Error Messages:
File ~/apps/miniconda3/envs/oscar_py39_tf28/lib/python3.9/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback..error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
File ~/apps/miniconda3/envs/oscar_py39_tf28/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py:1147, in func_graph_from_py_func..autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
ValueError: A tf.Variable created inside your tf.function has been garbage-collected. Your code needs to keep Python references to variables created inside `tf.function`s.
A common way to raise this error is to create and return a variable only referenced inside your function:
@tf.function
def f():
v = tf.Variable(1.0)
return v
v = f() # Crashes with this error message!
The reason this crashes is that @tf.function annotated function returns a **`tf.Tensor`** with the **value** of the variable when the function is called rather than the variable instance itself. As such there is no code holding a reference to the `v` created inside the function and Python garbage collects it.
The simplest way to fix this issue is to create variables outside the function and capture them:
v = tf.Variable(1.0)
@tf.function
def f():
return v
f() #
v.assign_add(1.)
f() #
Solution:
Modify the path that you import package.
Modify
from keras.layers import Layer, InputSpec
to
from tensorflow.python.keras.engine.base_layer import Layer, InputSpec