Hi,
I try to use dropcoonect, and already install on my machine,
but when i build and fit model, this error appear

a "Graph" tensor. It is possible to have Graph tensors
leak out of the function building context by including a
tf.init_scope in your function building code.
For example, the following function will fail:
  @tf.function
  def has_init_scope():
    my_constant = tf.constant(1.)
    with tf.init_scope():
      added = my_constant * 2
The graph tensor has name: Model/drop_connect/mul:0

I think there is a mismatch in tensorflow and keras version.
Tensorflow = 2.0.0
Keras = 2.4.0

This is how i use dropconnect

...
X = Dense(64, activation='relu')(X)  
X = DropConnect(layer=Dense(units=32),prob=0.2,)(X)
...

Thanks!

Hi,
Thanks for the implementation. However, I detected a bug in it.
It uses tf.nn.dropout which scales up the input by 1 / keep_prob (see its documentation). This is not required for dropconnect though. So you have to scale the weights and biases back. Here is the required change (given that the self.prob in you implementation is actually the probability of dropping, not keeping):

self.layer.kernel = K.in_train_phase(K.dropout(self.layer.kernel, self.prob), self.layer.kernel) * (1-self.prob)

self.layer.bias = K.in_train_phase(K.dropout(self.layer.bias, self.prob), self.layer.bias) * (1-self.prob)