弱对象消失了_什么意思? [英] weak object has gone away_what does it mean?
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问题描述
我正在使用 tensorflow 解决一个问题，其中有一个函数被调用一次并且它工作正常，但第二次被调用时出现错误弱对象已经消失"，我不明白这是什么意思以及问题可能出在哪里.
I am using tensorflow for a problem where there is a function which is called once and it works correctly but the second time it is called the error " weak object has gone away" comes up which I don't understand what it means and where the problem might be.
完整的引用如下:
----------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
108 try:
--> 109 hash(elem)
110 except TypeError:
TypeError: weak object has gone away
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-23-f1a95ac20255> in <module>
----> 1 default_settings['sur_model'].predict_with_grad(np.atleast_2d(xx))
<ipython-input-5-7e554cb74b1d> in predict_with_grad(self, x)
127
128 with tf.GradientTape() as t:
--> 129 m, v = self.predict(x)
130 dmdx = t.gradient(m, x)
131 with tf.GradientTape() as t:
<ipython-input-5-7e554cb74b1d> in predict(self, X)
116 """
117
--> 118 X_embed = self.embedding_model(X)
119 #print('X_embed',X_embed)
120 m, v = self.Pr_model.predict_y(X_embed)
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
889 with base_layer_utils.autocast_context_manager(
890 self._compute_dtype):
--> 891 outputs = self.call(cast_inputs, *args, **kwargs)
892 self._handle_activity_regularization(inputs, outputs)
893 self._set_mask_metadata(inputs, outputs, input_masks)
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
492 # In this case we have not created variables on the first call. So we can
493 # run the first trace but we should fail if variables are created.
--> 494 results = self._stateful_fn(*args, **kwds)
495 if self._created_variables:
496 raise ValueError("Creating variables on a non-first call to a function"
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
1820 def __call__(self, *args, **kwargs):
1821 """Calls a graph function specialized to the inputs."""
-> 1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2117
2118 with self._lock:
-> 2119 graph_function = self._function_cache.primary.get(cache_key, None)
2120 if graph_function is not None:
2121 return graph_function, args, kwargs
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __eq__(self, other)
115
116 def __eq__(self, other):
--> 117 return self._fields_safe == other._fields_safe # pylint: disable=protected-access
118
119
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _fields_safe(self)
91 def _fields_safe(self):
92 """Hash & equality-safe version of all the namedtuple fields."""
---> 93 return (self._hash_fix(self.input_signature), self.parent_graph,
94 self.device_functions, self.colocation_stack,
95 self.in_cross_replica_context)
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
99 # Descend into tuples
100 if isinstance(elem, tuple):
--> 101 return tuple(self._hash_fix(i) for i in elem)
102
103 if isinstance(elem, set):
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in <genexpr>(.0)
99 # Descend into tuples
100 if isinstance(elem, tuple):
--> 101 return tuple(self._hash_fix(i) for i in elem)
102
103 if isinstance(elem, set):
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
99 # Descend into tuples
100 if isinstance(elem, tuple):
--> 101 return tuple(self._hash_fix(i) for i in elem)
102
103 if isinstance(elem, set):
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in <genexpr>(.0)
99 # Descend into tuples
100 if isinstance(elem, tuple):
--> 101 return tuple(self._hash_fix(i) for i in elem)
102
103 if isinstance(elem, set):
~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _hash_fix(self, elem)
110 except TypeError:
111 v = elem()
--> 112 return (v.__class__, tensor_spec.TensorSpec(v.shape, v.dtype))
113
114 return elem
AttributeError: 'NoneType' object has no attribute 'shape'
我觉得它很奇怪，还没有找到线索来知道我应该从哪里修复它.
I find it rather strange and haven't found a clue to know from where I should fix it.
推荐答案
我想您在代码中的某处使用 tf.function/@tf.function 并且您可能试图重新定义一个对象导致 @tf.function 不回溯您的图形.这是因为这两个对象共享相同的轨迹，即使使用不同的参数也没有影响.以下代码段重现了上述行为
I suppose your using tf.function /@tf.function somewhere in your code and you may be trying to redefine an object which results in @tf.function not retracing your graph. This is because the two objects share the same trace and that even using different parameters has no effect. the following snippet reproduces the behavior described above
import tensorflow as tf
@tf.function
def square(x):
return x**2
a = square(tf.Variable(2))
print(a)
a = square(tf.Variable(3))
print(a)
但是，如果您希望不同的对象具有不同的跟踪，即不共享跟踪，您可以使用不同的 @tf.function 对象，如下所示；
However, if you want different objects to have different traces .i.e not share traces, you may use different @tf.function objects as below;
@tf.function
def square1(x):
return x**2
@tf.function
def square2(x):
return x**2
print(square1(tf.Variable(2)))
print(square2(tf.Variable(3)))
有关详细信息，请查看official_documentation.希望这有帮助
For more details, checkout the official_documentation. Hope this helps
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