我正在研究TensorFlow上的项目，并且正在尝试使用线性回归器训练模型。要将数据添加到估算器上，我正在使用函数 tf.estimator.inputs.pandas_input_fn（），但是由于存在一些问题，我无法启动训练。我收到此错误：
TypeError: Failed to convert object of type <class 'dict'> to Tensor. Contents: {'DispositionSoldAmount': <tf.Tensor 'random_shuffle_queue_DequeueMany:4' shape=(128,) dtype=float64>}. Consider casting elements to a supported type.
我试图将 yData 更改为pandas.core.series.Series，但这并没有改变结果。
有人可以解决我的问题吗？
此外，我用sklearn.linear_regression用相同的DataSet训练了另一个模型，并且可以正常工作。
这是我的代码：
FEATURES = ["DispositionMileage", "PurchasePrice", "Age"] # X
feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
estimator = tf.estimator.LinearRegressor(feature_columns=feature_cols,model_dir="train")
def get_input_fn( num_epochs=None, n_batch = 128, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=Xdata,
y=ydata,
batch_size=n_batch,
num_epochs=num_epochs,
shuffle=shuffle)
estimator.train(input_fn=get_input_fn(num_epochs=None,n_batch = 128,shuffle=True),steps=1000)
使用的数据：
Xdata type is pandas.core.frame.DataFrame:
DispositionMileage PurchasePrice Age
9741 3849 16472.0 0
9744 3849 16472.0 0
9745 3849 16472.0 0
9748 3849 16472.0 0
...
[18105 rows x 3 columns]
ydata type is pandas.core.frame.DataFrame:
DispositionSoldAmount
9741 1650.0
9744 1650.0
9745 1650.0
9748 1650.0
13465 7750.0
...
[18105 rows x 1 columns]
完整的追溯：
WARNING:tensorflow:From /home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
Traceback (most recent call last):
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 527, in make_tensor_proto
str_values = [compat.as_bytes(x) for x in proto_values]
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 527, in <listcomp>
str_values = [compat.as_bytes(x) for x in proto_values]
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/util/compat.py", line 61, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got {'DispositionSoldAmount': <tf.Tensor 'random_shuffle_queue_DequeueMany:4' shape=(128,) dtype=float64>}
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "tuto.py", line 85, in <module>
estimator.train(input_fn=get_input_fn(num_epochs=None,n_batch = 128,shuffle=True),steps=1000)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 354, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1207, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1237, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1195, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/linear.py", line 537, in _model_fn
sparse_combiner=sparse_combiner)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/linear.py", line 215, in _linear_model_fn
logits=logits)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py", line 239, in create_estimator_spec
regularization_losses))
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py", line 1482, in _create_tpu_estimator_spec
features=features, mode=mode, logits=logits, labels=labels)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py", line 1381, in create_loss
expected_labels_dimension=self._logits_dimension)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py", line 305, in _check_dense_labels_match_logits_and_reshape
labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/sparse_tensor.py", line 279, in convert_to_tensor_or_sparse_tensor
value, dtype=dtype, name=name)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1146, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 229, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 208, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/home/USER/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 531, in make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'dict'> to Tensor. Contents: {'DispositionSoldAmount': <tf.Tensor 'random_shuffle_queue_DequeueMany:4' shape=(128,) dtype=float64>}. Consider casting elements to a supported type.
答案 0 :(得分：2)
您需要将ydata数据框转换为pandas.Series
ydata = pd.Series(ydata[column_name])
经过随机数据检查，它正在工作。我实际上感到很惊讶，在新的TF版本tf.estimator.inputs.pandas_input_fn中似乎不接受数据框作为标签。
答案 1 :(得分：0)
可以与 pandas.Series 一起使用。感谢您的回答。 pandas.DataFrame看起来与 tf.estimator.inputs.pandas_input_fn函数上的 y 参数无关。