蟒蛇：3.6.9
TensorFlow：1.15.0
尽管在 SO 上看到了类似问题的答案，但我一直无法检测和解决代码中的错误。所以我来这里寻求你的帮助。
我正在 Iris 数据集上训练分类器，但出现以下错误：
TypeError：预期单个张量时的张量列表
但是，在此错误发生之前，我在堆栈跟踪中看到另一个错误：
ValueError: Tensor("IteratorGetNext:4", shape=(10,), dtype=string)
其中 10 是批量大小。
相关代码：
def input_data(features, labels, batch_size=1, epochs=None, shuffle=False):
# Create dictionaries of features and labels
features = {str(key):np.array(value) for key,value in dict(features).items()}
labels = {str(labels.name):np.array(labels.values)}
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
# `drop_remainder` discards last batch in the epoch if its size is less than `batch_size`
dataset.batch(batch_size, drop_remainder=True).repeat(epochs)
if shuffle:
dataset.shuffle(buffer_size=100)
features, labels = dataset.make_one_shot_iterator().get_next()
return features, labels
training_input_fn = lambda: input_data(train_dataset_features, train_dataset_labels,
batch_size=10, epochs=100, shuffle=True)
linear_classifier.train(input_fn=training_input_fn, steps=100)
堆栈跟踪：
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
323 try:
--> 324 fn(values)
325 except ValueError as e:
20 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in _check_not_tensor(values)
275 def _check_not_tensor(values):
--> 276 _ = [_check_failed(v) for v in nest.flatten(values)
277 if isinstance(v, ops.Tensor)]
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in <listcomp>(.0)
276 _ = [_check_failed(v) for v in nest.flatten(values)
--> 277 if isinstance(v, ops.Tensor)]
278 # pylint: enable=invalid-name
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in _check_failed(v)
247 # it is safe to use here.
--> 248 raise ValueError(v)
249
ValueError: Tensor("IteratorGetNext:4", shape=(10,), dtype=string, device=/device:CPU:0)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-76-4dd60e9636ae> in <module>()
1 linear_classifier.train(
2 input_fn = training_input_fn,
----> 3 steps = 100
4 )
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
368
369 saving_listeners = _check_listeners_type(saving_listeners)
--> 370 loss = self._train_model(input_fn, hooks, saving_listeners)
371 logging.info('Loss for final step: %s.', loss)
372 return self
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1159 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1160 else:
-> 1161 return self._train_model_default(input_fn, hooks, saving_listeners)
1162
1163 def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1189 worker_hooks.extend(input_hooks)
1190 estimator_spec = self._call_model_fn(
-> 1191 features, labels, ModeKeys.TRAIN, self.config)
1192 global_step_tensor = training_util.get_global_step(g)
1193 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1147
1148 logging.info('Calling model_fn.')
-> 1149 model_fn_results = self._model_fn(features=features, **kwargs)
1150 logging.info('Done calling model_fn.')
1151
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py in _model_fn(features, labels, mode, config)
989 partitioner=partitioner,
990 config=config,
--> 991 sparse_combiner=sparse_combiner)
992
993 super(LinearClassifier, self).__init__(
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py in _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, partitioner, config, sparse_combiner)
753 labels=labels,
754 optimizer=optimizer,
--> 755 logits=logits)
756
757
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/head.py in create_estimator_spec(self, features, mode, logits, labels, optimizer, train_op_fn, regularization_losses)
239 self._create_tpu_estimator_spec(
240 features, mode, logits, labels, optimizer, train_op_fn,
--> 241 regularization_losses))
242 return tpu_estimator_spec.as_estimator_spec()
243 except NotImplementedError:
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/head.py in _create_tpu_estimator_spec(self, features, mode, logits, labels, optimizer, train_op_fn, regularization_losses)
894
895 training_loss, unreduced_loss, weights, label_ids = self.create_loss(
--> 896 features=features, mode=mode, logits=logits, labels=labels)
897 if regularization_losses:
898 regularization_loss = math_ops.add_n(regularization_losses)
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/head.py in create_loss(***failed resolving arguments***)
800 logits = ops.convert_to_tensor(logits)
801 labels = _check_dense_labels_match_logits_and_reshape(
--> 802 labels=labels, logits=logits, expected_labels_dimension=1)
803 label_ids = self._label_ids(labels)
804 if self._loss_fn:
/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/canned/head.py in _check_dense_labels_match_logits_and_reshape(labels, logits, expected_labels_dimension)
305 'returns labels.')
306 with ops.name_scope(None, 'labels', (labels, logits)) as scope:
--> 307 labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
308 if isinstance(labels, sparse_tensor.SparseTensor):
309 raise ValueError(
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/sparse_tensor.py in convert_to_tensor_or_sparse_tensor(value, dtype, name)
412 (dtype.name, value.dtype.name))
413 return value
--> 414 return ops.internal_convert_to_tensor(value, dtype=dtype, name=name)
415
416
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accepted_result_types)
1295
1296 if ret is None:
-> 1297 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1298
1299 if ret is NotImplemented:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
284 as_ref=False):
285 _ = as_ref
--> 286 return constant(v, dtype=dtype, name=name)
287
288
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py in constant(value, dtype, shape, name)
225 """
226 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 227 allow_broadcast=True)
228
229
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
263 tensor_util.make_tensor_proto(
264 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 265 allow_broadcast=allow_broadcast))
266 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
267 const_tensor = g.create_op(
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
447 nparray = np.empty(shape, dtype=np_dt)
448 else:
--> 449 _AssertCompatible(values, dtype)
450 nparray = np.array(values, dtype=np_dt)
451 # check to them.
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
326 [mismatch] = e.args
327 if dtype is None:
--> 328 raise TypeError("List of Tensors when single Tensor expected")
329 else:
330 raise TypeError("Expected %s, got %s of type '%s' instead." %
TypeError: List of Tensors when single Tensor expected
字典的值features以及labels在它们被传递到之前tf.data.Dataset.from_tensor_slices()：
# features
{'0': array([7.2, 6. , 4.3, 5.7, 4.7, 7. , 5. , 5.4, 5.8, 5.6, 4.6, 6.4, 4.9,
6.2, 4.9, 5. , 5. , 6.9, 4.8, 6.1, 5. , 5.3, 6.3, 6.7, 6.1, 5.7,
4.4, 5.8, 4.6, 7.9, 4.5, 6.2, 7.7, 5.5, 6. , 6.3, 5.1, 7.3, 5.2,
6.2, 7.2, 4.8, 5.2, 6.6, 6.3, 5.6, 5. , 7.7, 6.7, 6.9, 5.5, 6.5,
6.7, 6.9, 5.1, 6. , 5.5, 6.1, 5.7, 5.4, 5.1, 6.7, 4.6, 6.8, 5.1,
4.9, 6.1, 6.3, 6.1, 7.4, 4.8, 5.1, 5.7, 6.7, 6.8, 6. , 6.2, 6.5,
7.2, 4.7, 5.8, 6.9, 6.7, 6. , 6.1, 7.6, 4.4, 6.4, 5.8, 5.7, 5.6,
6.3, 5.1, 5.1, 6.5, 6.4, 6.3, 5.8, 6.3, 5.8, 5.2, 6.5, 5.5, 4.9,
4.4]), '1': array([3.6, 2.7, 3. , 3. , 3.2, 3.2, 3.4, 3.9, 2.7, 3. , 3.6, 3.1, 2.4,
3.4, 2.5, 3. , 3.2, 3.1, 3.1, 3. , 3.5, 3.7, 2.5, 3.1, 2.8, 3.8,
2.9, 2.7, 3.4, 3.8, 2.3, 2.8, 3. , 3.5, 2.2, 2.5, 3.8, 2.9, 2.7,
2.9, 3.2, 3.4, 3.4, 2.9, 2.7, 2.5, 2. , 2.6, 2.5, 3.2, 2.4, 3. ,
3.3, 3.1, 2.5, 3. , 2.3, 3. , 2.5, 3.4, 3.5, 3.1, 3.2, 3.2, 3.8,
3.1, 2.9, 2.9, 2.6, 2.8, 3. , 3.8, 2.9, 3. , 3. , 2.2, 2.2, 3.2,
3. , 3.2, 2.7, 3.1, 3. , 3.4, 2.8, 3. , 3. , 2.9, 2.8, 4.4, 2.7,
3.4, 3.4, 3.5, 2.8, 3.2, 3.3, 4. , 2.3, 2.7, 4.1, 3. , 2.6, 3.6,
3.2]), '2': array([6.1, 5.1, 1.1, 4.2, 1.3, 4.7, 1.6, 1.7, 5.1, 4.5, 1. , 5.5, 3.3,
5.4, 4.5, 1.6, 1.2, 5.4, 1.6, 4.9, 1.3, 1.5, 4.9, 5.6, 4. , 1.7,
1.4, 5.1, 1.4, 6.4, 1.3, 4.8, 6.1, 1.3, 5. , 5. , 1.9, 6.3, 3.9,
4.3, 6. , 1.9, 1.4, 4.6, 4.9, 3.9, 3.5, 6.9, 5.8, 5.7, 3.7, 5.8,
5.7, 5.1, 3. , 4.8, 4. , 4.6, 5. , 1.5, 1.4, 4.4, 1.4, 5.9, 1.5,
1.5, 4.7, 5.6, 5.6, 6.1, 1.4, 1.6, 4.2, 5. , 5.5, 4. , 4.5, 5.1,
5.8, 1.6, 4.1, 4.9, 5.2, 4.5, 4.7, 6.6, 1.3, 4.3, 5.1, 1.5, 4.2,
5.6, 1.5, 1.4, 4.6, 5.3, 6. , 1.2, 4.4, 3.9, 1.5, 5.5, 4.4, 1.4,
1.3]), '3': array([2.5, 1.6, 0.1, 1.2, 0.2, 1.4, 0.4, 0.4, 1.9, 1.5, 0.2, 1.8, 1. ,
2.3, 1.7, 0.2, 0.2, 2.1, 0.2, 1.8, 0.3, 0.2, 1.5, 2.4, 1.3, 0.3,
0.2, 1.9, 0.3, 2. , 0.3, 1.8, 2.3, 0.2, 1.5, 1.9, 0.4, 1.8, 1.4,
1.3, 1.8, 0.2, 0.2, 1.3, 1.8, 1.1, 1. , 2.3, 1.8, 2.3, 1. , 2.2,
2.1, 2.3, 1.1, 1.8, 1.3, 1.4, 2. , 0.4, 0.3, 1.4, 0.2, 2.3, 0.3,
0.2, 1.4, 1.8, 1.4, 1.9, 0.3, 0.2, 1.3, 1.7, 2.1, 1. , 1.5, 2. ,
1.6, 0.2, 1. , 1.5, 2.3, 1.6, 1.2, 2.1, 0.2, 1.3, 2.4, 0.4, 1.3,
2.4, 0.2, 0.2, 1.5, 2.3, 2.5, 0.2, 1.3, 1.2, 0.1, 1.8, 1.2, 0.1,
0.2])}
# labels
{'4': array(['Iris-virginica', 'Iris-versicolor', 'Iris-setosa',
'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',
'Iris-setosa', 'Iris-virginica', 'Iris-versicolor', 'Iris-setosa',
'Iris-virginica', 'Iris-versicolor', 'Iris-virginica',
'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',
'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',
'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',
'Iris-setosa', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',
'Iris-virginica', 'Iris-setosa', 'Iris-virginica',
'Iris-virginica', 'Iris-setosa', 'Iris-virginica',
'Iris-virginica', 'Iris-setosa', 'Iris-virginica',
'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',
'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',
'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',
'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',
'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',
'Iris-versicolor', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',
'Iris-versicolor', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',
'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',
'Iris-virginica', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',
'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',
'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',
'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',
'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',
'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',
'Iris-versicolor', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',
'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',
'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',
'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa'],
dtype=object)}