作为Udemy课程的一部分，我正在尝试创建一个线性分类器，该分类器将预测一个人的年收入是否大于或小于5万。不幸的是，我收到了这个tensorflow错误：-----------------------------------------------------------
TypeError Traceback (most recent call last)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
526 try:
--> 527 str_values = [compat.as_bytes(x) for x in proto_values]
528 except TypeError:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in <listcomp>(.0)
526 try:
--> 527 str_values = [compat.as_bytes(x) for x in proto_values]
528 except TypeError:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/util/compat.py in as_bytes(bytes_or_text, encoding)
60 raise TypeError('Expected binary or unicode string, got %r' %
---> 61 (bytes_or_text,))
62
TypeError: Expected binary or unicode string, got {'income_bracket': <tf.Tensor 'random_shuffle_queue_DequeueUpTo:14' shape=(?,) dtype=int64>}
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-90-56c52ca792f4> in <module>
----> 1 model.train(input_fn=input_func, steps=5000)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1205 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1206 else:
-> 1207 return self._train_model_default(input_fn, hooks, saving_listeners)
1208
1209 def _train_model_default(self, input_fn, hooks, saving_listeners):
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1235 worker_hooks.extend(input_hooks)
1236 estimator_spec = self._call_model_fn(
-> 1237 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1238 global_step_tensor = training_util.get_global_step(g)
1239 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1193
1194 logging.info('Calling model_fn.')
-> 1195 model_fn_results = self._model_fn(features=features, **kwargs)
1196 logging.info('Done calling model_fn.')
1197
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/linear.py in _model_fn(features, labels, mode, config)
382 partitioner=partitioner,
383 config=config,
--> 384 sparse_combiner=sparse_combiner)
385
386 super(LinearClassifier, self).__init__(
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/linear.py in _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, partitioner, config, sparse_combiner)
213 labels=labels,
214 optimizer=optimizer,
--> 215 logits=logits)
216
217
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py in create_estimator_spec(self, features, mode, logits, labels, optimizer, train_op_fn, regularization_losses)
237 self._create_tpu_estimator_spec(
238 features, mode, logits, labels, optimizer, train_op_fn,
--> 239 regularization_losses))
240 return tpu_estimator_spec.as_estimator_spec()
241 except NotImplementedError:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py in _create_tpu_estimator_spec(self, features, mode, logits, labels, optimizer, train_op_fn, regularization_losses)
1207 (training_loss, unreduced_loss, weights, processed_labels) = (
1208 self.create_loss(
-> 1209 features=features, mode=mode, logits=logits, labels=labels))
1210 if regularization_losses:
1211 regularization_loss = math_ops.add_n(regularization_losses)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py in create_loss(***failed resolving arguments***)
1108 logits = ops.convert_to_tensor(logits)
1109 labels = _check_dense_labels_match_logits_and_reshape(
-> 1110 labels=labels, logits=logits, expected_labels_dimension=1)
1111 if self._label_vocabulary is not None:
1112 labels = lookup_ops.index_table_from_tensor(
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/estimator/canned/head.py in _check_dense_labels_match_logits_and_reshape(labels, logits, expected_labels_dimension)
303 'returns labels.')
304 with ops.name_scope(None, 'labels', (labels, logits)) as scope:
--> 305 labels = sparse_tensor.convert_to_tensor_or_sparse_tensor(labels)
306 if isinstance(labels, sparse_tensor.SparseTensor):
307 raise ValueError(
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/sparse_tensor.py in convert_to_tensor_or_sparse_tensor(value, dtype, name)
277 return value
278 return ops.internal_convert_to_tensor(
--> 279 value, dtype=dtype, name=name)
280
281
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
1144
1145 if ret is None:
-> 1146 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1147
1148 if ret is NotImplemented:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
227 as_ref=False):
228 _ = as_ref
--> 229 return constant(v, dtype=dtype, name=name)
230
231
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
206 tensor_value.tensor.CopyFrom(
207 tensor_util.make_tensor_proto(
--> 208 value, dtype=dtype, shape=shape, verify_shape=verify_shape))
209 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
210 const_tensor = g.create_op(
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
529 raise TypeError("Failed to convert object of type %s to Tensor. "
530 "Contents: %s. Consider casting elements to a "
--> 531 "supported type." % (type(values), values))
532 tensor_proto.string_val.extend(str_values)
533 return tensor_proto
TypeError: Failed to convert object of type <class 'dict'> to Tensor. Contents: {'income_bracket': <tf.Tensor 'random_shuffle_queue_DequeueUpTo:14' shape=(?,) dtype=int64>}. Consider casting elements to a supported type.
这是错误：
import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split
census_data = pd.read_csv("census_data.csv")
census_data["income_bracket"] =
census_data["income_bracket"].apply(lambda x: 0 if x==" <=50K" else 1)
y_census_data = pd.DataFrame(census_data["income_bracket"])
x_census_data = census_data.drop("income_bracket", axis=1)
test_data = {
"y":None,
"x":None
}
train_data = {
"y":None,
"x":None
}
train_data["x"], test_data["x"], train_data["y"], test_data["y"] = train_test_split(
x_census_data,
y_census_data,
test_size=0.33
)
def feature_column_prefill(col_name):
return_val =
tf.feature_column.categorical_column_with_vocabulary_list(
col_name,
list(census_data[col_name].unique())
)
return return_val
workclass = feature_column_prefill("workclass")
education = feature_column_prefill("education")
marital_status = feature_column_prefill("marital_status")
occupation = feature_column_prefill("occupation")
relationship = feature_column_prefill("relationship")
race = feature_column_prefill("race")
gender = feature_column_prefill("gender")
native_country = feature_column_prefill("native_country")
age = tf.feature_column.numeric_column("age")
education_num = tf.feature_column.numeric_column("education_num")
capital_gain = tf.feature_column.numeric_column("capital_gain")
capital_loss = tf.feature_column.numeric_column("capital_loss")
hours_per_week = tf.feature_column.numeric_column("hours_per_week")
feat_cols = [ age, workclass, education, education_num, marital_status,
occupation, relationship, race, gender, capital_gain, capital_loss,
hours_per_week, native_country ]
input_func = tf.estimator.inputs.pandas_input_fn(
x=train_data["x"],
y=train_data["y"],
batch_size=10,
num_epochs=1000,
shuffle=True
)
model = tf.estimator.LinearClassifier(
feature_columns=feat_cols,
n_classes=2
)
model.train(input_fn=input_func, steps=5000)
这是我的代码：
28, Private, Bachelors,13, Married-civ-spouse, Prof-specialty, Wife, Black, Female,0,0,40, Cuba, <=50K
37, Private, Masters,14, Married-civ-spouse, Exec-managerial, Wife, White, Female,0,0,40, United-States, <=50K
49, Private, 9th,5, Married-spouse-absent, Other-service, Not-in-family, Black, Female,0,0,16, Jamaica, <=50K
52, Self-emp-not-inc, HS-grad,9, Married-civ-spouse, Exec-managerial, Husband, White, Male,0,0,45, United-States, >50K
31, Private, Masters,14, Never-married, Prof-specialty, Not-in-family, White, Female,14084,0,50, United-States, >50K
42, Private, Bachelors,13, Married-civ-spouse, Exec-managerial, Husband, White, Male,5178,0,40, United-States, >50K
37, Private, Some-college,10, Married-civ-spouse, Exec-managerial, Husband, Black, Male,0,0,80, United-States, >50K
这是csv文件的一小部分样本：
{{1}}
我很困，所以如果有人能帮上忙，那就太好了。非常感谢。
我希望模型能够开始训练，但是，我得到了错误。