Beginner's Guide for Keras2DML users


Keras2DML is an experimental API that converts a Keras specification to DML through the intermediate Caffe2DML module. It is designed to fit well into the mllearn framework and hence supports NumPy, Pandas as well as PySpark DataFrame.

Getting Started

To create a Keras2DML object, one needs to create a Keras model through the Funcitonal API. please see the Functional API. This module utilizes the existing Caffe2DML backend to convert Keras models into DML. Keras models are parsed and translated into Caffe prototext and caffemodel files which are then piped into Caffe2DML. Thus one can follow the Caffe2DML documentation for further information.

Model Conversion

Keras models are parsed based on their layer structure and corresponding weights and translated into the relative Caffe layer and weight configuration. Be aware that currently this is a translation into Caffe and there will be loss of information from keras models such as intializer information, and other layers which do not exist in Caffe.

To create a Keras2DML object, simply pass the keras object to the Keras2DML constructor. It’s also important to note that your models should be compiled so that the loss can be accessed for Caffe2DML

```python from systemml.mllearn import Keras2DML import keras from keras.applications.resnet50 import preprocess_input, decode_predictions, ResNet50

model = ResNet50(weights=’imagenet’,include_top=True,pooling=’None’,input_shape=(224,224,3)) model.compile(optimizer=’sgd’, loss= ‘categorical_crossentropy’)

resnet = Keras2DML(spark,model,input_shape=(3,224,224)) resnet.summary() ```