Import External Code

To include any existing code in your Tensorleap Dataset Script you can create a library and import all your existing classes and functions to your script.
Here is an example of how to import a Custom Layer from an external file.

Custom Layer Import from File

  1. 1.
    First, create a file that includes the custom layers that are included in your model. Here we show an example of a custom dense layer.
import tensorflow as tf
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
2. In your Tensorleap Data Folder, create a new folder that will include the file you've created and an empty file. For example:
├── ....
| ├── custom_layers/
| ├──
| └──
3. Now, use one of two possible methods to import the classes Into the dataset script.

Append system path

Tensorleap dataset script
# custom function imports
import sys
from custom_dense import CustomDense
leap_binder.set_custom_layer(CustomDense, 'CustomDense')

Import using importlib

Tensorleap dataset script
# custom function imports
import importlib.util
import sys
from inspect import getmembers, isclass
# adding custom layers from a file using imports
spec = importlib.util.spec_from_file_location('custom_layers.custom_dense',
custom_layers_module = importlib.util.module_from_spec(spec)
custom_layers_titles = getmembers(custom_layers_module, isclass)
for i in range(len(custom_layers_titles)):
class_name = custom_layers_titles[i][0]
function_class = getattr(custom_layers_module, class_name)
leap_binder.set_custom_layer(function_class, class_name)
Parsing the dataset will now import the custom layers from the external files and add it to your Tensorleap environment.
Last modified 1mo ago