我为此感到发疯。
我使用tensorflow keras定义了一个顺序模型：
model = tf.keras.Sequential([tf.keras.layer.Dense(128,input_shape(784,),activation="relu"),
tf.keras.layer.Dense(10,activation="softmax"])
model.compile(optimizer="adam",loss="mse")
keras.experimental.export_saved_model(model,"keras_model")
我使用c_api.h在C program中训练了所述模型
C程序将权重保存在检查点文件中。
当尝试使用以下命令从检查点文件恢复python中的权重时：
keras.experimental.load_from_saved_model("keras_model/")
#OR
model = tf.keras.Sequential([tf.keras.layer.Dense(128,input_shape(784,),activation="relu"),
tf.keras.layer.Dense(10,activation="softmax"])
model.load_weights("keras_model/variables/variables")
#OR
checkpoint = tf.train.Checkpoint(model=model)
status = checkpoint.restore("keras_model/variables/variables")
我最终收到一个错误，并且没有恢复任何权重。
我能够恢复体重并继续进行C程序训练
keras.experimental.load_from_saved_model("keras_model/")
WARNING: Logging before flag parsing goes to stderr.
W0918 15:18:04.350199 140418474760000 deprecation.py:323] From <ipython-input-2-06ea110fdc8e>:1: load_from_saved_model (from tensorflow.python.keras.saving.saved_model_experimental) is deprecated and will be removed in a future version.
Instructions for updating:
The experimental save and load functions have been deprecated. Please switch to `tf.keras.models.load_model`.
2019-09-18 15:18:04.390271: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1696040000 Hz
2019-09-18 15:18:04.390913: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4bf4790 executing computations on platform Host. Devices:
2019-09-18 15:18:04.390961: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version
W0918 15:18:04.436281 140418474760000 deprecation.py:323] From /home/jregalado/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/util.py:1249: NameBasedSaverStatus.__init__ (from tensorflow.python.training.tracking.util) is deprecated and will be removed in a future version.
Instructions for updating:
Restoring a name-based tf.train.Saver checkpoint using the object-based restore API. This mode uses global names to match variables, and so is somewhat fragile. It also adds new restore ops to the graph each time it is called when graph building. Prefer re-encoding training checkpoints in the object-based format: run save() on the object-based saver (the same one this message is coming from) and use that checkpoint in the future.
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-2-06ea110fdc8e> in <module>
----> 1 keras.experimental.load_from_saved_model("keras_model/")
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py in new_func(*args, **kwargs)
322 'in a future version' if date is None else ('after %s' % date),
323 instructions)
--> 324 return func(*args, **kwargs)
325 return tf_decorator.make_decorator(
326 func, new_func, 'deprecated',
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/saved_model_experimental.py in load_from_saved_model(saved_model_path, custom_objects)
425 compat.as_text(constants.VARIABLES_DIRECTORY),
426 compat.as_text(constants.VARIABLES_FILENAME))
--> 427 model.load_weights(checkpoint_prefix)
428 return model
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in load_weights(self, filepath, by_name)
179 raise ValueError('Load weights is not yet supported with TPUStrategy '
180 'with steps_per_run greater than 1.')
--> 181 return super(Model, self).load_weights(filepath, by_name)
182
183 @trackable.no_automatic_dependency_tracking
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/network.py in load_weights(self, filepath, by_name)
1372 # streaming restore for any variables created in the future.
1373 trackable_utils.streaming_restore(status=status, session=session)
-> 1374 status.assert_nontrivial_match()
1375 return status
1376 if h5py is None:
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/util.py in assert_nontrivial_match(self)
964 # assert_nontrivial_match and assert_consumed (and both are less
965 # useful since we don't touch Python objects or Python state).
--> 966 return self.assert_consumed()
967
968 def _gather_saveable_objects(self):
~/Projects/tensorflow/env/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/util.py in assert_consumed(self)
941 raise AssertionError(
942 "Some objects had attributes which were not restored:{}".format(
--> 943 "".join(unused_attribute_strings)))
944 for trackable in self._graph_view.list_objects():
945 # pylint: disable=protected-access
AssertionError: Some objects had attributes which were not restored:
<tf.Variable 'a/kernel:0' shape=(784, 128) dtype=float32, numpy=
array([[-0.03716458, -0.04911711, -0.01023878, ..., 0.0636776 ,
0.02892563, -0.05542086],
[-0.02324755, -0.07362694, -0.0399951 , ..., 0.0680329 ,
0.05201877, -0.05149256],
[ 0.00954343, 0.05673491, 0.05108347, ..., 0.01994208,
-0.01107961, 0.06192174],
...,
[ 0.07091486, -0.07734856, -0.04417738, ..., 0.01921409,
-0.01908814, -0.05070668],
[ 0.01353646, -0.05189713, -0.01391671, ..., -0.05795977,
0.04801518, 0.00801209],
[-0.05304915, 0.01870193, 0.05657425, ..., -0.06819408,
-0.00760372, -0.0106293 ]], dtype=float32)>: ['a/kernel']
<tf.Variable 'a/bias:0' shape=(128,) dtype=float32, numpy=
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>: ['a/bias']
<tf.Variable 'b/kernel:0' shape=(128, 10) dtype=float32, numpy=
array([[-0.1759212 , -0.09282549, -0.11045764, ..., -0.13727605,
-0.02849793, 0.14510198],
[ 0.06857841, -0.01459177, 0.08369003, ..., 0.05089156,
-0.05319159, -0.08594933],
[-0.180914 , -0.18932283, 0.20551099, ..., -0.17210156,
-0.10069884, 0.06433241],
...,
[ 0.09097584, -0.03930017, -0.15125516, ..., 0.02359283,
-0.16158347, -0.13176063],
[-0.04145582, -0.03205152, 0.20097663, ..., -0.15124482,
0.16874255, -0.15434337],
[-0.13188484, 0.04145408, 0.05036192, ..., -0.10489662,
0.12316228, 0.08794598]], dtype=float32)>: ['b/kernel']
<tf.Variable 'b/bias:0' shape=(10,) dtype=float32, numpy=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>: ['b/bias']