tensorflow: Keras scikit-learn wrapper not compatible with keras functional model

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System information

• TensorFlow version (use command below): TF version: 2.0, 1.15
• Python version: 3.6 , 3.7

Describe the current behavior when using a keras functional api model via the keras scikit-learn wrapper a crash occurs. see: https://github.com/tensorflow/tensorflow/blob/13f2db1e7071ae109d2f51c7202867a154f587d2/tensorflow/python/keras/wrappers/scikit_learn.py#L241 Describe the expected behavior model.predict() should work on all keras model types besides sequential

Code to reproduce the issue

import numpy as np
import tensorflow as tf
import tensorflow.keras
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier

def build_model():
  input = tf.keras.layers.Input(shape=(2,))
  pred = tf.keras.layers.Dense(2, activation='softmax')(input)
  model = tf.keras.models.Model(inputs=input, outputs=pred)
  model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
  return model

X = np.array([[1,2],[3,1]])
Y = np.array([[1,0], [0,1]])
model = build_model()
model.fit(X, Y)
print(model.predict(X))  # this works

model_wrapped = KerasClassifier(build_model)
model_wrapped.fit(X, Y)
model_wrapped.predict(X)  # this crashes

Output: Train on 2 samples 2/2 [==============================] - 0s 62ms/sample - loss: 1.1024 - acc: 0.5000 [[0.62487346 0.37512657] [0.8205698 0.17943017]] Train on 2 samples 2/2 [==============================] - 0s 64ms/sample - loss: 0.2733 - acc: 1.0000

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-14-48bacae97b80> in <module>()
     19 model_wrapped = KerasClassifier(build_model)
     20 model_wrapped.fit(X, Y)
---> 21 model_wrapped.predict(X)  # this crashes
     22 
     23 

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/wrappers/scikit_learn.py in predict(self, x, **kwargs)
    239     """
    240     kwargs = self.filter_sk_params(Sequential.predict_classes, kwargs)
--> 241     classes = self.model.predict_classes(x, **kwargs)
    242     return self.classes_[classes]
    243 

AttributeError: 'Model' object has no attribute 'predict_classes'