Custom keras layers - deep learning

Hello,

We have added the possibility to use custom objects in the visual Deep Learning part of the Visual Machine Learning of DSS in release 5.1.3.

To use it, you need to register your custom object using the custom object handler, and then DSS will handle the serialization/deserialization for you.

For example, you can write a "MyDense" custom layer, that you put in a "my_layers.py" inside the libraries of the project on which you're going to build your DL model. It will look like:

# my_layers.py
from keras import backend as K
from keras.engine.topology import Layer

class MyDense(Layer):

    def __init__(self, output_dim=32, **kwargs):
        self.output_dim = output_dim
        super(MyDense, self).__init__(**kwargs)

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel', 
                                      shape=(input_shape[1], self.output_dim),
                                      initializer='uniform',
                                      trainable=True)
        super(MyDense, self).build(input_shape)

    def get_config(self):
        config = {'name': self.name,
                    'trainable': self.trainable,
                    'output_dim': self.output_dim
                    }
        if hasattr(self, 'batch_input_shape'):
            config['batch_input_shape'] = self.batch_input_shape
        if hasattr(self, 'dtype'):
            config['dtype'] = self.dtype
        return config        

    def call(self, x):
        y = K.dot(x, self.kernel)
        return y

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.output_dim)

Note that in this example, you must implement a "get_config" method, that converts your object into a dict. Otherwise, Keras will not be able to figure out how to serialize it.

Then, in the "Architecture" tab of your DL algorithm, you can use the "MyDense" layer, and need to register it for DSS to later save it. The architecture code will look like:

from keras.layers import Input, Dense
from keras.models import Model

from my_layers import MyDense
import dataiku.doctor.deep_learning.custom_objects_handler as coh
coh.register_object("MyDense", MyDense)


def build_model(input_shapes, n_classes=None):
    inputs = Input(shape=input_shapes['main'], name='main')
    x = Dense(128, activation='relu')(inputs)
    x = Dense(128, activation='relu')(x)
    x = MyDense(64)(x)
    predictions = Dense(n_classes, activation="softmax")(x)
    model = Model(inputs=inputs, outputs=predictions)
    return model

def compile_model(model):            
    model.compile(optimizer='rmsprop',
                  loss='binary_crossentropy')
    return model

Don't hesitate if you have further questions.

Best regards,