class_docstr: Deep neural network (MLP) implementation.
class_docstr_tok: ["Deep","neural","network","(","MLP",")","implementation","."]
docstr: Creates DNNet instance.
  
  Args:
      layer_sizes: number of neurons for each layer.
      activation: layer activation.
docstr_tok: ["Creates","DNNet","instance",".","Args",":","layer_sizes",":","number","of","neurons","for","each","layer",".","activation",":","layer","activation","."]
code: def __init__(self, layer_sizes: Iterable[int], activation: Callable):
      """Creates DNNet instance.
  
      Args:
          layer_sizes: number of neurons for each layer.
          activation: layer activation.
      """
      layer_sizes = list(layer_sizes)
      assert len(layer_sizes) >= 2
      ops = []
      for i in range(1, len(layer_sizes)):
          ops.extend(
              [Linear(layer_sizes[i - 1], layer_sizes[i]), activation]
          )
      super().__init__(ops)
  
imports: from typing import Callable, Iterable
  from objax.nn import Linear, Sequential
label_desc: Use image node_id 1 to create a new DNNet object from inherited base classes: Sequential with example: obj = DNNet(layer_sizes, activation)