I’m getting the following errors on running the CNN cifar 10 example code from the website, after this line of code:
(thank you in advance)
# use rate neurons always by setting learning_phase_scope
with tf.keras.backend.learning_phase_scope(1), nengo_dl.Simulator(
I've added the following prints for better understanding:
Build finished in 0:00:00
Optimization finished in 0:00:00
Construction finished in 0:00:01
starting training...
y_keys:
['output_p', 'input-layer_p', 'conv-layer1_p', 'conv-layer2_p', 'conv-layer3_p', 'conv-layer4_p', 'conv-layer5_p', 'dense-layer_p']
Y type:
<class 'list'>
X type:
<class 'list'>
train X shape:
(50000, 32, 32, 3)
train t shape:
(50000, 10)
train X:
[[[[-0.9999385 -0.9961553 -0.9965244 ]
[-0.99996924 -0.9987697 -0.9973856 ]
[-0.9996617 -0.9969858 -0.99701655]
...
[-0.9976932 -0.9997232 -0.9976932 ]
[-0.9997232 -0.9976932 -0.9997232 ]
[-0.9976932 -0.99990773 -0.9976932 ]]
[[-0.9999385 -0.9976932 -0.99981546]
[-0.9976932 -0.999877 -0.9976932 ]
[-0.99990773 -0.9976932 -0.99981546]
...
[-0.9976932 -0.99975395 -0.9976932 ]
[-0.9999385 -0.9976932 -0.99996924]
[-0.9976932 -0.99996924 -0.9976932 ]]
[[-0.999877 -0.9976932 -0.9997232 ]
[-0.9976932 -0.9997847 -0.9976932 ]
[-0.99975395 -0.9976932 -0.9998462 ]
...
[-0.9976932 -0.9997232 -0.9976932 ]
[-0.99975395 -0.9976932 -0.99996924]
[-0.9976932 -0.9997847 -0.9976932 ]]
...
train t:
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 1.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
Constructing graph: build stage finished in 0:00:01
the error output is:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-9c9fe265cc41> in <module>
99
100 for epoch in range(n_epochs):
--> 101 sim.fit(
102 train_data,
103 steps_per_epoch=steps_per_epoch,
~\anaconda3\lib\site-packages\nengo\utils\magic.py in __call__(self, *args, **kwargs)
179 return self.wrapper(wrapped, instance, args, kwargs)
180 else:
--> 181 return self.wrapper(self.__wrapped__, self.instance, args, kwargs)
182 else:
183 instance = getattr(self.__wrapped__, "__self__", None)
~\anaconda3\lib\site-packages\nengo_dl\simulator.py in require_open(wrapped, instance, args, kwargs)
65 )
66
---> 67 return wrapped(*args, **kwargs)
68
69
~\anaconda3\lib\site-packages\nengo_dl\simulator.py in fit(self, x, y, n_steps, stateful, **kwargs)
866 kwargs["validation_data"] = (x_val, y_val, validation_data[2])
867
--> 868 return self._call_keras(
869 "fit", x=x, y=y, n_steps=n_steps, stateful=stateful, **kwargs
870 )
~\anaconda3\lib\site-packages\nengo\utils\magic.py in __call__(self, *args, **kwargs)
179 return self.wrapper(wrapped, instance, args, kwargs)
180 else:
--> 181 return self.wrapper(self.__wrapped__, self.instance, args, kwargs)
182 else:
183 instance = getattr(self.__wrapped__, "__self__", None)
~\anaconda3\lib\site-packages\nengo_dl\simulator.py in with_self(wrapped, instance, args, kwargs)
48 try:
49 with tf.device(instance.tensor_graph.device):
---> 50 output = wrapped(*args, **kwargs)
51 finally:
52 tf.keras.backend.set_floatx(keras_dtype)
~\anaconda3\lib\site-packages\nengo_dl\simulator.py in _call_keras(self, func_type, x, y, n_steps, stateful, **kwargs)
1042 func_args = dict(x=x, y=y, **kwargs)
1043
-> 1044 outputs = getattr(self.keras_model, func_type)(**func_args)
1045
1046 # update n_steps/time
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
724 self._concrete_stateful_fn = (
--> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
726 *args, **kwds))
727
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3194 arg_names = base_arg_names + missing_arg_names
3195 graph_function = ConcreteFunction(
-> 3196 func_graph_module.func_graph_from_py_func(
3197 self._name,
3198 self._python_function,
~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
AttributeError: in user code:
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function *
return step_function(self, iterator)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step **
outputs = model.train_step(data)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:755 train_step
loss = self.compiled_loss(
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:186 __call__
self.build(y_pred)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:139 build
self._losses = nest.map_structure(self._get_loss_object, self._losses)
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\util\nest.py:659 map_structure
structure[0], [func(*x) for x in entries],
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\util\nest.py:659 <listcomp>
structure[0], [func(*x) for x in entries],
C:\Users\Aeon\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:264 _get_loss_object
loss_name = loss.__name__
AttributeError: 'functools.partial' object has no attribute '__name__'