我正在使用tensorflow解决一个函数，该函数被调用一次并且可以正常工作，但是第二次被称为错误”弱对象已消失”的问题，我不明白它的含义，并且问题可能出在哪里。
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'
我发现它很奇怪，并且没有找到从哪里修复的线索。
[我想您在代码中的某处使用了tf.function /@tf.function，并且您可能正在尝试重新定义一个对象，导致@tf.function无法重新绘制图形。这是因为两个对象共享同一条迹线，即使使用不同的参数也无效。以下代码段重现了上述行为]
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轨迹，即不共享轨迹，则可以如下使用不同的@tf.function对象；
@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)))
希望这会有所帮助