# compute_delta_l_per_data_point()

Compute the value of $\delta^l$ 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

kero version: 0.6.2