How does tf.layers create variables?

Question

Everytime we call tf.layers.conv2d , Tensorflow will automatically creates new kernel, bias and new name for this tensor by adding _1 , _2 , like:

<tf.Variable 'conv2d/kernel:0' shape=(3, 3, 10, 10) dtype=float32_ref>, 
<tf.Variable 'conv2d/bias:0' shape=(10,) dtype=float32_ref>, 
<tf.Variable 'conv2d_1/kernel:0' shape=(3, 3, 10, 10) dtype=float32_ref>,  
<tf.Variable 'conv2d_1/bias:0' shape=(10,) dtype=float32_ref>
conv2d/BiasAdd:0
conv2d_1/BiasAdd:0

If I would like to define a similar layer with variables inside, for example,

def some_layer(input):
    gamma = tf.get_variable(name='gamma', shape=[10], dtype=tf.float32,
                        initializer=tf.constant_initializer(1.0))

x = some_layer(input)
y = some_layer(input)

It will cause ValueError: Variable gamma already exists . I know there are methods to give each variable a name scope or variable scope, but I am wondering is there any method to automatically create new variables gamma_1:0 , gamma_2:0 like conv2d/kernel:0 , conv2d_1/kernel:0 after calling tf.layers.conv2d .

The definition of tf.layers.conv2d gives me no hint.

Answer 1

The Tensorflow tutorial custom layers gives the answer. If we need to build a custom layer, we will need to extend the class tf.keras.layers.Layer .