Can not invoke 'summary' on the instance of AdversarialRegularization instance

[ksachdeva]

Hi,

nsl.keras.AdversarialRegularization is a subclass of tf.keras.Model

When I invoke summary() on the instance of AdversarialRegularization I get an error saying the model needs to be built

ValueError                                Traceback (most recent call last)
<ipython-input-47-d160d5b3365c> in <module>()
      1 
----> 2 adv_model.summary()

/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/network.py in summary(self, line_length, positions, print_fn)
   1255     """
   1256     if not self.built:
-> 1257       raise ValueError('This model has not yet been built. '
   1258                        'Build the model first by calling `build()` or calling '
   1259                        '`fit()` with some data, or specify '

ValueError: This model has not yet been built. Build the model first by calling `build()` or calling `fit()` with some data, or specify an `input_shape` argument in the first layer(s) for automatic build.

This is happening because even though the 'base_model' is built the wrapper one i.e. instance of AdversarialRegularization is not.

[csferng]

Thanks for your interest.

nsl.keras.AdversarialRegularization creates subclassed Keras models, so calling .summary() won't work until the model is called with some input. The reason is that the computation of a subclassed Keras model is defined in its call function (a piece of Python code), and the model architecture is unknown to Tensorflow engine until the call function is executed. This behavior is different from Keras models created by Sequential or functional API, where all the computation can be defined in the constructor.

Here is an example of doing compile, call, and then summary for a AdversarialRegularization model:

>>> base_model = tf.keras.Sequential([tf.keras.Input(shape=(2,)), tf.keras.layers.Dense(1)])
>>> adv_model = nsl.keras.AdversarialRegularization(base_model, label_keys=['label'])
>>> adv_model.compile('sgd', loss='mse')
>>> adv_model({'input': tf.constant([[1.0, -1.0]]), 'label': tf.constant([[0.0]])})
<tf.Tensor: id=492, shape=(1, 1), dtype=float32, numpy=array([[-0.8385217]], dtype=float32)>
>>> adv_model.summary()
Model: "AdversarialRegularization"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
sequential_1 (Sequential)    (None, 1)                 3         
=================================================================
Total params: 3
Trainable params: 3
Non-trainable params: 0
_________________________________________________________________

More details about model subclassing can be found here: https://www.tensorflow.org/guide/keras/overview#model_subclassing