'str' object has no attribute '_keras_mask' error when using tf.keras.Sequential

Question

Background

I am using Tensorflow for the first time following a tutorial on featurization with the new Google Recommenders package: https://www.tensorflow.org/recommenders/examples/featurization

I ran into trouble swapping out their dataset (MovieLens) for one based on the Kaggle wine data. The following code works as expected:

wine_title_lookup= tf.keras.layers.experimental.preprocessing.StringLookup()

wine_title_lookup.adapt(np.unique(wine_train['title']))
print(f"Vocabulary: {wine_title_lookup.get_vocabulary()[:3]}")

Vocabulary: ['', '[UNK]', 'Žitavské Vinice Rhine Riesling']

wine_title_embedding = tf.keras.layers.Embedding(
    # Let's use the explicit vocabulary lookup.
    input_dim=wine_title_lookup.vocab_size(),
    output_dim=32
)

x= wine_title_lookup(["Susana Balbo Signature Malbec"])

x= wine_title_embedding(x)

x

<tf.Tensor: shape=(1, 32), dtype=float32, numpy= array([[-0.03861505, -0.02146437, 0.04332292, -0.02598745, 0.03842534, -0.01066433, 0.0292404 , 0.02783312, 0.03364438, 0.00054752, -0.0295071 , 0.03200008, 0.01224083, -0.00100452, -0.04346857, 0.00105418, -0.01640136, -0.01778026, 0.00171928, 0.03215903, 0.00020416, -0.02083766, -0.00323264, 0.02582215, 0.04805436, 0.0325211 , 0.0100181 , -0.04965406, 0.02548517, 0.01569786, 0.03761304, 0.01659941]], dtype=float32)>

However the following produces an error

wine_title_model = tf.keras.Sequential([wine_title_lookup, wine_title_embedding])

wine_title_model(["Susana Balbo Signature Malbec"])

AttributeError Traceback (most recent call last) in () ----> 1 wine_title_model(["Susana Balbo Signature Malbec"])

3 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in call(self, *args, **kwargs) 983 984 with ops.enable_auto_cast_variables(self._compute_dtype_object): --> 985 outputs = call_fn(inputs, *args, **kwargs) 986 987 if self._activity_regularizer:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in call(self, inputs, training, mask) 370 if not self.built: 371 self._init_graph_network(self.inputs, self.outputs) --> 372 return super(Sequential, self).call(inputs, training=training, mask=mask) 373 374 outputs = inputs # handle the corner case where self.layers is empty

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in call(self, inputs, training, mask) 384 """ 385 return self._run_internal_graph( --> 386 inputs, training=training, mask=mask) 387 388 def compute_output_shape(self, input_shape):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in _run_internal_graph(self, inputs, training, mask) 482 masks = self._flatten_to_reference_inputs(mask) 483 for input_t, mask in zip(inputs, masks): --> 484 input_t._keras_mask = mask 485 486 # Dictionary mapping reference tensors to computed tensors.

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

Notable differences with the source material

The Google code I based my script on uses a data format I am unfamiliar with which allows them to run map on their data. I tried converting my data into some tensorflow formats but could not seem to replicate their functionality. However this is the only step that is different and I cannot understand why the pieces of the Sequence op work individually but not as a whole.

I looked at some other examples from when this error has popped up on SO but could not find a solution to my problem. This what the raw data looks like.

wine_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 108655 entries, 0 to 120727
Data columns (total 16 columns):
    Column               Non-Null Count   Dtype  
---  ------               --------------   -----  
 0   country              108600 non-null  object 
 1   description          108652 non-null  object 
 2   designation          77150 non-null   object 
 3   points               108336 non-null  float64
 4   price                100871 non-null  float64
 5   province             108600 non-null  object 
 6   region_1             108655 non-null  object 
 7   region_2             42442 non-null   object 
 8   title                108655 non-null  object 
 9   variety              108655 non-null  object 
 10  winery               108655 non-null  object 
 11  designation_replace  108655 non-null  object 
 12  user_id              108655 non-null  int64  
 13  price_isna           108655 non-null  bool   
 14  price_imputed        108650 non-null  float64
 15  wine_id              108655 non-null  int64  
dtypes: bool(1), float64(3), int64(2), object(10)
memory usage: 13.4+ MB

sahilmanekia sahilmanekia · Accepted Answer · 2020-12-08T06:01:23

I solved this problem by fixing the following areas of my code

Converting wine_train to a Tensorflow format

When I posted this question I had already tried running tf.data.Dataset.from_tensor_slices on my pandas dataframe. However it will not work. Instead convert the dataframe to a dictionary as so: wine_features_dict = {name: np.array(value) for name, value in wine_train.items()} and then everything runs smoothly.

Tensorflow is very sensitive to missing or NaN values.

I thought I got everything but just dropping all rows with any missing data seemed to get rid of the error. If it's happening to you make sure that there is no missing data and all your data is either integer or string.

'str' object has no attribute '_keras_mask' error when using tf.keras.Sequential

Background

Notable differences with the source material

2 Answers