生成我自己的TFRecords，我似乎无法在我的模型中正确使用数据集。为了测试是否是我当前的文件或模型代码中的某些东西，我使用了MNIST的tfds，但出现了同样的错误。
这个错误是。IndexError: tuple index out of range完整的输出在下面 我是从jupyter笔记本上做的，如果它改变了什么。
1/Unknown - 0s 47ms/step
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-302-f8e9089d7285> in <module>
----> 1 model.fit(dataset['train'].batch(4096))
~/miniconda3/envs/keras/lib/python3.7/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)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
~/miniconda3/envs/keras/lib/python3.7/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, max_queue_size, workers, use_multiprocessing,
**kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
~/miniconda3/envs/keras/lib/python3.7/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)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
613 # This is the first call of __call__, so we have to initialize.
614 initializers = []
--> 615 self._initialize(args, kwds, add_initializers_to=initializers)
616 finally:
617 # At this point we know that the initialization is complete (or less
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
495 self._concrete_stateful_fn = (
496 self._stateful_fn._get_concrete_function_internal_garbage_collected(
# pylint: disable=protected-access
--> 497 *args, **kwds))
498
499 def invalid_creator_scope(*unused_args, **unused_kwds):
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args,
**kwargs) 2387 args, kwargs = None, None 2388 with self._lock:
-> 2389 graph_function, _, _ = self._maybe_define_function(args, kwargs) 2390 return graph_function 2391
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs) 2701 2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs) 2704 self._function_cache.primary[cache_key] = graph_function 2705 return graph_function, args, kwargs
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 2591 arg_names=arg_names, 2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value), 2594 self._function_attributes, 2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
83 args = _prepare_feed_values(model, input_iterator, mode, strategy)
84 outputs = strategy.experimental_run_v2(
---> 85 per_replica_function, args=args)
86 # Out of PerReplica outputs reduce or pick values to return.
87 all_outputs = dist_utils.unwrap_output_dict(
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
761 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
762 convert_by_default=False)
--> 763 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
764
765 def reduce(self, reduce_op, value, axis):
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs) 1817 kwargs
= {} 1818 with self._container_strategy().scope():
-> 1819 return self._call_for_each_replica(fn, args, kwargs) 1820 1821 def _call_for_each_replica(self, fn, args, kwargs):
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs) 2162 self._container_strategy(), 2163 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2164 return fn(*args, **kwargs) 2165 2166 def _reduce_to(self, reduce_op, value, destinations):
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics, standalone)
414 x, y, sample_weights = model._standardize_user_data(
415 x, y, sample_weight=sample_weight, class_weight=class_weight,
--> 416 extract_tensors_from_dataset=True)
417 batch_size = array_ops.shape(nest.flatten(x, expand_composites=True)[0])[0]
418 # If `model._distribution_strategy` is True, then we are in a replica context
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset) 2381 is_dataset=is_dataset, 2382 class_weight=class_weight,
-> 2383 batch_size=batch_size) 2384 2385 def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size) 2467 shapes=None, 2468 check_batch_axis=False, # Don't enforce the batch size.
-> 2469 exception_prefix='target') 2470 2471 # Generate sample-wise weight values given the `sample_weight` and
~/miniconda3/envs/keras/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
510 'for each key in: ' + str(names))
511 elif isinstance(data, (list, tuple)):
--> 512 if isinstance(data[0], (list, tuple)):
513 data = [np.asarray(d) for d in data]
514 elif len(names) == 1 and isinstance(data[0], (float, int)):
IndexError: tuple index out of range
最小的代码来重现这个问题。
from tensorflow.keras.layers import Dense, Embedding, Flatten, Lambda, Subtract, Input, Concatenate, Average, Reshape, GlobalAveragePooling1D, Dot, Dropout
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.utils import Sequence
from tensorflow.keras import initializers
import tensorflow_datasets as tfds
tfds.list_builders()
dataset, info = tfds.load("mnist", with_info=True)
inputs = Input((28, 28, 1), name="image")
First = Dense(128, activation="relu")
Second = Dropout(0.2)
Third = Dense(10, activation="softmax", name="label")
first = First(inputs)
second = Second(first)
third = Third(second)
model = Model(inputs=[inputs], outputs=[third])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(dataset['train'].batch(4096))
我想我一定是在文档中遗漏了什么 但我想不通 我已经敲了几个小时的代码了 模型从生成器中训练得很好，但随着数据集越来越大，我想转换一下。
添加 as_supervised=True 到 tfds.load() 就能解决这个问题。另一个问题是为什么会出现这个问题，可能是TF的一个BUG。