class_docstr: Neural Network class.
  
  Parameters
  ----------
  layers : list
      Layers of the Neural Network.
  
  Examples
  --------
  Create a NeuralNetwork from the given layers.
  
  >>> from madmom.ml.nn.layers import FeedForwardLayer
  >>> from madmom.ml.nn.activations import tanh, sigmoid
  >>> l1_weights = np.array([[0.5, -1., -0.3 , -0.2]])
  >>> l1_bias = np.array([0.05, 0., 0.8, -0.5])
  >>> l1 = FeedForwardLayer(l1_weights, l1_bias, activation_fn=tanh)
  >>> l2_weights = np.array([-1, 0.9, -0.2 , 0.4])
  >>> l2_bias = np.array([0.5])
  >>> l2 = FeedForwardLayer(l2_weights, l2_bias, activation_fn=sigmoid)
  >>> nn = NeuralNetwork([l1, l2])
  >>> nn  # doctest: +ELLIPSIS
  <madmom.ml.nn.NeuralNetwork object at 0x...>
  >>> nn(np.array([[0], [0.5], [1], [0], [1], [2], [0]]))
  ... # doctest: +NORMALIZE_WHITESPACE
  array([0.53305, 0.36903, 0.265 , 0.53305, 0.265 , 0.18612, 0.53305])
docstr: Reset the neural network to its initial state.
docstr_tok: ["Reset","the","neural","network","to","its","initial","state","."]
code: def reset(self):
      """
      Reset the neural network to its initial state.
  
      """
      for layer in self.layers:
          layer.reset()
  
file_path: madmom/madmom/ml/nn/__init__.py
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from ...processors import Processor, ParallelProcessor, SequentialProcessor
label_desc: Use image node_id 3 for calling the NeuralNetwork obj's underlying member method code with example usage: obj.reset() without return types