我试图在tensorflow中创建逻辑回归模型。
当我尝试执行model.fit(input_fn=train_input_fn, steps=200)时，我收到以下错误。
TypeError Traceback (most recent call last)
<ipython-input-44-fd050d8188b5> in <module>()
----> 1 model.fit(input_fn=train_input_fn, steps=200)
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc in fit(self, x, y, input_fn, steps, batch_size, monitors)
180 feed_fn=feed_fn,
181 steps=steps,
--> 182 monitors=monitors)
183 logging.info('Loss for final step: %s.', loss)
184 return self
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.pyc in _train_model(self, input_fn, steps, feed_fn, init_op, init_feed_fn, init_fn, device_fn, monitors, log_every_steps, fail_on_nan_loss)
447 features, targets = input_fn()
448 self._check_inputs(features, targets)
--> 449 train_op, loss_op = self._get_train_ops(features, targets)
450
451 # Add default monitors.
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.pyc in _get_train_ops(self, features, targets)
105 if self._linear_feature_columns is None:
106 self._linear_feature_columns = layers.infer_real_valued_columns(features)
--> 107 return super(LinearClassifier, self)._get_train_ops(features, targets)
108
109 @property
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.pyc in _get_train_ops(self, features, targets)
154 global_step = contrib_variables.get_global_step()
155 assert global_step
--> 156 logits = self._logits(features, is_training=True)
157 with ops.control_dependencies([self._centered_bias_step(
158 targets, self._get_weight_tensor(features))]):
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.pyc in _logits(self, features, is_training)
298 logits = self._dnn_logits(features, is_training=is_training)
299 else:
--> 300 logits = self._linear_logits(features)
301
302 return nn.bias_add(logits, self._centered_bias())
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.pyc in _linear_logits(self, features)
255 num_outputs=self._num_label_columns(),
256 weight_collections=[self._linear_weight_collection],
--> 257 name="linear")
258 return logits
259
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.pyc in weighted_sum_from_feature_columns(columns_to_tensors, feature_columns, num_outputs, weight_collections, name, trainable)
173 transformer = _Transformer(columns_to_tensors)
174 for column in sorted(set(feature_columns), key=lambda x: x.key):
--> 175 transformed_tensor = transformer.transform(column)
176 predictions, variable = column.to_weighted_sum(transformed_tensor,
177 num_outputs,
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.pyc in transform(self, feature_column)
353 return self._columns_to_tensors[feature_column]
354
--> 355 feature_column.insert_transformed_feature(self._columns_to_tensors)
356
357 if feature_column not in self._columns_to_tensors:
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column.pyc in insert_transformed_feature(self, columns_to_tensors)
410 mapping=list(self.lookup_config.keys),
411 default_value=self.lookup_config.default_value,
--> 412 name=self.name + "_lookup")
413
414
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/contrib/lookup/lookup_ops.pyc in string_to_index(tensor, mapping, default_value, name)
349 with ops.op_scope([tensor], name, "string_to_index") as scope:
350 shared_name = ""
--> 351 keys = ops.convert_to_tensor(mapping, dtypes.string)
352 vocab_size = array_ops.size(keys)
353 values = math_ops.cast(math_ops.range(vocab_size), dtypes.int64)
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, as_ref)
618 for base_type, conversion_func in funcs_at_priority:
619 if isinstance(value, base_type):
--> 620 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
621 if ret is NotImplemented:
622 continue
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.pyc in _constant_tensor_conversion_function(v, dtype, name, as_ref)
177 as_ref=False):
178 _ = as_ref
--> 179 return constant(v, dtype=dtype, name=name)
180
181
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.pyc in constant(value, dtype, shape, name)
160 tensor_value = attr_value_pb2.AttrValue()
161 tensor_value.tensor.CopyFrom(
--> 162 tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
163 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
164 const_tensor = g.create_op(
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc in make_tensor_proto(values, dtype, shape)
351 nparray = np.empty(shape, dtype=np_dt)
352 else:
--> 353 _AssertCompatible(values, dtype)
354 nparray = np.array(values, dtype=np_dt)
355 # check to them.
/home/praveen/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc in _AssertCompatible(values, dtype)
288 else:
289 raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 290 (dtype.name, repr(mismatch), type(mismatch).__name__))
291
292
TypeError: Expected string, got 1 of type 'int64' instead.
我不确定要检查哪个功能。有人可以告诉我怎么可以调试这个？提前致谢
答案 0 :(得分：2)
我的分类列功能很少，其数据类型为int64。所以，我将列从int转换为string。之后，适合步骤完成。显然，tensorflow期望分类特征dtype为字符串。