Regularization DropBLock : When I add the regularization block for each Colvolution Layer I got the followinf probem .

[hamzaca]

TypeError: An op outside of the function building code is being passed 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: strided_slice:0

**I added a regularization block after each convolution layer.

Here is my code :**

    import keras
    from keras  import datasets, layers, models, optimizers    
    import matplotlib.pyplot as plt
    from keras_drop_block import DropBlock2D

    input_shape = (32,32,3)
    def Build_Model_Block_Drop() : 

        model = models.Sequential()
        model.add(layers.Conv2D(32,(3,3), activation= 'relu', input_shape =input_shape))
        model.add(DropBlock2D(block_size=5, keep_prob=0.8, name='Dropout_Block-1'))
        model.add(layers.Conv2D(64,(3,3), activation='relu'))
        model.add(layers.MaxPool2D((2,2)))
        model.add(DropBlock2D(block_size=5, keep_prob=0.8, name='Dropout_Block-2'))

        model.add(layers.Conv2D(64,(3,3), activation='relu'))
        model.add(DropBlock2D(block_size=5, keep_prob=0.8,name='Dropout_Block-3'))

        model.add(layers.Conv2D(64,(3,3), activation='relu'))
        model.add(layers.MaxPool2D((2,2)))
        model.add(DropBlock2D(block_size=5, keep_prob=0.8, name='Dropout_Block-4'))

        model.add(layers.Flatten())
        model.add(layers.Dense(60,activation='relu'))
        model.add(layers.Dense(64,activation='relu'))
        model.add(layers.Dense(10,activation='softmax'))
                model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',
                                         metrics = ['accuracy'])

        return model