Closed yug95 closed 4 years ago
I am using tensorflow 2.0 and got this error , can you please let me know how to resolve that.
Train on 6132 samples Epoch 1/10 32/6132 [..............................] - ETA: 1:17:35 - loss: 2130.8425 --------------------------------------------------------------------------- NotFoundError Traceback (most recent call last) <ipython-input-54-d5c9353e9609> in <module>() 4 nnT2V.compile(loss='mse', optimizer='adam') 5 ----> 6 nnT2V.fit(X_train, y_train, epochs=10) ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\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_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 726 max_queue_size=max_queue_size, 727 workers=workers, --> 728 use_multiprocessing=use_multiprocessing) 729 730 def evaluate(self, ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs) 322 mode=ModeKeys.TRAIN, 323 training_context=training_context, --> 324 total_epochs=epochs) 325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN) 326 ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs) 121 step=step, mode=mode, size=current_batch_size) as batch_logs: 122 try: --> 123 batch_outs = execution_function(iterator) 124 except (StopIteration, errors.OutOfRangeError): 125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError? ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn) 84 # `numpy` translates Tensors to values in Eager mode. 85 return nest.map_structure(_non_none_constant_value, ---> 86 distributed_function(input_fn)) 87 88 return execution_function ~\AppData\Roaming\Python\Python36\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() ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds) 485 # In this case we have created variables on the first call, so we run the 486 # defunned version which is guaranteed to never create variables. --> 487 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable 488 elif self._stateful_fn is not None: 489 # Release the lock early so that multiple threads can perform the call ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\function.py in __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 1825 @property ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs) 1139 if isinstance(t, (ops.Tensor, 1140 resource_variable_ops.BaseResourceVariable))), -> 1141 self.captured_inputs) 1142 1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None): ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager) 1222 if executing_eagerly: 1223 flat_outputs = forward_function.call( -> 1224 ctx, args, cancellation_manager=cancellation_manager) 1225 else: 1226 gradient_name = self._delayed_rewrite_functions.register() ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager) 509 inputs=args, 510 attrs=("executor_type", executor_type, "config_proto", config), --> 511 ctx=ctx) 512 else: 513 outputs = execute.execute_with_cancellation( ~\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 65 else: 66 message = e.message ---> 67 six.raise_from(core._status_to_exception(e.code, message), None) 68 except TypeError as e: 69 keras_symbolic_tensors = [ ~\Anaconda3\lib\site-packages\six.py in raise_from(value, from_value) NotFoundError: Resource AnonymousIterator/AnonymousIterator6/class tensorflow::data::IteratorResource does not exist. [[node IteratorGetNext (defined at C:\Users\yogesh\AppData\Roaming\Python\Python36\site-packages\tensorflow_core\python\framework\ops.py:1751) ]] [Op:__inference_distributed_function_25761] Function call stack: distributed_function
https://github.com/tensorflow/tensorflow/issues/36728
I am using tensorflow 2.0 and got this error , can you please let me know how to resolve that.