我的 TensorFlow 输入函数中的错误：“TypeError: List of Tensor when single Tensor expected”
[英]Error in my TensorFlow input function: "TypeError: List of Tensors when single Tensor expected"
问题描述
Python: 3.6.9
蟒蛇：3.6.9
TensorFlow: 1.15.0 TensorFlow：1.15.0
Despite seeing answers to similar questions on SO, I have been unable to detect and resolve the bug in my code.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。 So I have come here to ask you for your help.所以我来这里请你帮忙。
I am training a classifier on the Iris dataset and I get the following error:我正在 Iris 数据集上训练分类器，但出现以下错误：
TypeError: List of Tensors when single Tensor expected
But, before this error occurs I see another error in the stack trace:但是，在发生此错误之前，我在堆栈跟踪中看到了另一个错误：
ValueError: Tensor("IteratorGetNext:4", shape=(10,), dtype=string)
where 10 is the batch size.其中 10 是批量大小。
Relevant Code: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)
Stack Trace: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
Values of the dictionaries features and labels before they are passed to tf.data.Dataset.from_tensor_slices() :字典features和labels在传递给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)}
1 个解决方案
解决方案1
0 已采纳 2020-01-31 14:07:21
As pointed out in the comment to the question by @GPhilo正如@GPhilo 在对问题的评论中指出的那样
String labels cannot be used for training.字符串标签不能用于训练。 They must be converted to an integer.它们必须转换为整数。 Therefore, we can map each class to an integer and use the new 'numerical' labels for training.因此，我们可以将每个类映射到一个整数，并使用新的“数字”标签进行训练。
So in the above case, the classes can be changed to,所以在上面的例子中，类可以更改为，
'Iris-setosa' -> 0
'Iris-versicolor' -> 1
'Iris-viginica' -> 2
One way to code this in Python using Pandas is:使用 Pandas 在 Python 中进行编码的一种方法是：
# 'string_labels' are the labels in string format
# provied in a list-like structure (eg. pd.Series)
numerical_labels = pd.Categorical(string_labels).codes
# the above 'numerical_labels' is an array of dtype 'int8'
# to convert it into pd.Series of dtype 'int32' or 'int64'
numerical_labels = pd.Series(numerical_labels, dtype=np.int64)
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