I have used a similar LSTM model on previous Jetpack versions but for some reason I cant run model.fit() as I get the following error message.
I am very new to data science and I dont understand what this error message means. I will really appreciate if anyone can help. Thanks in advance!
AttributeError: module 'tensorflow.compat.v2.__internal__.distribute' has no attribute 'strategy_supports_no_merge_call'
Full Log:
Epoch 1/100
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-18fefb168cfe> in <module>
----> 1 regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
/usr/local/lib/python3.6/dist-packages/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)
1382 _r=1):
1383 callbacks.on_train_batch_begin(step)
-> 1384 tmp_logs = self.train_function(iterator)
1385 if data_handler.should_sync:
1386 context.async_wait()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
908
909 with OptionalXlaContext(self._jit_compile):
--> 910 result = self._call(*args, **kwds)
911
912 new_tracing_count = self.experimental_get_tracing_count()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
956 # This is the first call of __call__, so we have to initialize.
957 initializers = []
--> 958 self._initialize(args, kwds, add_initializers_to=initializers)
959 finally:
960 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
779 self._concrete_stateful_fn = (
780 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 781 *args, **kwds))
782
783 def invalid_creator_scope(*unused_args, **unused_kwds):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3155 args, kwargs = None, None
3156 with self._lock:
-> 3157 graph_function, _ = self._maybe_define_function(args, kwargs)
3158 return graph_function
3159
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3555
3556 self._function_cache.missed.add(call_context_key)
-> 3557 graph_function = self._create_graph_function(args, kwargs)
3558 self._function_cache.primary[cache_key] = graph_function
3559
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3400 arg_names=arg_names,
3401 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3402 capture_by_value=self._capture_by_value),
3403 self._function_attributes,
3404 function_spec=self.function_spec,
/usr/local/lib/python3.6/dist-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, acd_record_initial_resource_uses)
1141 _, original_func = tf_decorator.unwrap(python_func)
1142
-> 1143 func_outputs = python_func(*func_args, **func_kwargs)
1144
1145 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
670 # the function a weak reference to itself to avoid a reference cycle.
671 with OptionalXlaContext(compile_with_xla):
--> 672 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
673 return out
674
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1127 except Exception as e: # pylint:disable=broad-except
1128 if hasattr(e, "ag_error_metadata"):
-> 1129 raise e.ag_error_metadata.to_exception(e)
1130 else:
1131 raise
AttributeError: in user code:
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 863, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/usr/local/lib/python3.6/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 532, in minimize
return self.apply_gradients(grads_and_vars, name=name)
File "/usr/local/lib/python3.6/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 668, in apply_gradients
grads_and_vars = self._aggregate_gradients(grads_and_vars)
File "/usr/local/lib/python3.6/dist-packages/keras/optimizer_v2/optimizer_v2.py", line 484, in _aggregate_gradients
return self.gradient_aggregator(grads_and_vars)
File "/usr/local/lib/python3.6/dist-packages/keras/optimizer_v2/utils.py", line 33, in all_reduce_sum_gradients
if tf.__internal__.distribute.strategy_supports_no_merge_call():
AttributeError: module 'tensorflow.compat.v2.__internal__.distribute' has no attribute 'strategy_supports_no_merge_call'