Joerg Kiegeland

The problems with MxNet is fixed by using "take" operation instead of "pick". TrainMnist executes now 4 times faster for MxNet .

Tests added

HI @zachgk , thanks for your thoughts. You sketched a class `Symbolic`, which I assume would capture the DJL custom format you mentioned. MXNet has a similar concept of symbolic...

As to give an example how the block-level model built by TrainMnist.java would be converted to tensorflow: ``` model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile(...

Similar to your abstraction, it makes sense to divide all blocks in DJL as primitive blocks on the one side and compound / lambda blocks on the other side. If...