AttributeError: 'str' 对象在 Tensorflow 中没有属性 'name'

Question

I am trying to predict the prices of items using Dnnregressor and I couldn't figure out this error that keeps coming. I created tf numeric and categorical columns from pandas dataframe and fed it into the DNNRegressor. There is not much help online regarding this particular error.

Please help me fix this error. Thanks

AttributeError                            Traceback (most recent call last)
<ipython-input-27-790ecef8c709> in <module>()
 92 
 93 if __name__ == '__main__':
---> 94     main()

<ipython-input-27-790ecef8c709> in main()
 81      #   learning_rate=0.1, l1_regularization_strength=0.001))
 82     est = tf.estimator.DNNRegressor(feature_columns = feature_columns,  hidden_units = [10, 10], model_dir = 'data')
---> 83     est.train(input_fn = get_train_input_fn(Xtrain, ytrain), steps = 500)
 84     scores = est.evaluate(input_fn = get_test_input_fn(Xtest, ytest))
 85     print('Loss Score: {0:f}' .format(scores['average_loss']))

C:\Users\user\Anaconda3\lib\site-   packages\tensorflow\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps)
239       hooks.append(training.StopAtStepHook(steps, max_steps))
240 
--> 241     loss = self._train_model(input_fn=input_fn, hooks=hooks)
242     logging.info('Loss for final step: %s.', loss)
243     return self

C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model(self, input_fn, hooks)
628           input_fn, model_fn_lib.ModeKeys.TRAIN)
629       estimator_spec = self._call_model_fn(features, labels,
--> 630                                            model_fn_lib.ModeKeys.TRAIN)
631       ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss)
632       all_hooks.extend(hooks)

C:\Users\user\Anaconda3\lib\site- packages\tensorflow\python\estimator\estimator.py in _call_model_fn(self, features, labels, mode)
613     if 'config' in model_fn_args:
614       kwargs['config'] = self.config
--> 615     model_fn_results = self._model_fn(features=features, **kwargs)
616 
617     if not isinstance(model_fn_results, model_fn_lib.EstimatorSpec):

C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\dnn.py in _model_fn(features, labels, mode, config)
389           dropout=dropout,
390           input_layer_partitioner=input_layer_partitioner,
--> 391           config=config)
392     super(DNNRegressor, self).__init__(
393         model_fn=_model_fn, model_dir=model_dir, config=config)

C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\dnn.py in _dnn_model_fn(features, labels, mode, head, hidden_units, feature_columns, optimizer, activation_fn, dropout, input_layer_partitioner, config)
100       net = feature_column_lib.input_layer(
101           features=features,
--> 102           feature_columns=feature_columns)
103 
104     for layer_id, num_hidden_units in enumerate(hidden_units):

C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column.py in input_layer(features, feature_columns, weight_collections, trainable)
205     ValueError: if an item in `feature_columns` is not a `_DenseColumn`.
206   """
--> 207   _check_feature_columns(feature_columns)
208   for column in feature_columns:
209     if not isinstance(column, _DenseColumn):

 C:\Users\user\Anaconda3\lib\site-  packages\tensorflow\python\feature_column\feature_column.py in     _check_feature_columns(feature_columns)
1660   name_to_column = dict()
1661   for column in feature_columns:
-> 1662     if column.name in name_to_column:
1663       raise ValueError('Duplicate feature column name found for columns: {} '
1664                        'and {}. This usually means that these columns refer to '

C:\Users\user\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column.py in name(self)
2451   @property
2452   def name(self):
-> 2453     return '{}_indicator'.format(self.categorical_column.name)
2454 
2455   def _transform_feature(self, inputs):

AttributeError: 'str' object has no attribute 'name'

And below is code:

def get_train_input_fn(Xtrain, ytrain):
return tf.estimator.inputs.pandas_input_fn(
        x = Xtrain,
        y = ytrain,
        batch_size = 30,
        num_epochs = None,
        shuffle = True)

def get_test_input_fn(Xtest, ytest):
return tf.estimator.inputs.pandas_input_fn(
        x = Xtest,
        y = ytest,
        batch_size = 32,
        num_epochs = 1,
        shuffle = False)
def main():
Xtrain, Xtest, ytrain, ytest = train_test_split(merc, ytr, test_size = 0.4, random_state = 42)
feature_columns = []
brand_rating = tf.feature_column.numeric_column('brand_rating')
feature_columns.append(brand_rating)
sentiment = tf.feature_column.numeric_column('description_polarity')
feature_columns.append(sentiment)
item_condition = tf.feature_column.numeric_column('item_condition_id')
feature_columns.append(item_condition)
shipping = tf.feature_column.indicator_column('shipping')
feature_columns.append(shipping)
name = tf.feature_column.embedding_column('item_name', 34) #(column name, dimension(no. of unique values ** 0.25))
feature_columns.append(name)
general = tf.feature_column.categorical_column_with_hash_bucket('General', 12)
feature_columns.append(general)
sc1 = tf.feature_column.categorical_column_with_hash_bucket('SC1', 120)
feature_columns.append(sc1)
sc2 = tf.feature_column.categorical_column_with_hash_bucket('SC2', 900)
feature_columns.append(sc2)
print(feature_columns)
#est = tf.estimator.DNNRegressor(feature_columns, hidden_units = [10, 10], optimizer=tf.train.ProximalAdagradOptimizer(
 #   learning_rate=0.1, l1_regularization_strength=0.001))
est = tf.estimator.DNNRegressor(feature_columns = feature_columns, hidden_units = [10, 10], model_dir = 'data')
est.train(input_fn = get_train_input_fn(Xtrain, ytrain), steps = 500)

Answer 1

The first argument to tf.feature_column.embedding_column must be a categorical column, not a string. See API spec .

The offending line in your code is:

tf.feature_column.embedding_column('item_name', 34) 

Answer 2

After using

general = tf.feature_column.categorical_column_with_hash_bucket('General', 12)

and other feature_column.categorical_column_with..., you should use

general_indicator = tf.feature_column.indicator_column(general)

and then append it to your feature_columns list.

feature_columns.append(general_indicator)