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Compute the value of shown in Neural Network and Back Propagation.

kero.multib.NeuralNetwork.py class NetworkUpdater: def compute_delta_l_per_data_point(self, w_l_plus_1, delta_l_plus_1, z_l, AF, verbose=False, print_format="%6.8f"): return delta_l

**Arguments/Return**

w_l_plus_1 | numpy matrix. Matrix of size m x n, where m and n are the number of neurons in the (l+1)-th and l-th layers respectively. In the neural network, this is the weights between layer l and layer l+1. |

delta_l_plus_1 | numpy matrix. delta value from layer l+1. We are back-propagating using this function. |

z_l | numpy matrix. Vector of size m x 1, where m is the number of neurons in layer l. In the neural network this is the values at layer l before activation function. |

AF | AF (activationFunction). Assume it is initiated. |

verbose | Bool False or integer
The larger the integer, the more information is printed. Set them to suitable integers for debugging. Default=False |

print_format | String. Format for printing numpy matrices when verbose is beyond some value.
Default=”%6.8f” |

return delta_l | numpy matrix. Vector of size m x 1 where m is the number of neurons in layer l. |

**Example Usage 1**

See compute_delta_L_per_data_point().

*kero version: 0.6.2*