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()
"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.