from SpectralNormalizationKeras import DenseSN, ConvSN2D

(...)

 def build_critic(self, spectral_normalization=True):

        model = Sequential()

        model.add(ConvSN2D(16, kernel_size=3, strides=2,kernel_initializer='glorot_uniform', input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(ConvSN2D(32, kernel_size=3, strides=2,kernel_initializer='glorot_uniform', padding="same"))
        model.add(ZeroPadding2D(padding=((0,1),(0,1))))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(ConvSN2D(64, kernel_size=3, strides=2,kernel_initializer='glorot_uniform', padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(ConvSN2D(128, kernel_size=3, strides=1,kernel_initializer='glorot_uniform',padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Flatten())
        model.add(DenseSN(1,kernel_initializer='glorot_uniform'))

        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-24-da7bad03b7a7> in <module>
      1 if __name__ == '__main__':
----> 2     wgan = WGANGP()
      3     wgan.train(epochs=30001, batch_size=256, sample_interval=1500)

<ipython-input-23-9f58d066c64d> in __init__(self)
     27         # Build the generator and critic
     28         self.generator = self.build_generator()
---> 29         self.critic = self.build_critic()
     30 
     31         #-------------------------------

<ipython-input-23-9f58d066c64d> in build_critic(self, spectral_normalization)
    141         model = Sequential()
    142 
--> 143         model.add(ConvSN2D(16, kernel_size=3, strides=2,kernel_initializer='glorot_uniform', input_shape=self.img_shape, padding="same"))
    144         model.add(LeakyReLU(alpha=0.2))
    145         model.add(Dropout(0.25))

~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    126       raise TypeError('The added layer must be '
    127                       'an instance of class Layer. '
--> 128                       'Found: ' + str(layer))
    129     self.built = False
    130     if not self._layers:

TypeError: The added layer must be an instance of class Layer. Found: <SpectralNormalizationKeras.ConvSN2D object at 0x000001BF340526D8>

The following Variables were used a Lambda layer's call (tf.nn.bias_add), but
are not present in its tracked objects:
  <tf.Variable 'discriminator_ConvSN2D_1/bias:0' shape=(64,) dtype=float32>
It is possible that this is intended behavior, but it is more likely
an omission. This is a strong indication that this layer should be
formulated as a subclassed Layer rather than a Lambda layer.```

Looking forward to your reply. Thank you very much.

Best regards