masks = layer._call_and_compute_mask( computed_tensors, **kwargs) else: - output_tensors = layer.call(computed_tensors, **kwargs) + if context.executing_eagerly(): + output_tensors = layer(computed_tensors, **kwargs) + else: + output_tensors = layer.call(computed_tensors, **kwargs) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensors, computed_masks) |