adding_new_layers_tensorflow.md

Exporting and Importing layer - Tensorflow

Follow the guide here to add your new layer to Fabrik's frontend. Then follow through this guide to add support for importing and exporting your layer for Tensorflow.

Importing a layer

• Open layers_import.py.

  • Add a function to import your layer.

    • Create a function name import_<layer_name> that takes one parameter, layer_ops, that is a list of all ops in the layer being imported.
    • Get layer parameters from the operations in layer_ops and build a dictionary mapping parameter names to values.
    • Get a list of input layers to the layer being processed using get_input_layers(layer_ops).
    • Create and return a json layer for new layer, calling jsonLayer with the new layer_type, parameters, input layers and output_layers(optional).

    def import_dropout(layer_ops):
        layer_params = {}
        for node in layer_ops:
        if ('rate' in node.node_def.attr):
            layer_params['rate'] = node.get_attr('rate')
        if ('seed' in node.node_def.attr):
            layer_params['seed'] = node.get_attr('seed')
        if ('training' in node.node_def.attr):
            layer_params['trainable'] = node.get_attr('training')
        return jsonLayer('Dropout', layer_params, get_input_layers(layer_ops), [])
    

• Open import_graphdef.py

  • From layers_import, import the layer import function you just defined.

    from layers_import import_dropout

  • Add your layer import function to either layer_map or name_map:

    • layer_map if the layer type can be determined directly by the type of nodes in the layer's ops. 'name_map if the laye type can only be determined from the name of the layer.

    name_map = {
        'flatten': import_flatten,
        'lrn': import_lrn,
        ...
    +   'dropout': import_dropout,
        ...
        'concatenate': import_concat
    }
    

Exporting a layer

Fabrik exports Tensorflow models using Keras. See this guide for exporting a layer for Keras.