Sequential model doesn't have outputs

Shouldn’t this code work?

from sklearn.datasets import make_classification
from scikeras.wrappers import KerasClassifier
import tensorflow as tf

def model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(8))
    model.add(tf.keras.layers.Dense(1))
    return model

X, y = make_classification(n_features=8)
est = KerasClassifier(model=model, loss="sparse_categorical_crossentropy")
est.fit(X, y=y)

This throws a ValueError: object of type NoneType [self.model_.outputs] has no len().

Full traceback

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/Downloads/_junk2.py in <module>
     11 X, y = make_classification(n_features=8)
     12 est = KerasClassifier(model=model, loss="sparse_categorical_crossentropy")
---> 13 est.fit(X, y=y)

~/anaconda3/envs/scikeras/lib/python3.7/site-packages/scikeras/wrappers.py in fit(self, X, y, sample_weight, **kwargs)
   1375             sample_weight = 1 if sample_weight is None else sample_weight
   1376             sample_weight *= compute_sample_weight(class_weight=self.class_weight, y=y)
-> 1377         super().fit(X=X, y=y, sample_weight=sample_weight, **kwargs)
   1378         return self
   1379

~/anaconda3/envs/scikeras/lib/python3.7/site-packages/scikeras/wrappers.py in fit(self, X, y, sample_weight, **kwargs)
    739             epochs=getattr(self, "fit__epochs", self.epochs),
    740             initial_epoch=0,
--> 741             **kwargs,
    742         )
    743

~/anaconda3/envs/scikeras/lib/python3.7/site-packages/scikeras/wrappers.py in _fit(self, X, y, sample_weight, warm_start, epochs, initial_epoch, **kwargs)
    855         X = self.feature_encoder_.transform(X)
    856
--> 857         self._check_model_compatibility(y)
    858
    859         self._fit_keras_model(

~/anaconda3/envs/scikeras/lib/python3.7/site-packages/scikeras/wrappers.py in _check_model_compatibility(self, y)
    541             # we recognize the attribute but do not force it to be
    542             # generated
--> 543             if self.n_outputs_expected_ != len(self.model_.outputs):
    544                 raise ValueError(
    545                     "Detected a Keras model input of size"

TypeError: object of type 'NoneType' has no len()

[stsievert]

Eh, I like specific and narrow PRs. Let’s keep them separate.

[invexed]

The original model definitely works as expected - it’s a default model from TensorFlow Decision Forests and does indeed train and predict correctly. It looks like the warning is something unique to TFDF (I think Keras’ functional API is confusing it for whatever reason), however the wrapped model seems to be working correctly. And you were right about the Flatten being superfluous.

Thanks a lot for your help. It’s much appreciated.