Issue:
i have a successfully running nearest neighbour tensorflow model on colab, named top_classify. but when comes to saving, getting the error message below:
KeyError: "Failed to add concrete function
'b'__inference_model_layer_call_fn_158405'' to object-based SavedModel as it captures
tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which is
unsupported or not reachable from root. One reason could be that a stateful object or
a variable that the function depends on is not assigned to an attribute of the
serialized trackable object (see SaveTest.test_captures_unreachable_variable)."
Details:
the nearest neighbour model of interest is using output of an already trained model (embedding_network). and it is comparing input's output with train data set's outputs (nearest distance). i mean we are not directly comparing images, comparing their outputs. normally i wouldn't bother to code NN on tensorflow, since model is not iterative, but i need a tflite model to use on an android app. so, i don't have much choice.
first i trained the embedding_network model (via transfer learning & siamese), which is giving output size of (None, 27). xc is the constant output matrix of the entire training set (752 examples, of size (752, 27) ), yc is the correct labels of the traing set. both are tf constants. the below example is for best match (1NN). code can also be modified to work for any number of desired matches (KNN).
xc = tf.constant(embedding_network(x_train))
yc = tf.constant(y_train)
inputs = keras.Input(shape=(TARGET_SIZE, TARGET_SIZE, 3))
x0 = embedding_network(inputs, training=False)
distance = tf.reduce_sum(tf.abs(tf.add(xc, tf.negative(x0))), axis=1)
findKClosestTrImages = tf.argsort(distance, direction='ASCENDING')
closest0 = tf.gather(yc, findKClosestTrImages[0])
out=tf.one_hot(closest0, DEPTH)
top_classify = keras.Model(inputs=inputs, outputs=out)
top_classify.summary()
as you can see i'm not doing any training with the model, no loss function, neither compile & fit. since i will just use it on inference.
here is the summary of the new nearest neighbour model, top_classify:
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_6 (InputLayer) [(None, 224, 224, 3)] 0
model (Functional) (None, 27) 2292571
tf.math.negative_1 (TFOpLam (None, 27) 0
bda)
tf.math.add_1 (TFOpLambda) (752, 27) 0
tf.math.abs_1 (TFOpLambda) (752, 27) 0
tf.math.reduce_sum_1 (TFOpL (752,) 0
ambda)
tf.argsort_1 (TFOpLambda) (752,) 0
tf.__operators__.getitem_1 () 0
(SlicingOpLambda)
tf.compat.v1.gather_1 (TFOp () 0
Lambda)
tf.one_hot_1 (TFOpLambda) (27,) 0
=================================================================
Total params: 2,292,571
Trainable params: 0
Non-trainable params: 2,292,571
_________________________________________________________________
anyway model works as a charm on colab. i'm getting really good results. but i can't save the model, whether i use tf.saved_model.save or top_classify.save
errors if try tf.saved_model.save
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/function_serialization.py in serialize_concrete_function(concrete_function, node_ids, coder)
64 for capture in concrete_function.captured_inputs:
---> 65 bound_inputs.append(node_ids[capture])
66 except KeyError:
7 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/object_identity.py in __getitem__(self, key)
138 def __getitem__(self, key):
--> 139 return self._storage[self._wrap_key(key)]
140
KeyError: <_ObjectIdentityWrapper wrapping <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>>>
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-33-13ced446758f> in <module>()
15 TOP_CLASSIFY_SAVE_LOC = "/content/top_classify"
16 #top_classify.save("/content/top_classify")
---> 17 tf.saved_model.save(top_classify, TOP_CLASSIFY_SAVE_LOC)
18 #top_classify.save("/content/top_classify", save_format='h5')
19 # Convert the model
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in save(obj, export_dir, signatures, options)
1278 # pylint: enable=line-too-long
1279 metrics.IncrementWriteApi(_SAVE_V2_LABEL)
-> 1280 save_and_return_nodes(obj, export_dir, signatures, options)
1281 metrics.IncrementWrite(write_version="2")
1282
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in save_and_return_nodes(obj, export_dir, signatures, options, experimental_skip_checkpoint)
1313
1314 _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (
-> 1315 _build_meta_graph(obj, signatures, options, meta_graph_def))
1316 saved_model.saved_model_schema_version = (
1317 constants.SAVED_MODEL_SCHEMA_VERSION)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in _build_meta_graph(obj, signatures, options, meta_graph_def)
1485
1486 with save_context.save_context(options):
-> 1487 return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
1449
1450 object_graph_proto = _serialize_object_graph(
-> 1451 saveable_view, asset_info.asset_index)
1452 meta_graph_def.object_graph_def.CopyFrom(object_graph_proto)
1453
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in _serialize_object_graph(saveable_view, asset_file_def_index)
1015 name = saveable_view.function_name_map.get(name, name)
1016 serialized = function_serialization.serialize_concrete_function(
-> 1017 concrete_function, saveable_view.captured_tensor_node_ids, coder)
1018 if serialized is not None:
1019 proto.concrete_functions[name].CopyFrom(serialized)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/function_serialization.py in serialize_concrete_function(concrete_function, node_ids, coder)
66 except KeyError:
67 raise KeyError(
---> 68 f"Failed to add concrete function '{concrete_function.name}' to object-"
69 f"based SavedModel as it captures tensor {capture!r} which is unsupported"
70 " or not reachable from root. "
KeyError: "Failed to add concrete function
'b'__inference_model_layer_call_fn_158405'' to object-based SavedModel as it captures
tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which is
unsupported or not reachable from root. One reason could be that a stateful object or
a variable that the function depends on is not assigned to an attribute of the
serialized trackable object (see SaveTest.test_captures_unreachable_variable)."
errors if try top_classify.save:
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.
layer_config = serialize_layer_fn(layer)
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-34-7275f8fa7bba> in <module>()
18 top_classify.save("/content/top_classify", save_format='h5')
19 # Convert the model
---> 20 converter = tf.lite.TFLiteConverter.from_saved_model(TOP_CLASSIFY_SAVE_LOC) # path to the SavedModel directory
21 tflite_model = converter.convert()
22
4 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/lite.py in from_saved_model(cls, saved_model_dir, signature_keys, tags)
1603
1604 with context.eager_mode():
-> 1605 saved_model = _load(saved_model_dir, tags)
1606 if not signature_keys:
1607 signature_keys = saved_model.signatures
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in load(export_dir, tags, options)
898 ValueError: If `tags` don't match a MetaGraph in the SavedModel.
899 """
--> 900 result = load_internal(export_dir, tags, options)["root"]
901 return result
902
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in load_internal(export_dir, tags, options, loader_cls, filters)
911 tags = nest.flatten(tags)
912 saved_model_proto, debug_info = (
--> 913 loader_impl.parse_saved_model_with_debug_info(export_dir))
914
915 if (len(saved_model_proto.meta_graphs) == 1 and
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/loader_impl.py in parse_saved_model_with_debug_info(export_dir)
58 parsed. Missing graph debug info file is fine.
59 """
---> 60 saved_model = _parse_saved_model(export_dir)
61
62 debug_info_path = file_io.join(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/loader_impl.py in parse_saved_model(export_dir)
117 else:
118 raise IOError(
--> 119 f"SavedModel file does not exist at: {export_dir}{os.path.sep}"
120 f"{{{constants.SAVED_MODEL_FILENAME_PBTXT}|"
121 f"{constants.SAVED_MODEL_FILENAME_PB}}}")
OSError: SavedModel file does not exist at: /content/top_classify/{saved_model.pbtxt|saved_model.pb}
if you come up with a solution or suggestion, it would be really appreciated. ty
