Custom Layers

You can write your own custom layers, and integrate them into your Tensorleap model.

To set up your custom layer, open the Dataset editor and add a class that describes your custom layer. Then, connect your custom layer with the leap_binder object to support your layer. For example, here we add a custom dense layer:

# This class must inherit from tf.keras.layers.Layer
class CustomDense(tf.keras.layers.Layer):
    def __init__(self, n, **args):
        super(CustomDense, self).__init__()
        self.n = n
        self.dense = tf.keras.layers.Dense(self.n)
    
    def call(self, inputs):
        return self.dense(inputs) 
    
    def get_config(self):
        config = super().get_config()
        config.update({
            "n": self.n,
        })
        return config

leap_binder.set_custom_layer(CustomDense, "CustomDense")

Now, you can upload a model that includes these layers by following the Import Model guide.